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<@U0667DNC02Y> How can I trigger a job in one user...
# ask-ai
a
@Scout How can I trigger a job in one usercode repo, from another usercode repo, under the same deployment?
s
To trigger a job in one user code repository from another user code repository under the same deployment, you would typically use a cross-repository mechanism such as a sensor or a schedule that is aware of both repositories. However, the documentation does not provide a direct example or explicit instructions on how to set up cross-repository triggers within the same deployment. In general, sensors and schedules in Dagster can be used to monitor conditions and trigger jobs when those conditions are met. If you have a sensor in one repository, it could potentially be configured to trigger a job in another repository if it has access to the necessary context or information about the other repository. If you are looking for a specific example or detailed instructions on how to achieve this, and the documentation does not cover it, you might consider exploring the following general steps: 1. Ensure that both user code repositories are accessible within the same Dagster instance or deployment. 2. Define a sensor or schedule in the repository where you want to initiate the trigger. 3. Within the sensor or schedule, use the appropriate Dagster APIs to create a 
RunRequest
or similar mechanism to trigger the job in the other repository. Please note that cross-repository interactions can be complex and may require careful consideration of dependencies, permissions, and the overall architecture of your Dagster deployment. Since the documentation does not provide a concrete example for this scenario, if you encounter difficulties or need further guidance, you may want to reach out to the Dagster community or support channels for more specific advice tailored to your use case.
a
@Scout is it possible to automatically restart a job from failure?
s
Yes, it is possible to automatically restart a job from failure in Dagster. The documentation describes a feature called "Run Retries," which allows you to configure automatic retries for jobs that fail. You can set this up using tags either on job definitions or in the Dagster UI Launchpad. Here is an example from the documentation on how to configure a job with automatic retries using tags:
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python from dagster import job  @job(tags={"dagster/max_retries": 3}) def sample_job():     pass
The 
dagster/max_retries
tag specifies the maximum number of retries that will be attempted for the job. Additionally, you can control the retry strategy with the 
dagster/retry_strategy
tag, which determines which steps will be retried: - `FROM_FAILURE`: Retries will re-execute from the point of failure, skipping any successful steps but using their outputs for downstream steps. - `ALL_STEPS`: All steps in the job will be retried. Please note that for retries to work effectively, especially with the 
FROM_FAILURE
strategy, you need an IOManager that can access outputs from previous runs. This is important for ensuring that the outputs of successful steps are available to downstream steps during the retry. If you want to set up automatic retries for your jobs, you should configure the appropriate tags and ensure that your IOManager supports cross-run artifact storage. Keep in mind that the exact configuration might depend on your deployment environment, such as Kubernetes, and the storage systems you are using.
a
@Scout I have a dynamicly partitioned job that typically has 60-70 partitions. It is a k8s one-pod-per-op job deployed on GKE. When the partitions are generated, they don't all spin up pods on the cluster at once - can I change any configuration so that all the partitions spin up pods immediately?
s
In Dagster, when you have a dynamically partitioned job that runs on Kubernetes with the 
k8s_job_executor
, each step of the job runs in its own pod. By default, Dagster does not limit the number of pods that can run concurrently for a single job run. However, Kubernetes itself may impose limits on the number of pods that can be scheduled and run concurrently based on available cluster resources. If you're noticing that not all partitions are spinning up pods immediately, it could be due to Kubernetes resource constraints or other scheduling behaviors within the cluster. Here are a few things you can check or configure to influence pod scheduling: 1. Resource Requests and Limits: Ensure that the resource requests and limits specified in your job's Kubernetes configuration are appropriate for the number of concurrent pods you want to run. If the requests are too high, the Kubernetes scheduler may not be able to find enough resources to schedule all the pods at once. 2. Kubernetes Cluster Autoscaling: If you're using the Kubernetes Cluster Autoscaler, make sure it's configured correctly to scale up the number of nodes as needed to accommodate the number of desired pods. 3. Dagster Concurrency Configuration: While Dagster itself doesn't limit concurrency by default, you can control concurrency using the 
max_concurrent
configuration within the 
k8s_job_executor
. Setting this to a higher number or removing the limit can allow more pods to run concurrently if Kubernetes resources permit. 4. Kubernetes Scheduler: The Kubernetes scheduler may be delaying the scheduling of pods based on its own internal logic and priorities. You can look into Kubernetes scheduling policies to see if there's any tuning that can be done to prioritize your job's pods. 5. Pod Priority and Preemption: You can use Kubernetes pod priority and preemption to ensure that your job's pods are given higher scheduling priority over other workloads in the cluster. 6. Kubernetes Namespace Resource Quotas: If you have resource quotas set on the Kubernetes namespace where your job's pods are being scheduled, this could limit the number of pods that can run concurrently. Adjusting these quotas may help. 7. Kubernetes Pod Disruption Budgets: If you have Pod Disruption Budgets (PDBs) set, they could be preventing the Kubernetes scheduler from creating all the pods at once. Review any PDBs that might be affecting your job's pods. Here's an example of how you might configure the 
k8s_job_executor
to control concurrency (note that this is just an example and may not directly apply to your situation): ```python from dagster_k8s import k8s_job_executor my_k8s_executor = k8s_job_executor.configured({ "job_config": { "container_config": {
"resources": { "requests": {"cpu": "100m", "memory": "128Mi"}, "limits": {"cpu": "200m", "memory": "256Mi"}, } } }, "max_concurrent": 70, # Adjust this to the desired concurrency level }) ``` If you've checked these configurations and are still experiencing issues with pod scheduling, you may need to investigate the Kubernetes events and logs to identify any scheduling problems or resource bottlenecks. Additionally, consider reaching out to the Dagster community or support channels for further assistance with your specific deployment scenario.
a
If I am running dynamic partitions through a series of three ops, and there is some condition met in op_3, is it possible to restart the running of the partition from op_1? @Scout
s
In Dagster, if a condition is met in 
op_3
and you want to restart the execution from 
op_1
, you would typically handle this through the orchestration logic in your job definition. However, Dagster does not natively support automatically restarting a job from the beginning while it is already running. Once an operation (
op
) starts executing, the job will proceed through the remaining operations unless an error occurs or the job is manually terminated. To achieve a restart from 
op_1
based on a condition in 
op_3
, you would need to implement custom logic. Here are a couple of approaches you could consider: 1. Manual Retry: If 
op_3
detects a condition that requires a restart, you could fail the job intentionally and then manually trigger a retry from the beginning. This could be done by raising an exception in 
op_3
when the condition is met. 2. Custom Retry Logic: Implement custom logic within your operations to check for specific conditions and use Dagster's APIs to launch a new run of the job from the beginning. This would involve stopping the current run and manually triggering a new run with the same partition key. 3. External Monitoring: Use an external system or a Dagster sensor to monitor the runs and apply the custom logic to trigger a restart of the job from 
op_1
when specific conditions are detected. Here's a conceptual example of how you might implement a manual retry within an operation:
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python from dagster import op, job, RetryRequested  @op def op_1(context):     # Your logic here     pass  @op def op_2(context):     # Your logic here     pass  @op def op_3(context):     # Your logic here     if condition_to_restart:         # You can raise an exception or use the RetryRequested mechanism         raise RetryRequested(max_retries=1)  @job def my_job():     op_1_result = op_1()     op_2_result = op_2(op_1_result)     op_3(op_2_result)
In this example, if 
condition_to_restart
is 
True
in 
op_3
, the 
RetryRequested
exception is raised, indicating that the job should be retried. Note that this will only retry 
op_3
, not the entire job. To retry the entire job, you would need to implement a more complex solution that involves stopping the current run and triggering a new run, which is not a native feature of Dagster. Keep in mind that implementing such a pattern may have implications for idempotency and consistency within your data pipeline, and it should be carefully considered within the context of your specific use case. If you need further guidance on implementing such a pattern, consider reaching out to the Dagster community or support channels for assistance.
a
@Scout can I have an op output dynamic partitions and some standard, non partitioned outputs?
s
As of my knowledge cutoff date in early 2023, in Dagster, an operation (
op
) can produce dynamic outputs using the 
DynamicOut
and 
DynamicOutput
constructs. These are used to dynamically determine the number of outputs at runtime and can be used to fan out subsequent operations. However, the documentation does not explicitly state that an 
op
can have both dynamic outputs and standard, non-partitioned outputs simultaneously. Typically, an 
op
is designed to either produce a single output, multiple named outputs, or dynamic outputs. The dynamic outputs are specifically used for cases where the number of outputs is not known until runtime and each output can be mapped to downstream operations. If you need to produce both dynamic and non-dynamic outputs from the same 
op
, you might consider splitting the logic into separate operations. One 
op
could handle the dynamic partitioning logic, while another 
op
could produce the standard, non-partitioned outputs. This separation of concerns can help maintain clarity in your job definitions and ensure that each 
op
has a single responsibility. Here's a conceptual example of how you might structure your job:
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python from dagster import op, DynamicOut, DynamicOutput, Out, job  @op(out=DynamicOut()) def dynamic_partition_op(context):     # Your logic to produce dynamic partitions     for partition in partitions:         yield DynamicOutput(value=partition, mapping_key=str(partition))  @op(out=Out()) def standard_output_op(context):     # Your logic to produce a standard output     return some_value  @job def my_job():     dynamic_partitions = dynamic_partition_op()     standard_output = standard_output_op()      # Further logic to use dynamic_partitions and standard_output
In this example, 
dynamic_partition_op
produces dynamic partitions, while 
standard_output_op
produces a standard, non-partitioned output. Both can be used within the same job, and their outputs can be passed to downstream operations as needed. If you have a use case that requires an 
op
to produce both dynamic and non-dynamic outputs and you're unsure how to implement it, I recommend reaching out to the Dagster community or support channels for more specific guidance. They may be able to provide insights into whether this capability has been introduced in a version of Dagster released after my knowledge cutoff date or suggest alternative design patterns to achieve your goal.
a
If I run a job and need to restart from any failures, is there any id I can access from the context that is constant over the restarts?
If I run a job and need to restart from any failures, is there any id I can access from the context that is constant over the restarts? @Scout
s
There was an error processing your request. Please try again.
a
If I run a job and need to restart from any failures, is there any id I can access from the context that is constant over the restarts? @Scout
s
When you run a job in Dagster and need to restart from any failures, you can access the original run ID from the context, which remains constant over restarts. This original run ID is associated with the first run of the job and is preserved across re-executions for traceability. Here's how you can access the original run ID from the context:
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python from dagster import op, job, RunRequest, DagsterInstance  @op def my_op(context):     # Access the original run ID     original_run_id = context.pipeline_run.parent_run_id or context.pipeline_run.run_id     <http://context.log.info|context.log.info>(f"Original run ID: {original_run_id}")      # Your operation logic here  @job def my_job():     my_op()  # Example of how you might trigger a re-execution programmatically def trigger_reexecution(instance: DagsterInstance, run_id: str):     reexecution_options = ReexecutionOptions(         parent_run_id=run_id,         step_selection=["*"],     )     result = execute_job(         reconstructable(my_job),         instance=instance,         reexecution_options=reexecution_options,     )     return result
In this example, 
context.pipeline_run.parent_run_id
is used to access the ID of the parent run if the current run is a re-execution. If the current run is the first run (not a re-execution), 
context.pipeline_run.parent_run_id
will be 
None
, and 
context.pipeline_run.run_id
will give you the run ID of the current run. When you trigger a re-execution, you can specify the 
parent_run_id
in the 
ReexecutionOptions
. This links the re-execution to the original run, and you can use this information within your operations to maintain a consistent reference across restarts. Please note that the code example provided is conceptual and may need to be adapted to fit your specific use case. If you need further assistance with handling run IDs and re-executions, the Dagster community and support channels are good resources for additional help.
a
@Scout how can I add default values to this config class? class LocalRunConfig(Config): org_id: str region: str run_type: str
s
To add default values to a config class in Dagster, you can use the 
Field
class from Pydantic, which allows you to specify metadata for config fields, including default values. Here's an example of how you might define a config class with default values:
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python from dagster import Config from pydantic import Field  class MyConfig(Config):     my_str: str = Field(default="default_value", description="A string field with a default value")     my_int: int = Field(default=42, description="An integer field with a default value")     my_bool: bool = Field(default=True, description="A boolean field with a default value")
In this example, 
Field
is used to define default values for the fields in the 
MyConfig
class. The 
default
parameter is used to specify the default value for each field. Additionally, you can provide a description for each field, which can be useful for documentation and when viewing the config schema in the Dagster UI. When you use this config class in an operation or job, Dagster will use the specified default values if no values are provided in the run config. Here's an example of how you might use the 
MyConfig
class in an operation:
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python from dagster import op  @op(config_schema=MyConfig) def my_op(context):     config = context.op_config     <http://context.log.info|context.log.info>(f"my_str: {config['my_str']}")     <http://context.log.info|context.log.info>(f"my_int: {config['my_int']}")     <http://context.log.info|context.log.info>(f"my_bool: {config['my_bool']}")      # Your operation logic here
In this operation, if you do not provide a value for 
my_str
, 
my_int
, or 
my_bool
in the run config, Dagster will use the default values 
"default_value"
, 
42
, and 
True
, respectively. Remember that the 
Config
class from Dagster is a subclass of Pydantic's 
BaseModel
, so you can use Pydantic's features to define and validate your config schema.
To add default values to a config class in Dagster, you can use the 
Field
class from Dagster's configuration system to specify default values for each field. Here's how you can modify your 
LocalRunConfig
class to include default values:
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python from dagster import Field, Config  class LocalRunConfig(Config):     org_id: str = Field(default_value="default_org_id")     region: str = Field(default_value="default_region")     run_type: str = Field(default_value="default_run_type")
In this example, 
Field
is used to define each configuration field with a 
default_value
parameter. The 
default_value
parameter specifies the default value for the field if it is not provided by the user. Please note that the 
Field
class is used to provide metadata about the configuration field, including the default value, description, and any validation constraints. If you want to provide additional information or constraints, you can add them as additional parameters to the 
Field
constructor. Keep in mind that the code example provided is based on the typical usage of Dagster's configuration system, and you may need to adjust it according to the specific version of Dagster you are using and your project's requirements. If you encounter any issues or have further questions, the Dagster community and support channels can provide additional assistance.
a
My default values aren't showing in the launchpad @Scout
s
If your default values aren't showing in the launchpad, it could be due to a few reasons. Based on the documentation and previous issues reported by users, here are some steps you can take to troubleshoot the issue: 1. Check Dagster Version: Ensure that you are using a version of Dagster that supports displaying default values in the launchpad. If you are using an older version, consider upgrading to the latest version where this feature is available. 2. Verify Config Schema: Make sure that your config schema is correctly defined with default values using the 
Field
class. The default values should be specified using the 
default_value
parameter within the 
Field
. 3. Clear Local Storage: There have been instances where the launchpad might not display the default values due to local storage issues. Try opening the launchpad in an incognito window or clearing your browser's local storage to see if that resolves the issue. 4. Create a New Config Tab: If you have previously edited the config in the launchpad, it might not show the default values. Try creating a new config tab in the launchpad to see if the default values appear. 5. Check for Known Issues: Look at the Dagster GitHub issues and discussions to see if there are any known bugs or issues related to default values not appearing in the launchpad. It's possible that you might be encountering a known issue that the Dagster team is already aware of and working to resolve. 6. Engage with the Community: If you're still having trouble, consider reaching out to the Dagster community or support channels. Provide details about your Dagster version, your config schema, and the steps you've taken to troubleshoot the issue. The community or the maintainers might be able to provide further assistance or identify if there's an ongoing issue with the launchpad. Remember to check the documentation for any updates or changes related to the launchpad and default values, as there may have been improvements or fixes introduced after my knowledge cutoff date.
a
@Scout Here is my job definition: dataprep_local_job = dataprep_local_graph.to_job( resource_defs={}, config=local_run_config, name='dataprep_local_job', ) class LocalRunConfig(Config): org_id: str region: str run_type: str = "DEV" incremental_update: bool = False save_local: bool = True @config_mapping def local_run_config(val: LocalRunConfig) -> RunConfig: _, project_id = google.auth.default() org_id = val.org_id # Getting from ProdRunConfig region = val.region # Getting from ProdRunConfig
s
It looks like you're trying to define default values for a 
LocalRunConfig
class and use it in a 
config_mapping
for a job in Dagster. However, there are a few issues with the code snippet you've provided, and I'll help you correct them. Firstly, the 
Config
class from Dagster is not intended to be subclassed directly to define configuration schemas with default values. Instead, you should use Python data classes or Pydantic models to define configuration schemas. Dagster uses these schemas to automatically generate configuration types with default values. Secondly, the 
config_mapping
decorator is used to transform a more user-friendly config into the config that Dagster expects. It's not used to directly set default values for configs. Here's an example of how you might define a configuration schema with default values using a Python data class and then use it in a `config_mapping`:
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python
from dagster import job, config_mapping, RunConfig, graph
from dataclasses import dataclass

# Define a data class for your run configuration
@dataclass
class LocalRunConfig:
    org_id: str
    region: str
    run_type: str = "DEV"
    incremental_update: bool = False
    save_local: bool = True

# Define a config mapping function that transforms the user-friendly config
# into the config that Dagster expects
@config_mapping
def local_run_config(config: dict) -> dict:
    # Extract values from the config dictionary
    org_id = config['org_id']
    region = config['region']
    run_type = config.get('run_type', "DEV")  # Use default value if not provided
    incremental_update = config.get('incremental_update', False)
    save_local = config.get('save_local', True)

    # Construct the actual run config expected by Dagster
    return {
        'resources': {
            # Define your resource configurations here
        },
        'ops': {
            # Define your op configurations here
        },
        # Add other necessary configurations
    }

# Define your graph
@graph
def dataprep_local_graph():
    # Define your graph here
    pass

# Create a job with the config mapping
dataprep_local_job = dataprep_local_graph.to_job(
    resource_defs={},
    config=local_run_config,
    name='dataprep_local_job',
)
In this example, the 
LocalRunConfig
data class defines the schema for your configuration with default values. The 
local_run_config
function is a 
config_mapping
that takes a dictionary of config values, applies defaults if necessary, and then returns the actual run config expected by Dagster. When you define your job, you can provide the 
config
parameter with the 
config_mapping
function. This allows users to provide simplified configuration when launching runs, and the 
config_mapping
function will handle transforming it into th
e full configuration needed by the job. Please note that the actual implementation may vary based on your specific use case and the version of Dagster you are using. If you have further questions or need assistance, consider reaching out to the Dagster community or support channels.
a
@Scout I want the default values to show in the launchpad though so the user knows what they are
s
The documentation indicates that in the launchpad, you can preview any default configuration by hovering over elements in the configuration schema panel. This feature allows users to see the default values that are set within the configuration schema. However, if you're finding that the default values are not showing up as expected in the launchpad, there are a few potential reasons and steps you can take: 1. Check for Updates: Ensure that you are using the latest version of Dagster, as improvements and bug fixes are regularly made. The ability to preview default values in the launchpad is a feature that may have been added or improved in recent updates. 2. Verify Configuration Schema: Make sure that your configuration schema is correctly defined. If you are using Pydantic models or data classes to define your configuration schema, ensure that the default values are set correctly. 3. Review Known Issues: There have been issues reported on GitHub where users have experienced similar problems with default values not appearing in the launchpad. It's possible that you might be encountering a known issue that the Dagster team is aware of. Reviewing the GitHub issues might provide insights or workarounds suggested by the community or maintainers. 4. Community and Support Channels: If you've checked the above and are still experiencing issues, consider reaching out to the Dagster community or support channels for assistance. Provide details about your Dagster version, your configuration schema, and the steps you've taken to troubleshoot the issue. Based on the information provided, it's important to ensure that your Dagster instance is up to date and that your configuration schema is defined correctly. If the issue persists, engaging with the community or support channels may help resolve the problem.
a
@Scout Why do I get this error: Error dagster._core.errors.DagsterInvalidConfigError: Error in config for job dataprep_local_job Error 1: Received unexpected config entry "run_config" at path root:resources. Expected: "{ io_manager?: { config?: Any } }". File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_grpc/impl.py", line 530, in get_external_execution_plan_snapshot create_execution_plan( File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/execution/api.py", line 732, in create_execution_plan resolved_run_config = ResolvedRunConfig.build(job_def, run_config) File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/system_config/objects.py", line 169, in build raise DagsterInvalidConfigError( from this dataprep_local_job = dataprep_local_graph.to_job( config=local_run_config, name='dataprep_local_job', )
s
The error message you're encountering indicates that there is an unexpected config entry named 
run_config
at the root level of the 
resources
configuration for the job 
dataprep_local_job
. Specifically, the error is saying that the 
resources
configuration was expecting something like an 
io_manager
configuration, but instead, it found 
run_config
, which is not valid in that location. Here's the relevant part of the error message:
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Error 1: Received unexpected config entry "run_config" at path root:resources. Expected: "{ io_manager?: { config?: Any } }".
This suggests that there might be a mismatch between the configuration you are providing and the schema expected by the job. In Dagster, the 
resources
configuration is used to configure resources such as IO managers, databases, or any other external dependencies that your ops might need. To resolve this issue, you should review the configuration you are providing to ensure it matches the expected schema for the job. Here are some steps you can take: 1. Review Job Definition: Check the 
dataprep_local_job
definition to ensure that the 
resources
configuration is defined correctly. If you are using custom resources, make sure that they are specified within the 
resource_defs
argument of the job. 2. Check Config Mapping: If you are using a 
config_mapping
function (like 
local_run_config
), ensure that it is transforming the input configuration into the correct structure expected by the job. The 
config_mapping
function should return a dictionary that matches the job's configuration schema. 3. Correct Configuration Format: When providing the configuration for the job, make sure that it is structured correctly. For example, if you are using an 
io_manager
, the configuration should be something like this:
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yaml resources:   io_manager:     config:       # Your io_manager config here
4. Validate Configuration Locally: You can use the 
execute_in_process
method to test the configuration locally before deploying it. This method will raise a 
DagsterInvalidConfigError
if the configuration is invalid, allowing you to debug the issue more easily. 5. Consult Documentation: Review the Dagster documentation on resources and configuration to ensure that you are following the correct practices for defining and providing configuration for your job. If you continue to face issues after checking the above points, you may want to share more details about your job definition and the configuration you are providing. This will help in diagnosing the issue more accurately. Additionally, you can reach out to the Dagster community or support channels for further assistance.
a
@Scout If I have two k8s jobs that created from the same graph of ops, can I independently define the k8s resources the ops need for the jobs seperately, so I don't need to duplicate op definitions?
s
Yes, you can independently define the Kubernetes (k8s) resources that ops need for jobs created from the same graph without duplicating op definitions. In Dagster, you can create multiple jobs from a single graph and provide different resource definitions for each job. This allows you to customize the behavior of the ops within each job based on the environment or other requirements. Here's how you can do it: 1. Define your graph using the 
@graph
decorator. 2. Use the 
GraphDefinition.to_job
method to create jobs from the graph. 3. Provide different 
resource_defs
for each job to specify the k8s resources needed. Here's a conceptual example:
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python from dagster import graph, op, job, ResourceDefinition  # Define your ops @op(required_resource_keys={"k8s_resource"}) def my_op(context):     # Your op logic here     pass  # Define your graph @graph def my_graph():     my_op()  # Define k8s resources for different environments prod_k8s_resource = ResourceDefinition.hardcoded_resource({"k8s_config": "prod_config"}) dev_k8s_resource = ResourceDefinition.hardcoded_resource({"k8s_config": "dev_config"})  # Create jobs from the graph with different k8s resources prod_job = my_graph.to_job(     resource_defs={"k8s_resource": prod_k8s_resource},     name="my_graph_prod" )  dev_job = my_graph.to_job(     resource_defs={"k8s_resource": dev_k8s_resource},     name="my_graph_dev" )
In this example, 
my_op
requires a k8s resource, which is provided by the 
resource_defs
when creating the jobs. The 
prod_job
and 
dev_job
are two separate jobs created from the same graph 
my_graph
, but they use different k8s resource definitions (
prod_k8s_resource
and 
dev_k8s_resource
respectively). By doing this, you can maintain a single definition of your ops and graph while configuring each job to run with different k8s resources. This approach is useful for managing different environments, such as production and development, without duplicating op definitions. Remember to replace the 
ResourceDefinition.hardcoded_resource
with the actual k8s resource definitions that are appropriate for your use case. The hardcoded resource is just a placeholder for the purpose of this example. For more detailed information on how to define resources and configure jobs, you can refer to the Dagster documentation on resources and job configuration.
a
What would the k8s resource look like if all the prod ops needed a pod with 10gb extra memory. but it is a one pod per op job? @Scout
s
If you have a job configured with the 
k8s_job_executor
that runs each op in its own Kubernetes pod and you want to ensure that all ops in the production environment have a pod with 10GB of extra memory, you can define a custom resource that includes the necessary Kubernetes resource requests and limits. Here's an example of how you might define such a resource in Dagster:
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python from dagster_k8s import k8s_job_executor from dagster import job, graph, op, make_values_resource  # Define your ops @op def my_op():     # Your op logic here     pass  # Define your graph @graph def my_graph():     my_op()  # Define a resource that specifies the extra memory requirement extra_memory_resource = make_values_resource(     "k8s_config",     {         "container_config": {             "resources": {                 "requests": {                     "memory": "10Gi"                 },                 "limits": {                     "memory": "10Gi"                 }             }         }     } )  # Create a job with the k8s_job_executor and the extra memory resource @job(     resource_defs={         "k8s_config": extra_memory_resource,     },     executor_def=k8s_job_executor.configured(         {"job_config": {"container_config": {"resources": {"requests": {"memory": "10Gi"}, "limits": {"memory": "10Gi"}}}}}     ) ) def prod_job():     my_graph()
In this example, the 
extra_memory_resource
is a resource that specifies the Kubernetes container configuration for resource requests and limits. The 
k8s_job_executor
is configured with this resource to ensure that each op's pod requests and limits 10GB of memory. When you run the 
prod_job
, each op will execute in its own pod with the specified memory configuration. This allows you to control the Kubernetes resources for each op independently, even though they are part of the same job. Please note that the 
make_values_resource
function is used here to create a resource definition from a dictionary. You should replace this with the actual method of defining resources that matches your Dagster version and setup. For more detailed information on configuring Kubernetes resources for Dagster jobs and ops, you can refer to the Dagster documentation on Kubernetes deployment and the 
k8s_job_executor
.
a
@Scout what if the individual ops have different resource requirements job 1: op1 10gb op2 20gb op3 30gb job 2: op1 20gb op2 30gb op3 40gb
s
If individual ops within a job have different Kubernetes resource requirements, you can specify these requirements using the 
op_tags
argument within each op definition. These tags can include the necessary Kubernetes resource configurations for each op. Here's an example of how you might define ops with different resource requirements and then create two different jobs with varying resource configurations for the same ops:
Copy code
python
from dagster import op, graph, job
from dagster_k8s import k8s_job_executor

