How to use the Native IBM MQ Client Receiver with Spark Streaming

How to use the Native IBM MQ Client Receiver

Non-CheckPoint Model:

JavaStreamingContext ssc = new JavaStreamingContext(sparkconf, new Duration(1000));  
JavaDStream<String> mqStream = ssc.receiverStream(new IBMMQReceiver("mqp1.example.com", 1414, "qmgrName", "MQ.CLIENT.CHANNEL.NAME", "userName", "password", "mqTimeWaitMS", "keepMessagesTrue/False"));  
  
mqStream.foreach(rdd -> {  
    rdd.foreach(record -> {  
        JSONArray json = new JSONArray(record);  
        String key ((JSONObject) json.get(0)).getString("key");  
        String value ((JSONObject) json.get(1)).getString("value");  
        System.out.println("Key: "+key" :: Value:"+value);  
    });  
});  
  
ssc.start();  
ssc.awaitTermination();
  

Checkpoint/WriteAheadLogs Model:

sparkConf = new SparkConf().setAppName(jobName);
if (sparkUser.equalsIgnoreCase("")) {
   sparkUser = System.getProperty("user.name");
}
sparkCheckPoint = "/user/"+sparkUser+"/sparkstreaming/"+jobName;
JavaStreamingContext ssc = JavaStreamingContext.getOrCreate(sparkCheckPoint,
   new Function0<JavaStreamingContext>() {
      private static final long serialVersionUID = 1L;
      @Override
      public JavaStreamingContext call() throws Exception {
         JavaSparkContext jsc = new JavaSparkContext(sparkConf);
         JavaStreamingContext ssc = new JavaStreamingContext(jsc, new Duration(sparkBatchDuration));
         ssc.checkpoint(sparkCheckPoint);
         sparkUser = ssc.sparkContext().sparkUser();
         JavaDStream<String> mqStream = ssc.receiverStream(new IBMMQReceiver(qmgrHost, Integer.parseInt(qmgrPort), qmgrName, qmgrChannel, mqQueue, mqUser, mqPassword, mqWait, mqKeepMessages, mqRateLimit));
         mqStream.foreach(rdd -> {  
            rdd.foreach(record -> {  
      	        JSONArray json = new JSONArray(record);  
               String key ((JSONObject) json.get(0)).getString("key");  
               String value ((JSONObject) json.get(1)).getString("value");  
               System.out.println("Key: "+key" :: Value:"+value);  
            });  
         });          
         return ssc;
     }
   });       
   ssc.start();  
   ssc.awaitTermination();
               

Set Spark Streaming Job with the following parameters if using this to keep order from say DB2QREP or some other Queue based Data Replication Tool

spark.executor.instances=1
spark.cores.max=2
spark.streaming.receiver.maxRate=1000
spark.cleaner.ttl=120
spark.streaming.concurrentJobs=1
spark.serializer=org.apache.spark.serializer.JavaSerializer
spark.executor.cores=2
spark.hadoop.fs.hdfs.impl.disable.cache=true
spark.dynamicAllocation.enabled=false
spark.streaming.backpressure.enabled=true

Use Sparks writeAheadLog Feature when using IBM MQ if not messages can be lost

spark.streaming.receiver.blockStoreTimeout=300
spark.streaming.receiver.writeAheadLog.enable=true
spark.streaming.driver.writeAheadLog.closeFileAfterWrite=true
spark.streaming.receiver.writeAheadLog.closeFileAfterWrite=true

For long running Spark or Flink Jobs on YARN the following yarn-site.xml entry needs to be set

yarn.resourcemanager.proxy-user-privileges.enabled=true