BigQuery Query#

This example shows how to use a Flyte BigQueryTask to execute a query.

    from typing import Annotated
except ImportError:
    from typing_extensions import Annotated

import pandas as pd
from flytekit import StructuredDataset, kwtypes, task, workflow
from flytekitplugins.bigquery import BigQueryConfig, BigQueryTask

This is the world’s simplest query. Note that in order for registration to work properly, you’ll need to give your BigQuery task a name that’s unique across your project/domain for your Flyte installation.

bigquery_task_no_io = BigQueryTask(
    query_template="SELECT 1",
    task_config=BigQueryConfig(ProjectID="flyte", Location="us-west1-b"),

def no_io_wf():
    return bigquery_task_no_io()

Of course, in real world applications we are usually more interested in using BigQuery to query a dataset. In this case we use crypto_dogecoin data which is public dataset in BigQuery. here

Let’s look out how we can parameterize our query to filter results for a specific transaction version, provided as a user input specifying a version.

DogeCoinDataset = Annotated[
    StructuredDataset, kwtypes(hash=str, size=int, block_number=int)

bigquery_task_templatized_query = BigQueryTask(
    # Define inputs as well as their types that can be used to customize the query.
    task_config=BigQueryConfig(ProjectID="flyte", Location="us-west1-b"),
    query_template="SELECT * FROM `bigquery-public-data.crypto_dogecoin.transactions` WHERE @version = 1 LIMIT 10;",

StructuredDataset transformer can convert query result to pandas dataframe here. We can also change “pandas.dataframe” to “pyarrow.Table”, and convert result to Arrow table.

def convert_bq_table_to_pandas_dataframe(sd: DogeCoinDataset) -> pd.DataFrame:

def full_bigquery_wf(version: int) -> pd.DataFrame:
    sd = bigquery_task_templatized_query(version=version)
    return convert_bq_table_to_pandas_dataframe(sd=sd)

Check query result on bigquery console:

Total running time of the script: ( 0 minutes 0.000 seconds)

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