Apache Flink Google BigQuery Connector (Under Development)

The connector supports streaming data from Google BigQuery tables to Apache Flink. This is done by using the Flink’s Datastream API to communicate with BigQuery.

Public Preview

This connector is a work in progress, and here we’re providing the first preview of its capabilities. It currently offers the feature to read data from a BigQuery table into a Flink application, while the ability to write results of Flink jobs to BigQuery tables will be offered in the near future. Users should note this is an experimental instrument, and we guarantee no SLOs at this stage.

Apache Flink

Apache Flink is an open source framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Learn more about Flink here.

BigQuery Storage API

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

Following are some benefits of using the Storage API:

Direct Streaming

Rows are read directly from BigQuery servers using the Avro wire format.

Filtering

The 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.

Dynamic Sharding

The API rebalances records between readers until they all complete.

Requirements

Enable the BigQuery Storage API

Follow these instructions.

Downloading and Using the Connector

Users are expected to clone and build this repository locally in order to obtain the connector jar and/or maven artifact. Being an experimental preview, this connector has not been published to maven central yet.

Prerequisites

• Unix-like environment (we use Linux, Mac OS X)
• Git
• Maven (we recommend version 3.8.6)
• Java 11

Steps

git clone https://github.com/GoogleCloudDataproc/flink-bigquery-connector
cd flink-bigquery-connector
git checkout tags/v0.1.0-preview
mvn clean install -DskipTests

Resulting jars can be found in the target directory of respective modules, i.e. flink-bigquery-connector/flink-connector-bigquery/target for the connector, and flink-bigquery-connector/flink-connector-bigquery-examples/target for a sample application.

Maven artifacts are installed under .m2/repository.

If only the jars are needed, then execute maven package instead of install.

Connector to Flink Compatibility

Connector tag \ Flink version	1.15.4	1.17.1
0.1.0-preview	✓	✓

Create a Google Cloud Dataproc cluster (Optional)

If you do not have an Apache Flink environment you can create a Cloud Dataproc cluster with pre-configured auth. Here we attach relevant documentation to execute Flink applications on Cloud Dataproc, but you can deploy the Flink runtime in any environment and submit jobs using Flink CLI or web UI.

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.

Dataproc Flink Component

Follow this document.

Connector to Dataproc Image Compatibility Matrix

Connector tag \ Dataproc Image	2.1
0.1.0-preview	✓

Usage

The connector uses Flink’s Datastream API, and can be used in Java applications. For a Flink source, it offers two read modes, bounded and unbounded.

Compiling against the connector

Maven Dependency

<dependency>
  <groupId>com.google.cloud.flink</groupId>
  <artifactId>flink-connector-bigquery</artifactId>
  <version>0.1.0-preview</version>
</dependency>

Relevant Files

• Source factory methods are defined at com.google.cloud.flink.bigquery.source.BigQuerySource.
• Source configs are defined at com.google.cloud.flink.bigquery.source.config.BigQueryReadOptions.
• BigQuery connection config is defined at com.google.cloud.flink.bigquery.common.config.BigQueryConnectOptions.
• Sample Flink application using connector is defined at com.google.cloud.flink.bigquery.examples.BigQueryExample.

Unbounded Source

A timestamp partitioned table will be continuously checked for “completed” partitions, which the connector will stream into the Flink application.

BigQuerySource<GenericRecord> source =
    BigQuerySource.streamAvros(
        BigQueryReadOptions.builder()
            .setBigQueryConnectOptions(
                BigQueryConnectOptions.builder()
                    .setProjectId(...)
                    .setDataset(...)
                    .setTable(...)
                    .build())
            .setColumnNames(...)
            .setLimit(...)
            .setMaxRecordsPerSplitFetch(...)
            .setMaxStreamCount(...)
            .setOldestPartitionId(...)
            .setPartitionDiscoveryRefreshIntervalInMinutes(...)
            .setRowRestriction(...)
            .setSnapshotTimestampInMillis(...)
            .build());

• A partition is considered “complete” if the table’s write buffer’s oldest entry’s ingestion time is after the partition’s end.
• If the table’s write buffer is empty, then a partition is considered complete if java.time.Instant.now() is after the partition’s end.
• This approach is susceptible to out-of-order data, and we plan to replace it with a lateness tolerance beyond the partition’s end in future releases.

Bounded Source

Table

A table will be read once, and its rows at the time will be streamed into the Flink application.

BigQuerySource<GenericRecord> source =
    BigQuerySource.readAvros(
        BigQueryReadOptions.builder()
        .setBigQueryConnectOptions(
            BigQueryConnectOptions.builder()
            .setProjectId(...)
            .setDataset(...)
            .setTable(...)
            .build())
        .setColumnNames(...)
        .setLimit(...)
        .setMaxRecordsPerSplitFetch(...)
        .setMaxStreamCount(...)
        .setRowRestriction(...)
        .setSnapshotTimestampInMillis(...)
        .build());

Query

A SQL query will be executed in the GCP project, and its view will be streamed into the Flink application.

BigQuerySource<GenericRecord> bqSource =
    BigQuerySource.readAvrosFromQuery(query, projectId, limit);
    // OR
    BigQuerySource.readAvrosFromQuery(query, projectId);

• Operations (like JOINs) which can be performed as queries in BigQuery should be executed this way because they’ll be more efficient than Flink, and only the result will be transmitted over the wire.
• Since BigQuery executes the query and stores results in a temporary table, this may add additional costs on your BigQuery account.
• The connector’s query source offers limited configurability compared to bounded/unbounded table reads. This will be addressed in future releases.
• The connector does not manage query generated views beyond creation and read. Users will need to manage these views on their own, until future releases expose a configuration in the connector to delete them or assign a time-to-live.

Connector Configurations

The connector supports a number of options to configure the source.

Property	Data Type	Description
projectId	String	Google Cloud Project ID of the table. This config is required, and assumes no default value.
dataset	String	Dataset containing the table. This config is required for standard tables, but not when loading query results.
table	String	BigQuery table in the format [[projectId:]dataset.]table. This config is required for standard tables, but not when loading query results.
credentialsOptions	CredentialsOptions	Google credentials for connecting to BigQuery. This config is optional, and default behavior is to use the GOOGLE_APPLICATION_CREDENTIALS environment variable. Note: The query bounded source only uses default application credentials.
query	String	BigQuery SQL query used in the bounded query source. This config is not required for bounded table or unbounded source.
columnNames	List<String>	Columns to project from the table. This config is used in bounded table or unbounded source. If unspecified, all columns are fetched.
limit	Integer	Maximum number of rows to read from table. This config is used in all source types. If unspecified, all rows are fetched.
maxRecordsPerSplitFetch	Integer	Maximum number of records to read from a split once Flink requests fetch. This config is used in bounded table or unbounded source. If unspecified, the default value used is 10000. Note: Configuring this number too high may cause memory pressure in the task manager, depending on the BigQuery record's size and total rows on the stream.
maxStreamCount	Integer	Maximum read streams to open during a read session. BigQuery can return a lower number of streams than specified based on internal optimizations. This config is used in bounded table or unbounded source. If unspecified, this config is not set and BigQuery has complete control over the number of read streams created.
rowRestriction	String	BigQuery SQL query for row filter pushdown. This config is used in bounded table or unbounded source. If unspecified, all rows are fetched.
snapshotTimeInMillis	Long	Time (in milliseconds since epoch) for the BigQuery table snapshot to read. This config is used in bounded table or unbounded source. If unspecified, the latest snapshot is read.
oldestPartitionId	String	Earliest table partition to consider for unbounded reads. This config is used in unbounded source. If unspecified, all partitions are read.
partitionDiscoveryRefreshIntervalInMinutes	Integer	Periodicity (in minutes) of partition discovery in table. This config is used in unbounded source. If unspecified, the default value used is 10 minutes.

Datatypes

All the current BigQuery datatypes are being handled when transforming data from BigQuery to Avro’s GenericRecord.

BigQuery Data Type	Converted Avro Datatype
STRING	STRING
GEOGRAPHY	STRING
BYTES	BYTES
INTEGER	LONG
INT64	LONG
FLOAT	DOUBLE
FLOAT64	DOUBLE
NUMERIC	BYTES
BIGNUMERIC	BYTES
BOOLEAN	BOOLEAN
BOOL	BOOLEAN
TIMESTAMP	LONG
RECORD	RECORD
STRUCT	RECORD
DATE	STRING, INT
DATETIME	STRING
TIME	STRING, LONG
JSON	STRING

Example Application

The flink-connector-bigquery-examples module offers a sample Flink application powered by the connector. It can be found at com.google.cloud.flink.bigquery.examples.BigQueryExample. It offers an intuitive hands-on application with elaborate guidance to test out the connector and its various configurations across different source read modes.

FAQ

What is the pricing for the Storage API?

See the BigQuery Pricing Documentation.

How do I authenticate outside GCE / Dataproc?

The connector needs an instance of a GoogleCredentials in order to connect to the BigQuery APIs. There are multiple options to provide it:

• The default is to load the JSON key from the GOOGLE_APPLICATION_CREDENTIALS environment variable, as described here.
• In case the environment variable cannot be changed, the credentials file can be configured as a connector option. The file should reside on the same path on all the nodes of the cluster.

How to fix classloader error in Flink application?

Change Flink’s classloader strategy to parent-first. This can be made default in the flink-conf yaml.

How to fix issues with checkpoint storage when running Flink on Dataproc?

Point flink-conf yaml’s state.checkpoints.dir to a bucket in Google Storage, as file system storages are more suitable for yarn applications.