Do not append Scala versions to the generated artifacts orgnization name (e.g., the package name of the project) Set the build.sbt file contents to: name := "diybigdata-udf" #Pyspark udf example how toNow, create the file that will tell SBT how to build your UDF jar. Mkdir -p udf-development/src/test/java/net/diybigdata/udf/Īdjust the path for the first folder ending in udf as you wish according to the Java package name you will want to use. One SBT is installed, create a folder structure in the hduser home as follows: mkdir -p udf-development/src/main/java/net/diybigdata/udf/ To install SBT onto the master node, you will need to follow the instructions given at the SBT site, which outlines how to use apt-get to install SBT onto an Ubuntu distribution. To do this, I used SBT as my Java build tool. To use your Java-based Hive UDFs within PySpark, you need to first package them in a jar file which is given to PySpark when it is launched. To illustrate this, I will rework the flow I created in my last post on average airline flight delays to transform a Python UDF to a Hive UDF written in Java. To avoid the JVM-to-Python data serialization costs, you can use a Hive UDF written in Java. Creating a Hive UDF and then using it within PySpark can be a bit circuitous, but it does speed up your PySpark data frame flows if they are using Python UDFs. This changes if you ever write a UDF in Python. If you do most of your data manipulation using data frames in PySpark, you generally avoid this serialization cost because the Python code ends up being more of a high-level coordinator of the data frame operations rather than doing low-level operations on the data itself. However, due to the fact that Spark runs in a JVM, when your Python code interacts with the underlying Spark system, there can be an expensive process of data serialization and deserialization between the JVM and the Python interpreter. Using Python to develop on Apache Spark is easy and familiar for many developers.
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