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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).

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

### Build with Scala 2.12

The default Scala version supported is 2.11. To build for Scala 2.12 version, build using `scala-2.12` profile

```
mvn clean package -DskipTests -Dscala-2.12
```

### Build with Spark 3

The default Spark version supported is 2.4.4. To build for different Spark 3 versions, use the corresponding profile

```
# Build against Spark 3.2.1 (the default build shipped with the public Spark 3 bundle)
mvn clean package -DskipTests -Dspark3

# Build against Spark 3.1.2
mvn clean package -DskipTests -Dspark3.1.x
```

### Build without spark-avro module

The default hudi-jar bundles spark-avro module. To build without spark-avro module, build using `spark-shade-unbundle-avro` profile

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

# Start command
spark-2.4.4-bin-hadoop2.7/bin/spark-shell \
  --packages org.apache.spark:spark-avro_2.11:2.4.4 \
  --jars `ls packaging/hudi-spark-bundle/target/hudi-spark-bundle_2.11-*.*.*-SNAPSHOT.jar` \
  --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
```

## 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. 
