e5a2baeed0
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.
This directory contains examples code that uses hudi.
To run the demo:
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Configure your
SPARK_MASTERenv variable, yarn-cluster mode by default. -
For hudi write client demo and hudi data source demo, just use spark-submit as common spark app
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For hudi delta streamer demo of custom source, run
bin/custom-delta-streamer-example.sh -
For hudi delta streamer demo of dfs source:
4.1 Prepare dfs data, we have provided
src/main/resources/delta-streamer-config/dfs/source-file.jsonfor test4.2 Run
bin/dfs-delta-streamer-example.sh -
For hudi delta streamer demo of dfs source:
5.1 Start Kafka server
5.2 Configure your Kafka properties, we have provided
src/main/resources/delta-streamer-config/kafka/kafka-source.propertiesfor test5.3 Run
bin/kafka-delta-streamer-example.sh5.4 Continuously write source data to the Kafka topic your configured with
hoodie.deltastreamer.source.kafka.topicinkafka-source.properties -
Some notes delta streamer demo:
6.1 The configuration files we provided is just the simplest demo, you can change it according to your specific needs.
6.2 You could also use Intellij to run the example directly by configuring parameters as "Program arguments"