Configuration Reference for Google BigQuery Sink Connector for Confluent Platform (Legacy)

The BigQuery Sink connector can be configured using a variety of configuration properties.

Note

defaultDataset

The default dataset to be used

  • Type: string
  • Importance: high

Note

defaultDataset replaced the datasets parameter of older versions of this connector.

project

The BigQuery project to write to.

  • Type: string
  • Importance: high
topics

A list of Kafka topics to read from.

  • Type: list
  • Importance: high
autoCreateTables

Create BigQuery tables if they don’t already exist. This property should only be enabled for Schema Registry-based inputs: Avro, Protobuf, or JSON Schema (JSON_SR). Table creation is not supported for JSON input.

  • Type: boolean
  • Default: false
  • Importance: high
gcsBucketName

The name of the bucket where Google Cloud Storage (GCS) blobs are located. These blobs are used to batch-load to BigQuery. This is applicable only if enableBatchLoad is configured.

  • Type: string
  • Default: “”
  • Importance: high
queueSize

The maximum size (or -1 for no maximum size) of the worker queue for BigQuery write requests before all topics are paused. This is a soft limit; the size of the queue can go over this before topics are paused. All topics resume once a flush is triggered or the size of the queue drops under half of the maximum size.

  • Type: long
  • Default: -1
  • Valid Values: [-1,…]
  • Importance: high
bigQueryRetry

The number of retry attempts made for a BigQuery request that fails with a backend error or a quota exceeded error.

  • Type: int
  • Default: 0
  • Valid Values: [0,…]
  • Importance: medium
bigQueryRetryWait

The minimum amount of time, in milliseconds, to wait between retry attempts for a BigQuery backend or quota exceeded error.

  • Type: long
  • Default: 1000
  • Valid Values: [0,…]
  • Importance: medium
bigQueryMessageTimePartitioning

Whether or not to use the message time when inserting records. Default uses the connector processing time.

  • Type: boolean
  • Default: false
  • Importance: high
bigQueryPartitionDecorator

Whether or not to append partition decorator to BigQuery table name when inserting records. Default is true. Setting this to true appends partition decorator to table name (e.g. table$yyyyMMdd depending on the configuration set for bigQueryPartitionDecorator). Setting this to false bypasses the logic to append the partition decorator and uses raw table name for inserts.

  • Type: boolean
  • Default: true
  • Importance: high
timestampPartitionFieldName

The name of the field in the value that contains the timestamp to partition by in BigQuery and enable timestamp partitioning for each table. Leave this configuration blank, to enable ingestion time partitioning for each table.

  • Type: string
  • Default: null
  • Importance: low
clusteringPartitionFieldNames

Comma-separated list of fields where data is clustered in BigQuery.

  • Type: list
  • Default: null
  • Importance: low
timePartitioningType

The time partitioning type to use when creating tables. Existing tables will not be altered to use this partitioning type.

  • Type: string
  • Default: DAY
  • Valid Values: (case insensitive) [MONTH, YEAR, HOUR, DAY]
  • Importance: low
keySource

Determines whether the keyfile configuration is the path to the credentials JSON file or to the JSON itself. Available values are FILE and JSON. This property is available in BigQuery sink connector version 1.3 (and later).

  • Type: string
  • Default: FILE
  • Importance: medium
keyfile

keyfile can be either a string representation of the Google credentials file or the path to the Google credentials file itself. The string representation of the Google credentials file is supported in BigQuery sink connector version 1.3 (and later).

  • Type: string
  • Default: null
  • Importance: medium
sanitizeTopics

Designates whether to automatically sanitize topic names before using them as table names. If not enabled, topic names are used as table names.

  • Type: boolean
  • Default: false
  • Importance: medium
schemaRetriever

A class that can be used for automatically creating tables and/or updating schemas. Note that in version 2.0.0, SchemaRetriever API changed to retrieve the schema from each SinkRecord, which will help support multiple schemas per topic. SchemaRegistrySchemaRetriever has been removed as it retrieves schema based on the topic.

  • Type: class
  • Default: com.wepay.kafka.connect.bigquery.retrieve.IdentitySchemaRetriever
  • Importance: medium
threadPoolSize

The size of the BigQuery write thread pool. This establishes the maximum number of concurrent writes to BigQuery.

  • Type: int
  • Default: 10
  • Valid Values: [1,…]
  • Importance: medium
allBQFieldsNullable

If true, no fields in any produced BigQuery schema are REQUIRED. All non-nullable Avro fields are translated as NULLABLE (or REPEATED, if arrays).

  • Type: boolean
  • Default: false
  • Importance: low
avroDataCacheSize

The size of the cache to use when converting schemas from Avro to Kafka Connect.

  • Type: int
  • Default: 100
  • Valid Values: [0,…]
  • Importance: low
batchLoadIntervalSec

The interval, in seconds, in which to attempt to run GCS to BigQuery load jobs. Only relevant if enableBatchLoad is configured.

  • Type: int
  • Default: 120
  • Importance: low
convertDoubleSpecialValues

Designates whether +Infinity is converted to Double.MAX_VALUE and whether -Infinity and NaN are converted to Double.MIN_VALUE to ensure successfull delivery to BigQuery.

