Project showing how the spark hdfs backed state store can be used to deduplicate streaming data
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Spark Structured Streaming Data Deduplication using State Store
This is a project showing how the spark in-built HDFS backed state store can be used to deduplicate data in a stream. The related blog post can be found at https://www.barrelsofdata.com/data-deduplication-spark-state-store
Build instructions
From the root of the project execute the below commands
- To clear all compiled classes, build and log directories
./gradlew clean
- To run tests
./gradlew test
- To build jar
./gradlew shadowJar
- All combined
./gradlew clean test shadowJar
Run
Ensure your local hadoop cluster is running (hadoop cluster tutorial) and start two kafka brokers (kafka tutorial).
- Create kafka topic
kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 2 --partitions 2 --topic streaming-data
- Start streaming job
spark-submit --master yarn --deploy-mode cluster build/libs/spark-state-store-data-deduplication-1.0.jar <KAFKA_BROKER> <KAFKA_TOPIC> <FULL_OUTPUT_PATH> <DEDUPLICATED_OUTPUT_PATH> <WINDOW_SIZE_SECONDS>
Example: spark-submit --master yarn --deploy-mode client build/libs/spark-state-store-data-deduplication-1.0.jar localhost:9092 streaming-data fullOutput deduplicatedOutput 5
- You can feed simulated data to the kafka topic
- Open new terminal and run the shell script located at src/test/resources/dataProducer.sh
- Produces the two instances of the following json structure every 1 second: {"ts":1594307307,"usr":"user1","tmp":98}
cd src/test/resources
./dataProducer.sh localhost:9092 streaming-data
View Results
Open a spark-shell and use the following code, do change the paths to where the outputs are stored.
spark.read.parquet("fullOutput").orderBy("user","eventTime").show(truncate = false)
spark.read.parquet("deduplicatedOutput").orderBy("user","eventTime").show(truncate = false)