https://dagster.io/ logo
#ask-ai
Title
# ask-ai
t

Tara Larrue

02/14/2024, 11:45 AM
Can I access the attributes of an IOManager from inside an asset definition function?
s

Scout

02/14/2024, 11:45 AM
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:
Copy code
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:
Copy code
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!
t

Tara Larrue

02/14/2024, 11:50 AM
@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.
s

Scout

02/14/2024, 11:50 AM
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:
Copy code
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
need to handle partition keys appropriately in your IOManager's
load_input
method, potentially by using the
asset_partition_key
property from the
InputContext
to filter the data for the specific partition.
t

Tara Larrue

02/14/2024, 11:57 AM
@Scout if I have a multipartition, how do I access the different multipartition keys from
asset_partition_key
?
s

Scout

02/14/2024, 11:57 AM
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:
Copy code
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.
t

Tara Larrue

02/14/2024, 12:47 PM
@Scout can I log an AssetObservation from an IO Manager method?
s

Scout

02/14/2024, 12:47 PM
There was an error processing your request. Please try again.