Flink 消费 Kafka 数据实时落Apache doris数据仓库(KFD)

张家锋

1.概述

Apache Doris(原百度 Palo)是一款基于大规模并行处理技术的分布式 SQL 数据仓库,由百度在 2017 年开源,2018 年 8 月进入 Apache 孵化器。

Apache Doris是一个现代化的MPP分析型数据库产品。仅需亚秒级响应时间即可获得查询结果,有效地支持实时数据分析。Apache Doris的分布式架构非常简洁,易于运维,并且可以支持10PB以上的超大数据集。

Apache Doris可以满足多种数据分析需求,例如固定历史报表,实时数据分析,交互式数据分析和探索式数据分析等。令您的数据分析工作更加简单高效!

2.场景介绍

这里我们介绍的是通过Doris提供的Stream load 结合Flink计算引擎怎么实现数据实时快速入库操作。

使用环境如下:

  mysql 5.x/8.x (主要是业务数据库)
  kafka 2.11 (消息队列)
  flink 1.10.1 (流式计算引擎)
  doris 0.14.7  (核心数仓)
  Canal (Mysql binlog数据采集工具)

3.实现方案

这里我们采用的历史数据离线处理+增量数据实时处理的架构

3.1 历史数据离线处理

历史数据离线处理方式,这里我们使用是Doris ODBC外表方式,将mysql的表映射到doris里,然后使用

 insert into <doris_table_name>  select * from <mysql_odbc_external_table>

3.1.1 外表创建方法

首先Apache Doris 0.13.x以上版本
要在所有的BE节点安装对应数据的ODBC驱动
创建外表
具体可以参考我的另外一篇文章,这里不多做介绍

[Apache doris ODBC外表使用方式]

https://mp.weixin.qq.com/s/J0suRGPNkxD6oHSRFK6KTA

3.2 增量数据实时处理

增量数据的实时处理,这里我们是通过 Canal 监控 Mysql binlog 解析并推送到指定的 Kafka 队列,然后通过 Flink 去实时消费Kafka队列的数据,然后你可以根据自己的需要对数据进行处理,算法等,最后将明细数据或者实时计算的中间结果保存到对应的doris数据表中,这里使用的是stream load,你可以使用Flink doris connector。

