5755ff25a4
* `ZCurveOptimizeHelper` > `ZOrderingIndexHelper`; Moved Z-index helper under `hudi.index.zorder` package * Tidying up `ZOrderingIndexHelper` * Fixing compilation * Fixed index new/original table merging sequence to always prefer values from new index; Cleaned up `HoodieSparkUtils` * Added test for `mergeIndexSql` * Abstracted Z-index name composition w/in `ZOrderingIndexHelper`; * Fixed `DataSkippingUtils` to interrupt prunning in case data filter contains non-indexed column reference * Properly handle exceptions origination during pruning in `HoodieFileIndex` * Make sure no errors are logged upon encountering `AnalysisException` * Cleaned up Z-index updating sequence; Tidying up comments, java-docs; * Fixed Z-index to properly handle changes of the list of clustered columns * Tidying up * `lint` * Suppressing `JavaDocStyle` first sentence check * Fixed compilation * Fixing incorrect `DecimalType` conversion * Refactored test `TestTableLayoutOptimization` - Added Z-index table composition test (against fixtures) - Separated out GC test; Tidying up * Fixed tests re-shuffling column order for Z-Index table `DataFrame` to align w/ the one by one loaded from JSON * Scaffolded `DataTypeUtils` to do basic checks of Spark types; Added proper compatibility checking b/w old/new index-tables * Added test for Z-index tables merging * Fixed import being shaded by creating internal `hudi.util` package * Fixed packaging for `TestOptimizeTable` * Revised `updateMetadataIndex` seq to provide Z-index updating process w/ source table schema * Make sure existing Z-index table schema is sync'd to source table's one * Fixed shaded refs * Fixed tests * Fixed type conversion of Parquet provided metadata values into Spark expected schemas * Fixed `composeIndexSchema` utility to propose proper schema * Added more tests for Z-index: - Checking that Z-index table is built correctly - Checking that Z-index tables are merged correctly (during update) * Fixing source table * Fixing tests to read from Parquet w/ proper schema * Refactored `ParquetUtils` utility reading stats from Parquet footers * Fixed incorrect handling of Decimals extracted from Parquet footers * Worked around issues in javac failign to compile stream's collection * Fixed handling of `Date` type * Fixed handling of `DateType` to be parsed as `LocalDate` * Updated fixture; Make sure test loads Z-index fixture using proper schema * Removed superfluous scheme adjusting when reading from Parquet, since Spark is actually able to perfectly restore schema (given Parquet was previously written by Spark as well) * Fixing race-condition in Parquet's `DateStringifier` trying to share `SimpleDataFormat` object which is inherently not thread-safe * Tidying up * Make sure schema is used upon reading to validate input files are in the appropriate format; Tidying up; * Worked around javac (1.8) inability to infer expression type properly * Updated fixtures; Tidying up * Fixing compilation after rebase * Assert clustering have in Z-order layout optimization testing * Tidying up exception messages * XXX * Added test validating Z-index lookup filter correctness * Added more test-cases; Tidying up * Added tests for string expressions * Fixed incorrect Z-index filter lookup translations * Added more test-cases * Added proper handling on complex negations of AND/OR expressions by pushing NOT operator down into inner expressions for appropriate handling * Added `-target:jvm-1.8` for `hudi-spark` module * Adding more tests * Added tests for non-indexed columns * Properly handle non-indexed columns by falling back to a re-write of containing expression as `TrueLiteral` instead * Fixed tests * Removing the parquet test files and disabling corresponding tests 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
- 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 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.