src-kafka/docs/streams.html

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<script id="streams-template" type="text/x-handlebars-template">
    <h1>Streams</h1>

        <ol class="toc">
            <li>
                <a href="#streams_overview">Overview</a>
            </li>
            <li>
                <a href="#streams_developer">Developer guide</a>
                <ul>
                    <li><a href="#streams_concepts">Core concepts</a>
                    <li><a href="#streams_processor">Low-level processor API</a>
                    <li><a href="#streams_dsl">High-level streams DSL</a>
                </ul>
            </li>
        </ol>

        <h2><a id="streams_overview" href="#overview">Overview</a></h2>

        <p>
        Kafka Streams is a client library for processing and analyzing data stored in Kafka and either write the resulting data back to Kafka or send the final output to an external system. It builds upon important stream processing concepts such as properly distinguishing between event time and processing time, windowing support, and simple yet efficient management of application state.
        Kafka Streams has a <b>low barrier to entry</b>: You can quickly write and run a small-scale proof-of-concept on a single machine; and you only need to run additional instances of your application on multiple machines to scale up to high-volume production workloads. Kafka Streams transparently handles the load balancing of multiple instances of the same application by leveraging Kafka's parallelism model.
        </p>
        <p>
        Some highlights of Kafka Streams:
        </p>

        <ul>
            <li>Designed as a <b>simple and lightweight client library</b>, which can be easily embedded in any Java application and integrated with any existing packaging, deployment and operational tools that users have for their streaming applications.</li>
            <li>Has <b>no external dependencies on systems other than Apache Kafka itself</b> as the internal messaging layer; notably, it uses Kafka's partitioning model to horizontally scale processing while maintaining strong ordering guarantees.</li>
            <li>Supports <b>fault-tolerant local state</b>, which enables very fast and efficient stateful operations like joins and windowed aggregations.</li>
            <li>Employs <b>one-record-at-a-time processing</b> to achieve low processing latency, and supports <b>event-time based windowing operations</b>.</li>
            <li>Offers necessary stream processing primitives, along with a <b>high-level Streams DSL</b> and a <b>low-level Processor API</b>.</li>

        </ul>

        <h2><a id="streams_developer" href="#streams_developer">Developer Guide</a></h2>

        <p>
        There is a <a href="#quickstart_kafkastreams">quickstart</a> example that provides how to run a stream processing program coded in the Kafka Streams library.
        This section focuses on how to write, configure, and execute a Kafka Streams application.
        </p>

        <h4><a id="streams_concepts" href="#streams_concepts">Core Concepts</a></h4>

        <p>
        We first summarize the key concepts of Kafka Streams.
        </p>

        <h5><a id="streams_topology" href="#streams_topology">Stream Processing Topology</a></h5>

        <ul>
            <li>A <b>stream</b> is the most important abstraction provided by Kafka Streams: it represents an unbounded, continuously updating data set. A stream is an ordered, replayable, and fault-tolerant sequence of immutable data records, where a <b>data record</b> is defined as a key-value pair.</li>
            <li>A <b>stream processing application</b> written in Kafka Streams defines its computational logic through one or more <b>processor topologies</b>, where a processor topology is a graph of stream processors (nodes) that are connected by streams (edges).</li>
            <li>A <b>stream processor</b> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently producing one or more output records to its downstream processors.</li>
        </ul>

        <p>
        Kafka Streams offers two ways to define the stream processing topology: the <a href="#streams_dsl"><b>Kafka Streams DSL</b></a> provides
        the most common data transformation operations such as <code>map</code> and <code>filter</code>; the lower-level <a href="#streams_processor"><b>Processor API</b></a> allows
        developers define and connect custom processors as well as to interact with <a href="#streams_state">state stores</a>.
        </p>

        <h5><a id="streams_time" href="#streams_time">Time</a></h5>

        <p>
        A critical aspect in stream processing is the notion of <b>time</b>, and how it is modeled and integrated.
        For example, some operations such as <b>windowing</b> are defined based on time boundaries.
        </p>
        <p>
        Common notions of time in streams are:
        </p>