# Define ops with different resource requirements using op_tags
@op(tags={"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "10Gi"}, "limits": {"memory": "10Gi"}}}}})
def op1():
    # Your op logic here
    pass

@op(tags={"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "20Gi"}, "limits": {"memory": "20Gi"}}}}})
def op2():
    # Your op logic here
    pass

@op(tags={"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "30Gi"}, "limits": {"memory": "30Gi"}}}}})
def op3():
    # Your op logic here
    pass

# Define your graph
@graph
def my_graph():
    op1()
    op2()
    op3()

# Create job 1 with the default resource requirements defined in the ops
job1 = my_graph.to_job(
    name="job1",
    executor_def=k8s_job_executor
)

# Create job 2 with overridden resource requirements for each op
job2 = my_graph.to_job(
    name="job2",
    op_selection={
        "op1": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "20Gi"}, "limits": {"memory": "20Gi"}}}}},
        "op2": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "30Gi"}, "limits": {"memory": "30Gi"}}}}},
        "op3": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "40Gi"}, "limits": {"memory": "40Gi"}}}}},
    },
    executor_def=k8s_job_executor
)
In this example, 
op1
, 
op2
, and 
op3
are defined with their default Kubernetes resource requirements using 
op_tags
. The 
job1
is created with these default resource configurations. For 
job2
, we want to override the resource requirements for each op. However, as of my knowledge cutoff date, Dagster does not support directly overriding op tags at the job level. Therefore, you would typically need to redefine the ops with the new resource requirements if you want to create a job with different resource configurations. If you need to frequently change resource requirements for ops across different jobs without redefining the ops, you might consider using a custom resource definition that can dynamically adjust the resource requirements based on some condition or configuration. This would require a more advanced setup and potentially custom d
evelopment within your Dagster deployment. For the most accurate and up-to-date information on configuring Kubernetes resources for individual ops and jobs, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout if a generate a random id in the run_config resource of a job and pass that into the ops via the context.op_config, If for any reason the job fails, and I restart from failure, will that id remain the same or will it be regenerated for the downstream ops?
s
When you restart a job from failure in Dagster, the 
run_config
used for the original run is preserved for the re-execution. This means that any values, including a randomly generated ID that was part of the original 
run_config
, will remain the same for the downstream ops during the re-execution. The 
run_config
is not re-evaluated or regenerated when you restart from failure; instead, Dagster uses the configuration from the initial run to ensure consistency and to allow for the reuse of outputs from successful steps. This behavior is important for traceability and for ensuring that re-executions are predictable and based on the same parameters as the original execution. Therefore, if you generate a random ID in the 
run_config
and use it in your ops, that ID will be the same for all ops during both the initial run and any restarts from failure. If you need a new random ID for each execution, you would have to trigger a completely new run rather than restarting from failure. For more detailed information on Dagster's behavior during re-executions and how 
run_config
is handled, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout does the context.run_id change?
s
When you restart a job from failure in Dagster, the 
context.run_id
for the re-execution will be different from the original run. Each run in Dagster, including re-executions, is assigned a unique 
run_id
. This unique identifier allows you to distinguish between the original run and subsequent retries or re-executions. However, Dagster provides a way to trace back to the original run through the 
parent_run_id
attribute. When you restart a job from failure, the 
context
of the re-execution will have a 
parent_run_id
that points to the 
run_id
of the initial run that failed. This linkage allows you to track the lineage of runs and understand the relationship between the original run and its re-executions. To summarize: - 
context.run_id
will change and be unique for each re-execution. - 
context.parent_run_id
will point to the 
run_id
of the original run that is being retried. This behavior ensures that each execution attempt is uniquely identifiable while still maintaining a connection to the original run for tracking and analysis purposes. For more information on run IDs and execution contexts, you can refer to the Dagster documentation.
a
@Scout In my graph, can I create placeholder ops/pods to allow GKE to pre-emptively scale up required nodes for downstream ops?
s
In Dagster, each op corresponds to a unit of computation, and when using the 
k8s_job_executor
, each op can be executed in its own Kubernetes pod. However, creating placeholder ops solely for the purpose of scaling up nodes preemptively in Google Kubernetes Engine (GKE) is not a typical pattern and might not be the best use of the framework's capabilities. Instead of using placeholder ops, you should consider leveraging GKE's cluster autoscaling features to manage the scaling of nodes based on actual workload demands. GKE's cluster autoscaler will automatically adjust the number of nodes in your cluster when pods fail to run due to resource constraints. Here are some strategies you can use to ensure that GKE scales up nodes preemptively for your actual workload: 1. Resource Requests: Set resource requests for your ops that accurately reflect the expected resource consumption. GKE uses these requests to schedule pods on nodes with sufficient resources. If there aren't enough resources available, the cluster autoscaler will attempt to provision new nodes. 2. Overprovisioning: You can create a deployment with a number of "pause" pods that have resource requests but do not use these resources. This can trigger the cluster autoscaler to add nodes preemptively. Once your actual workload starts, these pause pods can be preemptively evicted to make room for your real pods. 3. Node Pools with Different Sizes: Use node pools with different machine types and sizes to better match the resource needs of your workload. You can use node selectors and affinity to schedule your ops on appropriate node pools. 4. Pod Priority and Preemption: Use Kubernetes pod priority and preemption to ensure that your critical ops are given higher scheduling priority. This can help ensure that nodes are scaled up in time for your high-priority workloads. 5. Scheduled Scaling: If your workload has predictable patterns, you can use scheduled scaling to adjust the size of your cluster in anticipation of workload changes. 6. Horizontal Pod Autoscaling: Use Horizontal Pod Autoscaling (HPA) to automatically scale the number of pods in a deployment based on observed CPU utilization or other select metrics. While Dagster does not directly manage the scaling of Kubernetes nodes, it operates within the Kubernetes ecosystem, which provides these mechanisms to ensure that your workloads have the necessary resources. For more detailed information on how to configure these features in GKE, please refer to the GKE documentation or reach out to the Dagster community for assistance.
a
@Scout I can't find a parent_run_id
s
As of my last update, Dagster's execution context provides a 
pipeline_run
object, which contains information about the current run, including the 
run_id
. If you're looking for a 
parent_run_id
in the context of a re-execution or retry of a pipeline run, you should be able to access it through the 
pipeline_run
object's 
parent_run_id
attribute. Here's an example of how you might access the 
parent_run_id
within an op:
Copy code
python @op def my_op(context):     run_id = context.pipeline_run.run_id     parent_run_id = context.pipeline_run.parent_run_id      <http://context.log.info|context.log.info>(f"Run ID: {run_id}")     if parent_run_id:         <http://context.log.info|context.log.info>(f"Parent Run ID: {parent_run_id}")     else:         <http://context.log.info|context.log.info>("This run does not have a Parent Run ID.")
In this example, 
parent_run_id
will be 
None
if the current run is not a retry or re-execution. If the current run is a retry or re-execution, 
parent_run_id
will contain the 
run_id
of the original run that is being retried. If you are unable to find the 
parent_run_id
or if it is not behaving as expected, it's possible that there may have been changes or updates to Dagster after my last update. In that case, I recommend checking the latest Dagster documentation or reaching out to the Dagster community for the most current information and assistance.
a
@Scout will this be constant the the job is restarted from failure twice?
s
When a Dagster job is restarted from failure, the 
parent_run_id
is designed to reference the 
run_id
of the initial run that encountered the failure. If you restart the job from failure multiple times, each restart should maintain the same 
parent_run_id
, which points back to the original run. Here's what happens step by step: 1. The initial run starts and gets a unique 
run_id
. 2. The job fails, and you restart it from failure. 3. The first restart gets a new 
run_id
, but the 
parent_run_id
points to the 
run_id
from step 1. 4. If the first restart also fails and you restart from failure again, the second restart will get another new 
run_id
. However, the 
parent_run_id
for this second restart should still point to the 
run_id
from step 1, the original run. This behavior ensures that you can trace all restarts back to the original run that they are attempting to complete. It provides a consistent reference across multiple restarts for auditing and debugging purposes. If you find that the 
parent_run_id
is not consistent across multiple restarts from failure, it may be worth checking the Dagster documentation or reaching out to the community for support, as there might have been changes or updates to this behavior after my last update.
a
@Scout this also doesn't exist, is there a job id?
s
In Dagster, each pipeline run is associated with a unique identifier called 
run_id
. This 
run_id
serves as the job ID for that particular execution and is used to track the run within the system. There isn't a separate concept of a "job ID" distinct from the 
run_id
in Dagster's data model. When you execute a job, Dagster generates a 
run_id
for that execution, which you can use to reference the run in the Dagit UI, through the GraphQL API, or within your code when interacting with the Dagster instance. Here's an example of how you might access the 
run_id
within an op:
Copy code
python @op def my_op(context):     run_id = context.run_id     <http://context.log.info|context.log.info>(f"Run ID: {run_id}")
In this example, 
context.run_id
provides the unique identifier for the current run. If you need to keep track of this run ID across restarts or for other operational purposes, you can store it or log it as needed. If you're looking for a way to reference the job itself, which is a persistent entity that defines what to execute, you would typically use the job's name. The job name is defined when you construct the job using 
@job
or when converting a graph to a job with 
.to_job()
. If you have further questions about run IDs, job names, or other identifiers in Dagster, or if you're experiencing unexpected behavior, I recommend checking the latest Dagster documentation or reaching out to the Dagster community for assistance.
a
But run_id isn't consistent across restarts, and you've given me two incorrect uses of parent_run_id @Scout
s
I apologize for the confusion. You are correct that the 
run_id
is not consistent across restarts; each new run or restart will have its own unique 
run_id
. Regarding the 
parent_run_id
, as of my last update, Dagster does not have a built-in 
parent_run_id
attribute that is preserved across restarts and accessible in the same way as 
run_id
. If you are looking for a way to link restarts to the original run, you might consider using tags or custom logic to maintain this association. For example, you could add a custom tag to your run configuration that specifies the original 
run_id
or some other identifier that you want to preserve across restarts. When you initiate a restart, you would carry over this tag to the new run configuration. Here's a conceptual example of how you might add a custom tag to your run configuration:
Copy code
python from dagster import RunConfig, job  @job def my_job():     # Job definition  # When starting the initial run initial_run_config = RunConfig(tags={"original_run_id": "your_custom_identifier"}) result = my_job.execute_in_process(run_config=initial_run_config)  # When restarting the job from failure, carry over the original_run_id tag restart_run_config = RunConfig(tags={"original_run_id": result.run_id}) restarted_result = my_job.execute_in_process(run_config=restart_run_config)
In this example, the 
original_run_id
tag is used to keep track of the association with the initial run. You would need to ensure that this tag is included in the run configuration for any restarts. Please note that the actual implementation may vary based on your specific use case and the version of Dagster you are using. For the most accurate and up-to-date information on handling run IDs and restarts, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout How does dagster handle a situation where an op running on a spot vm is pre-empted by GKE?
@Scout How does dagster handle a situation where an op running on a spot vm is pre-empted by GKE?
s
When an op running on a spot VM (preemptible node) in Google Kubernetes Engine (GKE) is preempted, it is similar to any other scenario where a Kubernetes pod is unexpectedly terminated. Dagster, running within the Kubernetes ecosystem, relies on Kubernetes to handle pod lifecycle events, including preemptions. Here's how the process generally works: 1. Pod Termination: When GKE preempts a spot VM, any pods running on that VM receive a termination signal (SIGTERM), and Kubernetes gives them a grace period (default is 30 seconds) to shut down gracefully. 2. Job Failure: If an op is running in a pod that gets preempted, the op will likely fail because its pod is terminated. Dagster will detect this as a step failure within the job. 3. Run Monitoring: If you have run monitoring configured, Dagster can detect that the run worker (pod) has been terminated. The documentation mentions run monitoring as a feature that allows you to detect hanging runs and restart crashed run workers for certain run launchers. 4. Run Retries: If you have configured run retries, Dagster can automatically retry the failed run. The retry behavior can be configured to retry from the beginning of the job or from the point of failure, depending on your settings. 5. Kubernetes Job Rescheduling: Kubernetes will attempt to reschedule the preempted pod onto a different node in the cluster. If you have cluster autoscaling enabled, GKE may provision a new non-preemptible node to accommodate the rescheduled pod. 6. Manual Intervention: If automatic retries are not configured or do not succeed, you may need to manually intervene to restart the job or address any issues caused by the preemption. It's important to design your jobs with idempotency in mind, especially when using spot VMs, as preemptions can happen at any time. This means ensuring that ops can be retried without causing adverse effects and that any external state changes are handled correctly. For the most accurate and up-to-date information on how Dagster and Kubernetes interact, and how to configure run monitoring and retries, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.
a
Does the op_selection passed into the job definition just pass the ops config tags? op_selection={ "op1": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "20Gi"}, "limits": {"memory": "20Gi"}}}}}, "op2": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "30Gi"}, "limits": {"memory": "30Gi"}}}}}, "op3": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "40Gi"}, "limits": {"memory": "40Gi"}}}}}, }, @Scout
s
The 
op_selection
argument in Dagster is not used to pass configuration tags or resource requirements for individual ops. Instead, 
op_selection
is used to specify a subset of ops to execute within a job. It allows you to select specific ops or sets of ops based on their names or relationships (such as ancestors or descendants within the job's graph). The configuration you've shown in your question appears to be an attempt to assign Kubernetes resource configurations to specific ops using a syntax similar to 
op_selection
. However, this is not the correct way to specify Kubernetes resource requirements for ops in Dagster. To specify Kubernetes resource requirements for individual ops, you should use the 
tags
parameter on the 
@op
decorator to include the 
dagster-k8s/config
tag with the desired resource configuration. Here's an example of how to do this:
Copy code
python from dagster import op  @op(tags={     "dagster-k8s/config": {         "container_config": {             "resources": {                 "requests": {"memory": "20Gi"},                 "limits": {"memory": "20Gi"}             }         }     } }) def op1(context):     # Op logic here     pass  # Repeat for op2 and op3 with their respective resource requirements
When you define your job, you do not need to specify 
op_selection
for resource configurations. The tags you've set on each op will be used by the 
k8s_job_executor
to create Kubernetes pods with the specified resource requirements. If you want to execute a subset of ops within a job, you can use 
op_selection
like this:
Copy code
python job_with_selected_ops = my_job.with_op_selection(["op1", "op2+"])
In this example, 
op_selection
is used to select 
op1
and 
op2
along with all of `op2`'s downstream dependencies. For the most accurate and up-to-date information on configuring Kubernetes resources for individual ops and using 
op_selection
, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.
a
But I want to select all the ops, but configure the k8s resources on them, so I can have multiple jobs from the same graph of ops, but with different k8s resources @Scout
s
To create multiple jobs from the same graph of ops with different Kubernetes resource configurations for each job, you should define the resource requirements within the job definition rather than trying to pass them through 
op_selection
. Each job can have its own set of resource definitions that apply to the ops when that job is executed. Here's a conceptual example of how you might define a graph and then create two jobs from it, each with different Kubernetes resource configurations:
Copy code
python
from dagster import graph, op, job
from dagster_k8s import k8s_job_executor

# Define your ops without specific resource requirements
@op
def op1():
    # Op logic here
    pass

@op
def op2():
    # Op logic here
    pass

@op
def op3():
    # Op logic here
    pass

# Define your graph
@graph
def my_graph():
    op1()
    op2()
    op3()

# Define resource requirements for job1
job1_resources = {
    "op1": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "10Gi"}, "limits": {"memory": "10Gi"}}}}},
    "op2": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "20Gi"}, "limits": {"memory": "20Gi"}}}}},
    "op3": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "30Gi"}, "limits": {"memory": "30Gi"}}}}},
}

# Define resource requirements for job2
job2_resources = {
    "op1": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "20Gi"}, "limits": {"memory": "20Gi"}}}}},
    "op2": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "30Gi"}, "limits": {"memory": "30Gi"}}}}},
    "op3": {"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "40Gi"}, "limits": {"memory": "40Gi"}}}}},
}

# Create job1 from the graph with specific resource requirements
job1 = my_graph.to_job(
    name="job1",
    tags=job1_resources,
    executor_def=k8s_job_executor
)

# Create job2 from the graph with different resource requirements
job2 = my_graph.to_job(
    name="job2",
    tags=job2_resources,
    executor_def=k8s_job_executor
)
In this example, 
job1
and 
job2
are created from the same graph 
my_graph
, but they have different Kubernetes resource configurations specified in the 
tags
parameter of the 
to_job
method. The 
k8s_job_executor
will use these tags to set the resource requirements for the Kubernetes pods that run the ops. Please note that the actual implementation may vary based on your specific use case and the version of Dagster you are using. For the most accurate and up-to-date information on configuring Kubernetes resources for jobs and using the 
k8s_job_executor
, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance
.
a
I can see the tags appear in the UI launchpad using this method, but when I run the job, the k8s resources assigned to the pods are just default values and not those specified under job1_resources @Scout
s
If the Kubernetes resource configurations specified in the tags are visible in the UI launchpad but are not being applied when the job runs, it's possible that there is a configuration issue or that the tags are not being correctly interpreted by the 
k8s_job_executor
. Here are some steps you can take to troubleshoot this issue: 1. Check Executor Configuration: Ensure that the 
k8s_job_executor
is configured to use tags for setting Kubernetes resource configurations. The executor should be set up to read the relevant tags and apply them to the pods it launches. 2. Review Tag Format: Verify that the format of the tags matches the expected schema for Kubernetes resource configurations in Dagster. The tags should be structured in a way that the 
k8s_job_executor
understands. 3. Check Helm Chart Configuration: If you are using the Dagster Helm chart to deploy to Kubernetes, ensure that the Helm chart configuration does not override the resource configurations you've set in the tags. 4. Review Kubernetes Namespace: Make sure that the Kubernetes namespace where the pods are being launched does not have restrictions or default resource configurations that could be overriding your settings. 5. Examine Logs and Events: Look at the logs for the Dagster instance and the Kubernetes events for the pods to see if there are any errors or warnings that could indicate why the resource configurations are not being applied. 6. Consult Documentation: Review the latest Dagster documentation on Kubernetes integration, specifically the sections related to the 
k8s_job_executor
and resource configuration using tags. 7. Reach Out for Support: If you've gone through these steps and are still facing issues, consider reaching out to the Dagster community or support channels for assistance. Provide details about your configuration, the behavior you're observing, and any relevant logs or error messages. It's important to ensure that the configuration is correct and that there are no conflicting settings that could be causing the resource configurations to be ignored. The Dagster community or support channels may be able to provide further insights based on the specifics of your deployment and configuration.
a
Can i somehow use an op factory in my graph, which accepts some values in its params to generate the tags for the returned op, and use the returned op in the graph? def main_op_1_factory(tags): @op( tags = tags) def main_op_1_built: ..... return main_op_1_built @Scout
s
Yes, you can use an op factory in your graph to generate ops with dynamic tags based on the parameters passed to the factory function. This is a common pattern when you need to create multiple ops that have similar functionality but require different configurations, such as Kubernetes resource requests and limits. Here's an example of how you might define an op factory and use it within a graph:
Copy code
python from dagster import op, graph  # Define the op factory function def main_op_factory(tags):     @op(tags=tags)     def main_op_built(context):         # Op logic here         <http://context.log.info|context.log.info>("Running op with tags: {}".format(tags))     return main_op_built  # Use the factory to create ops with different tags main_op_1 = main_op_factory(tags={"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "10Gi"}, "limits": {"memory": "10Gi"}}}}}) main_op_2 = main_op_factory(tags={"dagster-k8s/config": {"container_config": {"resources": {"requests": {"memory": "20Gi"}, "limits": {"memory": "20Gi"}}}}})  # Define a graph that uses the ops created by the factory @graph def my_graph():     main_op_1()     main_op_2()  # Create a job from the graph my_job = my_graph.to_job()
In this example, 
main_op_factory
is a function that takes a 
tags
parameter and returns an op with those tags applied. We then create two ops, 
main_op_1
and 
main_op_2
, using the factory with different resource tags. These ops are then used within the 
my_graph
graph definition. When 
my_job
is executed, each op will have its own set of Kubernetes resource configurations as specified by the tags. This approach allows you to customize the resource requirements for each op without duplicating the op logic. Remember to adjust the resource tags and op logic according to your specific requirements. For the most accurate and up-to-date information on using op factories and configuring resources, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.
a
I want the main_op_1 to created at runtime when I pass in some resource values @Scout
s
Creating ops with different configurations at runtime based on passed-in values is a common pattern in Dagster. You can achieve this by defining an op factory function that accepts parameters and returns an op with the desired configuration. This op can then be included in a graph and instantiated at runtime with specific values. Here's an example of how you might define an op factory and use it within a graph:
Copy code
python from dagster import op, graph, job  # Define the op factory function def main_op_factory(memory_request, memory_limit):     @op(         tags={             "dagster-k8s/config": {                 "container_config": {                     "resources": {                         "requests": {"memory": memory_request},                         "limits": {"memory": memory_limit},                     }                 }             }         }     )     def main_op(context):         # Op logic here         <http://context.log.info|context.log.info>(f"Running with memory request: {memory_request}, limit: {memory_limit}")      return main_op  # Define a graph that uses the ops created by the factory @graph def my_graph(op1, op2):     op1()     op2()  # At runtime, create specific instances of the ops with the desired configurations @job def my_job():     op1 = main_op_factory("10Gi", "10Gi")     op2 = main_op_factory("20Gi", "20Gi")     my_graph(op1, op2)
In this example, 
main_op_factory
is a function that takes 
memory_request
and 
memory_limit
parameters and returns an op configured with those values in its Kubernetes resource tags. The 
my_graph
graph takes two ops as parameters and executes them. The 
my_job
job creates specific instances of these ops with the desired memory configurations and passes them to the graph. When you execute 
my_job
, it will run the ops with the memory configurations specified at the time the job was defined. If you need to pass the resource values at the time of job execution (rather than at job definition), you would typically use runtime configuration to parameterize the ops. However, as of my last update, Dagster does not support changing the op-level resource definitions at runtime through the run config. The resource configurations are defined at job construction time. For the most accurate and up-to-date information on dynamically configuring ops at runtime, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout I seem to get this error when calling the op created from a op factory, in the graph : The above exception was caused by the following exception: TypeError: 'InputMappingNode' object is not callable
s
The error message you're encountering, 
TypeError: 'InputMappingNode' object is not callable
, suggests that there's an issue with how the op is being used within the graph. This error typically occurs when an object that is not callable (like a class or instance that doesn't implement the 
__call__
method) is being used as if it were a function. In the context of Dagster, this error can happen if you're trying to invoke an op that has been returned from a factory function incorrectly. When you use an op factory, you need to ensure that the returned op is being called (invoked) correctly within the graph. Here's a revised example that demonstrates how to use an op factory within a graph:
Copy code
python from dagster import op, graph, job  # Define the op factory function def main_op_factory(memory_request, memory_limit):     @op(         tags={             "dagster-k8s/config": {                 "container_config": {                     "resources": {                         "requests": {"memory": memory_request},                         "limits": {"memory": memory_limit},                     }                 }             }         }     )     def main_op(context):         # Op logic here         <http://context.log.info|context.log.info>(f"Running with memory request: {memory_request}, limit: {memory_limit}")      return main_op  # Define a graph that uses the ops created by the factory @graph def my_graph():     # Create specific instances of the ops with the desired configurations     op1 = main_op_factory("10Gi", "10Gi")     op2 = main_op_factory("20Gi", "20Gi")          # Invoke the ops within the graph     op1()     op2()  # Create a job from the graph my_job = my_graph.to_job()
In this revised example, the ops 
op1
and 
op2
are created within the 
my_graph
function using the factory, and then they are invoked as part of the graph's definition. This ensures that the ops are callable objects when the graph is constructed. If you're still encountering issues, make sure that the ops are being instantiated and invoked correctly within the graph. If the problem persists, it may be helpful to provide the full code snippet or context in which the error occurs, as there might be other factors at play. For further assistance, consider reaching out to the Dagster community with details about your implementation and the error you're experiencing. They may be able to provide more specific guidance based on the latest version of Dagster and its features.
a
@Scout what is causing this error when I retry from failure? dagster._core.errors.DagsterInvariantViolationError: Attempting to access run_id, but it was not provided when constructing the OutputContext File "/usr/local/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_plan.py", line 275, in dagster_event_sequence_for_step for step_event in check.generator(step_events): File "/usr/local/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 443, in core_dagster_event_sequence_for_step for event_or_input_value in step_input.source.load_input_object(step_context, input_def): File "/usr/local/lib/python3.10/site-packages/dagster/_core/execution/plan/inputs.py", line 486, in load_input_object yield from _load_input_with_input_manager(input_manager, load_input_context) File "/usr/local/lib/python3.10/site-packages/dagster/_core/execution/plan/inputs.py", line 831, in _load_input_with_input_manager value = input_manager.load_input(context) File "/usr/local/lib/python3.10/site-packages/dagster_gcp/gcs/io_manager.py", line 166, in load_input return self._internal_io_manager.load_input(context) File "/usr/local/lib/python3.10/site-packages/dagster/_core/storage/upath_io_manager.py", line 401, in load_input path = self._get_path(context) File "/usr/local/lib/python3.10/site-packages/dagster/_core/storage/upath_io_manager.py", line 162, in _get_path path = self._get_path_without_extension(context) File "/usr/local/lib/python3.10/site-packages/dagster/_core/storage/upath_io_manager.py", line 128, in _get_path_without_extension context_path = self.get_op_output_relative_path(context) File "/usr/local/lib/python3.10/site-packages/dagster_gcp/gcs/io_manager.py", line 48, in get_op_output_relative_path parts = context.get_identifier() File "/usr/local/lib/python3.10/site-packages/dagster/_core/execution/context/input.py", line 448, in get_identifier return self.upstream_output.get_identifier() File "/usr/local/lib/python3.10/site-packages/dagster/_core/execution/context/output.py", line 559, in get_identifier run_id = self.run_id File "/usr/local/lib/python3.10/site-packages/dagster/_core/execution/context/output.py", line 204, in run_id raise DagsterInvariantViolationError(
s
The error message you're encountering indicates that there is an attempt to access the 
run_id
within an 
OutputContext
, but it was not provided when the 
OutputContext
was constructed. This is a 
DagsterInvariantViolationError
, which typically indicates that a well-defined invariant of the system has been violated at runtime. In the context of Dagster, this error could occur if the 
OutputContext
is being manually constructed or manipulated in a way that does not include all the necessary information, such as the 
run_id
. The 
run_id
is a critical piece of information that is used to track the execution of a run and is typically provided by the Dagster framework itself during normal execution. When retrying from failure, Dagster attempts to reconstruct the necessary context to resume the job. If the 
run_id
is missing from the context during this process, it could lead to the error you're seeing. Here are some steps you can take to troubleshoot this issue: 1. Review Custom Code: If you have custom code that constructs or modifies 
OutputContext
objects, ensure that it properly includes the 
run_id
. The 
run_id
should not be omitted when creating these contexts. 2. Check IO Manager Implementation: If you're using a custom IO manager or have overridden methods in an existing IO manager, make sure that the implementation correctly handles the 
OutputContext
and includes the 
run_id
. 3. Examine Retry Logic: Look at how the retry from failure is being initiated. Ensure that the retry logic is correctly using the Dagster APIs and that the 
run_id
is being preserved and passed through as expected. 4. Consult Documentation: Review the Dagster documentation on retries and IO managers to ensure that your implementation aligns with the expected patterns and practices. 5. Reach Out for Support: If the issue is not resolved by reviewing your code and the documentation, consider reaching out to the Dagster community for support. Provide the full stack trace, details about your Dagster setup, and any custom code related to the error. It's important to ensure that the system's invariants are maintained throughout the execution and retry processes. The 
run_id
should be automatically managed by Dagster and available in the context where it is expected. If there's a mismatch or missing information, it could indicate a problem with how the execution context is being handled in your code or a potential issue with Dagster's retry mechanism.
a
@Scout I am using a process similar to this to dynamically produce ops with custom k8s resources for different jobs. Where should I define the dictionary that contains the various sets k8s resource values, and how can I pass the chosen set into the factory functions in the graph?
Copy code
@graph
def my_graph():
    # Create specific instances of the ops with the desired configurations
    op1 = main_op_factory("10Gi", "10Gi")
    op2 = main_op_factory("20Gi", "20Gi")
    
