We've been exploring using Glue to transform some JSON data to parquet. One scenario we tried was adding a column to the parquet table. So partition 1 has columns [A] and partition 2 has columns [A,B]. Then we wanted to write further Glue ETL jobs to aggregate the parquet table but the new column was not available. Using glue_context.create_dynamic_frame.from_catalog to load the dynamic frame our
Window functions: Something like this should do the trick: import org.apache.spark.sql.functions.{row_number, max, broadcast} import org.apache.spark.sql.expressions.Window val df = sc.parallelize(Seq( (0,"cat26",30.9), (0,"cat13",22.1), (0,"cat95",19.6), (0,"cat105",1.3), (1,"cat67",28.5), (1,"cat4",26.8), (1,"cat13",12.6), (1,"cat23",5.3), (2,"cat56",39.6), (2,"cat40",29.7), (2,"cat187",27.9), (
MockitoJUnitRunner gives you automatic validation of framework usage, as well as an automatic initMocks(). The automatic validation of framework usage is actually worth having. It gives you better reporting if you make one of these mistakes. You call the static when method, but don't complete the stubbing with a matching thenReturn, thenThrow or then. (Error 1 in the code below) You call verify on
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