在本人当前项目架构中有多个Flink作业需要在数据处理过程中查询并关联HBase中的数据。由于对最终的结果展现有时间上的要求，所以引出了本次关于HBase外部数据系统的查询方面的优化。

1. 同步I/O

传统情况下Flink查询HBase时是同步I/O连接。在查询a和b两条数据时，只有a的请求发送到数据库并且返回结果后，才能发送查询b的请求，所以这样在大数据量的查询情况下同步I/O效率是非常低的。可能会进一步出现反压等问题。

2. Async I/O

因此在Flink1.2版本时引入Async I/O，该功能由阿里捐献给社区。当查询数据时客户端发送多个异步请求，数据库先接收到哪个请求就优先处理哪个请求，并且Flink在处理时也是哪个结果先返回就先将结果传输到下游继续处理，这样好处就是不需要等待其他的结果，从而加快了Flink流处理时和外部系统交互时的速度。

3. 性能测试

本人项目中Flink在process算子中会按照用户ID作为RowKey到HBase的用户宽表查询用户的最新数据进行合并，并最终将用户的全量结果写入到Elasticsearch。

基于此场景在本地集群中准备了五千万条用户数据，包含用户ID、身份证号、手机号、性别、年龄、省份、城市几个属性字段。分别以全局并行度为 1 测试同步I/O和Async I/O所需的处理时长。

测试结果：

从两次测试结果来看Async I/O 相对于同步 I/O 的速度提升了大约 35%。

4. 代码

4.1. SyncIOProcessFunc

package com.dengdz.func;

import com.alibaba.fastjson.JSON;
import com.dengdz.utils.SyncHBase;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.util.Collector;

import java.util.Map;
import java.util.concurrent.atomic.AtomicLong;

public class SyncIOProcessFunc extends ProcessFunction<String, String> {
    public static final String HBASE_ASYNC_DEMO = "hbase_async_demo";
    private SyncHBase syncHBase;
    private final long bufferFlushMaxMutations = 1000;
    private long star;
    private long end;
    private transient AtomicLong numPendingRequests;
    private transient AtomicLong total;

    @Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);

        star = System.currentTimeMillis();

        System.out.println("Start processing, current time:" + star);

        syncHBase = new SyncHBase("Flink-Project/flink-async-hbase/src/main/resources/hbase-site.xml",
                "Flink-Project/flink-async-hbase/src/main/resources/core-site.xml");

    }


    @Override
    public void close() throws Exception {
        super.close();
        end = System.currentTimeMillis();
        System.out.println("Start processing, current time:" + end + " ,time consuming:" + (end - star) + " ms");
        syncHBase.close();
    }


    private void flush() {
        System.out.println("Has been inserted：" + total.addAndGet(numPendingRequests.get()));
        numPendingRequests.set(0);
    }

    @Override
    public void processElement(String value, ProcessFunction<String, String>.Context ctx, Collector<String> out) throws Exception {
        Map<String, Map<String, String>> row = syncHBase.getRow(HBASE_ASYNC_DEMO, value);

        String jsonString = JSON.toJSONString(row);

        out.collect(jsonString);
        if (numPendingRequests.incrementAndGet() >= bufferFlushMaxMutations) {
            flush();
        }
    }
}

4.2. AsyncIOProcessFunc

package com.dengdz.func;

import com.alibaba.fastjson.JSON;
import com.dengdz.utils.AsyncHBase;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.async.ResultFuture;
import org.apache.flink.streaming.api.functions.async.RichAsyncFunction;

import java.util.Collections;
import java.util.concurrent.CompletableFuture;
import java.util.concurrent.atomic.AtomicLong;
import java.util.function.Consumer;
import java.util.function.Supplier;

public class AsyncIOProcessFunc extends RichAsyncFunction<String, String> {

    public static final String HBASE_ASYNC_DEMO = "hbase_async_demo";
    private final long bufferFlushMaxMutations = 1000;
    private AsyncHBase asyncHBase;
    private long star;
    private long end;
    private transient AtomicLong numPendingRequests;
    private transient AtomicLong total;

    @Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);
        asyncHBase = new AsyncHBase("Flink-Project/flink-async-hbase/src/main/resources/hbase-site.xml",
                "Flink-Project/flink-async-hbase/src/main/resources/core-site.xml");
        star = System.currentTimeMillis();
        System.out.println("Start processing, current time:" + star);
        this.total = new AtomicLong(0);
        this.numPendingRequests = new AtomicLong(0);
    }

    @Override
    public void asyncInvoke(String input, ResultFuture<String> resultFuture) throws Exception {

        CompletableFuture.supplyAsync(new Supplier<String>() {
                    @Override
                    public String get() {

                        return JSON.toJSONString(asyncHBase.getRow(HBASE_ASYNC_DEMO, input));

                    }

                })
                .thenAccept(new Consumer<String>() {
                    @Override
                    public void accept(String s) {
                        resultFuture.complete(Collections.singletonList(s));
                        if (numPendingRequests.incrementAndGet() >= bufferFlushMaxMutations) {
                            flush();
                        }
                    }
                });
    }

    private void flush() {
        System.out.println("Has been inserted：" + total.addAndGet(numPendingRequests.get()));
        numPendingRequests.set(0);
    }

    @Override
    public void close() throws Exception {
        super.close();
        end = System.currentTimeMillis();
        System.out.println("Start processing, current time:" + end + " ,time consuming:" + (end - star) + " ms");
        asyncHBase.close();
    }

    @Override
    public void timeout(String input, ResultFuture<String> resultFuture) throws Exception {
        super.timeout(input, resultFuture);
    }
}

5. 总结

从两次测试样本来看，Async I/O 确实要比同步I/O速度要快，但是从监控来看Async I/O时内存使用以及GC也比同步I/O要消耗的资源更大，所以在优化时也要考虑外部系统的承受压力，合理设置参数。有精力的兄弟可以自己测试一下~