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Event-time join with kafka Streams

This is a proof of concept scala project to illustrate event-time join in Kafka Stream 0.11.0.0.

The full description is available in my blog post: Event-time join with kafka Streams

Generate test data

In order to play with this POC, you first need to create the test topics:

> kafka-topics --create --zookeeper localhost:2181 --topic etj-moods --replication-factor 1 --partitions 4
> kafka-topics --create --zookeeper localhost:2181 --topic etj-events --replication-factor 1 --partitions 4

You can the execute then start the python scripts in gen_data:

> python gen_moods.py
> python gen_events.py

I recommend starting them together so produced timestamps are similar and kafka stream is able to apply flow control (TODO: review this: this might no longer be necessary since I handle timestamps explicitly in the scripts).

The mood event generator should produce beautiful consultant's recommendations, as follows:

 {"ingestion_time": 689, "consultant": "Adrian", "event_time": 131, "recommendation": "implement intuitive vortals"}
 {"ingestion_time": 697, "consultant": "Sheila", "event_time": 520, "recommendation": "seize proactive interfaces"}
 {"ingestion_time": 782, "consultant": "Tammy", "event_time": 629, "recommendation": "orchestrate efficient platforms"}
 {"ingestion_time": 949, "consultant": "Rhonda", "event_time": 257, "recommendation": "matrix integrated web services"}
 {"ingestion_time": 995, "consultant": "Rhonda", "event_time": 59, "recommendation": "enable B2C web-readiness"}
 {"ingestion_time": 1000, "consultant": "Adrian", "event_time": 603, "recommendation": "morph wireless initiatives"}
 {"ingestion_time": 1009, "consultant": "Scott", "event_time": 957, "recommendation": "orchestrate compelling platforms"}
 {"ingestion_time": 1029, "consultant": "Tammy", "event_time": 693, "recommendation": "generate vertical portals"}
 {"ingestion_time": 1078, "consultant": "Scott", "event_time": 526, "recommendation": "harness virtual schemas"}
 {"ingestion_time": 1127, "consultant": "Adrian", "event_time": 567, "recommendation": "grow integrated vortals"}
 {"ingestion_time": 1189, "consultant": "Tammy", "event_time": 1057, "recommendation": "revolutionize visionary content"}
 {"ingestion_time": 1284, "consultant": "Tammy", "event_time": 1255, "recommendation": "drive wireless e-tailers"}
 {"ingestion_time": 1297, "consultant": "Scott", "event_time": 973, "recommendation": "embrace mission-critical content"}
 {"ingestion_time": 1310, "consultant": "Tammy", "event_time": 1306, "recommendation": "embrace back-end networks"}
 {"ingestion_time": 1323, "consultant": "Tammy", "event_time": 1034, "recommendation": "target transparent systems"}
 {"ingestion_time": 1349, "consultant": "Tammy", "event_time": 1305, "recommendation": "expedite virtual info-mediaries"}
 {"ingestion_time": 1386, "consultant": "Scott", "event_time": 738, "recommendation": "engineer strategic e-business"}
 {"ingestion_time": 1399, "consultant": "Tammy", "event_time": 1106, "recommendation": "morph strategic networks"}
 {"ingestion_time": 1514, "consultant": "Adrian", "event_time": 1406, "recommendation": "innovate impactful content"}

Which are generated with python Faker's bs() method (which stands for "business statement", right?)

You can check that have been posted to kafka as follows:

>kafka-console-consumer --bootstrap-server localhost:9092 --topic etj-moods --from-beginning
>kafka-console-consumer --bootstrap-server localhost:9092 --topic etj-events --from-beginning

Since Kafka is persistent we're using it more as a storage than as a communication channel here, so this only needs to be executed once. THe stream processing will automatically start processing them from the start.

Run the Kafka Stream application

Just launch the main EventTimeJoinExampleApp.scala.

Note that since we set a different application ID at each startup, this is equivalent to reseting the offset of the application to 0 at each startup, which is rather dirty but handy for tests. Do not do that with a real application ^^

p.put(StreamsConfig.APPLICATION_ID_CONFIG, s"event-time-join-example-${System.currentTimeMillis()}")

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Scala project to illustrate my approach to event-time join in Kafka Stream

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