Help Center/ MapReduce Service/ Developer Guide (LTS)/ Spark2x Development Guide (Common Mode)/ Developing Spark Applications/ Sample Project for Spark Structured Streaming Status Operations/ Development Plan
Updated on 2024-08-10 GMT+08:00
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Development Plan

Overview

Assume that you need to collect statistics on the number of events in each session and the start and end timestamp of the sessions.

You need to export the sessions that are in the updated state in this batch.

Preparing Data

  1. Generate simulated data in Kafka (the Kafka permission is required).
  2. Ensure that the cluster is installed with HDFS, YARN, Spark2x, and Kafka.
  3. Create a topic.

    {zkQuorum} indicates ZooKeeper cluster information in the IP address:Port number format.

    $KAFKA_HOME/bin/kafka-topics.sh --create --zookeeper {zkQuorum}/kafka --replication-factor 1 --partitions 1 --topic {Topic}

  4. Start Kafka Producer and enable it to send data to Kafka.

    {ClassPath} indicates the storage path of the project JAR package that is specified by the user. For details, see Commissioning a Spark Application in a Linux Environment.

    java -cp $SPARK_HOME/conf:$SPARK_HOME/jars/*:$SPARK_HOME/jars/streamingClient010/*:{ClassPath} com.huawei.bigdata.spark.examples.KafkaProducer {brokerlist} {topic} {number of events produce every 0.02s}

    The following is an example:

    java -cp /opt/client/Spark2x/spark/conf:/opt/StructuredStreamingState-1.0.jar:/opt/client/Spark2x/spark/jars/*:/opt/client/Spark2x/spark/jars/streamingClient010/* com.huawei.bigdata.spark.examples.KafkaProducer xxx.xxx.xxx.xxx:21005,xxx.xxx.xxx.xxx:21005,xxx.xxx.xxx.xxx:21005 mytopic 10

Development Guidelines

  1. Receive data from Kafka and generate the corresponding DataStreamReader.
  2. Collect statistics by category.
  3. Calculate and print the result.

Packaging the Project

  • Use the Maven tool provided by IDEA to pack the project and generate a JAR file.
  • Upload the JAR package to any directory (for example, /opt) on the server where the Spark client is located.

Running the Task

When running the sample application, you need to specify <brokers>, <subscribe-type>, <topic>, and <checkpointLocation>.

  • <brokers> indicates the Kafka address for obtaining metadata.
  • <subscribe-type> indicates the Kafka consumption mode.
  • <topic> indicates the Kafka topic to be consumed.
  • <checkpointLocation> indicates the path for storing the checkpoint of the Spark task.

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 Streaming Structured 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[*]}").

Go to the Spark client directory and run the following command to invoke the bin/spark-submit script to run the code (The class name and file name must be the same as those in the actual code. The following is only an example.):

  • bin/spark-submit --master yarn --deploy-mode client --jars $(files=($SPARK_HOME/jars/streamingClient010/*.jar); IFS=,; echo "${files[*]}") --class com.huawei.bigdata.spark.examples.kafkaSessionization /opt/StructuredStreamingState-1.0.jar <brokers> <subscribe-type> <topic> <checkpointLocation>

    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-clientsjar file from the $SPARK_HOME/jars/streamingClient010 directory to the $SPARK_HOME/jars directory. Otherwise, the "class not found" error is reported.