The Lakehouse source connector (currently only including the Delta Lake source connector) fetches the Lakehouse table's changelog and saves changelogs into a Pulsar topic.

How to get

This section describes how to build the Lakehouse source connector.

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

Download the NAR package from the download page.
Build it from the source code.

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

Clone the source code to your machine.

git clone https://github.com/streamnative/pulsar-io-lakehouse.git

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-3.2.2.1.nar
```

How to configure

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

Name	Type	Required	Default	Description
`type`	String	true	N/A	The type of the Lakehouse source connector. Available values: `delta`.
`checkpointInterval`	int	false	30	The checkpoint interval (in units of seconds). By default, it is set to 30s.
`queueSize`	int	false	10_000	The buffer queue size of the Lakehouse source connector. The buffer queue is used for store records before they are sent to Pulsar topics. By default, it is set to `10_000`.
`fetchHistoryData`	bool	false	false	Configure whether to fetch the history data of the table. By default, it is set to `false`.
`startSnapshotVersion`	long	false	-1	The Delta snapshot version to start capturing data change. Available values: [-1: LATEST, -2: EARLIEST]. The `startSnapshotVersion` and `startTimestamp` are mutually exclusive.
`startTimestamp`	long	false	N/A	The Delta snapshot timestamp (in units of seconds) to start capturing data change. The `startSnapshotVersion` and `startTimestamp` are mutually exclusive.
`tablePath`	String	true	N/A	The path of the Delta table.
`parquetParseThreads`	int	false	Runtime.getRuntime().availableProcessors()	The parallelism of paring Delta Parquet files. By default, it is set to `Runtime.getRuntime().availableProcessors()`.
`maxReadBytesSizeOneRound`	long	false	Total memory * 0.2	The maximum read bytes size from Parquet files in one fetch round. By default, it is set to 20% of the heap memory.
`maxReadRowCountOneRound`	int	false	100_000	The maximum read number of rows processed in one round. By default, it is set to `1_000_000`.

Note

The Lakehouse source 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 Delta table that is stored in the file system

{
    "tenant":"public",
    "namespace":"default",
    "name":"delta_source",
    "parallelism":1,
    "topicName": "delta_source",
    "processingGuarantees":"ATLEAST_ONCE",
    "archive": "connectors/pulsar-io-lakehouse-3.2.2.1.nar",
    "configs":{
        "type":"delta",
        "checkpointInterval": 180,
        "queueSize": 10000,
        "fatchHistoryData": false,
        "startSnapshotVersion": -1,
        "tablePath": "file:///tmp/data/delta-source",
        "parquetParseThreads": 3,
        "maxReadBytesSizeOneRound": 134217728,
        "maxReadRowCountOneRound": 100000
    }
}

The Delta table that is stored in cloud storage (AWS S3, GCS, or Azure)

{
    "tenant":"public",
    "namespace":"default",
    "name":"delta_source",
    "parallelism":1,
    "topicName": "delta_source",
    "processingGuarantees":"ATLEAST_ONCE",
    "archive": "connectors/pulsar-io-lakehouse-3.2.2.1-cloud.nar",
    "configs":{
        "type":"delta",
        "checkpointInterval": 180,
        "queueSize": 10000,
        "fatchHistoryData": false,
        "startSnapshotVersion": -1,
        "tablePath": "s3a://test-dev-us-west-2/lakehouse/delta_source",
        "hadoop.fs.s3a.aws.credentials.provider": "com.amazonaws.auth.DefaultAWSCredentialsProviderChain",
        "parquetParseThreads": 3,
        "maxReadBytesSizeOneRound": 134217728,
        "maxReadRowCountOneRound": 100000
    }
}

Data format types

Currently, The Lakehouse source connector only supports reading Delta table changelogs, which adopt a parquet storage format.

How to use

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

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

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

PULSAR_HOME/bin/pulsar-admin sources create \
--source-config-file <lakehouse-source-config.yaml>

Demos

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

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

Connector	Link
Delta Lake	For details, see the Delta Lake demo.
Hudi
Iceberg