Apache Spark SQL connector for Google BigQuery (Beta)

The connector supports reading Google BigQuery tables into Spark's DataFrames, and writing DataFrames back into BigQuery. This is done by using the Spark SQL Data Source API to communicate with BigQuery.

Beta Disclaimer

The BigQuery Storage API and this connector are in Beta and are subject to change.

Changes may include, but are not limited to:

• Type conversion
• Partitioning
• Parameters

Breaking changes will be restricted to major and minor versions.

BigQuery Storage API

The Storage API streams data in parallel directly from BigQuery via gRPC without using Google Cloud Storage as an intermediary.

It has a number of advantages over using the previous export-based read flow that should generally lead to better read performance:

Direct Streaming

It does not leave any temporary files in Google Cloud Storage. Rows are read directly from BigQuery servers using the Arrow or Avro wire formats.

Filtering

The new API allows column and predicate filtering to only read the data you are interested in.

Column Filtering

Since BigQuery is backed by a columnar datastore, it can efficiently stream data without reading all columns.

Predicate Filtering

The Storage API supports arbitrary pushdown of predicate filters. Connector version 0.8.0-beta and above support pushdown of arbitrary filters to Bigquery.

There is a known issue in Spark that does not allow pushdown of filters on nested fields. For example - filters like address.city = "Sunnyvale" will not get pushdown to Bigquery.

Dynamic Sharding

The API rebalances records between readers until they all complete. This means that all Map phases will finish nearly concurrently. See this blog article on how dynamic sharding is similarly used in Google Cloud Dataflow.

See Configuring Partitioning for more details.

Requirements

Enable the BigQuery Storage API

Follow these instructions.

Create a Google Cloud Dataproc cluster (Optional)

If you do not have an Apache Spark environment you can create a Cloud Dataproc cluster with pre-configured auth. The following examples assume you are using Cloud Dataproc, but you can use spark-submit on any cluster.

Any Dataproc cluster using the API needs the 'bigquery' or 'cloud-platform' scopes. Dataproc clusters have the 'bigquery' scope by default, so most clusters in enabled projects should work by default e.g.

MY_CLUSTER=...
gcloud dataproc clusters create "$MY_CLUSTER"

Downloading and Using the Connector

The latest version of the connector is publicly available in the following links:

The connector is also available from the Maven Central repository. It can be used using the --packages option or the spark.jars.packages configuration property. Use the following value

If you want to keep up with the latest version of the connector the following links can be used. Notice that for production environments where the connector version should be pinned, one of the above links should be used.

Hello World Example

You can run a simple PySpark wordcount against the API without compilation by running

Dataproc image 1.5 and above

gcloud dataproc jobs submit pyspark --cluster "$MY_CLUSTER" \
  --jars gs://spark-lib/bigquery/spark-bigquery-latest_2.12.jar \
  examples/python/shakespeare.py

Dataproc image 1.4 and below

gcloud dataproc jobs submit pyspark --cluster "$MY_CLUSTER" \
  --jars gs://spark-lib/bigquery/spark-bigquery-latest_2.11.jar \
  examples/python/shakespeare.py

Example Codelab

https://codelabs.developers.google.com/codelabs/pyspark-bigquery

Usage

The connector uses the cross language Spark SQL Data Source API:

Reading data from a BigQuery table

df = spark.read \
  .format("bigquery") \
  .load("bigquery-public-data.samples.shakespeare")

or the Scala only implicit API:

import com.google.cloud.spark.bigquery._
val df = spark.read.bigquery("bigquery-public-data.samples.shakespeare")

For more information, see additional code samples in Python, Scala and Java.

Reading data from a BigQuery query

The connector allows you to run any Standard SQL SELECT query on BigQuery and fetch its results directly to a Spark Dataframe. This is easily done as described in the following code sample:

spark.conf.set("viewsEnabled","true")
spark.conf.set("materializationDataset","<dataset>")

sql = """
  SELECT tag, COUNT(*) c
  FROM (
    SELECT SPLIT(tags, '|') tags
    FROM `bigquery-public-data.stackoverflow.posts_questions` a
    WHERE EXTRACT(YEAR FROM creation_date)>=2014
  ), UNNEST(tags) tag
  GROUP BY 1
  ORDER BY 2 DESC
  LIMIT 10
  """
df = spark.read.format("bigquery").load(sql)
df.show()

