Google BigQuery - StreamNative Documentation

The Google Cloud BigQuery sink connector pulls data from Pulsar topics and persists data to Google Cloud BigQuery tables.

Quick start

Data can only be synchronized to Standard BigQuery tables. External tables and View are not supported.

Prerequisites

The prerequisites for connecting an Google BigQuery sink connector to external systems include:

Create GoogleBigQuery, DataSet in Google Cloud.
Create the Gcloud ServiceAccount and create a public key certificate.
Create the Gcloud Role, ensure the Google Cloud role have the following permissions to the Google BigQuery API:

- bigquery.tables.create
- bigquery.tables.get
- bigquery.tables.getData
- bigquery.tables.list
- bigquery.tables.update
- bigquery.tables.updateData

Grant the service account the above role permissions.

1. Create a connector

The following command shows how to use pulsarctl to create a builtin connector. If you want to create a non-builtin connector, you need to replace --sink-type bigquery with --archive /path/to/pulsar-io-bigquery.nar. You can find the button to download the nar package at the beginning of the document.

If you are a StreamNative Cloud user, you need set up your environment first.

pulsarctl sinks create \
  --sink-type bigquery \
  --name bigquery-sink \
  --tenant public \
  --namespace default \
  --inputs "Your topic name" \
  --parallelism 1 \
  --sink-config \
  '{
    "projectId": "Your BigQuery project Id", 
    "datasetName": "Your Bigquery DataSet name",
    "tableName": "The name of the table you want to write data to is automatically created by default",
    "credentialJsonString": "Public key certificate you created above"
  }'

The --sink-config is the minimum necessary configuration for starting this connector, and it is a JSON string. You need to substitute the relevant parameters with your own. If you want to configure more parameters, see Configuration Properties for reference.

You can also choose to use a variety of other tools to create a connector:

pulsar-admin: The command arguments for pulsar-admin are similar to those of pulsarctl. You can find an example for StreamNative Cloud Doc.
RestAPI: You can find an example for StreamNative Cloud Doc.
Terraform: You can find an example for StreamNative Cloud Doc.
Function Mesh: The docker image can be found at the beginning of the document.

2. Send messages to the topic

If your connector is created on StreamNative Cloud, you need to authenticate your clients. See Build applications using Pulsar clients for more information.

@Data
@ToString
public class TestMessage {
    private String testString;
    private String testInt;
 
    public static void main(String[] args) {
        PulsarClient client = PulsarClient.builder()
        .serviceUrl("{{Your Pulsar URL}}")
        .build();

        Producer<TestMessage> producer = client.newProducer(Schema.AVRO(TestMessage.class))
            .topic("{{Your topic name}}")
            .create();

        AvroDataConvertTestIntegration testMessage = new AvroDataConvertTestIntegration();
        testMessage.setTestString("test string");
        testMessage.setTestInt(123);
        MessageId msgID = producer.send(testMessage);
        System.out.println("Publish " + testMessage + " and message ID " + msgID);
        
        producer.flush();
        producer.close();
        client.close();  
    }
}

3. Show data on Google BigQuery

This connector will automatically create the table structure according to the schema. You can use sql to query the data in the console.

SELECT * FROM `{{Your project id}}.{{Your dataset name}}.{{Your table name}}`

+-----------------+-----------------+--------------------------------+----------------------------+-------------+---------+
| __meessage_id__ | __sequence_id__ |         __event_time__         |     __producer_name__      | testString  | testInt |
+-----------------+-----------------+--------------------------------+----------------------------+-------------+---------+
| 9:20:-1         |               0 | 2023-09-14 14:05:29.657000 UTC | test-bigquery-produce-name | test string |     123 |
+-----------------+-----------------+--------------------------------+----------------------------+-------------+---------+

Configuration Properties

Before using the Google Cloud BigQuery sink connector, you need to configure it. This table outlines the properties and the descriptions.

