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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.

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.

When Spark Streaming is used as data input for Kafka, the following stable and experimental Kafka versions are supported:

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

For specific installation steps, please see Configuring environment.

Step 4. Connect to CKafka

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
   

3. 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
   

4. 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
   

3. Grant admin permissions.

   visudo
   

4. Add the following in a new line under root ALL=(ALL) ALL:
   hadoop ALL=(ALL) ALL
   Save and exit.
5. Use Hadoop for operations.

   su hadoop
   

6. 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
   

7. Install Java.

   sudo yum install java-1.8.0-openjdk java-1.8.0-openjdk-devel
   

8. 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.

9. Decompress Hadoop and enter the directory.

   ./bin/hadoop version
   

   The display of version information indicates a successful installation.

10. 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.

11. 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>

12. 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>

13. 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
    

14. Format the NameNode.

    ./bin/hdfs namenode -format
    

    The display of Exitting with status 0 indicates a success.

15. 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.