Distributedly Scanning HBase Tables

Scenario

Users can use HBaseContext to perform operations on HBase in Spark applications and use HBase RDDs to scan HBase tables based on specific rules.

Data Planning

Use HBase tables created in 3.5.1 Performing Operations Data in Avro Format.

Development Guideline

1. Set the scanning rule, for example: setCaching.
2. Use specific rules to scan the HBase table.

Packaging the Project

• Use the Maven tool provided by IDEA to pack the project and generate a JAR file. For details, see Compiling and Running the Application.
• 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 example program, 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

Java Sample Code

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

 public static void main(String[] args) throws IOException{
    if (args.length < 1) {
      System.out.println("JavaHBaseDistributedScan {tableName}");
      return;
    }
    String tableName = args[0];
    SparkConf sparkConf = new SparkConf().setAppName("JavaHBaseDistributedScan " + tableName);
    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
    try {
      Configuration conf = HBaseConfiguration.create();
      JavaHBaseContext hbaseContext = new JavaHBaseContext(jsc, conf);
      Scan scan = new Scan();
      scan.setCaching(100);
      JavaRDD<Tuple2<ImmutableBytesWritable, Result>> javaRdd =
              hbaseContext.hbaseRDD(TableName.valueOf(tableName), scan);
      List<String> results = javaRdd.map(new ScanConvertFunction()).collect();
      System.out.println("Result Size: " + results.size());
    } finally {
      jsc.stop();
    }
  }

Scala Sample Code

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

  def main(args: Array[String]) {
    if (args.length < 1) {
      println("HBaseDistributedScanExample {tableName} missing an argument")
      return
    }
    val tableName = args(0)
    val sparkConf = new SparkConf().setAppName("HBaseDistributedScanExample " + tableName )
    val sc = new SparkContext(sparkConf)
    try {
      val conf = HBaseConfiguration.create()
      val hbaseContext = new HBaseContext(sc, conf)
      val scan = new Scan()
      scan.setCaching(100)
      val getRdd = hbaseContext.hbaseRDD(TableName.valueOf(tableName), scan)
      getRdd.foreach(v => println(Bytes.toString(v._1.get())))
      println("Length: " + getRdd.map(r => r._1.copyBytes()).collect().length);
    } finally {
      sc.stop()
    }
  }

Python Sample Code

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

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