Sink data from RisingWave to Apache Iceberg

This guide describes how to sink data from RisingWave to Apache Iceberg using the Iceberg sink connector in RisingWave. Apache Iceberg is a table format designed to support huge tables. For more information, see Apache Iceberg.

Beta feature

The Iceberg sink connector in RisingWave is currently in Beta. Please contact us if you encounter any issues or have feedback.

Prerequisites

• Ensure you already have an Iceberg table that you can sink data to. For additional guidance on creating a table and setting up Iceberg, refer to this quickstart guide on creating an Iceberg table.

• Ensure you have an upstream materialized view or source that you can sink data from.

Syntax

CREATE SINK [ IF NOT EXISTS ] sink_name
[FROM sink_from | AS select_query]
WITH (
   connector='iceberg',
   connector_parameter = 'value', ...
);

Parameters

Parameter Names	Description
type	Required. Allowed values: appendonly and upsert.
force_append_only	Optional. If true, forces the sink to be append-only, even if it cannot be.
s3.endpoint	Optional. Endpoint of the S3. For MinIO object store backend, it should be http://${MINIO_HOST}:${MINIO_PORT}. For AWS S3, refer to S3
s3.region	Optional. The region where the S3 bucket is hosted. Either s3.endpoint or s3.region must be specified.
s3.access.key	Required. Access key of the S3 compatible object store.
s3.secret.key	Required. Secret key of the S3 compatible object store.
database.name	Required. The database of the target Iceberg table.
table.name	Required. The name of the target Iceberg table.
catalog.name	Conditional. The name of the Iceberg catalog. It can be omitted for storage catalog but required for other catalogs.
catalog.type	Optional. The catalog type used in this table. Currently, the supported values are storage, rest, hive and jdbc. If not specified, storage is used. For details, see Catalogs.
warehouse.path	Conditional. The path of the Iceberg warehouse. Currently, only S3-compatible object storage systems, such as AWS S3 and MinIO, are supported. It's required if the catalog.type is not rest.
catalog.url	Conditional. The URL of the catalog. It is required when catalog.type is not storage.
primary_key	The primary key for an upsert sink. It is only appliable to the upsert mode.

Data type mapping

RisingWave converts risingwave data types from/to Iceberg according to the following data type mapping table:

RisingWave Type	Iceberg Type
boolean	boolean
int	integer
bigint	long
real	float
double	double
varchar	string
date	date
timestamptz	timestamptz
timestamp	timestamp

Catalogs

Iceberg supports these types of catalogs:

• Storage catalog: The Storage catalog stores all metadata in the underlying file system, such as Hadoop or S3. Currently, we only support S3 as the underlying file system.
• REST catalog: RisingWave supports the REST catalog, which acts as a proxy to other catalogs like Hive, JDBC, and Nessie catalog. This is the recommended approach to use RisingWave with Iceberg tables.
• Hive catalog: RisingWave supports the Hive catalog. You need to set catalog.type to hive to use it. See the full example in this configuration file.
• Jdbc Catalog: RisingWave supports the JDBC catalog. See the full example in this configuration file.

Iceberg table format

Currently, RisingWave only supports Iceberg tables in format v2.

Examples

This section includes several examples that you can use if you want to quickly experiment with sinking data to Iceberg.

Create an Iceberg table (if you do not already have one)

For example, the following spark-sql command creates an Iceberg table named table under the database dev in AWS S3. The table is in an S3 bucket named my-iceberg-bucket in region ap-southeast-1 and under the path path/to/warehouse. The table has the property format-version=2, so it supports the upsert option. There should be a folder named s3://my-iceberg-bucket/path/to/warehouse/dev/table/metadata.