    # Invoke the ops within the graph
    op1()
    op2()

# Create a job from the graph
my_job = my_graph.to_job()
s
To dynamically produce ops with custom Kubernetes resource configurations for different jobs, you can define a dictionary that contains the various sets of Kubernetes resource values outside of your graph and job definitions. You can then pass the chosen set of resource values into the factory functions when constructing the job. Here's an example of how you might structure your code:
Copy code
python from dagster import graph, op, job  # Define the op factory function def main_op_factory(memory_request, memory_limit):     @op(         tags={             "dagster-k8s/config": {                 "container_config": {                     "resources": {                         "requests": {"memory": memory_request},                         "limits": {"memory": memory_limit},                     }                 }             }         }     )     def main_op(context):         # Op logic here         <http://context.log.info|context.log.info>(f"Running with memory request: {memory_request}, limit: {memory_limit}")      return main_op  # Define a dictionary with the resource configurations for different jobs k8s_resource_configs = {     "job1": [("10Gi", "10Gi"), ("20Gi", "20Gi")],     "job2": [("20Gi", "20Gi"), ("30Gi", "30Gi")],     # Add more job configurations as needed }  # Define a function to create a job with a specific resource configuration def create_my_job(job_name):     resource_values = k8s_resource_configs[job_name]          @graph     def my_graph():         # Create specific instances of the ops with the desired configurations         ops = [main_op_factory(memory_request, memory_limit) for memory_request, memory_limit in resource_values]                  # Invoke the ops within the graph         for op_instance in ops:             op_instance()          # Create a job from the graph     return my_graph.to_job()  # Create specific jobs with the desired Kubernetes resource configurations job1 = create_my_job("job1") job2 = create_my_job("job2")
In this example, 
k8s_resource_configs
is a dictionary that maps job names to lists of Kubernetes resource configurations. The 
create_my_job
function takes a 
job_name
, looks up the corresponding resource configurations, and constructs a graph with ops that have those resource configurations. It then returns a job constructed from that graph. When you want to create a job with a specific set of Kubernetes resource configurations, you call 
create_my_job
with the desired job name, and it will return a job with the ops configured accordingly. This approach allows you to define the resource configurations separately from your graph and job definitions, providing a clear separation of concerns and making it easy to manage different configurations for different jobs.
a
@Scout I don't want to dynamically create the job, I am defining all the jobs from the same graph, but how do I pass different k8s resources into the graph from the to_job invocation?
Copy code
op_factory_job = dataprep_graph.to_job(
    tags={"dagster/max_retries": 3},
    resource_defs={'run_config': prod_resource},
    config=k8s_prod_config,  # Attach the config schema here
    name='k8s_op_factory_job',
    executor_def=k8s_job_executor
)
s
To pass different Kubernetes resource configurations into the graph from the 
to_job
invocation, you can define the resource configurations as part of the 
tags
argument in the 
to_job
method. The 
tags
argument can be used to pass arbitrary metadata to the job execution, including Kubernetes resource configurations for the 
k8s_job_executor
. Here's an example of how you might pass different Kubernetes resource configurations for each job using the 
tags
argument:
Copy code
python from dagster import graph, op, job from dagster_k8s.executor import k8s_job_executor  # Define your op factory function def main_op_factory():     @op     def main_op(context):         # Op logic here         pass     return main_op  # Define your graph @graph def my_graph():     op_instance = main_op_factory()     op_instance()  # Define Kubernetes resource configurations for different jobs k8s_prod_resources = {     "dagster-k8s/config": {         "container_config": {             "resources": {                 "requests": {"memory": "10Gi"},                 "limits": {"memory": "10Gi"},             }         }     } }  k8s_dev_resources = {     "dagster-k8s/config": {         "container_config": {             "resources": {                 "requests": {"memory": "5Gi"},                 "limits": {"memory": "5Gi"},             }         }     } }  # Create jobs from the graph with different Kubernetes resource configurations prod_job = my_graph.to_job(     name="prod_job",     tags=k8s_prod_resources,     executor_def=k8s_job_executor )  dev_job = my_graph.to_job(     name="dev_job",     tags=k8s_dev_resources,     executor_def=k8s_job_executor )
In this example, 
k8s_prod_resources
and 
k8s_dev_resources
are dictionaries that contain the Kubernetes resource configurations for the production and development environments, respectively. These dictionaries are passed to the 
tags
argument when creating 
prod_job
and 
dev_job
from the 
my_graph
graph. The 
k8s_job_executor
will use the resource configurations specified in the tags to set the Kubernetes resource requests and limits for the pods that run the ops. Please note that the actual implementation may vary based on your specific use case and the version of Dagster you are using. For the most accurate and up-to-date information on configuring Kubernetes resources for jobs and using the 
k8s_job_executor
, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.
a
How can I pass a parameter from the job invocation into the graph params? @Scout
s
In Dagster, you can pass parameters from the job invocation into the graph by using the 
config
argument when defining the job. The 
config
argument allows you to specify runtime configuration for the job, which can include parameters that are passed to the ops within the graph. Here's an example of how you might define a graph with parameters and pass values to those parameters from the job invocation:
Copy code
python from dagster import graph, op, job, Field  # Define an op that accepts a parameter @op(config_schema={"param": Field(str)}) def my_op(context):     param = context.op_config["param"]     <http://context.log.info|context.log.info>(f"Op received parameter: {param}")  # Define a graph that uses the op @graph def my_graph():     my_op()  # Define a job from the graph with specific parameters my_job = my_graph.to_job(     name="my_job",     config={         "ops": {             "my_op": {                 "config": {                     "param": "value_from_job_invocation"                 }             }         }     } )
In this example, 
my_op
is defined with a 
config_schema
that expects a parameter named 
param
. The 
my_graph
graph includes 
my_op
. When creating 
my_job
from 
my_graph
, the 
config
argument is used to provide a value for the 
param
parameter that 
my_op
expects. When you execute 
my_job
, it will run 
my_op
with the parameter value specified in the job's 
config
. If you want to pass parameters dynamically at the time of job execution (rather than at job definition), you can use the Dagster UI or the command line to provide a run configuration that includes the parameters for the ops. For example, if you're using the Dagster UI, you can enter the run configuration in the playground when launching the job. If you're using the command line, you can pass a YAML or JSON file with the run configuration using the 
-c
or 
--config
flag when executing the job. For the most accurate and up-to-date information on passing parameters to jobs and graphs, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.
a
I want to pass it into my_graph(param) @Scout
s
To pass parameters directly into a graph function in Dagster, you can define inputs for the graph. These inputs can then be used within the ops that make up the graph. When you convert the graph to a job using 
to_job
, you can provide the input values using the 
input_values
argument. Here's an example of how to define a graph with an input parameter and pass a value to it when creating a job:
Copy code
python from dagster import graph, op, job  # Define an op that uses an input parameter @op def my_op(context, param):     <http://context.log.info|context.log.info>(f"Op received parameter: {param}")  # Define a graph that accepts an input parameter @graph def my_graph(param):     my_op(param)  # Create a job from the graph and provide the input value my_job = my_graph.to_job(     name="my_job",     input_values={"param": "value_from_job_invocation"} )
In this example, 
my_graph
is defined with a parameter 
param
, which is passed to 
my_op
. When creating 
my_job
, the 
input_values
argument is used to provide the value for 
param
. When you execute 
my_job
, it will run 
my_graph
with the 
param
value specified in 
input_values
. This approach allows you to define graphs that accept parameters and create jobs with specific values for those parameters. The values provided in 
input_values
will be used each time the job is executed. For more information on how to use inputs in graphs and jobs, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout I get this error when I pass param to my_op (which in my case is a factory function that generates an op) Stack Trace: File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_grpc/server.py", line 408, in init self._loaded_repositories: Optional[LoadedRepositories] = LoadedRepositories( File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_grpc/server.py", line 235, in init with user_code_error_boundary( File "/usr/lib/python3.10/contextlib.py", line 153, in exit self.gen.throw(typ, value, traceback) File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/errors.py", line 297, in user_code_error_boundary raise new_error from e The above exception was caused by the following exception: dagster._check.ParameterCheckError: Param "tags" is not one of ['Mapping']. Got InputMappingNode(input_def=<dagster._core.definitions.input.InputDefinition object at 0x7f501cd6ba30>) which is type <class 'dagster._core.definitions.composition.InputMappingNode'>. Stack Trace: File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/errors.py", line 287, in user_code_error_boundary yield File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_grpc/server.py", line 242, in init loadable_targets = get_loadable_targets( File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_grpc/utils.py", line 50, in get_loadable_targets else loadable_targets_from_python_module(module_name, working_directory) File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/workspace/autodiscovery.py", line 35, in loadable_targets_from_python_module module = load_python_module( File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/code_pointer.py", line 135, in load_python_module return importlib.import_module(module_name) File "/usr/lib/python3.10/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1050, in _gcd_import File "<frozen importlib._bootstrap>", line 1027, in _find_and_load File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 688, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 883, in exec_module File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed File "/home/alexsmith/Repositories/sbx-etl-services/elt_repositories/dataprep_repo/dataprep_repo/__init__.py", line 5, in <module> from .jobs import k8s_dataprep_JAD_prod_job, k8s_dataprep_RI_prod_job, k8s_dataprep_RI_partition_job,k8s_dataprep_RI_partition_job_test, dataprep_local_job, ri_factory_job, jad_factory_job File "/home/alexsmith/Repositories/sbx-etl-services/elt_repositories/dataprep_repo/dataprep_repo/jobs/__init__.py", line 11, in <module> from .k8s_dataprep_jobs import ri_factory_job, jad_factory_job File "/home/alexsmith/Repositories/sbx-etl-services/elt_repositories/dataprep_repo/dataprep_repo/jobs/k8s_dataprep_jobs.py", line 11, in <module> def dataprep_graph(k8s_resources): File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/definitions/decorators/graph_decorator.py", line 79, in call ) = do_composition( File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/definitions/composition.py", line 1032, in do_composition output = fn(**kwargs) File "/home/alexsmith/Repositories/sbx-etl-services/elt_repositories/dataprep_repo/dataprep_repo/jobs/k8s_dataprep_jobs.py", line 15, in dataprep_graph read_data_files_op = read_data_files_factory(k8s_resources) File "/home/alexsmith/Repositories/sbx-etl-services/elt_repositories/dataprep_repo/dataprep_repo/jobs/op_factory.py", line 62, in read_data_files_factory def read_data_files_op(context: OpExecutionContext): File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/definitions/decorators/op_decorator.py", line 127, in call op_def = OpDefinition.dagster_internal_init( File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_definition.py", line 205, in dagster_internal_init return OpDefinition( File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/decorator_utils.py", line 203, in wrapped_with_pre_call_fn return fn(*args, **kwargs) File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_definition.py", line 187, in init tags=check.opt_mapping_param(tags, "tags", key_type=str), File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_check/__init__.py", line 941, in opt_mapping_param return mapping_param(obj, param_name, key_type, value_type, additional_message) File "/home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_check/__init__.py", line 921, in mapping_param raise _param_type_mismatch_exception(
s
The error you're encountering suggests that there's a mismatch between the expected type for the 
tags
parameter and the object being passed to it. The error message indicates that the 
tags
parameter is expected to be a mapping (such as a dictionary), but an 
InputMappingNode
object is being provided instead. This type of error typically occurs when there's a confusion between the construction of ops and the construction of graphs, especially when using decorators and factory functions. To resolve this issue, ensure that the 
tags
parameter you pass to the 
@op
decorator within your factory function is a dictionary that conforms to the expected structure for Kubernetes resource tags. Here's a simplified example of how you might define an op factory function:
Copy code
python from dagster import op  # Define the op factory function def op_factory(resource_tags):     @op(tags=resource_tags)     def my_op(context):         # Op logic here         pass     return my_op
When you call 
op_factory
, you should pass a dictionary that represents the Kubernetes resource tags:
Copy code
python # Example usage of the op factory function my_op_instance = op_factory({     "dagster-k8s/config": {         "container_config": {             "resources": {                 "requests": {"memory": "10Gi"},                 "limits": {"memory": "10Gi"},             }         }     } })
In your graph definition, you should then use the instances of the ops created by the factory function:
Copy code
python from dagster import graph  @graph def my_graph():     op1 = op_factory({...})  # Replace with actual resource tags     op2 = op_factory({...})  # Replace with actual resource tags          op1()     op2()
Finally, when creating jobs from the graph, you don't need to pass Kubernetes resource tags at this point since they are already embedded within the ops:
Copy code
python my_job = my_graph.to_job()
If you're still experiencing issues, it would be helpful to review the full implementation of your op factory function and how it's being used within the graph. Make sure that the 
tags
parameter is always a dictionary with the correct structure and that you're not inadvertently passing graph-related objects where op-related parameters are expected. For further assistance, consider reaching out to the Dagster community with the full code and context of the error. They may be able to provide more specific guidance based on the details of your implementation.
a
But I want to pass the dictionary contains the resource information to the job invocation's input_values: input_values={"params": params} where params is a dictonary of the required resource tag @Scout
s
To pass a dictionary containing resource information to the job invocation's 
input_values
, you would typically use config mappings or resources in Dagster. However, 
input_values
is used for passing inputs to the graph, not for configuring resources or tags. If you want to pass different Kubernetes resource configurations to ops within a graph and have those configurations be determined at job invocation time, you would use resources. You can define a resource that holds the configuration, and then access that resource within your ops. Here's a conceptual example of how you might set up a resource to hold Kubernetes resource tags and use it within your ops:
Copy code
python
from dagster import op, graph, job, resource, make_values_resource

# Define a resource that holds Kubernetes resource tags
@resource
def k8s_resource_tags(context):
    return context.resource_config

# Define an op that uses the Kubernetes resource tags from the resource
@op(required_resource_keys={"k8s_resource_tags"})
def my_op(context):
    tags = context.resources.k8s_resource_tags
    # Use the tags as needed, for example, to configure Kubernetes resources
    <http://context.log.info|context.log.info>(f"Kubernetes resource tags: {tags}")

# Define a graph that uses the op
@graph
def my_graph():
    my_op()

# Create a job from the graph with the resource
my_job = my_graph.to_job(
    name="my_job",
    resource_defs={
        "k8s_resource_tags": make_values_resource(
            memory_request="10Gi",
            memory_limit="10Gi",
            # Add other Kubernetes resource configurations as needed
        )
    }
)
In this example, 
k8s_resource_tags
is a resource that holds the Kubernetes resource tags. The 
my_op
op requires this resource and retrieves the tags from the context. The 
my_graph
graph uses 
my_op
, and 
my_job
is a job created from the graph with the 
k8s_resource_tags
resource definition. When you want to invoke 
my_job
with different Kubernetes resource configurations, you can provide a different resource configuration at job invocation time. Here's how you might do that:
Copy code
python
# When invoking the job, provide a different configuration for the resource
run_config = {
    "resources": {
        "k8s_resource_tags": {
            "config": {
                "memory_request": "20Gi",
                "memory_limit": "20Gi",
                # Add other Kubernetes resource configurations as needed
            }
        }
    }
}

result = my_job.execute_in_process(run_config=run_config)
In this example, 
run_config
is a dictionary that provides the configuration for the 
k8s_resource_tags
resource when invoking the job. This allows you to dynamically configure the Kubernetes resources for the ops within the job at invocation time. Please note that this is a
conceptual example, and you may need to adjust the implementation to fit your specific use case. For more detailed guidance, please refer to the Dagster documentation or reach out to the Dagster community. Resources passed to Definitions should b... button BindResourcesToJobs is great but if hard... button Generate multiple runs while incrementin... button Dagster Docs button Thank you for your feedback!
a
How can I use the graphs input_defs? @Scout
s
In Dagster, you can define inputs for a graph using the 
input_defs
parameter in the 
@graph
decorator. These inputs can then be passed to the ops within the graph. When you convert the graph to a job using 
to_job
, you can provide the input values using the 
input_values
argument. Here's an example of how to define a graph with input definitions and pass values to it when creating a job:
Copy code
python from dagster import graph, op, job, In, Nothing  # Define an op that uses an input parameter @op def my_op(context, my_input):     <http://context.log.info|context.log.info>(f"Op received input: {my_input}")  # Define a graph that accepts an input parameter @graph(input_defs=[In(name="my_input", dagster_type=str)]) def my_graph(my_input):     my_op(my_input)  # Create a job from the graph and provide the input value my_job = my_graph.to_job(     name="my_job",     input_values={"my_input": "value_from_job_invocation"} )
In this example, 
my_graph
is defined with an input definition for 
my_input
, which is passed to 
my_op
. When creating 
my_job
, the 
input_values
argument is used to provide the value for 
my_input
. When you execute 
my_job
, it will run 
my_graph
with the 
my_input
value specified in 
input_values
. This approach allows you to define graphs that accept inputs and create jobs with specific values for those inputs. The values provided in 
input_values
will be used each time the job is executed. For more information on how to use inputs in graphs and jobs, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout The above exception was caused by the following exception: TypeError: In.__new__() got an unexpected keyword argument 'name'
s
The error message you're encountering indicates that there's a misuse of the 
In
class when defining input definitions for a graph. The 
In
class in Dagster does not accept a 
name
keyword argument in its constructor. Instead, the name of the input is determined by the parameter name in the graph function itself. Here's the correct way to define a graph with input definitions:
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python from dagster import graph, op, job, In  # Define an op that uses an input parameter @op def my_op(context, my_input):     <http://context.log.info|context.log.info>(f"Op received input: {my_input}")  # Define a graph that accepts an input parameter @graph def my_graph(my_input: str):     my_op(my_input)  # Create a job from the graph and provide the input value my_job = my_graph.to_job(     name="my_job",     input_values={"my_input": "value_from_job_invocation"} )
In this example, 
my_graph
is defined with a parameter 
my_input
, which is used as the input for the graph. The type of the input is specified using Python type annotations (
my_input: str
). When creating 
my_job
, the 
input_values
argument is used to provide the value for 
my_input
. When you execute 
my_job
, it will run 
my_graph
with the 
my_input
value specified in 
input_values
. If you need to provide additional configuration for the input, such as a default value or a description, you can use the 
In
class without the 
name
keyword argument, like this:
Copy code
python from dagster import In  my_input_def = In(dagster_type=str, description="My input description", default_value="default_value")
Then, you would use 
my_input_def
in the graph function signature:
Copy code
python @graph def my_graph(my_input: my_input_def):     my_op(my_input)
Please adjust your graph and job definitions accordingly, and ensure that you're using the 
In
class correctly without the 
name
keyword argument. For more detailed guidance, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
how do I use the ins param on the graph decorator? @Scout
s
The 
ins
parameter on the 
@graph
decorator in Dagster is used to define inputs for the graph. These inputs are then mapped to the inputs of the ops within the graph. The 
ins
parameter takes a dictionary where the keys are the names of the inputs and the values are instances of 
GraphIn
. Here's an example of how to use the 
ins
parameter with the 
@graph
decorator:
Copy code
python from dagster import graph, op, GraphIn, In  # Define an op that expects an input @op def my_op(context, my_input):     <http://context.log.info|context.log.info>(f"Received input: {my_input}")  # Define a graph with an input using the `ins` parameter @graph(ins={"graph_input": GraphIn()}) def my_graph(graph_input):     my_op(graph_input)  # When creating a job, you can provide the input values for the graph my_job = my_graph.to_job(     name="my_job",     input_values={"graph_input": "value_passed_to_graph"} )
In this example, 
my_graph
is defined with an input called 
graph_input
using the 
ins
parameter. The 
GraphIn
class is used to create the input definition for the graph. Inside the graph, 
graph_input
is passed to 
my_op
. When you create a job from this graph using 
to_job
, you can provide the input values using the 
input_values
argument, where the keys correspond to the input names defined in the 
ins
parameter of the 
@graph
decorator. This approach allows you to define inputs at the graph level, which can be particularly useful when you want to reuse the same graph in multiple jobs with different input values or when you want to expose graph-level inputs for testing and development purposes. For more information on using the 
ins
parameter and defining graph inputs, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
It's treating "value_passed_to_graph" as a tuple object in the graph, but surely it should be a string? @Scout
s
If "value_passed_to_graph" is being treated as a tuple instead of a string within the graph, it could be due to a misconfiguration or misunderstanding of how inputs are passed to the graph. The value you pass to 
input_values
should indeed be used as the value for the corresponding input within the graph. Let's clarify the correct usage with an example:
Copy code
python from dagster import graph, op, In, job  # Define an op that expects an input @op def my_op(context, my_input: str):     <http://context.log.info|context.log.info>(f"Received input: {my_input}")  # Define a graph with an input @graph def my_graph(my_input: str):     my_op(my_input)  # Create a job from the graph and provide the input value my_job = my_graph.to_job(     name="my_job",     input_values={"my_input": "value_passed_to_graph"} )
In this example, 
my_graph
has a single input 
my_input
of type 
str
. When creating 
my_job
, we provide a dictionary to 
input_values
where the key 
my_input
corresponds to the graph input, and the value is the string "value_passed_to_graph". If you're still encountering issues where the input is treated as a tuple, ensure that: 1. The input definition in the 
@op
and 
@graph
decorators correctly specifies the type (e.g., 
str
). 2. The 
input_values
provided to 
to_job
matches the input names and structure expected by the graph. If the problem persists, it may be helpful to review the full code and context in which the error occurs. There might be other factors at play, such as how the inputs are being used within the ops or how the job is being executed. For further assistance, consider reaching out to the Dagster community with the full code and details of the behavior you're experiencing. They may be able to provide more specific guidance based on the details of your implementation.
a
@Scout can I generate a hash id in op_1 of a graph and pass it to the op_config of all future ops?
s
In Dagster, you can generate a value in one op and pass it to subsequent ops within a graph. However, you cannot directly modify the 
op_config
of downstream ops at runtime, as the 
op_config
is determined by the job's configuration before execution begins. To pass a value such as a hash ID generated in 
op_1
to other ops, you can return the value from 
op_1
and use it as an input to the downstream ops. Here's a conceptual example:
Copy code
python from dagster import op, graph, job, Out, In  # Op that generates a hash ID and returns it @op(out=Out(str)) def op_1(context):     hash_id = generate_hash_id()  # Replace with your hash ID generation logic     return hash_id  # Downstream op that takes a hash ID as an input @op(ins={"hash_id": In(str)}) def op_2(context, hash_id):     <http://context.log.info|context.log.info>(f"Received hash ID: {hash_id}")     # Use the hash ID as needed  # Define a graph that connects the ops @graph def my_graph():     hash_id = op_1()     op_2(hash_id)  # Create a job from the graph my_job = my_graph.to_job()  # Function to generate a hash ID (replace with your actual implementation) def generate_hash_id():     import uuid     return str(uuid.uuid4())
In this example, 
op_1
generates a hash ID and returns it. The returned value is then passed as an input to 
op_2
. The 
my_graph
graph connects 
op_1
and 
op_2
, ensuring that the hash ID flows from one op to the other. When you execute 
my_job
, 
op_1
will run first, generate a hash ID, and pass it to 
op_2
. Please note that this approach allows you to pass dynamic values between ops at runtime, but it does not modify the 
op_config
of the ops. The 
op_config
is static and must be defined before execution starts, typically in the job definition or when launching the job. For more information on passing data between ops and configuring jobs, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout Can I pass in a fixed parameter to partitioned_op_1 alongside the mapped dynamic partitions ? dynamic_partitions = generate_dynamic_partitions_op(input_data) processed_partitions = dynamic_partitions.map(partitioned_op_1)
s
In Dagster, when you use the 
.map
method on the output of an op that produces dynamic outputs, each dynamic output is mapped to a new instance of the downstream op. If you want to pass a fixed parameter to 
partitioned_op_1
alongside the dynamically mapped partitions, you can do so by capturing the fixed parameter in a closure when defining 
partitioned_op_1
. Here's a conceptual example:
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python from dagster import op, DynamicOut, DynamicOutput, graph, job  # Op that generates dynamic partitions @op(out=DynamicOut()) def generate_dynamic_partitions_op(context, input_data):     # Your logic to generate partitions     for partition in input_data:         yield DynamicOutput(partition, mapping_key=str(partition['key']))  # Factory function to create an op that takes a fixed parameter and a dynamic input def create_partitioned_op(fixed_parameter):     @op     def partitioned_op(context, dynamic_partition):         # Use fixed_parameter and dynamic_partition as needed         <http://context.log.info|context.log.info>(f"Fixed parameter: {fixed_parameter}, Dynamic partition: {dynamic_partition}")         # Your processing logic here     return partitioned_op  # Define a graph that uses the ops @graph def my_graph(input_data, fixed_parameter):     dynamic_partitions = generate_dynamic_partitions_op(input_data)     # Create an instance of partitioned_op_1 with the fixed parameter     partitioned_op_1 = create_partitioned_op(fixed_parameter)     # Map the dynamic partitions to partitioned_op_1     processed_partitions = dynamic_partitions.map(partitioned_op_1)  # Create a job from the graph my_job = my_graph.to_job()
In this example, 
generate_dynamic_partitions_op
yields dynamic partitions, and 
create_partitioned_op
is a factory function that creates an op (
partitioned_op
) with a fixed parameter. When defining the graph 
my_graph
, we create an instance of 
partitioned_op_1
using the factory function and the fixed parameter. Then, we map the dynamic partitions to 
partitioned_op_1
. When you execute 
my_job
, 
generate_dynamic_partitions_op
will run first, generate dynamic partitions, and then each partition will be processed by an instance of 
partitioned_op_1
that also has access to the fixed parameter. Please note that this approach allows you to pass both dynamic and fixed parameters to the mapped op. The fixed parameter is captured by the closure created by the factory function, and the dynamic partitions are passed by the 
.map
method. For more information on working with dynamic outputs and mapping ops, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
The fixed parameter is returned from a previous op in the graph @Scout
s
If the fixed parameter is returned from a previous op in the graph, you can pass it to subsequent ops by capturing it as an output of the first op and then providing it as an input to the downstream ops. Here's how you can structure your graph to achieve this:
Copy code
python
from dagster import op, Out, DynamicOut, DynamicOutput, graph, job

