大数据实时流处理零数据丢失

1.整体流程：

    a)kafka：作为流处理程序的生产者
    b)sparkStreaming：作为消费者，设置合理batch
    c)DB：输出到redis/ES

2.存在问题：

雪崩效应： kill 出现，导致的数据丢失
sparkStreaming程序挂掉了，到知道的数据丢失

解决：
    1.使用checkpoint。维护太麻烦，流程序修改后需要删除checkpoint下的数据才可以，但是这回导致数据丢失或者重复。
    2.官方建议：同时启动新旧两个流程序，然后认为关掉旧的，并记录偏移量，然后新的流程序从指定的偏移量消费。

    3.自己保存偏移量到外部设备，每次启动流程序先读取外设，然后判断，offset的读取方式是earliest 还是指定偏移量

    4.配置kafak限速参数，以及流程序的序列化方式。

    5.关于偏移量的存储，可以存mysql，redis，HBASE，kafka，zk。。。


    6.关于数据重复，为了达到exactly once， 需要跟实际的业务结合。例如最后的结果如果是写入到HBase，可以将偏移量作为HBase对应的业务表中的一个列，实现事务性，幂等。

代码：

1.pom文件：

     <properties>
        <scala.version>2.11.8</scala.version>
        <spark.version>2.3.1</spark.version>
        <kafka.version>0.10.0.0</kafka.version>
    </properties>

    <repositories>
        <repository>
            <id>scala-tools.org</id>
            <name>Scala-Tools Maven2 Repository</name>
            <url>http://scala-tools.org/repo-releases</url>
        </repository>

        <repository>
            <id>scalikeJDBC</id>
            <name>scalikeJDBC</name>
            <url>https://mvnrepository.com/artifact/org.scalikejdbc/scalikejdbc</url>
        </repository>
    </repositories>

    <pluginRepositories>
        <pluginRepository>
            <id>scala-tools.org</id>
            <name>Scala-Tools Maven2 Repository</name>
            <url>http://scala-tools.org/repo-releases</url>
        </pluginRepository>
    </pluginRepositories>

    <dependencies>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${spark.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-kafka-0-10_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!--sparksql -kafka-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql-kafka-0-10_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.11</artifactId>
            <version>${kafka.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>${kafka.version}</version>
        </dependency>

        <!--json解析-->
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.36</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.40</version>
        </dependency>

        <!--redis-->
        <dependency>
            <groupId>redis.clients</groupId>
            <artifactId>jedis</artifactId>
            <version>2.9.0</version>
        </dependency>

        <!--config-->
        <dependency>
            <groupId>com.typesafe</groupId>
            <artifactId>config</artifactId>
            <version>1.3.1</version>
        </dependency>

        <!--RDBMS访问-->
        <dependency>
            <groupId>org.scalikejdbc</groupId>
            <artifactId>scalikejdbc_2.11</artifactId>
            <version>2.5.0</version>
        </dependency>
        <dependency>
            <groupId>org.scalikejdbc</groupId>
            <artifactId>scalikejdbc-core_2.11</artifactId>
            <version>2.5.0</version>
        </dependency>
        <dependency>
            <groupId>org.scalikejdbc</groupId>
            <artifactId>scalikejdbc-config_2.11</artifactId>
            <version>2.5.0</version>
        </dependency>


        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>

2.kafka topic的创建：

    a) bin/kafka-topics.sh --create --zookeeper pj-01:2181 --replication-factor 1 --partitions 1 --topic saprkProcess
    b) 修改kafka的分区数目：提高spark的并行度，提升消息的处理吞吐量。
    bin/kafka-topics.sh --alter --zookeeper pj-01:2181 --topic saprkProcess --partitions 3

    c) note：在0.8版本,修改完kafka 对应的topic的partition的后，流程序是侦测不到，导致数据丢失。其实，在kafka的logs下面数据已经有了，所以需要在代码中判断是否有新的分区，并获取到新的分区，然后将其偏移量初始化为0.
        但是在0.10版本后，就不存在这个问题了。

3.模拟kafka生产者：

    package Producer;
    import org.apache.kafka.clients.producer.KafkaProducer;
    import org.apache.kafka.clients.producer.ProducerRecord;

    import java.util.Properties;
    import java.util.Random;
    import java.util.UUID;


