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<@U0667DNC02Y> can we give arguments like paramete...
# ask-ai
v
@Scout can we give arguments like parameters while running the module?
s
Yes, you can provide arguments when running a module with Dagster. According to the documentation, when using the 
dagster
CLI to run a module, you can supply the 
-m
or 
--module-name
argument followed by the name of the module you wish to run. This allows Dagster to load the definitions at the top-level of the specified module. For example, to run a module named 
your_module_name
, you would use the following command:
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shell dagster dev -m your_module_name
This command loads the definitions in the variable containing the 
Definitions
object in the named module, which is defined as the root 
__init__.py
file, in the current Python environment. If you want to include multiple modules at a time, each module will be loaded as a separate code location. You can specify multiple modules by repeating the 
-m
argument with different module names:
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shell dagster dev -m your_first_module_name -m your_second_module_name
Additionally, you can configure your local instance using a 
pyproject.toml
file, which can include a 
tool.dagster
section with a 
module_name
variable. When defined, you can run the 
dagster dev
command in the same directory as the 
pyproject.toml
file without needing to supply the module name as a command-line argument. Keep in mind that the documentation also suggests using Python modules as code locations for Dagster projects deployed to production, as this approach can help eliminate a class of Python import errors.
In Dagster, when you run a module, you typically don't pass parameters directly to the module itself. Instead, you configure the behavior of the ops, resources, and jobs within the module using Dagster's configuration system. However, you can pass configuration values when you execute a job or a pipeline. These configurations can be provided in several ways, such as through YAML files, Python dictionaries, or the Dagster UI's Launchpad. The configuration can include parameters that ops or resources within the job or pipeline will use during execution. For example, when executing a job in a script, you can pass a configuration dictionary directly:
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python if __name__ == "__main__":     result = my_job.execute_in_process(         run_config={             "ops": {                 "my_op": {                     "config": {                         "param1": "value1",                         "param2": "value2",                     }                 }             }         }     )
In this example, 
my_op
is an op within 
my_job
, and it is being configured with parameters 
param1
and 
param2
. If you want to pass arguments via the command line, you can use the 
--config
flag to specify a path to a YAML file containing the configuration:
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shell dagster job execute -f my_module.py -c config.yaml
Here, 
config.yaml
is a file that contains the necessary configuration for the job. Additionally, you can use tags to pass metadata or simple key-value pairs when executing a job. For example, you can use the 
--tag
flag with the 
dagster job execute
command:
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shell dagster job execute -f my_module.py --tag key1=value1 --tag key2=value2
These tags can then be accessed within your ops or resources if needed. Remember that the exact method of passing arguments or parameters will depend on the context of what you're trying to achieve and the specific capabilities provided by Dagster's configuration system.
v
@Scout i want to do with arguments in the terminals and to run the module ?how can we do
s
To run a Dagster job from the terminal and pass arguments in the form of configuration parameters, you would typically use a YAML configuration file that specifies the parameters for your job's execution. You can then reference this file when executing the job using the Dagster CLI. Here's a step-by-step guide on how to do this: 1. Create a YAML configuration file: This file will contain the configuration for your job, including any parameters that your ops or resources require. Save this file with a 
.yaml
extension. For example, 
config.yaml
might look like this:
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yaml ops:   my_op:     config:       param1: "value1"       param2: "value2"
2. Execute the job with the configuration file: Use the 
dagster job execute
command in the terminal, and pass the 
-c
or 
--config
flag to specify the path to your YAML configuration file.
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shell dagster job execute -f my_module.py -c config.yaml
In this command: - 
-f my_module.py
specifies the Python file where your job is defined. - 
-c config.yaml
tells Dagster to use the 
config.yaml
file for the job's configuration. If you need to pass multiple configuration files, you can repeat the 
-c
flag with different file paths, and Dagster will merge these configurations:
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shell dagster job execute -f my_module.py -c config1.yaml -c config2.yaml
Remember that the configuration file should match the structure expected by your job's ops and resources. The example above assumes that 
my_op
is an op within your job that expects configuration parameters 
param1
and 
param2
. If you want to pass simple key-value pairs that are not part of the job's configuration schema, you can use tags with the 
--tag
flag:
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shell dagster job execute -f my_module.py --tag key1=value1 --tag key2=value2
These tags are not used for op configuration but can be useful for passing metadata or for use within the Dagster system. Please note that the exact commands and flags may vary depending on the version of Dagster you are using, so it's always a good idea to refer to the official Dagster documentation or use the 
--help
flag with the CLI to get the most accurate and up-to-date information.
v
@Scout ##assets.py from dagster import asset, AssetSpec from dagster_embedded_elt.sling import SlingMode, build_sling_asset from dagster_params.resource import file_paths_resource # Import the resource function def generate_assets(file_info_list): asset_defs = [] for file_info in file_info_list: file_path, file_name, table_name = file_info full_file_path = f"{file_path}/{file_name}" asset_key = table_name.replace('.', '_').replace('-', '_') sling_asset = build_sling_asset( sling_resource_key="sling_sql", asset_spec=AssetSpec(key=asset_key, group_name="ETL"), source_stream=full_file_path, target_object=table_name, mode=SlingMode.FULL_REFRESH, ) asset_defs.append(sling_asset) return asset_defs # Use the file_paths_resource to get the file paths and generate assets file_info_list = file_paths_resource() assets = generate_assets(file_info_list) this is the assets file and thisis resource file from dagster import get_dagster_logger, resource, ExperimentalWarning from dagster_embedded_elt.sling import SlingResource, SlingSourceConnection, SlingTargetConnection from sqlalchemy import create_engine, text from dagster_params.params import db_connection_params import warnings from sqlalchemy.exc import SQLAlchemyError from sqlalchemy import text warnings.filterwarnings("ignore", category=ExperimentalWarning) logger = get_dagster_logger() @resource def file_paths_resource(): try: engine = create_engine( f"mssql+pyodbc://{db_connection_params['user']}:{db_connection_params['password']}@" f"{db_connection_params['host']}:{db_connection_params['port']}/{db_connection_params['database']}?" f"driver=ODBC+Driver+17+for+SQL+Server" ) with engine.connect() as conn: query = text(""" SELECT propertyvalue, propertynameid FROM app.PropertyConfig WHERE PropertyId IN (SELECT id FROM app.Property WHERE id=1) """) result = conn.execute(query) # Initialize variables to store path, filename, and tablename path, filename, tablename = None, None, None # Iterate over the results and assign values based on propertynameid for row in result: if row[1] == 1: # Assuming 1 is the ID for the path path = row[0] elif row[1] == 2: # Assuming 2 is the ID for the filename filename = row[0] + '.csv' # Append '.csv' to the filename elif row[1] == 3: # Assuming 3 is the ID for the tablename tablename = 'app.' + row[0] # Prepend 'app.' to the tablename # Check if all values are present if not all([path, filename, tablename]): raise ValueError("Missing path, filename, or tablename from the database results.") # Return a list of tuples containing the path, filename, and tablename return [(path, filename, tablename)] except SQLAlchemyError as e: logger.error(f"An error occurred while fetching file paths: {e}") raise except ValueError as e: logger.error(f"An error occurred while processing file paths: {e}") raise # Define the SlingResource for SQL Server using the connection details from params.py sling_sql = SlingResource( source_connection=SlingSourceConnection(type="file"), target_connection=SlingTargetConnection(**db_connection_params) ) and i gave connection details on params file so here i need to provide id =1 not in the code but outside like in the arguments in terminals so how can i do that ?
s
To provide an external argument such as an ID from the terminal when running your Dagster job, you can use Dagster's configuration system to pass runtime parameters. You can define a configuration schema for your resource that accepts parameters, and then provide the values for these parameters through a YAML configuration file or directly via the CLI. Here's how you can modify your 
file_paths_resource
resource to accept a parameter:
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python # assets.py # ... (other imports and code)  @resource(config_schema={"property_id": int}) def file_paths_resource(context):     property_id = context.resource_config["property_id"]     # ... (rest of your code using property_id)
In the above code, 
config_schema
defines that 
file_paths_resource
expects a configuration with a key 
property_id
of type 
int
. Next, create a YAML configuration file (e.g., 
config.yaml
) that includes the value for `property_id`:
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yaml resources:   file_paths_resource:     config:       property_id: 1
Now, when you run your job from the terminal, you can use the 
-c
flag to specify the path to your configuration file:
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shell dagster job execute -f assets.py -c config.yaml
Alternatively, if you want to pass the 
property_id
directly via the CLI without using a YAML file, you can use the 
-a
or 
--config-args
flag to provide a JSON string with the configuration:
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shell dagster job execute -f assets.py -a '{"resources": {"file_paths_resource": {"config": {"property_id": 1}}}}'
Please note that the 
-a
or 
--config-args
flag is not standard and may not be available in all versions of Dagster. The standard way to pass configuration is through a YAML file using the 
-c
flag. Always refer to the official Dagster documentation or use the 
dagster job execute --help
command to get the most accurate and up-to-date information on the available CLI options for your version of Dagster.
v
@Scout dagster job execute -f assets.py -a '{"resources": {"file_paths_resource": {"config": {"property_id": 1}}}}' here assets is in dagster_params module and in that i have other files and to get the resulted output i need to execute to run the module so how can i run that?
s
To execute a Dagster job that is defined within a specific module, you would typically use the 
-m
or 
--module-name
flag to specify the module name when running the job from the CLI. However, based on the documentation, the 
-a
or 
--config-args
flag is not a standard option in Dagster for passing configuration arguments directly from the CLI. Instead, you should use a YAML configuration file to pass the parameters to your job. Here's how you can do it step by step: 1. Create a YAML configuration file (e.g., 
config.yaml
) that includes the configuration for your `file_paths_resource`:
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yaml resources:   file_paths_resource:     config:       property_id: 1
2. Run the job from the CLI using the 
-m
flag to specify the module name and the 
-c
flag to specify the configuration file:
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shell dagster job execute -m dagster_params -c config.yaml
In this command: - 
-m dagster_params
specifies the Python module where your job and assets are defined. - 
-c config.yaml
tells Dagster to use the 
config.yaml
file for the job's configuration. Make sure that the module 
dagster_params
is in your Python path and that it contains an 
__init__.py
file that imports the job you want to execute. Dagster will look for the job definition within the specified module. If you have multiple jobs defined within the 
dagster_params
module and you want to execute a specific job, you can use the 
-j
or 
--job
flag to specify the job name:
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shell dagster job execute -m dagster_params -c config.yaml -j your_job_name
Replace 
your_job_name
with the actual name of the job you want to execute. Remember to always refer to the official Dagster documentation for the most accurate and up-to-date CLI options for your version of Dagster. The flags and usage may vary between versions.
v
@Scout dagster job execute -m dagster_params -c config.yaml in this where can i give port like 3000
s
In Dagster, when you execute a job using the 
dagster job execute
command, the port specification is not directly related to the execution of the job itself. Instead, the port is typically used when you are running the Dagster webserver (
dagit
) or a gRPC server that serves your repository to the Dagster instance. If you need to specify a port for the Dagster webserver (
dagit
), you would do so when starting 
dagit
, not when executing a job. Here's how you would start 
dagit
with a specific port:
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shell dagit -p 3000
This command starts the Dagster webserver on port 3000. However, if you are running a gRPC server for your Dagster repository and you want to specify the port for that server, you would use the 
dagster api grpc
command. Here's an example of how to start a gRPC server on a specific port:
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shell dagster api grpc -m dagster_params -p 3000
This command starts a gRPC server that serves the repository defined in the 
dagster_params
module on port 3000. When you execute a job from the CLI using 
dagster job execute
, the job will run in the context of the Dagster instance that is configured to communicate with your repository. If your repository is being served by a gRPC server on a specific port, you would ensure that your Dagster instance (or workspace configuration) is set up to point to that gRPC server. The job execution command itself does not need the port because it's not starting a server; it's simply triggering a job run within the context of the already-running Dagster instance or gRPC server. If you are using a 
dagster.yaml
configuration file for your Dagster instance, you might specify the gRPC server and port there, under the workspace configuration. This is separate from the job execution command and is part of the setup of your Dagster environment. Always refer to the official Dagster documentation for the most accurate and up-to-date information on configuring and running Dagster components.
