spark-structured-streaming

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This is an example of structured-streaming with latest Spark v2.1.0.

A spark job reads from Kafka topic, manipulates data as datasets/dataframes and writes to Cassandra.

Usage:

1. Inside setup directory, run docker-compose up -d to launch instances of zookeeper, kafka and cassandra

2. Wait for a few seconds and then run docker ps to make sure all the three services are running.

3. Then run pip install -r requirements.txt

4. main.py generates some random data and publishes it to a topic in kafka.

5. Run the spark-app using sbt clean compile run in a console. This app will listen on topic (check Main.scala) and writes it to Cassandra.

6. Again run main.py to write some test data on a kafka topic.

7. Finally check if the data has been published in cassandra.

• Go to cqlsh docker exec -it cas_01_test cqlsh localhost
• And then run select * from my_keyspace.test_table ;

8. Another branch avro-example contains avro deserialization code.

Credits:

• This repository has borrowed some snippets from killrweather app.