3.2.1 doris sink实现

这里我们首先实现一个Flink doris sink


 import com.alibaba.fastjson.JSON;
 import org.apache.flink.configuration.Configuration;
 import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 ​
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.List;
 ​
 /**
  * 自定义flink doris sink
  */
 public class DorisSink extends RichSinkFunction<String> {
 ​
     private static final Logger log = LoggerFactory.getLogger(DorisSink.class);
 ​
     private final static List<String> DORIS_SUCCESS_STATUS = new ArrayList<>(Arrays.asList("Success", "Publish Timeout"));
 ​
     private DorisStreamLoad dorisStreamLoad;
 ​
     private String columns;
 ​
     private String jsonFormat;
 ​
     public DorisSink(DorisStreamLoad dorisStreamLoad, String columns, String jsonFormat) {
         this.dorisStreamLoad = dorisStreamLoad;
         this.columns = columns;
         this.jsonFormat = jsonFormat;
     }
 ​
     @Override
     public void open(Configuration parameters) throws Exception {
         super.open(parameters);
     }
 ​
 ​
     /**
      * 判断StreamLoad是否成功
      *
      * @param respContent streamload返回的响应信息(JSON格式)
      * @return
      */
     public static Boolean checkStreamLoadStatus(RespContent respContent) {
         if (DORIS_SUCCESS_STATUS.contains(respContent.getStatus())
                 && respContent.getNumberTotalRows() == respContent.getNumberLoadedRows()) {
             return true;
         } else {
             return false;
         }
     }
 ​
     @Override
     public void invoke(String value, Context context) throws Exception {
         DorisStreamLoad.LoadResponse loadResponse = dorisStreamLoad.loadBatch(value, columns, jsonFormat);
         if (loadResponse != null && loadResponse.status == 200) {
             RespContent respContent = JSON.parseObject(loadResponse.respContent, RespContent.class);
             if (!checkStreamLoadStatus(respContent)) {
                 log.error("Stream Load fail{}:", loadResponse);
             }
         } else {
             log.error("Stream Load Request failed:{}", loadResponse);
         }
     }
 }
3.2.2 Stream Load 工具类
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 ​
 ​
 import java.io.Serializable;
 import java.io.IOException;
 import java.io.BufferedOutputStream;
 import java.io.InputStream;
 import java.io.BufferedReader;
 import java.io.InputStreamReader;
 import java.net.HttpURLConnection;
 import java.net.URL;
 import java.nio.charset.StandardCharsets;
 import java.util.Base64;
 import java.util.Calendar;
 import java.util.UUID;
 ​
 ​
 /**
  * doris streamLoad
  */
 ​
 public class DorisStreamLoad implements Serializable {
 ​
     private static final Logger log = LoggerFactory.getLogger(DorisStreamLoad.class);
     //连接地址,这里使用的是连接FE
     private static String loadUrlPattern = "http://%s/api/%s/%s/_stream_load?";
     //fe ip地址
     private String hostPort;
     //数据库
     private String db;
     //要导入的数据表名
     private String tbl;
     //用户名
     private String user;
     //密码
     private String passwd;
     private String loadUrlStr;
     private String authEncoding;
 ​
 ​
     public DorisStreamLoad(String hostPort, String db, String tbl, String user, String passwd) {
         this.hostPort = hostPort;
         this.db = db;
         this.tbl = tbl;
         this.user = user;
         this.passwd = passwd;
         this.loadUrlStr = String.format(loadUrlPattern, hostPort, db, tbl);
         this.authEncoding = Base64.getEncoder().encodeToString(String.format("%s:%s", user, passwd).getBytes(StandardCharsets.UTF_8));
     }
     //获取http连接信息
     private HttpURLConnection getConnection(String urlStr, String label, String columns, String jsonformat) throws IOException {
         URL url = new URL(urlStr);
         HttpURLConnection conn = (HttpURLConnection) url.openConnection();
         conn.setInstanceFollowRedirects(false);
         conn.setRequestMethod("PUT");
         conn.setRequestProperty("Authorization", "Basic " + authEncoding);
         conn.addRequestProperty("Expect", "100-continue");
         conn.addRequestProperty("Content-Type", "text/plain; charset=UTF-8");
         conn.addRequestProperty("label", label);
         conn.addRequestProperty("max_filter_ratio", "0");
         conn.addRequestProperty("strict_mode", "true");
         conn.addRequestProperty("columns", columns);
         conn.addRequestProperty("format", "json");
         conn.addRequestProperty("jsonpaths", jsonformat);
         conn.addRequestProperty("strip_outer_array", "true");
         conn.setDoOutput(true);
         conn.setDoInput(true);
 ​
         return conn;
     }
 ​
     public static class LoadResponse {
         public int status;
         public String respMsg;
         public String respContent;
 ​
         public LoadResponse(int status, String respMsg, String respContent) {
             this.status = status;
             this.respMsg = respMsg;
             this.respContent = respContent;
         }
 ​
         @Override
         public String toString() {
             StringBuilder sb = new StringBuilder();
             sb.append("status: ").append(status);
             sb.append(", resp msg: ").append(respMsg);
             sb.append(", resp content: ").append(respContent);
             return sb.toString();
         }
     }
     //执行数据导入
     public LoadResponse loadBatch(String data, String columns, String jsonformat) {
         Calendar calendar = Calendar.getInstance();
         //导入的lable,全局唯一
         String label = String.format("flink_import_%s%02d%02d_%02d%02d%02d_%s",
                 calendar.get(Calendar.YEAR), calendar.get(Calendar.MONTH) + 1, calendar.get(Calendar.DAY_OF_MONTH),
                 calendar.get(Calendar.HOUR_OF_DAY), calendar.get(Calendar.MINUTE), calendar.get(Calendar.SECOND),
                 UUID.randomUUID().toString().replaceAll("-", ""));
 ​
         HttpURLConnection feConn = null;
         HttpURLConnection beConn = null;
         try {
             // build request and send to fe
             feConn = getConnection(loadUrlStr, label, columns, jsonformat);
             int status = feConn.getResponseCode();
             // fe send back http response code TEMPORARY_REDIRECT 307 and new be location
             if (status != 307) {
                 throw new Exception("status is not TEMPORARY_REDIRECT 307, status: " + status);
             }
             String location = feConn.getHeaderField("Location");
             if (location == null) {
                 throw new Exception("redirect location is null");
             }
             // build request and send to new be location
             beConn = getConnection(location, label, columns, jsonformat);
             // send data to be
             BufferedOutputStream bos = new BufferedOutputStream(beConn.getOutputStream());
             bos.write(data.getBytes());
             bos.close();
 ​
             // get respond
             status = beConn.getResponseCode();
             String respMsg = beConn.getResponseMessage();
             InputStream stream = (InputStream) beConn.getContent();
             BufferedReader br = new BufferedReader(new InputStreamReader(stream));
             StringBuilder response = new StringBuilder();
             String line;
             while ((line = br.readLine()) != null) {
                 response.append(line);
             }
             return new LoadResponse(status, respMsg, response.toString());
 ​
         } catch (Exception e) {
             e.printStackTrace();
             String err = "failed to load audit via AuditLoader plugin with label: " + label;
             log.warn(err, e);
             return new LoadResponse(-1, e.getMessage(), err);
         } finally {
             if (feConn != null) {
                 feConn.disconnect();
             }
             if (beConn != null) {
                 beConn.disconnect();
             }
         }
     }
 ​
 }