v
@Scout Resource initialization function has context argument, but no context was provided when invoking. got this error for the code @resource(config_schema={"property_id": int}) def file_paths_resource(context): property_id = context.resource_config["property_id"] try: engine = create_engine( f"mssql+pyodbc://{db_connection_params['user']}:{db_connection_params['password']}@" f"{db_connection_params['host']}:{db_connection_params['port']}/{db_connection_params['database']}?" f"driver=ODBC+Driver+17+for+SQL+Server" ) with engine.connect() as conn: query = text(""" SELECT propertyvalue, propertynameid FROM app.PropertyConfig WHERE PropertyId IN (SELECT id FROM app.Property WHERE id=1) """) result = conn.execute(query) # Initialize variables to store path, filename, and tablename path, filename, tablename = None, None, None # Iterate over the results and assign values based on propertynameid for row in result: if row[1] == 1: # Assuming 1 is the ID for the path path = row[0] elif row[1] == 2: # Assuming 2 is the ID for the filename filename = row[0] + '.csv' # Append '.csv' to the filename elif row[1] == 3: # Assuming 3 is the ID for the tablename tablename = 'app.' + row[0] # Prepend 'app.' to the tablename # Check if all values are present if not all([path, filename, tablename]): raise ValueError("Missing path, filename, or tablename from the database results.") # Return a list of tuples containing the path, filename, and tablename return [(path, filename, tablename)] except SQLAlchemyError as e: logger.error(f"An error occurred while fetching file paths: {e}") raise except ValueError as e: logger.error(f"An error occurred while processing file paths: {e}") raise # Define the SlingResource for SQL Server using the connection details from params.py sling_sql = SlingResource( source_connection=SlingSourceConnection(type="file"), target_connection=SlingTargetConnection(**db_connection_params) )
s
The error message you're encountering, "Resource initialization function has context argument, but no context was provided when invoking," typically indicates that a resource function is being called directly in Python code without the appropriate context that Dagster provides during job execution. In Dagster, resources are meant to be used within the context of a job or asset execution, and the framework is responsible for providing the 
context
object. When you define a resource with 
@resource
, you should not be calling it directly; instead, you should be referencing it within your job or asset definitions, and Dagster will handle the invocation and provide the context. Here's an example of how you might use the 
file_paths_resource
within a job:
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python from dagster import job  @job(resource_defs={"file_paths_resource": file_paths_resource}) def my_job():     # Your job logic here, which can access the file_paths_resource via context     pass
When you execute 
my_job
, Dagster will automatically provide the 
context
to the 
file_paths_resource
function. If you are trying to test your resource or invoke it outside of a Dagster job, you need to construct a 
build_init_resource_context
to simulate the Dagster context. Here's an example of how you might do that:
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python from dagster import build_init_resource_context  # Simulate the resource context with the necessary config resource_context = build_init_resource_context(config={"property_id": 1}) file_paths = file_paths_resource(resource_context)
This is typically used for testing purposes to simulate the resource invocation as it would occur within a Dagster job. If you're encountering this error during the normal execution of a Dagster job, it could be due to a misconfiguration in how the resource is being provided to the job or asset. Ensure that you have correctly defined the resource in your job's 
resource_defs
and that you are not trying to call the resource function directly in your code. If you continue to face issues, please check the latest Dagster documentation or consider reaching out to the Dagster community for more specific guidance based on your use case.
v
@Scout getthe modified resource and not to f=give id in the code and provide them usig arg in terminals or in yaml file
s
To modify the 
file_paths_resource
so that you can provide the 
property_id
from outside the code, such as through a YAML configuration file or a command-line argument, you need to ensure that your resource is set up to accept configuration parameters. It looks like you've already done this with the 
config_schema
in your resource definition. Here's the resource definition with a 
config_schema
that accepts `property_id`:
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python from dagster import resource, Field  @resource(config_schema={"property_id": Field(int, is_required=True)}) def file_paths_resource(context):     property_id = context.resource_config["property_id"]     # ... rest of your code using property_id
Now, to provide the 
property_id
when running a job, you can use a YAML configuration file. Create a 
config.yaml
file with the following content:
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yaml resources:   file_paths_resource:     config:       property_id: 1  # Replace with the desired property ID
Then, execute your job using the Dagster CLI and specify the configuration file:
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shell dagster job execute -f your_job_file.py -c config.yaml
In this command, replace 
your_job_file.py
with the Python file where your job is defined. If you want to provide the 
property_id
directly via the CLI without using a YAML file, you can use environment variables to pass the value. First, modify your resource to read from an environment variable:
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python import os from dagster import resource  @resource def file_paths_resource(_):     property_id = os.getenv('PROPERTY_ID')     if property_id is None:         raise Exception("Environment variable 'PROPERTY_ID' is not set")     property_id = int(property_id)     # ... rest of your code using property_id
Then, set the environment variable before running your job:
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shell export PROPERTY_ID=1 dagster job execute -f your_job_file.py
Remember to replace 
1
with the actual 
property_id
you want to use. Please note that the ability to pass configuration directly via the CLI using JSON or other inline methods is not a standard feature in Dagster, and you should use YAML configuration files or environment variables to pass external parameters to your jobs. Always refer to the latest Dagster documentation for the most accurate and up-to-date information on configuring resources and executing jobs.
v
@Scout # Define the Dagster definitions defs = Definitions( resources={ "sling_sql": resource.sling_sql, "file_paths": resource.file_paths_resource, # Add the file paths resource }, assets=[my_asset], ) is this in the job format
s
The code snippet you've provided is using the 
Definitions
object to define resources and assets for a Dagster project. The 
Definitions
object is a way to collect and organize various Dagster definitions such as assets, jobs, resources, schedules, sensors, and more. This is part of the setup for a Dagster project and is used to make these definitions available to Dagster tools. However, the snippet does not define a job directly. In Dagster, a job is a collection of ops (or, in the case of assets, a collection of assets) that can be executed. To create a job from assets, you would typically use the 
build_assets_job
function, which bundles assets into an executable job. Here's an example of how you might define a job from assets using the 
build_assets_job
function:
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python from dagster import build_assets_job  # Assuming `my_asset` is defined elsewhere and is an instance of `AssetIn` my_job = build_assets_job(     name="my_job",     assets=[my_asset],     resource_defs={         "sling_sql": resource.sling_sql,         "file_paths": resource.file_paths_resource,     }, )
In this example, 
my_job
is a job that includes the 
my_asset
asset and uses the 
sling_sql
and 
file_paths
resources. The 
name
parameter gives a name to the job, and the 
assets
parameter is a list of assets that the job will execute. The 
resource_defs
parameter is a dictionary that maps resource keys to resource definitions, which are used by the assets during execution. If you want to use the 
Definitions
object to organize your project's definitions, you can still define a job within that structure. Here's how you might include a job in your `Definitions`:
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python from dagster import Definitions, build_assets_job  # Define the job from assets my_job = build_assets_job(     name="my_job",     assets=[my_asset],     resource_defs={         "sling_sql": resource.sling_sql,         "file_paths": resource.file_paths_resource,     }, )  # Define the Dagster definitions defs = Definitions(     jobs=[my_job],  # Include the job in the Definitions     resources={         "sling_sql": resource.sling_sql,         "file_paths": resource.file_paths_resource,     },     assets=[my_asset], )
In this updated example, the 
Definitions
object now includes a 
jobs
parameter with a list of jobs, in addition to the resources and assets. Please note that the exact syntax and functions may vary depending on the version of Dagster you are using, so always refer to the official Dagster documentation for the most accurate and up-to-date information.
v
@Scout export PROPERTY_ID=1 dagster job execute -f init.py bash: export: `-f': not a valid identifier bash: export: `__init__.py': not a valid identifier
s
The error you're seeing is because the 
export
command in Bash is being used incorrectly. The 
export
command is used to set environment variables, and it expects a format like 
VARIABLE_NAME=value
. When you run 
export PROPERTY_ID=1 dagster job execute -f __init__.py
, the shell interprets 
-f
and 
__init__.py
as additional arguments to 
export
, which is not correct. To set the 
PROPERTY_ID
environment variable and then execute the Dagster job, you should separate the commands with a semicolon (
;
) or run them on separate lines. Here's how you can do it with a semicolon:
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shell export PROPERTY_ID=1; dagster job execute -f __init__.py
Or, if you prefer to run them on separate lines:
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shell export PROPERTY_ID=1 dagster job execute -f __init__.py
This sets the 
PROPERTY_ID
environment variable to 
1
for the duration of your shell session, and then executes the Dagster job defined in 
__init__.py
. If you want to set the environment variable only for the duration of the 
dagster job execute
command, you can do so by prefixing the command with the variable assignment, like this:
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shell PROPERTY_ID=1 dagster job execute -f __init__.py
This syntax sets 
PROPERTY_ID
just for the execution of the 
dagster job execute
command and does not affect the environment variable for the rest of the shell session.
v
@Scout getting error as from dagster import Definitions, build_assets_job ImportError: cannot import name 'build_assets_job' from 'dagster' get the modified codes get this modified code from dagster import Definitions, build_assets_job # Define the job from assets my_job = build_assets_job( name="my_job", assets=[generate_assets], resource_defs={ "sling_sql": resource.sling_sql, "file_paths": resource.file_paths_resource, }, ) # Define the Dagster definitions defs = Definitions( jobs=[my_job], # Include the job in the Definitions resources={ "sling_sql": resource.sling_sql, "file_paths": resource.file_paths_resource, }, assets=[my_asset], )
s
It seems there might be a misunderstanding or an outdated reference in the code you're trying to run. As of my knowledge cutoff in early 2023, Dagster's asset-based APIs use 
@asset
to define assets, and jobs are typically defined using 
@job
. The 
build_assets_job
function is not a standard part of the Dagster API. To define assets and jobs in Dagster, you would typically do something like this:
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python from dagster import asset, job  @asset def my_asset():     # Asset definition here     pass  @job def my_job():     # Job definition that includes my_asset     my_asset()
If you have multiple assets and you want to create a job that includes all of them, you can use the 
@job
decorator and list all the assets within the job definition:
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python from dagster import asset, job  @asset def asset_one():     # Asset definition here     pass  @asset def asset_two():     # Asset definition here     pass  @job def my_job():     asset_one()     asset_two()
If you're trying to create a job from a list of assets programmatically, you might be looking for a way to do this dynamically. In that case, you can use the 
define_asset_job
function, which is part of the 
dagster
package:
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python from dagster import define_asset_job  my_job = define_asset_job(     name="my_job",     assets=[asset_one, asset_two],     # You can include resource definitions if needed     resource_defs={         "resource_name": resource_definition,     }, )
Please make sure you are using the correct version of Dagster and referring to the latest documentation for the most accurate and up-to-date information on defining assets and jobs. If you continue to encounter issues, it may be helpful to check the Dagster release notes or the official Dagster Slack community for assistance.
