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#ask-ai
Title
# ask-ai
b

Brandon Peebles

03/07/2024, 5:11 AM
give an example of how I can use the op factory pattern to create an op in two different code locations where I pass in different configs for each instance of the op. For instance, I want to use pass one string variable to one op and a different one to another. I also want to provide different resources to the ops from the factory.
s

Scout

03/07/2024, 5:11 AM
Certainly! The op factory pattern in Dagster allows you to create configurable ops that can be instantiated with different configurations and resources. Here's an example of how you can use this pattern to create two instances of an op with different string variables and resources:
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python from dagster import op, job, ResourceDefinition  # Define a resource that you want to pass to your ops class MyResource:     def __init__(self, resource_value):         self.resource_value = resource_value  # Define the op factory function def my_op_factory(string_var, resource_key):     @op(required_resource_keys={resource_key})     def my_op(context):         # Use the string_var and resource within the op         <http://context.log.info|context.log.info>(f"String variable: {string_var}")         <http://context.log.info|context.log.info>(f"Resource value: {context.resources[resource_key].resource_value}")         # Your op logic goes here      return my_op  # Create two different resource instances resource_1 = ResourceDefinition.hardcoded_resource(MyResource("resource_1_value"), "resource_1") resource_2 = ResourceDefinition.hardcoded_resource(MyResource("resource_2_value"), "resource_2")  # Instantiate the op with different configurations my_op_instance_1 = my_op_factory("string_1", "resource_1") my_op_instance_2 = my_op_factory("string_2", "resource_2")  # Define a job that uses the two op instances with their respective resources @job(resource_defs={"resource_1": resource_1, "resource_2": resource_2}) def my_job():     my_op_instance_1()     my_op_instance_2()  # Now you can execute the job, and it will run both ops with their specific configurations and resources
In this example,
my_op_factory
is a function that creates a new instance of an op with a given string variable and a resource key. The
required_resource_keys
parameter in the
@op
decorator specifies which resources the op requires. We then create two different resources,
resource_1
and
resource_2
, each with its own value. We instantiate the op twice using
my_op_factory
, passing in different string variables and resource keys for each instance. Finally, we define a job that includes both op instances and specifies the resources to use for each one. When you run
my_job
, Dagster will execute both instances of the op, each with its own string variable and resource.
b

Brandon Peebles

03/07/2024, 5:14 AM
@Scout can you give an example using the python config system instead?
s

Scout

03/07/2024, 5:14 AM
Certainly! In Dagster, you can use the Python config system to create configurable ops (now referred to as assets) with different configurations. Below is an example of how you can define a configurable asset using a Python class for the configuration schema, and then instantiate this asset with different configurations and resources. First, let's define a resource and a configuration schema class:
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python
from dagster import resource, Field, AssetIn, AssetOut, asset

# Define a resource with a simple configuration schema
@resource(config_schema={"resource_param": str})
def my_resource(context):
    return context.resource_config["resource_param"]

# Define a configuration schema class for your asset
class MyAssetConfig:
    def __init__(self, my_param: str):
        self.my_param = my_param
Next, we'll create an asset factory function that takes a configuration object and a resource key:
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python
# Define the asset factory function
def my_asset_factory(config: MyAssetConfig, resource_key: str):
    @asset(required_resource_keys={resource_key}, config_schema=MyAssetConfig)
    def my_asset(context):
        # Use the config and resource within the asset
        <http://context.log.info|context.log.info>(f"Config parameter: {config.my_param}")
        <http://context.log.info|context.log.info>(f"Resource value: {context.resources[resource_key]}")
        # Your asset logic goes here

    return my_asset
Now, let's instantiate the asset with different configurations and resources:
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python
# Instantiate the asset with different configurations
my_asset_instance_1 = my_asset_factory(MyAssetConfig(my_param="string_1"), "my_resource")
my_asset_instance_2 = my_asset_factory(MyAssetConfig(my_param="string_2"), "my_resource")

# Define a job that uses the two asset instances with their respective resources
@job(resource_defs={"my_resource": my_resource})
def my_job():
    my_asset_instance_1()
    my_asset_instance_2()
Finally, you can execute the job with specific run configurations for each asset:
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python
from dagster import execute_job

# Execute the job with specific configurations for each asset
result = execute_job(
    my_job,
    run_config={
        "resources": {
            "my_resource": {"config": {"resource_param": "resource_value"}}
        },
        "ops": {
            "my_asset_instance_1": {"config": {"my_param": "string_1"}},
            "my_asset_instance_2": {"config": {"my_param": "string_2"}},
        },
    },
)
In this example,
my_asset_factory
is a function that creates a new instance of an asset with a given configuration object and a resource key. The
required_resource_keys
parameter in the
@asset
decorator specifies which resources the asset requires. We then create two different instances of the asset, each with its own configuration. When y
ou run
my_job
with the provided
run_config
, Dagster will execute both instances of the asset, each with its own configuration and the shared resource.
b

Brandon Peebles

03/07/2024, 5:39 AM
@Scout if I have two snowflake accounts and multiple databases in each, is it best to create a single resource per database? or have one Snowflake resource that I configure with the correct account and database each time
s

