How do I define the IO manager that should be used for the output, eg to write the output to a postgres database?

To define an IO manager that writes output to a PostgreSQL database, you would typically create a custom IO manager class that extends the

IOManager

base class provided by Dagster. This custom IO manager would implement the

handle_output

method to write data to the database and the

load_input

method to read data from the database. Here's a simplified example based on the documentation:

python
from dagster import IOManager, OutputContext, InputContext, asset
import pandas as pd
import sqlalchemy

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

    def handle_output(self, context: OutputContext, obj: pd.DataFrame):
        # Write the DataFrame to a table in the PostgreSQL database
        table_name = context.asset_key.path[-1]  # Use the asset key as the table name
        obj.to_sql(table_name, self.engine, if_exists='replace', index=False)

    def load_input(self, context: InputContext):
        # Read a DataFrame from a table in the PostgreSQL database
        table_name = context.upstream_output.asset_key.path[-1]
        return pd.read_sql_table(table_name, self.engine)

# Create an instance of the IO manager with a connection to the PostgreSQL database
engine = sqlalchemy.create_engine('<postgresql://username:password@host:port/dbname>')
postgres_io_manager = PostgresIOManager(engine)

# Define your assets and specify the IO manager to use
@asset(io_manager_key="my_postgres_io_manager")
def my_asset():
    # Your asset logic here
    return pd.DataFrame(...)

# Define your Dagster repository and include the IO manager in the resources
from dagster import repository, define_asset_job

@repository
def my_repository():
    return [
        define_asset_job("my_job", selection="*"),
        my_asset,
        {"my_postgres_io_manager": postgres_io_manager},
    ]

In this example, the

PostgresIOManager

class is responsible for writing a Pandas DataFrame to a PostgreSQL table when

handle_output

is called and for reading a DataFrame from a PostgreSQL table when

load_input

is called. The

engine

is an SQLAlchemy engine that provides connectivity to the PostgreSQL database. When defining your assets, you can specify the IO manager to use by passing the

io_manager_key

argument to the

@asset

decorator. This key should match the key used to register the IO manager in the repository's resources. Please note that this is a simplified example, and in a real-world scenario, you would need to handle additional considerations such as database credentials, connection management, error handling, and potentially more complex logic for reading and writing data. Remember to install the necessary dependencies, such as

sqlalchemy

and the appropriate database driver, and to adjust the connec

@Scout So for an asset I can use

ins

to specify how to load an input asset and I use

io_manager_key

to write the asset?

Yes, that's correct. In Dagster, you can use the

ins

parameter within the

@asset

decorator to specify custom logic for loading input assets, and you can use the

io_manager_key

parameter to specify which IO manager should be used for writing the output of the asset. Here's a brief explanation of both: 1. `ins`: This parameter allows you to provide a dictionary where the keys are the names of the input parameters to your asset function, and the values are instances of

AssetIn

. The

AssetIn

can specify an

input_manager_key

which tells Dagster which IO manager to use when loading the input for that particular asset. 2. `io_manager_key`: This parameter is used to specify the key of the IO manager that should be used for handling the output of the asset. When you define your asset, you can set the

io_manager_key

to the key of a custom IO manager that you have defined and included in your repository's resources. Here's an example that illustrates how to use both

ins

and `io_manager_key`:

python
from dagster import asset, AssetIn, IOManager, InputContext, OutputContext

class MyCustomInputIOManager(IOManager):
    def load_input(self, context: InputContext):
        # Custom logic to load an input asset
        pass

class MyCustomOutputIOManager(IOManager):
    def handle_output(self, context: OutputContext, obj):
        # Custom logic to handle the output of an asset
        pass

@asset(
    ins={"input_asset": AssetIn(input_manager_key="my_custom_input_io_manager")},
    io_manager_key="my_custom_output_io_manager"
)
def my_asset(input_asset):
    # Your asset logic here
    pass

# Define your repository and include the custom IO managers in the resources
from dagster import repository, define_asset_job

@repository
def my_repository():
    return [
        define_asset_job("my_job", selection="*"),
        my_asset,
        {
            "my_custom_input_io_manager": MyCustomInputIOManager(),
            "my_custom_output_io_manager": MyCustomOutputIOManager(),
        },
    ]

In this example,

MyCustomInputIOManager

is used to load the input for

my_asset

, and

MyCustomOutputIOManager

is used to handle the output of

my_asset

. The keys used in the

ins

dictionary and the

io_manager_key

parameter must match the keys used to register the corresponding IO managers in the repository's resources. Configure the

io_manager_key

for asset... button I/O managers | Dagster button IOManagerDefinition.address_for_asset button Is there a way to specify/override the d... button Thank you for your feedback!