The error you're encountering suggests that 
build_assets_job
is not a function that can be imported from the 
dagster
module. This might be due to a version mismatch or an incorrect reference in the code. As of my knowledge cutoff, the typical way to define a job from assets in Dagster is by using the 
@job
decorator and including the assets within the job function. Here's how you can define a job from assets using the 
@job
decorator:
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python from dagster import job, AssetGroup  # Assuming `generate_assets` is a function that returns a list of assets # and `my_asset` is an individual asset defined elsewhere asset_group = AssetGroup(assets=generate_assets() + [my_asset])  # Define the job that includes all assets in the asset group @job(resource_defs={     "sling_sql": resource.sling_sql,     "file_paths": resource.file_paths_resource, }) def my_job():     asset_group.build_job(name="my_job")
In this example, 
my_job
is a job that includes all assets returned by 
generate_assets()
as well as 
my_asset
. The 
resource_defs
parameter is used to provide the necessary resources to the job. The 
Definitions
object is not a standard part of the Dagster API for defining jobs and assets. Instead, you would typically define assets and jobs directly using the 
@asset
and 
@job
decorators, respectively. If you need to organize multiple assets and jobs, you can use an 
AssetGroup
to group assets together and then build a job from that group. Please ensure that you are using the correct and up-to-date Dagster API. If you are following a guide or example, make sure it matches the version of Dagster you are using. For further assistance, you may want to refer to the latest Dagster documentation or seek help from the Dagster community.
v
@Scout /.local/lib/python3.8/site-packages/dagster/_core/definitions/resource_definition.py", line 255, in call raise DagsterInvalidInvocationError( dagster._core.errors.DagsterInvalidInvocationError: Resource initialization function has context argument, but no context was provided when invoking. got this error for the codes from dagster import job,asset from dagster_params import resource from dagster_params.assets import generate_assets # Assuming 
generate_assets
is a function that returns a list of assets # and 
my_asset
is an individual asset defined elsewhere asset_group = Asset(assets=generate_assets() + [my_asset]) # Define the job that includes all assets in the asset group @job(resource_defs={ "sling_sql": resource.sling_sql, "file_paths": resource.file_paths_resource, }) def my_job(): asset_group.build_job(name="my_job") ##assets.py from dagster import asset, AssetSpec from dagster_embedded_elt.sling import SlingMode, build_sling_asset from dagster_params.resource import file_paths_resource # Import the resource function def generate_assets(file_info_list): asset_defs = [] for file_info in file_info_list: file_path, file_name, table_name = file_info full_file_path = f"{file_path}/{file_name}" asset_key = table_name.replace('.', '_').replace('-', '_') sling_asset = build_sling_asset( sling_resource_key="sling_sql", asset_spec=AssetSpec(key=asset_key, group_name="ETL"), source_stream=full_file_path, target_object=table_name, mode=SlingMode.FULL_REFRESH, ) asset_defs.append(sling_asset) return asset_defs # Use the file_paths_resource to get the file paths and generate assets file_info_list = file_paths_resource() assets = generate_assets(file_info_list) from dagster import get_dagster_logger, resource, ExperimentalWarning from dagster_embedded_elt.sling import SlingResource, SlingSourceConnection, SlingTargetConnection from sqlalchemy import create_engine, text from dagster_params.params import db_connection_params import warnings from sqlalchemy.exc import SQLAlchemyError from sqlalchemy import text warnings.filterwarnings("ignore", category=ExperimentalWarning) logger = get_dagster_logger() @resource def file_paths_resource(_): property_id = os.getenv('PROPERTY_ID') if property_id is None: raise Exception("Environment variable 'PROPERTY_ID' is not set") property_id = int(property_id) try: engine = create_engine( f"mssql+pyodbc://{db_connection_params['user']}:{db_connection_params['password']}@" f"{db_connection_params['host']}:{db_connection_params['port']}/{db_connection_params['database']}?" f"driver=ODBC+Driver+17+for+SQL+Server" ) with engine.connect() as conn: query = text(""" SELECT propertyvalue, propertynameid FROM app.PropertyConfig WHERE PropertyId IN (SELECT id FROM app.Property WHERE id=1) """) result = conn.execute(query) # Initialize variables to store path, filename, and tablename path, filename, tablename = None, None, None # Iterate over the results and assign values based on propertynameid for row in result: if row[1] == 1: # Assuming 1 is the ID for the path path = row[0] elif row[1] == 2: # Assuming 2 is the ID for the filename filename = row[0] + '.csv' # Append '.csv' to the filename elif row[1] == 3: # Assuming 3 is the ID for the tablename tablename = 'app.' + row[0] # Prepend 'app.' to the tablename # Check if all values are present if not all([path, filename, tablename]): raise ValueError("Missing path, filename, or tablename from the database results.") # Return a list of tuples containing the path, filename, and tablename return [(path, filename, tablename)] except SQLAlchemyError as e: logger.error(f"An error occurred while fetching file paths: {e}") raise except ValueError as e: logger.error(f"An error occurred while processing file paths: {e}") raise # Define the SlingResource for SQL Server using the connection details from params.py sling_sql = SlingResource( source_connection=SlingSourceConnection(type="file"), target_connection=SlingTargetConnection(**db_connection_params) )
s
The error message you're encountering indicates that a resource function is being called directly, but without the necessary context that Dagster provides during execution. In Dagster, resources are designed to be initialized by the framework, which provides the context that includes configuration and other metadata. From the code you've provided, it looks like there's an attempt to directly invoke 
file_paths_resource()
to get 
file_info_list
, which is then used to generate assets:
Copy code
python # Use the file_paths_resource to get the file paths and generate assets file_info_list = file_paths_resource() assets = generate_assets(file_info_list)
This direct invocation is likely what's causing the error, as 
file_paths_resource
is a resource definition expecting a context object when called by Dagster. To resolve this issue, you should not call 
file_paths_resource
directly. Instead, you should define your assets and resources within the Dagster job, and Dagster will handle the initialization of resources and provide them to your assets as needed. Here's how you can define a job that includes your assets and uses the `file_paths_resource`:
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python from dagster import job, asset, op, AssetGroup  # Define your assets using the @asset decorator @asset(required_resource_keys={"file_paths"}) def my_asset(context):     file_info_list = context.resources.file_paths  # Dagster provides the resource via context     # Generate assets using the file info list     return generate_assets(file_info_list)  # Define the job that includes all assets and resources @job(resource_defs={     "sling_sql": resource.sling_sql,     "file_paths": resource.file_paths_resource, }) def my_job():     my_asset()  # If you have multiple assets, you can create an AssetGroup and include it in the job all_assets = AssetGroup(assets=generate_assets(file_info_list) + [my_asset])
In this example, 
my_asset
is an asset that requires the 
file_paths
resource. The 
@job
decorator is used to define a job that includes this asset and specifies the necessary resources. When 
my_job
is executed, Dagster will initialize the 
file_paths_resource
and provide it to 
my_asset
through the context. Please ensure that you are using the correct and up-to-date Dagster API, and refer to the latest Dagster documentation for more information on defining assets and jobs. If you continue to encounter issues, consider reaching out to the Dagster community for further assistance.
v
@Scout we dont have assetgroup in the new dagster so provide the code accordingly
s
In the latest versions of Dagster, you can define assets and jobs without the need for an 
AssetGroup
. Instead, you can use the 
@asset
decorator to define assets and the 
@job
decorator to define jobs that include these assets. Here's an example of how you might define a job that includes assets generated by a function, using the `file_paths_resource`:
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python from dagster import job, asset, define_asset_job, repository from dagster_params import resource from dagster_params.assets import generate_assets  # Define your assets using the @asset decorator @asset(required_resource_keys={"file_paths"}) def my_asset(context):     # Access the file paths resource from the context     file_info_list = context.resources.file_paths     # Generate assets using the file info list     return generate_assets(file_info_list)  # Define the job that includes all assets and resources @job(resource_defs={     "sling_sql": resource.sling_sql,     "file_paths": resource.file_paths_resource, }) def my_job():     # Reference the assets you want to include in the job     my_asset()  # Assuming you have a list of asset definitions returned by generate_assets # and you want to create a job for these assets programmatically def create_asset_job():     # Generate the list of asset definitions     asset_defs = generate_assets(file_info_list)     # Define the job from the list of asset definitions     return define_asset_job(         name="my_generated_assets_job",         assets=asset_defs,         resource_defs={             "sling_sql": resource.sling_sql,             "file_paths": resource.file_paths_resource,         },     )  # Define a repository to hold our jobs and assets @repository def my_repository():     # Return a list of jobs and assets to include in the repository     return [my_job, create_asset_job()]
In this example, 
my_asset
is an asset that depends on the 
file_paths
resource. The 
my_job
job includes this asset and specifies the necessary resources. The 
create_asset_job
function demonstrates how you can programmatically create a job from a list of asset definitions returned by a function like 
generate_assets
. The 
@repository
decorator is used to define a repository that includes the jobs and assets. When you execute 
my_job
, Dagster will initialize the 
file_paths_resource
and provide it to 
my_asset
through the context. Please ensure that you are using the correct and up-to-date Dagster API, and refer to the latest Dagster documentation for more information on defining assets and jobs. If you continue to encounter issues, consider reaching out to the Dagster community for further assistance.
v
@Scout so how can we run the code now using the whole module or with the init file
s
To run your Dagster code using the whole module or with the 
__init__.py
file, you can use the 
dagster
CLI to execute your repository. Here's how you can do it: 1. Organize your code in a module: Make sure your Dagster code is organized in a Python module. A module is typically a directory with an 
__init__.py
file and other Python files that contain your asset and job definitions. 2. Define a repository: In your module's 
__init__.py
file, define a repository that includes all the jobs and assets you want to make available for execution. Here's an example `__init__.py`:
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python # your_module_name/__init__.py from dagster import repository from .jobs import my_job  # Import your job from the jobs module  @repository def my_repository():     return [my_job]
3. Run the job using the CLI: Use the 
dagster job execute
command with the 
-m
flag to specify the module name. The module name should be the name of the Python package that contains your 
__init__.py
file.
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shell dagster job execute -m your_module_name -j my_job
In this command: - 
-m your_module_name
specifies the Python module where your repository is defined. - 
-j my_job
specifies the name of the job you want to execute. If you want to execute the job without specifying the job name (assuming there is only one job in the repository), you can omit the 
-j
flag:
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shell dagster job execute -m your_module_name
4. Alternatively, use a 
workspace.yaml
file: If you have a 
workspace.yaml
file that specifies your module, you can use the 
dagster
CLI to execute your job by pointing to the workspace file:
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shell dagster job execute -w path/to/workspace.yaml -j my_job
In this command, 
-w path/to/workspace.yaml
points to your workspace configuration file. Make sure that your Python environment is set up correctly and that the 
dagster
package is installed. The module should be importable from the environment where you're running the 
dagster
CLI commands. Refer to the latest Dagster documentation for more detailed instructions and best practices for organizing your code and running Dagster jobs. If you encounter any issues, the Dagster community is a good resource for getting help with your specific setup.
