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# ask-ai
p

Paul Herzog

03/09/2024, 12:33 PM
@Scout i currently use google bigquery as source for some assets. im my init.py i have a switch which switches my i/o manager depending on my environment - for prod env i use cloud storage, for local dev it uses local file aystem. i want the same logic to apply now for my database resource. im production i want to usw google bigquery, in local i want a local duckdb. how to implement this?
s

Scout

03/09/2024, 12:33 PM
To implement environment-specific resources for your assets in Dagster, you can define different resource configurations for each environment and then switch between them based on the environment you're running in. You can use the
configured
method to create environment-specific instances of your resources. Here's a step-by-step guide on how to set up your
__init__.py
to switch between Google BigQuery and a local DuckDB based on the environment: 1. Define your BigQuery resource and DuckDB resource. You can use the
BigQueryResource
for BigQuery and create a custom resource for DuckDB. 2. Create configured instances of your resources for each environment. For example, you can have a
bigquery_resource_configured
for production and a
duckdb_resource_configured
for local development. 3. In your
__init__.py
, you can set up a switch that determines the current environment and selects the appropriate resource configuration. 4. Use the selected resource configuration in your asset definitions. Here's an example of how you might set this up:
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python
from dagster import asset, ResourceDefinition
from dagster_gcp import BigQueryResource
from some_module import DuckDBResource  # Assuming you have a custom DuckDB resource

# Define your resources
bigquery_resource = BigQueryResource(...)
duckdb_resource = DuckDBResource(...)

# Configure your resources for each environment
bigquery_resource_configured = bigquery_resource.configured({...})
duckdb_resource_configured = duckdb_resource.configured({...})

# Determine the current environment
environment = "prod"  # This should be dynamically determined, e.g., from an environment variable

# Select the appropriate resource based on the environment
if environment == "prod":
    database_resource = bigquery_resource_configured
else:
    database_resource = duckdb_resource_configured

# Use the selected resource in your asset definitions
@asset(required_resource_keys={"database"})
def my_asset(context):
    # Use context.resources.database to interact with the database
    pass

# Include the resource in your repository definition
from dagster import repository

@repository
def my_repository():
    return [my_asset, ResourceDefinition("database", database_resource)]
Remember to replace the placeholders (
...
) with the actual configuration for your resources. Also, the way you determine the current environment (
environment = "prod"
) should be based on your specific setup, such as an environment variable. The documentation provides examples of how to configure resources and assets, and you can adapt these examples to fit your environment switching logic. Keep in mind that the actual implementation may vary based on your specific requirements and the details of your resources. Evaluating Dagster for Better Skiing - a... button Building an Outbound Reporting Pipeline ... button Usage of BigQuery Resouce + BigQuery IO ... button SqlTypeHandler - Proposing an additional... button Thank you for your feedback!
p

Paul Herzog

03/09/2024, 6:50 PM
can you show me how to make this usable for local storage as well as cloud storage? @Scout def all_ranking_data_to_gcs(context: AssetExecutionContext, all_ranking_data_to_df: pd.DataFrame, google_cloud_storage: GCSResource) -> tuple: accuranker_id = context.partition_key client = google_cloud_storage.get_client() bucket_name = f"{project_id}-accuranker-orchestration-test" blob_name = f"dagster/accuranker/{accuranker_id}/all_ranking_data/{today_date}.csv" df, bucket_name, blob_name = df_to_gcs(all_ranking_data_to_df, bucket_name, blob_name, client) context.add_output_metadata( metadata={ "bucket_name": bucket_name, "blob_name": blob_name, "num_records": len(df), "preview": MetadataValue.md(df.head().to_markdown()) } ) tuple_for_return = (bucket_name, blob_name) return tuple_for_return
s

Scout

03/09/2024, 6:50 PM
To make your asset function usable for both local storage and cloud storage, you can define a custom I/O manager that switches between writing to a local file system and writing to Google Cloud Storage (GCS) based on the environment. Below is an example of how you might implement such an I/O manager:
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python
from dagster import IOManager, io_manager, OutputContext
import pandas as pd
import os
from google.cloud import storage

class LocalOrGCSIOManager(IOManager):
    def __init__(self, gcs_client=None, local_root_dir="/tmp", gcs_bucket=None):
        self.gcs_client = gcs_client
        self.local_root_dir = local_root_dir
        self.gcs_bucket = gcs_bucket

    def handle_output(self, context: OutputContext, obj: pd.DataFrame):
        if self.gcs_client and self.gcs_bucket:
            # Write to GCS
            bucket = self.gcs_client.get_bucket(self.gcs_bucket)
            blob = bucket.blob(context.asset_key.path[-1])
            blob.upload_from_string(obj.to_csv(index=False), 'text/csv')
            context.add_output_metadata({
                "bucket": self.gcs_bucket,
                "blob": blob.name,
                "num_records": len(obj),
            })
        else:
            # Write to local filesystem
            filepath = os.path.join(self.local_root_dir, *context.asset_key.path)
            os.makedirs(os.path.dirname(filepath), exist_ok=True)
            obj.to_csv(filepath, index=False)
            context.add_output_metadata({
                "filepath": filepath,
                "num_records": len(obj),
            })

    def load_input(self, context: InputContext):
        # Implement this method based on how you want to load inputs
        pass

@io_manager
def local_or_gcs_io_manager(init_context):
    if init_context.resource_config["use_gcs"]:
        gcs_client = storage.Client()
        gcs_bucket = init_context.resource_config["gcs_bucket_name"]
        return LocalOrGCSIOManager(gcs_client=gcs_client, gcs_bucket=gcs_bucket)
    else:
        local_root_dir = init_context.resource_config.get("local_root_dir", "/tmp")
        return LocalOrGCSIOManager(local_root_dir=local_root_dir)

# In your repository definition, you would include the IO manager with the appropriate config
@repository
def my_repository():
    return [
        my_asset,
        local_or_gcs_io_manager.configured({
            "use_gcs": True,  # Set to False for local development
            "gcs_bucket_name": "my-gcs-bucket",
            # Optionally include "local_root_dir" for local development
        }),
    ]
In this example, the
LocalOrGCSIOManager
class is an I/O manager that can write to either GCS or the local file system. The
handle_output
method checks if a GCS client and bucket are provided; if so, it writes to GCS, oth
erwise, it writes to the local file system. The
local_or_gcs_io_manager
factory function creates an instance of the
LocalOrGCSIOManager
with the appropriate configuration based on the
use_gcs
flag. When configuring this I/O manager in your repository, you can set
use_gcs
to
True
to use GCS or
False
to use local storage. Finally, you would use this I/O manager in your asset definition by specifying the `io_manager_key`:
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python @asset(io_manager_key="local_or_gcs_io_manager") def my_asset(context, input_df):     # Your asset logic here     pass
Remember to replace
"my-gcs-bucket"
with your actual GCS bucket name and adjust the
local_root_dir
as needed for your local development environment.
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