So I am very new to pyspark but I am still unable to correctly create my own query. I try googling my problems but I just don't understand how most of this works. I'm not sure what I'm missing.
But anyway I have the following two dataframes, spark_p:
-------------------- ----- --------------------
|__record_timestamp__|cycle| profiles|
-------------------- ----- --------------------
| 1651737406300000000| 0|[0, 1, 1, 1, 3, 1...|
| 1651737406300000000| 16|[0, 0, 1, 0, 0, 0...|
| 1651737406300000000| 17|[1, 1, 1, 1, 0, 0...|
| 1651737406300000000| 18|[0, 0, 0, 0, 0, 1...|
| 1651737406300000000| 19|[1, 1, 1, 0, 0, 0...|
-------------------- ----- --------------------
and spark_m:
------------- --------------------
| current|__record_timestamp__|
------------- --------------------
| 0.007181627| 1651730407500000000|
| 8.3004625E-4| 1651730464000000000|
| 0.41976404| 1651730507000000000|
|-0.0017322368| 1651732761000000000|
|-2.5260705E-4| 1651732822500000000|
| 2.3460487E-4| 1651732824500000000|
------------- --------------------
And I need to add a column to spark_p that contains the current at that specific timestamp.
So the result would look something like:
-------------------- ----- -------------------- ---------
|__record_timestamp__|cycle| profiles| current|
-------------------- ----- -------------------- ---------
| 1651737406300000000| 0|[0, 1, 1, 1, 3, 1...| 0.07|
| 1651737406300000000| 16|[0, 0, 1, 0, 0, 0...| 12|
| 1651737406300000000| 17|[1, 1, 1, 1, 0, 0...| 0.0|
| 1651737406300000000| 18|[0, 0, 0, 0, 0, 1...| 5.235654|
| 1651737406300000000| 19|[1, 1, 1, 0, 0, 0...| 125|
-------------------- ----- -------------------- ---------
Now the time stamps won't exactly match up but I just need the closest timestamp, or to use the value of the previously recorded current, either is fine. I have no idea how...
When I try:
spark_p.join(spark_m, spark_p.__record_timestamp__ == spark_m.__record_timestamp__, "inner").show()
I just get:
-------------------- ----- -------- ----- --------------------
|__record_timestamp__|cycle|profiles|value|__record_timestamp__|
-------------------- ----- -------- ----- --------------------
-------------------- ----- -------- ----- --------------------
So I'm guessing none of them match exactly, but how would I just grab the nearest value? TIA
CodePudding user response:
This solution contains the answer:
SPLIT_COUNT = 90
SPLIT_SIZE = 1024
spark_p = data.select("profiles", '__record_timestamp__')
spark_p = spark_p.withColumn("profiles", F.col("profiles").getField("elements") )
slices = [F.slice(F.col('profiles'), i * SPLIT_SIZE 1, SPLIT_SIZE) for i in range(SPLIT_COUNT)]
spark_p = spark_p.select(F.posexplode(F.array(*slices)), F.col('__record_timestamp__'))
spark_p = spark_p.withColumn("cycle", F.col("pos") )
spark_p = spark_p.withColumn("profiles", F.col("col") )
spark_p = spark_p.drop('pos').drop('col')
spark_m = magnetData.select("value", '__record_timestamp__', )
spark_p = spark_p.withColumn('value', F.lit(None))
spark_m = spark_m.withColumn('profiles', F.lit(None))
spark_m = spark_m.withColumn('cycle', F.lit(None))
final_df = spark_p.unionByName(spark_m)
w = Window.orderBy('__record_timestamp__').rowsBetween(Window.unboundedPreceding, -1)
final_df = final_df.withColumn('value', F.last('value', True).over(w)).filter(~F.isnull('profiles'))
You must create a window with unboundedPreceding parameter.