v
@Scout call_ raise DagsterInvalidInvocationError( dagster._core.errors.DagsterInvalidInvocationError: Resource initialization function has context argument, bu t no context was provided when invoking. got this error if i run export PROPERTY_ID=1; dagster job execute -f init.py for the code from dagster import job, asset, define_asset_job, repository from dagster_params.assets import generate_assets # Define your assets using the @asset decorator @asset(required_resource_keys={"file_paths"}) def my_asset(context): # Access the file paths resource from the context file_info_list = context.resources.file_paths # Generate assets using the file info list return generate_assets(file_info_list) # Define the job that includes all assets and resources @job(resource_defs={ "sling_sql": resource.sling_sql, "file_paths": resource.file_paths_resource, }) def my_job(): # Reference the assets you want to include in the job my_asset() # Assuming you have a list of asset definitions returned by generate_assets # and you want to create a job for these assets programmatically def create_asset_job(): # Generate the list of asset definitions asset_defs = generate_assets(file_info_list) # Define the job from the list of asset definitions return define_asset_job( name="my_generated_assets_job", assets=asset_defs, resource_defs={ "sling_sql": resource.sling_sql, "file_paths": resource.file_paths_resource, }, ) # Define a repository to hold our jobs and assets @repository def my_repository(): # Return a list of jobs and assets to include in the repository return [my_job, create_asset_job()] ##assets.py from dagster import asset, AssetSpec from dagster_embedded_elt.sling import SlingMode, build_sling_asset from dagster_params.resource import file_paths_resource # Import the resource function def generate_assets(file_info_list): asset_defs = [] for file_info in file_info_list: file_path, file_name, table_name = file_info full_file_path = f"{file_path}/{file_name}" asset_key = table_name.replace('.', '_').replace('-', '_') sling_asset = build_sling_asset( sling_resource_key="sling_sql", asset_spec=AssetSpec(key=asset_key, group_name="ETL"), source_stream=full_file_path, target_object=table_name, mode=SlingMode.FULL_REFRESH, ) asset_defs.append(sling_asset) return asset_defs # Use the file_paths_resource to get the file paths and generate assets file_info_list = file_paths_resource() assets = generate_assets(file_info_list) from dagster import get_dagster_logger, resource, ExperimentalWarning from dagster_embedded_elt.sling import SlingResource, SlingSourceConnection, SlingTargetConnection from sqlalchemy import create_engine, text from dagster_params.params import db_connection_params import warnings from sqlalchemy.exc import SQLAlchemyError from sqlalchemy import text warnings.filterwarnings("ignore", category=ExperimentalWarning) logger = get_dagster_logger() @resource def file_paths_resource(_): property_id = os.getenv('PROPERTY_ID') if property_id is None: raise Exception("Environment variable 'PROPERTY_ID' is not set") property_id = int(property_id) try: engine = create_engine( f"mssql+pyodbc://{db_connection_params['user']}:{db_connection_params['password']}@" f"{db_connection_params['host']}:{db_connection_params['port']}/{db_connection_params['database']}?" f"driver=ODBC+Driver+17+for+SQL+Server" ) with engine.connect() as conn: query = text(""" SELECT propertyvalue, propertynameid FROM app.PropertyConfig WHERE PropertyId IN (SELECT id FROM app.Property WHERE id=1) """) result = conn.execute(query) # Initialize variables to store path, filename, and tablename path, filename, tablename = None, None, None # Iterate over the results and assign values based on propertynameid for row in result: if row[1] == 1: # Assuming 1 is the ID for the path path = row[0] elif row[1] == 2: # Assuming 2 is the ID for the filename filename = row[0] + '.csv' # Append '.csv' to the filename elif row[1] == 3: # Assuming 3 is the ID for the tablename tablename = 'app.' + row[0] # Prepend 'app.' to the tablename # Check if all values are present if not all([path, filename, tablename]): raise ValueError("Missing path, filename, or tablename from the database results.") # Return a list of tuples containing the path, filename, and tablename return [(path, filename, tablename)] except SQLAlchemyError as e: logger.error(f"An error occurred while fetching file paths: {e}") raise except ValueError as e: logger.error(f"An error occurred while processing file paths: {e}") raise # Define the SlingResource for SQL Server using the connection details from params.py sling_sql = SlingResource( source_connection=SlingSourceConnection(type="file"), target_connection=SlingTargetConnection(**db_connection_params) ) so get the changes and i want to gve argments in the terminal
s
The error you're encountering, "Resource initialization function has context argument, but no context was provided when invoking," typically occurs when a resource function that expects a context is called directly, rather than being invoked by the Dagster framework during job execution. In the code you've provided, the 
file_paths_resource
function is a resource definition that should be provided with a context by Dagster. However, it looks like you're trying to call 
file_paths_resource
directly to get 
file_info_list
, which is then used to generate assets:
Copy code
python # This direct invocation is likely causing the error file_info_list = file_paths_resource() assets = generate_assets(file_info_list)
To fix this issue, you should not call 
file_paths_resource
directly. Instead, you should access the resource via the context within an asset or job definition, where Dagster will provide the context automatically. Here's how you can modify the code to use the 
file_paths_resource
within an asset:
Copy code
python from dagster import asset, repository, job, ResourceDefinition  # Define your resource with the correct context argument @resource def file_paths_resource(context):     property_id = context.resource_config['property_id']     # ... rest of your code ...  # Define your asset, which uses the file_paths_resource @asset(required_resource_keys={'file_paths'}) def my_asset(context):     # Access the file paths resource from the context     file_info_list = context.resources.file_paths     # Generate assets using the file info list     return generate_assets(file_info_list)  # Define the job that includes the asset and specifies the necessary resources @job(resource_defs={     'sling_sql': sling_sql,     'file_paths': file_paths_resource, }) def my_job():     my_asset()  # Define a repository to hold our job @repository def my_repository():     return [my_job]
Now, to run the job and provide the 
property_id
argument, you should use a YAML configuration file. Create a 
config.yaml
file with the following content:
Copy code
yaml resources:   file_paths:     config:       property_id: 1  # Replace with the desired property ID
Then, execute your job using the Dagster CLI and specify the configuration file:
Copy code
shell dagster job execute -f your_job_file.py -c config.yaml
In this command, replace 
your_job_file.py
with the Python file where your job is defined. By using this approach, you ensure that Dagster manages the lifecycle of resources and provides the necessary context during job execution. This should resolve the error you're encountering.
v
@Scout the assets code is this so get the code accordingly for resource and assets ##assets.py from dagster import asset, AssetSpec from dagster_embedded_elt.sling import SlingMode, build_sling_asset from dagster_params.resource import file_paths_resource # Import the resource function def generate_assets(file_info_list): asset_defs = [] for file_info in file_info_list: file_path, file_name, table_name = file_info full_file_path = f"{file_path}/{file_name}" asset_key = table_name.replace('.', '_').replace('-', '_') sling_asset = build_sling_asset( sling_resource_key="sling_sql", asset_spec=AssetSpec(key=asset_key, group_name="ETL"), source_stream=full_file_path, target_object=table_name, mode=SlingMode.FULL_REFRESH, ) asset_defs.append(sling_asset) return asset_defs # Use the file_paths_resource to get the file paths and generate assets file_info_list = file_paths_resource() assets = generate_assets(file_info_list)
s
To correctly use the 
file_paths_resource
within your assets, you need to ensure that it is accessed through the Dagster context during job execution. The 
file_paths_resource
should not be called directly as it is in the code snippet you provided. Instead, it should be used within an asset or op that is part of a job. Here's how you can modify your 
assets.py
to define assets that use the `file_paths_resource`:
Copy code
python
# assets.py
from dagster import asset
from dagster_embedded_elt.sling import SlingMode, build_sling_asset
from dagster_params.resource import file_paths_resource

# Define an asset that uses the file_paths_resource
@asset(required_resource_keys={"file_paths"})
def my_asset(context):
    # Access the file paths resource from the context
    file_info_list = context.resources.file_paths
    # Generate assets using the file info list
    asset_defs = []
    for file_info in file_info_list:
        file_path, file_name, table_name = file_info
        full_file_path = f"{file_path}/{file_name}"
        asset_key = table_name.replace('.', '_').replace('-', '_')
        sling_asset = build_sling_asset(
            sling_resource_key="sling_sql",
            asset_spec=AssetSpec(key=asset_key, group_name="ETL"),
            source_stream=full_file_path,
            target_object=table_name,
            mode=SlingMode.FULL_REFRESH,
        )
        asset_defs.append(sling_asset)
    return asset_defs
Now, you need to define a job that includes this asset and specifies the necessary resources:
Copy code
python
# jobs.py
from dagster import job
from .assets import my_asset
from dagster_params.resource import file_paths_resource, sling_sql

# Define the job that includes the asset and specifies the necessary resources
@job(resource_defs={
    "sling_sql": sling_sql,
    "file_paths": file_paths_resource,
})
def my_job():
    my_asset()
Finally, you need to define a repository that includes this job:
Copy code
python
# repository.py
from dagster import repository
from .jobs import my_job

# Define a repository to hold our job
@repository
def my_repository():
    return [my_job]
To run the job and provide the 
property_id
argument, you should use a YAML configuration file. Create a 
config.yaml
file with the following content:
Copy code
yaml
resources:
  file_paths:
    config:
      property_id: 1  # Replace with the desired property ID
Then, execute your job using the Dagster CLI and specify the configuration file:
Copy code
shell
dagster job execute -f repository.py -c config.yaml
In this command, replace 
repository.py
with the Python file where your repository is defined. By using this approach, you ensure that Dagster manages the lifecycle of resources and provides the necessary context during job execution. This should resolve t
he error you're encountering.
v
@Scout repository.py. Consider using the module-based options 
-m
for CLI-based targets or the 
python_module
workspace target. got this errr for the above code
s
To run your Dagster job using the module-based option 
-m
, you should ensure that your repository is defined in a Python module that can be imported. The module should contain an 
__init__.py
file, which makes it a package, and it should import and expose the repository definition. Here's an example structure for your module:
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your_module/     __init__.py     repository.py     jobs.py     assets.py     resources.py     ...
In 
your_module/__init__.py
, you would import the repository from `repository.py`:
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python # your_module/__init__.py from .repository import my_repository
In 
your_module/repository.py
, you define your repository:
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python # your_module/repository.py from dagster import repository from .jobs import my_job  @repository def my_repository():     return [my_job]
Now, to run the job using the CLI, navigate to the directory that contains your module and execute the following command:
Copy code
shell dagster job execute -m your_module -c config.yaml
In this command: - 
-m your_module
specifies the Python module where your repository is defined. - 
-c config.yaml
points to the YAML configuration file that contains the necessary configuration for the 
file_paths
resource. Make sure that the 
your_module
directory is in a location where Python can import it, typically by ensuring it's in your 
PYTHONPATH
, or by running the command from the directory that contains the 
your_module
directory. If you're using a 
workspace.yaml
file to manage your workspace, you can specify the module there as well:
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yaml # workspace.yaml load_from:   - python_module:       module_name: your_module
Then, you can run the job by pointing to the workspace file:
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shell dagster job execute -w workspace.yaml
This approach allows you to run Dagster jobs without specifying the module or file each time, as the workspace file contains the necessary information about where to load your code from.
The error message you're encountering suggests that there's an issue with how the repository is being loaded. When using the 
-m
option with the Dagster CLI, you should specify the name of the Python module (not the 
.py
file) that contains your repository definition. Here's how you can run your job using the module-based option: 1. Ensure your Python module is importable: Your module should be in a directory that contains an 
__init__.py
file, and it should be on the Python path. For example, if your repository is defined in 
repository.py
within a module named 
my_dagster_module
, your directory structure might look like this:
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my_dagster_module/     __init__.py     repository.py     assets.py     jobs.py     ...
2. Use the 
-m
option with the module name: When running the job, use the 
-m
option followed by the module name, not the file name. For example:
Copy code
shell dagster job execute -m my_dagster_module -c config.yaml
In this command: - 
-m my_dagster_module
specifies the Python module where your repository is defined. Replace 
my_dagster_module
with the actual name of your module. - 
-c config.yaml
specifies the configuration file that contains the 
property_id
. 3. Check your 
workspace.yaml
: If you're using a 
workspace.yaml
file to load your repository, make sure it's correctly pointing to your module. Here's an example `workspace.yaml`:
Copy code
yaml load_from:   - python_module:       module_name: my_dagster_module
Replace 
my_dagster_module
with the actual name of your module. Make sure that your module is correctly installed or available in your Python environment, and that you're running the Dagster CLI command from a directory where the module can be imported. If you're still encountering issues, you may need to adjust your Python path or check for typos in your module name or 
workspace.yaml
configuration.
v
@Scout return fn(context, **args_to_pass) if context_arg_provided else fn(**args_to_pass) dagster_params/assets.py", line 18, in my_asset asset_spec=AssetSpec(key=asset_key, group_name="ETL"), got this eror for the code from dagster import asset from dagster_embedded_elt.sling import SlingMode, build_sling_asset from dagster_params.resource import file_paths_resource # Define an asset that uses the file_paths_resource @asset(required_resource_keys={"file_paths"}) def my_asset(context): # Access the file paths resource from the context file_info_list = context.resources.file_paths # Generate assets using the file info list asset_defs = [] for file_info in file_info_list: file_path, file_name, table_name = file_info full_file_path = f"{file_path}/{file_name}" asset_key = table_name.replace('.', '_').replace('-', '_') sling_asset = build_sling_asset( sling_resource_key="sling_sql", asset_spec=AssetSpec(key=asset_key, group_name="ETL"), source_stream=full_file_path, target_object=table_name, mode=SlingMode.FULL_REFRESH, ) asset_defs.append(sling_asset) return asset_defs
s
The error message you're encountering suggests that there's an issue with how the 
build_sling_asset
function is being called or how the 
AssetSpec
is being used. The error message seems to be incomplete, so I can't provide a precise solution, but I can offer some general advice that might help you resolve the issue. First, ensure that the 
build_sling_asset
function from the 
dagster_embedded_elt.sling
module is being used correctly according to the documentation. The function signature and the expected arguments should match how you're calling it in your code. Second, the 
AssetSpec
object you're creating seems to be used as an argument to 
build_sling_asset
. Make sure that the 
AssetSpec
class is imported correctly and that you're using it as expected by 
build_sling_asset
. Here's a general structure of how you might define an asset using `build_sling_asset`:
Copy code
python
from dagster import asset
from dagster_embedded_elt.sling import SlingMode, build_sling_asset, AssetSpec

@asset(required_resource_keys={"file_paths"})
def my_asset(context):
    file_info_list = context.resources.file_paths
    asset_defs = []
    for file_info in file_info_list:
        file_path, file_name, table_name = file_info
        full_file_path = f"{file_path}/{file_name}"
        asset_key = table_name.replace('.', '_').replace('-', '_')
        sling_asset = build_sling_asset(
            sling_resource_key="sling_sql",
            asset_spec=AssetSpec(key=asset_key, group_name="ETL"),
            source_stream=full_file_path,
            target_object=table_name,
            mode=SlingMode.FULL_REFRESH,
        )
        asset_defs.append(sling_asset)
    return asset_defs
If you're still encountering issues, here are some steps you can take to debug the problem: 1. Check the documentation: Ensure that you're using the 
build_sling_asset
function and the 
AssetSpec
class according to the latest documentation for the 
dagster_embedded_elt.sling
module. 2. Review the error message: Look at the full error message to understand where the issue might be occurring. The message will often contain information about the line number and the specific problem. 3. Validate the arguments: Make sure that all the arguments you're passing to 
build_sling_asset
are of the correct type and format. 4. Check for updates: If there have been recent updates to the 
dagster_embedded_elt
package, make sure you're using a version that is compatible with your code. 5. Seek support: If you're unable to resolve the issue, consider reaching out to the Dagster community for support. You can provide the full error message and the relevant code snippets to get more targeted help. Remember to always refer to the official Dagster documentation and ensure th
at your code is compatible with the version of Dagster and its related packages that you're using.
