Iceberg
Cloud version is still in development iterations. DO NOT use for production purposes.
This page guides you through the process of setting up the Iceberg destination connector.
Sync overview
Output schema
The incoming airbyte data is structured in keyspaces and tables and is partitioned and replicated
across different nodes in the cluster. This connector maps an incoming stream to an Iceberg
table and a namespace to an Iceberg database. Fields in the airbyte message become different
columns in the Iceberg tables. Each table will contain the following columns.
_airbyte_ab_id: A random generated uuid._airbyte_emitted_at: a timestamp representing when the event was received from the data source._airbyte_data: a json text representing the extracted data.
Features
This section should contain a table with the following format:
| Feature | Supported?(Yes/No) | Notes |
|---|---|---|
| Full Refresh Sync | ✅ | |
| Incremental Sync | ✅ | |
| Replicate Incremental Deletes | ❌ | |
| SSH Tunnel Support | ❌ |
Performance considerations
Every ten thousand pieces of incoming airbyte data in a stream ————we call it a batch, would produce
one data file( Parquet/Avro) in an Iceberg table. This batch size can be configurabled by
Data file flushing batch size property. As the quantity of Iceberg data files grows, it causes an
unnecessary amount of metadata and less efficient queries from file open costs. Iceberg provides
data file compaction action to improve this case, you can read more about compaction
HERE. This connector also
provides auto compact action when stream closes, by Auto compact data files property. Any you can
specify the target size of compacted Iceberg data file.
Getting started
Requirements
- Iceberg catalog : Iceberg uses
catalogto manage tables. this connector already supports:- HiveCatalog connects to a Hive metastore to keep track of Iceberg tables.
- HadoopCatalog
doesn’t need to connect to a Hive MetaStore, but can only be used with HDFS or similar file
systems that support atomic rename. For
HadoopCatalog, this connector use Storage Config (S3 or HDFS) to manage Iceberg tables. - JdbcCatalog uses a table in a relational database to manage Iceberg tables through JDBC. So far, this connector supports PostgreSQL only.
- RESTCatalog connects to a REST server, which manages Iceberg tables.
- GlueCatalog
- Storage medium means where Iceberg data files storages in. So far, this connector supports S3/S3N/S3N object-storage. When using the RESTCatalog, it is possible to have storage be managed by the server.
Changelog
Expand to review
| Version | Date | Pull Request | Subject |
|---|---|---|---|
| 0.2.1 | 2024-09-20 | 45711 | Initial Cloud version for registry purpose [UNTESTED ON CLOUD] |
| 0.2.0 | 2024-09-20 | 45707 | Add support for AWS Glue Catalog |
| 0.1.8 | 2024-09-16 | 45206 | Fixing tests to work in airbyte-ci |
| 0.1.7 | 2024-05-17 | 38283 | Bump Iceberg library to 1.5.2 and Spark to 3.5.1 |
| 0.1.6 | 2024-04-04 | #36846 | Remove duplicate S3 Region |
| 0.1.5 | 2024-01-03 | #33924 | Add new ap-southeast-3 AWS region |
| 0.1.4 | 2023-07-20 | 28506 | Support server-managed storage config |
| 0.1.3 | 2023-07-12 | 28158 | Bump Iceberg library to 1.3.0 and add REST catalog support |
| 0.1.2 | 2023-07-14 | 28345 | Trigger rebuild of image |
| 0.1.1 | 2023-02-27 | 23201 | Bump Iceberg library to 1.1.0 |
| 0.1.0 | 2022-11-01 | 18836 | Initial Commit |