前言：当前为实时数仓，Lambda 架构，相比于目前比较主流的Kappa 架构相比，Lambda机构相对十分稳定。但是系统运维，维护十分繁琐。 两种架构对比。

博主代码样例如下：

package com.dataInPutHdfs;

import com.utils.DateUtil;
import com.utils.RedisUtil;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.common.TopicPartition;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.*;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaInputDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka010.*;
import scala.Tuple2;
import java.util.*;

/**
 *  sparkStreaming消费kafka数据每1分钟
 */

public class Hl_MWD_Data_InputHdfs {

    public static Boolean ExceptionFlag = true;
    private static String topics = "topics";                                                             //指定topic
    private static String groupId = "groupId001";                                                        //指定消费者组id
    private static String Hl_MWD_InputHdfs = "InputHdfs_offset";
    private static String brokers = "IP:9092,IP:9092,IP:9092";                //指定kafka地

    public static void main(String[] args) {
        //设置hadoop 文件备份为1，Hadoop 系统默认3份  减少数据同步延迟性
        Configuration hdfs = new Configuration();
        hdfs.set("dfs.replication","1");

        //初始化spark 并且优化spark
        SparkConf conf = new SparkConf().setAppName("Hl_MWD_Data_InputHdfs");
        conf.set("spark.dynamicAllocation.enabled", "false")
                .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
                //启用反压
                .set("spark.streaming.backpressure.enabled", "true")
                //最小条数
                .set("spark.streaming.backpressure.pid.minRate","1")
                //最大条数
                .set("spark.streaming.kafka.maxRatePerPartition","2000")
                //开启资源动态调用
                .set("spark.speculation", "true");
        System.setProperty("es.set.netty.runtime.available.processors", "false");

        JavaSparkContext sc = new JavaSparkContext(conf);
        JavaStreamingContext ssc = new JavaStreamingContext(sc, Durations.seconds(60));
        Set<String> topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
        Map<String, Object> kafkaParams = new HashMap<>();
        kafkaParams.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers);
        kafkaParams.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
        kafkaParams.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        kafkaParams.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        kafkaParams.put("auto.offset.reset", "latest");
        kafkaParams.put("enable.auto.commit", "false");

        HashMap<TopicPartition, Long> mapTopic = new HashMap<>();

        Boolean flag = RedisUtil.FlagExits(Hl_MWD_InputHdfs, 1);
        JavaInputDStream<ConsumerRecord<String, String>> messages = null;

        String[] s =null;
        String offsetlast =null;

        if (flag) {
            Map<String, String> offsets = RedisUtil.getAll(Hl_MWD_InputHdfs, 1);
            for (Map.Entry<String, String> entry : offsets.entrySet()) {
                String partition = entry.getKey();
                String offset = entry.getValue();
                //偏移量切割  将下标为0 数据拿到 为当前kafak 偏移量
                s = offset.split("_", -1);
                offsetlast = s[0];
                TopicPartition topicPartition = new TopicPartition(topics, Integer.valueOf(partition));
                mapTopic.put(topicPartition, Long.valueOf(offsetlast));
            }
            messages = KafkaUtils.createDirectStream(ssc, LocationStrategies.PreferConsistent(), ConsumerStrategies.<String, String>Subscribe(topicsSet, kafkaParams, mapTopic));
        } else {
            messages = KafkaUtils.createDirectStream(ssc, LocationStrategies.PreferConsistent(), ConsumerStrategies.<String, String>Subscribe(topicsSet, kafkaParams));
        }

        //讲获得的kafka数据转换为K,V格式对象
        JavaPairDStream<String, String> lines = messages.mapToPair(new PairFunction<ConsumerRecord<String, String>, String, String>() {
            @Override
            public Tuple2<String, String> call(ConsumerRecord<String, String> record) throws Exception {
                String[] lst = record.key().split("_");
                if(record.key().startsWith("BASIC") || record.key().startsWith("DATA")){
                    return new Tuple2<>(record.key(), lst[0]+ "_"+ lst[1]  + "|" + record.value());
                }else{
                    return new Tuple2<>(record.key(), lst[0]+ "_"+ lst[1] +  "_"+ lst[2] + "|" + record.value());
                }
            }
        });

        //根据接收到的数据的key按照每天一个文件夹的方式保存到/origin_data/目录下 2个文件
		lines.repartition(2).saveAsHadoopFiles("/origin_data/a","", Text.class,Text.class, TextOutputFormat.class);

        messages.foreachRDD(new VoidFunction<JavaRDD<ConsumerRecord<String, String>>>() {
            @Override
            public void call(JavaRDD<ConsumerRecord<String, String>> v3) throws Exception {
                v3.repartition(1).foreachPartition(new VoidFunction<Iterator<ConsumerRecord<String, String>>>() {
                    @Override
                    public void call(Iterator<ConsumerRecord<String, String>> st) throws Exception {
                        HashMap<String, String> redisMapOk = new HashMap<>();
                        HashMap<String, String> redisMapErro = new HashMap<>();