@Scout how would that look like if I wanted to use the same IO Manager to read from and write to a Postgres database? How can I specify the location (schema and table name) of the input and output assets?

To use the same IO Manager for both reading from and writing to a PostgreSQL database, you would create a custom IO Manager that knows how to interact with PostgreSQL. You can specify the location (schema and table name) of the input and output assets using metadata on the assets or by using configuration on the IO Manager. Here's an example of how you might define such an IO Manager and use it with assets:

python
from dagster import IOManager, InputContext, OutputContext, asset, repository, define_asset_job
import pandas as pd
import sqlalchemy

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

    def handle_output(self, context: OutputContext, obj: pd.DataFrame):
        # Use metadata to determine the schema and table name
        table_name = context.metadata.get("table_name")
        schema = context.metadata.get("schema", "public")  # default to 'public' schema if not specified
        obj.to_sql(table_name, self.engine, schema=schema, if_exists='replace', index=False)

    def load_input(self, context: InputContext):
        # Use metadata to determine the schema and table name
        table_name = context.upstream_output.metadata.get("table_name")
        schema = context.upstream_output.metadata.get("schema", "public")
        return pd.read_sql_table(table_name, self.engine, schema=schema)

# Create an instance of the IO manager with a connection to the PostgreSQL database
engine = sqlalchemy.create_engine('<postgresql://username:password@host:port/dbname>')
postgres_io_manager = PostgresIOManager(engine)

# Define your assets and specify the IO manager to use
@asset(
    metadata={"table_name": "my_input_table", "schema": "my_input_schema"},
    io_manager_key="my_postgres_io_manager"
)
def my_input_asset():
    # This asset represents a table in PostgreSQL and doesn't contain business logic
    pass

@asset(
    metadata={"table_name": "my_output_table", "schema": "my_output_schema"},
    io_manager_key="my_postgres_io_manager"
)
def my_transformed_asset(my_input_asset: pd.DataFrame):
    # Transform the input DataFrame and return the result
    transformed_df = my_input_asset  # Add your transformation logic here
    return transformed_df

# Define your Dagster repository and include the IO manager in the resources
@repository
def my_repository():
    return [
        define_asset_job("my_job", selection="*"),
        my_input_asset,
        my_transformed_asset,
        {"my_postgres_io_manager": postgres_io_manager},
    ]

In this example, the

PostgresIOManager

class is responsible for writing a Pandas DataFrame to a PostgreSQL table and reading a DataFrame from a PostgreSQL table. The schema and table name are specified using metadata on the assets. When defining your assets,

@Scout in your example, what is the name of the input table? How is that defined?

In the example provided, the name of the input table is specified in the metadata of the

my_input_asset

asset using the key

"table_name"

. The metadata is a dictionary that contains various pieces of information about the asset, including the table name and schema where the data is located. Here's the relevant part of the code that defines the input table name:

python @asset(     metadata={"table_name": "my_input_table", "schema": "my_input_schema"},     io_manager_key="my_postgres_io_manager" ) def my_input_asset():     # This asset represents a table in PostgreSQL and doesn't contain business logic     pass

In this snippet, the

metadata

dictionary for

my_input_asset

specifies that the table name is

"my_input_table"

and the schema is

"my_input_schema"

. When the `PostgresIOManager`'s

load_input

method is called, it will use this metadata to construct the full table identifier and load the data from the specified table and schema in the PostgreSQL database. The

my_input_asset

function itself doesn't contain any business logic because it represents a table in the database. The actual data loading is handled by the

PostgresIOManager

when another asset depends on

my_input_asset

. The IO Manager will use the metadata to know from which table to load the data.