Ben Chuanlong Du's Blog

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Process Big Data Using PySpark

  1. PySpark 2.4 and older does not support Python 3.8. You have to use Python 3.7 with PySpark 2.4 or older.

  2. It can be extremely helpful to run a PySpark application locally to detect possible issues before submitting it to the Spark cluster.

    #!/usr/bin/env bash …

Control Number of Partitions of a DataFrame in Spark

Tips and Traps

  1. DataFrame.repartition repartitions the DataFrame by hash code of each row. If you specify a (multiple) column(s) (instead of number of partitions) to the method DataFrame.repartition, then hash code of the column(s) are calculated for repartition. In some situations, there are lots of hash conflictions even if the total number of rows is small (e.g., a few thousand), which means that partitions generated might be skewed

User-defined Function (UDF) in PySpark

Tips and Traps

  1. The easist way to define a UDF in PySpark is to use the @udf tag, and similarly the easist way to define a Pandas UDF in PySpark is to use the @pandas_udf tag. Pandas UDFs are preferred to UDFs for server reasons. First, pandas UDFs are typically much faster than UDFs. Second, pandas UDFs are more flexible than UDFs on parameter passing. Both UDFs and pandas UDFs can take multiple columns as parameters. In addition, pandas UDFs can take a DataFrame as parameter (when passed to the apply

Date Functions in Spark

Tips and Traps

  1. HDFS table might contain invalid data (I'm not clear about the reasons at this time) with respct to the column types (e.g., Date and Timestamp). This will cause issues when Spark tries to load the data. For more discussions, please refer to Unrecognized column type:TIMESTAMP_TYP.

  1. datetime.datetime or datetime.date