  • Type: boolean
  • Default: false
  • Importance: low
enableBatchLoad

Beta Feature Use with caution. The sublist of topics to be batch loaded through GCS.

  • Type: list
  • Default: “”
  • Importance: low
includeKafkaData

Whether to include an extra block containing the Kafka source topic, offset, and partition information in the resulting BigQuery rows.

  • Type: boolean
  • Default: false
  • Importance: low
upsertEnabled

Enable upsert functionality on the connector through the use of record keys, intermediate tables, and periodic merge flushes. Row-matching will be performed based on the contents of record keys. This feature won’t work with SMTs that change the name of the topic and doesn’t support JSON input.

  • Type: boolean
  • Default: false
  • Importance: low
deleteEnabled

Enable delete functionality on the connector through the use of record keys, intermediate tables, and periodic merge flushes. A delete will be performed when a record with a null value (that is–a tombstone record) is read. This feature will not work with SMTs that change the name of the topic and doesn’t support JSON input.

  • Type: boolean
  • Default: false
  • Importance: low
intermediateTableSuffix

A suffix that will be appended to the names of destination tables to create the names for the corresponding intermediate tables. Multiple intermediate tables may be created for a single destination table, but their names will always start with the name of the destination table, followed by this suffix, and possibly followed by an additional suffix.

  • Type: string
  • Default: “tmp”
  • Importance: low
mergeIntervalMs

How often (in milliseconds) to perform a merge flush, if upsert/delete is enabled. Can be set to -1 to disable periodic flushing.

  • Type: long
  • Default: 60_000L
  • Importance: low
mergeRecordsThreshold

How many records to write to an intermediate table before performing a merge flush, if upsert/delete is enabled. Can be set to -1 to disable record count-based flushing.

  • Type: long
  • Default: -1
  • Importance: low
autoCreateBucket

Whether to automatically create the given bucket, if it does not exist.

  • Type: boolean
  • Default: true
  • Importance: medium
allowNewBigQueryFields

If true, new fields can be added to BigQuery tables during subsequent schema updates.

  • Type: boolean
  • Default: false
  • Importance: medium
allowBigQueryRequiredFieldRelaxation

If true, fields in BigQuery Schema can be changed from REQUIRED to NULLABLE. Note that allowNewBigQueryFields and allowBigQueryRequiredFieldRelaxation replaced the autoUpdateSchemas parameter of older versions of this connector.

  • Type: boolean
  • Default: false
  • Importance: medium
allowSchemaUnionization

If true, the existing table schema (if one is present) will be unionized with new record schemas during schema updates. If false, the record of the last schema in a batch will be used for any necessary table creation and schema update attempts.

Note that setting allowSchemaUnionization to false and allowNewBigQueryFields and allowBigQueryRequiredFieldRelaxation to true is equivalent to setting autoUpdateSchemas to true in older (pre-2.0.0) versions of this connector. This should only be enabled for Schema Registry-based inputs: Avro, Protobuf, or JSON Schema (JSON_SR). Table schema updates are not supported for JSON input.

If you set allowSchemaUnionization to false and allowNewBigQueryFields and allowBigQueryRequiredFieldRelaxation to true if BigQuery raises a schema validation exception or a table doesn’t exist when a writing a batch, the connector will try to remediate by required field relaxation and/or adding new fields.

If allowSchemaUnionization, allowNewBigQueryFields, and allowBigQueryRequiredFieldRelaxation are true, the connector will create or update tables with a schema whose fields are a union of the existing table schema’s fields and the ones present in all of the records of the current batch.

The key difference is that with unionization disabled, new record schemas have to be a superset of the table schema in BigQuery.

In general when enabled, allowSchemaUnionization is useful to make things work. For instance, if you’d like to remove fields from data upstream, the updated schemas still work in the connector. Similarly it is useful when different tasks see records whose schemas contain different fields that are not in the table. However note with caution that if allowSchemaUnionization is set and some bad records are in the topic, the BigQuery schema may be permanently changed. This presents two issues: first, since BigQuery doesn’t allow columns to be dropped from tables, they’ll add unnecessary noise to the schema. Second, since BigQuery doesn’t allow column types to be modified, they could completely break pipelines down the road where well-behaved records have schemas whose field names overlap with the accidentally-added columns in the table, but use a different type.

  • Type: boolean
  • Default: false
  • Importance: medium
kafkaDataFieldName

The Kafka data field name. The default value is null, which means the Kafka Data field will not be included.

  • Type: string
  • Default: null
  • Importance: low
kafkaKeyFieldName

The Kafka key field name. The default value is null, which means the Kafka Key field will not be included.

  • Type: string
  • Default: null
  • Importance: low
topic2TableMap

Map of topics to tables (optional). Format: comma-separated tuples, e.g. <topic-1>:<table-1>,<topic-2>:<table-2>,.. Note that topic name should not be modified using regex SMT while using this option. Also note that SANITIZE_TOPICS_CONFIG would be ignored if this config is set. Lastly, if the topic2table map doesn’t contain the topic for a record, a table with the same name as the topic name would be created.

  • Type: string
  • Default: “”
  • Importance: low