# Op that generates a fixed parameter
@op(out=Out())
def generate_fixed_parameter(context):
    fixed_parameter = "some_value"  # Replace with your logic to generate the fixed parameter
    return fixed_parameter

# Op that generates dynamic partitions
@op(out=DynamicOut())
def generate_dynamic_partitions_op(context):
    # Your logic to generate partitions
    for i in range(3):  # Example loop to generate dynamic partitions
        yield DynamicOutput(value=f"partition_{i}", mapping_key=str(i))

# Factory function to create an op that takes a fixed parameter and a dynamic input
def create_partitioned_op(fixed_parameter):
    @op
    def partitioned_op(context, dynamic_partition):
        # Use fixed_parameter and dynamic_partition as needed
        <http://context.log.info|context.log.info>(f"Fixed parameter: {fixed_parameter}, Dynamic partition: {dynamic_partition}")
        # Your processing logic here
    return partitioned_op

# Define a graph that uses the ops
@graph
def my_graph():
    fixed_parameter = generate_fixed_parameter()
    dynamic_partitions = generate_dynamic_partitions_op()
    # Create an instance of partitioned_op with the fixed parameter
    partitioned_op_1 = create_partitioned_op(fixed_parameter)
    # Map the dynamic partitions to partitioned_op_1
    processed_partitions = dynamic_partitions.map(partitioned_op_1)

# Create a job from the graph
my_job = my_graph.to_job()
In this example, 
generate_fixed_parameter
is an op that generates a fixed parameter and returns it. The 
generate_dynamic_partitions_op
yields dynamic partitions. The 
create_partitioned_op
is a factory function that creates an op (
partitioned_op
) with a fixed parameter. The 
my_graph
graph captures the fixed parameter from 
generate_fixed_parameter
and then uses it to create an instance of 
partitioned_op_1
using the factory function. The dynamic partitions are then mapped to 
partitioned_op_1
. When you execute 
my_job
, 
generate_fixed_parameter
will run first, generate the fixed parameter, and then 
generate_dynamic_partitions_op
will generate dynamic partitions. Each dynamic partition will be processed by an instance of 
partitioned_op_1
that also has access to the fixed parameter. This approach allows you to pass both the dynamically generated partitions and the fixed parameter to the downstream op created by the factory function. The fixed parameter is threaded through the graph as a regular output and input, while the dyna
mic partitions are handled using the 
.map
method. For more information on working with dynamic outputs and passing parameters through a graph, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
I want to pass the fixed param with the dynamic partition into the op @Scout
s
To pass a fixed parameter alongside dynamically generated partitions into an op within a Dagster graph, you can use the 
fan_in
function to combine the fixed parameter with each dynamic partition into a tuple. Then, you can map this tuple to the downstream op. Here's an example of how you can structure your graph to achieve this:
Copy code
python from dagster import op, Out, DynamicOut, DynamicOutput, graph, job, fan_in  # Op that generates a fixed parameter @op(out=Out()) def generate_fixed_parameter(context):     fixed_parameter = "some_value"  # Replace with your logic to generate the fixed parameter     return fixed_parameter  # Op that generates dynamic partitions @op(out=DynamicOut()) def generate_dynamic_partitions_op(context):     # Your logic to generate partitions     for i in range(3):  # Example loop to generate dynamic partitions         yield DynamicOutput(value=f"partition_{i}", mapping_key=str(i))  # Op that takes a fixed parameter and a dynamic partition @op def partitioned_op(context, inputs):     fixed_parameter, dynamic_partition = inputs     # Use fixed_parameter and dynamic_partition as needed     <http://context.log.info|context.log.info>(f"Fixed parameter: {fixed_parameter}, Dynamic partition: {dynamic_partition}")     # Your processing logic here  # Define a graph that uses the ops @graph def my_graph():     fixed_parameter = generate_fixed_parameter()     dynamic_partitions = generate_dynamic_partitions_op()     # Combine the fixed parameter with each dynamic partition     combined_inputs = fan_in([fixed_parameter, dynamic_partitions.collect()])     # Map the combined inputs to the partitioned_op     partitioned_op(combined_inputs)  # Create a job from the graph my_job = my_graph.to_job()
In this example, 
generate_fixed_parameter
produces a fixed parameter, and 
generate_dynamic_partitions_op
yields dynamic partitions. The 
fan_in
function is used to combine the fixed parameter with the dynamically collected partitions into a list of tuples. Each tuple contains the fixed parameter and one dynamic partition. The 
partitioned_op
is then invoked with these combined inputs. When you execute 
my_job
, 
generate_fixed_parameter
will run first to produce the fixed parameter. Then 
generate_dynamic_partitions_op
will generate dynamic partitions. Finally, 
partitioned_op
will be executed with both the fixed parameter and each dynamic partition. This approach allows you to pass both the fixed parameter and the dynamic partitions to the downstream op within the same execution step. For more information on working with dynamic outputs and the 
fan_in
function, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout if ia have a job created on a graph of ops 1, 2, 3, 4. Once the job has run op 2, can I disabled retries from op 2 and previous ops, but still allow retries on 3 and 4?
s
As of the last update to the documentation, Dagster does not provide a built-in feature to selectively disable retries for specific ops once a job has started running. Retries in Dagster are typically configured at the job level using the 
@job
decorator with tags to specify the retry policy, or at the op level using the 
retry_policy
argument in the 
@op
decorator. These configurations are static and apply to the entire execution of the job. However, you can design your ops to handle retries conditionally based on custom logic. For example, you could check the 
context.retry_number
within each op to determine whether to proceed with a retry or to skip the retry logic. Here's a conceptual example:
Copy code
python from dagster import op, RetryRequested, job  @op def op_1(context):     if context.retry_number > 0:         # Custom logic to skip retries or perform necessary actions before retrying         pass     # Op logic...  @op def op_2(context):     if context.retry_number > 0:         # Custom logic to skip retries or perform necessary actions before retrying         pass     # Op logic...  @op def op_3(context):     # This op will still follow the default retry policy     # Op logic...  @op def op_4(context):     # This op will still follow the default retry policy     # Op logic...  @job def my_job():     op_1()     op_2()     op_3()     op_4()
In this example, 
op_1
and 
op_2
include checks for 
context.retry_number
to determine if the op is being retried. You can implement custom logic to decide whether to proceed with the operation or to perform any other necessary actions. For 
op_3
and 
op_4
, the default retry policy will apply, allowing retries as configured for the job. If you want to implement a more dynamic approach to enabling and disabling retries for certain ops during job execution, you might need to manage this outside of Dagster, such as by using an external system to track the state of the job and make decisions about retries. For the most accurate and up-to-date information on configuring retries and handling retry logic in Dagster, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout how can I add a run config to my job which accepts either a string or none value from the fpath variable in the lauchpad?
s
To add a run config to your job that accepts either a string or 
None
value for a variable (let's call it 
fpath
) from the launchpad, you can define the run config schema for your job using Dagster's configuration system. You would typically use a 
Field
with 
StringSource
for the string type and make it optional to allow 
None
values. Here's an example of how you might define such a run config schema within your job:
Copy code
python from dagster import job, op, Field, StringSource  # Define an op that uses the fpath parameter @op(config_schema={"fpath": Field(StringSource, is_required=False, default_value=None)}) def process_file(context):     fpath = context.op_config["fpath"]     if fpath:         <http://context.log.info|context.log.info>(f"Processing file at path: {fpath}")     else:         <http://context.log.info|context.log.info>("No file path provided, skipping processing.")  # Define a job that uses the process_file op @job def my_job():     process_file()
In this example, the 
process_file
op has a config schema defined with a field 
fpath
that can be a string or 
None
. The 
Field
is marked with 
is_required=False
to indicate that it's optional, and 
default_value=None
specifies that the default value is 
None
if not provided. When you launch the job from the Dagster launchpad, you can provide a run config that includes the 
fpath
variable. If you provide a string, it will be used as the file path; if you leave it out or explicitly set it to 
None
, no file path will be provided, and the op can handle that accordingly. Here's an example of what the run config might look like in YAML format, which you can input in the launchpad:
Copy code
yaml ops:   process_file:     config:       fpath: "/path/to/my/file.txt"  # Replace with the actual file path or omit for None
Or, if you want to explicitly set 
fpath
to `None`:
Copy code
yaml ops:   process_file:     config:       fpath:
This setup allows you to provide an optional 
fpath
parameter when launching the job from the Dagster launchpad, and the op will receive this value in its configuration. For more information on configuring jobs and handling optional config values, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout can I make an op in my graph conditional based on a param generated in the config mapper of a a jobs config?
s
Yes, you can make the execution of an op within a graph conditional based on a parameter generated in the config mapper of a job's config. The config mapping feature in Dagster allows you to transform the provided run config into the config that the ops within the job expect. You can use this mechanism to include conditional logic that determines whether or not an op should execute. Here's a conceptual example of how you might set up a config mapping to conditionally execute an op based on a parameter:
Copy code
python
from dagster import job, op, graph, ConfigMapping, Field, Bool

# Define your ops
@op
def conditional_op(context):
    <http://context.log.info|context.log.info>("Executing conditional_op")

@op
def always_run_op(context):
    <http://context.log.info|context.log.info>("Executing always_run_op")

# Define your graph
@graph
def my_graph(should_run_conditional_op):
    always_run_op()
    if should_run_conditional_op:
        conditional_op()

# Define a config mapping that includes a parameter to control the conditional op
@ConfigMapping(config_schema={"run_conditional_op": Field(Bool, is_required=False, default_value=True)})
def my_config_mapping(config):
    return {
        "ops": {
            "my_graph": {
                "inputs": {
                    "should_run_conditional_op": config["run_conditional_op"]
                }
            }
        }
    }

# Create a job with the config mapping
@job(config=my_config_mapping)
def my_job():
    my_graph()

# Example run config to pass to the job
run_config = {
    "run_conditional_op": False  # Set to True or False to control the conditional op
}
In this example, 
my_graph
takes a parameter 
should_run_conditional_op
that determines whether 
conditional_op
should execute. The 
my_config_mapping
function transforms the provided run config into the config that 
my_graph
expects, including the value for 
should_run_conditional_op
. When you execute 
my_job
with the 
run_config
, the 
my_config_mapping
function will be called, and the value of 
run_conditional_op
from the run config will be used to determine whether 
conditional_op
is executed. Please note that this is a simplified example, and the actual implementation may vary based on your specific use case. For more detailed guidance on using config mappings and conditional execution in Dagster, please refer to the Dagster documentation or reach out to the Dagster community for assistance. Support mapping partition key in op/asse... button Op jobs | Dagster button Op Graphs | Dagster button Dagster Docs button Thank you for your feedback!
a
@Scout 2024-02-23 093859 +0000 - dagster.code_server - INFO - Started Dagster code server for module dataprep_repo in process 2869 /home/alexsmith/Repositories/dataprep-venv/lib/python3.10/site-packages/dagster/_core/workspace/context.py619 UserWarning: Error loading repository location dataprep_repodagster. core.errors.DagsterInvalidDefinitionError @graph 'dataprep_graph' has unmapped input 'save_local_diagnostic'. Remove it or pass it to the appropriate op/graph invocation.
s
The warning you're encountering indicates that the 
@graph
named 
dataprep_graph
has an input defined (
save_local_diagnostic
) that is not being passed to any op or graph invocation within the graph's body. This can happen if you've defined an input in the graph but haven't used it in any of the ops, or if there's a mismatch between the input names. To resolve this issue, you need to ensure that all inputs defined in the 
@graph
decorator are correctly mapped and passed to the ops within the graph. Here's a general approach to addressing the warning: 1. Check the definition of 
dataprep_graph
to identify the input 
save_local_diagnostic
. 2. Ensure that 
save_local_diagnostic
is being used within the graph. If it's intended to be passed to an op, make sure that the op accepts this input and that you are passing it correctly. 3. If 
save_local_diagnostic
is not needed, remove it from the graph's input definitions. Here's a conceptual example of how you might define a graph with an input and use it within an op:
Copy code
python
from dagster import graph, op, job, In

@op
def my_op(context, save_local_diagnostic):
    # Use the save_local_diagnostic input as needed
    pass

@graph
def dataprep_graph(save_local_diagnostic):
    # Pass the input to the op
    my_op(save_local_diagnostic)

# Create a job from the graph
dataprep_job = dataprep_graph.to_job()
In this example, the 
dataprep_graph
graph has an input 
save_local_diagnostic
, which is passed to 
my_op
. If 
save_local_diagnostic
is not used within any op, you would either remove it from the graph's input definitions or ensure it's passed to the appropriate op. If you continue to encounter issues, review the full definition of 
dataprep_graph
and all ops within it to ensure that inputs are correctly mapped and used. If you need further assistance, consider reaching out to the Dagster community with the full code and context of the error for more specific guidance. [docs] - include instructions for gettin... button got struck with the simple error button Dagster code is failing to load with: "A... button Running Dagster on-prem with User Code button Thank you for your feedback!
a
@Scout but thats the equivalent of the param you passed, should_run_conditional_op, to the graph to determine whether or not an op was called: were you wrong?
Copy code
@graph
def my_graph(should_run_conditional_op):
    always_run_op()
    if should_run_conditional_op:
        conditional_op()
s
I apologize for any confusion. The example I provided earlier was meant to illustrate how you could use a parameter within a graph to conditionally execute an op. However, based on the warning message you're receiving, it seems that there might be a different issue at hand related to the 
@graph
decorator and its inputs. The warning message you provided suggests that there is an input 
save_local_diagnostic
defined for the 
dataprep_graph
that is not being used correctly. This could mean that the input is defined but not passed to any op within the graph, or there is a mismatch in the expected inputs for the ops within the graph. To address this warning, you should ensure that all inputs defined in the 
@graph
decorator are either used within the graph or removed if they are not needed. Here's an example of how you might define a graph with an input and use it within an op:
Copy code
python
from dagster import graph, op, job, In

@op
def my_op(context, save_local_diagnostic):
    # Use the save_local_diagnostic input as needed
    pass

@graph
def dataprep_graph(save_local_diagnostic):
    # Pass the input to the op
    my_op(save_local_diagnostic)

# Create a job from the graph
dataprep_job = dataprep_graph.to_job()
In this example, the 
dataprep_graph
graph has an input 
save_local_diagnostic
, which is passed to 
my_op
. If 
save_local_diagnostic
is not used within any op, you would either remove it from the graph's input definitions or ensure it's passed to the appropriate op. If you are using a config mapping to generate a parameter and want to use it to conditionally execute an op within a graph, you would typically handle this within the config mapping function and then pass the resulting configuration to the graph. Here's an example of how you might do that:
Copy code
python
from dagster import graph, op, job, ConfigMapping, Field, Bool

@op
def conditional_op(context):
    # Op logic...

@op
def always_run_op(context):
    # Op logic...

@graph
def my_graph():
    always_run_op()
    # The conditional op is not included here; it will be added conditionally in the job

@ConfigMapping(config_schema={"run_conditional_op": Field(Bool, is_required=False, default_value=True)})
def my_config_mapping(config):
    graph_config = {"ops": {"always_run_op": {}}}
    if config["run_conditional_op"]:
        graph_config["ops"]["conditional_op"] = {}
    return graph_config

@job(config=my_config_mapping)
def my_job():
    my_graph()
In this updated example, the 
my_config_mapping
function checks the 
run_conditional_op
parameter and conditionally includes the 
conditional_op
in the graph configuration. The 
my_graph
graph definition does not include 
conditional_op
directly; instead, it's added based on the job configuration. Please review y
our graph and job definitions to ensure that inputs are correctly mapped and used. If you need further assistance, consider reaching out to the Dagster community with the full code and context of the error for more specific guidance. Got an unclear warning running a graph button Nesting Graphs | Dagster button Please help me with ops parameters and c... button Calling an asset from a graph button Thank you for your feedback!
a
I don't want to call my_op if save_local_diagnostic is False @Scout
s
If you want to conditionally execute 
my_op
based on the value of 
save_local_diagnostic
within a Dagster graph, you can do so by checking the value of 
save_local_diagnostic
within the graph function and only invoking 
my_op
if the condition is met. Here's an example of how to implement this:
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python from dagster import graph, op, job, In  @op def my_op(context):     # Op logic...     <http://context.log.info|context.log.info>("my_op is running because save_local_diagnostic is True.")  @graph def my_graph(save_local_diagnostic: In(bool)):     if save_local_diagnostic:         my_op()  # Create a job from the graph my_job = my_graph.to_job()
In this example, the 
my_graph
graph has a boolean input 
save_local_diagnostic
. Within the graph function, there is a conditional check on the value of 
save_local_diagnostic
, and 
my_op
is only invoked if the value is 
True
. When you execute 
my_job
, you will provide the value for 
save_local_diagnostic
in the run config. If 
save_local_diagnostic
is 
True
, 
my_op
will be executed; otherwise, it will be skipped. Here's an example of how you might provide the run config when launching the job:
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yaml # Run config in YAML format ops:   my_graph:     inputs:       save_local_diagnostic: true  # Set to true or false
Or in Python, when executing the job programmatically:
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python # Run config in Python run_config = {     "ops": {         "my_graph": {             "inputs": {                 "save_local_diagnostic": True  # Set to True or False             }         }     } }  result = my_job.execute_in_process(run_config=run_config)
This approach allows you to control the execution of 
my_op
based on the provided run config value for 
save_local_diagnostic
. For more information on conditional execution and run configs in Dagster, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout what are benefits of importing the resources into the repo?
s
Importing resources into a Dagster repository provides several benefits: 1. Reusability: Resources encapsulate logic for external services or systems, such as databases, APIs, or custom computation resources. By defining resources once and importing them into your repository, you can reuse the same resource across multiple assets, ops, or jobs, promoting DRY (Don't Repeat Yourself) principles. 2. Environment Abstraction: Resources allow you to abstract away environment-specific details from your business logic. You can define different resource implementations for different environments (e.g., development, staging, production) and switch between them without changing the core logic of your assets or ops. 3. Configuration Management: Resources can be configured centrally, and their configurations are surfaced in the Dagster UI. This makes it easy to see where resources are used and how they're configured, which can simplify debugging and operational management. 4. Testing and Mocking: When testing your Dagster assets or ops, you can swap out real resources with mock implementations. This is particularly useful for unit testing, where you might want to avoid interacting with real external systems. 5. Observability: Resources show up in the Dagster UI, providing visibility into their usage and configuration. This observability can help with monitoring resource health and usage patterns. 6. Lifecycle Management: Resources have lifecycle hooks, such as 
resource_init
and 
resource_cleanup
, which you can use to manage setup and teardown logic for external systems. This can help ensure that resources are managed correctly throughout the execution of your pipelines. 7. Consistency: By using resources, you ensure a consistent way of interacting with external systems throughout your Dagster repository. This consistency can reduce errors and make the codebase easier to understand and maintain. 8. Isolation: Resources can help isolate external dependencies, making your system more robust. If an external service changes or needs to be replaced, you can update the corresponding resource without impacting the rest of your code. By importing resources into your Dagster repository, you can leverage these benefits to create more maintainable, scalable, and testable data pipelines and workflows.
a
@Scout is there a way I can delay a downstream op by 5 minutes, after the current op completes?
s
In Dagster, there isn't a built-in way to introduce a delay or sleep directly within the execution plan between ops. Dagster's execution model is designed to run ops as soon as their dependencies are satisfied without intentional delay. However, if you need to introduce a delay for a specific use case, you could implement it within the op itself. Here's a conceptual example of how you might add a delay at the end of an op's execution:
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python import time from dagster import op  @op def upstream_op(context):     # Your upstream op logic     <http://context.log.info|context.log.info>("Upstream op completed.")  @op def downstream_op_with_delay(context):     # Introduce a delay     <http://context.log.info|context.log.info>("Waiting for 5 minutes before executing downstream op.")     time.sleep(300)  # Delay for 5 minutes (300 seconds)     # Your downstream op logic     <http://context.log.info|context.log.info>("Downstream op is now executing.")
In this example, 
downstream_op_with_delay
includes a 
time.sleep(300)
call, which will pause the execution for 5 minutes before proceeding with the rest of the op's logic. Please note that using 
time.sleep
to introduce delays is generally not recommended in production workflows, as it can tie up worker resources and make the system less responsive. It's better to design your workflow to handle timing and scheduling at a higher level, possibly using Dagster's scheduling system or sensors to control when jobs should run. If the delay is needed to ensure that some external condition is met (like waiting for a file to become available or for an external process to complete), consider using a sensor or a polling mechanism within the op to check for the condition rather than a fixed delay. For more sophisticated scheduling needs or if you have a use case that requires precise timing control between ops, you might need to look into external orchestration tools or custom scheduling logic within your application. For the most accurate and up-to-date information on handling delays and scheduling in Dagster, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout what does the "upstream code version" tag mean on an asset in a job DAG?
s
The "upstream code version" tag on an asset in a job DAG in Dagster is used to indicate that the code version of an upstream asset has changed since the last materialization of the current asset. This tag helps users identify when an asset may be out of date due to changes in the assets it depends on. In Dagster, each asset can have an associated 
code_version
, which is a user-defined string that typically represents the version of the code used to compute the asset. When you materialize an asset, Dagster records its 
code_version
along with the materialization. If an upstream asset's 
code_version
changes (for example, due to a code update), Dagster can compare the recorded 
code_version
of the downstream asset's last materialization against the current 
code_version
of the upstream asset. If they differ, Dagster will mark the downstream asset with the "upstream code version" tag to communicate that the upstream asset's code has changed since the downstream asset was last materialized. This feature is particularly useful for maintaining data lineage and understanding the impact of code changes on your data assets. It allows you to track dependencies and ensure that your assets are up-to-date with the latest code changes. For more information on code versions and how they are used in Dagster, please refer to the Dagster documentation or reach out to the Dagster community for assistance.
a
@Scout how do I start integrating great expectations into dagster?
s
To integrate Great Expectations with Dagster, you can use the 
dagster-ge
library, which provides built-in support for running Great Expectations validations within your Dagster pipelines. Here's a step-by-step guide to get started: 1. Install the 
dagster-ge
library: Make sure you have both Dagster and Great Expectations installed, and then install the 
dagster-ge
library using pip:
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shell
   pip install dagster-ge
2. Set up Great Expectations: If you haven't already, set up a Great Expectations project by following the [Great Expectations documentation](https://docs.greatexpectations.io/). This typically involves creating a 
great_expectations
directory with a 
great_expectations.yml
configuration file, expectation suites, and other configuration artifacts. 3. Create Expectation Suites: Define expectation suites using Great Expectations. These suites specify the expectations that your data should meet and can be created using the 
great_expectations
CLI or programmatically. 4. Use the GE Op Factory: The 
dagster-ge
library provides an op factory function that you can use to create Dagster ops that run Great Expectations validation suites. Here's an example of how to use it:
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python
   from dagster import job
   from dagster_ge.factory import ge_validation_op_factory