        <ul>
            <li><b>Event time</b> - The point in time when an event or data record occurred, i.e. was originally created "at the source".</li>
            <li><b>Processing time</b> - The point in time when the event or data record happens to be processed by the stream processing application, i.e. when the record is being consumed. The processing time may be milliseconds, hours, or days etc. later than the original event time.</li>
            <li><b>Ingestion time</b> - The point in time when an event or data record is stored in a topic partition by a Kafka broker. The difference to event time is that this ingestion timestamp is generated when the record is appended to the target topic by the Kafka broker, not when the record is created "at the source". The difference to processing time is that processing time is when the stream processing application processes the record. For example, if a record is never processed, there is no notion of processing time for it, but it still has an ingestion time.
        </ul>
        <p>
        The choice between event-time and ingestion-time is actually done through the configuration of Kafka (not Kafka Streams): From Kafka 0.10.x onwards, timestamps are automatically embedded into Kafka messages. Depending on Kafka's configuration these timestamps represent event-time or ingestion-time. The respective Kafka configuration setting can be specified on the broker level or per topic. The default timestamp extractor in Kafka Streams will retrieve these embedded timestamps as-is. Hence, the effective time semantics of your application depend on the effective Kafka configuration for these embedded timestamps.
        </p>
        <p>
        Kafka Streams assigns a <b>timestamp</b> to every data record
        via the <code>TimestampExtractor</code> interface.
        Concrete implementations of this interface may retrieve or compute timestamps based on the actual contents of data records such as an embedded timestamp field
        to provide event-time semantics, or use any other approach such as returning the current wall-clock time at the time of processing,
        thereby yielding processing-time semantics to stream processing applications.
        Developers can thus enforce different notions of time depending on their business needs. For example,
        per-record timestamps describe the progress of a stream with regards to time (although records may be out-of-order within the stream) and
        are leveraged by time-dependent operations such as joins.
        </p>

        <p>
        Finally, whenever a Kafka Streams application writes records to Kafka, then it will also assign timestamps to these new records. The way the timestamps are assigned depends on the context:
        <ul>
            <li> When new output records are generated via processing some input record, for example, <code>context.forward()</code> triggered in the <code>process()</code> function call, output record timestamps are inherited from input record timestamps directly.</li>
            <li> When new output records are generated via periodic functions such as <code>punctuate()</code>, the output record timestamp is defined as the current internal time (obtained through <code>context.timestamp()</code>) of the stream task.</li>
            <li> For aggregations, the timestamp of a resulting aggregate update record will be that of the latest arrived input record that triggered the update.</li>
        </ul>
        </p>

        <h5><a id="streams_state" href="#streams_state">States</a></h5>

        <p>
        Some stream processing applications don't require state, which means the processing of a message is independent from
        the processing of all other messages.
        However, being able to maintain state opens up many possibilities for sophisticated stream processing applications: you
        can join input streams, or group and aggregate data records. Many such stateful operators are provided by the <a href="#streams_dsl"><b>Kafka Streams DSL</b></a>.
        </p>
        <p>
        Kafka Streams provides so-called <b>state stores</b>, which can be used by stream processing applications to store and query data.
        This is an important capability when implementing stateful operations.
        Every task in Kafka Streams embeds one or more state stores that can be accessed via APIs to store and query data required for processing.
        These state stores can either be a persistent key-value store, an in-memory hashmap, or another convenient data structure.
        Kafka Streams offers fault-tolerance and automatic recovery for local state stores.
        </p>
        <p>
        Kafka Streams allows direct read-only queries of the state stores by methods, threads, processes or applications external to the stream processing application that created the state stores. This is provided through a feature called <b>Interactive Queries</b>. All stores are named and Interactive Queries exposes only the read operations of the underlying implementation. 
        </p>
        <br>
        <p>
        As we have mentioned above, the computational logic of a Kafka Streams application is defined as a <a href="#streams_topology">processor topology</a>.
        Currently Kafka Streams provides two sets of APIs to define the processor topology, which will be described in the subsequent sections.
        </p>

        <h4><a id="streams_processor" href="#streams_processor">Low-Level Processor API</a></h4>

        <h5><a id="streams_processor_process" href="#streams_processor_process">Processor</a></h5>

        <p>
        Developers can define their customized processing logic by implementing the <code>Processor</code> interface, which
        provides <code>process</code> and <code>punctuate</code> methods. The <code>process</code> method is performed on each
        of the received record; and the <code>punctuate</code> method is performed periodically based on elapsed time.
        In addition, the processor can maintain the current <code>ProcessorContext</code> instance variable initialized in the
        <code>init</code> method, and use the context to schedule the punctuation period (<code>context().schedule</code>), to
        forward the modified / new key-value pair to downstream processors (<code>context().forward</code>), to commit the current
        processing progress (<code>context().commit</code>), etc.
        </p>

        <pre>
            public class MyProcessor extends Processor<String, String> {
                private ProcessorContext context;
                private KeyValueStore<String, Integer> kvStore;

