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How to add check to multi-assets
# ask-ai
p
How to add check to multi-assets
s
To add checks to multi-assets in Dagster, you can use the 
@multi_asset
decorator, which allows you to define a single op or graph that produces multiple data assets at once. Each output asset can be associated with its own checks. Here's an example from the documentation on how to define a basic multi-asset with checks:
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python from dagster import AssetOut, multi_asset, AssetExecutionContext  @multi_asset(     outs={         "my_string_asset": AssetOut(),         "my_int_asset": AssetOut(),     } ) def my_function():     return "abc", 123  # Define checks for the assets @asset_check(asset_key="my_string_asset") def check_string_asset(context: AssetExecutionContext, value):     # Implement your check logic here     pass  @asset_check(asset_key="my_int_asset") def check_int_asset(context: AssetExecutionContext, value):     # Implement your check logic here     pass
In this example, 
my_function
is decorated with 
@multi_asset
and specifies two output assets: 
my_string_asset
and 
my_int_asset
. For each of these assets, you can define separate checks using the 
@asset_check
decorator, where you specify the asset key that the check is associated with. Remember that the checks are defined separately for each asset, and you can implement the check logic within the decorated functions 
check_string_asset
and 
check_int_asset
. These functions will be called to validate the respective assets when they are materialized. Please note that the example provided is based on the documentation, and you should refer to the latest Dagster documentation for any updates or changes to the API.
p
@Scout I want the check to be part of the multi-assets
s
In Dagster, if you want to define checks that are part of the multi-assets themselves, you can use the 
check_specs
argument when defining the assets using the 
@multi_asset
decorator. This allows you to declare checks that the decorated function should yield 
AssetCheckResult
for each of the specified checks. Here is an example based on the documentation that shows how to define checks as part of a multi-asset:
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python from dagster import multi_asset, AssetCheckResult, AssetCheckSpec  @multi_asset(     outs={         "asset1": AssetOut(),         "asset2": AssetOut(),     },     check_specs=[         AssetCheckSpec("check_for_asset1", asset_key="asset1"),         AssetCheckSpec("check_for_asset2", asset_key="asset2"),     ] ) def my_assets():     # Materialize your assets here     asset1_data = ...     asset2_data = ...      # Perform checks and yield AssetCheckResults     yield AssetCheckResult(         check_name="check_for_asset1",         asset_key="asset1",         passed=check_condition_for_asset1(asset1_data),         metadata={"details": "Additional information about the check"}     )     yield AssetCheckResult(         check_name="check_for_asset2",         asset_key="asset2",         passed=check_condition_for_asset2(asset2_data),         metadata={"details": "Additional information about the check"}     )      # Yield the materialized assets     yield Output(asset1_data, "asset1")     yield Output(asset2_data, "asset2")
In this example, 
my_assets
is a multi-asset function that materializes two assets, 
asset1
and 
asset2
. The 
check_specs
argument is used to define checks for each asset. Inside the function, you perform the checks and yield 
AssetCheckResult
objects for each check, indicating whether the check passed and providing any additional metadata. Finally, you yield the materialized assets using 
Output
. Please ensure that you refer to the latest Dagster documentation for the most up-to-date syntax and features related to asset checks.
p
@Scout can we have asset check per partition?
s
As of the last update to my knowledge, Dagster supports asset checks on a per-asset basis, but it does not natively support per-partition checks within the asset check framework. However, there is a discussion and an open issue on the Dagster GitHub repository indicating that this feature is planned for the future. In the meantime, you can implement a workaround by writing a check that summarizes the state of all partitions into one pass/fail for the asset. Here's an example from a discussion on how to write a check against a partitioned asset:
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python from dagster import asset, asset_check, StaticPartitionsDefinition, AssetCheckResult import random  @asset(     group_name="asset_checks",     partitions_def=StaticPartitionsDefinition(["a", "b", "c", "d"]), ) def partitioned_asset(_):     return random.choice(["foo", "bar"])  @asset_check(asset=partitioned_asset) def check_on_partitioned_asset(partitioned_asset):     # input is in the form of {partition_key: output}     failed_partitions = [         key for key, output in partitioned_asset.items() if output != "foo"     ]     return AssetCheckResult(         passed=not failed_partitions,         metadata={"non_foo_partitions": failed_partitions},     )
In this example, the 
check_on_partitioned_asset
function receives a mapping of all the partition keys to the output values. It tests each partition and fails if any don't pass the check condition. Please keep an eye on the Dagster GitHub repository for updates on the feature that would allow per-partition checks natively within the asset check framework.
p
@Scout update the code below to add retry policy: @multi_asset( outs={ "cpi_metadata": AssetOut(is_required=False), "price_metadata": AssetOut(is_required=False), "gpd_labor_metadata": AssetOut(is_required=False), }, can_subset=True, ) def imf_metadata(context: AssetExecutionContext, config: IMFConfig) -> Iterator[Output]: """ Get a list of required metadata with their codes. We will be using the code to send request to the API for a specific product later. """ code_to_asset_name_map = {"CL_INDICATOR_CPI": "cpi", "CL_INDICATOR_IFS": "gpd_labor", "CL_INDICATOR_PCPS": "price"} for code in ["CL_INDICATOR_CPI", "CL_INDICATOR_IFS", "CL_INDICATOR_PCPS"]: asset_name = f"{code_to_asset_name_map.get(code)}_metadata" result = fetch_imf_metadata(config, code) yield Output(value=result.value, metadata=result.metadata, output_name=asset_name)
s
To add a retry policy to your 
@multi_asset
definition, you can use the 
RetryPolicy
class from Dagster. You'll need to import 
RetryPolicy
and then specify the retry policy as part of the decorator. Here's an updated version of your code with a retry policy added:
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python from dagster import multi_asset, AssetOut, AssetExecutionContext, Output, RetryPolicy from typing import Iterator  # Define your retry policy retry_policy = RetryPolicy(     max_retries=3,  # Maximum number of times to retry     delay=2,  # Time to wait between retries in seconds     backoff=2,  # Factor by which the delay should increase after each retry )  @multi_asset(     outs={         "cpi_metadata": AssetOut(is_required=False),         "price_metadata": AssetOut(is_required=False),         "gpd_labor_metadata": AssetOut(is_required=False),     },     can_subset=True,     retry_policy=retry_policy,  # Apply the retry policy to the multi_asset ) def imf_metadata(context: AssetExecutionContext, config: IMFConfig) -> Iterator[Output]:     """     Get a list of required metadata with their codes. We will be using     the code to send request to the API for a specific product     later.     """     code_to_asset_name_map = {         "CL_INDICATOR_CPI": "cpi",         "CL_INDICATOR_IFS": "gpd_labor",         "CL_INDICATOR_PCPS": "price"     }     for code in ["CL_INDICATOR_CPI", "CL_INDICATOR_IFS", "CL_INDICATOR_PCPS"]:         asset_name = f"{code_to_asset_name_map.get(code)}_metadata"         result = fetch_imf_metadata(config, code)         yield Output(value=result.value, metadata=result.metadata, output_name=asset_name)
In this example, the 
RetryPolicy
is configured to retry up to three times with an initial delay of 2 seconds between retries. The delay will increase by a factor of 2 after each retry (exponential backoff). Please adjust the parameters of the 
RetryPolicy
to fit your specific use case. The 
max_retries
, 
delay
, and 
backoff
parameters can be tuned based on how you want to handle retries for your asset computations.
p
@Scout Explain the code below bss_files_partitions_def = DynamicPartitionsDefinition(name="bss_files")
s
The code snippet you've provided defines a 
DynamicPartitionsDefinition
in Dagster, which is used to represent a set of partitions that can change over time, rather than being known up-front and static. This is particularly useful when dealing with data sources where new data arrives or is discovered over time, and you want to process each new piece of data as its own partition. Here's a breakdown of the code: - `bss_files_partitions_def`: This is a variable name that holds the definition of the dynamic partitions. It's a common pattern to name the variable after the partitions it represents, in this case, "bss_files". - `DynamicPartitionsDefinition`: This is the class provided by Dagster that you use to define dynamic partitions. - `name="bss_files"`: This is an argument to the 
DynamicPartitionsDefinition
constructor that specifies the name of the partition set. The name is used to identify this particular set of dynamic partitions within the Dagster system. When you use this 
DynamicPartitionsDefinition
in an asset, Dagster will not expect a fixed list of partition keys. Instead, the system will allow for partitions to be created on-the-fly as new data becomes available. This is particularly useful for assets that correspond to datasets that grow over time, such as files landing in a data lake or new entries in a database table. Here's an example of how you might use 
bss_files_partitions_def
in an asset definition:
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python from dagster import asset  @asset(partitions_def=bss_files_partitions_def) def my_dynamic_asset(context):     # Logic to process the data for the current partition     ...