v
@Scout dagster - ERROR - __ASSET_JOB - 1ef30075-b741-4ac0-9682-bc5e461b45bb - 2130521 - my_asset - STEP_FAILURE - Execution of step "my_asset" failed. dagster._core.errors.DagsterInvariantViolationError: Object [<dagster._core.definitions.assets.AssetsDefinition object at 0x7f4f5be8d1f0>] is not picklable. You are currently using the fs_io_manager and the multi_or_in_process_executor. You will need to use a different io manager to continue using this output. For example, you can use the mem_io_manager with the in_process_executor. For more information on io managers, visit https://docs.dagster.io/concepts/io-management/io-managers For more information on executors, vist https://docs.dagster.io/deployment/executors#overview got this err
s
The error message you're encountering indicates that the 
AssetsDefinition
object is not picklable, which is a requirement when using the 
fs_io_manager
with the 
multi_or_in_process_executor
. This is because the 
fs_io_manager
relies on the ability to pickle objects to store them on the filesystem, and the 
multi_or_in_process_executor
may run steps in separate processes where pickling is necessary to pass objects between processes. To resolve this issue, you have a few options: 1. Use a different IO manager: If the data you're working with is not picklable, you can use an IO manager that doesn't rely on pickling. For example, the 
mem_io_manager
stores objects in memory and doesn't require them to be picklable. However, this is only suitable for single-process execution or when all steps are running within the same process. 2. Make your objects picklable: If possible, modify the objects you're working with to ensure they are picklable. This might involve changing how you define your assets or the data structures you're using. 3. Use a custom IO manager: If the built-in IO managers don't meet your needs, you can define a custom IO manager that handles your specific data types appropriately. Here's an example of how you might switch to using the 
mem_io_manager
for a job:
Copy code
python from dagster import job, mem_io_manager  @job(resource_defs={"io_manager": mem_io_manager}) def my_job():     my_asset()
In this example, the 
mem_io_manager
is specified as the IO manager for 
my_job
. This change means that the job will store intermediate values in memory rather than on the filesystem, avoiding the need for pickling. Please note that using the 
mem_io_manager
is only appropriate for certain scenarios, such as when running jobs in a single process. For distributed execution or when using multiple processes, you'll need to ensure that your data can be pickled or use a different IO manager that is compatible with your data types. For more information on IO managers and executors, please refer to the Dagster documentation on [IO Managers](https://docs.dagster.io/concepts/io-management/io-managers) and [Executors](https://docs.dagster.io/deployment/executors#overview). If you need further assistance, consider reaching out to the Dagster community for support.
v
@Scout # jobs.py from dagster import job from .assets import my_asset from dagster_params.resource import file_paths_resource, sling_sql # Define the job that includes the asset and specifies the necessary resources @job(resource_defs={ "sling_sql": sling_sql, "file_paths": file_paths_resource, }) def my_job(): my_asset() so we have this code in job how can we import iomanager init
s
To import and use an IO manager in your job, you first need to decide which IO manager you want to use based on your requirements. Dagster provides several built-in IO managers, such as 
fs_io_manager
for filesystem-based storage and 
mem_io_manager
for in-memory storage. Once you've chosen an IO manager, you can import it and attach it to your job definition in the 
resource_defs
dictionary. Here's an example of how to import and use the `fs_io_manager`:
Copy code
python # jobs.py from dagster import job, fs_io_manager from .assets import my_asset from dagster_params.resource import file_paths_resource, sling_sql  # Define the job that includes the asset and specifies the necessary resources @job(resource_defs={     "io_manager": fs_io_manager,  # Attach the filesystem IO manager     "sling_sql": sling_sql,     "file_paths": file_paths_resource, }) def my_job():     my_asset()
In this example, 
fs_io_manager
is used as the default IO manager for all assets within 
my_job
. If you need to configure the 
fs_io_manager
with specific settings, such as a custom base directory, you can do so using the 
.configured
method:
Copy code
python from dagster import fs_io_manager  # Configure the filesystem IO manager with a custom base directory configured_fs_io_manager = fs_io_manager.configured({"base_dir": "/path/to/base/dir"})  # Then use the configured IO manager in your job definition @job(resource_defs={     "io_manager": configured_fs_io_manager,     # ... other resources ... }) def my_job():     # ...
Replace 
"/path/to/base/dir"
with the actual path where you want the IO manager to store output files. If you're using a custom IO manager or a different built-in IO manager, the import path and configuration method may vary. Always refer to the documentation for the specific IO manager you're using for detailed instructions on how to import and configure it. Remember to ensure that the IO manager you choose is appropriate for the types of data your assets are producing and consuming, and that it meets the requirements of your deployment environment.
v
@Scout Using I/O managers with Software-defined Assets# Applying I/O managers to assets# By default, materializing an asset named 
my_asset
will pickle it to a local file named 
my_asset
. The directory that file lives underneath is determined by the rules in 
FilesytemIOManager
. I/O managers enable fully overriding this behavior and storing asset contents in any way you wish - e.g. writing them as tables in a database or as objects in a cloud object store such as s3. You can use one of Dagster's built-in I/O managers, or you can write your own. To apply an I/O manager to a set of assets, you can use `Definitions`:
Copy code
from dagster_aws.s3 import S3PickleIOManager, S3Resource

from dagster import Definitions, asset


@asset
def upstream_asset():
    return [1, 2, 3]


@asset
def downstream_asset(upstream_asset):
    return upstream_asset + [4]


defs = Definitions(
    assets=[upstream_asset, downstream_asset],
    resources={
        "io_manager": S3PickleIOManager(
            s3_resource=S3Resource(), s3_bucket="my-bucket"
        ),
__ASSET_JOB - 1eebf2da-f3f4-4cb1-8bb3-1a645b7cae20 - 2131417 - my_asset - STEP_FAILURE - Execution of step "my_asset" failed. dagster._core.errors.DagsterInvariantViolationError: Object [<dagster._core.definitions.assets.AssetsDefinition object at 0x7f68dd00d1c0>] is not picklable. You are currently using the fs_io_manager and the multi_or_in_process_executor. You will need to use a different io manager to continue using this output. For example, you can use the mem_io_manager with the in_process_executor. For more information on io managers, visit https://docs.dagster.io/concepts/io-management/io-managers For more information on executors, vist https://docs.dagster.io/deployment/executors#overview got this error by providing the example give the code from dagster import job, fs_io_manager from .assets import my_asset from dagster_params.resource import file_paths_resource, sling_sql # Define the job that includes the asset and specifies the necessary resources @job(resource_defs={ "io_manager": fs_io_manager, # Attach the filesystem IO manager "sling_sql": sling_sql, "file_paths": file_paths_resource, }) def my_job(): my_asset() we provide job code here so get the code and the resource codeis from dagster import get_dagster_logger, resource, ExperimentalWarning from dagster_embedded_elt.sling import SlingResource, SlingSourceConnection, SlingTargetConnection from sqlalchemy import create_engine, text from dagster_params.params import db_connection_params import warnings from sqlalchemy.exc import SQLAlchemyError from sqlalchemy import text warnings.filterwarnings("ignore", category=ExperimentalWarning) logger = get_dagster_logger() @resource def file_paths_resource(context): property_id = context.resource_config['property_id'] if property_id is None: raise Exception("Environment variable 'PROPERTY_ID' is not set") property_id = int(property_id) try: engine = create_engine( f"mssql+pyodbc://{db_connection_params['user']}:{db_connection_params['password']}@" f"{db_connection_params['host']}:{db_connection_params['port']}/{db_connection_params['database']}?" f"driver=ODBC+Driver+17+for+SQL+Server" ) with engine.connect() as conn: query = text(""" SELECT propertyvalue, propertynameid FROM app.PropertyConfig WHERE PropertyId IN (SELECT id FROM app.Property WHERE id=1) """) result = conn.execute(query) # Initialize variables to store path, filename, and tablename path, filename, tablename = None, None, None # Iterate over the results and assign values based on propertynameid for row in result: if row[1] == 1: # Assuming 1 is the ID for the path path = row[0] elif row[1] == 2: # Assuming 2 is the ID for the filename filename = row[0] + '.csv' # Append '.csv' to the filename elif row[1] == 3: # Assuming 3 is the ID for the tablename tablename = 'app.' + row[0] # Prepend 'app.' to the tablename # Check if all values are present if not all([path, filename, tablename]): raise ValueError("Missing path, filename, or tablename from the database results.") # Return a list of tuples containing the path, filename, and tablename return [(path, filename, tablename)] except SQLAlchemyError as e: logger.error(f"An error occurred while fetching file paths: {e}") raise except ValueError as e: logger.error(f"An error occurred while processing file paths: {e}") raise # Define the SlingResource for SQL Server using the connection details from params.py sling_sql = SlingResource( source_connection=SlingSourceConnection(type="file"), target_connection=SlingTargetConnection(**db_connection_params) )
s
Based on the documentation, to apply an IO manager to your assets within a job, you need to define the job with the appropriate 
resource_defs
that include your chosen IO manager. Given the error message you encountered, it seems that the default 
fs_io_manager
is not suitable because the objects you're working with are not picklable. Instead, you can use the 
mem_io_manager
for in-memory storage, which does not require objects to be picklable. Here's how you can modify your job definition to use the `mem_io_manager`:
Copy code
python from dagster import job, mem_io_manager from .assets import my_asset from dagster_params.resource import file_paths_resource, sling_sql  # Define the job that includes the asset and specifies the necessary resources @job(resource_defs={     "io_manager": mem_io_manager,  # Use the in-memory IO manager     "sling_sql": sling_sql,     "file_paths": file_paths_resource, }) def my_job():     my_asset()
In this job definition, we've replaced 
fs_io_manager
with 
mem_io_manager
. This change should resolve the issue with non-picklable objects, as the 
mem_io_manager
does not require serialization to the filesystem. Please note that using the 
mem_io_manager
means that the intermediate results will not be persisted between job runs, as they are only stored in memory during execution. This is suitable for development and testing but may not be appropriate for production environments where persistence is required. For production scenarios where persistence is necessary, you would need to ensure that your objects are picklable or use a custom IO manager that can handle your specific object types. If you need to store objects in a cloud object store like S3, you can use the 
S3PickleIOManager
as shown in the documentation example you provided. Remember to configure your resources and IO managers according to the environment in which you're running your jobs. For more information on IO managers and how to configure them, please refer to the [Dagster documentation on IO managers](https://docs.dagster.io/concepts/io-management/io-managers).
v
@Scout using PickledObjectFilesystemIOManager... 2024-03-12 104522 +0000 - dagster - ERROR - __ASSET_JOB - 851a6e50-4058-49f4-a1ec-c8ef6c0ed8ff - 2131616 - my_asset - STEP_FAILURE - Execution of step "my_asset" failed. dagster._core.errors.DagsterInvariantViolationError: Object [<dagster._core.definitions.assets.AssetsDefinition object at 0x7f94fe9e01c0>] is not picklable. You are currently using the fs_io_manager and the multi_or_in_process_executor. You will need to use a different io manager to continue using this output. For example, you can use the mem_io_manager with the in_process_executor. again getting the same issue and where can i see this in the server as im running dagster job execute -m dagster_params -c run.yaml
s
The error you're encountering indicates that the 
AssetsDefinition
object generated by your asset is not picklable, which is a requirement when using the 
fs_io_manager
(or 
PickledObjectFilesystemIOManager
) with an executor that runs steps in separate processes, such as the 
multi_or_in_process_executor
. To resolve this issue, you have two main options: 1. Use the 
mem_io_manager
: This IO manager stores objects in memory and does not require them to be picklable. However, it is only suitable for single-process execution or when all steps are running within the same process. Here's how you can switch to using the `mem_io_manager`:
Copy code
python from dagster import job, mem_io_manager from .assets import my_asset from dagster_params.resource import file_paths_resource, sling_sql  @job(resource_defs={     "io_manager": mem_io_manager,  # Use the in-memory IO manager     "sling_sql": sling_sql,     "file_paths": file_paths_resource, }) def my_job():     my_asset()
2. Make your objects picklable: If you need to use the 
fs_io_manager
for persistence and distributed execution, you'll need to ensure that the objects you're working with are picklable. This might involve changing how you define your assets or the data structures you're using. To see the logs and error messages on the server when running the job, you can check the following: - Dagit UI: If you have Dagit running, you can navigate to the Dagit UI in your web browser to see the logs and error messages for your job runs. The URL is typically 
<http://localhost:3000>
unless you've configured it to run on a different host or port. - Log files: If you have configured Dagster to write logs to a file, you can check the log files on the server where Dagster is running. - Standard output: If you're running the job from a terminal or command line, the logs and error messages will be printed to the standard output. - Dagster event log: Dagster maintains an event log for each run, which includes detailed information about the execution of the job. You can query this event log using Dagster's GraphQL API, or view it in the Dagit UI. Remember that using the 
mem_io_manager
means that the intermediate results will not be persisted between job runs. If persistence is required, you will need to ensure that your objects are picklable or use a custom IO manager that can handle your specific object types. For more information on IO managers and executors, please refer to the [Dagster documentation on IO managers](https://docs.dagster.io/concepts/io-management/io-managers) and [executors](https://docs.dagster.io/deployment/executors#overview). If you need further assistance, consider reaching out to the Dagster community for support.
v
@Scout The above exception was caused by the following exception: AttributeError: Can't pickle local object 'build_sling_asset.<locals>.sync again getting the same issue and im running dagster_etl$ dagster job execute -m dagster_params -c config.yaml and the codes are ##init.py from dagster import Definitions, load_assets_from_modules from dagster_params import assets # Import the dynamically generated asset # Load assets from the assets module all_assets = load_assets_from_modules([assets]) # Define the Dagster definitions defs = Definitions( resources={ "sling_sql": resource.sling_sql, "file_paths": resource.file_paths_resource, # Add the file paths resource }, assets=all_assets, )#asset from dagster import asset, AssetSpec from dagster_embedded_elt.sling import SlingMode, build_sling_asset from dagster_params.resource import file_paths_resource # Define an asset that uses the file_paths_resource @asset(required_resource_keys={"file_paths"}) def my_asset(context): file_info_list = context.resources.file_paths asset_defs = [] for file_info in file_info_list: file_path, file_name, table_name = file_info full_file_path = f"{file_path}/{file_name}" asset_key = table_name.replace('.', '_').replace('-', '_') sling_asset = build_sling_asset( sling_resource_key="sling_sql", asset_spec=AssetSpec(key=asset_key, group_name="ETL"), source_stream=full_file_path, target_object=table_name, mode=SlingMode.FULL_REFRESH, ) asset_defs.append(sling_asset) return asset_defs ##resource.py from dagster import get_dagster_logger, resource, ExperimentalWarning from dagster_embedded_elt.sling import SlingResource, SlingSourceConnection, SlingTargetConnection from sqlalchemy import create_engine, text from dagster_params.params import db_connection_params import warnings from sqlalchemy.exc import SQLAlchemyError from sqlalchemy import text warnings.filterwarnings("ignore", category=ExperimentalWarning) logger = get_dagster_logger() @resource def file_paths_resource(context): property_id = context.resource_config['property_id'] if property_id is None: raise Exception("Environment variable 'PROPERTY_ID' is not set") property_id = int(property_id) try: engine = create_engine( f"mssql+pyodbc://{db_connection_params['user']}:{db_connection_params['password']}@" f"{db_connection_params['host']}:{db_connection_params['port']}/{db_connection_params['database']}?" f"driver=ODBC+Driver+17+for+SQL+Server" ) with engine.connect() as conn: query = text(""" SELECT propertyvalue, propertynameid FROM app.PropertyConfig WHERE PropertyId IN (SELECT id FROM app.Property WHERE id=1) """) result = conn.execute(query) # Initialize variables to store path, filename, and tablename path, filename, tablename = None, None, None # Iterate over the results and assign values based on propertynameid for row in result: if row[1] == 1: # Assuming 1 is the ID for the path path = row[0] elif row[1] == 2: # Assuming 2 is the ID for the filename filename = row[0] + '.csv' # Append '.csv' to the filename elif row[1] == 3: # Assuming 3 is the ID for the tablename tablename = 'app.' + row[0] # Prepend 'app.' to the tablename # Check if all values are present if not all([path, filename, tablename]): raise ValueError("Missing path, filename, or tablename from the database results.") # Return a list of tuples containing the path, filename, and tablename return [(path, filename, tablename)] except SQLAlchemyError as e: logger.error(f"An error occurred while fetching file paths: {e}") raise except ValueError as e: logger.error(f"An error occurred while processing file paths: {e}") raise # Define the SlingResource for SQL Server using the connection details from params.py sling_sql = SlingResource( source_connection=SlingSourceConnection(type="file"), target_connection=SlingTargetConnection(**db_connection_params) )##job.py from dagster import job, mem_io_manager from .assets import my_asset from dagster_params.resource import file_paths_resource, sling_sql @job(resource_defs={ "io_manager": mem_io_manager, # Use the in-memory IO manager "sling_sql": sling_sql, "file_paths": file_paths_resource, }) def my_job(): my_asset()resources: file_paths: config: property_id: 1 # Replace with the desired property ID and we have parasm file for connection details of sql so now where im getting error and im not able to see the error in the server
s
The error message you're encountering, which mentions that an object is not picklable, suggests that there's a problem with serializing the 
AssetsDefinition
object for storage. This can happen when using an IO manager that relies on pickling, such as the 
fs_io_manager
, with objects that are not inherently serializable (picklable). The 
AttributeError: Can't pickle local object 'build_sling_asset.<locals>.sync
specifically points to the 
build_sling_asset
function, which seems to be creating a local function or object that cannot be pickled. This is often the case with dynamically generated functions or when using closures that reference local variables. Since you've already switched to using the 
mem_io_manager
, which stores objects in memory and doesn't require them to be picklable, the issue might be related to how the assets are being defined or used within the job. To debug this issue, you can try the following steps: 1. Check the 
build_sling_asset
function: Ensure that the 
build_sling_asset
function from the 
dagster_embedded_elt.sling
module is being used correctly. If this function is creating non-picklable objects, you might need to refactor the code or use a different approach to define your assets. 2. Review the asset definitions: Look at how your assets are defined and ensure that they don't rely on non-picklable objects. If your assets are using functions or objects that can't be serialized, you'll need to modify those definitions. 3. Check the execution environment: Ensure that the environment where you're running the job has the necessary permissions and configurations to execute the job and access the resources it needs. 4. View logs and error messages: To see the logs and error messages on the server, you can: - Access the Dagit UI in your web browser, typically at 
<http://localhost:3000>
, to view the logs and error messages for your job runs. - Check the log files on the server where Dagster is running if you have configured logging to a file. - Look at the standard output if you're running the job from a terminal or command line. - Query the Dagster event log using Dagster's GraphQL API or view it in the Dagit UI. 5. Simplify the asset: As a test, try simplifying the asset to return a basic, easily picklable object, such as a string or a number, to see if the problem is with the asset content. 6. Reach out for support: If you're unable to resolve the issue, consider reaching out to the Dagster community for support. Provide the full error message and relevant code snippets to get more targeted help. Remember to refer to the [Dagster documentation on IO managers](https://docs.dagster.io/concepts/io-management/io-managers) for more information on how to configure and use them.
If you need further assistance, the Dagster community is a good resource for getting help with your specific setup.
v
@Scout im only getting tht eror so modify the code annd get me ?
s
The error you're encountering suggests that there's an issue with the serialization of objects created by the 
build_sling_asset
function. Since the 
mem_io_manager
did not resolve the issue, it's possible that the problem lies with how the assets are being constructed or with the 
build_sling_asset
function itself. To address this, you can try the following steps: 1. Refactor the Asset Definitions: Instead of creating assets dynamically within the 
my_asset
function, define each asset explicitly using the 
@asset
decorator. This can help ensure that the assets are more easily serializable. 2. Simplify the Asset: Temporarily simplify the asset to return a basic, easily picklable object to confirm that the issue is with the asset content. Here's an example of how you might refactor the asset definition:
Copy code
python
from dagster import asset
from dagster_embedded_elt.sling import SlingMode, build_sling_asset, AssetSpec

# Define an asset that uses the file_paths_resource
@asset(required_resource_keys={"file_paths"})
def my_asset(context):
    file_info_list = context.resources.file_paths
    # Temporarily return a simple value to test serialization
    return "simple test value"

    # Comment out the dynamic asset generation for now
    # asset_defs = []
    # for file_info in file_info_list:
    #     file_path, file_name, table_name = file_info
    #     full_file_path = f"{file_path}/{file_name}"
    #     asset_key = table_name.replace('.', '_').replace('-', '_')
    #     sling_asset = build_sling_asset(
    #         sling_resource_key="sling_sql",
    #         asset_spec=AssetSpec(key=asset_key, group_name="ETL"),
    #         source_stream=full_file_path,
    #         target_object=table_name,
    #         mode=SlingMode.FULL_REFRESH,
    #     )
    #     asset_defs.append(sling_asset)
    # return asset_defs
By returning a simple string, you can test whether the issue is with the serialization of the assets. If this change resolves the error, the problem is likely with the objects being created by 
build_sling_asset
. 3. Review the 
build_sling_asset
Function: If the 
build_sling_asset
function is creating non-picklable objects, you may need to refactor the function or avoid using it to create assets dynamically. Instead, define each asset explicitly and ensure that all objects involved are picklable. 4. Check for Nested Functions: If 
build_sling_asset
uses nested functions or closures, these may not be picklable. Refactor the code to avoid such patterns. 5. Run the Job: After making the changes, try running the job again:
Copy code
shell
dagster job execute -m dagster_params -c config.yaml
If the job runs successfully with the simplified asset, you'll need to incrementally add complexity back to th
e asset definition until you identify the specific part of the code that causes the serialization issue. Remember to refer to the [Dagster documentation on IO managers](https://docs.dagster.io/concepts/io-management/io-managers) for more information on how to configure and use them. If you need further assistance, the Dagster community is a good resource for getting help with your specific setup.
v
@Scout dagster job execute -m dagster_params -c config.yaml -p 3005 Usage: dagster job execute [OPTIONS] Try "dagster job execute -h" for help. Error: no such option: -p
s
The error message you're seeing indicates that the 
-p
option is not recognized by the 
dagster job execute
command. The 
-p
flag is typically used to specify a port when running services like Dagit, the Dagster web interface, but it is not used for executing jobs. When you want to execute a Dagster job using the CLI, you should specify the module name with 
-m
and the configuration file with 
-c
, as you have done. There is no need to specify a port because job execution does not require opening a network port; it runs within the local execution environment. Here's the correct way to execute a job using the Dagster CLI:
Copy code
shell dagster job execute -m dagster_params -c config.yaml
If you were trying to specify a port for some other reason, such as running Dagit or a gRPC server, you would need to use the appropriate command for that service and include the 
-p
option there. For example, to run Dagit on a specific port:
Copy code
shell dagit -p 3005
To run a gRPC server on a specific port:
Copy code
shell dagster api grpc -p 3005
However, these commands are separate from job execution and are used for different purposes, such as serving the repository to Dagit or other Dagster tooling. If you're trying to connect to a specific instance of Dagit or a gRPC server that is running on a different port, you would configure that in your workspace file (
workspace.yaml
) rather than passing a port to the 
dagster job execute
command.