                @Override
                @SuppressWarnings("unchecked")
                public void init(ProcessorContext context) {
                    this.context = context;
                    this.context.schedule(1000);
                    this.kvStore = (KeyValueStore<String, Integer>) context.getStateStore("Counts");
                }

                @Override
                public void process(String dummy, String line) {
                    String[] words = line.toLowerCase().split(" ");

                    for (String word : words) {
                        Integer oldValue = this.kvStore.get(word);

                        if (oldValue == null) {
                            this.kvStore.put(word, 1);
                        } else {
                            this.kvStore.put(word, oldValue + 1);
                        }
                    }
                }

                @Override
                public void punctuate(long timestamp) {
                    KeyValueIterator<String, Integer> iter = this.kvStore.all();

                    while (iter.hasNext()) {
                        KeyValue<String, Integer> entry = iter.next();
                        context.forward(entry.key, entry.value.toString());
                    }

                    iter.close();
                    context.commit();
                }

                @Override
                public void close() {
                    this.kvStore.close();
                }
            };
        </pre>

        <p>
        In the above implementation, the following actions are performed:

        <ul>
            <li>In the <code>init</code> method, schedule the punctuation every 1 second and retrieve the local state store by its name "Counts".</li>
            <li>In the <code>process</code> method, upon each received record, split the value string into words, and update their counts into the state store (we will talk about this feature later in the section).</li>
            <li>In the <code>punctuate</code> method, iterate the local state store and send the aggregated counts to the downstream processor, and commit the current stream state.</li>
        </ul>
        </p>

        <h5><a id="streams_processor_topology" href="#streams_processor_topology">Processor Topology</a></h5>

        <p>
        With the customized processors defined in the Processor API, developers can use the <code>TopologyBuilder</code> to build a processor topology
        by connecting these processors together:

        <pre>
            TopologyBuilder builder = new TopologyBuilder();

            builder.addSource("SOURCE", "src-topic")

                .addProcessor("PROCESS1", MyProcessor1::new /* the ProcessorSupplier that can generate MyProcessor1 */, "SOURCE")
                .addProcessor("PROCESS2", MyProcessor2::new /* the ProcessorSupplier that can generate MyProcessor2 */, "PROCESS1")
                .addProcessor("PROCESS3", MyProcessor3::new /* the ProcessorSupplier that can generate MyProcessor3 */, "PROCESS1")

                .addSink("SINK1", "sink-topic1", "PROCESS1")
                .addSink("SINK2", "sink-topic2", "PROCESS2")
                .addSink("SINK3", "sink-topic3", "PROCESS3");
        </pre>

        There are several steps in the above code to build the topology, and here is a quick walk through:

        <ul>
            <li>First of all a source node named "SOURCE" is added to the topology using the <code>addSource</code> method, with one Kafka topic "src-topic" fed to it.</li>
            <li>Three processor nodes are then added using the <code>addProcessor</code> method; here the first processor is a child of the "SOURCE" node, but is the parent of the other two processors.</li>
            <li>Finally three sink nodes are added to complete the topology using the <code>addSink</code> method, each piping from a different parent processor node and writing to a separate topic.</li>
        </ul>
        </p>

        <h5><a id="streams_processor_statestore" href="#streams_processor_statestore">Local State Store</a></h5>

        <p>
        Note that the Processor API is not limited to only accessing the current records as they arrive, but can also maintain local state stores
        that keep recently arrived records to use in stateful processing operations such as aggregation or windowed joins.
        To take advantage of this local states, developers can use the <code>TopologyBuilder.addStateStore</code> method when building the
        processor topology to create the local state and associate it with the processor nodes that needs to access it; or they can connect a created
        local state store with the existing processor nodes through <code>TopologyBuilder.connectProcessorAndStateStores</code>.

        <pre>
            TopologyBuilder builder = new TopologyBuilder();

            builder.addSource("SOURCE", "src-topic")

                .addProcessor("PROCESS1", MyProcessor1::new, "SOURCE")
                // create the in-memory state store "COUNTS" associated with processor "PROCESS1"
                .addStateStore(Stores.create("COUNTS").withStringKeys().withStringValues().inMemory().build(), "PROCESS1")
                .addProcessor("PROCESS2", MyProcessor3::new /* the ProcessorSupplier that can generate MyProcessor3 */, "PROCESS1")
                .addProcessor("PROCESS3", MyProcessor3::new /* the ProcessorSupplier that can generate MyProcessor3 */, "PROCESS1")

                // connect the state store "COUNTS" with processor "PROCESS2"
                .connectProcessorAndStateStores("PROCESS2", "COUNTS");

                .addSink("SINK1", "sink-topic1", "PROCESS1")
                .addSink("SINK2", "sink-topic2", "PROCESS2")
                .addSink("SINK3", "sink-topic3", "PROCESS3");
        </pre>

        </p>

        In the next section we present another way to build the processor topology: the Kafka Streams DSL.

