@Scout I want to use google cloud storage and store my files there. can you give me some best practises on how to implement this?

Certainly! When using Google Cloud Storage (GCS) with Dagster, here are some best practices based on the documentation: 1. Use GCS Resources and IO Managers: We provide

GCSResource

and

GCSPickleIOManager

for interacting with GCS. These abstractions help you manage connections and interactions with GCS in a clean and reusable way. 2. Configuration: Configure your GCS resources and IO managers with the necessary parameters such as

project

,

gcs_bucket

, and

gcs_prefix

. This ensures that your assets and operations know where to store and retrieve data. 3. Client Management: When defining an asset that interacts with GCS, use the

GCSResource

to obtain a client. This client is a

google.cloud.storage.Client

object that you can use to interact with GCS. For example:

python    @asset    def my_asset(gcs: GCSResource):        with gcs.get_client() as client:            # client is a google.cloud.storage.Client            ...

4. Serialization: The

GCSPickleIOManager

serializes objects via pickling, which is suitable for object storage for distributed executors. Ensure that your data types are pickleable. 5. File Path Management: The IO manager assigns each op output to a unique filepath containing run ID, step key, and output name. It also assigns each asset to a single filesystem path. Be mindful of how your file paths are constructed to avoid conflicts and ensure data is stored and retrieved correctly. 6. Credentials Management: If you cannot upload a file to your Dagster deployment or authenticate with GCP via a standard method, you can provide a service account key as the

gcp_credentials

configuration. This key must be base64 encoded to avoid issues with newlines in the keys. 7. Cold Storage: If you are dealing with infrequently accessed data, consider using cold storage options like Google Cloud Storage Nearline for cost savings. 8. Data Archiving: For long-term data retention, use cloud providers like GCP to transfer and store data securely in their cloud infrastructure. 9. Security: Always ensure that your GCP credentials are handled securely and follow best practices for managing sensitive information. 10. Testing: Test your GCS interactions in a development environment before deploying to production to ensure that everything works as expected. By following these best practices, you can effectively use Google Cloud Storage within your Dagster pipelines to manage data storage and retrieval.

@Scout can you give me an example of implementing GCSPickelManager as resource?

Certainly! Below is an example of how to implement

GCSPickleIOManager

as a resource in a Dagster job, based on the documentation: First, you need to define the resource configuration for

GCSPickleIOManager

. This includes specifying the GCS bucket and optional prefix where the data will be stored:

python from dagster import job from dagster_gcp.gcs import GCSPickleIOManager, GCSResource  @job(     resource_defs={         "io_manager": GCSPickleIOManager(             gcs=GCSResource(project="my-cool-project"),             gcs_bucket="my-cool-bucket",             gcs_prefix="my-cool-prefix"         ),     } ) def my_job():     ...

In this example,

GCSPickleIOManager

is used as the

io_manager

resource for the job

my_job

. It is configured with a

GCSResource

that specifies the GCP project name, and the

GCSPickleIOManager

itself is configured with the GCS bucket and prefix. When you run

my_job

, any outputs from the operations within that job will be automatically managed by the

GCSPickleIOManager

, which will serialize the outputs using pickle and store them in the specified GCS bucket with the given prefix. Remember to replace

"my-cool-project"

,

"my-cool-bucket"

, and

"my-cool-prefix"

with your actual GCP project ID, GCS bucket name, and desired prefix for your use case.

@Scout Can you provide info why this is not working? @asset(ins={"by_url_data": AssetIn(key=AssetKey("by_url_data"))}, resource_defs={ "io_manager": GCSPickleIOManager( gcs=GCSResource(project=f"{project_id}"), gcs_bucket=f"{project_id}-accuranker-orchestration-test", gcs_prefix="first_time_test_prefix_gcs_resource" ), }) def upload_by_url_data_to_gcs(context: AssetExecutionContext, google_cloud_storage: GCSResource, by_url_data): with google_cloud_storage.get_client() as client: print(client) Error is: dagster._check.ParameterCheckError: Invariant violation for parameter Cannot specify resource requirements in both @op decorator and as arguments to the decorated function. Description: None

The error message you're encountering indicates that there is a conflict in how resources are being specified for your asset. In Dagster, you can define resources either as part of the