   # Create a Dagster op that runs a Great Expectations validation suite
   ge_validation_op = ge_validation_op_factory(
       name="ge_validation_op",
       datasource_name="my_datasource",
       suite_name="my_expectation_suite",
   )

   @job
   def my_data_validation_job():
       ge_validation_op()
In this example, replace 
"my_datasource"
with the name of your Great Expectations datasource and 
"my_expectation_suite"
with the name of your expectation suite. 5. Run the Job: Execute the job to run the Great Expectations validation. You can do this via the Dagster CLI, the Dagit UI, or programmatically:
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shell
   dagster job execute -f my_dagster_job_file.py -j my_data_validation_job
Or programmatically:
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python
   result = my_data_validation_job.execute_in_process()
6. Review Validation Results: After running the job, you can review the validation results in the Dagit UI or by inspecting the 
result
object if you executed the job programmatically. Remember to configure the 
ge_data_context
resource in your Dagster repository to point to your Great Expectations directory. This resource is necessary for the 
dagster-ge
integration to locate and use your Great Expectations configuration. For more detailed instructions and examples, please refer to the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectation
s) and the 
dagster-ge
library's API reference. If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout where would the great_expectations repository sit in relation to the dagster repository?
s
The Great Expectations repository typically contains your Great Expectations project with its configuration files, datasets, checkpoints, and expectation suites. This repository can be placed in relation to your Dagster repository in a few different ways, depending on your project structure and preferences: 1. Separate Directories: You can have the Great Expectations repository in a separate directory from your Dagster repository. This keeps the two projects distinct and allows for separate version control and management. You would then configure Dagster to point to the location of the Great Expectations directory when setting up the 
ge_data_context
resource. 2. Subdirectory: You can place the Great Expectations repository as a subdirectory within your Dagster repository. This can simplify project management by keeping all related code in a single version-controlled repository. Your Dagster project would reference the Great Expectations directory via a relative path. 3. Same Directory: If your project is small or if you prefer to have a single repository for simplicity, you can place the Great Expectations configuration files directly in your Dagster repository. This approach is less common and can lead to a more cluttered project structure, but it may be suitable for simple or small projects. Here's an example project structure with Great Expectations as a subdirectory within the Dagster repository:
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my_dagster_project/ ├── dagster_repository/ │   ├── pipelines/ │   ├── resources/ │   └── ... └── great_expectations/     ├── expectations/     ├── checkpoints/     ├── great_expectations.yml     └── ...
In this structure, 
my_dagster_project
is the root directory containing both the Dagster and Great Expectations code. The 
dagster_repository
directory contains Dagster-related code such as pipelines and resources, while the 
great_expectations
directory contains the Great Expectations project. When configuring the 
ge_data_context
resource in Dagster, you would provide the path to the 
great_expectations
directory so that Dagster can interact with your Great Expectations project. The choice of where to place the Great Expectations repository in relation to the Dagster repository depends on your project's needs, the size of your team, and how you want to manage development workflows. It's important to choose a structure that supports your development process and makes it easy to maintain and deploy your data pipelines.
a
I have multiple user code repositories that are managed and run on the same dagster webserver @Scout
s
When you have multiple user code repositories that need to be managed and run on the same Dagster webserver, you can use the concept of "code locations" to organize and load your Dagster definitions from these separate repositories. Code locations allow you to define multiple sources of Dagster definitions, each potentially corresponding to a different user code repository. Here's how you can set up multiple code locations for your Dagster webserver: 1. Define Code Locations: In your Dagster instance's workspace configuration file (commonly 
workspace.yaml
), you can define multiple code locations, each pointing to a different user code repository. Each code location should specify the Python environment and the repository or repositories it contains. 2. Workspace Configuration: Your 
workspace.yaml
file might look something like this, with each code location pointing to a different repository:
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yaml load_from:   - python_file:       relative_path: "path/to/first_repository/repository.py"       location_name: "first_repository"   - python_file:       relative_path: "path/to/second_repository/repository.py"       location_name: "second_repository"   # Add more code locations as needed
3. Python Environments: Each code location can be associated with a separate Python environment. This allows teams to manage their dependencies independently. If you're using virtual environments, each repository can have its own 
venv
with its specific dependencies installed. 4. Run the Webserver: Start the Dagster webserver with the workspace configuration file. The webserver will load the definitions from all specified code locations, making them available in the Dagit UI. 5. Isolation: By using multiple code locations, you can achieve a level of isolation between projects and teams. Each project can have its own CI/CD pipeline and be deployed independently, while still being managed by the same Dagster webserver. 6. RPC Communication: Code locations are loaded in a different process and communicate with the Dagster system processes over an RPC mechanism. This architecture allows the webserver/UI to pick up changes to user code without requiring a restart. By following this approach, you can manage multiple user code repositories on the same Dagster webserver, providing a unified interface for running and monitoring your data pipelines while maintaining separation between different projects' codebases and dependencies. For more detailed information on setting up and managing code locations, please refer to the [Dagster documentation on code locations](https://docs.dagster.io/concepts/code-locations). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
I think I will put it in the subdirectories of the user code repos, which subdirectory would be best? @Scout
s
When integrating Great Expectations with your Dagster project, and you decide to place it within the subdirectories of your user code repositories, it's important to choose a location that is logical, easily accessible, and consistent with the structure of your project. Here are some recommendations based on the documentation: 1. Create a Dedicated Directory: It's a good practice to create a dedicated directory for Great Expectations within each user code repository. This directory will contain all the Great Expectations artifacts, including expectation suites, checkpoints, and the 
great_expectations.yml
configuration file. 2. Consider the 
resources
Directory: If your Dagster project follows a structure where external services or systems are encapsulated as resources, you might consider placing the Great Expectations directory within a 
resources
subdirectory. This aligns with the concept that Great Expectations serves as a data validation resource for your pipelines. 3. Use a Descriptive Name: Name the directory descriptively, such as 
great_expectations
, so that it's clear to other developers what the directory contains. This helps with maintainability and onboarding new team members. Here's an example of how you might structure the directories:
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user_code_repository/ ├── dagster/ │   ├── pipelines/ │   ├── resources/ │   └── ... ├── great_expectations/ │   ├── expectations/ │   ├── checkpoints/ │   ├── great_expectations.yml │   └── ... └── ...
In this structure, 
user_code_repository
is the root of your user code repo, containing both Dagster-related code and the Great Expectations project. The 
great_expectations
directory is at the same level as the 
dagster
directory, clearly separating Dagster code from data validation artifacts. Remember to configure the 
ge_data_context
resource in your Dagster repository to point to the 
great_expectations
directory. This resource is necessary for the 
dagster-ge
integration to locate and use your Great Expectations configuration. For more detailed information on structuring your Dagster project, please refer to the [Dagster documentation on recommended project structure](https://docs.dagster.io/guides/dagster/recommended-project-structure). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
Can you give me a step by step guide for installing great expectations, setting up the directory and some initial expectations and checkpoints to test the data of my asset group 'asset_group_1' @Scout
s
Certainly! Here's a step-by-step guide to installing Great Expectations, setting up the directory, and creating some initial expectations and checkpoints to test the data of your asset group 'asset_group_1': ### Step 1: Install Great Expectations 1. Create a virtual environment for your project (optional but recommended):
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shell
   python -m venv my_venv
   source my_venv/bin/activate  # On Windows use `my_venv\Scripts\activate`
2. Install Great Expectations within the virtual environment:
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shell
   pip install great_expectations
### Step 2: Set Up the Great Expectations Directory 1. Initialize a new Great Expectations project:
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shell
   great_expectations init
This command will create a new directory called 
great_expectations
in your current working directory, with a pre-defined project structure. ### Step 3: Connect to Your Data 1. Configure a Datasource in Great Expectations to connect to your data. This could be a database, a file system, or any other type of data source. Follow the prompts from the 
great_expectations init
command to set up your Datasource. ### Step 4: Create Expectation Suites 1. Create a new Expectation Suite for your asset group:
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shell
   great_expectations suite new
Follow the prompts to create a new suite. You can name the suite after your asset group, e.g., 
asset_group_1_suite
. 2. The CLI will guide you through creating some initial expectations. You can also edit the suite later by modifying the JSON file in the 
great_expectations/expectations
directory or by using the interactive 
great_expectations suite edit
command. ### Step 5: Create Checkpoints 1. Create a checkpoint to bundle the validation of your Expectation Suite:
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shell
   great_expectations checkpoint new asset_group_1_checkpoint
Replace 
asset_group_1_checkpoint
with a name that makes sense for your use case. Follow the prompts to associate the checkpoint with your Expectation Suite and Datasource. ### Step 6: Validate Your Data 1. Run the checkpoint to validate your data against the Expectation Suite:
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shell
   great_expectations checkpoint run asset_group_1_checkpoint
This command will run the validation and provide a report on whether your data meets the defined expectations. ### Step 7: Integrate with Dagster 1. Install the 
dagster-ge
library if you haven't already:
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shell
   pip install dagster-ge
2. Configure the 
ge_data_context
resource in your Dagster repository to point to the Great Expectations directory: ```python from dagster import ResourceDefinition from dagster_ge import ge_data_context ge_data_context_resource = ResourceDefinition.resource_from_config( ge_data_context, config_schema={"ge_root_dir": str}
)
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3. Use the `ge_data_context` resource in your Dagster jobs to run Great Expectations validations:    ```python    from dagster import job    from dagster_ge.factory import ge_validation_op_for_checkpoint     @job(resource_defs={"ge_data_context": ge_data_context_resource})    def my_validation_job():        ge_validation_op_for_checkpoint("asset_group_1_checkpoint")
Now you have Great Expectations installed, an initial setup with an Expectation Suite and a checkpoint, and integration with Dagster to run validations on your data assets. For more detailed instructions and examples, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/) and the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectations). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout how can I make an expectation for a dbt asset?
s
To create an expectation for a dbt asset in Dagster, you can use Asset Checks, which are a way to define and execute checks that validate the properties of your assets. Asset Checks can be used to ensure that your dbt assets meet certain conditions or expectations. Here's a step-by-step guide on how to create an expectation for a dbt asset: 1. Define Asset Checks: Asset Checks are defined using the 
@asset
decorator in Dagster. You can define checks within the body of the asset computation function. 2. Use ExpectationResult: Within the asset computation function, you can yield 
ExpectationResult
objects that represent the outcome of each check. These results can indicate success or failure based on the conditions you define. 3. Integrate with dbt: If you're using Dagster's dbt integration, you can load dbt models as assets using the 
load_assets_from_dbt_project
function. You can then define Asset Checks for these dbt assets. Here's a conceptual example of how you might define an Asset Check for a dbt asset:
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python
from dagster import asset, ExpectationResult
from dagster_dbt import load_assets_from_dbt_project

# Load dbt models as assets
dbt_assets = load_assets_from_dbt_project(project_dir="path/to/dbt/project")

# Define an Asset Check for a specific dbt asset
@asset(required_resource_keys={"dbt"}, keys=dbt_assets.keys())
def my_dbt_asset(context):
    # Run dbt model
    context.resources.dbt.run_op(context.solid_def.name)
    
    # Perform your custom checks here
    # For example, check if a table row count meets an expectation
    row_count = get_row_count_from_table("my_dbt_model_table")  # Replace with your logic
    expected_row_count = 100  # Define your expectation
    
    # Yield an ExpectationResult based on the check
    yield ExpectationResult(
        success=row_count >= expected_row_count,
        label="row_count_check",
        description=f"Check if row count is at least {expected_row_count}",
    )

    # Continue with the rest of your asset logic if needed
In this example, 
my_dbt_asset
is defined as an asset that corresponds to a dbt model. Within the asset function, we run the dbt model and then perform a custom check on the row count of the resulting table. We yield an 
ExpectationResult
that indicates whether the expectation is met. Please note that this is a simplified example, and you would need to implement the actual logic for running the dbt model and performing the check based on your specific use case. For more detailed instructions and examples on using Asset Checks and integrating with dbt, please refer to the [Dagster documentation on Asset Checks](https://docs.dagster.io/concepts/assets/asset-checks) and the [dagster-dbt integration reference](https://docs.dagster.io/integrati
ons/dbt/reference). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout how can I use the ge_validation_op_factory to test the results of one of my dbt assets
s
To use the 
ge_validation_op_factory
to test the results of one of your dbt assets, you'll need to follow these steps: 1. Set Up Great Expectations: Ensure you have a Great Expectations suite that corresponds to the dbt asset you want to validate. This suite should be defined within your Great Expectations project. 2. Configure the Great Expectations DataContext: Make sure your Dagster environment is configured to access the Great Expectations DataContext. This typically involves setting up a resource in Dagster that points to your Great Expectations configuration directory. 3. Create a Validation Op: Use the 
ge_validation_op_factory
to create a Dagster op that will run the Great Expectations suite against the output of your dbt asset. 4. Add the Validation Op to Your Job: Include the validation op in your Dagster job, ensuring it runs after the dbt asset has been materialized. Here's a conceptual example of how you might set this up:
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python
from dagster import job, op, resource
from dagster_ge import ge_data_context, ge_validation_op_factory

# Define a resource for the Great Expectations DataContext
@resource(config_schema={"ge_root_dir": str})
def ge_data_context_resource(context):
    return ge_data_context(context.resource_config["ge_root_dir"])

# Create a validation op using the ge_validation_op_factory
validate_dbt_asset = ge_validation_op_factory(
    name="validate_dbt_asset",
    datasource_name="my_datasource",  # Replace with your GE datasource name
    suite_name="my_expectation_suite",  # Replace with your GE suite name
)

# Define your job
@job(resource_defs={"ge_data_context": ge_data_context_resource})
def my_validation_job():
    # Assume dbt_run_op is an op that materializes your dbt asset
    dbt_result = dbt_run_op()
    
    # Run the Great Expectations validation after the dbt asset is materialized
    validate_dbt_asset(dbt_result)
In this example, 
ge_data_context_resource
is a resource definition that creates a Great Expectations DataContext. The 
validate_dbt_asset
op is created using the 
ge_validation_op_factory
and is configured to run a specific Great Expectations suite against the output of a dbt asset. The 
my_validation_job
job includes both the dbt asset materialization op (
dbt_run_op
) and the Great Expectations validation op (
validate_dbt_asset
). The validation op is dependent on the dbt asset op, ensuring that the validation occurs after the dbt asset has been materialized. Please adjust the datasource name, suite name, and other details to match your Great Expectations and dbt setup. For more detailed instructions and examples, please refer to the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectations) and the `
dagster-ge` library's API reference. If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout so for every table in my last group of dbt assets that I want to validate with great expectations, do I need to create an ge_validation_op for each one? Where does the validation code sit, what should the ge resource look like? If one of my dbt tables is call 'sales_out' with three columns, how could I validate all columns only contain integer values?
s
To validate each table in your last group of dbt assets with Great Expectations, you will indeed need to create a separate 
ge_validation_op
for each table you want to validate. The validation code will be part of your Great Expectations project, where you define Expectation Suites that specify the validations for each table. Here's how you can set this up: 1. Create Expectation Suites in Great Expectations: For each dbt asset (table) you want to validate, create an Expectation Suite in Great Expectations. For example, to validate that all columns in the 'sales_out' table contain only integer values, you would define an expectation suite with the appropriate expectations. 2. Configure the Great Expectations DataContext Resource: The Great Expectations resource in Dagster provides the DataContext required to run validations. It should be configured to point to your Great Expectations project directory. 3. Create Validation Ops Using 
ge_validation_op_factory
: For each dbt asset, use the 
ge_validation_op_factory
to create a Dagster op that will run the corresponding Great Expectations suite. Here's a conceptual example of what this might look like:
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python
from dagster import job, op, resource
from dagster_ge import ge_data_context, ge_validation_op_factory

# Define a resource for the Great Expectations DataContext
@resource(config_schema={"ge_root_dir": str})
def ge_data_context_resource(context):
    return ge_data_context(context.resource_config["ge_root_dir"])

# Create a validation op for the 'sales_out' table
validate_sales_out = ge_validation_op_factory(
    name="validate_sales_out",
    datasource_name="my_datasource",  # Replace with your GE datasource name
    suite_name="sales_out_expectation_suite",  # Replace with your GE suite name for 'sales_out'
)

# Define your job
@job(resource_defs={"ge_data_context": ge_data_context_resource})
def my_dbt_validation_job():
    # ... (dbt ops to materialize your tables)
    
    # Run the Great Expectations validation for 'sales_out'
    validate_sales_out()

# Example resource configuration pointing to your Great Expectations project directory
ge_resource_config = {
    "ge_root_dir": "/path/to/your/great_expectations"
}
In this example, 
validate_sales_out
is a Dagster op created using the 
ge_validation_op_factory
to validate the 'sales_out' table. You would repeat this process for each dbt asset you want to validate, creating a separate op for each one. The 
my_dbt_validation_job
job includes the dbt ops to materialize your tables (not shown here) and the Great Expectations validation ops. To define the expectations for the 'sales_out' table in Great Expectations, you would create an Expectation Suite similar to this: ```python import great_expectations as ge
# Load your data into a Great Expectations dataset data = ge.read_table("sales_out") # Replace with the actual method to load your table # Add expectations for column in data.columns: data.expect_column_values_to_be_of_type(column, "integer") # Save the expectation suite data.save_expectation_suite("sales_out_expectation_suite.json") ``` This is a simplified example, and you would need to adjust it to fit your actual data loading method and the specifics of your Great Expectations setup. For more detailed instructions and examples, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/) and the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectations). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout so would the validate_sales_out op be in the dagster assets folder, and the expectation suite be in the ge directory?
s
Yes, that's correct. In a typical setup: - The 
validate_sales_out
op, which is a Dagster op created using the 
ge_validation_op_factory
, would be defined in your Dagster project, potentially alongside your other Dagster assets. This op would be part of your Dagster job definitions, which could be located in a directory such as 
dagster/assets/
or wherever you organize your Dagster code. - The expectation suite for the 
sales_out
table would be defined within your Great Expectations project directory. This directory contains all the artifacts related to Great Expectations, such as expectation suites, data docs, and the 
great_expectations.yml
configuration file. The expectation suite would be located in a subdirectory like 
great_expectations/expectations/
. Here's how the project structure might look:
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my_project/ ├── dagster/ │   ├── assets/ │   │   ├── validate_sales_out.py  # The Dagster op for GE validation │   │   └── ...  # Other Dagster assets and ops │   └── ...  # Other Dagster-related code └── great_expectations/     ├── expectations/     │   ├── sales_out_expectation_suite.json  # The GE expectation suite for 'sales_out'     │   └── ...  # Other expectation suites     ├── checkpoints/     ├── great_expectations.yml     └── ...  # Other Great Expectations configuration and artifacts
In this structure: - 
my_project/
is the root directory of your overall project. - 
my_project/dagster/
contains your Dagster code, including the 
validate_sales_out
op definition. - 
my_project/great_expectations/
contains your Great Expectations project, including the expectation suite for the 
sales_out
table. When you run your Dagster job, the 
validate_sales_out
op will use the Great Expectations DataContext to execute the expectation suite against the 
sales_out
table and yield the validation results. For more detailed instructions and examples, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/) and the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectations). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout what should an expectation suite look like?
s
An expectation suite in Great Expectations is a collection of expectations that define the data validation rules you want to apply to a dataset. Each expectation is a declarative statement that specifies a particular condition that your data should meet. Here's an example of what an expectation suite might look like for a table called 
sales_out
with three columns, where you want to validate that all columns contain only integer values:
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json {   "meta": {     "great_expectations_version": "x.y.z"   },   "expectation_suite_name": "sales_out_expectation_suite",   "data_asset_type": "Dataset",   "expectations": [     {       "expectation_type": "expect_column_values_to_be_of_type",       "kwargs": {         "column": "column_1",         "type_": "integer"       }     },     {       "expectation_type": "expect_column_values_to_be_of_type",       "kwargs": {         "column": "column_2",         "type_": "integer"       }     },     {       "expectation_type": "expect_column_values_to_be_of_type",       "kwargs": {         "column": "column_3",         "type_": "integer"       }     }   ] }
In this JSON structure: - 
meta
contains metadata about the expectation suite, such as the version of Great Expectations. - 
expectation_suite_name
is the name of the expectation suite. - 
data_asset_type
indicates the type of data asset the suite applies to. - 
expectations
is an array of individual expectation definitions. Each expectation definition includes: - 
expectation_type
, which specifies the type of expectation (e.g., 
expect_column_values_to_be_of_type
). - 
kwargs
, which contains the keyword arguments for the expectation, such as the name of the column to check (
column
) and the expected data type (
type_
). To create and edit expectation suites, you can use the Great Expectations CLI or the interactive data docs feature. The CLI provides commands like 
great_expectations suite new
and 
great_expectations suite edit
to help you build and modify your suites. Once you have defined your expectation suite, you can save it as a JSON file within your Great Expectations project under the 
expectations
directory. Great Expectations will use this suite when running validations against your data. For more detailed guidance on creating and managing expectation suites, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/docs/terms/expectation_suite). If you have any questions or need further assistance, the Great Expectations community is also a great resource for support.
a
@Scout I have pip installed dagster-ge but I m unable to import from it. It doesn't appear and I need the ge_validation_op_factory function
s
If you've installed 
dagster-ge
but are unable to import from it, there are a few steps you can take to troubleshoot the issue: 1. Verify Installation: Ensure that 
dagster-ge
is installed in the correct Python environment. If you're using a virtual environment, make sure it's activated. You can check the installed packages with 
pip list
. 2. Check for Errors: Look for any error messages during the installation process. If there were errors, they might indicate what went wrong, such as missing dependencies or permission issues. 3. Reinstall the Package: Try reinstalling 
dagster-ge
to see if that resolves the issue. Use the following command:
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shell    pip install --upgrade --force-reinstall dagster-ge
4. Check Python Path: Ensure that the Python path is set correctly and that you're running the Python interpreter from the same environment where 
dagster-ge
is installed. 5. Import Correctly: Make sure you're using the correct import statement. The import for 
ge_validation_op_factory
should look like this:
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python    from dagster_ge.factory import ge_validation_op_factory
6. Check for Compatibility: Ensure that the versions of 
dagster
, 
dagster-ge
, and 
great_expectations
are compatible. Incompatibilities between versions could cause issues with imports. 7. Inspect the Package: Check the contents of the installed 
dagster-ge
package in your environment's 
site-packages
directory to confirm that the 
ge_validation_op_factory
function is present. 8. Review Documentation: Consult the [Dagster integration documentation for Great Expectations](https://docs.dagster.io/integrations/great-expectations) for any additional setup steps or known issues. If you've gone through these steps and are still facing issues, consider reaching out to the Dagster community for support. Provide details about your environment, the exact commands you've run, and any error messages you've encountered. The community or the maintainers might be able to provide further insights into why the import isn't working as expected.
a
from dagster_ge.factory @Scout this import doesn't work, I have created a new venv and pip installed dagster-ge, but it is not recognised as existing
s
If you're encountering issues with importing from 
dagster_ge.factory
even after installing 
dagster-ge
in a new virtual environment, here are some steps you can take to further troubleshoot the problem: 1. Check the Installation: Verify that 
dagster-ge
is indeed installed in your virtual environment by running:
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shell    pip show dagster-ge
This command should display information about the installed package. If it doesn't show up, the package may not have been installed correctly. 2. Check for Error Messages: Look at the output from the 
pip install dagster-ge
command for any error messages that might indicate what went wrong during the installation. 3. Check Python Version: Ensure that the Python version in your virtual environment is compatible with 
dagster-ge
. You can check your Python version with:
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shell    python --version
4. Inspect the Virtual Environment: Make sure that you are working within the correct virtual environment where 
dagster-ge
was installed. Sometimes, the Python environment might not be activated, or you might be using a different environment by mistake. 5. Check the 
dagster-ge
Version: Ensure that you have installed a version of 
dagster-ge
that includes the 
factory
module. It's possible that you have an outdated version or a pre-release version that doesn't include what you're trying to import. 6. Review the 
dagster-ge
Source: If possible, inspect the source code of the installed 
dagster-ge
package in your virtual environment's 
site-packages
directory to confirm that the 
factory
module and 
ge_validation_op_factory
function are present. 7. Consult the Documentation: Double-check the [Dagster documentation](https://docs.dagster.io/integrations/great-expectations) to ensure that there haven't been any changes to the way 
dagster-ge
should be installed or imported. 8. Seek Support: If you've tried the above steps and the issue persists, reach out to the Dagster community for support. Provide detailed information about the steps you've taken, the output of 
pip show dagster-ge
, the Python version you're using, and any error messages you've received. The Dagster community can be a valuable resource when encountering such issues, and they may be able to provide additional assistance or identify if there's an ongoing issue with the 
dagster-ge
package.
a
If I have a dbt pipeline imported into my repo, and the downstream assets from the dbt tables all have their dependenies defined so my my asset graph is already linked and my job can run all assets - how can I inject an op from ge_validation_op_factory into the pipeline to test the end dbt tables? @Scout
s
To inject an op created with 
ge_validation_op_factory
into your pipeline to test the dbt tables, you can follow these steps: 1. Define the GE Validation Op: Use the 
ge_validation_op_factory
to create a Dagster op that will run the Great Expectations suite against the output of your dbt table. 2. Add the GE Validation Op to Your Asset Graph: Place the GE validation op downstream of the dbt table asset within your asset graph. This ensures that the validation runs after the dbt table has been materialized. 3. Define Dependencies: Make sure that the GE validation op has a dependency on the dbt table asset. This can be done by passing the output of the dbt table asset to the GE validation op. Here's a conceptual example of how you might set this up:
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python
from dagster import asset, AssetsDefinition, SourceAsset
from dagster_dbt import dbt_cli_resource, dbt_run_op
from dagster_ge import ge_data_context, ge_validation_op_factory