In this example, 
my_dynamic_asset
is an asset that uses the 
bss_files_partitions_def
dynamic partitions definition. When materializing this asset, Dagster will allow you to specify which partition key to use, and that key can be one that was not known at the time the pipeline was initially defined.
p
@Scout should assets and assets be on the same .py file or different files?
s
The organization of assets within 
.py
files in a Dagster project can vary depending on the size and complexity of the project, as well as team preferences and practices. The documentation provides guidance on structuring your Dagster project, and here are some general principles to consider: 1. Small Projects: For smaller projects with a limited number of assets, it may be convenient to define multiple assets in a single 
.py
file. This keeps related logic grouped together and can make it easier to understand the relationships between assets. 2. Large Projects: For larger projects, or as your project grows, it's often beneficial to organize assets into separate files or directories. This can help with readability, maintainability, and collaboration. Grouping assets by business domain or functionality can make the codebase easier to navigate and understand. 3. Reusability: If certain assets are reusable across different parts of your project, it may make sense to define them in their own files or modules to facilitate reuse. 4. Collaboration: When working in a team, having assets in separate files can reduce merge conflicts and make it clearer who owns which part of the codebase. 5. Testing: Separating assets into different files can also make it easier to write and manage tests, as each test can focus on a smaller piece of functionality. Ultimately, the decision on whether to place assets in the same or different 
.py
files should be guided by the needs of your project and your team's workflow. It's important to choose a structure that promotes clarity and efficiency for your specific context. As your project evolves, you may need to refactor your code organization to keep it manageable.
p
@Scout should assets and assets checks be on the same .py file or different files?
s
In Dagster, assets and their corresponding asset checks can be defined in the same 
.py
file or in separate files, depending on the project's organization and the developer's preference. The documentation provides examples where assets and asset checks are defined within the same file, which can be convenient for understanding the relationship between an asset and its checks, as well as for maintaining them together. Here are some considerations for deciding whether to keep assets and asset checks in the same file: 1. Cohesion: Keeping an asset and its checks in the same file can improve cohesion, making it easier to see the checks associated with a particular asset at a glance. 2. Maintainability: If the checks are closely related to the logic of the asset and are likely to change together, keeping them in the same file can make maintenance easier. 3. Size: If the file becomes too large or complex due to the number of assets and checks, it might be beneficial to split them into separate files to improve readability. 4. Reusability: If certain checks are generic and can be applied to multiple assets, it might make sense to define these checks in a separate file that can be imported and reused across asset definitions. 5. Testing: Similar to assets, having checks in separate files can make it easier to write and manage tests for those checks. 6. Collaboration: If multiple developers are working on different assets and checks, separating them into different files can help reduce merge conflicts and clarify ownership. Ultimately, the choice of whether to define assets and asset checks in the same or different files should be guided by what makes the codebase more understandable, maintainable, and navigable for your team. It's also worth noting that as your project grows and evolves, you may need to refactor your code organization to keep it manageable.
p
@Scout asset check for the multi- asset below that access the metadata key dataupload see if its equal to success: @multi_asset( outs={ "imported_cpi_remote_data": AssetOut(is_required=False), # "imported_price_remote_data": AssetOut(is_required=False), "imported_gpd_remote_data": AssetOut(is_required=False), "imported_labor_remote_data": AssetOut(is_required=False), }, internal_asset_deps={ "imported_cpi_remote_data": {AssetKey("validated_cpi_remote_data")}, # "imported_price_remote_data": {AssetKey("validated_price_remote_data")}, "imported_gpd_remote_data": {AssetKey("validated_gpd_remote_data")}, "imported_labor_remote_data": {AssetKey("validated_labor_remote_data")}, }, can_subset=True, partitions_def=monthly_partition_def, ) def import_imf_data( context: AssetExecutionContext, config: IMFConfig, validated_cpi_remote_data: pd.DataFrame, validated_gpd_remote_data: pd.DataFrame, validated_labor_remote_data: pd.DataFrame, ) -> Output[None]: """ Import a set of validated remote data into FDW using a DataUpload. """ result_cpi = import_priceindex_data_points(config, validated_cpi_remote_data) yield Output(result_cpi.value, metadata=result_cpi.metadata, output_name="imported_cpi_remote_data") result_gpd = import_remote_data_points(config, validated_gpd_remote_data) yield Output(result_gpd.value, metadata=result_gpd.metadata, output_name="imported_gpd_remote_data") result_labor = import_remote_data_points(config, validated_labor_remote_data) yield Output(result_labor.value, metadata=result_labor.metadata, output_name="imported_labor_remote_data")
s
To add an asset check for the multi-asset 
import_imf_data
that accesses the metadata key 
dataupload
to see if it's equal to 
success
, you can define an asset check function and yield an 
AssetCheckResult
within the multi-asset function itself. Here's how you can do it:
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python
from dagster import multi_asset, AssetOut, AssetExecutionContext, Output, AssetCheckResult, AssetKey
import pandas as pd

# Define your multi_asset function
@multi_asset(
    outs={
        "imported_cpi_remote_data": AssetOut(is_required=False),
        "imported_gpd_remote_data": AssetOut(is_required=False),
        "imported_labor_remote_data": AssetOut(is_required=False),
    },
    internal_asset_deps={
        "imported_cpi_remote_data": {AssetKey("validated_cpi_remote_data")},
        "imported_gpd_remote_data": {AssetKey("validated_gpd_remote_data")},
        "imported_labor_remote_data": {AssetKey("validated_labor_remote_data")},
    },
    can_subset=True,
    # Assuming monthly_partition_def is defined elsewhere in your code
    partitions_def=monthly_partition_def,
)
def import_imf_data(
    context: AssetExecutionContext,
    config: IMFConfig,
    validated_cpi_remote_data: pd.DataFrame,
    validated_gpd_remote_data: pd.DataFrame,
    validated_labor_remote_data: pd.DataFrame,
) -> None:
    """
    Import a set of validated remote data into FDW using a DataUpload.
    """
    result_cpi = import_priceindex_data_points(config, validated_cpi_remote_data)
    yield Output(result_cpi.value, metadata=result_cpi.metadata, output_name="imported_cpi_remote_data")
    # Check the 'dataupload' metadata key for 'imported_cpi_remote_data'
    if result_cpi.metadata.get('dataupload') == 'success':
        yield AssetCheckResult(
            asset_key=context.asset_key_for_output("imported_cpi_remote_data"),
            check_name="dataupload_check",
            passed=True,
            metadata=result_cpi.metadata
        )
    else:
        yield AssetCheckResult(
            asset_key=context.asset_key_for_output("imported_cpi_remote_data"),
            check_name="dataupload_check",
            passed=False,
            metadata=result_cpi.metadata
        )

    result_gpd = import_remote_data_points(config, validated_gpd_remote_data)
    yield Output(result_gpd.value, metadata=result_gpd.metadata, output_name="imported_gpd_remote_data")
    # Similar checks for 'imported_gpd_remote_data' and 'imported_labor_remote_data' can be added here
In this example, after each 
Output
is yielded, an 
AssetCheckResult
is also yielded. The check verifies whether the 
dataupload
key in the metadata is equal to 
success
. If it is, the check passes; otherwise, it fails. The 
AssetCheckResult
includes the 
asset_key
for the specific output, the 
check_name
, a boolean
indicating whether the check 
passed
, and any relevant 
metadata
. Please ensure that the 
import_priceindex_data_points
and 
import_remote_data_points
functions return an object with a 
metadata
attribute that contains the 
dataupload
key. Remember to adjust the example to fit the actual structure of your 
result_cpi
, 
result_gpd
, and 
result_labor
objects, as well as the actual checks you want to perform.
p
@Scout update to keep multi-asset and asset check separate
s
To keep the multi-asset and asset check separate, you can define an 
@asset_check
decorator for each asset that you want to check. This allows you to maintain a clear separation between the asset definitions and their checks. Here's an example of how you can define separate asset checks for the 
import_imf_data
multi-asset:
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python from dagster import asset_check, AssetCheckResult, AssetKey  # Define your asset checks separately @asset_check(asset_key=AssetKey("imported_cpi_remote_data")) def check_cpi_dataupload(context, data):     # Access the metadata from the asset materialization event     metadata = context.get_output_event_metadata("imported_cpi_remote_data")     dataupload_status = metadata.get("dataupload", "")     passed = dataupload_status == "success"     return AssetCheckResult(         passed=passed,         metadata={"dataupload_status": dataupload_status}     )  @asset_check(asset_key=AssetKey("imported_gpd_remote_data")) def check_gpd_dataupload(context, data):     # Similar check for GPD data     metadata = context.get_output_event_metadata("imported_gpd_remote_data")     dataupload_status = metadata.get("dataupload", "")     passed = dataupload_status == "success"     return AssetCheckResult(         passed=passed,         metadata={"dataupload_status": dataupload_status}     )  @asset_check(asset_key=AssetKey("imported_labor_remote_data")) def check_labor_dataupload(context, data):     # Similar check for Labor data     metadata = context.get_output_event_metadata("imported_labor_remote_data")     dataupload_status = metadata.get("dataupload", "")     passed = dataupload_status == "success"     return AssetCheckResult(         passed=passed,         metadata={"dataupload_status": dataupload_status}     )
In this example, separate 
@asset_check
functions are defined for each asset produced by the 
import_imf_data
multi-asset. Each check function takes the context and the data (which is the output of the asset) as arguments. The check function retrieves the metadata from the asset materialization event and checks if the 
dataupload
key is equal to 
success
. Please note that the 
context.get_output_event_metadata
function is a placeholder to illustrate how you might access the metadata for the asset. You will need to replace this with the actual method of accessing the metadata from your asset materialization event. By defining the asset checks separately, you maintain a clear separation between the asset definitions and their checks, which can improve the readability and maintainability of your code.
p
@Scout TypeError: asset_check() got an unexpected keyword argument 'asset_key' The above exception was the direct cause of the following exception:
s
The error message you're seeing indicates that the 
asset_check
decorator does not accept an argument named 
asset_key
. Instead, the 
asset_check
decorator expects an argument named 
asset
to specify the asset that the check is associated with. Here's how you can correctly use the 
asset_check
decorator based on the documentation:
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python from dagster import asset_check, AssetCheckResult, AssetKey  # Assume 'imported_cpi_remote_data' is an asset defined elsewhere in your code. # You would use the asset itself as an argument to the asset_check decorator.  @asset_check(asset=imported_cpi_remote_data) def check_cpi_dataupload(context, data):     # Access the metadata from the asset materialization event     metadata = context.get_output_event_metadata("imported_cpi_remote_data")     dataupload_status = metadata.get("dataupload", "")     passed = dataupload_status == "success"     return AssetCheckResult(         passed=passed,         metadata={"dataupload_status": dataupload_status}     )  # Repeat for other assets as needed, replacing 'imported_cpi_remote_data' # with the appropriate asset function or object.
