Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/apache/iceberg
Apache Iceberg
https://github.com/apache/iceberg
apache hacktoberfest iceberg
Last synced: 4 days ago
JSON representation
Apache Iceberg
- Host: GitHub
- URL: https://github.com/apache/iceberg
- Owner: apache
- License: apache-2.0
- Created: 2018-11-19T16:26:46.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2024-10-28T21:02:56.000Z (about 2 months ago)
- Last Synced: 2024-10-29T11:24:44.037Z (about 1 month ago)
- Topics: apache, hacktoberfest, iceberg
- Language: Java
- Homepage: https://iceberg.apache.org/
- Size: 67.9 MB
- Stars: 6,399
- Watchers: 160
- Forks: 2,212
- Open Issues: 748
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-dataops - Apache Iceberg - Open table format for huge analytic datasets. (Data Serialization / Data Table Format)
- awesome-datalake - Apache Iceberg - Iceberg is a high-performance format for huge analytic tables. Iceberg brings the reliability and simplicity of SQL tables to big data, while making it possible for engines like Spark, Trino, Flink, Presto, Hive and Impala to safely work with the same tables, at the same time. (Open Table Formats)
- awesome-iceberg - Apache Iceberg - Core project repository. (📂 Additional Sections / 3. Open-source Projects)
- awesome-iceberg - Apache Iceberg - Core project repository. (📂 Additional Sections / 3. Open-source Projects)
- awesome-datalake - Apache Iceberg - Iceberg is a high-performance format for huge analytic tables. Iceberg brings the reliability and simplicity of SQL tables to big data, while making it possible for engines like Spark, Trino, Flink, Presto, Hive and Impala to safely work with the same tables, at the same time. (Open Table Formats)
- StarryDivineSky - apache/iceberg
- awesome-production-machine-learning - Apache Iceberg - Iceberg is an ACID-compliant, high-performance format built for huge analytic tables (containing tens of petabytes of data), and it brings the reliability and simplicity of SQL tables to big data, while making it possible for engines like Spark, Trino, Flink, Presto, Hive and Impala to safely work with the same tables, at the same time. More info [here](https://iceberg.apache.org/). (Data Storage Optimisation)
- awesome-spark - Apache Iceberg - commit/apache/iceberg.svg"> - Upserts, Deletes And Incremental Processing on Big Data.. (Packages / Storage)
README
[](https://github.com/apache/iceberg/actions/workflows/java-ci.yml)
[](https://apache-iceberg.slack.com/)
Iceberg is a high-performance format for huge analytic tables. Iceberg brings the reliability and simplicity of SQL tables to big data, while making it possible for engines like Spark, Trino, Flink, Presto, Hive and Impala to safely work with the same tables, at the same time.
Background and documentation is available at
## Status
Iceberg is under active development at the Apache Software Foundation.
The [Iceberg format specification][iceberg-spec] is stable and new features are added with each version.
The core Java library is located in this repository and is the reference implementation for other libraries.
[Documentation][iceberg-docs] is available for all libraries and integrations.
[iceberg-docs]: https://iceberg.apache.org/docs/latest/
[iceberg-spec]: https://iceberg.apache.org/spec/
## Collaboration
Iceberg tracks issues in GitHub and prefers to receive contributions as pull requests.
Community discussions happen primarily on the [dev mailing list][dev-list] or on specific issues.
[dev-list]: mailto:[email protected]
### Building
Iceberg is built using Gradle with Java 11, 17, or 21.
* To invoke a build and run tests: `./gradlew build`
* To skip tests: `./gradlew build -x test -x integrationTest`
* To fix code style for default versions: `./gradlew spotlessApply`
* To fix code style for all versions of Spark/Hive/Flink:`./gradlew spotlessApply -DallModules`
Iceberg table support is organized in library modules:
* `iceberg-common` contains utility classes used in other modules
* `iceberg-api` contains the public Iceberg API
* `iceberg-core` contains implementations of the Iceberg API and support for Avro data files, **this is what processing engines should depend on**
* `iceberg-parquet` is an optional module for working with tables backed by Parquet files
* `iceberg-arrow` is an optional module for reading Parquet into Arrow memory
* `iceberg-orc` is an optional module for working with tables backed by ORC files
* `iceberg-hive-metastore` is an implementation of Iceberg tables backed by the Hive metastore Thrift client
* `iceberg-data` is an optional module for working with tables directly from JVM applications
Iceberg also has modules for adding Iceberg support to processing engines:
* `iceberg-spark` is an implementation of Spark's Datasource V2 API for Iceberg with submodules for each spark versions (use runtime jars for a shaded version)
* `iceberg-flink` contains classes for integrating with Apache Flink (use iceberg-flink-runtime for a shaded version)
* `iceberg-mr` contains an InputFormat and other classes for integrating with Apache Hive
---
**NOTE**
The tests require Docker to execute. On MacOS (with Docker Desktop), you might need to create a symbolic name to the docker socket in order to be detected by the tests:
```
sudo ln -s $HOME/.docker/run/docker.sock /var/run/docker.sock
```
---
### Engine Compatibility
See the [Multi-Engine Support](https://iceberg.apache.org/multi-engine-support/) page to know about Iceberg compatibility with different Spark, Flink and Hive versions.
For other engines such as Presto or Trino, please visit their websites for Iceberg integration details.
### Implementations
This repository contains the Java implementation of Iceberg. Other implementations can be found at:
* **Go**: [iceberg-go](https://github.com/apache/iceberg-go)
* **PyIceberg** (Python): [iceberg-python](https://github.com/apache/iceberg-python)
* **Rust**: [iceberg-rust](https://github.com/apache/iceberg-rust)