#Apple java and kafka sunnyvale code#

Next, we'll create a state store (key-value) for all the computed word counts: groupedByWord groupBy((key, word) -> word, Grouped.with(stringSerde, stringSerde)) flatMapValues(value -> Arrays.asList(value.toLowerCase().split('\W+'))) Firstly, we'll define the processing topology, in our case, the word count algorithm: KStream textLines =īuilder.stream(TEXT_LINES_TOPIC, Consumed.with(Serdes.String(), Serdes.String())) įinal KGroupedStream groupedByWord = textLines Let's see an example using interactive queries. With the following entries: 'one', 'two', 'three', 'four', 'five' Materialized.with(Serdes.String(), Serdes.Long())) aggregate(() -> 0L, (aggKey, newValue, aggValue) -> aggValue + newValue.length(), Grouped.with(Serdes.String(), Serdes.String()))

groupBy((key, value) -> (value != null & value.length() > 0) The result is the following: (key= 1) -> (left=left, right=null)įor the aggregation example, we'll compute the word count algorithm but using as key the first two letters of each word: KTable aggregated = input So we'll put in the left stream value=left with key=1 and the right stream value=right and key=2. JoinWindows.of(Duration.ofSeconds(5))).groupByKey() (leftValue, rightValue) -> 'left=' + leftValue + ', right=' + rightValue,

Finally, it is possible to apply windowing, to group records with the same key in join or aggregation functions.Īn example of joining with 5s windowing will merge records grouped by key from two streams into one stream: KStream leftRightSource = leftSource.outerJoin(rightSource, We are also able to aggregate, or combe multiple records from streams/tables into one single record in a new table. We can join, or merge two input streams/tables with the same key to produce a new stream/table. String TEXT_EXAMPLE_2 = 'test filter filter this sentence' ĭSL covers several transformation features. We'll send those two strings to the topic: String TEXT_EXAMPLE_1 = 'test test and test' count(Materialized.> as('counts-store')) flatMapValues(value -> Arrays.asList(value In other words, any table or state store can be restored using the changelog topic.Īn example of stateful transformation is the word count algorithm: KTable wordCounts = textLines The processing of a message depends on the processing of other messages (state store). Stateful transformations depend on the state to fulfil the processing operations. Finally, Kafka Streams API interacts with the cluster, but it does not run directly on top of it. Another important capability supported is the state stores, used by Kafka Streams to store and query data coming from the topics.

Besides, it uses threads to parallelize processing within an application instance. Kafka Streams uses the concepts of partitions and tasks as logical units strongly linked to the topic partitions. Stream partitions and tasks as logical units for storing and transporting messages.Interact only with a single Kafka Cluster.

#Apple java and kafka sunnyvale code#

Write an application requires few lines of code.

Support stateless and stateful operations.

Single Kafka Stream to consume and produce.

It deals with messages as an unbounded, continuous, and real-time flow of records, with the following characteristics: Built on top of Kafka client libraries, it provides data parallelism, distributed coordination, fault tolerance, and scalability. Kafka Streams greatly simplifies the stream processing from topics.