Custom Analysis with Spark


Apache Spark is a general-purpose cluster computing system - it allows users to run general execution graphs. APIs are available in Python, Scala, R, and Java. It is designed to be fast and easy to use.

Here are some useful introductory materials:

Spark can be used from Google's Dataproc, and works with data stored in BigQuery.

There are a number of methods of both reading from and writing to BigQuery using Spark.

Accessing BigQuery data from Spark

Using the Storage API Connector

This method requires BigQuery Access to be provisioned.

If you want to use Spark locally (or via an arbitrary GCP instance in the cloud), we recommend the Storage API Connector for accessing BigQuery tables in Spark as it is the most modern and actively developed connector. It works well with the BigQuery client library which is useful if you need to run arbitrary SQL queries and load their results into Spark.

Using Dataproc

This method requires BigQuery Access to be provisioned.

Dataproc is Google's managed Spark cluster service.

You can spin up a Dataproc cluster with Jupyter using the following command. Insert your values for cluster-name, bucket-name, and project-id there. Your notebooks are stored in Cloud Storage under gs://bucket-name/notebooks/jupyter:

gcloud beta dataproc clusters create cluster-name \
    --optional-components=ANACONDA,JUPYTER \
    --image-version=1.4 \
    --enable-component-gateway \
    --properties=^#^spark:spark.jars=gs://spark-lib/bigquery/spark-bigquery-latest.jar \
    --num-workers=3 \
    --max-idle=3h \
    --bucket bucket-name \
    --region=us-west1 \
    --project project-id

You can retrieve the Jupyter URL with the following command:

gcloud beta dataproc clusters describe cluster-name --region=us-west1 --project project-id | grep Jupyter

After you've finished your work, it's a good practice to delete your cluster:

gcloud beta dataproc clusters delete cluster-name --region=us-west1 --project project-id --quiet

Reading data from BigQuery into Spark

There are two main ways to read data from BigQuery into Spark: using either the storage API and the query API.

Storage API

First, using the Storage API - this bypasses BigQuery's execution engine and directly reads from the underlying storage.

This is the preferred method of loading data from BigQuery into Spark.

It is more efficient for reading large amounts of data into Spark, and supports basic column and partitioning filters.

Example of using the Storage API from Databricks:

dbutils.library.installPyPI("google-cloud-bigquery", "1.16.0")

from import bigquery

def get_table(view):
    """Helper for determining what table underlies a user-facing view, since the Storage API can't read views."""
    bq = bigquery.Client()
    view = view.replace(":", ".")
    # partition filter is required, so try a couple options
    for partition_column in ["DATE(submission_timestamp)", "submission_date"]:
            job = bq.query(
                f"SELECT * FROM `{view}` WHERE {partition_column} = CURRENT_DATE",
        except Exception:
        raise ValueError("could not determine partition column")
    assert len(job.referenced_tables) == 1, "View combines multiple tables"
    table = job.referenced_tables[0]
    return f"{table.project}:{table.dataset_id}.{table.table_id}"

# Read one day of main pings and select a subset of columns.
core_pings_single_day ="bigquery") \
    .option("table", get_table("moz-fx-data-shared-prod.telemetry.main")) \
    .load() \
    .where("submission_timestamp >= to_date('2019-08-25') submission_timestamp < to_date('2019-08-26')") \
    .select("client_id", "experiments", "normalized_channel")

A couple of things are worth noting in the above example.

  • get_table is necessary because an actual table name is required to read from BigQuery here, fully qualified with project name and dataset name. The Storage API does not support accessing VIEWs, so the convenience names such as telemetry.core are not available via this API.
  • You must supply a filter on the table's date partitioning column, in this case submission_timestamp. Additionally, you must use the to_date function to make sure that predicate push-down works properly for these filters.

Query API

If you want to read the results of a query (rather than directly reading tables), you may also use the Query API.

This pushes the execution of the query into BigQuery's computation engine, and is typically suitable for reading smaller amounts of data. If you need to read large amounts of data, prefer the Storage API as described above.


from import bigquery

bq = bigquery.Client()

query = """
  count(distinct client_id) AS client_count
  event_category = 'normandy'
  AND event_method = 'enroll'
  AND submission_date = '2019-06-01'
  client_count DESC

query_job = bq.query(query)

# Wait for query execution, then fetch results as a pandas dataframe.
rows = query_job.result().to_dataframe()

Persisting data

You can save data resulting from your Spark analysis as a BigQuery table or to Google Cloud Storage.

You can also save data to the Databricks Filesystem.