v
@Scout so get the workspace yaml file for fixing the port num
s
To specify a port number for a Dagster gRPC server in a 
workspace.yaml
file, you would define the server configuration under the 
load_from
key. Here's an example of how you might set up a 
workspace.yaml
file to connect to a gRPC server running on port 3005:
Copy code
yaml load_from:   - grpc_server:       host: localhost       port: 3005       location_name: "my_grpc_server_location"
In this example: - 
grpc_server
indicates that you're loading a code location from a running gRPC server. - 
host
is set to 
localhost
, which is where the gRPC server is running. If it's running on a different machine, you would specify the appropriate hostname or IP address. - 
port
is set to 
3005
, which is the port number where the gRPC server is listening. - 
location_name
is a user-friendly name for the code location, which will appear in Dagit. With this 
workspace.yaml
file in place, you can use the 
-w
flag to specify the workspace file when running Dagster commands, such as starting Dagit:
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shell dagit -w workspace.yaml
Or when executing a job:
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shell dagster job execute -w workspace.yaml
The 
-w
flag tells Dagster to load the workspace configuration from the specified file, which includes the connection details for the gRPC server. Please ensure that the gRPC server is running and listening on the specified port before attempting to connect to it with Dagster tooling. If you need to start a gRPC server on a specific port, you can use the 
dagster api grpc
command with the 
-p
flag, as mentioned earlier.
v
@Scout so now im having runspace yaml and workspace yaml so how to run the module now?
s
If you have both a 
runspace.yaml
and a 
workspace.yaml
file, it's important to understand the purpose of each: - 
workspace.yaml
is used to configure how Dagster loads your repository or repositories, which can include local file paths, Python modules, or gRPC servers. - 
runspace.yaml
is not a standard Dagster configuration file. It might be a custom configuration file you're using for a specific purpose, but it's not recognized by Dagster by default. To run a Dagster job using a 
workspace.yaml
file, you would execute the job with the 
-w
flag to specify the workspace configuration. Here's how you can do it:
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shell dagster job execute -w path/to/workspace.yaml
In this command, replace 
path/to/workspace.yaml
with the actual path to your 
workspace.yaml
file. If you have a custom 
runspace.yaml
file that you need to use for some reason, you would need to incorporate its configuration into your Dagster setup manually, as Dagster does not recognize a file with that name by default. You might need to write custom scripts or tooling to parse and apply the configurations from your 
runspace.yaml
file. If 
runspace.yaml
is meant to be a 
workspace.yaml
file with a different name, you would simply use the 
-w
flag with the correct file path when running your Dagster commands, as shown above. Remember to ensure that your 
workspace.yaml
file is correctly configured to point to the code locations where your Dagster assets and jobs are defined. If you're unsure about the configuration, you can refer to the [Dagster documentation on workspace files](https://docs.dagster.io/concepts/code-locations/workspace-files) for more information and examples.
v
@Scout in the runspace ysml we have resources: file_paths: config: property_id: 1 # Replace with the desired property ID so can we provide port number here? so ifnot how can i run the runconfig along with workspace yaml and i have module to run ?
s
The 
runspace.yaml
file you're referring to seems to contain run configuration for resources, specifically for the 
file_paths
resource with a 
property_id
set to 
1
. In Dagster, run configuration is typically provided when executing a job to configure resources, ops, and other runtime parameters. To use this configuration when executing a job, you should rename the file to a standard name recognized by Dagster, such as 
config.yaml
, and then pass it to the 
dagster job execute
command using the 
-c
flag. Here's how you can do it: 1. Rename 
runspace.yaml
to 
config.yaml
(or another appropriate name). 2. Execute the job with the following command:
Copy code
shell dagster job execute -m dagster_params -c path/to/config.yaml
In this command, replace 
path/to/config.yaml
with the actual path to your configuration file. The 
config.yaml
file should be structured to match the expected configuration schema for your job. For example, if you have a resource named 
file_paths
that requires a 
property_id
configuration, your 
config.yaml
might look like this:
Copy code
yaml resources:   file_paths:     config:       property_id: 1  # Replace with the desired property ID
This configuration will be passed to the 
file_paths
resource when the job is executed, allowing you to dynamically set the 
property_id
for each run. If you have additional configuration for other resources or for the job itself, you can include that in the 
config.yaml
file as well. Make sure to follow the schema expected by your job and resources. For more information on how to configure jobs and resources, you can refer to the [Dagster documentation on configuration](https://docs.dagster.io/concepts/configuration/config-schema).
No, you cannot specify a port number for the Dagster instance or a gRPC server within the 
resources
section of a run configuration file like 
config.yaml
or 
runspace.yaml
. The 
resources
section is used to configure resources for a job, such as database connections or other external services that your job might interact with during execution. If you need to specify a port number for a gRPC server in a Dagster workspace, you would do so in a 
workspace.yaml
file, not in the run configuration. The 
workspace.yaml
file is used to configure how Dagster loads your repository or repositories, which can include local file paths, Python modules, or gRPC servers. Here's an example of how you might set up a 
workspace.yaml
file to connect to a gRPC server running on a specific port:
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yaml load_from:   - grpc_server:       host: localhost       port: 3005       location_name: "my_grpc_server_location"
In this example, the 
port
key is set to 
3005
, which is the port number where the gRPC server is listening. When you run a job using the 
dagster job execute
command, you can specify the workspace configuration file with the 
-w
flag:
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shell dagster job execute -w path/to/workspace.yaml
This tells Dagster to load the workspace configuration from the specified file, which includes the connection details for the gRPC server. The run configuration file (e.g., 
config.yaml
) is then specified with the 
-c
flag to provide runtime configuration for the job:
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shell dagster job execute -w path/to/workspace.yaml -c path/to/config.yaml
Remember to ensure that your 
workspace.yaml
file is correctly configured to point to the code locations where your Dagster assets and jobs are defined, and that your run configuration file (
config.yaml
) matches the expected schema for your job and resources. For more information on workspace and run configuration, you can refer to the [Dagster documentation](https://docs.dagster.io/concepts/configuration/config-schema).
You cannot provide a port number in the 
resources
section of a run configuration file like 
runspace.yaml
(which you might rename to 
config.yaml
for clarity). The port number for a Dagster instance or a gRPC server is specified in the 
workspace.yaml
file, not in the run configuration. To run a Dagster job with both a workspace configuration and a run configuration, you would use the 
-w
flag to specify the workspace file and the 
-c
flag to specify the run configuration file when executing the job. Additionally, you use the 
-m
flag to specify the Python module that contains your job and assets. Here's how you can run the job with both configurations: 1. Ensure your 
workspace.yaml
file is correctly set up to point to your code locations. For example:
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yaml # workspace.yaml load_from:   - python_module:       module_name: my_dagster_module
2. Ensure your run configuration file (e.g., 
config.yaml
) is correctly set up with the necessary resource configurations. For example:
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yaml # config.yaml resources:   file_paths:     config:       property_id: 1
3. Execute the job using the Dagster CLI with the 
-w
, 
-c
, and 
-m
flags:
Copy code
shell dagster job execute -w path/to/workspace.yaml -c path/to/config.yaml -m my_dagster_module
In this command: - 
-w path/to/workspace.yaml
specifies the path to your workspace configuration file. - 
-c path/to/config.yaml
specifies the path to your run configuration file. - 
-m my_dagster_module
specifies the name of the Python module where your job and assets are defined. By using these flags together, you tell Dagster to load the code locations from the workspace file and apply the run configuration to the job execution. Make sure that the paths to the 
workspace.yaml
and 
config.yaml
files are correct and that the module name matches the actual name of your Python module. For more information on how to configure workspaces and run configurations, you can refer to the [Dagster documentation on workspaces](https://docs.dagster.io/concepts/code-locations/workspace-files) and [configuration](https://docs.dagster.io/concepts/configuration/config-schema).
v
Copy code
# workspace.yaml
load_from:
  - python_module:
      module_name: my_dagster_module
in the yaml file provide port 3005 @Scout
s
To specify a port number for a Dagster gRPC server in your 
workspace.yaml
file, you would use the 
grpc_server
key instead of 
python_module
. The 
grpc_server
key allows you to specify the host and port of a running gRPC server that serves your Dagster repository. Here's how you can modify your 
workspace.yaml
file to connect to a gRPC server running on port 3005:
Copy code
yaml # workspace.yaml load_from:   - grpc_server:       host: localhost       port: 3005       location_name: "my_grpc_server_location"
In this configuration: - 
grpc_server
indicates that you're loading a code location from a running gRPC server. - 
host
is set to 
localhost
, which is where the gRPC server is running. If it's running on a different machine, you would specify the appropriate hostname or IP address. - 
port
is set to 
3005
, which is the port number where the gRPC server is listening. - 
location_name
is a user-friendly name for the code location, which will appear in Dagit. Please ensure that the gRPC server is running and listening on the specified port before attempting to connect to it with Dagster tooling. If you need to start a gRPC server on a specific port, you can use the 
dagster api grpc
command with the 
-p
flag:
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shell dagster api grpc -p 3005
Once your gRPC server is running and your 
workspace.yaml
is configured, you can execute your job using the Dagster CLI with the 
-w
flag to specify the workspace file:
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shell dagster job execute -w path/to/workspace.yaml
Remember to replace 
path/to/workspace.yaml
with the actual path to your 
workspace.yaml
file. This command will instruct Dagster to load the repository from the gRPC server running on the specified port.
v
@Scout how can i keep all the files in one yaml file and run them instead of running module and provide property value in the terminal
s
To keep all configurations in one YAML file and run them without specifying a module or providing the property value in the terminal, you can combine your workspace configuration and run configuration into a single YAML file. However, Dagster typically expects these to be separate as they serve different purposes: - The 
workspace.yaml
file tells Dagster how to load your code. - The run configuration file (e.g., 
config.yaml
) tells Dagster how to execute a specific job, including setting resource configurations. Dagster does not natively support combining these into a single file. Instead, you would typically have a 
workspace.yaml
file that specifies how to load your repository and a separate run configuration file that you pass to the 
dagster job execute
command with the 
-c
flag. If you want to avoid passing the property value in the terminal and instead have it set automatically, you can hard-code the value in your run configuration file. Here's an example of how you might set up your 
config.yaml
file:
Copy code
yaml # config.yaml resources:   file_paths:     config:       property_id: 1  # The property ID is hard-coded here
Then, you can execute your job using the following command:
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shell dagster job execute -w path/to/workspace.yaml -c path/to/config.yaml
If you are looking to streamline the process and minimize the number of commands and files you need to manage, you could create a shell script that wraps the Dagster CLI command and specifies the paths to the 
workspace.yaml
and 
config.yaml
files. This way, you can run your job with a single command without manually entering the property value each time. Here's an example of what the shell script might look like:
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bash #!/bin/bash  # Set the path to your workspace.yaml file WORKSPACE_PATH="/path/to/workspace.yaml"  # Set the path to your config.yaml file CONFIG_PATH="/path/to/config.yaml"  # Execute the Dagster job dagster job execute -w $WORKSPACE_PATH -c $CONFIG_PATH
Save this script as 
run_job.sh
, make it executable with 
chmod +x run_job.sh
, and then you can run your job by simply executing 
./run_job.sh
. Please note that while this approach can help automate the process, it does not combine the workspace and run configurations into a single YAML file, as that is not supported by Dagster.
v
@Scout get the workspace yaml file template
s
A 
workspace.yaml
file in Dagster is used to specify code locations for Dagster to load. Here's a template for a 
workspace.yaml
file that you can use as a starting point:
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yaml # workspace.yaml  load_from:   - python_file:       relative_path: "path/to/my_file.py"       location_name: "my_location"   - python_module:       module_name: "my_dagster_module"       location_name: "my_module_location"   - python_package:       package_name: "my_dagster_package"       location_name: "my_package_location"   - grpc_server:       host: "localhost"       port: 3005       location_name: "my_grpc_server_location"
In this template: - 
python_file
specifies a code location from a Python file. Replace 
"path/to/my_file.py"
with the relative path to your Python file that contains your Dagster repository or 
Definitions
object. - 
python_module
specifies a code location from a Python module. Replace 
"my_dagster_module"
with the name of your Python module. - 
python_package
specifies a code location from an installed Python package. Replace 
"my_dagster_package"
with the name of your Python package. - 
grpc_server
specifies a code location served by a running gRPC server. Replace 
"localhost"
and 
3005
with the host and port where your gRPC server is running. The 
location_name
is an optional field that provides a user-friendly name for each code location. You can replace 
"my_location"
, 
"my_module_location"
, 
"my_package_location"
, and 
"my_grpc_server_location"
with names that make sense for your setup. You only need to include the sections relevant to your deployment in your 
workspace.yaml
file. For example, if you're not using a gRPC server, you can omit that section. Remember to place your 
workspace.yaml
file in a location that's accessible when you run Dagster commands, and refer to it using the 
-w
flag when executing jobs or starting Dagit. For more detailed instructions and examples, you can refer to the [Dagster documentation on workspace files](https://docs.dagster.io/concepts/code-locations/workspace-files).
