Spark reads HBase table (newAPIHadoopRDD method)

Hbase relies on zookeeper and hadoop. Spark reads hbase data and needs to connect to zookeeper and access hbase through zookeeper: the
first is to add the hbase-site.xml file to the classpath.
The second is to set
it in the HBaseConfiguration instance. If not set, the default connection Localhost:2181 will report an error: connection refused
This article uses the second method.

Dependencies need to be introduced in maven

<?xml version="1.0" encoding="UTF-8"?>

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.fuyun.bigdata</groupId>
    <artifactId>learning-spark_2.11</artifactId>
    <version>1.0-SNAPSHOT</version>

    <name>learning-spark_2.11</name>
    <!-- FIXME change it to the project's website -->
    <url>http://www.example.com</url>

    <repositories>
        <repository>
            <id>aliyun</id>
            <url>http://maven.aliyun.com/nexus/content/groups/public/</url>
        </repository>
        <repository>
            <id>cloudera</id>
            <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
        </repository>
        <repository>
            <id>jboss</id>
            <url>http://repository.jboss.com/nexus/content/groups/public</url>
        </repository>
    </repositories>

    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <maven.compiler.source>1.7</maven.compiler.source>
        <maven.compiler.target>1.7</maven.compiler.target>
        <hadoop.version>2.6.0-cdh5.7.6</hadoop.version>
        <spark.version>2.2.0</spark.version>
        <hbase.version>1.2.0-cdh5.7.6</hbase.version>
    </properties>


    <dependencies>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>${hbase.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-hadoop2-compat</artifactId>
            <version>1.2.0-cdh5.7.6</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.2.0-cdh5.7.6</version>
        </dependency>
    </dependencies>

    <build>
        <pluginManagement><!-- lock down plugins versions to avoid using Maven defaults (may be moved to parent pom) -->
            <plugins>
                <plugin>
                    <artifactId>maven-clean-plugin</artifactId>
                    <version>3.0.0</version>
                </plugin>
                <!-- see http://maven.apache.org/ref/current/maven-core/default-bindings.html#Plugin_bindings_for_jar_packaging -->
                <plugin>
                    <artifactId>maven-resources-plugin</artifactId>
                    <version>3.0.2</version>
                </plugin>
                <plugin>
                    <artifactId>maven-compiler-plugin</artifactId>
                    <version>3.7.0</version>
                </plugin>
                <plugin>
                    <artifactId>maven-surefire-plugin</artifactId>
                    <version>2.20.1</version>
                </plugin>
                <plugin>
                    <artifactId>maven-jar-plugin</artifactId>
                    <version>3.0.2</version>
                </plugin>
                <plugin>
                    <artifactId>maven-install-plugin</artifactId>
                    <version>2.5.2</version>
                </plugin>
                <plugin>
                    <artifactId>maven-deploy-plugin</artifactId>
                    <version>2.8.2</version>
                </plugin>
            </plugins>
        </pluginManagement>
    </build>
</project>

import org.apache.hadoop.hbase.{
    
    CellUtil, HBaseConfiguration}
import org.apache.hadoop.hbase.client.Result
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.spark.{
    
    SparkConf, SparkContext}
import org.apache.spark.serializer.KryoSerializer

/**
  * SparkCore从HBase表中读取数据：
  *   表的名称： event_logs20201011
  */
object ReadEventLogsSparkDemo {
    
    

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

    /**
      * 构建SparkContext实例对象，用于读取要处理的数据及调度应用执行
      */
    // TODO: 设置Spark Application的配置信息，比如应用的名称、应用运行地方
    val spatkConf = new SparkConf().setAppName("ReadEventLogsSparkDemo").setMaster("local[2]")
    // TODO: 设置使用Kryo方式序列化，默认情况下对 simple types, arrays of simple types, or string type
      .set("spark.serializer", classOf[KryoSerializer].getName)
    // To register your own custom classes with Kryo, use the registerKryoClasses method
      .registerKryoClasses(Array(classOf[ImmutableBytesWritable],classOf[Result]))

    // 创建SparkContext 实例对象
    val sc = SparkContext.getOrCreate(spatkConf)
    // 设置日志级别  Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN
    sc.setLogLevel("WARN")

    // a. 读取配置信息
    val conf = HBaseConfiguration.create()
    conf.set("hbase.zookeeper.quorum", "192.168.235.66")
    conf.set("hbase.zookeeper.property.clientPort", "2181")

    // b. 设置从HBase那张表读取数据  "event_logs20201011"
    conf.set(TableInputFormat.INPUT_TABLE,"event_logs20201011")

    /**
      *   def newAPIHadoopRDD[K, V, F <: NewInputFormat[K, V]](
            conf: Configuration = hadoopConfiguration,
            fClass: Class[F],
            kClass: Class[K],
            vClass: Class[V]
          ): RDD[(K, V)]
      */
    // c. 调用SparkContext中newAPIHadoopRDD读取表中的数据
    val resultRDD = sc.newAPIHadoopRDD(
      conf,
      classOf[TableInputFormat],
      classOf[ImmutableBytesWritable],
      classOf[Result]
    )

    // 测试获取的数据
    println(s"count = ${resultRDD.count()}")

    /**
      *  当使用RDD.take(3).foreach() 报如下错误，ImmutableBytesWritable不能进进行序列化
      *     java.io.NotSerializableException: org.apache.hadoop.hbase.io.ImmutableBytesWritable
      * -i. 原因在于：
      *    RDD.take(3) 将数据从Executor中返回Driver端，需要经过网络传输，所以需要对数据进行序列化操作，然而
      *  ImmutableBytesWritable和Result类型都没有实现Java中序列化接口Serializable，所以出错。
      * -ii. 如何解决问题呢？？
      *     Spark大数据分析计算框架来说，默认情况使用Java Serializable对数据进行序列化，设置其他序列化方式。
      *     http://spark.apache.org/docs/2.2.0/tuning.html#data-serialization
      */
    resultRDD.take(5).foreach{
    
    
      case (key, result) =>
        println(s"rowKey = ${Bytes.toString(key.get())}")
        for (cell <- result.rawCells()) {
    
    
          val cf = Bytes.toString(CellUtil.cloneFamily(cell))
          val colum = Bytes.toString(CellUtil.cloneQualifier(cell))
          val value = Bytes.toString(CellUtil.cloneValue(cell))
          println(s"\t$cf:$colum = $value -> ${cell.getTimestamp}")
        }
    }

    // 为了监控显示界面，线程休眠
    Thread.sleep(1000000)

    // 关闭资源
    sc.stop()
  }
}