user-defined-functions

来源： https://stackoverflow.com/questions/63147904/querying-delta-lake-from-inside-of-udf-in-databricks

来源： https://stackoverflow.com/questions/52025024/read-file-that-is-added-to-hive-resource-using-add-file-from-udf

来源： https://stackoverflow.com/questions/63494242/pyspark-how-to-calculate-poisson-distribution-using-udf

来源： https://stackoverflow.com/questions/63494242/pyspark-how-to-calculate-poisson-distribution-using-udf

来源： https://stackoverflow.com/questions/43411234/spark-sql-whether-to-use-row-transformation-or-udf

来源： https://stackoverflow.com/questions/46540252/spark-2-0-timestamp-difference-in-milliseconds-using-scala

来源： https://stackoverflow.com/questions/46540252/spark-2-0-timestamp-difference-in-milliseconds-using-scala

来源： https://stackoverflow.com/questions/63357901/how-to-convert-a-dictionary-like-structure-in-azure-stream-analytics-to-a-mult

问题 Is there a way to select the entire row as a column to input into a Pyspark filter udf? I have a complex filtering function "my_filter" that I want to apply to the entire DataFrame: my_filter_udf = udf(lambda r: my_filter(r), BooleanType()) new_df = df.filter(my_filter_udf(col("*")) But col("*") throws an error because that's not a valid operation. I know that I can convert the dataframe to an RDD and then use the RDD's filter method, but I do NOT want to convert it to an RDD and then back

问题 I've been developing a few stored procedure and I have been repeating a portion of codes that derives a column based on a few other columns. So instead of copy this piece of code from one stored procedure to another, I'm thinking of having a function that takes the input columns and produces the output columns. Basically, the function goes as: SELECT columnA, columnB, columnC, myFunction(columnA, columnB) as columnD FROM myTable As we can see, this function will take column A and column B as