BigQuery agent example usage#

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

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

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",

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.
    query_template="SELECT * FROM `bigquery-public-data.crypto_dogecoin.transactions` WHERE version = @version 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: