The connector supports streaming data from Google BigQuery tables to Apache Flink, and writing results back to BigQuery tables. This is done by using the Flink’s Datastream API to communicate with BigQuery.
This connector is a work in progress, and here we’re providing a preview of its capabilities. It currently offers the feature to read data from a BigQuery table into a Flink application, and the ability to write results of Flink jobs to BigQuery tables with at-least-once write consistency. Exactly-once consistency will be offered soon. Users should note this is an experimental instrument, and we guarantee no SLOs at this stage.
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.
The Storage write API is a high-performance data-ingestion API for BigQuery.
Write data to a stream and commit the data as a single transaction. If the commit operation fails, safely retry the operation. Multiple workers can create their own streams to process data independently.
The Storage Write API uses gRPC streaming rather than REST over HTTP. The Storage Write API also supports binary formats in the form of protocol buffers, which are a more efficient wire format than JSON. Write requests are asynchronous with guaranteed ordering.
The Storage Write API supports exactly-once semantics through the use of stream offsets. This will be used when offering an exactly-once Sink for BigQuery.
The Storage read 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:
It does not leave any temporary files in Google Cloud Storage. Rows are read directly from BigQuery servers using the Avro wire format.
The API allows column and predicate filtering to only read the data you are interested in.
Since BigQuery is backed by a columnar datastore, it can efficiently stream data without reading all columns.
The Storage API supports arbitrary pushdown of predicate filters.
The API rebalances records between readers until they all complete.
Follow these instructions. For write APIs, ensure following pemissions are granted. For read APIs, ensure following permissions are granted.
- Unix-like environment (we use Linux, Mac OS X)
- Git
- Maven (we recommend version 3.8.6)
- Java 8
There are two ways to access the connector.
The connector is available on the Maven Central repository.
| Flink version | Connector Artifact |
|---|---|
| Flink 1.17.x | com.google.cloud.flink:flink-1.17-connector-bigquery:0.2.0 |
Users can obtain the connector artifact from our GitHub repository.
git clone https://github.com/GoogleCloudDataproc/flink-bigquery-connector
cd flink-bigquery-connector
git checkout tags/0.2.0
mvn clean install -DskipTests -Pflink_1.17Resulting jars can be found in the target directory of respective modules, i.e.
flink-bigquery-connector/flink-1.17-connector-bigquery/flink-connector-bigquery/target for the connector,
and flink-bigquery-connector/flink-1.17-connector-bigquery/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 tag \ Flink version | 1.15.x | 1.17.x |
|---|---|---|
| 0.1.0-preview | ✓ | ✓ |
| 0.2.0-preview | ✓ | ✓ |
| 0.2.0 | ✓ | ✓ |
A Google Cloud Dataproc cluster can be used as an execution environment for Flink runtime. Here we attach relevant documentation to execute Flink applications on Cloud Dataproc, but you can deploy the Flink runtime in other Google Cloud environments (like GKE) and submit jobs using Flink CLI or web UI.
Dataproc clusters will need the bigquery or cloud-platform scopes. Dataproc clusters have the bigquery scope
by default, so most clusters in enabled projects should work by default.
Follow this document.
| Connector tag \ Dataproc Image | 2.1 | 2.2 |
|---|---|---|
| 0.1.0-preview | ✓ | ✓ |
| 0.2.0-preview | ✓ | ✓ |
| 0.2.0 | ✓ | ✓ |
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.
<dependency>
<groupId>com.google.cloud.flink</groupId>
<artifactId>flink-1.17-connector-bigquery</artifactId>
<version>0.2.0</version>
</dependency>- Sink factory methods are defined at
com.google.cloud.flink.bigquery.sink.BigQuerySink. - Sink configs are defined at
com.google.cloud.flink.bigquery.sink.BigQuerySinkConfig. - 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.
Flink Sink is the base interface for developing a sink. With checkpointing enabled, it can offer at-least-once consistency. Our implementation uses BigQuery Storage's default write stream in Sink's Writers.
// StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// env.enableCheckpointing(checkpointInterval);
BigQueryConnectOptions sinkConnectOptions =
BigQueryConnectOptions.builder()
.setProjectId(...)
.setDataset(...)
.setTable(...)
.build();
BigQuerySchemaProvider schemaProvider = new BigQuerySchemaProviderImpl(sinkConnectOptions);
BigQuerySinkConfig sinkConfig =
BigQuerySinkConfig.newBuilder()
.connectOptions(sinkConnectOptions)
.deliveryGuarantee(DeliveryGuarantee.AT_LEAST_ONCE)
.schemaProvider(schemaProvider)
.serializer(new AvroToProtoSerializer())
.build();
Sink<GenericRecord> sink = BigQuerySink.get(sinkConfig, env);- BigQuery sinks require that checkpoint is enabled for at-least-once consistency.
- Delivery guarantee must be at-least-once.
- BigQueryConnectOptions stores information needed to connect to a BigQuery table.
- AvroToProtoSerializer is the only out-of-the-box serializer offered for now. It expects data to arrive at the sink as avro's GenericRecord. Other relevant data formats will be supported soon. Also, users can create their own implementation of BigQueryProtoSerializer for other data formats.
- BigQuerySchemaProvider exposes schema related information about the BigQuery table. This is needed by the sink to write data to BigQuery tables. It can also be used by the serializer if needed (for instance, the AvroToProtoSerializer uses BigQuery table's schema).
- Flink cannot automatically serialize avro's GenericRecord, hence users must explicitly specify type information when using the AvroToProtoSerializer. Check Flink's blog on non-trivial serialization. Note that the avro schema needed here can be obtained from BigQuerySchemaProvider.
- The maximum parallelism of BigQuery sinks has been capped at 100. This is to respect BigQuery storage write quotas while adhering to best usage practices. Users should either set sink level parallelism explicitly, or ensure that default job level parallelism is under 100.
- Users are recommended to choose their application's restart strategy wisely, so as to avoid incessant retries which can potentially disrupt the BigQuery Storage API backend. Regardless of which strategy is adopted, the restarts must be finite and graciously spaced.
- If a data record cannot be serialized by BigQuery sink, then the record is dropped with a warning getting logged. Moving on, we plan to introduce a Flink metric for tracking such data. Additionally, a dead letter queue will be introduced in the future to store this data.
Important: Please refer to data ingestion pricing to understand the BigQuery Storage Write API pricing.
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.
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());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.
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. |
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 |
The flink-1.17-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.
See the BigQuery Pricing Documentation.
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_CREDENTIALSenvironment 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.
Change Flink’s classloader strategy to parent-first. This can be made default in the flink-conf yaml.
Point flink-conf yaml’s state.checkpoints.dir to a bucket in Google Storage, as file system storages are more suitable
for yarn applications.
The maximum parallelism of BigQuery sinks has been capped at 100. Please set sink level parallelism or default job level parallelism as 100 or less.
Records that cannot be serialized to BigQuery protobuf format are dropped with a warning being logged. In future, a Flink metric and dead letter queues will be supported to better track such records.