# Assume 'my_dbt_table' is a SourceAsset representing the dbt table
my_dbt_table = SourceAsset(key="my_dbt_table", ...)

# Create a GE validation op for the 'my_dbt_table'
validate_my_dbt_table = ge_validation_op_factory(
    name="validate_my_dbt_table",
    datasource_name="my_datasource",  # Replace with your GE datasource name
    suite_name="my_dbt_table_expectation_suite",  # Replace with your GE suite name
)

# Define the asset graph with dependencies
@asset(required_resource_keys={"dbt", "ge_data_context"})
def my_dbt_table_asset(context):
    # Run the dbt model that materializes 'my_dbt_table'
    dbt_run_op(context, models=["my_dbt_table"])

    # Run the GE validation after the dbt table is materialized
    validate_my_dbt_table()

# Define the job with the necessary resources
my_job = AssetsDefinition(
    asset_deps={"validate_my_dbt_table": {my_dbt_table}},
    resource_defs={
        "dbt": dbt_cli_resource.configured({"project_dir": "path/to/dbt/project"}),
        "ge_data_context": ge_data_context.configured({"ge_root_dir": "path/to/great_expectations"}),
    },
).build_job(name="my_dbt_validation_job")
In this example, 
my_dbt_table_asset
is an asset that represents the dbt table. The 
validate_my_dbt_table
op is created using the 
ge_validation_op_factory
and is configured to run a Great Expectations suite against the 
my_dbt_table
after it has been materialized by dbt. The 
my_job
job includes the necessary resources for dbt and Great Expectations and defines the dependencies between the assets and the GE validation op. Please adjust the example to fit your actual dbt and Great Expectations setup. For more detailed instructions and examples, please refer to the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectations
) and the [Dagster documentation on dbt integration](https://docs.dagster.io/integrations/dbt). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
How can I turn an op into an asset? @Scout
s
To turn an op into an asset in Dagster, you can use the 
@asset
decorator instead of the 
@op
decorator. The 
@asset
decorator allows you to define a software-defined asset, which is a unit of data that Dagster can track over time. Assets can be materialized, have dependencies on other assets, and be part of an asset graph. Here's a step-by-step guide to converting an op into an asset: 1. Replace 
@op
with 
@asset
: Change the decorator from 
@op
to 
@asset
on the function that defines your computation. 2. Define Outputs: If your op returns a value, ensure that the asset function also returns that value. Assets are expected to produce a materializable output. 3. Add Asset Dependencies: If your op depends on other ops or assets, use the 
dependencies
parameter of the 
@asset
decorator to specify these dependencies. Here's a conceptual example of converting an op into an asset:
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python # Original op definition @op def my_op(context, input_data):     # Op logic...     return processed_data  # Converted asset definition from dagster import asset  @asset def my_asset(input_data):     # Asset logic (same as op logic)...     return processed_data
In this example, 
my_op
is the original op, and 
my_asset
is the converted asset. The logic within the function remains the same, but the decorator and the way you define dependencies change. If your original op had dependencies, you would define them in the asset like this:
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python from dagster import asset, AssetsDefinition  @asset def upstream_asset():     # Logic for upstream asset...     return upstream_data  @asset(dependencies={"input_data": AssetsDefinition(asset_key="upstream_asset")}) def my_asset(input_data):     # Asset logic that depends on upstream_asset...     return processed_data
In this updated example, 
my_asset
depends on 
upstream_asset
, and this dependency is specified using the 
dependencies
parameter of the 
@asset
decorator. By following these steps, you can convert an op into an asset in Dagster, allowing you to take advantage of Dagster's asset management features. For more detailed information on defining assets, please refer to the [Dagster documentation on software-defined assets](https://docs.dagster.io/concepts/assets/software-defined-assets). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout should the path "ge_root_dir" start at the user code repo root, or the full repo root? And how can I view what the root repo is?
s
The 
ge_root_dir
should be the path to the root of your Great Expectations project directory, where the 
great_expectations.yml
file is located. This directory is typically named 
great_expectations
and contains all the configurations, expectations, checkpoints, and other metadata related to your Great Expectations setup. If your Great Expectations project is within a user code repository, the 
ge_root_dir
should be the path from the root of that user code repository to the Great Expectations directory. For example, if your project structure looks like this:
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my_user_code_repo/ ├── dagster/ │   └── ...  # Dagster-related code └── great_expectations/     ├── expectations/     ├── checkpoints/     ├── great_expectations.yml     └── ...  # Other Great Expectations configuration and artifacts
Then your 
ge_root_dir
would be set to 
great_expectations/
relative to the root of 
my_user_code_repo
. To view what the root of the repo is, you can typically look at the structure of your project in your file system. If you're using a version control system like Git, the root of the repo is usually the top-level directory that contains the 
.git
directory. In a Dagster context, if you're running Dagit or executing a job, Dagster considers the directory where you invoke the command as the working directory. If you're using a 
workspace.yaml
file to configure your Dagster workspace, the paths you define there are relative to the location of the 
workspace.yaml
file. If you want to programmatically determine the root directory of your user code repository, you could use Python's 
os
module to navigate the file system. Here's an example of how you might do that:
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python import os  # Get the current working directory cwd = os.getcwd()  # Assuming the current file is at the root of the user code repository repo_root = cwd  # Print the repository root directory print(f"The root of the user code repository is: {repo_root}")
Remember to configure the 
ge_root_dir
correctly in your Dagster resource definitions to ensure that Dagster can locate and use your Great Expectations project. For more information on configuring resources and paths in Dagster, please refer to the [Dagster documentation](https://docs.dagster.io/). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout 2024-02-29 164717 +0000 - dagster - ERROR - __ASSET_JOB - 921c11da-6fdd-4831-a0ce-0bc77458387a - 19084 - weather_gx_asset - STEP_FAILURE - Execution of step "weather_gx_asset" failed. dagster._core.errors.DagsterInvalidInvocationError: Decorated function '_ge_validation_fn' has context argument, but no context was provided when invoking. Stack Trace: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_plan.py", line 286, in dagster_event_sequence_for_step for step_event in check.generator(step_events): File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 519, in core_dagster_event_sequence_for_step for user_event in _step_output_error_checked_user_event_sequence( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 201, in _step_output_error_checked_user_event_sequence for user_event in user_event_sequence: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 99, in _process_asset_results_to_events for user_event in user_event_sequence: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 214, in execute_core_compute for step_output in _yield_compute_results(step_context, inputs, compute_fn, compute_context): File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 183, in _yield_compute_results for event in iterate_with_context( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 131, in _coerce_op_compute_fn_to_iterator result = invoke_compute_fn( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 125, in invoke_compute_fn return fn(context, **args_to_pass) if context_arg_provided else fn(**args_to_pass) File "/home/alexsmith/Repositories/sbx-etl-services/elt_repositories/repo/assets/outputs/__init__.py", line 234, in weather_gx_asset weather_gx(context) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_definition.py", line 456, in call return direct_invocation_result(self, *args, **kwargs) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_invocation.py", line 164, in direct_invocation_result raise DagsterInvalidInvocationError(
s
The error message you're encountering, 
DagsterInvalidInvocationError
, indicates that the decorated function 
_ge_validation_fn
is expecting a context argument, but no context was provided when the function was invoked. This typically happens when an op that requires a context is being called directly as a Python function, rather than being executed within the Dagster framework, which automatically provides the context. To resolve this issue, ensure that you are not directly calling the op function from your code. Instead, you should define the op with the 
@op
decorator and include it in a job or graph, allowing Dagster to handle the execution and provide the necessary context. Here's a conceptual example of how you might define and use an op within a Dagster job:
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python from dagster import op, job  @op def weather_gx(context):     # Your op logic here, using context as needed     pass  @job def my_job():     weather_gx()  # Execute the job, which will run the op within the Dagster framework result = my_job.execute_in_process()
In this example, 
weather_gx
is defined as an op with the 
@op
decorator, and it's included in the 
my_job
job. When you execute 
my_job
, Dagster will provide the context to 
weather_gx
automatically. If 
weather_gx
is supposed to be a Great Expectations validation op created with 
ge_validation_op_factory
, make sure you're using it correctly within a Dagster job or graph. The 
ge_validation_op_factory
will create an op that expects to be executed within the Dagster framework, which will provide the context. Here's an example of how you might include a Great Expectations validation op in a job:
Copy code
python from dagster import job from dagster_ge.factory import ge_validation_op_factory  # Create a Great Expectations validation op ge_validation_op = ge_validation_op_factory(     name="ge_validation_op",     datasource_name="my_datasource",     suite_name="my_expectation_suite", )  @job def my_ge_validation_job():     ge_validation_op()  # Execute the job to run the Great Expectations validation result = my_ge_validation_job.execute_in_process()
In this example, 
ge_validation_op
is created using 
ge_validation_op_factory
and included in the 
my_ge_validation_job
job. When executed, Dagster will run the validation op with the appropriate context. If you're still encountering issues, review the code where 
weather_gx_asset
is defined and ensure that any ops are being used correctly within the Dagster framework. If you need further assistance, consider reaching out to the Dagster community for support.
a
@Scout this isn't how I'm running my pipeline. I am using an asset selection for my job. I want to define the ge_validation op as an asset with the dbt model asset as an upstream, and have a downstream asset called asset_1, dependent on it's successful validation.
s
To define a Great Expectations validation op as an asset with a dbt model asset as an upstream dependency and have another asset (
asset_1
) dependent on its successful validation, you can follow these steps: 1. Define the dbt Model Asset: If you haven't already, define your dbt model as an asset in Dagster. This can be done using Dagster's dbt integration. 2. Create the Great Expectations Validation Asset: Use the 
ge_validation_op_factory
to create a validation op for your dbt model. Then, define an asset that wraps this op, ensuring it has the dbt model asset as an upstream dependency. 3. Define the Downstream Asset: Create your downstream asset (
asset_1
) and specify the Great Expectations validation asset as its upstream dependency. Here's a conceptual example:
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python
from dagster import asset, AssetsDefinition, SourceAsset
from dagster_dbt import dbt_cli_resource, dbt_run_op
from dagster_ge import ge_data_context, ge_validation_op_factory

# Assume 'my_dbt_model' is a SourceAsset representing the dbt model
my_dbt_model = SourceAsset(key="my_dbt_model", ...)

# Create a GE validation op for the 'my_dbt_model'
validate_my_dbt_model = ge_validation_op_factory(
    name="validate_my_dbt_model",
    datasource_name="my_datasource",  # Replace with your GE datasource name
    suite_name="my_dbt_model_expectation_suite",  # Replace with your GE suite name
)

# Define the GE validation asset
@asset(required_resource_keys={"ge_data_context"}, non_argument_deps={my_dbt_model})
def my_dbt_model_validation(context):
    # Run the GE validation op
    validate_my_dbt_model()

# Define the downstream asset that depends on the validation
@asset(non_argument_deps={my_dbt_model_validation})
def asset_1(context):
    # Logic for asset_1 that depends on the successful validation of my_dbt_model
    pass

# Define the job with the necessary resources
my_job = AssetsDefinition(
    asset_deps={
        "my_dbt_model_validation": {my_dbt_model},
        "asset_1": {my_dbt_model_validation},
    },
    resource_defs={
        "dbt": dbt_cli_resource.configured({"project_dir": "path/to/dbt/project"}),
        "ge_data_context": ge_data_context.configured({"ge_root_dir": "path/to/great_expectations"}),
    },
).build_job(name="my_dbt_validation_job")
In this example, 
my_dbt_model
is a SourceAsset representing the dbt model. The 
validate_my_dbt_model
op is created using the 
ge_validation_op_factory
and is configured to run a Great Expectations suite against the 
my_dbt_model
. The 
my_dbt_model_validation
asset wraps the validation op and has 
my_dbt_model
as an upstream dependency. The 
asset_1
asset is defined with 
my_dbt_model_validation
as its upstream dependency, ensuring that it runs after the validation is successful. Please
adjust the example to fit your actual dbt and Great Expectations setup. For more detailed instructions and examples, please refer to the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectations) and the [Dagster documentation on dbt integration](https://docs.dagster.io/integrations/dbt). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout I have followed your steps above and have wrapped a function generated from ge_validation_op_factory in an asset. However when I try materialise this here: weather_gx = ge_validation_op_factory( name="ge_validation_op", datasource_name="datasource", suite_name="test_suite" ) @asset( name = "weather_gx_asset", key_prefix = 'gx', required_resource_keys={"ge_data_context"}, group_name = 'gx', ins = {"upstream": AssetIn(**{"key": get_asset_key_for_model([dbt_project_assets], "weather")})}, ) def weather_gx_asset(context, upstream): weather_gx(context) I get the following error regarding the invocation of weather_gx: 2024-03-01 103948 +0000 - dagster - ERROR - __ASSET_JOB - 4eb80b48-14a0-4f14-83e4-5c735a9e5649 - 10354 - gx__weather_gx_asset - STEP_FAILURE - Execution of step "gx__weather_gx_asset" failed. dagster._core.errors.DagsterInvalidInvocationError: Decorated function '_ge_validation_fn' has context argument, but no context was provided when invoking. Stack Trace: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_plan.py", line 286, in dagster_event_sequence_for_step for step_event in check.generator(step_events): File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 519, in core_dagster_event_sequence_for_step for user_event in _step_output_error_checked_user_event_sequence( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 201, in _step_output_error_checked_user_event_sequence for user_event in user_event_sequence: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 99, in _process_asset_results_to_events for user_event in user_event_sequence: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 214, in execute_core_compute for step_output in _yield_compute_results(step_context, inputs, compute_fn, compute_context): File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 183, in _yield_compute_results for event in iterate_with_context( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 131, in _coerce_op_compute_fn_to_iterator result = invoke_compute_fn( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 125, in invoke_compute_fn return fn(context, **args_to_pass) if context_arg_provided else fn(**args_to_pass) File "/home/alexsmith/Repositories/sbx-etl-services/elt_repositories/debenhams_repo/debenhams_repo/assets/outputs/__init__.py", line 264, in weather_gx_asset weather_gx() File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_definition.py", line 456, in call return direct_invocation_result(self, *args, **kwargs) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_invocation.py", line 158, in direct_invocation_result raise DagsterInvalidInvocationError(
s
The error you're encountering indicates that the 
weather_gx
op, which is generated by 
ge_validation_op_factory
, is being called without the required context. In Dagster, ops generated by factories like 
ge_validation_op_factory
are expected to be executed within the Dagster framework, which automatically provides the context. However, when you wrap this op in an asset, you need to ensure that the context is passed correctly. The 
weather_gx
function generated by 
ge_validation_op_factory
is an op, not an asset, so it expects an 
OpExecutionContext
rather than an 
AssetExecutionContext
. To resolve this issue, you should not call 
weather_gx
directly within the body of 
weather_gx_asset
. Instead, you should use the 
build_op_context
utility to construct an appropriate context for invoking the op. Here's how you can modify your asset definition:
Copy code
python from dagster import build_op_context, asset, AssetIn, get_dagster_logger from dagster_ge.factory import ge_validation_op_factory  # Assuming 'weather_gx' is the op generated by ge_validation_op_factory weather_gx = ge_validation_op_factory(     name="ge_validation_op",     datasource_name="datasource",     suite_name="test_suite", )  @asset(     name="weather_gx_asset",     key_prefix='gx',     required_resource_keys={"ge_data_context"},     group_name='gx',     ins={"upstream": AssetIn(key="my_dbt_model")}, ) def weather_gx_asset(context, upstream):     # Construct the op context with required resources and config     op_context = build_op_context(resources=context.resources)          # Invoke the op with the constructed context     weather_gx(op_context)          # Log the result or perform additional actions     get_dagster_logger().info("Great Expectations validation completed.")
In this modified example, 
build_op_context
is used to create an 
OpExecutionContext
that includes the necessary resources from the asset context. Then, 
weather_gx
is invoked with this op context. Please note that 
build_op_context
is a utility function for testing purposes and may not be suitable for all production use cases. If you need to perform Great Expectations validation within an asset, consider using the 
dagster-ge
integration in a way that aligns with asset execution, or create a custom asset that encapsulates the validation logic. For more detailed instructions and examples, please refer to the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectations) and the [Dagster documentation on testing](https://docs.dagster.io/concepts/testing). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
Can I use this ge_validation_op_factory to create an asset directly? @Scout
s
As of the latest update, the 
ge_validation_op_factory
is designed to create an op that runs a Great Expectations validation suite. To use this within the context of assets, you would typically wrap the op in an asset definition. The op itself cannot be directly converted into an asset, but you can create an asset that invokes the op. Here's a conceptual example of how you might wrap a 
ge_validation_op_factory
-generated op in an asset:
Copy code
python from dagster import asset, AssetIn from dagster_ge.factory import ge_validation_op_factory  # Create a Great Expectations validation op using the factory ge_validation_op = ge_validation_op_factory(     name="validate_my_dbt_model",     datasource_name="my_datasource",     suite_name="my_expectation_suite", )  # Define an asset that wraps the Great Expectations validation op @asset(     ins={"upstream": AssetIn(key="my_dbt_model")},     required_resource_keys={"ge_data_context"}, ) def my_ge_validation_asset(context, upstream):     # Invoke the op within the asset     ge_validation_op(context)
In this example, 
my_ge_validation_asset
is an asset that wraps the 
ge_validation_op
. The asset takes an input from an upstream asset (presumably a dbt model) and invokes the Great Expectations validation op within its body. The 
required_resource_keys
parameter ensures that the necessary Great Expectations resources are available to the asset. When you materialize this asset, Dagster will execute the 
ge_validation_op
as part of the asset computation. The results of the validation will be handled according to how the op is configured—typically, they will be logged or could potentially be used to influence downstream computations. Remember to configure the 
ge_data_context
resource in your Dagster repository to point to your Great Expectations directory, as this resource is necessary for the 
dagster-ge
integration to locate and use your Great Expectations configuration. For more detailed instructions and examples, please refer to the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectations). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout I dont't think I am mapping, or have set up, my ge resource correctly - dagster._check.ParameterCheckError: Param "resources" is not one of ['Mapping']. .... sensitive resource info.... }) which is type <class 'dagster._core.definitions.scoped_resources_builder.ScopedResourcesBuilder.build.<locals>._ScopedResourcesContainsGenerator'>. File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/utils.py", line 54, in op_execution_error_boundary yield File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 131, in _coerce_op_compute_fn_to_iterator result = invoke_compute_fn( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 125, in invoke_compute_fn return fn(context, **args_to_pass) if context_arg_provided else fn(**args_to_pass) File "/home/alexsmith/Repositories/sbx-etl-services/elt_repositories/debenhams_repo/debenhams_repo/assets/outputs/__init__.py", line 264, in weather_gx_asset op_context = build_op_context(resources=context.resources) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/context/invocation.py", line 906, in build_op_context resources_dict=check.opt_mapping_param(resources, "resources", key_type=str), File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_check/__init__.py", line 941, in opt_mapping_param return mapping_param(obj, param_name, key_type, value_type, additional_message) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_check/__init__.py", line 921, in mapping_param raise _param_type_mismatch_exception(
s
The error message you're encountering suggests that there is an issue with the 
resources
parameter being passed to the 
build_op_context
function. The 
resources
parameter is expected to be a mapping (such as a dictionary) from resource keys to resource instances, but it seems that a different type of object is being passed instead. To resolve this issue, you need to ensure that you're passing a dictionary of resources to 
build_op_context
. Here's how you might construct the 
op_context
correctly:
Copy code
python from dagster import build_op_context, asset, AssetIn, get_dagster_logger, ResourceDefinition from dagster_ge.factory import ge_validation_op_factory  # Define your Great Expectations DataContext resource @resource(config_schema={"ge_root_dir": str}) def ge_data_context_resource(init_context):     # Assuming 'ge_root_dir' is the path to your Great Expectations directory     ge_root_dir = init_context.resource_config["ge_root_dir"]     # Initialize and return the DataContext object     from great_expectations.data_context import DataContext     return DataContext(context_root_dir=ge_root_dir)  # Create a Great Expectations validation op using the factory ge_validation_op = ge_validation_op_factory(     name="validate_my_dbt_model",     datasource_name="my_datasource",     suite_name="my_expectation_suite", )  # Define an asset that wraps the Great Expectations validation op @asset(     ins={"upstream": AssetIn(key="my_dbt_model")},     required_resource_keys={"ge_data_context"}, ) def my_ge_validation_asset(context, upstream):     # Construct the op context with the required resources     op_context = build_op_context(         resources={             "ge_data_context": context.resources.ge_data_context,         }     )          # Invoke the op with the constructed context     ge_validation_op(op_context)          # Log the result or perform additional actions     get_dagster_logger().info("Great Expectations validation completed.")
In this example, 
ge_data_context_resource
is a resource definition that creates a Great Expectations DataContext. The 
my_ge_validation_asset
asset wraps the 
ge_validation_op
and constructs the 
op_context
by passing a dictionary of resources, where the key 
"ge_data_context"
is mapped to the actual resource instance from the asset context. Make sure that the 
ge_root_dir
in the resource configuration points to the correct path of your Great Expectations project directory. If you continue to encounter issues, review your resource definitions and how you're constructing the 
op_context
. Ensure that the resources dictionary contains the correct resource instances and keys. If you need further assistance, consider reaching out to the Dagster community for support.
a
@Scout do I always need to pass a 'dataset' into ge_validation_op, even though the ge validation is checking a bigquery table?
s
When using 
ge_validation_op_factory
to create a Great Expectations validation op in Dagster, you do not always need to pass a 'dataset' directly into the op. Instead, the op will use the Great Expectations suite and datasource configuration to determine which dataset or table to validate. The 
datasource_name
parameter you provide when creating the op with 
ge_validation_op_factory
should correspond to a datasource configured in your Great Expectations 
great_expectations.yml
file. This datasource configuration should include the necessary details for connecting to BigQuery and accessing the table you want to validate. Here's an example of how you might set up the validation op without explicitly passing a 'dataset':
Copy code
python from dagster_ge.factory import ge_validation_op_factory  # Create a Great Expectations validation op using the factory ge_validation_op = ge_validation_op_factory(     name="validate_my_dbt_model",     datasource_name="my_bigquery_datasource",  # This should match your GE datasource name     suite_name="my_expectation_suite",  # This should match your GE suite name )
In this example, 
my_bigquery_datasource
is the name of the datasource defined in your Great Expectations configuration that points to BigQuery. The expectation suite 
my_expectation_suite
should contain the expectations for the BigQuery table you want to validate. When you run the op, Great Expectations will use the datasource configuration to connect to BigQuery and perform the validation against the specified table. The table name and other details should be part of the expectations defined in the suite. If you need to pass additional runtime parameters to the validation op, you can do so using the 
op_config
parameter when invoking the op within a job or graph. However, the connection details and table name should typically be handled by the datasource and suite configurations in Great Expectations. For more information on configuring datasources and expectation suites in Great Expectations, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/docs/terms/data_source). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout But i get this error if I pass nothing into my ge op: @asset( name = "weather_gx_asset", key_prefix = 'gx', required_resource_keys={"ge_data_context"}, group_name = 'gx', ins = {"upstream": AssetIn(**{"key": get_asset_key_for_model([dbt_project_assets], "weather")})}, ) def weather_gx_asset(context, upstream): op_context = build_op_context( resources={ "ge_data_context": context.resources.ge_data_context, } ) weather_gx(op_context) get_dagster_logger().info("Great Expectations validation completed.") 2024-03-01 114051 +0000 - dagster - ERROR - __ASSET_JOB - 71e37bc1-db1d-46a5-a8b9-04e51633a992 - 13585 - gx__weather_gx_asset - STEP_FAILURE - Execution of step "gx__weather_gx_asset" failed. dagster._core.errors.DagsterInvalidInvocationError: No value provided for required input "dataset". Stack Trace: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_plan.py", line 286, in dagster_event_sequence_for_step for step_event in check.generator(step_events): File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 519, in core_dagster_event_sequence_for_step for user_event in _step_output_error_checked_user_event_sequence( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 201, in _step_output_error_checked_user_event_sequence for user_event in user_event_sequence: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 99, in _process_asset_results_to_events for user_event in user_event_sequence: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 214, in execute_core_compute for step_output in _yield_compute_results(step_context, inputs, compute_fn, compute_context): File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 183, in _yield_compute_results for event in iterate_with_context( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 131, in _coerce_op_compute_fn_to_iterator result = invoke_compute_fn( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 125, in invoke_compute_fn return fn(context, **args_to_pass) if context_arg_provided else fn(**args_to_pass) File "/home/alexsmith/Repositories/sbx-etl-services/elt_repositories/debenhams_repo/debenhams_repo/assets/outputs/__init__.py", line 277, in weather_gx_asset weather_gx(op_context) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_definition.py", line 456, in call return direct_invocation_result(self, *args, **kwargs) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_invocation.py", line 225, in direct_invocation_result input_dict = _resolve_inputs(op_def, input_args, input_kwargs, bound_context) # type: ignore (pyright bug) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_invocation.py", line 316, in _resolve_inputs raise DagsterInvalidInvocationError( Exception ignored in: <function DirectOpExecutionContext.del at 0x7f3b87885b40> Traceback (most recent call last): File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/context/invocation.py", line 256, in del self._exit_stack.close() File "/usr/lib/python3.10/contextlib.py", line 584, in close self.__exit__(None, None, None) File "/usr/lib/python3.10/contextlib.py", line 576, in exit raise exc_details[1] File "/usr/lib/python3.10/contextlib.py", line 153, in exit self.gen.throw(typ, value, traceback) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/api.py", line 293, in ephemeral_instance_if_missing yield ephemeral_instance File "/usr/lib/python3.10/contextlib.py", line 561, in exit if cb(*exc_details): File "/usr/lib/python3.10/contextlib.py", line 142, in exit next(self.gen) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/build_resources.py", line 112, in build_resources list(resources_manager.generate_teardown_events()) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 535, in generate_teardown_events yield from self.generator File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/resources_init.py", line 276, in resource_initialization_event_generator yield from manager.generate_teardown_events() File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 535, in generate_teardown_events yield from self.generator File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/resources_init.py", line 344, in single_resource_event_generator with user_code_error_boundary(DagsterResourceFunctionError, msg_fn, log_manager=context.log): File "/usr/lib/python3.10/contextlib.py", line 142, in exit next(self.gen) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/errors.py", line 283, in user_code_error_boundary with raise_execution_interrupts(): File "/usr/lib/python3.10/contextlib.py", line 142, in exit next(self.gen) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/errors.py", line 250, in raise_execution_interrupts with raise_interrupts_as(DagsterExecutionInterruptedError): File "/usr/lib/python3.10/contextlib.py", line 142, in exit next(self.gen) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/interrupts.py", line 98, in raise_interrupts_as _replace_interrupt_signal(original_signal_handler) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/interrupts.py", line 27, in _replace_interrupt_signal signal.signal(signal.SIGINT, new_signal_handler) File "/usr/lib/python3.10/signal.py", line 56, in signal handler = _signal.signal(_enum_to_int(signalnum), _enum_to_int(handler)) TypeError: signal handler must be signal.SIG_IGN, signal.SIG_DFL, or a callable object
s
The error message you're encountering indicates that the 
weather_gx
op, which is generated by 
ge_validation_op_factory
, is expecting an input named "dataset" that has not been provided. In the context of Great Expectations, the "dataset" typically refers to the batch of data that you want to validate against your expectation suite. When using Great Expectations with Dagster, especially when validating data in a BigQuery table, you don't need to manually pass a "dataset" to the op. Instead, you should configure your Great Expectations datasource to point to the BigQuery table, and the expectation suite should reference this datasource. The 
ge_validation_op_factory
will then use this configuration to determine which data to validate. Here's how you can adjust your asset definition to avoid this error: 1. Ensure your Great Expectations datasource is correctly configured to connect to BigQuery and access the table you want to validate. 2. Make sure your expectation suite is set up to use the correct datasource and includes expectations for the BigQuery table. 3. When defining the asset that wraps the 
ge_validation_op_factory
-generated op, you do not need to manually build an op context or pass a "dataset" input. Instead, you should directly include the op in your asset computation function, and Dagster will handle the context and inputs. Here's a revised example:
Copy code
python
from dagster import asset, AssetIn, get_dagster_logger
from dagster_ge.factory import ge_validation_op_factory

