80011df995
As of now, delete partitions will ensure all file groups are deleted, but the partition as such is not deleted. So, get all partitions might be returning the deleted partitions as well. but no data will be served since all file groups are deleted. With this patch, we are fixing it. We are letting cleaner take care of deleting the partitions when all file groups pertaining to a partitions are deleted. - Fixed the CleanPlanActionExecutor to return meta info about list of partitions to be deleted. If there are no valid file groups for a partition, clean planner will include the partition to be deleted. - Fixed HoodieCleanPlan avro schema to include the list of partitions to be deleted - CleanActionExecutor is fixed to delete partitions if any (as per clean plan) - Same info is added to HoodieCleanMetadata - Metadata table when applying clean metadata, will check for partitions to be deleted and will update the "all_partitions" record for the deleted partitions. Co-authored-by: sivabalan <n.siva.b@gmail.com>
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).
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 + Avro).
- 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).
Learn more about Hudi at 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 different Spark versions
The default Spark version supported is 2.4.4. To build for different Spark versions and Scala 2.12, use the corresponding profile
| Label | Artifact Name for Spark Bundle | Maven Profile Option | Notes |
|---|---|---|---|
| Spark 2.4, Scala 2.11 | hudi-spark2.4-bundle_2.11 | -Pspark2.4 |
For Spark 2.4.4, which is the same as the default |
| Spark 2.4, Scala 2.12 | hudi-spark2.4-bundle_2.12 | -Pspark2.4,scala-2.12 |
For Spark 2.4.4, which is the same as the default and Scala 2.12 |
| Spark 3.1, Scala 2.12 | hudi-spark3.1-bundle_2.12 | -Pspark3.1 |
For Spark 3.1.x |
| Spark 3.2, Scala 2.12 | hudi-spark3.2-bundle_2.12 | -Pspark3.2 |
For Spark 3.2.x |
| Spark 3, Scala 2.12 | hudi-spark3-bundle_2.12 | -Pspark3 |
This is the same as Spark 3.2, Scala 2.12 |
| Spark, Scala 2.11 | hudi-spark-bundle_2.11 | Default | The default profile, supporting Spark 2.4.4 |
| Spark, Scala 2.12 | hudi-spark-bundle_2.12 | -Pscala-2.12 |
The default profile (for Spark 2.4.4) with Scala 2.12 |
For example,
# Build against Spark 3.2.x (the default build shipped with the public Spark 3 bundle)
mvn clean package -DskipTests -Pspark3.2
# Build against Spark 3.1.x
mvn clean package -DskipTests -Pspark3.1
# Build against Spark 2.4.4 and Scala 2.12
mvn clean package -DskipTests -Pspark2.4,scala-2.12
What about "spark-avro" module?
Starting from versions 0.11, Hudi no longer requires spark-avro to be specified using --packages
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 to quickly explore Hudi's capabilities using spark-shell.