3.2.3 Flink Job

这个地方演示的是单表,如果是你通过Canal监听的多个表的数据,这里你需要根据表名进行区分,并和你mysql表和doris里的表建好对应关系,解析相应的数据即可

import org.apache.doris.demo.flink.DorisSink;
import org.apache.doris.demo.flink.DorisStreamLoad;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
​
import java.util.Properties;
​
/**
 *
 * This example mainly demonstrates how to use flink to stream Kafka data.
 *  And use the doris streamLoad method to write the data into the table specified by doris
 * <p>
 * Kafka data format is an array, For example: ["id":1,"name":"root"]
 */
​
public class FlinkKafka2Doris {
    //kafka address
    private static final String bootstrapServer = "xxx:9092,xxx:9092,xxx:9092";
    //kafka groupName
    private static final String groupName = "test_flink_doris_group";
    //kafka topicName
    private static final String topicName = "test_flink_doris";
    //doris ip port
    private static final String hostPort = "xxx:8030";
    //doris dbName
    private static final String dbName = "db1";
    //doris tbName
    private static final String tbName = "tb1";
    //doris userName
    private static final String userName = "root";
    //doris password
    private static final String password = "";
    //doris columns
    private static final String columns = "name,age,price,sale";
    //json format
    private static final String jsonFormat = "[\"$.name\",\"$.age\",\"$.price\",\"$.sale\"]";
​
    public static void main(String[] args) throws Exception {
​
        Properties props = new Properties();
        props.put("bootstrap.servers", bootstrapServer);
        props.put("group.id", groupName);
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("auto.offset.reset", "earliest");
        props.put("max.poll.records", "10000");
​
        StreamExecutionEnvironment blinkStreamEnv = StreamExecutionEnvironment.getExecutionEnvironment();
        blinkStreamEnv.enableCheckpointing(10000);
        blinkStreamEnv.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
​
        FlinkKafkaConsumer<String> flinkKafkaConsumer = new FlinkKafkaConsumer<>(topicName,
                new SimpleStringSchema(),
                props);
​
        DataStreamSource<String> dataStreamSource = blinkStreamEnv.addSource(flinkKafkaConsumer);
​
        DorisStreamLoad dorisStreamLoad = new DorisStreamLoad(hostPort, dbName, tbName, userName, password);
​
        dataStreamSource.addSink(new DorisSink(dorisStreamLoad,columns,jsonFormat));
​
        blinkStreamEnv.execute("flink kafka to doris");
​
    }
}

然后将Flink Job提交到集群上就可以运行了,数据就可以试试入库

这里其实是一个微批处理,你可以自己完善以下几部分:

每个批次最大入库记录数,或者每个多少秒进行一次入库,如果你的实时数据量比较小,或者你的数据比较大,这两条件哪个先到执行哪个
这里连接是FE,你可以通过FE的 rest api接口拿到所有的BE节点,直接连接BE进行入库,URL地址只是将FE的ip和端口换成BE的IP及http 端口即可
为了避免你连接这个BE或者FE的时候,正好这个节点挂了,你可以进行重试其他FE或者BE
为了避免单个节点压力,你可以进行轮训BE节点,不要每次都连接同一个BE节点
设置最大重试次数,如果超过这个次数,可以将导入失败的数据推送到Kafka队列,以方便后续人工手动处理

4.总结

本文只是抛砖引玉的方式给大家一个使用Stream load进行数据接入的使用方式及示例,Doris还有很多数据接入的方式等待大家去探索

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