Which yields the result

+----------+-------+
|       tag|      c|
+----------+-------+
|javascript|1643617|
|    python|1352904|
|      java|1218220|
|   android| 913638|
|       php| 911806|
|        c#| 905331|
|      html| 769499|
|    jquery| 608071|
|       css| 510343|
|       c++| 458938|
+----------+-------+

A second option is to use the query option like this:

df = spark.read.format("bigquery").option("query", sql).load()

Notice that the execution should be faster as only the result is transmitted over the wire. In a similar fashion the queries can include JOINs more efficiently then running joins on Spark or use other BigQuery features such as subqueries, BigQuery user defined functions, wildcard tables, BigQuery ML and more.

In order to use this feature the following configurations MUST be set:

• viewsEnabled must be set to true.
• materializationDataset must be set to a dataset where the GCP user has table creation permission. materializationProject is optional.

Important: This feature is implemented by running the query on BigQuery and saving the result into a temporary table, of which Spark will read the results from. This may add additional costs on your BigQuery account.

Writing data to BigQuery

Writing a DataFrame to BigQuery is done in a similar manner. Notice that the process writes the data first to GCS and then loads it to BigQuery, a GCS bucket must be configured to indicate the temporary data location.

df.write \
  .format("bigquery") \
  .option("temporaryGcsBucket","some-bucket") \
  .save("dataset.table")

The data is temporarily stored using the Apache parquet format. An alternative format is Apache ORC.

The GCS bucket and the format can also be set globally using Spark"s RuntimeConfig like this:

spark.conf.set("temporaryGcsBucket","some-bucket")
df.write \
  .format("bigquery") \
  .save("dataset.table")

When streaming a DataFrame to BigQuery, each batch is written in the same manner as a non-streaming DataFrame. Note that a HDFS compatible checkpoint location (eg: path/to/HDFS/dir or gs://checkpoint-bucket/checkpointDir) must be specified.

df.writeStream \
  .format("bigquery") \
  .option("temporaryGcsBucket","some-bucket") \
  .option("checkpointLocation", "some-location") \
  .option("table", "dataset.table")

Important: The connector does not configure the GCS connector, in order to avoid conflict with another GCS connector, if exists. In order to use the write capabilities of the connector, please configure the GCS connector on your cluster as explained here.

Properties

The API Supports a number of options to configure the read

Options can also be set outside of the code, using the --conf parameter of spark-submit or --properties parameter of the gcloud dataproc submit spark. In order to use this, prepend the prefix spark.datasource.bigquery. to any of the options, for example spark.conf.set("temporaryGcsBucket", "some-bucket") can also be set as --conf spark.datasource.bigquery.temporaryGcsBucket=some-bucket.

Data types

With the exception of DATETIME and TIME all BigQuery data types directed map into the corresponding Spark SQL data type. Here are all of the mappings:

Spark ML Data Types Support

The Spark ML Vector and Matrix are supported, including their dense and sparse versions. The data is saved as a BigQuery RECORD. Notice that a suffix is added to the field's description which includes the spark type of the field.

In order to write those types to BigQuery, use the ORC or Avro intermediate format, and have them as column of the Row (i.e. not a field in a struct).

Filtering

The connector automatically computes column and pushdown filters the DataFrame's SELECT statement e.g.

spark.read.bigquery("bigquery-public-data:samples.shakespeare")
  .select("word")
  .where("word = 'Hamlet' or word = 'Claudius'")
  .collect()

filters to the column word and pushed down the predicate filter word = 'hamlet' or word = 'Claudius'.

If you do not wish to make multiple read requests to BigQuery, you can cache the DataFrame before filtering e.g.:

val cachedDF = spark.read.bigquery("bigquery-public-data:samples.shakespeare").cache()
val rows = cachedDF.select("word")
  .where("word = 'Hamlet'")
  .collect()
// All of the table was cached and this doesn't require an API call
val otherRows = cachedDF.select("word_count")
  .where("word = 'Romeo'")
  .collect()

You can also manually specify the filter option, which will override automatic pushdown and Spark will do the rest of the filtering in the client.

