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Hudi will be taking on promise for it bundles to stay compatible with Spark minor versions (for ex 2.4, 3.1, 3.2): meaning that single build of Hudi (for ex "hudi-spark3.2-bundle") will be compatible with ALL patch versions in that minor branch (in that case 3.2.1, 3.2.0, etc) To achieve that we'll have to remove (and ban) "spark-avro" as a dependency, which on a few occasions was the root-cause of incompatibility b/w consecutive Spark patch versions (most recently 3.2.1 and 3.2.0, due to this PR). Instead of bundling "spark-avro" as dependency, we will be copying over some of the classes Hudi depends on and maintain them along the Hudi code-base to make sure we're able to provide for the aforementioned guarantee. To workaround arising compatibility issues we will be applying local patches to guarantee compatibility of Hudi bundles w/in the Spark minor version branches. Following Hudi modules to Spark minor branches is currently maintained: "hudi-spark3" -> 3.2.x "hudi-spark3.1.x" -> 3.1.x "hudi-spark2" -> 2.4.x Following classes hierarchies (borrowed from "spark-avro") are maintained w/in these Spark-specific modules to guarantee compatibility with respective minor version branches: AvroSerializer AvroDeserializer AvroUtils Each of these classes has been correspondingly copied from Spark 3.2.1 (for 3.2.x branch), 3.1.2 (for 3.1.x branch), 2.4.4 (for 2.4.x branch) into their respective modules. SchemaConverters class in turn is shared across all those modules given its relative stability (there're only cosmetical changes from 2.4.4 to 3.2.1). All of the aforementioned classes have their corresponding scope of visibility limited to corresponding packages (org.apache.spark.sql.avro, org.apache.spark.sql) to make sure broader code-base does not become dependent on them and instead relies on facades abstracting them. Additionally, given that Hudi plans on supporting all the patch versions of Spark w/in aforementioned minor versions branches of Spark, additional build steps were added to validate that Hudi could be properly compiled against those versions. Testing, however, is performed against the most recent patch versions of Spark with the help of Azure CI. Brief change log: - Removing spark-avro bundling from Hudi by default - Scaffolded Spark 3.2.x hierarchy - Bootstrapped Spark 3.1.x Avro serializer/deserializer hierarchy - Bootstrapped Spark 2.4.x Avro serializer/deserializer hierarchy - Moved ExpressionCodeGen,ExpressionPayload into hudi-spark module - Fixed AvroDeserializer to stay compatible w/ both Spark 3.2.1 and 3.2.0 - Modified bot.yml to build full matrix of support Spark versions - Removed "spark-avro" dependency from all modules - Fixed relocation of spark-avro classes in bundles to assist in running integ-tests.
117 lines
4.9 KiB
Markdown
117 lines
4.9 KiB
Markdown
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The ASF licenses this file to You under the Apache License, Version 2.0
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Unless required by applicable law or agreed to in writing, software
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# Apache Hudi
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Apache Hudi (pronounced Hoodie) stands for `Hadoop Upserts Deletes and Incrementals`.
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Hudi manages the storage of large analytical datasets on DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage).
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<https://hudi.apache.org/>
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[](https://github.com/apache/hudi/actions/workflows/bot.yml)
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[](https://dev.azure.com/apache-hudi-ci-org/apache-hudi-ci/_build/latest?definitionId=3&branchName=master)
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[](https://www.apache.org/licenses/LICENSE-2.0.html)
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[](http://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.hudi%22)
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[](https://join.slack.com/t/apache-hudi/shared_invite/enQtODYyNDAxNzc5MTg2LTE5OTBlYmVhYjM0N2ZhOTJjOWM4YzBmMWU2MjZjMGE4NDc5ZDFiOGQ2N2VkYTVkNzU3ZDQ4OTI1NmFmYWQ0NzE)
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## Features
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* Upsert support with fast, pluggable indexing
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* Atomically publish data with rollback support
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* Snapshot isolation between writer & queries
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* Savepoints for data recovery
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* Manages file sizes, layout using statistics
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* Async compaction of row & columnar data
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* Timeline metadata to track lineage
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* Optimize data lake layout with clustering
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Hudi supports three types of queries:
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* **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)).
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* **Incremental Query** - Provides a change stream with records inserted or updated after a point in time.
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* **Read Optimized Query** - Provides excellent snapshot query performance via purely columnar storage (e.g. [Parquet](https://parquet.apache.org/)).
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Learn more about Hudi at [https://hudi.apache.org](https://hudi.apache.org)
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## Building Apache Hudi from source
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Prerequisites for building Apache Hudi:
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* Unix-like system (like Linux, Mac OS X)
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* Java 8 (Java 9 or 10 may work)
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* Git
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* Maven (>=3.3.1)
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```
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# Checkout code and build
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git clone https://github.com/apache/hudi.git && cd hudi
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mvn clean package -DskipTests
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# Start command
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spark-2.4.4-bin-hadoop2.7/bin/spark-shell \
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--jars `ls packaging/hudi-spark-bundle/target/hudi-spark-bundle_2.11-*.*.*-SNAPSHOT.jar` \
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--conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
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```
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To build the Javadoc for all Java and Scala classes:
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```
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# Javadoc generated under target/site/apidocs
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mvn clean javadoc:aggregate -Pjavadocs
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```
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### Build with Scala 2.12
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The default Scala version supported is 2.11. To build for Scala 2.12 version, build using `scala-2.12` profile
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```
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mvn clean package -DskipTests -Dscala-2.12
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```
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### Build with Spark 3
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The default Spark version supported is 2.4.4. To build for different Spark 3 versions, use the corresponding profile
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```
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# Build against Spark 3.2.1 (the default build shipped with the public Spark 3 bundle)
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mvn clean package -DskipTests -Dspark3
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# Build against Spark 3.1.2
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mvn clean package -DskipTests -Dspark3.1.x
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```
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### What about "spark-avro" module?
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Starting from versions 0.11, Hudi no longer requires `spark-avro` to be specified using `--packages`
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## Running Tests
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Unit tests can be run with maven profile `unit-tests`.
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```
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mvn -Punit-tests test
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```
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Functional tests, which are tagged with `@Tag("functional")`, can be run with maven profile `functional-tests`.
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```
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mvn -Pfunctional-tests test
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```
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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.
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```
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mvn -Punit-tests test -DSPARK_EVLOG_DIR=/path/for/spark/event/log
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```
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## Quickstart
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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.
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