sink
Lakehouse Sink Connector
pulsar lakehouse connector
Authored by
hangc0276,zymap,Huanli-Meng,horizonzy
Support type
streamnative
License
Apache License 2.0

The Lakehouse sink connector (including the Hudi, Iceberg, and Delta Lake sink connectors) fetches data from a Pulsar topic and saves data to the Lakehouse tables.

How to get

This section describes how to build the Lakehouse sink connector.

You can get the Lakehouse sink connector using one of the following methods:

To build the Lakehouse sink connector from the source code, follow these steps.

  1. Clone the source code to your machine.

    git clone https://github.com/streamnative/pulsar-io-lakehouse.git
    
  2. Build the connector in the pulsar-io-lakehouse directory.

    • Build the NAR package for your local file system.

      mvn clean install -DskipTests
      
    • Build the NAR package for your cloud storage (Including AWS, GCS and Azure related package dependency).

      mvn clean install -P cloud -DskipTests
      

    After the connector is successfully built, a NAR package is generated under the target directory.

    ls target
    pulsar-io-lakehouse-3.1.1.2.nar
    

How to configure

Before using the Lakehouse sink connector, you need to configure it. This table lists the properties and the descriptions.

For a list of Hudi configurations, see Write Client Configs.

NameTypeRequiredDefaultDescription
typeStringtrueN/AThe type of the Lakehouse source connector. Available values: hudi, iceberg, and delta.
maxCommitIntervalIntegerfalse120The maximum flush interval (in units of seconds) for each batch. By default, it is set to 120s.
maxRecordsPerCommitIntegerfalse10_000_000The maximum number of records for each batch to commit. By default, it is set to 10_000_000.
maxCommitFailedTimesIntegerfalse5The maximum commit failure times until failing the process. By default, it is set to 5.
sinkConnectorQueueSizeIntegerfalse10_000The maximum queue size of the Lakehouse sink connector to buffer records before writing to Lakehouse tables.
partitionColumnsList<String>falseCollections.empytList()The partition columns for Lakehouse tables.
processingGuaranteesInttrue" " (empty string)The processing guarantees. Currently the Lakehouse connector only supports EFFECTIVELY_ONCE.
hudi.table.nameStringtrueN/AThe name of the Hudi table that Pulsar topic sinks data to.
hoodie.table.typeStringfalseCOPY_ON_WRITEThe type of the Hudi table of the underlying data for one write. It cannot be changed between writes.
hoodie.base.pathStringtrueN/AThe base path of the lake storage where all table data is stored. It always has a specific prefix with the storage scheme (for example, hdfs://, s3:// etc). Hudi stores all the main metadata about commits, savepoints, cleaning audit logs etc in the .hoodie directory.
hoodie.datasource.write.recordkey.fieldStringfalseUUIDThe record key field. It is used as the recordKey component of HoodieKey. You can obtain the value by invoking .toString() on the field value. You can use the dot notation for nested fields such as a.b.c.
hoodie.datasource.write.partitionpath.fieldStringtrueN/AThe partition path field. It is used as the partitionPath component of the HoodieKey. You can obtain the value by invoking .toString().

Note

The Lakehouse sink connector uses the Hadoop file system to read and write data to and from cloud objects, such as AWS, GCS, and Azure. If you want to configure Hadoop related properties, you should use the prefix hadoop..

Examples

You can create a configuration file (JSON or YAML) to set the properties if you use Pulsar Function Worker to run connectors in a cluster.

  • The Hudi table that is stored in the file system

     {
         "tenant": "public",
         "namespace": "default",
         "name": "hudi-sink",
         "inputs": [
           "test-hudi-pulsar"
         ],
         "archive": "connectors/pulsar-io-hudi-3.1.1.2.nar",
         "processingGuarantees": "EFFECTIVELY_ONCE",
         "parallelism": 1,
         "configs":   {
             "type": "hudi",
             "hoodie.table.name": "hudi-connector-test",
             "hoodie.table.type": "COPY_ON_WRITE",
             "hoodie.base.path": "file:///tmp/data/hudi-sink",
             "hoodie.datasource.write.recordkey.field": "id",
             "hoodie.datasource.write.partitionpath.field": "id",
         }
     }
    
  • The Hudi table that is stored in the AWS S3

     {
         "tenant": "public",
         "namespace": "default",
         "name": "hudi-sink",
         "inputs": [
           "test-hudi-pulsar"
         ],
         "archive": "connectors/pulsar-io-hudi-3.1.1.2-cloud.nar",
         "parallelism": 1,
         "processingGuarantees": "EFFECTIVELY_ONCE",
         "configs":   {
             "type": "hudi",
             "hoodie.table.name": "hudi-connector-test",
             "hoodie.table.type": "COPY_ON_WRITE",
             "hoodie.base.path": "s3a://bucket/path/to/hudi",
             "hoodie.datasource.write.recordkey.field": "id",
             "hoodie.datasource.write.partitionpath.field": "id",
             "hadoop.fs.s3a.aws.credentials.provider": "com.amazonaws.auth.DefaultAWSCredentialsProviderChain"
         }
     }
    

Data format types

The Lakehouse sink connector provides multiple output format options, including Avro and Parquet. The default format is Parquet. With the current implementation, there are some limitations for different formats:

This table lists the Pulsar Schema types supported by the writers.

Pulsar SchemaWriter: AvroWriter: Parquet
Primitive
Avro
Json
Protobuf *
ProtobufNative *

*: The Protobuf schema is based on the Avro schema. It uses Avro as an intermediate format, so it may not provide the best effort conversion.

*: The ProtobufNative record holds the Protobuf descriptor and the message. When writing to Avro format, the connector uses avro-protobuf to do the conversion.

How to use

You can use the Lakehouse sink connector with Function Worker. You can use the Lakehouse sink connector as a non built-in connector or a built-in connector.

If you already have a Pulsar cluster, you can use the Lakehouse sink connector as a non built-in connector directly.

This example shows how to create a Lakehouse sink connector on a Pulsar cluster using the pulsar-admin sinks create command.

PULSAR_HOME/bin/pulsar-admin sinks create \
--sink-config-file <lakehouse-sink-config.yaml>

Demos

This table lists demos that show how to run the Delta Lake, Hudi, and Iceberg sink connectors with other external systems.

Currently, only the demo on the Delta Lake sink connector is available.

ConnectorLink
Delta LakeFor details, see the Delta Lake demo.
Hudi
Iceberg