Partitioned Tables

The pseudo columns _PARTITIONDATE and _PARTITIONTIME are not part of the table schema. Therefore in order to query by the partitions of partitioned tables do not use the where() method shown above. Instead, add a filter option in the following manner:

val df = spark.read.format("bigquery")
  .option("filter", "_PARTITIONDATE > '2019-01-01'")
  ...
  .load(TABLE)

Configuring Partitioning

By default the connector creates one partition per 400MB in the table being read (before filtering). This should roughly correspond to the maximum number of readers supported by the BigQuery Storage API. This can be configured explicitly with the parallelism property. BigQuery may limit the number of partitions based on server constraints.

Reading From Views

The connector has a preliminary support for reading from BigQuery views. Please note there are a few caveats:

• BigQuery views are not materialized by default, which means that the connector needs to materialize them before it can read them. This process affects the read performance, even before running any collect() or count() action.
• The materialization process can also incur additional costs to your BigQuery bill.
• By default, the materialized views are created in the same project and dataset. Those can be configured by the optional viewMaterializationProject and viewMaterializationDataset options, respectively. These options can also be globally set by calling spark.conf.set(...) before reading the views.
• Reading from views is disabled by default. In order to enable it, either set the viewsEnabled option when reading the specific view (.option("viewsEnabled", "true")) or set it globally by calling spark.conf.set("viewsEnabled", "true").

Using in Jupyter Notebooks

The connector can be used in Jupyter notebooks even if it is not installed on the Spark cluster. It can be added as an external jar in using the following code:

Python:

from pyspark.sql import SparkSession
spark = SparkSession.builder\
  .config("spark.jars.packages", "com.google.cloud.spark:spark-bigquery-with-dependencies_2.11:0.19.1")\
  .getOrCreate()
df = spark.read.format("bigquery")\
  .load("dataset.table")

Scala:

val spark = SparkSession.builder
  .config("spark.jars.packages", "com.google.cloud.spark:spark-bigquery-with-dependencies_2.11:0.19.1")
  .getOrCreate()
val df = spark.read.format("bigquery")
  .load("dataset.table")

In case Spark cluster is using Scala 2.12 (it's optional for Spark 2.4.x, mandatory in 3.0.x), then the relevant package is com.google.cloud.spark:spark-bigquery-with-dependencies_2.12:0.19.1. In order to know which Scala version is used, please run the following code:

Python:

spark.sparkContext._jvm.scala.util.Properties.versionString()

Scala:

scala.util.Properties.versionString

Compiling against the connector

Unless you wish to use the implicit Scala API spark.read.bigquery("TABLE_ID"), there is no need to compile against the connector.

To include the connector in your project:

Maven

<dependency>
  <groupId>com.google.cloud.spark</groupId>
  <artifactId>spark-bigquery-with-dependencies_${scala.version}</artifactId>
  <version>0.19.1</version>
</dependency>

SBT

libraryDependencies += "com.google.cloud.spark" %% "spark-bigquery-with-dependencies" % "0.19.1"

Building the Connector

The connector is built using SBT. Following command creates a jar with shaded dependencies:

sbt assembly

FAQ

What is the Pricing for the Storage API?

See the BigQuery pricing documentation.

I have very few partitions

You can manually set the number of partitions with the maxParallelism property. BigQuery may provide fewer partitions than you ask for. See Configuring Partitioning.

You can also always repartition after reading in Spark.

How do I authenticate outside GCE / Dataproc?

Use a service account JSON key and GOOGLE_APPLICATION_CREDENTIALS as described here.

Credentials can also be provided explicitly either as a parameter or from Spark runtime configuration. It can be passed in as a base64-encoded string directly, or a file path that contains the credentials (but not both).

Example:

spark.read.format("bigquery").option("credentials", "<SERVICE_ACCOUNT_JSON_IN_BASE64>")

or

spark.conf.set("credentials", "<SERVICE_ACCOUNT_JSON_IN_BASE64>")

Alternatively, specify the credentials file name.

spark.read.format("bigquery").option("credentialsFile", "</path/to/key/file>")

or

spark.conf.set("credentialsFile", "</path/to/key/file>")

Another alternative to passing the credentials, is to pass the access token used for authenticating the API calls to the Google Cloud Platform APIs. You can get the access token by running gcloud auth application-default print-access-token.

spark.read.format("bigquery").option("gcpAccessToken", "<acccess-token>")

or

spark.conf.set("gcpAccessToken", "<access-token>")