Connecting Spark Streaming to CKafka

Last updated: 2021-08-10 14:58:11

    As an extension of Spark Core, Spark Streaming is used for high-throughput and fault-tolerant processing of continuous data. Currently supported external input sources include Kafka, Flume, HDFS/S3, Kinesis, Twitter, and TCP socket.
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    Spark Streaming abstracts continuous data into a Discretized Stream (DStream), which consists of a series of continuous resilient distributed datasets (RDDs). Each RDD contains data generated at a certain time interval. Processing DStream with functions is actually processing these RDDs.
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    When Spark Streaming is used as data input for Kafka, the following stable and experimental Kafka versions are supported:

    Kafka Version spark-streaming-kafka-0.8 spark-streaming-kafka-0.10
    Broker Version 0.8.2.1 or higher 0.10.0 or higher
    API Maturity Deprecated Stable
    Language Support Scala, Java, and Python Scala and Java
    Receiver DStream Yes No
    Direct DStream Yes Yes
    SSL / TLS Support No Yes
    Offset Commit API No Yes
    Dynamic Topic Subscription No Yes

    Currently, CKafka is compatible with version above 0.9. The Kafka dependency of v0.10.2.1 is used in this practice scenario.

    In addition, Spark Streaming in EMR also supports direct connection to CKafka. For more information, please see Integrating Spark Streaming with Ckafka.

    Directions

    Step 1. Get the CKafka instance access address

    1. Log in to the CKafka console.
    2. Select Instance List on the left sidebar and click the ID of an instance to enter the instance basic information page.
    3. On the instance basic information page, get the instance access address in the Access Mode module, which is the bootstrap-server required by production and consumption.

    Step 2. Create a topic

    1. On the instance basic information page, select the Topic Management tab on the top.
    2. On the topic management page, click Create to create a topic named test. This topic is used as an example below to describe how to produce and consume messages.

    Step 3. Prepare the CVM environment

    CentOS 6.8

    Package Version
    sbt 0.13.16
    Hadoop 2.7.3
    Spark 2.1.0
    Protobuf 2.5.0
    SSH Installed on CentOS by default
    Java 1.8

    For specific installation steps, please see Configuring environment.

    Step 4. Connect to CKafka

    The Kafka dependency of v0.10.2.1 is used here.

    1. Add dependencies to build.sbt:
      name := "Producer Example"
      version := "1.0"
      scalaVersion := "2.11.8"
      libraryDependencies += "org.apache.kafka" % "kafka-clients" % "0.10.2.1"
      
    2. Configure producer_example.scala:
      import java.util.Properties
      import org.apache.kafka.clients.producer._
      object ProducerExample extends App {
      val props = new Properties()
      props.put("bootstrap.servers", "172.16.16.12:9092") // Private IP and port in the instance information
      props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
      props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")
      val producer = new KafkaProducerString, String
      val TOPIC="test" // Specify the topic to produce to
      for(i<- 1 to 50){
      val record = new ProducerRecord(TOPIC, "key", s"hello $i") // Produce a message whose key is "key" and value is "hello i"
      producer.send(record)
      }
      val record = new ProducerRecord(TOPIC, "key", "the end "+new java.util.Date)
      producer.send(record)
      producer.close() // Disconnect at the end
      }

    For more information on how to use ProducerRecord, please see ProducerRecord.

    Configuring environment

    Installing sbt

    1. Download the sbt package from sbt's official website.

    2. After decompression, create an sbt_run.sh script with the following content in the sbt directory and add executable permissions:

      #!/bin/bash
      SBT_OPTS="-Xms512M -Xmx1536M -Xss1M -XX:+CMSClassUnloadingEnabled -XX:MaxPermSize=256M"
      java $SBT_OPTS -jar `dirname $0`/bin/sbt-launch.jar "$@"
      
      chmod u+x ./sbt_run.sh
      
    1. Run the following command:
      ./sbt-run.sh sbt-version
      

    The display of sbt version indicates a successful installation.