        <h4><a id="streams_dsl" href="#streams_dsl">High-Level Streams DSL</a></h4>

        To build a processor topology using the Streams DSL, developers can apply the <code>KStreamBuilder</code> class, which is extended from the <code>TopologyBuilder</code>.
        A simple example is included with the source code for Kafka in the <code>streams/examples</code> package. The rest of this section will walk
        through some code to demonstrate the key steps in creating a topology using the Streams DSL, but we recommend developers to read the full example source
        codes for details. 

        <h5><a id="streams_kstream_ktable" href="#streams_kstream_ktable">KStream and KTable</a></h5>
        The DSL uses two main abstractions. A <b>KStream</b> is an abstraction of a record stream, where each data record represents a self-contained datum in the unbounded data set. A <b>KTable</b> is an abstraction of a changelog stream, where each data record represents an update. More precisely, the value in a data record is considered to be an update of the last value for the same record key, if any (if a corresponding key doesn't exist yet, the update will be considered a create). To illustrate the difference between KStreams and KTables, let’s imagine the following two data records are being sent to the stream: <code>("alice", 1) --> ("alice", 3)</code>. If these records a KStream and the stream processing application were to sum the values it would return <code>4</code>. If these records were a KTable, the return would be <code>3</code>, since the last record would be considered as an update.



        <h5><a id="streams_dsl_source" href="#streams_dsl_source">Create Source Streams from Kafka</a></h5>

        <p>
        Either a <b>record stream</b> (defined as <code>KStream</code>) or a <b>changelog stream</b> (defined as <code>KTable</code>)
        can be created as a source stream from one or more Kafka topics (for <code>KTable</code> you can only create the source stream
        from a single topic).
        </p>

        <pre>
            KStreamBuilder builder = new KStreamBuilder();

            KStream<String, GenericRecord> source1 = builder.stream("topic1", "topic2");
            KTable<String, GenericRecord> source2 = builder.table("topic3", "stateStoreName");
        </pre>

        <h5><a id="streams_dsl_windowing" href="#streams_dsl_windowing">Windowing a stream</a></h5>
        A stream processor may need to divide data records into time buckets, i.e. to <b>window</b> the stream by time. This is usually needed for join and aggregation operations, etc. Kafka Streams currently defines the following types of windows:
        <ul>
        <li><b>Hopping time windows</b> are windows based on time intervals. They model fixed-sized, (possibly) overlapping windows. A hopping window is defined by two properties: the window's size and its advance interval (aka "hop"). The advance interval specifies by how much a window moves forward relative to the previous one. For example, you can configure a hopping window with a size 5 minutes and an advance interval of 1 minute. Since hopping windows can overlap a data record may belong to more than one such windows.</li>
        <li><b>Tumbling time windows</b> are a special case of hopping time windows and, like the latter, are windows based on time intervals. They model fixed-size, non-overlapping, gap-less windows. A tumbling window is defined by a single property: the window's size. A tumbling window is a hopping window whose window size is equal to its advance interval. Since tumbling windows never overlap, a data record will belong to one and only one window.</li>
        <li><b>Sliding windows</b> model a fixed-size window that slides continuously over the time axis; here, two data records are said to be included in the same window if the difference of their timestamps is within the window size. Thus, sliding windows are not aligned to the epoch, but on the data record timestamps. In Kafka Streams, sliding windows are used only for join operations, and can be specified through the <code>JoinWindows</code> class.</li>
        </ul>

        <h5><a id="streams_dsl_joins" href="#streams_dsl_joins">Joins</a></h5>
        A <b>join</b> operation merges two streams based on the keys of their data records, and yields a new stream. A join over record streams usually needs to be performed on a windowing basis because otherwise the number of records that must be maintained for performing the join may grow indefinitely. In Kafka Streams, you may perform the following join operations:
        <ul>
        <li><b>KStream-to-KStream Joins</b> are always windowed joins, since otherwise the memory and state required to compute the join would grow infinitely in size. Here, a newly received record from one of the streams is joined with the other stream's records within the specified window interval to produce one result for each matching pair based on user-provided <code>ValueJoiner</code>. A new <code>KStream</code> instance representing the result stream of the join is returned from this operator.</li>
        