@asset

decorator or by passing them as arguments to the decorated function, but not both. The

@asset

decorator in your code snippet is attempting to define an

io_manager

resource, while the function

upload_by_url_data_to_gcs

is also expecting a

google_cloud_storage

resource as an argument. This is what's causing the conflict. To resolve this issue, you should define the resources in one place. If you want to use the

io_manager

within the asset, you should not pass it as an argument to the function. Instead, you can access it via the

context

object that is automatically passed to the asset function. Here's how you can modify your asset definition to avoid the error:

python from dagster import asset, AssetIn, AssetExecutionContext from dagster_gcp.gcs import GCSPickleIOManager, GCSResource  @asset(ins={"by_url_data": AssetIn(key=AssetKey("by_url_data"))}) def upload_by_url_data_to_gcs(context: AssetExecutionContext, by_url_data):     # Access the GCSResource from the context     google_cloud_storage = context.resources.gcs      # Use the GCS client without the context manager, based on the issue you mentioned     client = google_cloud_storage.get_client()     print(client)     # ... rest of your code to upload data to GCS

And then, when you define your job or repository, you can specify the resource definitions there:

python from dagster import job, repository  @job(resource_defs={     "gcs": GCSResource(project=f"{project_id}"),     "io_manager": GCSPickleIOManager(         gcs_bucket=f"{project_id}-accuranker-orchestration-test",         gcs_prefix="first_time_test_prefix_gcs_resource"     ), }) def my_job():     upload_by_url_data_to_gcs()  @repository def my_repo():     return [my_job]

In this revised code, the

GCSResource

is defined as a resource with the key

"gcs"

in the job's

resource_defs

. The asset function

upload_by_url_data_to_gcs

can then access this resource via

context.resources.gcs

. Please ensure that you replace

project_id

with your actual GCP project ID and adjust the bucket name and prefix as needed. Also, make sure that the

GCSPickleIOManager

is correctly configured in the job's resource definitions.

@Scout can i define the io manager in Definitions as well in init.py? If so, how can i pass individual variables to gcs prefix? Also show me an example on how to upload data to the storage.

Yes, you can define the

io_manager

in the

Definitions

in your

__init__.py

file. However, when you want to pass individual variables to the GCS prefix or any other configuration, you would typically use the Dagster configuration system to inject these values at runtime. Here's an example of how you might set up your

Definitions

with a

GCSPickleIOManager

and how to pass a dynamic GCS prefix using the configuration system:

python
# __init__.py
from dagster import Definitions, asset, Field, String
from dagster_gcp.gcs import GCSPickleIOManager, GCSResource

# Define your GCS IO Manager with configuration for the GCS prefix
gcs_io_manager = GCSPickleIOManager.configured({
    "gcs_bucket": "my-cool-bucket",
    "gcs_prefix": Field(String, is_required=False, default_value="default_prefix"),
})

# Define your Definitions with the GCS IO Manager
defs = Definitions(
    resources={
        "io_manager": gcs_io_manager,
        "gcs": GCSResource(project="my-cool-project"),
    },
)

# Define your asset
@asset(required_resource_keys={"io_manager", "gcs"})
def my_asset(context):
    # Access the GCS client from the context
    gcs_client = context.resources.gcs.get_client()
    bucket = gcs_client.bucket("my-cool-bucket")

    # Prepare your data to upload
    data_to_upload = "my data"

    # Create a blob in the bucket
    blob = bucket.blob(f"{context.resources.io_manager.gcs_prefix}/my_file.txt")

    # Upload the data to the blob
    blob.upload_from_string(data_to_upload)

    # Return something or perform other operations as needed
    return "Data uploaded"

When you run your job, you can provide a configuration for the

gcs_prefix

like so:

yaml
# config.yaml
resources:
  io_manager:
    config:
      gcs_prefix: "dynamic_prefix_for_this_run"

And then execute your job with the configuration file:

bash
dagster job execute -f __init__.py -c config.yaml

This will use the

dynamic_prefix_for_this_run

as the GCS prefix for this particular execution. Please note that the

GCSPickleIOManager

is a resource that needs to be configured with the GCS bucket and optional prefix. The

GCSResource

is a separate resource that provides a GCS client and needs to be configured with your GCP project ID. Both resources should be included in your

Definitions

if they are required by your assets or operations. Passing arbitrary Python objects to `Con... button I/O managers | Dagster button Tutorial, part six: Using Dagster to sav... button Pushing REST-API data to Google Sheets w... button Thank you for your feedback!

