Using UPathIOManager, how can I modify the path, so that it depends on the partition key if present. Eg. If the partition key is ‘2023-01-01’ I want the path to be {base_path}/2023/01/2023-01-01.parquet

To modify the path based on the partition key when using

UPathIOManager

, you can subclass

UPathIOManager

and override the

dump_to_path

and

load_from_path

methods to incorporate the partition key into the file path. Here's an example based on the documentation:

python import pandas as pd from upath import UPath  from dagster import OutputContext, UPathIOManager  class MyPartitionedParquetIOManager(UPathIOManager):     extension: str = ".parquet"      def _get_partitioned_path(self, context: OutputContext, path: UPath) -> UPath:         if context.has_asset_partitions:             partition_key = context.asset_partition_key             # Assuming partition_key is in the format 'YYYY-MM-DD'             year, month, day = partition_key.split('-')             # Construct the new path with partition directories             partitioned_path = path / year / month / f"{partition_key}{self.extension}"             return partitioned_path         else:             # If there's no partition, use the default behavior             return super()._get_partitioned_path(context, path)      def dump_to_path(self, context: OutputContext, obj: pd.DataFrame, path: UPath):         partitioned_path = self._get_partitioned_path(context, path)         with partitioned_path.open("wb") as file:             obj.to_parquet(file)      def load_from_path(self, context: OutputContext, path: UPath) -> pd.DataFrame:         partitioned_path = self._get_partitioned_path(context, path)         with partitioned_path.open("rb") as file:             return pd.read_parquet(file)

In this example, the

_get_partitioned_path

method is used to modify the path based on the partition key. If the

OutputContext

has a partition key, it splits the key into year, month, and day components and constructs a new path that includes these components. The

dump_to_path

and

load_from_path

methods use this new path to write and read the parquet files, respectively. Please note that this is a simplified example, and you may need to adjust the path construction logic to fit your specific use case and ensure that the directories exist before writing to them.

@Scout How can I add metadata to an asset using the IO manager?

To add metadata to an asset using an IO manager, you can use the

add_output_metadata

method within the

handle_output

method of your custom IO manager class. This method allows you to attach arbitrary metadata to the output of an asset when it is being handled by the IO manager. The metadata you add will be attached to the materialization event for that asset and will be displayed on the Asset Details page in the Dagit UI. Here's an example of how you might implement this in a custom IO manager:

python from dagster import IOManager, OutputContext  class DataframeTableIOManagerWithMetadata(IOManager):     def handle_output(self, context: OutputContext, obj):         table_name = context.name         # Perform your logic to write the dataframe to a table         write_dataframe_to_table(name=table_name, dataframe=obj)                  # Add metadata entries         context.add_output_metadata({             "num_rows": len(obj),             "table_name": table_name         })      def load_input(self, context):         # Perform your logic to load the dataframe from a table         table_name = context.upstream_output.name         return read_dataframe_from_table(name=table_name)

In this example, the

handle_output

method is used to write a dataframe to a table and then add metadata about the number of rows and the table name to the output. This metadata will be recorded and displayed in Dagit. Please note that the

write_dataframe_to_table

and

read_dataframe_from_table

functions are placeholders for your actual logic to write and read dataframes to and from a table or other storage system. Remember to configure your pipeline to use this custom IO manager for the relevant assets or outputs. You can do this by setting the

io_manager

key in the resource configuration when defining your pipeline or job.

@Scout How to read the asset output metadata from the iomanager?

As of my knowledge cutoff date in early 2023, Dagster does not provide a built-in way for an IOManager to directly read the metadata of an asset's output during the

load_input

method. The

OutputContext

object available in the

handle_output

method allows you to add metadata using

add_output_metadata

, which is then associated with the materialization event for that asset. However, this metadata is not directly accessible in the

InputContext

during the loading phase. If you need to access output metadata within an IOManager, you would typically need to use a different approach, such as: 1. Storing the metadata in a place where it can be retrieved later, such as a database or a metadata store, and then reading it from there in the

load_input

method. 2. Using the Dagster event log to query for materialization events and extract the metadata from there. This would involve using Dagster's instance to query the event log for the specific run and asset key. Here's a conceptual example of how you might use the event log to access metadata:

python
from dagster import IOManager, InputContext, AssetKey
from dagster.core.definitions.events import EventRecordsFilter, DagsterEventType

class MyIOManager(IOManager):
    def load_input(self, context: InputContext):
        # Assuming you want to access metadata for a specific asset
        asset_key = AssetKey(["my_asset"])
        
        # Query the event log for materialization events for the asset
        event_records = context.instance.get_event_records(
            EventRecordsFilter(
                event_type=DagsterEventType.ASSET_MATERIALIZATION,
                asset_key=asset_key,
            ),
            ascending=False,  # Get the most recent event first
            limit=1,  # We only want the most recent materialization
        )
        
        if event_records:
            # Extract metadata from the most recent materialization event
            metadata = event_records[0].event_log_entry.dagster_event.event_specific_data.materialization.metadata_entries
            
            # Now you can use the metadata as needed
            # ...

        # Perform your logic to load the input
        # ...

In this example,

get_event_records

is used to retrieve the most recent materialization event for a specific asset, and then the metadata is extracted from that event. This is a more advanced use case and may not be suitable for all scenarios, especially if you have a high volume of events or need to access metadata frequently. Please note that the code above is a conceptual example and may require adjustments to fit your specific use case. Additionally, the actual implementation details may vary depending on the version of Dagster you are using and any changes to the API that

@Scout, how can I pass parameters to the io manager dynamically (i.e. from the asset code)

In Dagster, IO managers are resources, and their configuration is typically static and defined at pipeline/job construction time. However, if you need to pass parameters dynamically to an IO manager from within an asset or op, you can use the

resource config

field in the context object to access the configuration values. While you cannot directly modify the IO manager's configuration at runtime from within an asset or op, you can pass parameters to the IO manager by including them in the configuration when you define your pipeline or job. Then, within the IO manager's methods, you can access these parameters through the context object. Here's a conceptual example of how you might set up an IO manager to accept dynamic parameters via its configuration:

python from dagster import IOManager, OutputContext, InputContext, io_manager, resource  class MyDynamicIOManager(IOManager):     def __init__(self, config):         self.config = config      def handle_output(self, context: OutputContext, obj):         # Use the dynamic parameter from the config         dynamic_param = self.config.get('dynamic_param')         # Perform the output handling logic using the dynamic parameter         # ...      def load_input(self, context: InputContext):         # Use the dynamic parameter from the config         dynamic_param = self.config.get('dynamic_param')         # Perform the input loading logic using the dynamic parameter         # ...  @io_manager(config_schema={"dynamic_param": str}) def my_dynamic_io_manager(init_context):     return MyDynamicIOManager(config=init_context.resource_config)  # In your job or pipeline definition, you can then configure the IO manager: my_job = my_pipeline.to_job(resource_defs={     "io_manager": my_dynamic_io_manager.configured({"dynamic_param": "my_value"}) })

In this example,

my_dynamic_io_manager

is an IO manager that accepts a configuration with a

dynamic_param

. When you define your job, you use the

configured

method to provide the dynamic parameter's value. If you need to pass parameters that are only known at runtime from within an asset or op, you would typically need to include that information as part of the data being passed between assets or ops, or store it in a place where the IO manager can retrieve it, such as a database or a metadata store. As of my knowledge cutoff date, Dagster does not support dynamically altering the configuration of an IO manager from within an asset or op at runtime. If you need to pass different parameters for different runs, you would need to configure your job or pipeline with the appropriate parameters before each run.