298808baaf
- Introduced an internal metadata table, that stores file listings. - metadata table is kept upto date with - Fixed handling of CleanerPlan. - [HUDI-842] Reduce parallelism to speed up the test. - [HUDI-842] Implementation of CLI commands for metadata operations and lookups. - [HUDI-842] Extend rollback metadata to include the files which have been appended to. - [HUDI-842] Support for rollbacks in MOR Table. - MarkerBasedRollbackStrategy needs to correctly provide the list of files for which rollback blocks were appended. - [HUDI-842] Added unit test for rollback of partial commits (inflight but not completed yet). - [HUDI-842] Handled the error case where metadata update succeeds but dataset commit fails. - [HUDI-842] Schema evolution strategy for Metadata Table. Each type of metadata saved (FilesystemMetadata, ColumnIndexMetadata, etc.) will be a separate field with default null. The type of the record will identify the valid field. This way, we can grow the schema when new type of information is saved within in which still keeping it backward compatible. - [HUDI-842] Fix non-partitioned case and speedup initial creation of metadata table.Choose only 1 partition for jsc as the number of records is low (hundreds to thousands). There is more overhead of creating large number of partitions for JavaRDD and it slows down operations like WorkloadProfile. For the non-partitioned case, use "." as the name of the partition to prevent empty keys in HFile. - [HUDI-842] Reworked metrics pusblishing. - Code has been split into reader and writer side. HoodieMetadata code to be accessed by using HoodieTable.metadata() to get instance of metdata for the table. Code is serializable to allow executors to use the functionality. - [RFC-15] Add metrics to track the time for each file system call. - [RFC-15] Added a distributed metrics registry for spark which can be used to collect metrics from executors. This helps create a stats dashboard which shows the metadata table improvements in real-time for production tables. - [HUDI-1321] Created HoodieMetadataConfig to specify configuration for the metadata table. This is safer than full-fledged properties for the metadata table (like HoodieWriteConfig) as it makes burdensome to tune the metadata. With limited configuration, we can control the performance of the metadata table closely. [HUDI-1319][RFC-15] Adding interfaces for HoodieMetadata, HoodieMetadataWriter (apache#2266) - moved MetadataReader to HoodieBackedTableMetadata, under the HoodieTableMetadata interface - moved MetadataWriter to HoodieBackedTableMetadataWriter, under the HoodieTableMetadataWriter - Pulled all the metrics into HoodieMetadataMetrics - Writer now wraps the metadata, instead of extending it - New enum for MetadataPartitionType - Streamlined code flow inside HoodieBackedTableMetadataWriter w.r.t initializing metadata state - [HUDI-1319] Make async operations work with metadata table (apache#2332) - Changes the syncing model to only move over completed instants on data timeline - Syncing happens postCommit and on writeClient initialization - Latest delta commit on the metadata table is sufficient as the watermark for data timeline archival - Cleaning/Compaction use a suffix to the last instant written to metadata table, such that we keep the 1-1 - .. mapping between data and metadata timelines. - Got rid of a lot of the complexity around checking for valid commits during open of base/log files - Tests now use local FS, to simulate more failure scenarios - Some failure scenarios exposed HUDI-1434, which is needed for MOR to work correctly co-authored by: Vinoth Chandar <vinoth@apache.org>
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
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
# 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 3.0.0
The default Spark version supported is 2.4.4. To build for Spark 3.0.0 version, build using spark3 profile
mvn clean package -DskipTests -Dspark3
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 to quickly explore Hudi's capabilities using spark-shell.