Note that only S3-compatible object store is supported, such as AWS S3 or MinIO.

spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.4_2.12:1.3.1,org.apache.hadoop:hadoop-aws:3.3.2\
    --conf spark.sql.catalog.demo=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.demo.type=hadoop \
    --conf spark.sql.catalog.demo.warehouse=s3a://my-iceberg-bucket/path/to/warehouse \
    --conf spark.sql.catalog.demo.hadoop.fs.s3a.endpoint=https://s3.ap-southeast-1.amazonaws.com \
    --conf spark.sql.catalog.demo.hadoop.fs.s3a.path.style.access=true \
    --conf spark.sql.catalog.demo.hadoop.fs.s3a.access.key=${ACCESS_KEY} \
    --conf spark.sql.catalog.demo.hadoop.fs.s3a.secret.key=${SECRET_KEY} \
    --conf spark.sql.defaultCatalog=demo \
    --e "drop table if exists demo.dev.`table`;

CREATE TABLE demo.dev.`table`
(
  seq_id bigint, 
  user_id bigint,
  user_name string
) TBLPROPERTIES ('format-version'='2')";

Create an upstream materialized view or source

The following query creates an append-only source. For more details on creating a source, see CREATE SOURCE .

CREATE SOURCE s1_source (
     seq_id bigint, 
     user_id bigint,
     user_name varchar)
WITH (                    
     connector = 'datagen',
     fields.seq_id.kind = 'sequence',
     fields.seq_id.start = '1',
     fields.seq_id.end = '10000000',
     fields.user_id.kind = 'random',
     fields.user_id.min = '1',
     fields.user_id.max = '10000000',
     fields.user_name.kind = 'random',
     fields.user_name.length = '10',
     datagen.rows.per.second = '20000'
 ) FORMAT PLAIN ENCODE JSON;

Another option is to create an upsert table, which supports in-place updates. For more details on creating a table, see CREATE TABLE .

CREATE TABLE s1_table (
     seq_id bigint, 
     user_id bigint,
     user_name varchar)
WITH (                    
     connector = 'datagen',
     fields.seq_id.kind = 'sequence',
     fields.seq_id.start = '1',
     fields.seq_id.end = '10000000',
     fields.user_id.kind = 'random',
     fields.user_id.min = '1',
     fields.user_id.max = '10000000',
     fields.user_name.kind = 'random',
     fields.user_name.length = '10',
     datagen.rows.per.second = '20000'
 ) FORMAT PLAIN ENCODE JSON;

Append-only sink from append-only source

If you have an append-only source and want to create an append-only sink, set type = append-only in the CREATE SINK SQL query.

CREATE SINK s1_sink FROM s1_source
WITH (
    connector = 'iceberg',
    type = 'append-only',
    warehouse.path = 's3a://my-iceberg-bucket/path/to/warehouse,
    s3.endpoint = 'https://s3.ap-southeast-1.amazonaws.com',
    s3.access.key = '${ACCESS_KEY}',
    s3.secret.key = '${SECRET_KEY},
    database.name='dev',
    table.name='table'
);

Append-only sink from upsert source

If you have an upsert source and want to create an append-only sink, set type = append-only and force_append_only = true. This will ignore delete messages in the upstream, and to turn upstream update messages into insert messages.

CREATE SINK s1_sink FROM s1_table
WITH (
    connector = 'iceberg',
    type = 'append-only',
    force_append_only = 'true',
    warehouse.path = 's3a://my-iceberg-bucket/path/to/warehouse,
    s3.endpoint = 'https://s3.ap-southeast-1.amazonaws.com',
    s3.access.key = '${ACCESS_KEY}',
    s3.secret.key = '${SECRET_KEY},
    database.name='dev',
    table.name='table'
);

Upsert sink from upsert source

In RisingWave, you can directly sink data as upserts into Iceberg tables.

CREATE SINK s1_sink FROM s1_table
WITH (
    connector = 'iceberg',
    warehouse.path = 's3a://my-iceberg-bucket/path/to/warehouse,
    s3.endpoint = 'https://s3.ap-southeast-1.amazonaws.com',
    s3.access.key = '${ACCESS_KEY}',
    s3.secret.key = '${SECRET_KEY},
    database.name='dev',
    table.name='table',
    primary_key='seq_id'
);