Development Plan

Scenarios

In Spark applications, use StructuredStreaming to invoke Kafka interface to obtain word records. Collect the statistics of records for each word.

Data Planning

Development Approach

1. Receive data from Kafka and generate DataStreamReader.
2. Collect the statistics of word records.
3. Calculate and print the result.

Configuration Operations Before Running

In security mode, the Spark Core sample code needs to read two files (user.keytab and krb5.conf). The user.keytab and krb5.conf files are authentication files in the security mode. Download the authentication credentials of the user principal on the FusionInsight Manager page. The user in the sample code is sparkuser, change the value to the prepared development user name.

Packaging the Project

• Upload the user.keytab and krb5.conf files to the server where the client is installed.

• Use the Maven tool provided by IDEA to pack the project and generate a JAR file. For details, see Writing and Running the Spark Program in the Linux Environment.
  

  Before compilation and packaging, change the paths of the user.keytab and krb5.conf files in the sample code to the actual paths on the client server where the files are located. Example: /opt/female/user.keytab and /opt/female/krb5.conf.

• Upload the JAR package to any directory (for example, /opt) on the server where the Spark client is located.

Running Tasks

When running the sample project, you need to specify <brokers>, <subscribe-type>, <topic>, <protocol>, <service>, <domain>, and <checkpointDir>.

• <brokers> indicates the Kafka address for obtaining metadata.
• <subscribe-type> indicates the Kafka subscription type (for example, subscribe),
• and <topic> indicates the name of the topic read from Kafka.
• <protocol> indicates the secure access protocol, such as SASL_PLAINTEXT.
• <service> indicates the Kerberos service name, for example, kafka.
• <domain> indicates the Kerberos domain name, for example, hadoop.<System domain name>.
• <checkpointDir> indicates the path for storing the checkpoint file, which can be a local path or an HDFS path.

The path of the Spark Structured Streaming Kafka dependency package on the client is different from that of other dependency packages. For example, the path of other dependency packages is $SPARK_HOME/jars. Whereas the path of the Spark Structured Streaming Kafka dependency package is $SPARK_HOME/jars/streamingClient010. Therefore, when running an application, you need to add a configuration item to the spark-submit command to specify the path of the dependency package of Spark Streaming Kafka, for example, --jars $(files=($SPARK_HOME/jars/streamingClient010/*.jar); IFS=,; echo "${files[*]}")

Because the cluster is security mode, you need to add configuration items and modify the command parameters.

1. Add configuration items to $SPARK_HOME/conf/jaas.conf:

   KafkaClient {
   com.sun.security.auth.module.Krb5LoginModule required
   useKeyTab=false
   useTicketCache=true
   debug=false;
   };

2. Add configuration items to $SPARK_HOME/conf/jaas-zk.conf:

   KafkaClient {
   com.sun.security.auth.module.Krb5LoginModule required
   useKeyTab=true
   keyTab="./user.keytab"
   principal="sparkuser@<system domain name>"
   useTicketCache=false
   storeKey=true
   debug=true;
   };

3. Use --files and relative path to submit the keytab file to ensure that the keytab file is loaded to the container of the executor.

When submitting a structured stream task, you need to run the --jars command to specify the path of the Kafka-related JAR file. For the current version, you need to cope the kafka-clients jar file from the $SPARK_HOME/jars/streamingClient010 directory to the $SPARK_HOME/jars directory. Otherwise, the "class not found" error is reported.