In this corrected example, the 
asset_check
decorator is used with the 
asset
argument, which should be the asset function or object that you want to associate the check with. The 
context.get_output_event_metadata
function is still a placeholder to illustrate how you might access the metadata for the asset. You will need to replace this with the actual method of accessing the metadata from your asset materialization event. Please ensure that 
imported_cpi_remote_data
and any other assets you reference in the 
asset_check
decorator are defined as asset functions or objects in your codebase. If you have defined your assets using the 
@multi_asset
decorator, you will need to reference the individual asset functions or objects that correspond to each output of the multi-asset.
p
@Scout I've defined my asset using the 
@multi_asset
decorator. How do i modify the asset check
s
If you've defined your asset using the 
@multi_asset
decorator and you want to add an asset check, you can do so by defining a separate function with the 
@asset_check
decorator. However, you need to ensure that you're referencing the correct asset when you apply the 
@asset_check
decorator. Here's an example of how you might define an asset check for an asset produced by a multi-asset:
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python from dagster import asset_check, AssetCheckResult, AssetKey, EventMetadataEntry  # Define your multi_asset function @multi_asset(     outs={         "imported_cpi_remote_data": AssetOut(is_required=False),         "imported_gpd_remote_data": AssetOut(is_required=False),         "imported_labor_remote_data": AssetOut(is_required=False),     },     # ... other parameters ... ) def import_imf_data(context, config, validated_cpi_remote_data, validated_gpd_remote_data, validated_labor_remote_data):     # ... implementation ...  # Define an asset check for one of the assets produced by the multi-asset @asset_check(asset_key=AssetKey("imported_cpi_remote_data")) def check_cpi_dataupload(context, data):     # Access the metadata from the asset materialization event     metadata = context.get_output_event_metadata("imported_cpi_remote_data")     dataupload_status = metadata.get("dataupload", "")     passed = dataupload_status == "success"     return AssetCheckResult(         passed=passed,         metadata_entries=[EventMetadataEntry.text(dataupload_status, "Data Upload Status")]     )  # Repeat for other assets as needed
In this example, 
import_imf_data
is the multi-asset function, and 
check_cpi_dataupload
is an asset check for the 
imported_cpi_remote_data
asset. The 
asset_check
decorator is applied to the 
check_cpi_dataupload
function, and the 
asset_key
argument is used to specify the asset key that the check is associated with. Please note that the 
context.get_output_event_metadata
function is a placeholder to illustrate how you might access the metadata for the asset. You will need to replace this with the actual method of accessing the metadata from your asset materialization event. Also, ensure that the asset key you use in the 
asset_check
decorator matches the asset key of the asset produced by the multi-asset. If you're using the default naming convention, the asset key will typically be the name of the output in the 
@multi_asset
decorator. If you've customized the asset key, make sure to use the customized key in the 
asset_check
decorator.
p
Update the job below to run with config IMFConfig imf_ingestion_job = define_asset_job(name="imf_ingestion_job", selection=assets_list)
@Scout Update the job below to run with config IMFConfig imf_ingestion_job = define_asset_job(name="imf_ingestion_job", selection=assets_list)
s
To update the 
imf_ingestion_job
to run with a specific configuration, such as 
IMFConfig
, you would typically pass the configuration when you execute the job. However, 
define_asset_job
does not directly accept a configuration class like 
IMFConfig
as an argument. Instead, you would define the configuration schema separately and then provide the configuration values when you launch the job. Here's an example of how you might execute the 
imf_ingestion_job
with a specific configuration:
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python from dagster import define_asset_job, RunConfig  # Assuming assets_list is defined elsewhere in your code imf_ingestion_job = define_asset_job(name="imf_ingestion_job", selection=assets_list)  # Define your configuration values according to the IMFConfig schema imf_config_values = {     "ops": {         "my_op": {  # Replace "my_op" with the actual op name that requires the IMFConfig             "config": {                 "param1": "value1",  # Replace with actual config parameters and values                 "param2": "value2",                 # ... other config parameters ...             }         }     } }  # Execute the job with the provided configuration result = imf_ingestion_job.execute_in_process(run_config=RunConfig(config=imf_config_values))
In this example, 
imf_config_values
is a dictionary that matches the structure expected by the 
IMFConfig
schema. You would replace 
"my_op"
with the actual op name within the job that requires the 
IMFConfig
, and 
"param1"
, 
"param2"
, etc., with the actual configuration parameters defined in 
IMFConfig
. Please note that 
execute_in_process
is typically used for testing or development purposes. For production scenarios, you would launch the job using the Dagster CLI or the Dagit UI, providing the configuration values through a YAML file or the UI's configuration editor. If 
IMFConfig
is a Python class that you've defined to represent your configuration schema, you would need to convert it to a dictionary format that Dagster can understand when executing the job. Dagster's configuration system works with dictionaries, YAML, or JSON-like structures.
p
@Scout how to use runconfig
s
The 
RunConfig
object in Dagster is used to provide runtime configuration for jobs or asset materializations when executing them programmatically within Python. It allows you to specify configuration for execution, loggers, resources, and individual ops or assets. Here's a step-by-step guide on how to use `RunConfig`: 1. Import RunConfig: First, you need to import the 
RunConfig
class from Dagster.
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python from dagster import RunConfig
2. Define Configuration: Define your configuration as a dictionary. The keys and structure of this dictionary should match the configuration schema of your job or assets. This includes configurations for resources, loggers, and ops, among others.
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python run_config_dict = {     "resources": {         "my_resource": {             "config": {                 "param": "value"             }         }     },     "ops": {         "my_op": {             "config": {                 "input_param": "input_value"             }         }     }     # ... other configurations ... }
3. Create RunConfig Object: Create an instance of 
RunConfig
by passing the configuration dictionary to it.
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python run_config = RunConfig(config=run_config_dict)
4. Execute Job or Materialize Assets: Use the 
RunConfig
instance when executing a job or materializing assets. For a job:
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python from dagster import job  @job def my_job():     # ... definition ...  # Execute the job with the run configuration result = my_job.execute_in_process(run_config=run_config)
For asset materialization:
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python from dagster import materialize  # Assuming 'my_assets' is a list of asset objects result = materialize(my_assets, run_config=run_config)
5. Using RunConfig in Testing: 
RunConfig
is particularly useful in testing scenarios where you want to provide specific configurations without relying on external configuration files or environment variables. Remember that when you execute a job or materialize assets using the Dagster CLI or the Dagit UI, you typically provide the configuration as a YAML file or through the UI's configuration editor, rather than using 
RunConfig
. The 
RunConfig
object is a way to programmatically provide configuration for Dagster executions within Python code. It's important to ensure that the configuration you provide matches the schema expected by your job or assets, including the correct keys and value types.
p
@Scout fix the code below: class IMFConfig(Config): web_service: str = Field(default="IMF", description="The name of the web service that is being ingested") preview_rows: int = Field(default=10, description="The number of rows to show in DataFrame previews") dry_run: bool = Field( default=False, description="Boolean which determines if changes to the database are made or if the import is only simulated", ) start_date: datetime.datetime = Field(default=datetime.datetime(2015, 1, 1), description="The first date in the set of partitions will be midnight the sonnest first of the month following start_date") monthly_partition_def = MonthlyPartitionsDefinition(start_date=IMFConfig.start_date)
s
The code you've provided seems to be attempting to define a configuration class using Pydantic and then use one of its fields to define a 
MonthlyPartitionsDefinition
in Dagster. However, there are a few issues with the code: 1. The 
Config
class is not a standard Pydantic model class. You should inherit from 
pydantic.BaseModel
instead. 2. The 
Field
function is used incorrectly. It should be imported from 
pydantic
and used as a field type validator. 3. The 
start_date
field in 
IMFConfig
is a class attribute, but it's being accessed as if it were an instance attribute when defining 
monthly_partition_def
. Here's the corrected code:
Copy code
python from pydantic import BaseModel, Field import datetime from dagster import MonthlyPartitionsDefinition  class IMFConfig(BaseModel):     web_service: str = Field(default="IMF", description="The name of the web service that is being ingested")     preview_rows: int = Field(default=10, description="The number of rows to show in DataFrame previews")     dry_run: bool = Field(         default=False,         description="Boolean which determines if changes to the database are made or if the import is only simulated",     )     start_date: datetime.datetime = Field(default=datetime.datetime(2015, 1, 1), description="The first date in the set of partitions will be midnight the soonest first of the month following start_date")  # Create an instance of IMFConfig to access the start_date imf_config = IMFConfig()  # Use the start_date from the instance of IMFConfig monthly_partition_def = MonthlyPartitionsDefinition(start_date=imf_config.start_date)
In this corrected code, 
IMFConfig
now inherits from 
pydantic.BaseModel
, which is the correct base class for defining configuration schemas with Pydantic. The 
Field
function is used to provide default values and descriptions for the fields in the configuration model. An instance of 
IMFConfig
is created to access the 
start_date
attribute, which is then used to define 
monthly_partition_def
. This ensures that the 
start_date
used in 
MonthlyPartitionsDefinition
is an actual 
datetime
value from an instance of 
IMFConfig
, rather than a class attribute reference.
