kafka stream入门1

最近本人在单位经常有对于大量心跳数据进行汇总计算，然后更加计算汇总出不同种类的中间数据集合，来提供后期的处理的需求。
原先的方案是自己写了不少的job，然后利用zookeeper等进行job进度的控制，问题是这种模式下，需要大量的编码，保证数据不被重复消费，感觉自己的程序在出现异常的时候，
还是会有部分数据丢失的问题。

考虑采用一个业绩主流的流式计算的方案，同时也要支持对于历史数据的批量操作。

对比了spark，storm，kafka_stream，首先本人完全没有大数据的实战经验，个人感觉，前两者相对成熟很多，后者kafka_stream是新出来的，相对资源少。
但是前两者是框架级别的，以spark为例，看了下，一般要单独部署一套自己的spark集群（除非单位有现成的给你使用）我们这边是不具备的。搭建的硬件要求也很高。
对比kafkastream，其只是个库，依赖只有kafka，硬件资源需求较小，决定自己先研究下。如果可行，就投入生产。
以下摘录一个最简单的入门的案例。

后续继续补全。

package io.confluent.examples.streams;

import org.apache.kafka.common.serialization.Serde;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.KStreamBuilder;
import org.apache.kafka.streams.kstream.KTable;

import java.util.Arrays;
import java.util.Properties;
import java.util.regex.Pattern;

/**
 * Demonstrates, using the high-level KStream DSL, how to implement the WordCount program that
 * computes a simple word occurrence histogram from an input text. This example uses lambda
 * expressions and thus works with Java 8+ only.
 * <p>
 * In this example, the input stream reads from a topic named "TextLinesTopic", where the values of
 * messages represent lines of text; and the histogram output is written to topic
 * "WordsWithCountsTopic", where each record is an updated count of a single word, i.e. {@code word (String) -> currentCount (Long)}.
 * <p>
 * Note: Before running this example you must 1) create the source topic (e.g. via {@code kafka-topics --create ...}),
 * then 2) start this example and 3) write some data to the source topic (e.g. via {@code kafka-console-producer}).
 * Otherwise you won't see any data arriving in the output topic.
 * <p>
 * <br>
 * HOW TO RUN THIS EXAMPLE
 * <p>
 * 1) Start Zookeeper and Kafka. Please refer to <a href='http://docs.confluent.io/current/quickstart.html#quickstart'>QuickStart</a>.
 * <p>
 * 2) Create the input and output topics used by this example.
 * <pre>
 * {@code
 * $ bin/kafka-topics --create --topic TextLinesTopic \
 *                    --zookeeper localhost:2181 --partitions 1 --replication-factor 1
 * $ bin/kafka-topics --create --topic WordsWithCountsTopic \
 *                    --zookeeper localhost:2181 --partitions 1 --replication-factor 1
 * }</pre>
 * Note: The above commands are for the Confluent Platform. For Apache Kafka it should be {@code bin/kafka-topics.sh ...}.
 * <p>
 * 3) Start this example application either in your IDE or on the command line.
 * <p>
 * If via the command line please refer to <a href='https://github.com/confluentinc/examples/tree/master/kafka-streams#packaging-and-running'>Packaging</a>.
 * Once packaged you can then run:
 * <pre>
 * {@code
 * $ java -cp target/streams-examples-3.3.0-standalone.jar io.confluent.examples.streams.WordCountLambdaExample
 * }</pre>
 * 4) Write some input data to the source topic "TextLinesTopic" (e.g. via {@code kafka-console-producer}).
 * The already running example application (step 3) will automatically process this input data and write the
 * results to the output topic "WordsWithCountsTopic".
 * <pre>
 * {@code
 * # Start the console producer. You can then enter input data by writing some line of text, followed by ENTER:
 * #
 * #   hello kafka streams<ENTER>
 * #   all streams lead to kafka<ENTER>
 * #   join kafka summit<ENTER>
 * #
 * # Every line you enter will become the value of a single Kafka message.
 * $ bin/kafka-console-producer --broker-list localhost:9092 --topic TextLinesTopic
 * }</pre>
 * 5) Inspect the resulting data in the output topic, e.g. via {@code kafka-console-consumer}.
 * <pre>
 * {@code
 * $ bin/kafka-console-consumer --topic WordsWithCountsTopic --from-beginning \
 *                              --new-consumer --bootstrap-server localhost:9092 \
 *                              --property print.key=true \
 *                              --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer
 * }</pre>
 * You should see output data similar to below. Please note that the exact output
 * sequence will depend on how fast you type the above sentences. If you type them
 * slowly, you are likely to get each count update, e.g., kafka 1, kafka 2, kafka 3.
 * If you type them quickly, you are likely to get fewer count updates, e.g., just kafka 3.
 * This is because the commit interval is set to 10 seconds. Anything typed within
 * that interval will be compacted in memory.
 * <pre>
 * {@code
 * hello    1
 * kafka    1
 * streams  1
 * all      1
 * streams  2
 * lead     1
 * to       1
 * join     1
 * kafka    3
 * summit   1
 * }</pre>
 * 6) Once you're done with your experiments, you can stop this example via {@code Ctrl-C}. If needed,
 * also stop the Kafka broker ({@code Ctrl-C}), and only then stop the ZooKeeper instance (`{@code Ctrl-C}).
 */
public class WordCountLambdaExample {