    Installing Protobuf

    1. Download an appropriate version of Protobuf.

    2. Decompress and enter the directory.

      ./configure
      make && make install
      

      You should install gcc-g++ in advance, and the root permission may be required during installation.

    3. Log in again and enter the following on the command line:

      protoc --version
      
    1. The display of Protobuf version indicates a successful installation.

    Installing Hadoop

    1. Download the required version at Hadoop's official website.
    2. Add a Hadoop user.
      useradd -m hadoop -s /bin/bash
      
    1. Grant admin permissions.
      visudo
      
    1. Add the following in a new line under root ALL=(ALL) ALL:
      hadoop ALL=(ALL) ALL
      Save and exit.
    2. Use Hadoop for operations.
      su hadoop
      
    1. Configure SSH password-free login.
      cd ~/.ssh/                     # If there is no such directory, please run `ssh localhost` first
      ssh-keygen -t rsa              # There will be prompts. Simply press Enter
      cat id_rsa.pub >> authorized_keys  # Add authorization
      chmod 600 ./authorized_keys    # Modify file permission
      
    1. Install Java.
      sudo yum install java-1.8.0-openjdk java-1.8.0-openjdk-devel
      
    1. Configure ${JAVA_HOME}.

      vim /etc/profile
      

      Add the following at the end:

      export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.121-0.b13.el6_8.x86_64/jre
      export PATH=$PATH:$JAVA_HOME
      

      Modify the corresponding path based on the installation information.

    2. Decompress Hadoop and enter the directory.

      ./bin/hadoop version
      

      The display of version information indicates a successful installation.

    3. Configure the pseudo-distributed mode (so that you can build different forms of clusters as needed).

      vim /etc/profile
      

      Add the following at the end:

      export HADOOP_HOME=/usr/local/hadoop
      export PATH=$HADOOP_HOME/bin:$PATH
      

      Modify the corresponding path based on the installation information.

    4. Modify /etc/hadoop/core-site.xml.

      <configuration>    
      <property>
      <name>hadoop.tmp.dir</name>
      <value>file:/usr/local/hadoop/tmp</value>
      <description>Abase for other temporary directories.</description>
      </property>
      <property>
      <name>fs.defaultFS</name>
      <value>hdfs://localhost:9000</value>
      </property>
      </configuration>
    1. Modify /etc/hadoop/hdfs-site.xml.
      <configuration>
      <property>
      <name>dfs.replication</name>
      <value>1</value>
      </property>
      <property>
      <name>dfs.namenode.name.dir</name>
      <value>file:/usr/local/hadoop/tmp/dfs/name</value>
      </property>
      <property>
      <name>dfs.datanode.data.dir</name>
      <value>file:/usr/local/hadoop/tmp/dfs/data</value>
      </property>
      </configuration>
    1. Change JAVA_HOME in /etc/hadoop/hadoop-env.sh to the Java path.
      export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.121-0.b13.el6_8.x86_64/jre
      
    1. Format the NameNode.

      ./bin/hdfs namenode -format
      

      The display of Exitting with status 0 indicates a success.

    2. Start Hadoop.

      ./sbin/start-dfs.sh
      

    NameNode, DataNode, and SecondaryNameNode processes will exist upon successful startup.

    Installing Spark

    Download the required version at Spark's official website.
    As Hadoop has already been installed, select Pre-build with user-provided Apache Hadoop here.

    Note:

    This example also uses the hadoop user for operations.

    1. Decompress and enter the directory.

    2. Modify the configuration file.

      cp ./conf/spark-env.sh.template ./conf/spark-env.sh
      vim ./conf/spark-env.sh
      

      Add the following in the first line:

      export SPARK_DIST_CLASSPATH=$(/usr/local/hadoop/bin/hadoop classpath)
      

      Modify the path based on the Hadoop installation information.

    3. Run the example.

      bin/run-example SparkPi
      

    The display of an approximate value of π output by the program indicates a successful installation.