    public class KafkaProducerApp {
        public static void main(String[] args) throws InterruptedException {
            final Properties props = new Properties();
            //Assign topicName to string variable
            String topics = "saprkProcess";
            // create instance for properties to access producer configs
            //Assign localhost id
            props.put("bootstrap.servers", "pj-01:9092");
            //Set acknowledgements for producer requests.
            props.put("acks", "all");
            //If the request fails, the producer can automatically retry,
            props.put("retries", 0);
            //Specify buffer size in config
            props.put("batch.size", 16384);
            //Reduce the no of requests less than 0
            props.put("linger.ms", 1);
            //The buffer.memory controls the total amount of memory available to the producer for buffering.
            props.put("buffer.memory", 33554432);
            props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
            props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
            KafkaProducer<String, String> producer = new KafkaProducer<String, String>(props);
            String value[] = {"alibaba","baidu","tencent","facebook","amazon","apple","google","linkedin","twitter","ant","ICBC","Lakers","cleveland"};
            int i = value.length;
            final Random random = new Random();
            int ret = 100;
            while (ret>=0) {
                Thread.sleep(10);
                final ProducerRecord<String, String> msg = new ProducerRecord<String, String>(
                        topics
    //                    , (new Random()).nextInt(3)
                        ,0
                        ,System.currentTimeMillis()
                        , UUID.randomUUID().toString().substring(6, 14).replace("-", "")
                        , value[random.nextInt(i)]
                );
                producer.send(msg);
                System.out.println("msg = " + "alibaba -A: "+msg);
                ret --;
            }
        }
    }

4.创建：mysql 偏移量表：

  a)这里MySQL相关操作使用的scalikeJDBC，这个是针对scala的一款db库，操作api简单明了，默认数据库配置通过读取resources下的（applicaiton.conf/application.json,applicaiton.properties)文件。

  b)更新偏移量的api使用replace into：
    note：如果没有设置主键，默认replace操作就是新增，不会与表中已存在的数据进行逻辑比对。
        01：所以下面建表语句中：使用topic，groupid，partition作为联合主键，来确定一条记录。
        02：因为我们的流程序一经启动：一般就是7*24，所以表名暂定为streaming_offset_24。这个自定义程度很高。
    建表语句：

  create table streaming_offset_24(topic varchar(50),groupid varchar(20), partitions int, offset bigint, primary  key(topic,groupid,partitions));

5.流程序： spark streaming 处理demo：

    package pjnet
    import org.apache.kafka.common.TopicPartition
    import org.apache.kafka.common.serialization.StringDeserializer
    import org.apache.log4j.{Level, Logger}
    import org.apache.spark.SparkConf
    import org.apache.spark.rdd.RDD
    import org.apache.spark.streaming.kafka010._
    import org.apache.spark.streaming.{Seconds, StreamingContext}
    import scalikejdbc.config.DBs
    import scalikejdbc.{DB, _}


    object StreamingProApp {


      Logger.getLogger("org.apache").setLevel(Level.WARN)  //设置日志显示

      def main(args: Array[String]): Unit = {

        val conf = new SparkConf().setMaster("local[*]").setAppName("StreamingProApp")
          //指定每秒钟每个分区kafka拉取消息的速率
          .set("spark.streaming.kafka.maxRatePerPartition", "100")
          // 修改序列化为Kyro，减少shuffle量
          .set("spark.serilizer", "org.apache.spark.serializer.KryoSerializer")
          // 开启rdd的压缩
          .set("spark.rdd.compress", "true")
    //    设置批次的处理时间
        val ssc = new StreamingContext(conf, Seconds(10))

        //一参数设置
        val groupId = "1" //也可以通过读取配置文件，使用typesafe的 ConfigFactory读取

        val kafkaParams = Map[String, Object](
          "bootstrap.servers" -> "pj-01:9092",
          "key.deserializer" -> classOf[StringDeserializer],
          "value.deserializer" -> classOf[StringDeserializer],
          "group.id" -> groupId,
          "auto.offset.reset" -> "earliest",
          "enable.auto.commit" -> (false: java.lang.Boolean) //自己维护偏移量。连接kafka的集群。
        )
        val topics = Array("saprkProcess")

        //二参数设置
        DBs.setup()
        val fromdbOffset: Map[TopicPartition, Long] =
          DB.readOnly { implicit session =>
            SQL(s"select * from `streaming_offset_24` where groupId = '${groupId}'")
              .map(rs => (new TopicPartition(rs.string("topic"), rs.int("partitions")), rs.long("offset")))
              .list().apply()
          }.toMap

        //程序启动，拉取kafka的消息。
        val stream = if (fromdbOffset.size == 0) {
          KafkaUtils.createDirectStream[String, String](
            ssc,
            LocationStrategies.PreferConsistent,
            ConsumerStrategies.Subscribe[String, String](topics, kafkaParams)
          )
        } else {
          KafkaUtils.createDirectStream(
            ssc,
            LocationStrategies.PreferConsistent,
            ConsumerStrategies.Assign[String, String](fromdbOffset.keys, kafkaParams, fromdbOffset)
          )
        }


        stream.foreachRDD({
          rdd =>
            val offsetRanges: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
            //数据处理
            val resout: RDD[(String, Int)] = rdd.flatMap(_.value().split(" ")).map((_, 1)).reduceByKey(_ + _)

            //可以将结果保存至redis，hbase等db
            resout.foreach(println)
    //        resout.foreachPartition({
    ////          it =>
    ////            val jedis = RedisUtils.getJedis
    ////            it.foreach({
    ////              va =>
    ////                jedis.hincrBy(field，val1，val2)
    ////            })
    ////            jedis.close()
    //
    //        })

            //偏移量存入mysql，使用scalikejdbc框架，下面两种方式都可以。