  public static void main(final String[] args) throws Exception {
    final String bootstrapServers = args.length > 0 ? args[0] : "localhost:9092";
    final Properties streamsConfiguration = new Properties();
    // Give the Streams application a unique name.  The name must be unique in the Kafka cluster
    // against which the application is run.
    streamsConfiguration.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-lambda-example");
    streamsConfiguration.put(StreamsConfig.CLIENT_ID_CONFIG, "wordcount-lambda-example-client");
    // Where to find Kafka broker(s).
    streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
    // Specify default (de)serializers for record keys and for record values.
    streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
    streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
    // Records should be flushed every 10 seconds. This is less than the default
    // in order to keep this example interactive.
    streamsConfiguration.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 10 * 1000);
    // For illustrative purposes we disable record caches
    streamsConfiguration.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);

    // Set up serializers and deserializers, which we will use for overriding the default serdes
    // specified above.
    final Serde<String> stringSerde = Serdes.String();
    final Serde<Long> longSerde = Serdes.Long();

    // In the subsequent lines we define the processing topology of the Streams application.
    final KStreamBuilder builder = new KStreamBuilder();

    // Construct a `KStream` from the input topic "TextLinesTopic", where message values
    // represent lines of text (for the sake of this example, we ignore whatever may be stored
    // in the message keys).
    //
    // Note: We could also just call `builder.stream("TextLinesTopic")` if we wanted to leverage
    // the default serdes specified in the Streams configuration above, because these defaults
    // match what's in the actual topic.  However we explicitly set the deserializers in the
    // call to `stream()` below in order to show how that's done, too.
    final KStream<String, String> textLines = builder.stream(stringSerde, stringSerde, "TextLinesTopic");

    final Pattern pattern = Pattern.compile("\\W+", Pattern.UNICODE_CHARACTER_CLASS);

    final KTable<String, Long> wordCounts = textLines
      // Split each text line, by whitespace, into words.  The text lines are the record
      // values, i.e. we can ignore whatever data is in the record keys and thus invoke
      // `flatMapValues()` instead of the more generic `flatMap()`.
      .flatMapValues(value -> Arrays.asList(pattern.split(value.toLowerCase())))
      // Count the occurrences of each word (record key).
      //
      // This will change the stream type from `KStream<String, String>` to `KTable<String, Long>`
      // (word -> count).  In the `count` operation we must provide a name for the resulting KTable,
      // which will be used to name e.g. its associated state store and changelog topic.
      //
      // Note: no need to specify explicit serdes because the resulting key and value types match our default serde settings
      .groupBy((key, word) -> word)
      .count("Counts");

    // Write the `KStream<String, Long>` to the output topic.
    wordCounts.to(stringSerde, longSerde, "WordsWithCountsTopic");

    // Now that we have finished the definition of the processing topology we can actually run
    // it via `start()`.  The Streams application as a whole can be launched just like any
    // normal Java application that has a `main()` method.
    final KafkaStreams streams = new KafkaStreams(builder, streamsConfiguration);
    // Always (and unconditionally) clean local state prior to starting the processing topology.
    // We opt for this unconditional call here because this will make it easier for you to play around with the example
    // when resetting the application for doing a re-run (via the Application Reset Tool,
    // http://docs.confluent.io/current/streams/developer-guide.html#application-reset-tool).
    //
    // The drawback of cleaning up local state prior is that your app must rebuilt its local state from scratch, which
    // will take time and will require reading all the state-relevant data from the Kafka cluster over the network.
    // Thus in a production scenario you typically do not want to clean up always as we do here but rather only when it
    // is truly needed, i.e., only under certain conditions (e.g., the presence of a command line flag for your app).
    // See `ApplicationResetExample.java` for a production-like example.
    streams.cleanUp();
    streams.start();

    // Add shutdown hook to respond to SIGTERM and gracefully close Kafka Streams
    Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
  }

}

这里的逻辑答题上就是从kafka的输入stream TextLinesTopic里，不断读入用户输入的文本行，

 final KStream<String, String> textLines = builder.stream(stringSerde, stringSerde, "TextLinesTopic");

然后针对每行输入用正则表达式查封成各个word。flatMap到word 单词数据流

flatMapValues(value -> Arrays.asList(pattern.split(value.toLowerCase())))

接下来，按照不同的单词进行分组

groupBy((key, word) -> word)

最后把kstream 通过count进行转存到ktable里，后续可以通过ksql进行查询
切记，streams需要开启才能工作

streams.start();