org.apache.spark.sql.TypedColumn Java Examples

The following examples show how to use org.apache.spark.sql.TypedColumn. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: JavaUserDefinedTypedAggregation.java    From incubator-nemo with Apache License 2.0 5 votes vote down vote up 
/**
 * Main function.
 *
 * @param args arguments.
 */
public static void main(final String[] args) {
  SparkSession spark = SparkSession
    .builder()
    .appName("Java Spark SQL user-defined Datasets aggregation example")
    .getOrCreate();

  Encoder<Employee> employeeEncoder = Encoders.bean(Employee.class);
  String path = args[0];
  Dataset<Employee> ds = spark.read().json(path).as(employeeEncoder);
  ds.show();
  // +-------+------+
  // |   name|salary|
  // +-------+------+
  // |Michael|  3000|
  // |   Andy|  4500|
  // | Justin|  3500|
  // |  Berta|  4000|
  // +-------+------+

  MyAverage myAverage = new MyAverage();
  // Convert the function to a `TypedColumn` and give it a name
  TypedColumn<Employee, Double> averageSalary = myAverage.toColumn().name("average_salary");
  Dataset<Double> result = ds.select(averageSalary);
  result.show();
  // +--------------+
  // |average_salary|
  // +--------------+
  // |        3750.0|
  // +--------------+
  spark.stop();
}
Example #2
Source File: Dataset.java    From incubator-nemo with Apache License 2.0 5 votes vote down vote up 
@Override
public <U1> Dataset<U1> select(final TypedColumn<T, U1> c1) {
  final boolean userTriggered = initializeFunction(c1);
  final Dataset<U1> result = from(super.select(c1));
  this.setIsUserTriggered(userTriggered);
  return result;
}
Example #3
Source File: JavaUserDefinedTypedAggregation.java    From nemo with Apache License 2.0 5 votes vote down vote up 
/**
 * Main function.
 * @param args arguments.
 */
public static void main(final String[] args) {
  SparkSession spark = SparkSession
      .builder()
      .appName("Java Spark SQL user-defined Datasets aggregation example")
      .getOrCreate();

  Encoder<Employee> employeeEncoder = Encoders.bean(Employee.class);
  String path = args[0];
  Dataset<Employee> ds = spark.read().json(path).as(employeeEncoder);
  ds.show();
  // +-------+------+
  // |   name|salary|
  // +-------+------+
  // |Michael|  3000|
  // |   Andy|  4500|
  // | Justin|  3500|
  // |  Berta|  4000|
  // +-------+------+

  MyAverage myAverage = new MyAverage();
  // Convert the function to a `TypedColumn` and give it a name
  TypedColumn<Employee, Double> averageSalary = myAverage.toColumn().name("average_salary");
  Dataset<Double> result = ds.select(averageSalary);
  result.show();
  // +--------------+
  // |average_salary|
  // +--------------+
  // |        3750.0|
  // +--------------+
  spark.stop();
}
Example #4
Source File: Dataset.java    From nemo with Apache License 2.0 5 votes vote down vote up 
@Override
public <U1> Dataset<U1> select(final TypedColumn<T, U1> c1) {
  final boolean userTriggered = initializeFunction(c1);
  final Dataset<U1> result = from(super.select(c1));
  this.setIsUserTriggered(userTriggered);
  return result;
}
Example #5
Source File: UDFExample.java    From Apache-Spark-2x-for-Java-Developers with MIT License 4 votes vote down vote up 
public static void main(String[] args) {
	//Window Specific property if Hadoop is not instaalled or HADOOP_HOME is not set
	 System.setProperty("hadoop.home.dir", "E:\\hadoop");
	
	 //Build a Spark Session	
      SparkSession sparkSession = SparkSession
      .builder()
      .master("local")
	  .config("spark.sql.warehouse.dir","file:///E:/hadoop/warehouse")
      .appName("EdgeBuilder")
      .getOrCreate();
      Logger rootLogger = LogManager.getRootLogger();
	  rootLogger.setLevel(Level.WARN); 
	// Read the CSV data
		 Dataset<Row> emp_ds = sparkSession.read()
				 .format("com.databricks.spark.csv")
   		         .option("header", "true")
   		         .option("inferSchema", "true")
   		         .load("src/main/resources/employee.txt");    
    		
	    UDF2 calcDays=new CalcDaysUDF();
	  //Registering the UDFs in Spark Session created above      
	    sparkSession.udf().register("calcDays", calcDays, DataTypes.LongType);
	    
	    emp_ds.createOrReplaceTempView("emp_ds");
	    
	    emp_ds.printSchema();
	    emp_ds.show();
	    
	    sparkSession.sql("select calcDays(hiredate,'dd-MM-yyyy') from emp_ds").show();   
	    //Instantiate UDAF
	    AverageUDAF calcAvg= new AverageUDAF();
	    //Register UDAF to SparkSession
	    sparkSession.udf().register("calAvg", calcAvg);
	    //Use UDAF
	    sparkSession.sql("select deptno,calAvg(salary) from emp_ds group by deptno ").show(); 
	   
	    //
	    TypeSafeUDAF typeSafeUDAF=new TypeSafeUDAF();
	    
	    Dataset<Employee> emf = emp_ds.as(Encoders.bean(Employee.class));
	    emf.printSchema();
	    emf.show();
	    
	    TypedColumn<Employee, Double> averageSalary = typeSafeUDAF.toColumn().name("averageTypeSafe");
	    Dataset<Double> result = emf.select(averageSalary);
	   result.show();
	    

}