Using the mapPartition Interface

Scenario

You can use the HBaseContext method to perform operations on HBase in Spark applications and use the mapPartition interface to traverse HBase tables in parallel.

Data Planning

Use HBase tables created in Using the foreachPartition Interface.

Development Guideline

1. Construct RDDs corresponding to rowkey in HBase tables to be traversed.
2. Use the mapPartition interface to traverse the data corresponding to the rowkey and perform simple operations.

Packaging the Project

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

  To run the Spark on HBase sample project, set spark.yarn.security.credentials.hbase.enabled (false by default) in the spark-defaults.conf file on the Spark client to true. Changing the spark.yarn.security.credentials.hbase.enabled value does not affect existing services. (To uninstall the HBase service, you need to change the value of this parameter back to false.) Set the value of the configuration item spark.inputFormat.cache.enabled to false.

Submitting Commands

Assume that the JAR package name is spark-hbaseContext-test-1.0.jar that is stored in the $SPARK_HOME directory on the client. The following commands are executed in the $SPARK_HOME directory, and Java is displayed before the class name of the Java interface. For details, see the sample code.

• yarn-client mode:

  Java/Scala version (The class name must be the same as the actual code. The following is only an example.)

  bin/spark-submit --master yarn --deploy-mode client --class com.huawei.bigdata.spark.examples.hbasecontext.JavaHBaseMapPartitionExample SparkOnHbaseJavaExample-1.0.jar table2

  Python version. (The file name must be the same as the actual one. The following is only an example.)

  bin/spark-submit --master yarn --deploy-mode client --jars SparkOnHbaseJavaExample-1.0.jar HBaseMapPartitionExample.py table2

• yarn-cluster mode:

  Java/Scala version (The class name must be the same as the actual code. The following is only an example.)

  bin/spark-submit --master yarn --deploy-mode cluster --class com.huawei.bigdata.spark.examples.hbasecontext.JavaHBaseMapPartitionExample SparkOnHbaseJavaExample-1.0.jar table2

  Python version. (The file name must be the same as the actual one. The following is only an example.)

  bin/spark-submit --master yarn --deploy-mode cluster --jars SparkOnHbaseJavaExample-1.0.jar HBaseMapPartitionExample.py table2

Java Sample Code

The following code snippet is only for demonstration. For details about the code, see the JavaHBaseMapPartitionExample file in SparkOnHbaseJavaExample.

public static void main(String args[]) throws IOException {
        if(args.length <1){
            System.out.println("JavaHBaseMapPartitionExample {tableName} is missing an argument");
            return;
        }
        final String tableName = args[0];
        SparkConf sparkConf = new SparkConf().setAppName("HBaseMapPartitionExample " + tableName);
        JavaSparkContext jsc = new JavaSparkContext(sparkConf);
        try{
            List<byte []>  list = new ArrayList();
            list.add(Bytes.toBytes("1"));
            list.add(Bytes.toBytes("2"));
            list.add(Bytes.toBytes("3"));
            list.add(Bytes.toBytes("4"));
            list.add(Bytes.toBytes("5"));
            JavaRDD<byte []> rdd = jsc.parallelize(list);
            Configuration hbaseconf = HBaseConfiguration.create();
            JavaHBaseContext hbaseContext = new JavaHBaseContext(jsc, hbaseconf);
            JavaRDD  getrdd = hbaseContext.mapPartitions(rdd, new FlatMapFunction<Tuple2<Iterator<byte[]>,Connection>, Object>() {
                public Iterator call(Tuple2<Iterator<byte[]>, Connection> t)
                        throws Exception {
                    Table table = t._2.getTable(TableName.valueOf(tableName));
                    //go through rdd
                    List<String> list = new ArrayList<String>();
                    while(t._1.hasNext()){
                        byte[] bytes = t._1.next();
                        Result result = table.get(new Get(bytes));
                        Iterator<Cell> it = result.listCells().iterator();
                        StringBuilder sb = new StringBuilder();
                        sb.append(Bytes.toString(result.getRow()) + ":");
                        while(it.hasNext()){
                            Cell cell = it.next();
                            String column = Bytes.toString(cell.getQualifierArray());
                            if(column.equals("counter")){
                                sb.append("(" + column + "," + Bytes.toLong(cell.getValueArray()) + ")");
                            } else {
                                sb.append("(" + column + "," + Bytes.toString(cell.getValueArray()) + ")");
                            }
                        }
                        list.add(sb.toString());
                    }
                    return list.iterator();
                }
            });
            List<byte[]> resultList = getrdd.collect();
            if(null == resultList || 0 == resultList.size()){
                System.out.println("Nothing matches!");
            }else{
                for(int i =0; i< resultList.size(); i++){
                    System.out.println(resultList.get(i));
                }
            }
        } finally {
            jsc.stop();
        }
    }

Scala Sample Code

The following code snippet is only for demonstration. For details about the code, see the HBaseMapPartitionExample file in SparkOnHbaseScalaExample.

 def main(args: Array[String]) {
    if (args.length < 1) {
      println("HBaseMapPartitionExample {tableName} is missing an argument")
      return
    }
    val tableName = args(0)
    val sparkConf = new SparkConf().setAppName("HBaseMapPartitionExample " + tableName)
    val sc = new SparkContext(sparkConf)
    try {
      //[(Array[Byte])]
      val rdd = sc.parallelize(Array(
        Bytes.toBytes("1"),
        Bytes.toBytes("2"),
        Bytes.toBytes("3"),
        Bytes.toBytes("4"),
        Bytes.toBytes("5")))
      val conf = HBaseConfiguration.create()
      val hbaseContext = new HBaseContext(sc, conf)
      val b = new StringBuilder
      val getRdd = rdd.hbaseMapPartitions[String](hbaseContext, (it, connection) => {
        val table = connection.getTable(TableName.valueOf(tableName))
        it.map{r =>
          //batching would be faster.  This is just an example
          val result = table.get(new Get(r))
          val it = result.listCells().iterator()
          b.append(Bytes.toString(result.getRow) + ":")
          while (it.hasNext) {
            val cell = it.next()
            val q = Bytes.toString(cell.getQualifierArray)
            if (q.equals("counter")) {
              b.append("(" + q + "," + Bytes.toLong(cell.getValueArray) + ")")
            } else {
              b.append("(" + q + "," + Bytes.toString(cell.getValueArray) + ")")
            }
          }
          b.toString()
        }
      })
      getRdd.collect().foreach(v => println(v))
    } finally {
      sc.stop()
    }
  }

Python Sample Code

The following code snippet is only for demonstration. For details about the code, see the HBaseMapPartitionExample file in SparkOnHbasePythonExample.

# -*- coding:utf-8 -*-
"""
[Note]
PySpark does not provide HBase-related APIs. In this example, Python is used to invoke Java code.
"""
from py4j.java_gateway import java_import
from pyspark.sql import SparkSession
# Create a SparkSession instance. 
spark = SparkSession\
        .builder\
        .appName("JavaHBaseMapPartitionExample")\
        .getOrCreate()
#  Import the required class to sc._jvm. 
java_import(spark._jvm, 'com.huawei.bigdata.spark.examples.hbasecontext.JavaHBaseMapPartitionExample')
# Create a class instance and invoke the method. Transfer the sc._jsc parameter.  
spark._jvm.JavaHBaseMapPartitionExample().execute(spark._jsc, sys.argv)
# Stop the SparkSession instance. 
spark.stop()