        <li><b>KTable-to-KTable Joins</b> are join operations designed to be consistent with the ones in relational databases. Here, both changelog streams are materialized into local state stores first. When a new record is received from one of the streams, it is joined with the other stream's materialized state stores to produce one result for each matching pair based on user-provided ValueJoiner. A new <code>KTable</code> instance representing the result stream of the join, which is also a changelog stream of the represented table, is returned from this operator.</li>
        <li><b>KStream-to-KTable Joins</b> allow you to perform table lookups against a changelog stream (<code>KTable</code>) upon receiving a new record from another record stream (KStream). An example use case would be to enrich a stream of user activities (<code>KStream</code>) with the latest user profile information (<code>KTable</code>). Only records received from the record stream will trigger the join and produce results via <code>ValueJoiner</code>, not vice versa (i.e., records received from the changelog stream will be used only to update the materialized state store). A new <code>KStream</code> instance representing the result stream of the join is returned from this operator.</li>
        </ul>

        Depending on the operands the following join operations are supported: <b>inner joins</b>, <b>outer joins</b> and <b>left joins</b>. Their semantics are similar to the corresponding operators in relational databases.
        a
        <h5><a id="streams_dsl_transform" href="#streams_dsl_transform">Transform a stream</a></h5>

        <p>
        There is a list of transformation operations provided for <code>KStream</code> and <code>KTable</code> respectively.
        Each of these operations may generate either one or more <code>KStream</code> and <code>KTable</code> objects and
        can be translated into one or more connected processors into the underlying processor topology.
        All these transformation methods can be chained together to compose a complex processor topology.
        Since <code>KStream</code> and <code>KTable</code> are strongly typed, all these transformation operations are defined as
        generics functions where users could specify the input and output data types.
        </p>

        <p>
        Among these transformations, <code>filter</code>, <code>map</code>, <code>mapValues</code>, etc, are stateless
        transformation operations and can be applied to both <code>KStream</code> and <code>KTable</code>,
        where users can usually pass a customized function to these functions as a parameter, such as <code>Predicate</code> for <code>filter</code>,
        <code>KeyValueMapper</code> for <code>map</code>, etc:

        </p>

        <pre>
            // written in Java 8+, using lambda expressions
            KStream<String, GenericRecord> mapped = source1.mapValue(record -> record.get("category"));
        </pre>

        <p>
        Stateless transformations, by definition, do not depend on any state for processing, and hence implementation-wise
        they do not require a state store associated with the stream processor; Stateful transformations, on the other hand,
        require accessing an associated state for processing and producing outputs.
        For example, in <code>join</code> and <code>aggregate</code> operations, a windowing state is usually used to store all the received records
        within the defined window boundary so far. The operators can then access these accumulated records in the store and compute
        based on them.
        </p>

        <pre>
            // written in Java 8+, using lambda expressions
            KTable<Windowed<String>, Long> counts = source1.groupByKey().aggregate(
                () -> 0L,  // initial value
                (aggKey, value, aggregate) -> aggregate + 1L,   // aggregating value
                TimeWindows.of("counts", 5000L).advanceBy(1000L), // intervals in milliseconds
                Serdes.Long() // serde for aggregated value
            );

            KStream<String, String> joined = source1.leftJoin(source2,
                (record1, record2) -> record1.get("user") + "-" + record2.get("region");
            );
        </pre>

        <h5><a id="streams_dsl_sink" href="#streams_dsl_sink">Write streams back to Kafka</a></h5>

        <p>
        At the end of the processing, users can choose to (continuously) write the final resulted streams back to a Kafka topic through
        <code>KStream.to</code> and <code>KTable.to</code>.
        </p>

        <pre>
            joined.to("topic4");
        </pre>

        If your application needs to continue reading and processing the records after they have been materialized
        to a topic via <code>to</code> above, one option is to construct a new stream that reads from the output topic;
        Kafka Streams provides a convenience method called <code>through</code>:

        <pre>
            // equivalent to
            //
            // joined.to("topic4");
            // materialized = builder.stream("topic4");
            KStream<String, String> materialized = joined.through("topic4");
        </pre>


        <br>
        <p>
        Besides defining the topology, developers will also need to configure their applications
        in <code>StreamsConfig</code> before running it. A complete list of
        Kafka Streams configs can be found <a href="#streamsconfigs"><b>here</b></a>.
        </p>
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