# Create a Great Expectations validation op using the factory
ge_validation_op = ge_validation_op_factory(
    name="validate_weather_gx",
    datasource_name="my_bigquery_datasource",  # This should match your GE datasource name
    suite_name="weather_gx_expectation_suite",  # This should match your GE suite name
)

# Define an asset that wraps the Great Expectations validation op
@asset(
    name="weather_gx_asset",
    key_prefix='gx',
    required_resource_keys={"ge_data_context"},
    group_name='gx',
    ins={"upstream": AssetIn(key="my_dbt_model")},
)
def weather_gx_asset(context, upstream):
    # Directly invoke the op within the asset
    ge_validation_op()

    get_dagster_logger().info("Great Expectations validation completed.")
In this revised example, 
weather_gx_asset
directly invokes 
ge_validation_op
without manually constructing an op context. The 
required_resource_keys
parameter ensures that the necessary Great Expectations resources are available to the asset. Make sure that the 
ge_data_context
resource is correctly configured in your Dagster repository to point to your Great Expectations directory, as this resource is necessary for the 
dagster-ge
integration to locate and use your Great Expectations configuration. For more in
formation on configuring resources and assets in Dagster, please refer to the [Dagster documentation](https://docs.dagster.io/). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout this is exactly how my code is already
s
If your code is already set up as described and you're still encountering the error, it's possible that there may be an issue with how the Great Expectations validation op is being invoked within the Dagster asset framework. Since the 
ge_validation_op_factory
generates an op that expects certain inputs, including a "dataset", it's important to ensure that the Great Expectations context is being correctly provided. Here are some steps you can take to troubleshoot the issue: 1. Review Great Expectations Configuration: Ensure that your Great Expectations datasource and expectation suite are correctly configured. The datasource should point to your BigQuery table, and the expectation suite should be set up to validate that table. 2. Check Resource Definitions: Verify that the 
ge_data_context
resource is correctly defined and provided to the asset. The resource should point to the root directory of your Great Expectations project. 3. Asset Dependencies: Make sure that the dependencies of your asset are correctly specified. If your asset depends on the output of a dbt model, the dependency should be correctly represented in the asset's 
ins
parameter. 4. Dagster Version: Check that you are using a version of Dagster that is compatible with the 
dagster-ge
library. Incompatibilities between versions could lead to unexpected behavior. 5. Great Expectations Version: Similarly, ensure that the version of Great Expectations you are using is compatible with 
dagster-ge
. 6. Dagster Context: Instead of manually building an op context, allow Dagster to manage the context. The 
ge_validation_op_factory
-generated op should be included in the asset computation and executed within the Dagster-managed context. 7. Error Messages: Review the full error message and stack trace for any additional clues about what might be going wrong. 8. Dagster Community: If you're still unable to resolve the issue, consider reaching out to the Dagster community for support. Provide details about your setup, including the versions of Dagster and Great Expectations you're using, your Great Expectations configuration, and the exact error message you're encountering. The Dagster community can be a valuable resource when troubleshooting issues, and they may be able to provide additional insights or solutions based on their experience.
a
@Scout Do I need to add a value for input_dagster_type for the ge_validation_op_factory ?
s
When using 
ge_validation_op_factory
from the 
dagster-ge
library, you typically do not need to specify a value for 
input_dagster_type
unless you want to override the default input type for the op. By default, the input type is set to 
dagster_pandas.DataFrame
if you're working with Pandas dataframes. The 
input_dagster_type
parameter allows you to specify a custom Dagster type to use for type checking the input to the generated op. This can be useful if your dataset is not a Pandas dataframe or if you have specific type checking logic you want to enforce. If you're validating a BigQuery table and not passing a Pandas dataframe (or other in-memory object) to the op, you likely do not need to set 
input_dagster_type
. The Great Expectations validation will run against the table specified in your Great Expectations datasource configuration, and the dataset will be loaded based on the 
batch_kwargs
or 
batch_request
that you configure in your Great Expectations suite. Here's an example of how you might use 
ge_validation_op_factory
without specifying `input_dagster_type`:
Copy code
python from dagster_ge.factory import ge_validation_op_factory  # Create a Great Expectations validation op using the factory ge_validation_op = ge_validation_op_factory(     name="validate_my_dbt_model",     datasource_name="my_bigquery_datasource",  # This should match your GE datasource name     suite_name="my_expectation_suite",  # This should match your GE suite name )
In this example, the generated op 
ge_validation_op
will use the default input type, and you do not need to provide a 
dataset
when invoking the op within a Dagster job or asset. The validation will be performed against the data specified in the Great Expectations configuration. If you have a specific reason to use a custom input type, you can provide it using the 
input_dagster_type
parameter. Otherwise, it's not necessary to set this parameter for typical use cases. For more information on using 
ge_validation_op_factory
and integrating Great Expectations with Dagster, please refer to the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectations). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout I have this set up, but I'm getting this error?
s
The error you're encountering, 
DagsterInvalidInvocationError
, indicates that the 
ge_validation_op_factory
-generated op is expecting an input named "dataset" that has not been provided. This suggests that the op is configured to expect a dataset input, which is typically a Pandas DataFrame or similar data structure that Great Expectations can validate. To resolve this issue, you need to ensure that the op is correctly configured to validate data from a BigQuery table without requiring a dataset to be passed explicitly. Here are some steps to troubleshoot and resolve the issue: 1. Check Great Expectations Configuration: Ensure that your Great Expectations datasource is correctly configured to connect to BigQuery. The datasource should include the necessary credentials and reference the correct table. 2. Review Expectation Suite: Verify that your expectation suite is set up correctly for the BigQuery table and does not require a dataset to be passed at runtime. The suite should reference the table in BigQuery directly. 3. Inspect Batch Kwargs or Batch Request: In your Great Expectations suite, you might be using 
batch_kwargs
or 
batch_request
to specify which data batch to validate. Ensure that this configuration correctly targets your BigQuery table and does not rely on an input dataset. 4. Update Resource Configuration: Make sure that the 
ge_data_context
resource in your Dagster job is correctly configured with the path to your Great Expectations project directory. 5. Remove Dataset Input: If you have modified the generated op to include a "dataset" input, remove this modification. The op should not require a "dataset" input if it's validating a BigQuery table. 6. Use Asset Materialization: If you're defining an asset that represents the BigQuery table, ensure that the asset materialization process correctly reflects the data being validated. 7. Dagster and Great Expectations Versions: Check that you are using compatible versions of Dagster and Great Expectations. Incompatibilities between versions could lead to unexpected behavior. 8. Seek Support: If you've tried the above steps and the issue persists, consider reaching out to the Dagster community for support. Provide details about your setup, including the versions of Dagster and Great Expectations you're using, your Great Expectations configuration, and the exact error message you're encountering. Remember, the 
ge_validation_op_factory
-generated op should be able to run the validation against the BigQuery table based on the Great Expectations configuration without needing a "dataset" input. If the configuration is correct, the op will handle loading the data from BigQuery internally.
a
What credentials would I need to provide and where? @Scout
s
When using Great Expectations with a BigQuery table, you need to provide credentials that allow Great Expectations to access BigQuery. Typically, this involves using a service account with the necessary permissions to read the data in BigQuery. Here's how to provide the credentials: 1. Create a Service Account in Google Cloud: Go to your Google Cloud Console, create a new service account, and assign it the roles required to access BigQuery. Download the JSON key file for this service account. 2. Configure Great Expectations Datasource: In your Great Expectations 
great_expectations.yml
file, configure the datasource to use the service account key file. You'll need to provide the path to the JSON key file or set the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable to point to the key file. 3. Set Environment Variable: If you prefer not to store the path to the key file in your Great Expectations configuration, you can set the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable in your environment to the path of the service account JSON key file. Most Google Cloud client libraries, including the one used by Great Expectations, will automatically use the credentials specified by this environment variable. 4. Dagster Resource Configuration: If you're using Dagster, you can configure the 
ge_data_context
resource to include the credentials. This resource should be set up to initialize the Great Expectations 
DataContext
with the correct configuration, including the path to the Great Expectations directory and any necessary environment variables. Here's an example of how you might set up the 
ge_data_context
resource in Dagster:
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python
from dagster import resource
from great_expectations.data_context import DataContext

@resource(config_schema={"ge_root_dir": str})
def ge_data_context_resource(init_context):
    ge_root_dir = init_context.resource_config["ge_root_dir"]
    # Set the environment variable for the Google service account
    os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/service/account/key.json"
    return DataContext(context_root_dir=ge_root_dir)
In this example, the 
ge_data_context_resource
function sets the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable before returning the Great Expectations 
DataContext
. Replace 
"/path/to/service/account/key.json"
with the actual path to your service account JSON key file. Make sure to handle the service account key file securely and avoid exposing it in your code or version control. It's often best to use environment variables or secret management systems to manage credentials in production. For more information on setting up authentication for Google Cloud services, refer to the [Google Cloud authentication documentation](https://cloud.g
oogle.com/docs/authentication/provide-credentials-adc). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout I get this error when the ge validation op runs: Exception ignored in: <function DirectOpExecutionContext.del at 0x7faa818bdb40> Traceback (most recent call last): File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/context/invocation.py", line 256, in del self._exit_stack.close() File "/usr/lib/python3.10/contextlib.py", line 584, in close self.__exit__(None, None, None) File "/usr/lib/python3.10/contextlib.py", line 576, in exit raise exc_details[1] File "/usr/lib/python3.10/contextlib.py", line 153, in exit self.gen.throw(typ, value, traceback) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/api.py", line 293, in ephemeral_instance_if_missing yield ephemeral_instance File "/usr/lib/python3.10/contextlib.py", line 561, in exit if cb(*exc_details): File "/usr/lib/python3.10/contextlib.py", line 142, in exit next(self.gen) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/build_resources.py", line 112, in build_resources list(resources_manager.generate_teardown_events()) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 535, in generate_teardown_events yield from self.generator File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/resources_init.py", line 276, in resource_initialization_event_generator yield from manager.generate_teardown_events() File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 535, in generate_teardown_events yield from self.generator File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/resources_init.py", line 344, in single_resource_event_generator with user_code_error_boundary(DagsterResourceFunctionError, msg_fn, log_manager=context.log): File "/usr/lib/python3.10/contextlib.py", line 142, in exit next(self.gen) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/errors.py", line 283, in user_code_error_boundary with raise_execution_interrupts(): File "/usr/lib/python3.10/contextlib.py", line 142, in exit next(self.gen) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/errors.py", line 250, in raise_execution_interrupts with raise_interrupts_as(DagsterExecutionInterruptedError): File "/usr/lib/python3.10/contextlib.py", line 142, in exit next(self.gen) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/interrupts.py", line 98, in raise_interrupts_as _replace_interrupt_signal(original_signal_handler) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/interrupts.py", line 27, in _replace_interrupt_signal signal.signal(signal.SIGINT, new_signal_handler) File "/usr/lib/python3.10/signal.py", line 56, in signal handler = _signal.signal(_enum_to_int(signalnum), _enum_to_int(handler)) TypeError: signal handler must be signal.SIG_IGN, signal.SIG_DFL, or a callable object
s
The error message you're encountering is related to the teardown process of a 
DirectOpExecutionContext
in Dagster. It appears that an exception is being ignored during the cleanup of resources, which is not typical behavior and suggests that there might be an issue with resource management or an unexpected state during the execution. The stack trace indicates that the error occurs when trying to replace a signal handler during the teardown process. This could be due to a variety of reasons, such as a resource not being properly initialized or cleaned up, or an issue with the environment or Python version being used. Here are some steps you can take to troubleshoot the issue: 1. Review Resource Definitions: Check your resource definitions to ensure that they are correctly implemented, especially the initialization and teardown logic. Make sure that any external connections or sessions are being properly closed. 2. Check Python Version: Verify that you are using a supported Python version for your version of Dagster. Some features or behaviors might differ between Python versions, and using an unsupported version could lead to unexpected errors. 3. Inspect Environment: Look for any issues in your environment that might affect signal handling or process management. This could include other libraries that interact with signals or the way your execution environment is set up. 4. Update Dagster: Ensure that you are using the latest version of Dagster, as bugs and issues are regularly fixed in new releases. If you're not on the latest version, consider upgrading. 5. Review Great Expectations Integration: Double-check the integration with Great Expectations to ensure that it's set up correctly. Make sure that the 
ge_data_context
resource is properly configured and that the Great Expectations suite and datasource are correct. 6. Seek Support: If the issue persists after checking the above points, consider reaching out to the Dagster community for support. Provide details about your Dagster and Python versions, your resource definitions, and the full error message and stack trace. The Dagster community can be a valuable resource when encountering such issues, and they may be able to provide additional insights or solutions based on their experience.
a
@Scout in def ge_validation_op_factory_v3( name, datasource_name, data_connector_name, data_asset_name, suite_name, batch_identifiers: dict, input_dagster_type=DataFrame, runtime_method_type="batch_data", extra_kwargs=None, ) what should the data_connector_name and data_asset_name be?
s
There was an error processing your request. Please try again.
a
@Scout How do i use the ge_validation_op_factory_v3 function?
s
The 
ge_validation_op_factory_v3
function is used to create a Dagster op that integrates with Great Expectations version 3 (also known as the V3 API). This function generates an op that runs a Great Expectations checkpoint, which is a way to bundle the execution of one or more expectation suites along with optional additional actions. Here's how you can use the 
ge_validation_op_factory_v3
function: 1. Ensure Great Expectations V3 Setup: Make sure you have a Great Expectations V3 project set up with at least one checkpoint defined. Checkpoints in Great Expectations V3 are defined in the 
great_expectations.yml
file or in separate checkpoint files within the 
checkpoints/
directory. 2. Install the 
dagster-ge
Library: If you haven't already, install the 
dagster-ge
library in your environment:
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shell
   pip install dagster-ge
3. Import the Factory Function: Import the 
ge_validation_op_factory_v3
function from the 
dagster-ge
library:
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python
   from dagster_ge.factory import ge_validation_op_factory_v3
4. Create a Validation Op: Use the 
ge_validation_op_factory_v3
function to create a Dagster op that runs a Great Expectations checkpoint:
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python
   ge_validation_op = ge_validation_op_factory_v3(
       name="my_ge_validation_op",
       checkpoint_name="my_checkpoint",  # Replace with your checkpoint name
   )
5. Use the Op in a Job or Graph: Include the generated validation op in a Dagster job or graph. The op will run the specified Great Expectations checkpoint when the job or graph is executed:
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python
   from dagster import job

   @job
   def my_data_validation_job():
       ge_validation_op()
6. Execute the Job: Run the job to perform the Great Expectations validation:
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shell
   dagster job execute -f my_dagster_job_file.py -j my_data_validation_job
Or programmatically:
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python
   result = my_data_validation_job.execute_in_process()
7. Review Validation Results: After running the job, you can review the validation results in the Dagit UI or by inspecting the 
result
object if you executed the job programmatically. Please note that the 
ge_validation_op_factory_v3
function and its usage may vary depending on the version of the 
dagster-ge
library and any updates to the Great Expectations API. For the most accurate and up-to-date information on using 
ge_validation_op_factory_v3
, please refer to the [Dagster documentation](https://docs.dagster.io/integrations/great-expectations) or the 
dagster-ge
library's API reference. If you have any questions or need further assistance, the Dagster community is also a great resource for support. Dagster with Great Expectations | Dagste... button Using dagster-ge to create datadocs as a... button Great Expectations for Dagster | Dagster... button Dagster Docs button Thank you for your feedback!
a
@Scout ge_validation_op_factory_v3 doesnt have a param called bigquery_checkpoint
s
I apologize for any confusion. The 
ge_validation_op_factory_v3
function is used to create a Dagster op that runs a Great Expectations checkpoint. The parameter for specifying the checkpoint is typically called 
checkpoint_name
, not 
bigquery_checkpoint
. The 
checkpoint_name
parameter should match the name of the checkpoint you have defined in your Great Expectations project. Here's an example of how you might use the 
ge_validation_op_factory_v3
function:
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python from dagster_ge.factory import ge_validation_op_factory_v3  # Create a Great Expectations validation op using the factory ge_validation_op = ge_validation_op_factory_v3(     name="my_ge_validation_op",     checkpoint_name="my_checkpoint",  # Replace with your actual checkpoint name )
In this example, 
my_checkpoint
is the name of the checkpoint defined in your Great Expectations project. When you run the generated 
ge_validation_op
, it will execute the Great Expectations checkpoint with the name provided. If you are validating a BigQuery table, ensure that your Great Expectations datasource configuration is set up correctly to connect to BigQuery, and that your checkpoint configuration in Great Expectations is correctly defined to validate the BigQuery table. For more information on configuring Great Expectations checkpoints and integrating them with Dagster, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/docs/guides/validation/checkpoints/how_to_create_a_new_checkpoint) and the [Dagster documentation on Great Expectations integration](https://docs.dagster.io/integrations/great-expectations). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
It also doesnt have this param @Scout This is it's definition: def ge_validation_op_factory_v3( name, datasource_name, data_connector_name, data_asset_name, suite_name, batch_identifiers: dict, input_dagster_type=DataFrame, runtime_method_type="batch_data", extra_kwargs=None, ): """Generates ops for interacting with GE (v3 API). Args: name (str): the name of the op datasource_name (str): the name of your DataSource, see your great_expectations.yml data_connector_name (str): the name of the data connector for this datasource. This should point to a RuntimeDataConnector. For information on how to set this up, see: https://docs.greatexpectations.io/docs/guides/connecting_to_your_data/how_to_create_a_batch_of_data_from_an_in_memory_spark_or_pandas_dataframe data_asset_name (str): the name of the data asset that this op will be validating. suite_name (str): the name of your expectation suite, see your great_expectations.yml batch_identifier_fn (dict): A dicitonary of batch identifiers to uniquely identify this batch of data. To learn more about batch identifiers, see: https://docs.greatexpectations.io/docs/reference/datasources#batches. input_dagster_type (DagsterType): the Dagster type used to type check the input to the op. Defaults to 
dagster_pandas.DataFrame
. runtime_method_type (str): how GE should interperet the op input. One of ("batch_data", "path", "query"). Defaults to "batch_data", which will interperet the input as an in-memory object. extra_kwargs (Optional[dict]): adds extra kwargs to the invocation of `ge_data_context`'s 
get_validator
method. If not set, input will be: { "datasource_name": datasource_name, "data_connector_name": data_connector_name, "data_asset_name": data_asset_name, "runtime_parameters": { "<runtime_method_type>": <op input> }, "batch_identifiers": batch_identifiers, "expectation_suite_name": suite_name, } Returns: An op that takes in a set of data and yields both an expectation with relevant metadata and an output with all the metadata (for user processing) """ check.str_param(datasource_name, "datasource_name") check.str_param(data_connector_name, "data_connector_name") check.str_param(suite_name, "suite_name") _extra_kwargs: Dict[Any, Any] = check.opt_dict_param(extra_kwargs, "extra_kwargs") @op( name=name, ins={"dataset": In(input_dagster_type)}, out=Out( dict, description=""" This op yields an ExpectationResult with a structured dict of metadata from the GE suite, as well as the full result in case a user wants to process it differently. The structured dict contains both summary stats from the suite as well as expectation by expectation results/details. """, ), required_resource_keys={"ge_data_context"}, tags={"kind": "ge"}, ) def _ge_validation_fn(context: OpExecutionContext, dataset): data_context = context.resources.ge_data_context validator_kwargs = { "datasource_name": datasource_name, "data_connector_name": data_connector_name, "data_asset_name": datasource_name or data_asset_name, "runtime_parameters": {runtime_method_type: dataset}, "batch_identifiers": batch_identifiers, "expectation_suite_name": suite_name, **_extra_kwargs, } validator = data_context.get_validator(**validator_kwargs) run_id = { "run_name": datasource_name + " run", "run_time": datetime.datetime.now(datetime.timezone.utc), } results = validator.validate(run_id=run_id) validation_results_page_renderer = ValidationResultsPageRenderer(run_info_at_end=True) rendered_document_content_list = validation_results_page_renderer.render( validation_results=results ) md_str = "".join(DefaultMarkdownPageView().render(rendered_document_content_list)) yield ExpectationResult( success=bool(results["success"]), metadata={"Expectation Results": MetadataValue.md(md_str)}, ) yield Output(results.to_json_dict()) return _ge_validation_fn
s
The 
ge_validation_op_factory_v3
function you've provided is designed to create a Dagster op that runs a Great Expectations validation using the V3 Batch Request API. This function requires several parameters to configure the validation, including details about the datasource, data connector, data asset, and expectation suite. Here's a breakdown of the parameters you need to provide: - `name`: The name of the op. - `datasource_name`: The name of the datasource configured in your 
great_expectations.yml
. - `data_connector_name`: The name of the data connector that should point to a 
RuntimeDataConnector
. - `data_asset_name`: The name of the data asset that you will be validating. - `suite_name`: The name of the expectation suite you want to use for validation. - `batch_identifiers`: A dictionary of batch identifiers to uniquely identify the batch of data. - `input_dagster_type`: The Dagster type used to type check the input to the op (defaults to 
dagster_pandas.DataFrame
). - `runtime_method_type`: How Great Expectations should interpret the op input (e.g., "batch_data", "path", "query"). - `extra_kwargs`: Additional kwargs to pass to the 
get_validator
method of the Great Expectations 
DataContext
. To use this function to create an op that validates a BigQuery table, you would typically configure a Great Expectations datasource that connects to BigQuery and set up a 
RuntimeDataConnector
that can accept runtime parameters for the batch data. Here's an example of how you might use the 
ge_validation_op_factory_v3
to create a validation op:
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python
from dagster import job, Out, op, In
from dagster_ge.factory import ge_validation_op_factory_v3

