I'm writing small poc trying to rewrite piece of logic written in python to pyspark, where im processing logs stored in sqlite one by one:
logs = [...]processed_logs = []previous_log = EmptyDecoratedLog() #emptyfor log in logs: processed_log = with_outlet_value_closed(log, previous_log) previous_log = processed_log processed_logs.append(processed_log)
and
def with_outlet_value_closed(current_entry: DecoratedLog, previous_entry: DecoratedLog): if current_entry.sourceName == "GS2": self.outletValveClosed = current_entry.eventData else: self.outletValveClosed = previous_entry.outletValveClosed
which I wanted to represent in pyspark api as:
import pyspark.sql.functions as fwindow = W.orderBy("ID") #where ID is unique id on those logsdf.withColumn("testValveOpened", f.when((f.col("sourceName") == "GS2"), f.col("eventData")) .otherwise(f.lag("testValveOpened").over(window)), )
but this leads to AnalysisException: [UNRESOLVED_COLUMN.WITH_SUGGESTION] A column or function parameter with name outletValveClosed
cannot be resolved.
So my question is:Is it possible to represent such code where the value of a current row depends from previous row of the same column(i know that this will result in all records being processed on a single thread, but thats fine)
I've tried adding initialization of a column
df = df.withColumn("testValveOpened", f.lit(0))df.withColumn("testValveOpened", f.when((f.col("sourceName") == "GS2"), f.col("eventData")) .otherwise(f.lag("testValveOpened").over(window)), )
but then I'm getting
ID |sourceName|eventData|testValveOpened1 |GS3 |1 |02 |GS2 |1 |13 |GS2 |1 |14 |GS1 |1 |05 |GS1 |1 |06 |ABC |0 |07 |B123 |0 |08 |B423 |0 |09 |PTSD |168 |010 |XCD |0 |0
I would like to get
ID |sourceName|eventData|testValveOpened1 |GS3 |1 |02 |GS2 |1 |13 |GS2 |1 |14 |GS1 |1 |15 |GS1 |1 |16 |ABC |0 |17 |B123 |0 |18 |B423 |0 |19 |PTSD |168 |110 |XCD |0 |1
so when there's GS2 take value of eventData, otherwise cary value from previous testValueOpened