@Scout Can you show me an best practise example on how to load a dataframe in a bigquery table and append the data?

Certainly! To load a DataFrame into a BigQuery table and append the data, you can use the

google.cloud.bigquery

library, which provides a method

load_table_from_dataframe

that allows you to load data from a pandas DataFrame into a BigQuery table. Here's an example of how you might do this within a Dagster asset:

python import pandas as pd from dagster import asset from dagster_gcp import BigQueryResource from google.cloud import bigquery  # Define your asset @asset(required_resource_keys={"bigquery"}) def my_asset(context, df: pd.DataFrame):     # Access the BigQuery client from the context     bigquery_client = context.resources.bigquery.get_client()      # Define your BigQuery table and dataset     dataset_name = 'my_dataset'     table_name = 'my_table'     table_id = f'{bigquery_client.project}.{dataset_name}.{table_name}'      # Configure the job to append data to the destination table if it already exists     job_config = bigquery.LoadJobConfig(         schema=[             # Define your schema here if needed, or set to None to autodetect             # bigquery.SchemaField("name", "STRING"),             # bigquery.SchemaField("age", "INTEGER"),         ],         write_disposition=bigquery.WriteDisposition.WRITE_APPEND,     )      # Load the DataFrame into BigQuery     job = bigquery_client.load_table_from_dataframe(         dataframe=df,         destination=table_id,         job_config=job_config     )      # Wait for the load job to complete     job.result()      <http://context.log.info|context.log.info>(f"Loaded {job.output_rows} rows into {table_id}")      return "Data appended to BigQuery table"

In this example, the

my_asset

asset function takes a pandas DataFrame

df

as input and appends it to a BigQuery table specified by

dataset_name

and

table_name

. The

BigQueryResource

is used to get the BigQuery client, which is then used to load the DataFrame into BigQuery. The

job_config

is set up with

write_disposition=bigquery.WriteDisposition.WRITE_APPEND

, which specifies that the data should be appended to the table if it already exists. If the table does not exist, BigQuery will attempt to create it based on the schema defined in the DataFrame. To use this asset, you would define a job that includes it and provide the necessary configuration for the

bigquery

resource, including credentials and project information. Remember to replace

'my_dataset'

and

'my_table'

with your actual dataset and table names, and define the schema if you do not want BigQuery to autodetect it. Please note that this example assumes you have already installed the

google-cloud-bigquery

package and have the necessary permissions to access the BigQuery dataset and table.

im trying to get data into bq like this, please help @Scout : def by_url_data_to_bq(context: AssetExecutionContext, upload_by_url_data_to_gcs: tuple, bigquery: BigQueryResource, google_cloud_storage: GCSResource) -> None: bucket_name, blob_name = upload_by_url_data_to_gcs client_bq = bigquery.get_client() client_gcs = google_cloud_storage.get_client() job = client_bq.load_table_from_dataframe( dataframe=dataframe, destination=f"{bq_data_set}.{bq_table_name}", ) job.result() its not working, due to AttributeError: '_GeneratorContextManager' object has no attribute 'load_table_from_dataframe'

The error you're encountering,

AttributeError: '_GeneratorContextManager' object has no attribute 'load_table_from_dataframe'

, suggests that the

client_bq

object you're trying to call

load_table_from_dataframe

on is not an instance of

google.cloud.bigquery.Client

but rather a context manager that wraps around the client. In Dagster, when you use

bigquery.get_client()

as a context manager with the

with

statement, it will manage the lifecycle of the client for you, including closing the client once the block of code is exited. However, if you're using it without the

with

statement, you need to explicitly call

__enter__()

to enter the context manager and obtain the client. Here's how you can modify your function to use the BigQuery client correctly:

python
from dagster import asset
from dagster_gcp import BigQueryResource, GCSResource