                        String time = DateUtil.formatDateString(new Date(), DateUtil.DATE_FORMAT_12W);
                        OffsetRange[] offsetRanges = ((HasOffsetRanges) v3.rdd()).offsetRanges();
                        for (OffsetRange offsetRange : offsetRanges) {
                            //记录正确的偏移量 如果没有发生错误，则记录当前偏移量的结束位置，因为起始位置已经数据入库， 下次从上次的结束开始
                            redisMapOk.put(String.valueOf(offsetRange.partition()), offsetRange.untilOffset()+"_"+time+"_OK");
                            //记录错误的的偏移量 因为异常插入, 所以记录当前偏移量的起始位置。
                            redisMapErro.put(String.valueOf(offsetRange.partition()),offsetRange.fromOffset()+"_"+time+"_ERROR");
                        }
                        //当数据为空 不对数据添加到redis 然后减少redis 的压力
                        if(st.hasNext()){
                            if (ExceptionFlag) {
                                RedisUtil.PutAll(Hl_MWD_InputHdfs, redisMapOk, 1);
                            } else {
                                RedisUtil.PutAll(Hl_MWD_InputHdfs, redisMapErro, 1);
                            }
                        }
                    }
                });
            }
        });

        ssc.start();
        try {
            ssc.awaitTermination();
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }
}

上述文件以每分钟形式写入HDFS ，需要用Azkaban定时，每小时对小文件进行合并追加到一个大文件，脚本如下

#将当前文件夹下的数据，按照每分钟小文件合并，并且追加到指定服务器临时文件夹下。
#/bin/bash
num=`hdfs dfs -ls /origin_data|wc -l`
a=1
for i in `hdfs dfs -ls /origin_data|awk '{print $8}'`
  do
    if [ $a == $num ]
    then
      echo "===========================================zuihouyige"
      break
    else
      echo $i
      echo $a
      hdfs dfs -cat $i/* >> /home/work_space/combine/data_temp.txt
      hdfs dfs -rm -r -skipTrash $i
      a=`expr $a + 1`
    fi
  done

在每日离线数据更新的时候，对数据进行上传HDFS，同时对文件进行数据清空更新，脚本如下：

hadoop fs -put /home/work_space/combine/data_temp.txt /everyday_file
cd /home/work_space/combine/data/
cat /dev/null > /home/work_space/combine/data_temp.txt

POM坐标如下 

<properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <java.version>1.8</java.version>
        <spark.version>2.3.0</spark.version>
        <scala.version>2.11.8</scala.version>
        <!--<scala.version>2.10.0</scala.version>-->
        <elasticsearch.version>6.6.1</elasticsearch.version>
    </properties>
    <dependencies>
        <!-- spark start -->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
            <!--<artifactId>spark-streaming-kafka-0-8_2.11</artifactId>-->
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.11</version>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>redis.clients</groupId>
            <artifactId>jedis</artifactId>
            <version>3.0.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.1.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>2.6.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>0.10.0.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.zookeeper</groupId>
            <artifactId>zookeeper</artifactId>
            <version>3.4.5</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.6.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>2.6.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>2.3.0</version>
        </dependency>
        <dependency>
            <groupId>com.aliyun.hbase</groupId>
            <artifactId>alihbase-client</artifactId>
            <version>1.1.3</version>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.23</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.47</version>
        </dependency>

        <dependency>
            <groupId>org.elasticsearch.client</groupId>
            <artifactId>transport</artifactId>
            <version>6.5.4</version>
        </dependency>

# kafka # hdfs # 大数据
Logo
Kafka开源项目指南

Kafka开源项目指南提供详尽教程，助开发者掌握其架构、配置和使用，实现高效数据流管理和实时处理。它高性能、可扩展，适合日志收集和实时数据处理，通过持久化保障数据安全，是企业大数据生态系统的核心。

更多推荐

Kafka入门（一） 概述、部署与API的简单使用

Kafka概述、部署与API的简单使用

avatar Kafka开源项目指南

基于canal和kafka同步，实现binlog同步ElasticSearch

文章目录前言elasticsearch 安装canal安装canal-adapter 安装及配置mysql 安装zk及kafaka安装查看效果注意事项前言中间件版本elasticsearch7.5.2canal1.1.4client-adapter1.1.5-alpha-1zookeeper3.4.13kafka2.6.0mysql5.7.31elasticsearch 安装{"settings"

avatar Kafka开源项目指南

基于 Iceberg 的湖仓一体架构在 B 站的实践

背景在B站，每天都有PB级的数据注入到大数据平台，经过离线或实时的ETL建模后，提供给下游的分析、推荐及预测等场景使用。面对如此大规模的数据，如何高效低成本地满足下游数据的分析需求，一直是我们重点的工作方向。我们之前的数据处理流程基本上是这样的：采集端将客户端埋点、服务端埋点、日志、业务数据库等数据收集到HDFS、Kafka等存储系统中，然后通过Hive、Spark、Fl...

avatar Kafka开源项目指南