Ingest data from Apache Iceberg
This guide describes how to batch ingest data from Apache Iceberg to RisingWave using the Iceberg source in RisingWave. Apache Iceberg is a table format designed to support huge tables. For more information, see Apache Iceberg.
The Iceberg source connector in RisingWave is currently in Beta. Please contact us if you encounter any issues or have feedback.
Syntax
CREATE SOURCE [ IF NOT EXISTS ] source_name
WITH (
connector='iceberg',
connector_parameter='value', ...
);
You don’t need to specify the column name for the Iceberg source, as RisingWave can derive it from the Iceberg table metadata directly. Use DESCRIBE statement to view the column names and data types.
Parameters
| Field | Notes |
|---|---|
| type | Required. Allowed values: appendonly and upsert. |
| s3.endpoint | Optional. Endpoint of the 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. Name of the database that you want to ingest data from. |
| table.name | Required. Name of the table that you want to ingest data from. |
| 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. |
Data type mapping
RisingWave converts data types from Iceberg to RisingWave according to the following data type mapping table.
| Iceberg Type | RisingWave Type |
|---|---|
| boolean | boolean |
| integer | int |
| long | bigint |
| float | real |
| double | double |
| string | varchar |
| date | date |
| timestamptz | timestamptz |
| timestamp | timestamp |
| decimal | decimal |
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.typetohiveto 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.
Time travel
Our Iceberg source provides time travel capabilities, allowing you to query data from a specific point in time or version, rather than just the current state of the data. You can achieve this by specifying a timestamp or a version identifier.
Here is the syntax for specifying a system time. The timestamp here should be in a format like YYYY-MM-DD HH:MM:SS or a UNIX timestamp in seconds.
FOR SYSTEM_TIME AS OF [STRING | NUMBER];
Here is the syntax for specifying a system version:
FOR SYSTEM_VERSION AS OF [SNAPSHOT_ID];
Here are some examples:
-- Querying data using a timestamp
SELECT * FROM s FOR SYSTEM_TIME AS OF '2100-01-01 00:00:00+00:00';
SELECT * FROM s FOR SYSTEM_TIME AS OF 4102444800;
-- Querying data using a version identifier
SELECT * FROM s FOR SYSTEM_VERSION AS OF 3023402865675048688;
System tables
We currently support system tables rw_iceberg_files and rw_iceberg_snapshots. rw_iceberg_files contains the current files of the Iceberg source or table. Here is a simple example:
SELECT * FROM rw_iceberg_files;
source_id | schema_name | source_name | content | file_path | file_format | record_count | file_size_in_bytes | equality_ids | sort_order_id
-----------+-------------+----------------------------+---------+----------------------------------------------------------------------------------------------------------------------+-------------+--------------+--------------------+--------------+---------------
1 | public | source_merge_on_read_table | 0 | s3a://hummock001/demo_db/merge_on_read_table/data/00000-1-dcbf00a2-8722-4cd8-9d77-15880e334488-00001.parquet | parquet | 1 | 951 | | 0
1 | public | source_merge_on_read_table | 0 | s3a://hummock001/demo_db/merge_on_read_table/data/00000-0-0d0f5a54-b4b1-431d-8574-e13fc410296f-00001.parquet | parquet | 1 | 907 | | 0
1 | public | source_merge_on_read_table | 0 | s3a://hummock001/demo_db/merge_on_read_table/data/00001-1-0d0f5a54-b4b1-431d-8574-e13fc410296f-00001.parquet | parquet | 1 | 935 | | 0
1 | public | source_merge_on_read_table | 0 | s3a://hummock001/demo_db/merge_on_read_table/data/00000-0-66d3b597-a132-4e04-a40a-02972edcff24-00001.parquet | parquet | 1 | 907 | | 0