# Create a Great Expectations validation op using the factory
ge_validation_op = ge_validation_op_factory_v3(
    name="my_ge_validation_op",
    datasource_name="my_bigquery_datasource",
    data_connector_name="my_runtime_data_connector",
    data_asset_name="my_data_asset",
    suite_name="my_expectation_suite",
    batch_identifiers={"default_identifier_name": "default_identifier_value"},
)

# Define a job that includes the GE validation op
@job
def my_data_validation_job():
    ge_validation_op()

# Execute the job to perform the Great Expectations validation
result = my_data_validation_job.execute_in_process()
In this example, you would replace the placeholder values with the actual names and configurations from your Great Expectations setup. The 
batch_identifiers
would be specific to your use case and how you've configured your 
RuntimeDataConnector
. Please ensure that your Great Expectations project is properly set up with the correct datasource, data connector, and expectation suite configurations. For more information on configuring Great Expectations and using the V3 Batch Reque
st API, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout How would I set up "my_runtime_data_connector"
s
To set up a 
RuntimeDataConnector
in Great Expectations, you'll need to modify your 
great_expectations.yml
file to include the configuration for the connector. A 
RuntimeDataConnector
allows you to pass in data at runtime, which is useful when you want to validate data that isn't available at the time of setting up your Great Expectations environment, such as data from a pipeline run. Here's a step-by-step guide on how to set up a 
RuntimeDataConnector
for a BigQuery datasource: 1. Open your 
great_expectations.yml
file: This file is located in the root of your Great Expectations project directory. 2. Add a new datasource with a 
RuntimeDataConnector
: Under the 
datasources
section, define a new datasource or modify an existing one to include a 
RuntimeDataConnector
. Here's an example configuration for a BigQuery datasource:
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yaml
datasources:
  my_bigquery_datasource:
    class_name: Datasource
    execution_engine:
      class_name: SqlAlchemyExecutionEngine
      credentials:
        url: bigquery://<GCP_PROJECT>/<DATASET>
    data_connectors:
      my_runtime_data_connector:
        class_name: RuntimeDataConnector
        batch_identifiers:
          - default_identifier_name
        runtime_parameters:
          - query
In this configuration: - Replace 
<GCP_PROJECT>
with your Google Cloud project ID and 
<DATASET>
with the BigQuery dataset you're working with. - The 
url
under 
credentials
should be the SQLAlchemy URL for BigQuery. - 
my_runtime_data_connector
is the name of the 
RuntimeDataConnector
. - 
batch_identifiers
is a list of keys that will be used to uniquely identify a batch of data; you can specify one or more identifiers. - 
runtime_parameters
specifies the parameters that will be passed at runtime, such as a SQL query. 3. Set up authentication: Ensure that your environment is authenticated with Google Cloud and has the necessary permissions to access BigQuery. You can use the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable to point to your service account key file. 4. Use the 
RuntimeDataConnector
in your validation op: When you create the validation op with 
ge_validation_op_factory_v3
, you'll pass the name of the 
RuntimeDataConnector
you just configured. 5. Run a checkpoint that uses the 
RuntimeDataConnector
: When you execute a checkpoint that uses this connector, you'll provide the 
batch_identifiers
and 
runtime_parameters
that the 
RuntimeDataConnector
expects. For more detailed instructions on setting up 
RuntimeDataConnector
and other Great Expectations components, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/docs/guides/connecting_to_your_data/how_to_configure_a_runtime_data_connector). If you have any question
s or need further assistance, the Great Expectations community is also a great resource for support.
a
What info is on an AssetExecutionContext object and how can I print it out? @Scout
s
The 
AssetExecutionContext
object in Dagster provides context for the execution of an asset. It contains information about the system, such as resources, configuration, and logging, similar to the 
OpExecutionContext
. The 
AssetExecutionContext
is designed to expose only the fields that make sense in the context of asset computations. Here's what's typically available on an 
AssetExecutionContext
object: - `resources`: Provides access to the resources defined in the job or pipeline. Resources are reusable components that can be accessed by assets during execution. - `log`: Allows you to log messages that will appear in the Dagster event log. You can use different log levels like 
info
, 
warning
, 
error
, etc. - `run_id`: The ID of the current Dagster run. - `config`: The configuration for the asset, if any was provided. - `asset_key`: The key that uniquely identifies the asset. - `op_config`: The configuration specific to the op, if the asset is defined using the 
@op
decorator. - `output_capture`: A dictionary that captures outputs produced during execution. To print out information from the 
AssetExecutionContext
, you can access its properties and use the 
log
method to output the information. Here's an example of how you might do this within an asset:
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python from dagster import asset  @asset def my_asset(context):     # Accessing and printing various parts of the AssetExecutionContext     <http://context.log.info|context.log.info>(f"Run ID: {context.run_id}")     <http://context.log.info|context.log.info>(f"Asset Key: {context.asset_key}")     <http://context.log.info|context.log.info>(f"Resources: {context.resources}")     <http://context.log.info|context.log.info>(f"Config: {context.config}")     # If you have op-specific config     <http://context.log.info|context.log.info>(f"Op Config: {context.op_config}")     # If you want to capture and print outputs (assuming output_capture is used)     <http://context.log.info|context.log.info>(f"Output Capture: {context.output_capture}")          # Your asset logic here
In this example, the 
my_asset
function logs various pieces of information from the 
AssetExecutionContext
to the Dagster event log. You can view these logs in the Dagit UI or wherever your Dagster logs are aggregated. For more information on the 
AssetExecutionContext
and its properties, please refer to the [Dagster documentation](https://docs.dagster.io/_apidocs/execution#dagster.AssetExecutionContext). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout how do I call an op definition?
s
In Dagster, you typically use the 
@op
decorator to define an op, and then you include that op within a job or a graph to execute it. You don't usually call an op definition directly like a regular function because ops are designed to be executed within the Dagster framework, which provides the necessary context and manages execution. Here's how you can define and use an op within a job:
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python from dagster import op, job  @op def my_op(context):     <http://context.log.info|context.log.info>("Hello from my_op")  @job def my_job():     my_op()  # Execute the job result = my_job.execute_in_process()
In this example, 
my_op
is defined with the 
@op
decorator, and 
my_job
is a job that includes 
my_op
. When you execute 
my_job
, Dagster will run 
my_op
within its execution context. If you need to call an op outside of the standard Dagster execution context, such as in a test or a standalone script, you can use the 
build_op_context
utility to construct an appropriate context for invoking the op. Here's an example of how you might do that:
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python from dagster import build_op_context  # Assuming my_op is already defined as shown above  # Build a context for the op op_context = build_op_context()  # Invoke the op with the constructed context result = my_op(op_context)
In this example, 
build_op_context
creates a mock context that can be used to invoke 
my_op
directly. This approach is typically used for testing purposes. Please note that using 
build_op_context
is not the standard way to execute ops in Dagster, and it should generally be reserved for testing or other special cases where you need to run an op outside of a job or graph. For more information on defining and using ops, please refer to the [Dagster documentation](https://docs.dagster.io/concepts/ops-jobs-graphs/ops). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout If I have an op definition, how can I invoke it within an asset?
s
To invoke an op within an asset in Dagster, you can use the 
@asset
decorator to define your asset and include the op logic within the body of the asset function. Since assets are a higher-level abstraction that represent data artifacts, they are not typically used to directly invoke ops. However, you can encapsulate the logic of an op within an asset. Here's a conceptual example of how you might include the logic of an op within an asset:
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python from dagster import asset, Out  # Assume this is the logic you want to invoke, originally encapsulated in an op def my_logic(input_value):     # Perform some computation     return input_value * 2  # Define an asset that uses the logic from the op @asset def my_asset(input_value):     result = my_logic(input_value)     return result
In this example, 
my_logic
represents the computation you want to perform, which was originally part of an op. The 
my_asset
asset calls 
my_logic
directly and returns the result as the asset's output. If you have an existing op and want to use it within an asset, you would refactor the op's computation logic into a function that can be called both from the op and the asset. Here's how you might refactor an existing op to be used within an asset:
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python from dagster import op, asset, Out  # Refactor the op's logic into a function def compute_something(param):     # Your computation logic here     return param * 2  # Define the op using the refactored logic @op(out=Out()) def my_op(context, param):     return compute_something(param)  # Define the asset using the same refactored logic @asset def my_asset(param):     return compute_something(param)
In this refactored example, both 
my_op
and 
my_asset
use the 
compute_something
function to perform their computation. This allows you to maintain the logic in one place and use it in both ops and assets. Remember that assets in Dagster are meant to represent data artifacts and their dependencies, and they are typically used within asset jobs to define data pipelines. Ops, on the other hand, are units of computation that are used within jobs and graphs. For more information on assets and how they relate to ops, please refer to the [Dagster documentation](https://docs.dagster.io/concepts/assets/software-defined-assets). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
I want the asset to invoke the op definition with a value generated within the asset @Scout
s
To invoke an op definition within an asset and pass it a value generated within the asset, you can use the 
build_op_context
utility function to create an 
OpExecutionContext
and then invoke the op with that context. This approach is typically used for testing or when you need to execute an op outside of a job or graph. Here's a step-by-step example of how you might do this: 1. Define your op with the 
@op
decorator. 2. In your asset, generate the value you want to pass to the op. 3. Use 
build_op_context
to create an 
OpExecutionContext
. 4. Invoke the op with the context and the generated value.
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python from dagster import op, asset, build_op_context  # Define your op @op def my_op(context, my_input):     # Op logic here     <http://context.log.info|context.log.info>(f"Received input: {my_input}")     return my_input * 2  # Define your asset that invokes the op @asset def my_asset():     # Generate the value you want to pass to the op     generated_value = 42  # Replace with your logic to generate the value      # Build an op context (note: this is typically used for testing)     op_context = build_op_context()      # Invoke the op with the context and the generated value     result = my_op(op_context, generated_value)      # Optionally, you can do something with the result     # ...      return result
In this example, 
my_op
is a simple op that logs and processes the input value. The 
my_asset
asset generates a value and then invokes 
my_op
using a context created with 
build_op_context
. The result of the op is returned from the asset. Please note that using 
build_op_context
outside of testing is not a common pattern in Dagster, as it bypasses the standard execution plan and lifecycle management provided by the framework. In a typical Dagster pipeline, you would include both the op and the asset in a job, and the framework would manage the execution context and dependencies. If you need to invoke an op within an asset in a production scenario, you should consider refactoring the logic into a function that can be called both from the op and the asset, as described in the previous response. For more information on testing ops and assets, please refer to the [Dagster documentation](https://docs.dagster.io/concepts/ops-jobs-graphs/testing). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
The error message 
TypeError: 'generator' object is not subscriptable
indicates that the 
weather_gx
op is yielding results (most likely 
Output
or 
ExpectationResult
objects) as a generator, and you are attempting to subscript it as if it were a dictionary or list. In Dagster, when an op yields multiple results, you need to iterate over those results rather than trying to access them with a subscript. The 
weather_gx
op, created by 
ge_validation_op_factory
, is expected to yield its results, which is why you're encountering this error when you try to do 
result["success"]
. Here's how you can adjust your asset definition to handle the yielded results correctly:
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python from dagster import asset, AssetIn, get_dagster_logger, resource  # ... (other code, including ge_validation_op_factory and ge_data_context_resource)  @asset(     name="weather_gx_asset",     key_prefix='gx',     required_resource_keys={"ge_data_context"},     group_name='gx',     ins={"upstream": AssetIn(**{"key": get_asset_key_for_model([dbt_project_assets], "weather")})}, ) def weather_gx_asset(context, upstream):     # Build the op context     op_context = build_op_context(         resources={             "ge_data_context": context.resources.ge_data_context,         }     )          # Execute the op and iterate over the results     for result in weather_gx(op_context, pd.DataFrame()):         # Handle each result         if isinstance(result, ExpectationResult):             # Log the success value of the ExpectationResult             get_dagster_logger().info(f"Great Expectations validation success: {result.success}")         elif isinstance(result, Output):             # Do something with the output value             output_value = result.value             # ...
In this revised example, we iterate over the results yielded by the 
weather_gx
op and handle each one accordingly. If the result is an 
ExpectationResult
, we log its success value. If it's an 
Output
, we can do something with the output value. Please note that using 
build_op_context
outside of testing is not a common pattern in Dagster, as it bypasses the standard execution plan and lifecycle management provided by the framework. In a typical Dagster pipeline, you would include both the op and the asset in a job, and the framework would manage the execution context and dependencies. For more information on testing ops and assets, please refer to the [Dagster documentation](https://docs.dagster.io/concepts/ops-jobs-graphs/testing). If you have any questions or need further assistance, the Dagster community is also a great resource for support.
a
@Scout If I call my weather_gx function without an empty dataset I get this error? What dataset am I supposed to be passing through to the gx function? I would have thought the gx function would be querying the data in big query using the great expectations resource config? result = weather_gx(op_context) dagster._core.errors.DagsterInvalidInvocationError: No value provided for required input "dataset". Stack Trace: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_plan.py", line 286, in dagster_event_sequence_for_step for step_event in check.generator(step_events): File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 519, in core_dagster_event_sequence_for_step for user_event in _step_output_error_checked_user_event_sequence( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 201, in _step_output_error_checked_user_event_sequence for user_event in user_event_sequence: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 99, in _process_asset_results_to_events for user_event in user_event_sequence: File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 214, in execute_core_compute for step_output in _yield_compute_results(step_context, inputs, compute_fn, compute_context): File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 183, in _yield_compute_results for event in iterate_with_context( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 131, in _coerce_op_compute_fn_to_iterator result = invoke_compute_fn( File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 125, in invoke_compute_fn return fn(context, **args_to_pass) if context_arg_provided else fn(**args_to_pass) File "/home/alexsmith/Repositories/sbx-etl-services/elt_repositories/debenhams_repo/debenhams_repo/assets/outputs/__init__.py", line 282, in weather_gx_asset result = weather_gx(op_context) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_definition.py", line 456, in call return direct_invocation_result(self, *args, **kwargs) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_invocation.py", line 225, in direct_invocation_result input_dict = _resolve_inputs(op_def, input_args, input_kwargs, bound_context) # type: ignore (pyright bug) File "/home/alexsmith/test-gx-venv/lib/python3.10/site-packages/dagster/_core/definitions/op_invocation.py", line 316, in _resolve_inputs raise DagsterInvalidInvocationError(
s
The error you're encountering indicates that the 
weather_gx
op, created by 
ge_validation_op_factory_v3
, expects an input named "dataset" that has not been provided. The "dataset" input is required because the op is configured to perform validation on an in-memory dataset, such as a Pandas DataFrame, that you need to pass at runtime. If you expected the 
weather_gx
op to query data directly from BigQuery using the Great Expectations configuration, it's likely that the op was created with the assumption that it would be validating an in-memory dataset. To validate data directly in BigQuery, you would typically configure a Great Expectations 
Datasource
and 
RuntimeDataConnector
that can accept runtime parameters to identify the specific data batch (table, query, etc.) to validate. To resolve this issue, you have a couple of options: 1. Pass a Pandas DataFrame to the Op: If you have a DataFrame that you want to validate, you can pass it directly to the 
weather_gx
op when invoking it. This DataFrame should contain the data you want to validate against your Great Expectations suite. 2. Configure Great Expectations for Direct BigQuery Validation: If you want to validate data directly in BigQuery without passing a DataFrame, you need to ensure that your Great Expectations configuration is set up to do so. This involves configuring a 
Datasource
with a 
RuntimeDataConnector
and creating an expectation suite that references this configuration. Here's a high-level overview of what you need to do for the second option: - Update your 
great_expectations.yml
to include a 
Datasource
with a 
RuntimeDataConnector
that targets BigQuery. - Create an expectation suite that uses this datasource. - Configure the 
weather_gx
op to use the correct 
datasource_name
, 
data_connector_name
, and 
batch_identifiers
that correspond to your BigQuery table. Once you have the correct Great Expectations configuration, you should be able to run the 
weather_gx
op without needing to pass a "dataset" input, as Great Expectations will handle fetching the data from BigQuery based on the provided runtime parameters. For more detailed guidance on configuring Great Expectations to validate data in BigQuery, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/docs/guides/connecting_to_your_data/database/bigquery). If you have any questions or need further assistance, the Dagster community and the Great Expectations community are also great resources for support.
Based on the information provided, it seems that you are trying to run a Great Expectations validation on a BigQuery table using the 
ge_validation_op_factory
from Dagster. However, the error indicates that the op is expecting a "dataset" input, which suggests that the op is configured to validate an in-memory dataset (like a Pandas DataFrame) rather than directly querying BigQuery. The 
ge_validation_op_factory
function you're using appears to be from an older version of the 
dagster-ge
library that expects a dataset to be passed to the op. In Great Expectations V3 (the latest version), you would typically use a 
BatchRequest
to specify the data to be validated, which can include a SQL query or table name for databases like BigQuery. Here's what you need to do to fix the issue: 1. Update the Great Expectations Datasource Configuration: Ensure that your 
great_expectations.yml
is configured with a 
Datasource
that includes a 
RuntimeDataConnector
for BigQuery. The 
RuntimeDataConnector
should be set up to accept 
batch_identifiers
and 
runtime_parameters
that specify the data to be validated. 2. Use the Correct Op Factory Function: If you are using Great Expectations V3, make sure you are using the correct op factory function that supports the V3 API. The 
ge_validation_op_factory_v3
function should be used instead of 
ge_validation_op_factory
. This function will allow you to specify a 
BatchRequest
instead of passing a "dataset". 3. Configure the 
BatchRequest
: When creating the op with 
ge_validation_op_factory_v3
, you will need to provide a 
BatchRequest
that tells Great Expectations how to fetch the data from BigQuery. This request includes details such as the datasource name, data connector name, data asset name, and any additional identifiers needed to select the correct data batch. 4. Remove the Manual 
build_op_context
: You should not need to manually build an op context using 
build_op_context
when running within a Dagster job or graph. Dagster will automatically provide the necessary context when the op is executed as part of a job. Here's a conceptual example of how you might define the validation op using the V3 API:
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python
from dagster_ge.factory import ge_validation_op_factory_v3

# Create a Great Expectations validation op using the V3 factory
weather_gx = ge_validation_op_factory_v3(
    name="ge_validation_op",
    datasource_name="sparkbox_dev_datasource",
    data_connector_name="my_runtime_data_connector",  # Replace with your actual data connector name
    data_asset_name="weather",
    suite_name="test_bigquery_suite",
    batch_identifiers={"some_identifier": "some_value"},  # Replace with actual identifiers
    # Additional parameters as needed
)
Make sure to replace the placeh
olders with the actual names and configurations from your Great Expectations setup. You will also need to ensure that your 
great_expectations.yml
file and the associated configurations are correctly set up for BigQuery. For more detailed guidance on configuring Great Expectations with BigQuery and using the V3 API, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/docs/guides/connecting_to_your_data/database/bigquery). If you have any questions or need further assistance, the Dagster community and the Great Expectations community are also great resources for support.
a
@Scout how do I set up the runtime data connector?
s
To set up a 
RuntimeDataConnector
in Great Expectations, you'll need to modify your 
great_expectations.yml
configuration file to include the 
RuntimeDataConnector
as part of a datasource. This connector allows you to specify data at runtime, which is particularly useful when you want to validate data that isn't available at the time of setting up your Great Expectations environment. Here's a step-by-step guide on how to set up a `RuntimeDataConnector`: 1. Open your 
great_expectations.yml
file: This file is located in the root of your Great Expectations project directory. 2. Add a new datasource with a 
RuntimeDataConnector
: Under the 
datasources
section, define a new datasource or modify an existing one to include a 
RuntimeDataConnector
. Here's an example configuration for a BigQuery datasource:
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yaml
datasources:
  my_bigquery_datasource:
    class_name: Datasource
    execution_engine:
      class_name: SqlAlchemyExecutionEngine
      credentials: ${bigquery_credentials}  # Use environment variable or actual credentials
    data_connectors:
      my_runtime_data_connector:
        class_name: RuntimeDataConnector
        batch_identifiers:
          - default_identifier_name  # Define the identifiers you will use at runtime
        assets:
          my_table:  # This is a logical name for your asset
            class_name: Asset
            batch_spec_passthrough:
              bigquery_temp_table: my_temp_table  # Optional: Use a temp table in BigQuery
In this configuration: - 
my_bigquery_datasource
is the name of the datasource. - 
SqlAlchemyExecutionEngine
is used for BigQuery, and credentials are provided (either directly or via an environment variable). - 
my_runtime_data_connector
is the name of the 
RuntimeDataConnector
. - 
batch_identifiers
are the keys that will be used to uniquely identify a batch of data at runtime. - 
assets
defines logical names for your assets, which correspond to tables or queries in BigQuery. 3. Use the 
RuntimeDataConnector
in your Great Expectations Checkpoint: When you create a checkpoint in Great Expectations, you'll reference the 
RuntimeDataConnector
and provide the 
batch_identifiers
at runtime. Here's an example of a checkpoint configuration:
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yaml
checkpoints:
  my_checkpoint:
    class_name: Checkpoint
    run_name_template: "%Y%m%d-%H%M%S-my-checkpoint"
    validations:
      - batch_request:
          datasource_name: my_bigquery_datasource
          data_connector_name: my_runtime_data_connector
          data_asset_name: my_table
          # At runtime, you'll provide the actual batch_identifiers values
4. Run the Checkpoint with Runtime Parameters: When executing the checkpoint, you'll provide the 
batch_identifiers
that the `RuntimeDataConnec
tor` expects. This can be done via a script, the Great Expectations CLI, or within a Dagster op. For more detailed instructions on setting up 
RuntimeDataConnector
and other Great Expectations components, please refer to the [Great Expectations documentation](https://docs.greatexpectations.io/docs/guides/connecting_to_your_data/how_to_configure_a_runtime_data_connector). Remember to handle your BigQuery credentials securely and avoid exposing them in your code or version control. It's often best to use environment variables or secret management systems to manage credentials in production environments. If you have any questions or need further assistance, the Great Expectations community is also a great resource for support.
a
Is dagster configured to run with GEs fluent data sources? @Scout
s
As of my last update, Dagster's integration with Great Expectations does not specifically mention support for Great Expectations' "fluent" data sources. The integration primarily focuses on using the 
ge_data_context
resource to provide access to the Great Expectations DataContext within Dagster, and on using factory functions like 
ge_validation_op_factory
or 
ge_validation_op_factory_v3
to create ops that run Great Expectations validations. The 
ge_data_context
resource points to the root directory of your Great Expectations configuration, and from there, you can use all the standard features of Great Expectations, including any data sources defined in your 
great_expectations.yml
file. If you're using Great Expectations' fluent data sources, which are a way to define data sources and their configurations programmatically, you should still be able to use them within the context provided by the 
ge_data_context
resource in Dagster. However, you would need to ensure that your Great Expectations setup is compatible with the Dagster integration and that any data sources you define are accessible when running validations. For specific details on how to configure and use fluent data sources with Dagster, you would need to refer to the Great Expectations documentation and ensure that your Great Expectations version is compatible with the 
dagster-ge
integration. If you encounter any issues or have questions about using fluent data sources with Dagster, I recommend reaching out to the Dagster community for guidance and support. They may have more up-to-date information or examples of using fluent data sources within Dagster pipelines.
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