使用 kafka-streams 对 json 输入流进行有条件的排序 [英] using kafka-streams to conditionally sort a json input stream

问题描述

我是开发 kafka-streams 应用程序的新手.我的流处理器旨在根据输入 json 消息中用户键的值对 json 消息进行排序.

I am new to developing kafka-streams applications. My stream processor is meant to sort json messages based on a value of a user key in the input json message.

Message 1: {"UserID": "1", "Score":"123", "meta":"qwert"}
Message 2: {"UserID": "5", "Score":"780", "meta":"mnbvs"}
Message 3: {"UserID": "2", "Score":"0", "meta":"fghjk"}

我在这里阅读了动态连接Kafka输入流到多个输出流，没有动态解决方案.

I have read here Dynamically connecting a Kafka input stream to multiple output streams that there is no dynamic solution.

在我的用例中，我知道对输入流进行排序所需的用户键和输出主题.因此，我正在为每个用户编写单独的处理器应用程序，其中每个处理器应用程序都匹配不同的用户 ID.

In my use-case I know the user keys and output topics that I need to sort the input stream. So I am writing separate processor applications specific to each user where each processor application matches a different UserID.

所有不同的流处理器应用程序都从 kafka 中的同一个 json 输入主题中读取，但如果满足预设的用户条件，每个应用程序只会将消息写入特定用户的输出主题.

All the different stream processor applications read from the same json input topic in kafka but each one only writes the message to a output topic for a specific user if the preset user condition is met.

public class SwitchStream extends AbstractProcessor<String, String> {
        @Override
        public void process(String key, String value) {
            HashMap<String, String> message = new HashMap<>();
            ObjectMapper mapper = new ObjectMapper();
            try {
                message = mapper.readValue(value, HashMap.class);
            } catch (IOException e){}

            // User condition UserID = 1
            if(message.get("UserID").equals("1")) {
                context().forward(key, value);
                context().commit();
            }
        }

        public static void main(String[] args) throws Exception {
            Properties props = new Properties();
            props.put(StreamsConfig.APPLICATION_ID_CONFIG, "sort-stream-processor");
            props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
            props.put(StreamsConfig.KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
            props.put(StreamsConfig.VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

            props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
            TopologyBuilder builder = new TopologyBuilder();
            builder.addSource("Source", "INPUT_TOPIC");
            builder.addProcessor("Process", SwitchStream::new, "Source");
            builder.addSink("Sink", "OUTPUT_TOPIC", "Process");

            KafkaStreams streams = new KafkaStreams(builder, props);
            streams.start(); 
       }
}

问题 1:如果使用 Low-Level Processor API，是否可以使用 High-Level Streams DSL 轻松实现相同的功能?(我承认我发现它更难理解并遵循 High-Level Streams DSL 的其他在线示例)

Question 1: Is it possible to achieve the same functionality easily using the High-Level Streams DSL instead if the Low-Level Processor API? (I admit I found it harder understand and follow the other online examples of the High-Level Streams DSL)

问题 2:输入 json 主题正在以 20K-25K EPS 的高速率获取输入.我的处理器应用程序似乎无法跟上这个输入流的步伐.我曾尝试为每个流程部署多个实例，但结果与我希望的情况相差甚远.理想情况下，每个处理器实例应该能够处理 3-5K EPS.

Question 2: The input json topic is getting input at a high rate 20K-25K EPS. My processor applications don't seem to be able to keep pace with this input stream. I have tried deploying multiple instances of each process but the results are nowhere close to where I want them to be. Ideally each processor instance should be able to process 3-5K EPS.

有没有办法改进我的处理器逻辑或使用高级流 DSL 编写相同的处理器逻辑?这会有所作为吗?

Is there a way to improve my processor logic or write the same processor logic using the high level streams DSL? would that make a difference?

推荐答案

您可以通过 filter() 在高级 DSL 中执行此操作(您有效地实现了过滤器，因为您只在以下情况下返回消息它是 userID==1).您可以通过使用 KStream#branch() 来概括此过滤器模式(有关更多详细信息，请参阅文档:http://docs.confluent.io/current/streams/developer-guide.html#stateless-transformations).另请阅读 JavaDocs:http://kafka.apache.org/0102/javadoc/index.html?org/apache/kafka/streams

You can do this in high-level DSL via filter() (you effectively implemented a filter as you only return a message if it's userID==1). You could generalize this filter pattern, by using KStream#branch() (see the docs for further details: http://docs.confluent.io/current/streams/developer-guide.html#stateless-transformations). Also read the JavaDocs: http://kafka.apache.org/0102/javadoc/index.html?org/apache/kafka/streams

KStreamBuilder builder = new KStreamBuilder();
builder.stream("INPUT_TOPIC")
       .filter(new Predicate() {
           @Overwrite
           boolean test(String key, String value) {
               // put you processor logic here
               return message.get("UserID").equals("1")
           }
        })
       .to("OUTPUT_TOPIC");

关于性能.单个实例应该能够处理 10K+ 条记录.如果没有任何进一步的信息，很难判断问题可能是什么.我建议询问 Kafka 用户列表(参见 http://kafka.apache.org/contact)

About performance. A single instance should be able to process 10K+ records. It's hard to tell without any further information what the problem might be. I would recommend to ask at Kafka user list (see http://kafka.apache.org/contact)

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