p
@Scout Error defining Dagster config class <class 'dagster_pipelines.assets.ingestion.price.imf_ingestion.IMFConfig'> on field 'start_date'. Unable to resolve config type <class 'datetime.datetime'> to a supported Dagster config type. This config type can be a: - Python primitive type - int, float, bool, str, list - A Python Dict or List type containing other valid types - Custom data classes extending dagster.Config - A Pydantic discriminated union type (https://docs.pydantic.dev/usage/types/#discriminated-unions-aka-tagged-unions)
s
The error message you're encountering indicates that Dagster is unable to resolve the 
datetime.datetime
type to a supported config type. Dagster's configuration system expects types that can be serialized to and from JSON, and 
datetime.datetime
is not directly serializable. To resolve this issue, you can represent the 
start_date
as a string in ISO 8601 format, which is a common way to serialize datetime objects to JSON. You can then parse this string back into a 
datetime.datetime
object when you need to use it within your code. Here's how you can modify the 
IMFConfig
class to use a string for the `start_date`:
Copy code
python from pydantic import BaseModel, Field  class IMFConfig(BaseModel):     web_service: str = Field(default="IMF", description="The name of the web service that is being ingested")     preview_rows: int = Field(default=10, description="The number of rows to show in DataFrame previews")     dry_run: bool = Field(         default=False,         description="Boolean which determines if changes to the database are made or if the import is only simulated",     )     # Represent the start_date as a string in ISO 8601 format     start_date: str = Field(default="2015-01-01T00:00:00", description="The first date in the set of partitions will be midnight the soonest first of the month following start_date")  # Create an instance of IMFConfig to access the start_date imf_config = IMFConfig()  # Parse the start_date string back into a datetime object start_date_datetime = datetime.datetime.fromisoformat(imf_config.start_date)  # Use the parsed datetime object to define monthly_partition_def monthly_partition_def = MonthlyPartitionsDefinition(start_date=start_date_datetime)
In this updated code, the 
start_date
field is now a string that conforms to the ISO 8601 format. When you need to use the 
start_date
as a 
datetime.datetime
object, you can parse it using 
datetime.datetime.fromisoformat
. This approach ensures that the configuration can be serialized and deserialized properly by Dagster's configuration system, while still allowing you to work with 
datetime.datetime
objects in your code.
p
@Scout dagster._core.errors.DagsterInvariantViolationError: Cannot access partition_key for a non-partitioned run File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/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/pmburu/.venvs/fntfdwlcl/lib/python3.8/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/pmburu/.venvs/fntfdwlcl/lib/python3.8/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/pmburu/.venvs/fntfdwlcl/lib/python3.8/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/pmburu/.venvs/fntfdwlcl/lib/python3.8/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/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_core/execution/plan/compute.py", line 183, in _yield_compute_results for event in iterate_with_context( File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) File "/home/pmburu/git/fdw/dagster_pipelines/assets/ingestion/price/imf_ingestion.py", line 533, in remote_imf_data yield from fetch_and_yield_imf_data(gpd_labor_metadata, "Economic Statistics", GDP_indicators, "gpd_remote_data") File "/home/pmburu/git/fdw/dagster_pipelines/assets/ingestion/price/imf_ingestion.py", line 528, in fetch_and_yield_imf_data period_date=context.partition_key, File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_core/execution/context/compute.py", line 1690, in partition_key return self.op_execution_context.partition_key File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_core/execution/context/compute.py", line 306, in partition_key return self._step_execution_context.partition_key File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_core/execution/context/system.py", line 391, in partition_key raise DagsterInvariantViolationError(
s
The error message you're encountering, 
DagsterInvariantViolationError: Cannot access partition_key for a non-partitioned run
, indicates that the code is attempting to access the 
partition_key
property in a context where the run is not partitioned. This error typically occurs when you have an asset or op that is designed to work with partitioned runs, but the run was initiated without specifying a partition key. The 
partition_key
property is only available when the run is associated with a partition set and a specific partition key has been provided. To resolve this issue, you need to ensure that: 1. The asset or op that requires a partition key is indeed part of a partitioned job or asset group. 2. When you initiate the run, you are providing a partition key. Here are some steps you can take to debug and fix the issue: - Check the Asset or Job Definition: Ensure that the asset or job is configured with a partition set and that the partition set is properly defined. - Run Configuration: When initiating the run, make sure you are specifying the partition key. If you are using the Dagit UI, you should select the appropriate partition from the partition set. If you are using the Dagster CLI or initiating the run programmatically, you need to provide the partition key in the run configuration. - Conditional Access: If the asset or op should be able to run both with and without partitions, you should add a conditional check before accessing the 
partition_key
property. For example:
Copy code
python if context.has_partition_key:     period_date = context.partition_key else:     period_date = some_default_value
- Review Execution Context: Make sure that the execution context in which you're trying to access the 
partition_key
is appropriate. For example, if you're within an op, you should be using the op's context to access the 
partition_key
. By ensuring that the run is correctly partitioned and that the 
partition_key
is only accessed when available, you should be able to resolve the 
DagsterInvariantViolationError
. If the issue persists, you may need to review the configuration and execution details of your Dagster assets and jobs to ensure they align with the partitioning logic you've implemented.
p
@Scout how do job and schedule handle partition?
s
In Dagster, jobs and schedules handle partitions as follows: Jobs: A job in Dagster can be configured to work with partitioned assets. When you define a job that operates on partitioned assets, you can specify a 
partitions_def
argument that defines how the partitions are structured. This can be a static set of partitions or a dynamic definition that changes over time. For example, you might have a job that processes data on a daily basis, and each run of the job corresponds to a specific day's partition. The job's logic can access the 
partition_key
to determine which slice of data to process. Here's a simple example of a job that uses a static set of partitions:
Copy code
python
from dagster import job, StaticPartitionsDefinition

partitions_def = StaticPartitionsDefinition(["2021-01-01", "2021-01-02", "2021-01-03"])

@job(partitions_def=partitions_def)
def my_partitioned_job():
    # Job logic here
Schedules: A schedule in Dagster is used to trigger a job run at a specific time or interval. When you have a partitioned job, you can create a schedule that triggers runs for specific partitions. The schedule uses the partition set to determine which partition key to use for each run. Dagster provides a utility function 
build_schedule_from_partitioned_job
that creates a schedule from a partitioned job. The schedule executes at the cadence specified by the time partitioning of the job or assets. Here's an example of creating a schedule from a partitioned job:
Copy code
python
from dagster import build_schedule_from_partitioned_job

my_schedule = build_schedule_from_partitioned_job(my_partitioned_job)
The schedule created by 
build_schedule_from_partitioned_job
will automatically select the appropriate partition key for each run based on the current time and the partitioning scheme defined in the job. In cases where you want more control over the schedule or if the job's partitions are not time-based, you can use the 
@schedule
decorator to define custom logic for selecting partition keys and triggering runs. Here's an example of a custom schedule for a partitioned job:
Copy code
python
from dagster import schedule

@schedule(cron_schedule="0 0 * * *", job=my_partitioned_job)
def my_custom_schedule(context):
    # Custom logic to determine the partition key
    partition_key = "2021-01-01"  # Replace with logic to select the correct partition key
    return my_partitioned_job.run_request_for_partition(partition_key=partition_key)
In this custom schedule, you define the cron schedule and provide the logic to select the partition key that should be used for each run. It's important to note that when you trigger a run for a partitioned job, whether manually or via a schedule, you must provide the partition key if the job expects one. This
ensures that the job logic can correctly identify and process the data for the specified partition.
p
@Scout fix the code below: imf_data_ingestion_schedule = build_schedule_from_partitioned_job( name="imf_data_ingestion_schedule", job=imf_ingestion_job, cron_schedule="0 0 * * *", )
s
The 
build_schedule_from_partitioned_job
function in Dagster is used to create a schedule from a partitioned job, where the schedule's timing is derived from the partitioning of the job. However, the 
cron_schedule
parameter is not used with this function, as the schedule is meant to align with the partitioning scheme of the job itself. Here's how you can fix the code:
Copy code
python from dagster import build_schedule_from_partitioned_job  # Assuming imf_ingestion_job is a partitioned job defined elsewhere in your code imf_data_ingestion_schedule = build_schedule_from_partitioned_job(     job=imf_ingestion_job,     name="imf_data_ingestion_schedule", )
In this corrected code, we remove the 
cron_schedule
parameter because 
build_schedule_from_partitioned_job
automatically infers the schedule from the partitioning of the job. The 
name
parameter is optional and can be used to give a custom name to the schedule. If you need to specify a custom cron schedule that does not align with the partitioning scheme, you would not use 
build_schedule_from_partitioned_job
. Instead, you would define a custom schedule using the 
@schedule
decorator, where you can provide the 
cron_schedule
and the logic to select the appropriate partition key for each run. Here's an example of how you might define a custom schedule with a specific cron schedule:
Copy code
python from dagster import schedule, RunRequest  @schedule(     cron_schedule="0 0 * * *",  # This cron schedule runs daily at midnight     job=imf_ingestion_job,     name="imf_data_ingestion_schedule" ) def custom_imf_data_ingestion_schedule(context):     # Logic to determine the partition key based on the context     partition_key = "some_partition_key"  # Replace with actual logic to determine the partition key     return RunRequest(run_key=partition_key, run_config={"solids": {"my_solid": {"config": {"partition_key": partition_key}}}})
In this custom schedule, you define the cron schedule explicitly and provide the logic to create a 
RunRequest
with the appropriate 
run_key
and 
run_config
for the partitioned job.
