# hudi

**Repository Path**: bigdata-study/hudi

## Basic Information

- **Project Name**: hudi
- **Description**: Apache Hudi（发音为“hoodie”）是下一代流数据湖平台。Apache Hudi将核心仓库和数据库功能直接引入数据湖。Hudi 提供表、事务、高效的更新插入/删除、高级索引、流式引入服务、数据聚类/压缩优化和并发性，同时以开源文件格式保留数据。

Apache Hudi不仅非常适合流工作负载，而且还允许您创建高效的增量批处理管道。阅读文档以获取更多用例描述，并查看谁在使用Hudi，以了解世界上一些最大的数据湖（包括Uber，Amazon，ByteDance，Robinhood等）如何通过Hudi改变其生产数据湖。

Apache Hudi可以很容易地在任何云存储平台上使用。Hudi的高级性能优化，使分析工作负载与任何流行的查询引擎，包括Apache Spark，Flink，Presto，Trino，Hive等更快。
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: https://hudi.apache.org/cn
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 4
- **Created**: 2022-09-20
- **Last Updated**: 2022-09-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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# Apache Hudi

Apache Hudi (pronounced Hoodie) stands for `Hadoop Upserts Deletes and Incrementals`. Hudi manages the storage of large
analytical datasets on DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage).

<img src="https://hudi.apache.org/assets/images/hudi-logo-medium.png" alt="Hudi logo" height="80px" align="right" />

<https://hudi.apache.org/>

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## Features

* Upsert support with fast, pluggable indexing
* Atomically publish data with rollback support
* Snapshot isolation between writer & queries
* Savepoints for data recovery
* Manages file sizes, layout using statistics
* Async compaction of row & columnar data
* Timeline metadata to track lineage
* Optimize data lake layout with clustering
 
Hudi supports three types of queries:
 * **Snapshot Query** - Provides snapshot queries on real-time data, using a combination of columnar & row-based storage (e.g [Parquet](https://parquet.apache.org/) + [Avro](https://avro.apache.org/docs/current/mr.html)).
 * **Incremental Query** - Provides a change stream with records inserted or updated after a point in time.
 * **Read Optimized Query** - Provides excellent snapshot query performance via purely columnar storage (e.g. [Parquet](https://parquet.apache.org/)).

Learn more about Hudi at [https://hudi.apache.org](https://hudi.apache.org)

## Building Apache Hudi from source

Prerequisites for building Apache Hudi:

* Unix-like system (like Linux, Mac OS X)
* Java 8 (Java 9 or 10 may work)
* Git
* Maven (>=3.3.1)

```
# Checkout code and build
git clone https://github.com/apache/hudi.git && cd hudi
mvn clean package -DskipTests

# Start command
spark-2.4.4-bin-hadoop2.7/bin/spark-shell \
  --jars `ls packaging/hudi-spark-bundle/target/hudi-spark-bundle_2.11-*.*.*-SNAPSHOT.jar` \
  --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
```

To build for integration tests that include `hudi-integ-test-bundle`, use `-Dintegration-tests`.

To build the Javadoc for all Java and Scala classes:
```
# Javadoc generated under target/site/apidocs
mvn clean javadoc:aggregate -Pjavadocs
```

### Build with different Spark versions

The default Spark version supported is 2.4.4. Refer to the table below for building with different Spark and Scala versions.

| Maven build options       | Expected Spark bundle jar name               | Notes                                            |
|:--------------------------|:---------------------------------------------|:-------------------------------------------------|
| (empty)                   | hudi-spark-bundle_2.11 (legacy bundle name)  | For Spark 2.4.4 and Scala 2.11 (default options) |
| `-Dspark2.4`              | hudi-spark2.4-bundle_2.11                    | For Spark 2.4.4 and Scala 2.11 (same as default) |
| `-Dspark2.4 -Dscala-2.12` | hudi-spark2.4-bundle_2.12                    | For Spark 2.4.4 and Scala 2.12                   |
| `-Dspark3.1 -Dscala-2.12` | hudi-spark3.1-bundle_2.12                    | For Spark 3.1.x and Scala 2.12                   |
| `-Dspark3.2 -Dscala-2.12` | hudi-spark3.2-bundle_2.12                    | For Spark 3.2.x and Scala 2.12                   |
| `-Dspark3`                | hudi-spark3-bundle_2.12 (legacy bundle name) | For Spark 3.2.x and Scala 2.12                   |
| `-Dscala-2.12`            | hudi-spark-bundle_2.12 (legacy bundle name)  | For Spark 2.4.4 and Scala 2.12                   |

For example,
```
# Build against Spark 3.2.x
mvn clean package -DskipTests -Dspark3.2 -Dscala-2.12

# Build against Spark 3.1.x
mvn clean package -DskipTests -Dspark3.1 -Dscala-2.12

# Build against Spark 2.4.4 and Scala 2.12
mvn clean package -DskipTests -Dspark2.4 -Dscala-2.12
```

#### What about "spark-avro" module?

Starting from versions 0.11, Hudi no longer requires `spark-avro` to be specified using `--packages`

### Build with different Flink versions

The default Flink version supported is 1.14. Refer to the table below for building with different Flink and Scala versions.

| Maven build options        | Expected Flink bundle jar name | Notes                                           |
|:---------------------------|:-------------------------------|:------------------------------------------------|
| (empty)                    | hudi-flink1.14-bundle_2.11     | For Flink 1.14 and Scala 2.11 (default options) |
| `-Dflink1.14`              | hudi-flink1.14-bundle_2.11     | For Flink 1.14 and Scala 2.11 (same as default) |
| `-Dflink1.14 -Dscala-2.12` | hudi-flink1.14-bundle_2.12     | For Flink 1.14 and Scala 2.12                   |
| `-Dflink1.13`              | hudi-flink1.13-bundle_2.11     | For Flink 1.13 and Scala 2.11                   |
| `-Dflink1.13 -Dscala-2.12` | hudi-flink1.13-bundle_2.12     | For Flink 1.13 and Scala 2.12                   |

## Running Tests

Unit tests can be run with maven profile `unit-tests`.
```
mvn -Punit-tests test
```

Functional tests, which are tagged with `@Tag("functional")`, can be run with maven profile `functional-tests`.
```
mvn -Pfunctional-tests test
```

To run tests with spark event logging enabled, define the Spark event log directory. This allows visualizing test DAG and stages using Spark History Server UI.
```
mvn -Punit-tests test -DSPARK_EVLOG_DIR=/path/for/spark/event/log
```

## Quickstart

Please visit [https://hudi.apache.org/docs/quick-start-guide.html](https://hudi.apache.org/docs/quick-start-guide.html) to quickly explore Hudi's capabilities using spark-shell.