Name	Type	Required	Sensitive	Default	Description
`projectId`	String	Yes	false	"" (empty string)	The Google BigQuery project ID.
`datasetName`	String	Yes	false	"" (empty string)	The Google BigQuery dataset name.
`tableName`	String	Yes	false	"" (empty string)	The Google BigQuery table name.
`credentialJsonString`	String	Yes	true	"" (empty string)	The authentication JSON key. Set the environment variable `GOOGLE_APPLICATION_CREDENTIALS` to the path of the JSON file that contains your service account key when the `credentialJsonString` is set to an empty string. For details, see the Google documentation.
`visibleModel`	String	No	false	”Committed”	The mode that controls when data written to the stream becomes visible in BigQuery for reading. For details, see the Google documentation. Available options are `Committed` and `Pending`.
`pendingMaxSize`	int	No	false	10000	The maximum number of messages waiting to be committed in `Pending` mode.
`batchMaxSize`	int	No	false	20	The maximum number of batch messages. The actual batch bytes size cannot exceed 10 MB. If it does, the batch will be flushed first. https://cloud.google.com/bigquery/quotas
`batchMaxTime`	long	No	false	5000	The maximum batch waiting time (in units of milliseconds).
`batchFlushIntervalTime`	long	No	false	2000	The batch flush interval (in units of milliseconds).
`failedMaxRetryNum`	int	No	false	20	The maximum retries when appending fails. By default, it sets 2 seconds for each retry.
`autoCreateTable`	boolean	No	false	true	Automatically create a table if no table is available.
`autoUpdateTable`	boolean	No	false	true	Automatically update the table schema if the BigQuery table schema is incompatible with the Pulsar schema.
`partitionedTables`	boolean	No	false	true	Create a partitioned table when the table is automatically created. It will use the `__event_time__` as the partition key.
`partitionedTableIntervalDay`	int	No	false	7	The number of days between partitioning of the partitioned table.
`clusteredTables`	boolean	No	false	true	Create a clustered table when the table is automatically created. It will use the `__message_id__` as the cluster key.
`defaultSystemField`	String	No	false	"" (empty string)	Create the system fields when the table is automatically created. You can use commas to separate multiple fields. The supported system fields are: `__schema_version__` , `__partition__` , `__event_time__`, `__publish_time__` , `__message_id__` , `__sequence_id__` , `__producer_name__` and `__properties__`. The `__properties__` will be a repeat struct on bigquery. key and value will as a string type..

Advanced features

Delivery guarantees

The Pulsar IO connector framework provides three delivery guarantees: at-most-once, at-least-once, and effectively-once. Currently, the Google Cloud BigQuery sink connector only provides the at-least-once delivery guarantee.

Tables schema

The Google Cloud BigQuery sink connector supports automatically creating and updating a table’s schema based on the Pulsar topic schema. You can configure the following options:

autoCreataTables = true
autoUpdateSchema = true

If the Pulsar topic schema and BigQuery schema are different, the Google Cloud BigQuery sink connector updates schemas by merging them together. The Google Cloud BigQuery sink connector supports mapping schema structures to the BigQuery RECORD TYPE. In addition, the Google Cloud BigQuery sink connector supports writing some Pulsar-specific fields, as shown below:

#
# optional: __schema_version__ , __partition__   , __event_time__    , __publish_time__  
#           __message_id__     , __sequence_id__ , __producer_name__ , __key__ , __properties__ 
#
defaultSystemField = __event_time__,__message_id__

The Google Cloud BigQuery sink connector does not delete any fields. If you change a field name in a Pulsar topic, the Google Cloud BigQuery sink connector will preserve both fields.

This table lists the schema types that currently are supported to be converted.

Schema	Supported
AVRO	Yes
PRIMITIVE	Yes
PROTOBUF_NATIVE	Yes
PROTOBUF	No
JSON	No
KEY_VALUE	No

Partitioned tables

This feature is only available when autoCreateTable is set to true. If you create a table manually, you need to manually specify the partition key.

BigQuery supports partitioned tables. Partitioned tables can improve query and control costs by reducing the data read from the table. The Google Cloud BigQuery sink connector provides an option to create a partitioned table. The partitioned tables use the event_time as the partition key.

partitioned-tables = true

Clustered tables

This feature is only available when autoCreateTable is set to true. If you create a table manually, you need to manually specify the cluster key.

Clustered tables can improve the performance of certain queries, such as queries that use filter clauses and queries that aggregate data. The Google Cloud BigQuery sink connector provides an option to create a clustered table. The clustered tables use the message_id as the cluster key.

clustered-tables = true

Multiple tasks

You can leverage the Pulsar Functions scheduling mechanism to configure parallelism of the Google Cloud BigQuery sink connector. You can schedule multiple sink instances to run on different Function worker nodes. These sink instances consume messages according to the configured subscription mode.

parallelism = 4

It is an effective way to increase parallelism when you encounter write bottlenecks. In addition, you need to pay attention to whether the write rate is greater than BigQuery Rate Limits

Batch progress

To increase write throughput, the Google Cloud BigQuery sink connector supports configuring the batch size. You can set the batch size and latency using the following options.

batchMaxSize = 100
batchMaxTime = 4000
batchFlushIntervalTime = 2000

Sources

Sinks

Google bigquery sink

Quick start

Prerequisites

1. Create a connector

2. Send messages to the topic

3. Show data on Google BigQuery

Configuration Properties

Advanced features

Delivery guarantees

Tables schema

Partitioned tables

Clustered tables

Multiple tasks

Batch progress

Sources

Sinks

​Quick start

​Prerequisites

​1. Create a connector

​2. Send messages to the topic

​3. Show data on Google BigQuery

​Configuration Properties

​Advanced features

​Delivery guarantees

​Tables schema

​Partitioned tables

​Clustered tables

​Multiple tasks

​Batch progress

Quick start

Prerequisites

1. Create a connector

2. Send messages to the topic

3. Show data on Google BigQuery

Configuration Properties

Advanced features

Delivery guarantees

Tables schema

Partitioned tables

Clustered tables

Multiple tasks

Batch progress