p
@Scout the code fails: from typing import Optional, Callable from dagster import asset, Config from pydantic import Field class MyAssetConfig(Config): # ellipsis indicates that even though the type is Optional, # an input is required person_first_name: Optional[str] = ... # ellipsis can also be used with pydantic.Field to attach metadata person_last_name: Optional[Callable] = Field( default=..., description="The last name of the person to greet" ) @asset def goodbye(config: MyAssetConfig) -> str: full_name = f"{config.person_first_name} {config.person_last_name}".strip() if full_name: return f"Goodbye, {full_name}" else: return "Goodbye" Fails with: DagsterInvalidPythonicConfigDefinitionError: Error defining Dagster config class <class '__main__.MyAssetConfig'> on field 'person_last_name'. Unable to resolve config type typing.Callable to a supported Dagster config type. This config type can be a: - Python primitive type - int, float, bool, str, list - A Python Dict or List type containing other valid types - Custom data classes extending dagster.Config - A Pydantic discriminated union type (https://docs.pydantic.dev/usage/types/#discriminated-unions-aka-tagged-unions)
s
The error message you're encountering indicates that Dagster is unable to resolve the 
typing.Callable
type to a supported config type. Dagster's configuration system expects types that can be serialized to and from JSON, and 
typing.Callable
is not directly serializable. To fix this issue, you should change the type of 
person_last_name
to a serializable type that Dagster supports, such as 
str
. If you intended 
person_last_name
to be a string, you should not use 
typing.Callable
. Here's the corrected code:
Copy code
python from typing import Optional from dagster import asset, Field  class MyAssetConfig:     # ellipsis indicates that even though the type is Optional,     # an input is required     person_first_name: Optional[str] = Field(         default=..., description="The first name of the person to greet"     )      # Use str type for person_last_name     person_last_name: Optional[str] = Field(         default=..., description="The last name of the person to greet"     )  @asset(config_schema=MyAssetConfig) def goodbye(context) -> str:     config = context.op_config     full_name = f"{config['person_first_name']} {config['person_last_name']}".strip()     if full_name:         return f"Goodbye, {full_name}"     else:         return "Goodbye"
In this corrected code, 
MyAssetConfig
no longer inherits from 
Config
, and the 
Field
function from Dagster is used to define fields with defaults and descriptions. The 
config_schema
parameter of the 
@asset
decorator is used to specify the configuration schema for the asset. The 
goodbye
asset function now accesses the configuration through 
context.op_config
, which provides a dictionary of the configuration values. The 
person_first_name
and 
person_last_name
fields are both expected to be strings, and the ellipsis (
...
) indicates that they are required fields.
p
@Scout DagsterInvalidPythonicConfigDefinitionError: Error defining Dagster config class <class '__main__.MyAssetConfig'> on field 'start_date'. Unable to resolve config type <class 'datetime.datetime'> to a supported Dagster config type. This config type can be a: - Python primitive type - int, float, bool, str, list - A Python Dict or List type containing other valid types - Custom data classes extending dagster.Config - A Pydantic discriminated union type (https://docs.pydantic.dev/usage/types/#discriminated-unions-aka-tagged-unions)
s
The error message indicates that Dagster cannot use the 
datetime.datetime
type directly in a config class because it's not a supported config type that can be serialized to and from JSON. To resolve this issue, you should represent the 
start_date
as a string in a format that can be serialized, such as the ISO 8601 format. Here's how you can modify the 
MyAssetConfig
class to use a string for the `start_date`:
Copy code
python from typing import Optional from dagster import asset, Field  class MyAssetConfig:     # Define start_date as a string field     start_date: str = Field(         default="2021-01-01T00:00:00",  # Example default value in ISO 8601 format         description="The start date in ISO 8601 format"     )  @asset(config_schema=MyAssetConfig) def my_asset(context) -> str:     config = context.op_config     # Parse the start_date string back into a datetime object if needed     # start_date = datetime.datetime.fromisoformat(config['start_date'])     # Use the start_date as needed in your asset logic     # ...     return "Asset logic that uses start_date"
In this corrected code, 
start_date
is defined as a string field with a default value in ISO 8601 format. When you need to use 
start_date
as a 
datetime.datetime
object within your asset logic, you can parse the string using 
datetime.datetime.fromisoformat
. This approach ensures that the configuration can be serialized and deserialized properly by Dagster's configuration system, while still allowing you to work with 
datetime.datetime
objects in your code.
p
@Scout update the code below to include use RunConfig imf_ingestion_job = define_asset_job(name="imf_ingestion_job", selection=assets_list, partitions_def=monthly_partition_def)
s
The 
RunConfig
is used when you execute a job to provide runtime configuration. It is not used as part of the job definition itself. Therefore, you cannot include 
RunConfig
directly in the 
define_asset_job
function call. However, you can specify 
RunConfig
when you execute the job. Here's an example of how you might execute the 
imf_ingestion_job
with a `RunConfig`:
Copy code
python from dagster import define_asset_job, RunConfig  # Assuming assets_list and monthly_partition_def are defined elsewhere in your code imf_ingestion_job = define_asset_job(     name="imf_ingestion_job",     selection=assets_list,     partitions_def=monthly_partition_def )  # Define your configuration values according to the job's schema run_config_values = {     # ... your run configuration goes here ... }  # Create a RunConfig object run_config = RunConfig(config=run_config_values)  # Execute the job with the provided RunConfig result = imf_ingestion_job.execute_in_process(run_config=run_config)
In this example, 
run_config_values
should be a dictionary that matches the configuration schema expected by the job. The 
RunConfig
object is created with these values and then passed to the 
execute_in_process
method to run the job with the specified configuration. Please note that 
execute_in_process
is typically used for testing or development purposes. For production scenarios, you would launch the job using the Dagster CLI or the Dagit UI, providing the configuration values through a YAML file or the UI's configuration editor.
p
How do i enable Config to be available in the UI
@Scout How do i enable Config to be available in the UI?
s
To enable configuration to be available and editable in the Dagster UI (Dagit), you need to ensure that your assets or jobs are defined with a configuration schema. When you define an asset or job with a configuration schema, Dagit will automatically provide an interface to input and edit the configuration values. Here's how you can define an asset with a configuration schema:
Copy code
python from dagster import asset, Field  class MyAssetConfig:     my_param: str = Field(         default="default_value",         description="A configurable parameter for the asset."     )  @asset(config_schema=MyAssetConfig) def my_asset(context):     my_param_value = context.op_config["my_param"]     # Your asset logic here using my_param_value
In this example, 
MyAssetConfig
is a class that defines the configuration schema for the asset 
my_asset
. The 
Field
function is used to define a field 
my_param
with a default value and a description. The 
config_schema
argument of the 
@asset
decorator is used to associate the configuration schema with the asset. When you navigate to the asset in Dagit, you will see an interface that allows you to configure 
my_param
when you materialize the asset. If you click on the "Materialize" button while holding the Shift key, Dagit will open the configuration editor where you can input or modify the configuration values. For jobs, you can define a configuration schema directly on the job or on the individual ops within the job. When you launch the job from Dagit, you will be able to input and edit the configuration values in the UI. Remember to ensure that your Dagster instance is running and that you have access to Dagit. The configuration schema you define in your code will be reflected in the UI, allowing you to configure runs directly from the web interface.
p
@Scout what is the difference between op and job
s
In Dagster, the concepts of ops and jobs are distinct but related, and they play different roles in the construction of data pipelines: Ops: Ops are the core unit of computation in Dagster. An individual op performs a relatively simple task, like getting data from one place and storing it somewhere else. Ops are the building blocks for more complicated jobs and assets. They contain the business logic that transforms inputs into outputs and can be combined to form a graph, which is a collection of ops that are connected together to create a data pipeline. Here's a brief overview of ops: - Ops are functions that perform computation. - They can consume inputs and produce outputs. - Ops can be composed together in a graph to define dependencies and execution order. - Ops can be reused across different jobs. Jobs: Jobs are a logical, ordered grouping of Dagster assets or ops. The core of a job is a "graph" of "ops." Think of a graph as a diagram of how different tasks are connected. The ops are the individual tasks that need to be executed, and they're connected by lines that show which tasks depend on which others. Jobs are the main unit of execution and monitoring in Dagster: When you ‘run a pipeline,’ you ask Dagster to run a Job. In turn, that Job will materialize one or more Assets. Here's a brief overview of jobs: - Jobs define the orchestration of ops or assets. - They represent the execution plan that Dagster will run. - Jobs can be scheduled or triggered by sensors. - Jobs are the entities you run and monitor in Dagster. In summary, an op is a single computational step, while a job is a collection of these steps (ops) arranged to execute in a specific order, potentially with scheduling, retries, and other execution parameters. Jobs are what you execute when you want to run your pipeline in Dagster.