Scout

03/07/2024, 5:39 AM
When dealing with multiple Snowflake accounts and databases, the best approach depends on the complexity of your workflows and how often you switch between different accounts and databases. 1. Single Resource Per Database: If you have a relatively stable set of databases that you access frequently, and the access patterns to these databases are consistent, it might be simpler to define a separate resource for each database. This way, you can encapsulate the connection details within each resource and avoid having to pass configuration details at runtime. This approach also makes it easier to manage permissions and resource lifecycles.
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python
from dagster import resource, Field, StringSource

@resource(config_schema={
    "account": StringSource,
    "user": StringSource,
    "password": StringSource,
    "database": StringSource,
    "schema": StringSource,
})
def snowflake_resource(context):
    # Here you would create and return a connection object to Snowflake
    # using the provided configuration.
    pass

# Define resources for each Snowflake database
snowflake_db1_resource = snowflake_resource.configured({
    "account": "account1",
    "user": "user1",
    "password": "password1",
    "database": "database1",
    "schema": "schema1",
})

snowflake_db2_resource = snowflake_resource.configured({
    "account": "account2",
    "user": "user2",
    "password": "password2",
    "database": "database2",
    "schema": "schema2",
})
2. Single Snowflake Resource with Dynamic Configuration: If you frequently switch between accounts and databases or have a large number of them, it might be more efficient to have a single Snowflake resource that you configure dynamically at runtime. This approach is more flexible and allows you to pass the necessary configuration parameters when you launch a run. ```python from dagster import job, op, resource, Field, StringSource @resource(config_schema={ "account": StringSource, "user": StringSource, "password": StringSource, "database": StringSource, "schema": StringSource, }) def snowflake_resource(context): # Here you would create and return a connection object to Snowflake # using the provided configuration. pass @op(required_resource_keys={"snowflake"}) def my_op(context): # Use the Snowflake resource to perform operations pass @job(resource_defs={"snowflake": snowflake_resource}) def my_job(): my_op() # Execute the job with specific configurations for the Snowflake resource result = execute_job( my_job, run_config={ "resources": { "snowflake": { "config": { "account": "account1", "user": "user1", "password": "password1", "da
tabase": "database1", "schema": "schema1", } } } }, ) ``` In both cases, you should consider security best practices, such as using environment variables or secret management systems to handle sensitive information like passwords. Ultimately, the choice between these two approaches should be guided by the needs of your team and the specific requirements of your data workflows. If you need to maintain clear separation and access control between databases, the first approach might be preferable. If you need more flexibility and want to minimize the number of resources you define, the second approach might be more suitable.
b

Brandon Peebles

03/07/2024, 5:43 AM
@Scout which pattern should I choose if I want to use the op factory to generate two instances of the same op that connect to different databases?
s

Scout

03/07/2024, 5:43 AM
If you want to use an op factory to generate two instances of the same op that connect to different databases, you would typically choose the second pattern, where you have a single Snowflake resource that you configure dynamically at runtime. This approach allows you to create a generic op factory that can produce ops configured with different Snowflake connections. Here's an example of how you might implement this: ```python from dagster import job, op, resource, Field, StringSource, execute_job # Define the Snowflake resource with a dynamic configuration @resource(config_schema={ "account": StringSource, "user": StringSource, "password": StringSource, "database": StringSource, "schema": StringSource, }) def snowflake_resource(context): # Here you would create and return a connection object to Snowflake # using the provided configuration. pass # Define the op factory function def snowflake_op_factory(database_name): @op(required_resource_keys={"snowflake"}, config_schema={"query": str}) def snowflake_op(context): # Use the Snowflake resource to perform operations # The query and database are dynamically configured query = context.op_config["query"] database = context.resources.snowflake.config["database"] context.log.info(f"Running query '{query}' on database '{database}'") # Your logic to execute the query goes here return snowflake_op # Create two instances of the op with different configurations snowflake_op_db1 = snowflake_op_factory("database1") snowflake_op_db2 = snowflake_op_factory("database2") # Define a job that uses the two op instances @job(resource_defs={"snowflake": snowflake_resource}) def my_job(): snowflake_op_db1() snowflake_op_db2() # Execute the job with specific configurations for each op instance result = execute_job( my_job, run_config={ "resources": { "snowflake": { "config": { "account": "account_placeholder", "user": "user_placeholder", "password": "password_placeholder", "database": "database_placeholder", # This will be overridden per op "schema": "schema_placeholder", } } }, "ops": { "snowflake_op_db1": { "config": { "query": "SELECT * FROM table1" }, "resources": { "snowflake": { "config": { "database": "database1" } } } }, "snowflake_op_db2": { "config": { "qu
ery": "SELECT * FROM table2" }, "resources": { "snowflake": { "config": { "database": "database2" } } } } } }, ) ``` In this example,
snowflake_op_factory
is a function that creates a new instance of an op configured to connect to a specific Snowflake database. The
snowflake_resource
is defined with a dynamic configuration schema that allows you to specify the connection details at runtime. When you execute
my_job
, you provide a run configuration that includes the connection details for each database. The
snowflake_op_db1
and
snowflake_op_db2
instances are configured to run queries on "database1" and "database2", respectively. This pattern provides the flexibility to generate multiple op instances with different configurations while sharing the same underlying resource definition. It's a powerful way to create reusable and configurable ops for different environments or databases.
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