Complex Event Processing on top of Kafka Streams Processor API !

Apache Kafka is a high-throughput, distributed, publish-subscribe messaging system.

This library can be used to extend the Kafka Streams API in order to select complex event sequences from streams.

It provides a convenient DSL to build complex event queries.

Currently, this library supports the following event selection strategies :

Strict contiguity : Selected events must be contiguous in the input stream.
Skip till next match :Irrelevant events are skipped until an event matching the next pattern is encountered. If multiple events in the stream can match the next pattern only the first of them is selected.
Skip till any match : Irrelevant events are skipped until an event matching the next pattern is encountered. All events in the stream that can match a pattern are selected.

Maven dependency

    <dependency>
      <groupId>com.github.fhuss</groupId>
      <artifactId>kafka-streams-cep</artifactId>
      <version>0.1.0</version>
    </dependency>

Demonstration

The below example is based on the research paper Efficient Pattern Matching over Event Streams.

Implementation based on https://people.cs.umass.edu/~yanlei/publications/sase-sigmod08.pdf

CEP Query :

     PATTERN SEQ(Stock+ a[ ], Stock b)
       WHERE skip_till_next_match(a[ ], b) {
           [symbol]
       and
           a[1].volume > 1000
       and
           a[i].price > avg(a[..i-1].price)
       and
           b.volume < 80%*a[a.LEN].volume }
       WITHIN 1 hour

Build Query

        Pattern<Object, StockEvent> pattern = new QueryBuilder<Object, StockEvent>()
                .select()
                    .where((k, v, ts, store) -> v.volume > 1000)
                    .<Integer>fold("avg", (k, v, curr) -> v.price)
                    .then()
                .select()
                    .oneOrMore()
                    .skipTillNextMatch()
                    .where((k, v, ts, state) -> v.price > (int)state.get("avg"))
                    .<Integer>fold("avg", (k, v, curr) -> (curr + v.price) / 2)
                    .<Integer>fold("volume", (k, v, curr) -> v.volume)
                    .then()
                .select()
                    .skipTillNextMatch()
                    .where((k, v, ts, state) -> v.volume < (0.8 *  (int)state.get("volume")))
                    .within(1, TimeUnit.HOURS)
                .build();

KStreams API:

        Pattern<Object, StockEvent> pattern = ...

        KStreamBuilder builder = new KStreamBuilder();

        CEPStream<Object, StockEvent> stream = new CEPStream<>(builder.stream("StockEvents"));

        KStream<Object, Sequence<Object, StockEvent>> stocks = stream.query("Stocks", pattern);
        ...

        //Use the topologyBuilder and streamingConfig to start the kafka streams process
        KafkaStreams streaming = new KafkaStreams(builder, props);

Processor API:

        Pattern<Object, StockEvent> pattern = ...

        TopologyBuilder builder = new TopologyBuilder();
        topologyBuilder.addSource("source", "StockEvents")
                .addProcessor("cep", () -> new CEPProcessor<>(query), "source");
        ...
        
        //Use the topologyBuilder and streamingConfig to start the kafka streams process
        KafkaStreams streaming = new KafkaStreams(builder, props);
        streaming.start();

Demo

Run the demonstration class CEPStockKStreamsDemo :

Produce the following json events StockEvents:

./bin/kafka-console-producer --topic StockEvents --broker-list localhost:9092

Input

{"name":"e1","price":100,"volume":1010}
{"name":"e2","price":120,"volume":990}
{"name":"e3","price":120,"volume":1005}
{"name":"e4","price":121,"volume":999}
{"name":"e5","price":120,"volume":999}
{"name":"e6","price":125,"volume":750}
{"name":"e7","price":120,"volume":950}
{"name":"e8","price":120,"volume":700}

Consume from the sink topic "matches"

./bin/kafka-console-consumer --new-consumer --topic Matches --bootstrap-server localhost:9092

Output

{"0":["e1"],"1":["e2","e3","e4","e5"],"2":["e6"]}
{"0":["e3"],"1":["e4"],"2":["e6"]}
{"0":["e1"],"1":["e2","e3","e4","e5","e6","e7"],"2":["e8"]}
{"0":["e3"],"1":["e4","e6"],"2":["e8"]}

TODO

Improve test scenarios
Add support for streams grouped by key

Contributions

Any contribution is welcome

Licence

Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License

wking1986/kafkastreams-cep