p
@Scout 2024-02-29 122311 +0300 - dagster - INFO - To persist information across sessions, set the environment variable DAGSTER_HOME to a directory to use. Traceback (most recent call last): File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_serdes/config_class.py", line 98, in rehydrate module = importlib.import_module(self.module_name) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "<frozen importlib._bootstrap>", line 1204, in _gcd_import File "<frozen importlib._bootstrap>", line 1176, in _find_and_load File "<frozen importlib._bootstrap>", line 1140, in _find_and_load_unlocked ModuleNotFoundError: No module named 'dagster_postgres' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/pmburu/miniconda3/bin/dagster", line 8, in <module> sys.exit(main()) ^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_cli/__init__.py", line 48, in main cli(auto_envvar_prefix=ENV_PREFIX) # pylint:disable=E1123 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/click/core.py", line 1157, in call return self.main(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/click/core.py", line 1078, in main rv = self.invoke(ctx) ^^^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/click/core.py", line 1688, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/click/core.py", line 1434, in invoke return ctx.invoke(self.callback, **ctx.params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/click/core.py", line 783, in invoke return __callback(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_cli/dev.py", line 132, in dev_command with get_possibly_temporary_instance_for_cli("dagster dev", logger=logger) as instance: File "/home/pmburu/miniconda3/lib/python3.11/contextlib.py", line 137, in enter return next(self.gen) ^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_cli/utils.py", line 83, in get_possibly_temporary_instance_for_cli with _get_temporary_instance(cli_command, logger) as instance: File "/home/pmburu/miniconda3/lib/python3.11/contextlib.py", line 137, in enter return next(self.gen) ^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_cli/utils.py", line 42, in _get_temporary_instance with DagsterInstance.from_ref( ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_core/instance/__init__.py", line 616, in from_ref unified_storage = instance_ref.storage ^^^^^^^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_core/instance/ref.py", line 498, in storage return self.storage_data.rehydrate(as_type=DagsterStorage) if self.storage_data else None ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_serdes/config_class.py", line 100, in rehydrate check.failed( File "/home/pmburu/miniconda3/lib/python3.11/site-packages/dagster/_check/__init__.py", line 1627, in failed raise CheckError(f"Failure condition: {desc}") dagster._check.CheckError: Failure condition: Couldn't import module dagster_postgres when attempting to load the configurable class dagster_postgres.DagsterPostgresStorage
s
The error message you're encountering indicates that the 
dagster_postgres
module cannot be found. This module is typically required when you're using PostgreSQL as a backend for Dagster's run storage, event log storage, or for other components that require a PostgreSQL database. Here are some steps to resolve the issue: 1. Install the 
dagster-postgres
Package: If you haven't already, you need to install the 
dagster-postgres
package, which includes the necessary modules to integrate Dagster with a PostgreSQL database. You can install it using pip:
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shell    pip install dagster-postgres
2. Check Your 
PYTHONPATH
: Ensure that your Python environment is correctly set up and that the 
PYTHONPATH
includes the directory where the 
dagster_postgres
module is installed. 3. Check Dagster Instance Configuration: The error message suggests that Dagster is trying to rehydrate a configuration class for 
dagster_postgres.DagsterPostgresStorage
. Make sure that your 
dagster.yaml
instance configuration file is correctly pointing to the 
dagster_postgres
storage options and that the module is available in your environment. 4. Set 
DAGSTER_HOME
Environment Variable: The log message suggests setting the 
DAGSTER_HOME
environment variable to persist information across sessions. This should point to a directory where Dagster can store instance configuration and other runtime data. If not set, Dagster may attempt to use a temporary directory that gets cleaned up after the process exits.
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shell    export DAGSTER_HOME=/path/to/your/dagster_home
Make sure to replace 
/path/to/your/dagster_home
with the actual path to your Dagster home directory. 5. Review the Stack Trace: The stack trace can provide additional context on where the failure is occurring. It seems like the failure is happening when trying to load the instance from a reference, which suggests that there might be an issue with how the instance is configured or how the 
dagster_postgres
module is being referenced. If you've gone through these steps and the issue persists, it may be helpful to ensure that your Dagster instance is correctly configured to connect to your PostgreSQL database and that all necessary credentials and connection strings are properly set up in your 
dagster.yaml
file.
p
@Scout Traceback (most recent call last): File "/home/pmburu/.venvs/fntfdwlcl/bin/dagster", line 8, in <module> sys.exit(main()) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_cli/__init__.py", line 48, in main cli(auto_envvar_prefix=ENV_PREFIX) # pylint:disable=E1123 File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 1157, in call return self.main(*args, **kwargs) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 1078, in main rv = self.invoke(ctx) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 1688, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 1434, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 783, in invoke return __callback(*args, **kwargs) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_cli/dev.py", line 141, in dev_command with get_possibly_temporary_instance_for_cli("dagster dev", logger=logger) as instance: File "/usr/lib/python3.8/contextlib.py", line 113, in enter return next(self.gen) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_cli/utils.py", line 83, in get_possibly_temporary_instance_for_cli with _get_temporary_instance(cli_command, logger) as instance: File "/usr/lib/python3.8/contextlib.py", line 113, in enter return next(self.gen) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_cli/utils.py", line 42, in _get_temporary_instance with DagsterInstance.from_ref( File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_core/instance/__init__.py", line 617, in from_ref unified_storage = instance_ref.storage File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_core/instance/ref.py", line 499, in storage return self.storage_data.rehydrate(as_type=DagsterStorage) if self.storage_data else None File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_serdes/config_class.py", line 127, in rehydrate raise DagsterInvalidConfigError( dagster._core.errors.DagsterInvalidConfigError: Errors whilst loading configuration for {'postgres_url': Field(<dagster._config.source.StringSourceType object at 0x7fea4ec97970>, default=@, is_required=False), 'postgres_db': Field(<dagster._config.field_utils.Shape object at 0x7fea50912580>, default=@, is_required=False), 'should_autocreate_tables': Field(<dagster._config.config_type.Bool object at 0x7fea4ed1a760>, default=True, is_required=False)}. Error 1: Post processing at path rootpostgres dbusername of original value {'env': 'DAGSTER_PGUSER'} failed: dagster._config.errors.PostProcessingError: You have attempted to fetch the environment variable "DAGSTER_PGUSER" which is not set. In order for this execution to succeed it must be set in this environment. Stack Trace: File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_config/post_process.py", line 79, in _post_process new_value = context.config_type.post_process(config_value) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_config/source.py", line 40, in post_process return str(_ensure_env_variable(cfg)) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_config/source.py", line 16, in _ensure_env_variable raise PostProcessingError(
s
The error message you're encountering indicates that Dagster is trying to load configuration for a PostgreSQL database, but it's unable to find an environment variable that it expects to be set. Specifically, the error is occurring because the environment variable 
DAGSTER_PGUSER
, which is supposed to contain the username for the PostgreSQL database, is not set in your environment. To resolve this issue, you need to set the 
DAGSTER_PGUSER
environment variable to the username of your PostgreSQL database. You can do this by exporting the variable in your shell before running the Dagster command:
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shell export DAGSTER_PGUSER=your_postgres_username
Make sure to replace 
your_postgres_username
with the actual username that you use to connect to your PostgreSQL database. Additionally, if there are other environment variables that are required for your PostgreSQL configuration (such as 
DAGSTER_PGPASSWORD
for the password, 
DAGSTER_PGHOST
for the host, etc.), you'll need to ensure that those are also set in your environment. After setting the necessary environment variables, try running the Dagster command again. If you're running Dagster in a persistent environment (like a production server or a Docker container), you may want to set these environment variables in a more permanent way, such as in an environment file or as part of your container configuration.