v
@Scout
Copy code
# workspace.yaml

load_from:
  - python_file:
      relative_path: "path/to/my_file.py"
      location_name: "my_location"
  - python_module:
      module_name: "my_dagster_module"
      location_name: "my_module_location"
  - python_package:
      package_name: "my_dagster_package"
      location_name: "my_package_location"
  - grpc_server:
      host: "localhost"
      port: 3005
      location_name: "my_grpc_server_location"
i have 5 files and how to provide the yaml file
s
If you have five different files that you want to load as code locations in your 
workspace.yaml
, you can specify each one using either the 
python_file
, 
python_module
, or 
python_package
keys, depending on how your code is organized. If some of the files are part of the same Python module or package, you may only need to reference the module or package once. Here's an example of how you might set up a 
workspace.yaml
file with multiple code locations:
Copy code
yaml # workspace.yaml  load_from:   - python_file:       relative_path: "path/to/my_first_file.py"       location_name: "location_for_first_file"   - python_file:       relative_path: "path/to/my_second_file.py"       location_name: "location_for_second_file"   - python_module:       module_name: "my_third_module"       location_name: "location_for_third_module"   - python_module:       module_name: "my_fourth_module"       location_name: "location_for_fourth_module"   - python_package:       package_name: "my_fifth_package"       location_name: "location_for_fifth_package"
In this example: - The 
python_file
key is used to load code locations from individual Python files. You would replace 
"path/to/my_first_file.py"
and 
"path/to/my_second_file.py"
with the actual paths to your Python files. - The 
python_module
key is used to load code locations from Python modules. Replace 
"my_third_module"
and 
"my_fourth_module"
with the actual names of your Python modules. - The 
python_package
key is used to load code locations from an installed Python package. Replace 
"my_fifth_package"
with the name of your Python package. Each code location has a 
location_name
that provides a user-friendly name for the location. These names will appear in Dagit and can be used to identify the code locations. If you are using a gRPC server to serve any of these locations, you would include a 
grpc_server
entry with the appropriate host and port information. Make sure to adjust the paths and names to match your actual file structure and Python environment. Once your 
workspace.yaml
file is configured, you can use it with Dagster CLI commands by specifying it with the 
-w
flag. For more detailed instructions and examples, you can refer to the [Dagster documentation on workspace files](https://docs.dagster.io/concepts/code-locations/workspace-files).
v
@Scout ~/dagster_etl$ dagster job execute -w $WORKSPACE_PATH -c $CONFIG_PATH Usage: dagster job execute [OPTIONS] Try "dagster job execute -h" for help. Error: no such option: -w got lyk this
s
The error message you're seeing indicates that the 
-w
option is not recognized by the 
dagster job execute
command. This is because the 
-w
flag is not a valid option for the 
dagster job execute
command. Instead, the 
-w
flag is used with the 
dagit
command to specify a workspace file when launching the Dagit UI. To execute a job with Dagster, you should use the 
-c
flag to specify the run configuration file and the 
-m
or 
-f
flag to specify the module or file where your job is defined. Here's the correct way to execute a job using the Dagster CLI:
Copy code
shell dagster job execute -c path/to/config.yaml -m my_dagster_module
In this command: - 
-c path/to/config.yaml
specifies the path to your run configuration file. - 
-m my_dagster_module
specifies the name of the Python module where your job is defined. If your job is defined in a Python file rather than a module, you can use the 
-f
flag instead:
Copy code
shell dagster job execute -c path/to/config.yaml -f path/to/my_file.py
In this command: - 
-f path/to/my_file.py
specifies the path to the Python file where your job is defined. Make sure to replace 
path/to/config.yaml
, 
my_dagster_module
, and 
path/to/my_file.py
with the actual paths and names relevant to your project. If you have a 
workspace.yaml
file that specifies how to load your repository, you can use it when launching Dagit to view and interact with your repository in the UI:
Copy code
shell dagit -w path/to/workspace.yaml
In this command, 
-w path/to/workspace.yaml
specifies the path to your workspace configuration file. For more information on executing jobs and configuring workspaces, you can refer to the [Dagster documentation](https://docs.dagster.io/).
v
@Scout can we give yaml file in workspace yaml file?
s
In Dagster, the 
workspace.yaml
file is used to specify code locations, which tell Dagster where to find the definitions for your assets, jobs, resources, schedules, and sensors. The 
workspace.yaml
file itself does not directly include other YAML files, but it defines where Dagster should look for your code. If you want to use a YAML file to configure the execution of a specific job (a run configuration file), you would pass that YAML file to the 
dagster job execute
command using the 
-c
flag. The 
workspace.yaml
file and the run configuration YAML file serve different purposes and are used at different points in the Dagster execution process. Here's an example of how you might use a 
workspace.yaml
file and a separate run configuration YAML file: 1. Define your 
workspace.yaml
file to tell Dagster where to find your code:
Copy code
yaml # workspace.yaml load_from:   - python_module:       module_name: my_dagster_module       location_name: my_location
2. Create a separate run configuration YAML file (e.g., 
config.yaml
) to configure the execution of your job:
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yaml # config.yaml resources:   file_paths:     config:       property_id: 1
3. Execute your job using the Dagster CLI, specifying both the 
workspace.yaml
and the run configuration file:
Copy code
shell dagster job execute -w path/to/workspace.yaml -c path/to/config.yaml
In this command, 
-w path/to/workspace.yaml
specifies the path to your workspace configuration file, and 
-c path/to/config.yaml
specifies the path to your run configuration file. For more information on workspaces and run configurations, you can refer to the [Dagster documentation on workspaces](https://docs.dagster.io/concepts/code-locations/workspace-files) and [configuration](https://docs.dagster.io/concepts/configuration/config-schema).
v
@Scout dagster job execute -w path/to/workspace.yaml -c path/to/config.yaml in this i want tp add port also how can i do?
s
The 
dagster job execute
command does not have an option to specify a port because it is used to execute a job within the current environment where the Dagster CLI is invoked. The port number is typically specified when running services like Dagit or a Dagster gRPC server, which require network communication. If you need to specify a port for a Dagster gRPC server, you would do so in the 
workspace.yaml
file under the 
grpc_server
key. However, this is not related to the execution of a job via the CLI; it's for loading the repository in Dagit or other tools that connect to a gRPC server. Here's how you specify a port in the 
workspace.yaml
for a gRPC server:
Copy code
yaml # workspace.yaml load_from:   - grpc_server:       host: "localhost"       port: 3005       location_name: "my_grpc_server_location"
When you execute a job with the 
dagster job execute
command, Dagster uses the workspace configuration to load the repository, and the run configuration to execute the job with the specified resources and settings. The port number in the 
workspace.yaml
is used only if your workspace is set up to connect to a gRPC server. To execute a job with a workspace and run configuration, you would use the following command:
Copy code
shell dagster job execute -w path/to/workspace.yaml -c path/to/config.yaml
This command does not include a port number because it's not applicable to the job execution command. The job will run in the current environment, and Dagster will use the workspace configuration to load the code locations and the run configuration to execute the job. For more information on executing jobs and configuring workspaces, you can refer to the [Dagster documentation](https://docs.dagster.io/). If you have a specific scenario where you believe you need to specify a port number for job execution, please provide more details so we can assist you further.
v
@Scout ##init.py from dagster import Definitions, load_assets_from_modules from dagster_params import assets # Import the dynamically generated asset # Load assets from the assets module all_assets = load_assets_from_modules([assets]) # Define the Dagster definitions defs = Definitions( resources={ "sling_sql": resource.sling_sql, "file_paths": resource.file_paths_resource, # Add the file paths resource }, assets=all_assets, )##assets.py from dagster import asset, AssetSpec from dagster_embedded_elt.sling import SlingMode, build_sling_asset from dagster_params.resource import file_paths_resource # Import the resource function def generate_assets(file_info_list): asset_defs = [] for file_info in file_info_list: file_path, file_name, table_name = file_info full_file_path = f"{file_path}/{file_name}" asset_key = table_name.replace('.', '_').replace('-', '_') sling_asset = build_sling_asset( sling_resource_key="sling_sql", asset_spec=AssetSpec(key=asset_key, group_name="ETL"), source_stream=full_file_path, target_object=table_name, mode=SlingMode.FULL_REFRESH, ) asset_defs.append(sling_asset) return asset_defs # Use the file_paths_resource to get the file paths and generate assets file_info_list = file_paths_resource() assets = generate_assets(file_info_list)from dagster import get_dagster_logger, resource, ExperimentalWarning from dagster_embedded_elt.sling import SlingResource, SlingSourceConnection, SlingTargetConnection from sqlalchemy import create_engine, text from dagster_params.params import db_connection_params import warnings from sqlalchemy.exc import SQLAlchemyError from sqlalchemy import text warnings.filterwarnings("ignore", category=ExperimentalWarning) logger = get_dagster_logger() @resource def file_paths_resource(): try: engine = create_engine( f"mssql+pyodbc://{db_connection_params['user']}:{db_connection_params['password']}@" f"{db_connection_params['host']}:{db_connection_params['port']}/{db_connection_params['database']}?" f"driver=ODBC+Driver+17+for+SQL+Server" ) with engine.connect() as conn: query = text(""" SELECT propertyvalue, propertynameid FROM app.PropertyConfig WHERE PropertyId IN (SELECT id FROM app.Property WHERE id=1) """) result = conn.execute(query) # Initialize variables to store path, filename, and tablename path, filename, tablename = None, None, None # Iterate over the results and assign values based on propertynameid for row in result: if row[1] == 1: # Assuming 1 is the ID for the path path = row[0] elif row[1] == 2: # Assuming 2 is the ID for the filename filename = row[0] + '.csv' # Append '.csv' to the filename elif row[1] == 3: # Assuming 3 is the ID for the tablename tablename = 'app.' + row[0] # Prepend 'app.' to the tablename # Check if all values are present if not all([path, filename, tablename]): raise ValueError("Missing path, filename, or tablename from the database results.") # Return a list of tuples containing the path, filename, and tablename return [(path, filename, tablename)] except SQLAlchemyError as e: logger.error(f"An error occurred while fetching file paths: {e}") raise except ValueError as e: logger.error(f"An error occurred while processing file paths: {e}") raise # Define the SlingResource for SQL Server using the connection details from params.py sling_sql = SlingResource( source_connection=SlingSourceConnection(type="file"), target_connection=SlingTargetConnection(**db_connection_params) ) and we have params file tfr the connection details and i need propertyid = 1 to explicitly provide either through argv or through yaml file get the modified codes?
s
To provide the 
property_id
explicitly through a YAML configuration file or as an argument when running your Dagster job, you can modify the 
file_paths_resource
resource definition to accept configuration. Here's how you can modify the 
file_paths_resource
to accept 
property_id
from the run configuration:
Copy code
python # resource.py from dagster import get_dagster_logger, resource, Field, Int from dagster_embedded_elt.sling import SlingResource, SlingSourceConnection, SlingTargetConnection from sqlalchemy import create_engine, text from dagster_params.params import db_connection_params import warnings from sqlalchemy.exc import SQLAlchemyError  warnings.filterwarnings("ignore", category=ExperimentalWarning) logger = get_dagster_logger()  @resource(config_schema={"property_id": Field(Int, is_required=True)}) def file_paths_resource(context):     property_id = context.resource_config['property_id']     # ... rest of your code ...
With this change, you can now provide the 
property_id
in a YAML configuration file like this:
Copy code
yaml # config.yaml resources:   file_paths:     config:       property_id: 1
To run the job with this configuration, you would use the following command:
Copy code
shell dagster job execute -c path/to/config.yaml
If you want to provide 
property_id
through command-line arguments (argv), you would need to use environment variables, as Dagster does not support passing custom arguments directly through the CLI. Here's how you can modify your resource to accept an environment variable:
Copy code
python # resource.py import os  @resource def file_paths_resource(_):     property_id = os.getenv('PROPERTY_ID')     if property_id is None:         raise Exception("Environment variable 'PROPERTY_ID' is not set")     property_id = int(property_id)     # ... rest of your code ...
Then, you can set the environment variable before running your job:
Copy code
shell export PROPERTY_ID=1 dagster job execute
This will set the 
PROPERTY_ID
environment variable to 
1
, and the 
file_paths_resource
will use this value during job execution. Remember to replace 
path/to/config.yaml
with the actual path to your YAML configuration file when running the job.
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