Apache Spark Component Guide
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Calling Hive User-Defined Functions

You can call built-in Hive UDFs, UDAFs, and UDTFs and custom UDFs from Spark SQL applications if the functions are available in the standard Hive .jar file. When using Hive UDFs, use HiveContext (not SQLContext).

Using Built-in UDFs

The following interactive example reads and writes to HDFS under Hive directories, using hiveContext and the built-in collect_list(col) UDF. The collect_list(col) UDF returns a list of objects with duplicates. In a production environment, this type of operation runs under an account with appropriate HDFS permissions; the following example uses hdfs user.

  1. Launch the Spark Shell on a YARN cluster:

    su hdfs
    cd $SPARK_HOME
    ./bin/spark-shell --num-executors 2 --executor-memory 512m --master yarn-client
  2. At the Scala REPL prompt, construct a HiveContext instance:

    val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
  3. Invoke the Hive collect_list UDF:

    scala> hiveContext.sql("from TestTable SELECT key, collect_list(value) group by key order by key").collect.foreach(println)

Using Custom UDFs

You can register custom functions in Python, Java, or Scala, and use them within SQL statements.

When using a custom UDF, ensure that the .jar file for your UDF is included with your application, or use the --jars command-line option to specify the file.

The following example uses a custom Hive UDF. This example uses the more limited SQLContext, instead of HiveContext.

  1. Launch spark-shell with hive-udf.jar as its parameter:

    ./bin/spark-shell --jars <path-to-your-hive-udf>.jar
  2. From spark-shell, define a function:

    scala> sqlContext.sql("""create temporary function balance as 'org.package.hiveudf.BalanceFromRechargesAndOrders'""");
  3. From spark-shell, invoke your UDF:

    scala> sqlContext.sql("""
    create table recharges_with_balance_array as
      balance(orders,'date_order', 'order_value', reseller_id, date_recharge, phone_credit_value) as balance
    from orders