p
@Scout --- Logging error --- Traceback (most recent call last): File "/usr/lib/python3.8/logging/handlers.py", line 70, in emit self.doRollover() File "/usr/lib/python3.8/logging/handlers.py", line 394, in doRollover self.rotate(self.baseFilename, dfn) File "/usr/lib/python3.8/logging/handlers.py", line 111, in rotate os.rename(source, dest) FileNotFoundError: [Errno 2] No such file or directory: '/home/pmburu/git/fdw/log/django.log' -> '/home/pmburu/git/fdw/log/django.log.2024-02-29' Call stack: File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "/usr/lib/python3.8/runpy.py", line 87, in _run_code exec(code, run_globals) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/__main__.py", line 3, in <module> main() File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_cli/__init__.py", line 48, in main cli(auto_envvar_prefix=ENV_PREFIX) # pylint:disable=E1123 File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 1157, in call return self.main(*args, **kwargs) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 1078, in main rv = self.invoke(ctx) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 1688, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 1688, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 1434, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/click/core.py", line 783, in invoke return __callback(*args, **kwargs) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_cli/api.py", line 698, in grpc_command api_servicer = DagsterApiServer( File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_grpc/server.py", line 408, in init self._loaded_repositories: Optional[LoadedRepositories] = LoadedRepositories( File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_grpc/server.py", line 242, in init loadable_targets = get_loadable_targets( File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_grpc/utils.py", line 50, in get_loadable_targets else loadable_targets_from_python_module(module_name, working_directory) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_core/workspace/autodiscovery.py", line 35, in loadable_targets_from_python_module module = load_python_module( File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/dagster/_core/code_pointer.py", line 135, in load_python_module return importlib.import_module(module_name) File "/usr/lib/python3.8/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1014, in _gcd_import File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/git/fdw/dagster_pipelines/__init__.py", line 5, in <module> from .assets.ingestion.price.imf_ingestion import ( File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/git/fdw/dagster_pipelines/assets/ingestion/price/imf_ingestion.py", line 31, in <module> from ..semistructured.base import import_remote_data_points # NOQA: E402 File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/git/fdw/dagster_pipelines/assets/ingestion/semistructured/base.py", line 4, in <module> from ..warehouse.base import base_import_data # NOQA: E402 File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/git/fdw/dagster_pipelines/assets/ingestion/warehouse/base.py", line 19, in <module> django.setup() File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/django/__init__.py", line 24, in setup apps.populate(settings.INSTALLED_APPS) File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/django/apps/registry.py", line 114, in populate app_config.import_models() File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/django/apps/config.py", line 301, in import_models self.models_module = import_module(models_module_name) File "/usr/lib/python3.8/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1014, in _gcd_import File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/ddtrace/internal/module.py", line 190, in _exec_module self.loader.exec_module(module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/git/fdw/apps/ipc/models.py", line 23, in <module> from warehouse.models import DataPoint, DataPointManager, DataPointQuerySet, DataSeries, DataSet File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/ddtrace/internal/module.py", line 190, in _exec_module self.loader.exec_module(module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/git/fdw/apps/warehouse/models.py", line 43, in <module> from responsecache.utils import POLICY_ABBREV_NUM_CHARS File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/ddtrace/internal/module.py", line 190, in _exec_module self.loader.exec_module(module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/git/fdw/apps/responsecache/utils.py", line 160, in <module> from common.utils import timekeeper File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/ddtrace/internal/module.py", line 190, in _exec_module self.loader.exec_module(module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/git/fdw/apps/common/utils.py", line 37, in <module> from pyvirtualdisplay import Display File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/ddtrace/internal/module.py", line 190, in _exec_module self.loader.exec_module(module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/pyvirtualdisplay/__init__.py", line 4, in <module> from pyvirtualdisplay.display import Display File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/ddtrace/internal/module.py", line 190, in _exec_module self.loader.exec_module(module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/pyvirtualdisplay/display.py", line 3, in <module> from pyvirtualdisplay.xephyr import XephyrDisplay File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/ddtrace/internal/module.py", line 190, in _exec_module self.loader.exec_module(module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/pyvirtualdisplay/xephyr.py", line 3, in <module> from pyvirtualdisplay.abstractdisplay import AbstractDisplay File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/ddtrace/internal/module.py", line 190, in _exec_module self.loader.exec_module(module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/pyvirtualdisplay/abstractdisplay.py", line 11, in <module> from easyprocess import EasyProcess, EasyProcessError File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "/home/pmburu/.venvs/fntfdwlcl/lib/python3.8/site-packages/ddtrace/internal/module.py", line 190, in _exec_module self.loader.exec_module(module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module version__) Message: 'version=%s'
s
The error message you're seeing is related to Python's logging system, specifically with a 
RotatingFileHandler
that's trying to perform a log rotation. The 
RotatingFileHandler
is attempting to rename the current log file (
/home/pmburu/git/fdw/log/django.log
) to a backup file with a date suffix (
/home/pmburu/git/fdw/log/django.log.2024-02-29
), but it's failing because the original file does not exist. Here are some steps to troubleshoot and resolve the issue: 1. Check Log File Existence: Ensure that the log file 
/home/pmburu/git/fdw/log/django.log
actually exists. If it doesn't, you may need to create it or adjust your logging configuration to point to the correct file path. 2. Permissions: Verify that the user running the Dagster process has the necessary permissions to read and write to the log file and its directory. 3. Disk Space: Check if there's enough disk space on the device where the log file is stored. A lack of disk space could prevent file operations like renaming. 4. File Locks: Ensure that no other processes are holding a lock on the log file, which could prevent renaming. 5. Logging Configuration: Review your logging configuration to ensure that the 
RotatingFileHandler
is set up correctly. You may need to adjust the handler's parameters, such as 
maxBytes
and 
backupCount
, to match your requirements. 6. Dagster Configuration: Since the error occurs during the Dagster process, make sure that Dagster's logging is configured correctly in your 
dagster.yaml
file or wherever you set up logging for Dagster. 7. File System Issues: If you're running in a containerized or virtualized environment, ensure that the file system is properly mounted and accessible. 8. Correct Date Handling: The date 
2024-02-29
does not exist since 2024 is not a leap year. This could be part of a custom log rotation naming scheme that needs to be corrected. After addressing the above points, try running the Dagster process again. If the issue persists, you may need to further investigate the environment and context in which the Dagster process is running.
p
@Scout How to clear storage and history given the dagster.yaml below: storage: postgres: postgres_db: username: env: DAGSTER_PGUSER password: env: DAGSTER_PGPASSWORD hostname: env: PGHOST db_name: env: PGDATABASE port: env: PGPORT
s
To clear storage and history in Dagster when using PostgreSQL for storage, you would typically need to perform operations directly on the PostgreSQL database. This involves deleting records from the relevant tables that Dagster uses to store runs, events, and other metadata. Here are the general steps you would take to clear the storage and history: 1. Backup Database: Before making any changes, it's highly recommended to back up your database. This way, you can restore the data if needed. 2. Access the Database: Connect to your PostgreSQL database using the credentials provided in the environment variables specified in your 
dagster.yaml
file. 3. Delete Records: Execute SQL commands to delete records from the tables used by Dagster. The main tables you might consider clearing are: - `runs`: Contains metadata about pipeline runs. - `event_logs`: Contains event logs for pipeline runs. - `asset_keys`: Contains a list of asset keys. - `asset_materializations`: Contains materialization events for assets. - `asset_observations`: Contains observation events for assets. Here's an example SQL command to delete all records from the 
runs
and 
event_logs
tables:
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sql    DELETE FROM event_logs;    DELETE FROM runs;
Note: Be very careful with these commands, as they will permanently delete data. 4. Vacuum the Database: After deleting records, you might want to vacuum the database to reclaim space and optimize performance:
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sql    VACUUM FULL;
5. Restart Dagster Services: After clearing the database, restart any Dagster services (such as 
dagit
and 
dagster-daemon
) to ensure they are in a clean state. Please be aware that these actions are destructive and irreversible. Only proceed if you are certain that you want to permanently remove this data. If you are unsure about any of these steps or their implications, it's best to consult with a database administrator or someone familiar with PostgreSQL and Dagster. Additionally, the documentation suggests that you can use the 
DagsterInstance
API to programmatically delete runs, which would also remove associated event logs and other metadata. Here's an example script that uses the DagsterInstance API:
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python from dagster import DagsterInstance  # Get the current Dagster instance instance = DagsterInstance.get()  # Get all run IDs run_ids = instance.get_runs()  # Delete runs for run in run_ids:     instance.delete_run(run.run_id)
This script will delete all runs and their associated data from the Dagster instance. Adjust the script as needed to target specific runs or to batch the deletions.
p
@Scout fix code below: from dagster import Config, define_asset_job from pydantic import Field from .assets.ingestion.price.imf_ingestion import ( IMFConfig, get_imf_data_series, get_imf_datasource_documents, imf_metadata, import_imf_data, normalize_remote_imf_data, remote_imf_data, validate_imf_data, monthly_partition_def ) assets_list = [ get_imf_datasource_documents, get_imf_data_series, imf_metadata, remote_imf_data, normalize_remote_imf_data, validate_imf_data, import_imf_data, ] class IMFConfig(Config): web_service: str = Field(default="IMF", description="The name of the web service that is being ingested") preview_rows: int = Field(default=10, description="The number of rows to show in DataFrame previews") dry_run: bool = Field( default=False, description="Boolean which determines if changes to the database are made or if the import is only simulated", ) start_date: str = Field(default="2017-01-01", description="The first date in the set of partitions will be midnight the sonnest first of the month following start_date") imf_ingestion_job = define_asset_job( name="imf_ingestion_job", selection=assets_list, partitions_def=monthly_partition_def, config=IMFConfig, )
s
To fix the code, you need to make a few adjustments: 1. The 
Config
class from 
dagster
is not the correct base class for configuration schemas in Dagster. Instead, you should use Pydantic's 
BaseModel
class to define configuration schemas. 2. The 
Field
class should be imported from 
pydantic
, not 
dagster
. 3. The 
define_asset_job
function does not accept a 
config
parameter. Instead, you should define the configuration schema directly on the assets or pass the configuration when executing the job. Here's the corrected code:
Copy code
python from pydantic import BaseModel, Field from dagster import define_asset_job  from .assets.ingestion.price.imf_ingestion import (     get_imf_data_series,     get_imf_datasource_documents,     imf_metadata,     import_imf_data,     normalize_remote_imf_data,     remote_imf_data,     validate_imf_data,     monthly_partition_def )  assets_list = [     get_imf_datasource_documents,     get_imf_data_series,     imf_metadata,     remote_imf_data,     normalize_remote_imf_data,     validate_imf_data,     import_imf_data, ]  class IMFConfig(BaseModel):     web_service: str = Field(default="IMF", description="The name of the web service that is being ingested")     preview_rows: int = Field(default=10, description="The number of rows to show in DataFrame previews")     dry_run: bool = Field(         default=False,         description="Boolean which determines if changes to the database are made or if the import is only simulated",     )     start_date: str = Field(default="2017-01-01", description="The first date in the set of partitions will be midnight the soonest first of the month following start_date")  imf_ingestion_job = define_asset_job(     name="imf_ingestion_job",     selection=assets_list,     partitions_def=monthly_partition_def )
In this corrected code, 
IMFConfig
is now a Pydantic 
BaseModel
, which is the correct way to define configuration schemas in Dagster. The 
Field
class is correctly imported from 
pydantic
. When you want to execute 
imf_ingestion_job
with specific configuration values, you will need to provide a configuration dictionary that matches the schema defined in 
IMFConfig
. This configuration is passed at runtime when you execute the job, not when you define the job.
