Can I access the attributes of an IOManager from inside an asset definition function?

No, you typically cannot access the attributes of an IOManager directly from inside an asset definition function. Asset definition functions are meant to define the computation that produces the asset's data, while IOManagers are used to handle the storage and retrieval of the data produced by assets. However, you can configure your IOManager with resources, and those resources can be accessed within an asset definition function if they are provided in the context. The context object passed to the asset definition function includes resources that have been configured for the job that the asset is a part of. Here's an example from the documentation that shows how you might define an IOManager and pass a resource to it:

python
from dagster import (
    Definitions,
    AssetKey,
    OutputContext,
    InputContext,
    ConfigurableIOManager,
)

class MyIOManager(ConfigurableIOManager):
    root_path: str

    def _get_path(self, asset_key: AssetKey) -> str:
        return self.root_path + "/".join(asset_key.path)

    def handle_output(self, context: OutputContext, obj):
        write_csv(self._get_path(context.asset_key), obj)

    def load_input(self, context: InputContext):
        return read_csv(self._get_path(context.asset_key))

defs = Definitions(
    assets=...,
    resources={"io_manager": MyIOManager(root_path="/tmp/")},
)

In this example,

MyIOManager

is an IOManager that has a

root_path

attribute. This IOManager is then added to the resources under the key

"io_manager"

. If you need to access the

root_path

within an asset definition function, you would typically pass it as a resource to the context of the asset:

python
from dagster import asset

@asset(required_resource_keys={"io_manager"})
def my_asset(context):
    # You can access the root_path from the IOManager resource here
    root_path = context.resources.io_manager.root_path
    # Perform your asset computation

In this asset definition, the

required_resource_keys

parameter specifies that the asset requires the

"io_manager"

resource, which allows you to access the

root_path

attribute from the IOManager within the asset definition function through the context's resources. Keep in mind that this is a simplified example, and the actual implementation details may vary depending on your specific use case and the version of Dagster you are using. Enabling using assets as input for a job... button Pythonic Input/Output Metadata button I/O Managers | Dagster button IOManagerDefinition.address_for_asset button Thank you for your feedback!

@Scout alternatively, how do I define an input IOManager for a source asset that is a table in a postgres database? The asset downstream from the source asset will be partitioned.

To define an input IOManager for a source asset that is a table in a PostgreSQL database, you can create a custom IOManager that knows how to read from the PostgreSQL table. Then, you can associate this IOManager with the input of the downstream asset. Here's a high-level outline of the steps you would take, based on the documentation: 1. Define a custom IOManager that connects to the PostgreSQL database and reads the table data. This IOManager should implement the

load_input

method to fetch data from the database. 2. Register the custom IOManager as a resource in your Dagster repository. 3. Use the

input_manager_key

argument on

AssetIn

to specify that the downstream asset should use this custom IOManager to load its input. Here's an example that illustrates these steps:

python
from dagster import IOManager, InputContext, asset, AssetIn, repository, with_resources
import pandas as pd
import sqlalchemy

class PostgresTableIOManager(IOManager):
    def __init__(self, engine):
        self.engine = engine

    def load_input(self, context: InputContext):
        table_name = context.upstream_output.name
        query = f"SELECT * FROM {table_name}"
        with self.engine.connect() as conn:
            return pd.read_sql(query, conn)

# Define the resource that provides the IOManager
postgres_io_manager = PostgresTableIOManager(
    engine=sqlalchemy.create_engine('<postgresql://username:password@host:port/dbname>')
)

# Define the source asset
@asset
def source_asset():
    # Logic to load data from the PostgreSQL table
    ...

# Define the downstream asset with a partitioned input
@asset(ins={"source_asset": AssetIn(input_manager_key="postgres_io_manager")})
def downstream_asset(source_asset):
    # Logic that uses the data from the source_asset
    ...

# Define the repository and include the IOManager resource
@repository
def my_repo():
    return with_resources(
        [source_asset, downstream_asset],
        {"postgres_io_manager": postgres_io_manager}
    )

In this example,

PostgresTableIOManager

is a custom IOManager that reads from a PostgreSQL table. The

load_input

method constructs a SQL query to read the entire table and uses Pandas to load the result into a DataFrame. The

source_asset

is the asset representing the table in the PostgreSQL database. The

downstream_asset

is the asset that depends on the

source_asset

. It specifies that it should use the

postgres_io_manager

to load its input. Finally, the

my_repo

function defines the repository and attaches the

postgres_io_manager

resource to it. Please note that this is a simplified example, and you would need to adapt it to fit your specific schema, table names, and authentication details. Additionally, if your downstream asset is partitioned, you would

@Scout if I have a multipartition, how do I access the different multipartition keys from

asset_partition_key

?

When working with a multi-partitioned asset in Dagster, you can access the different partition keys from the

asset_partition_key

by using the

keys_by_dimension

method of the

MultiPartitionKey

object. This method returns the key for each dimension of the multi-partition. Here's an example of how you might access the partition keys from a multi-partitioned asset:

python from dagster import asset, MultiPartitionKey  @asset(partitions_def=MultiPartitionsDefinition({     "date": DailyPartitionsDefinition(start_date="2022-01-01"),     "color": StaticPartitionsDefinition(["red", "yellow", "blue"]), })) def multi_partitions_asset(context):     partition_keys = context.partition_key.keys_by_dimension     date_key = partition_keys['date']     color_key = partition_keys['color']     <http://context.log.info|context.log.info>(f"Date partition: {date_key}, Color partition: {color_key}")

In this example,

multi_partitions_asset

is an asset that is partitioned by both date and color. Inside the asset function,

context.partition_key.keys_by_dimension

is used to access the partition keys for each dimension. The

date_key

and

color_key

variables hold the partition keys for the "date" and "color" dimensions, respectively. This allows you to use the specific partition keys to tailor the behavior of your asset computation based on the partitions being processed.

@Scout can I log an AssetObservation from an IO Manager method?

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