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.
*
* 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)}.
*
* 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.
*
*
* HOW TO RUN THIS EXAMPLE
*
* 1) Start Zookeeper and Kafka. Please refer to QuickStart.
*
* 2) Create the input and output topics used by this example.
*
* {@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
* }
* Note: The above commands are for the Confluent Platform. For Apache Kafka it should be {@code bin/kafka-topics.sh ...}.
*
* 3) Start this example application either in your IDE or on the command line.
*
* If via the command line please refer to Packaging.
* Once packaged you can then run:
*
* {@code
* $ java -cp target/streams-examples-3.3.0-standalone.jar io.confluent.examples.streams.WordCountLambdaExample
* }
* 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".
*
* {@code
* # Start the console producer. You can then enter input data by writing some line of text, followed by ENTER:
* #
* # hello kafka streams
* # all streams lead to kafka
* # join kafka summit
* #
* # Every line you enter will become the value of a single Kafka message.
* $ bin/kafka-console-producer --broker-list localhost:9092 --topic TextLinesTopic
* }
* 5) Inspect the resulting data in the output topic, e.g. via {@code kafka-console-consumer}.
*
* {@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
* }
* 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.
*
* {@code
* hello 1
* kafka 1
* streams 1
* all 1
* streams 2
* lead 1
* to 1
* join 1
* kafka 3
* summit 1
* }
* 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 stringSerde = Serdes.String();
final Serde 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 textLines = builder.stream(stringSerde, stringSerde, "TextLinesTopic");
final Pattern pattern = Pattern.compile("\\W+", Pattern.UNICODE_CHARACTER_CLASS);
final KTable 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` to `KTable`
// (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` 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 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();