![]() PYSPARK UDF EXAMPLE REGISTRATIONThe registration of a User Defined Function is done with the following code in PySpark: sqlContext.registerJavaFunction("name", "io.", TYPE) The following table gives some matching between Java and PySpark types: Java type The types supported by PySpark are defined in the Python package, the Catalyst code can be looked up to understand type conversion. ![]() PySpark expects the datasets to be strongly typed, therefore when declaring the UDF in your job, you must also specify the types of its return values, with arrays and maps being strongly typed too. If the stack contained a single level after the execution, the object on top of the stack will be returned, otherwise an array with the levels of the stack will be returned, top first. the parameter following the WarpScript fragment will be on top of the stack when the fragment is executed.Īfter the WarpScript has been executed, the content of the stack will be returned to the Spark job. The extra parameters will be pushed onto the stack, in reverse order, i.e. The first of those parameters is expected to be the WarpScript code fragment to execute. The warp10-spark2 package defines a range of Spark SQL User Defined Functions which accept from 1 to 22 parameters (as allowed by the Spark SQL API). Those functions are implemented as Java classes and thus do not suffer from the Py4J overhead experienced when executing Python functions in Spark jobs. This artifact defines both User Defined Functions ( UDFs) and a User Defined Aggregate Function ( UDAF) which can be used in PySpark jobs to execute WarpScript code. ![]() The integration of WarpScript in PySpark is provided by the warp10-spark-x.y.z.jar built from source (use the pack Gradle task). ![]() It defines how the Spark analytics engine can be leveraged from the Python programming language and tools which support it such as Jupyter. PySpark is the name given to the Spark Python API. ![]()
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