@asset(required_resource_keys={"bigquery", "gcs"})
def by_url_data_to_bq(context, upload_by_url_data_to_gcs: tuple):
    bucket_name, blob_name = upload_by_url_data_to_gcs
    bq_data_set = "your_dataset"
    bq_table_name = "your_table"

    # Get the BigQuery client
    client_bq = context.resources.bigquery.get_client()
    # Get the GCS client
    client_gcs = context.resources.gcs.get_client()

    # Assuming you want to load data from GCS to BigQuery
    # Create a BigQuery job config
    job_config = bigquery.LoadJobConfig(
        source_format=bigquery.SourceFormat.NEWLINE_DELIMITED_JSON,
        autodetect=True,
        write_disposition=bigquery.WriteDisposition.WRITE_APPEND,
    )

    # Get the URI for the GCS object
    uri = f"gs://{bucket_name}/{blob_name}"

    # Start the load job
    load_job = client_bq.load_table_from_uri(
        uri,
        f"{bq_data_set}.{bq_table_name}",
        job_config=job_config
    )

    # Wait for the job to complete
    load_job.result()

    <http://context.log.info|context.log.info>(f"Loaded data from {uri} into {bq_data_set}.{bq_table_name}")

In this revised function, I've made the following changes: 1. Used the

@asset

decorator to define the asset and specified the required resource keys. 2. Removed the

dataframe

parameter from the

load_table_from_dataframe

method since it seems you want to load data from GCS, not a DataFrame. Instead, I used

load_table_from_uri

to load data directly from a GCS URI. 3. Added a

job_config

to specify the source format and other loading options. You'll need to adjust the

source_format

and other configurations based on your actual data format and requirements. 4. Used

load_table_from_uri

to start a load job that loads data from the GCS URI into the BigQuery table. Please ensure that you replace

"your_dataset"

and

"your_table"

with your actual dataset and table names, and adjust the

job_config

as needed based on yo

@Scout show me best practise to upload from uri to bigquery using with statement

To upload data from a Google Cloud Storage URI to a BigQuery table using the

with

statement, you can follow the best practices outlined in the documentation. Here's an example of how you might do this within a Dagster asset:

python from dagster import asset, Field, String, get_dagster_logger from dagster_gcp.bigquery.resources import bigquery_resource from google.cloud import bigquery  # Define your asset @asset(required_resource_keys={"bigquery"}) def upload_from_uri_to_bigquery(context, gcs_uri: str, dataset_id: str, table_id: str):     logger = get_dagster_logger()      # Get the BigQuery client from the context     with context.resources.bigquery.get_client() as client_bq:         # Define the destination table reference         table_ref = f"{client_bq.project}.{dataset_id}.{table_id}"          # Create a job configuration         job_config = bigquery.LoadJobConfig(             source_format=bigquery.SourceFormat.NEWLINE_DELIMITED_JSON,             autodetect=True,             write_disposition=bigquery.WriteDisposition.WRITE_APPEND,         )          # Start the load job         load_job = client_bq.load_table_from_uri(             source_uris=gcs_uri,             destination=table_ref,             job_config=job_config         )          # Wait for the job to complete         load_job.result()          <http://logger.info|logger.info>(f"Loaded data from {gcs_uri} into {table_ref}")

In this example, the

upload_from_uri_to_bigquery

asset function takes a GCS URI

gcs_uri

, a dataset ID

dataset_id

, and a table ID

table_id

as inputs. It uses the

bigquery_resource

to get the BigQuery client and then uploads the data from the GCS URI to the specified BigQuery table. The

job_config

is set up with

source_format=bigquery.SourceFormat.NEWLINE_DELIMITED_JSON

and

write_disposition=bigquery.WriteDisposition.WRITE_APPEND

, which specifies that the data should be appended to the table if it already exists. You should adjust the

source_format

based on the format of your data in GCS. To use this asset, you would define a job that includes it and provide the necessary configuration for the

bigquery

resource, including credentials and project information. Remember to replace the

gcs_uri

,

dataset_id

, and

table_id

with your actual GCS URI, dataset name, and table name, respectively. Also, ensure that the

job_config

is correctly configured based on your data format and requirements. Please note that this example assumes you have already installed the

google-cloud-bigquery

package and have the necessary permissions to access the BigQuery dataset and table.