Paul Herzog
03/09/2024, 12:33 PMScout
03/09/2024, 12:33 PMconfigured
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:
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!Paul Herzog
03/09/2024, 6:50 PMScout
03/09/2024, 6:50 PMpython
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, othScout
03/09/2024, 6:51 PMlocal_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`: 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.