1 | public | source_merge_on_read_table | 1 | s3a://hummock001/demo_db/merge_on_read_table/data/00000-1-dcbf00a2-8722-4cd8-9d77-15880e334488-00001-deletes.parquet | parquet | 1 | 1467 | |
1 | public | source_merge_on_read_table | 0 | s3a://hummock001/demo_db/merge_on_read_table/data/00000-0-6acaf4d1-512c-42b7-9a76-e81fc8c8ca8f-00004.parquet | parquet | 1 | 1690 | |
1 | public | source_merge_on_read_table | 2 | s3a://hummock001/demo_db/merge_on_read_table/data/00000-0-6e0c500e-6a8f-4252-863c-7c3d6ac20264-00048-eq-del.parquet | parquet | 1 | 721 | {1} |
(7 rows)
rw_iceberg_snapshots contains all Iceberg snapshots in RisingWave. Based on it, you can read a specific snapshot by a time travel query. For example, you can use the following query to read these snapshots:
SELECT * FROM rw_iceberg_snapshots;
source_id | schema_name |source_name| sequence_number | snapshot_id | timestamp_ms | manifest_list | summary
-----------+-------------+-----------------------------+-----------------+---------------------+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
1 | public | t | 1 | 4476030648521181855 | 2024-04-03 08:54:22.488+00:00 | s3a://hummock001/demo_db/t/metadata/snap-4476030648521181855-1-f06c9623-1ae5-4646-b117-b765d4154221.avro | {"added-data-files": "2", "added-files-size": "1814", "added-records": "2", "changed-partition-count": "1", "operation": "append", "spark.app.id": "local-1712134455383", "total-data-files": "2", "total-delete-files": "0", "total-equality-deletes": "0", "total-files-size": "1814", "total-position-deletes": "0", "total-records": "2"}
1 | public | t | 2 | 3368332126703695368 | 2024-04-03 08:54:36.795+00:00 | s3a://hummock001/demo_db/t/metadata/snap-3368332126703695368-1-3de95014-9e9e-4704-8ea3-44449525638a.avro | {"added-data-files": "2", "added-files-size": "1842", "added-records": "2", "changed-partition-count": "1", "operation": "append", "spark.app.id": "local-1712134465484", "total-data-files": "4", "total-delete-files": "0", "total-equality-deletes": "0", "total-files-size": "3656", "total-position-deletes": "0", "total-records": "4"}
1 | public | t | 3 | 8015994742063949347 | 2024-04-07 02:46:38.913+00:00 | s3a://hummock001/demo_db/t/metadata/snap-8015994742063949347-1-f31760cb-5df9-452a-938e-01eec7105e94.avro | {"changed-partition-count": "1", "deleted-data-files": "1", "deleted-records": "1", "operation": "delete", "removed-files-size": "907", "spark.app.id": "local-1712457982197", "total-data-files": "3", "total-delete-files": "0", "total-equality-deletes": "0", "total-files-size": "2749", "total-position-deletes": "0", "total-records": "3"}
1 | public | t | 4 | 4642583001850518920 | 2024-04-07 02:47:05.171+00:00 | s3a://hummock001/demo_db/t/metadata/snap-4642583001850518920-1-57d9b426-e584-4185-9b7e-eab890f20589.avro | {"added-data-files": "1", "added-delete-files": "1", "added-files-size": "2418", "added-position-delete-files": "1", "added-position-deletes": "1", "added-records": "1", "changed-partition-count": "1", "operation": "overwrite", "spark.app.id": "local-1712458005806", "total-data-files": "4", "total-delete-files": "1", "total-equality-deletes": "0", "total-files-size": "5167", "total-position-deletes": "1", "total-records": "4"}
SELECT * FROM t FOR SYSTEM_VERSION AS OF 4476030648521181855;
SELECT * FROM t FOR SYSTEM_TIME AS OF '2024-04-03 08:54:22.488+00:00';
Examples
Firstly, create an append-only Iceberg table, see Append-only sink from upsert source for details.
Secondly, create an Iceberg source:
CREATE SOURCE iceberg_source
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},
catalog.name='demo',
database.name='dev',
table.name='table'
);
Then, you can query the Iceberg source:
SELECT * FROM iceberg_source;