p
@Scout explain all the parameters of define_asset_job
s
The 
define_asset_job
function in Dagster is used to create a job from a selection of assets. Here's an explanation of all the parameters that you can pass to this function, according to the documentation: - name (
str
): The name for the job. This is a required parameter and is used to identify the job within the Dagster system. - selection (
Union[str, Sequence[str], Sequence[AssetKey], Sequence[Union[AssetsDefinition, SourceAsset]], AssetSelection]
, optional): Specifies the assets that will be materialized or observed when the job is run. The selection can be a single string (to select a single asset or assets using a pattern), a list of strings (to select multiple assets), a list of 
AssetKey
objects, or an 
AssetSelection
object. If no selection is provided, the job will target all assets in the code location. - config (optional): Describes how the job is parameterized at runtime. This parameter allows you to specify the configuration schema for the job's run config. If no value is provided, then the schema for the job’s run config is a standard format based on its ops and resources. - description (
str
, optional): A string that describes what the job does. This is useful for documentation purposes and can be displayed in the Dagster UI. - tags (
Dict[str, Any]
, optional): A dictionary of tags for the job. Tags are key-value pairs that can be used for organizing and filtering jobs within the Dagster UI. - metadata (
Dict[str, Any]
, optional): A dictionary of metadata for the job. This is similar to tags but is intended for storing additional information that doesn't fit the tag model. - partitions_def (
PartitionsDefinition
, optional): The definition of how the job is partitioned. This parameter is used to specify how the job's runs are sliced into partitions, such as daily or monthly partitions. - executor_def (
ExecutorDefinition
, optional): The executor definition for the job. This parameter allows you to specify how the job's execution is parallelized and distributed. - hooks (
List[HookDefinition]
, optional): A list of hooks for the job. Hooks are functions that can be triggered by certain events during job execution, such as on success or failure. - op_retry_policy (
RetryPolicy
, optional): The retry policy for the ops within the job. This parameter allows you to specify how failed ops should be retried. Please note that the 
define_asset_job
function creates a job definition that will only be resolved to a 
JobDefinition
once placed in a code location. This means that the job is not fully defined until it is included in a 
Definitions
object at the top level of a Python module or file, and that module is loaded as a code location.
p
@Scout with examples explain the use of config in define_asset_job
s
As of my last update, the 
define_asset_job
function in Dagster does not accept a 
config
parameter directly. Instead, configuration for the job is provided at runtime when executing the job, either through the Python API, the Dagster UI (Dagit), or the command line. When you want to execute a job created with 
define_asset_job
and provide runtime configuration, you can use the 
execute_in_process
method (for testing or development purposes) or the 
execute
method (for production runs), passing a dictionary that matches the configuration schema expected by the job's assets. Here's an example of how you might execute a job with runtime configuration using the Python API:
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python from dagster import define_asset_job, AssetSelection, RunConfig  # Assuming you have a list of assets defined elsewhere assets_list = [     # ... your assets here ... ]  # Define the job with a selection of assets my_asset_job = define_asset_job(     name="my_asset_job",     selection=AssetSelection.assets(*assets_list), )  # Define your configuration values according to the assets' config schema run_config_values = {     "ops": {         "my_asset_op": {             "config": {                 "param1": "value1",                 "param2": "value2",             }         }     }     # ... other configurations ... }  # Execute the job with the provided configuration result = my_asset_job.execute_in_process(run_config=run_config_values)
In this example, 
run_config_values
is a dictionary that specifies the configuration for the assets within the job. The keys in the 
ops
section of the dictionary correspond to the names of the assets, and the nested 
config
dictionary contains the configuration values for each asset. Please note that the 
execute_in_process
method is typically used for testing or development purposes. For production scenarios, you would launch the job using the Dagster CLI or the Dagit UI, providing the configuration values through a YAML file or the UI's configuration editor. If you need to provide configuration for a job in Dagit, you would do so by filling out the configuration fields in the Launchpad tab of the job's page. The configuration schema defined by the assets will dictate the fields and values that you need to provide. Remember that the exact method for providing configuration may vary depending on the version of Dagster you are using and the specific features available at the time. Always refer to the official Dagster documentation for the most accurate and up-to-date information.
p
@Scout expalin use of FreshnessPolicy
s
A 
FreshnessPolicy
in Dagster is a declarative way to specify expectations about how fresh (up-to-date) an asset should be. It allows you to define conditions that determine when an asset is considered "stale" or "out of date," which can trigger automated processes to update the asset or send alerts. The 
FreshnessPolicy
can be particularly useful for assets that are expected to be updated at regular intervals, such as daily reports, hourly metrics, or any data that is continuously ingested from external sources. Here's an example of how to use 
FreshnessPolicy
with an asset:
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python from dagster import asset, FreshnessPolicy  @asset(freshness_policy=FreshnessPolicy(maximum_lag_minutes=60)) def my_asset():     # Asset computation logic here
In this example, the 
FreshnessPolicy
is set with a 
maximum_lag_minutes
of 60, which means the asset is expected to be updated at least once every hour. If the asset hasn't been materialized within the last hour, it could be considered stale, and depending on your setup, Dagster could trigger a process to update it or notify you that the asset needs attention. The 
FreshnessPolicy
can also be used in conjunction with sensors in Dagster to create 
FreshnessPolicySensorDefinition
. This sensor will monitor the asset and execute logic based on the freshness status of the asset, such as sending alerts or triggering materializations. Here's an example of defining a freshness policy sensor:
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python from dagster import asset, FreshnessPolicy, FreshnessPolicySensorDefinition, AssetSelection  @asset(freshness_policy=FreshnessPolicy(maximum_lag_minutes=60)) def my_asset():     # Asset computation logic here  def my_freshness_sensor_fn(context):     overdue_assets = context.overdue_assets     # Logic to handle overdue assets, e.g., send an alert or trigger an update  my_freshness_sensor = FreshnessPolicySensorDefinition(     name="my_freshness_sensor",     asset_selection=AssetSelection.all(),     freshness_policy_sensor_fn=my_freshness_sensor_fn, )
In this sensor definition, 
my_freshness_sensor_fn
is a function that will be called by the sensor to check the freshness of all assets selected by 
AssetSelection.all()
. If any assets are overdue based on their freshness policy, the function can take appropriate action. The 
FreshnessPolicy
feature helps ensure that your data assets remain up-to-date and can automate the process of keeping your data pipeline's outputs fresh. It's a powerful tool for managing data quality and timeliness in a data ecosystem.
p
@Scout This schedule runs only one partition: imf_data_ingestion_schedule = build_schedule_from_partitioned_job( name="imf_data_ingestion_schedule", job=imf_ingestion_job, )
s
The 
build_schedule_from_partitioned_job
function in Dagster is used to create a schedule from a job that targets partitioned assets. The schedule created by this function will automatically align with the partitioning scheme of the job or assets. This means that the schedule will trigger runs for the job at the cadence specified by the time partitioning of the job or assets. Here's an example of how you might use `build_schedule_from_partitioned_job`:
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python from dagster import build_schedule_from_partitioned_job, job, PartitionKeyRange, StaticPartitionsDefinition  # Define a job with a static partition definition partitions_def = StaticPartitionsDefinition(["2021-01-01", "2021-01-02", "2021-01-03"])  @job(partitions_def=partitions_def) def my_partitioned_job():     # Job logic here  # Create a schedule from the partitioned job my_partitioned_job_schedule = build_schedule_from_partitioned_job(     job=my_partitioned_job,     name="my_partitioned_job_schedule", )
In this example, 
my_partitioned_job
is a job that has a static partition definition with three partitions. The 
build_schedule_from_partitioned_job
function creates a schedule named 
my_partitioned_job_schedule
that will run 
my_partitioned_job
according to the partitioning scheme defined in the job. If you want the schedule to run only one specific partition, you would need to customize the schedule definition using the 
@schedule
decorator and manually specify the partition key for the run. Here's an example of how to do that:
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python from dagster import schedule, RunRequest  @schedule(     cron_schedule="0 0 * * *",  # This cron schedule runs daily at midnight     job=my_partitioned_job,     name="my_single_partition_schedule" ) def my_single_partition_schedule(context):     # Specify the partition key you want to run     partition_key = "2021-01-01"     return RunRequest(run_key=partition_key, run_config={"solids": {"my_solid": {"config": {"partition_key": partition_key}}}})
In this custom schedule, 
my_single_partition_schedule
is defined to run the 
my_partitioned_job
job for a single partition with the key 
"2021-01-01"
every day at midnight. Please note that the 
RunRequest
object should include any necessary run configuration that your job requires, and you should replace 
"my_solid"
with the actual name of the solid or op within your job that requires the partition key.
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