I'm trying to use Spark 1.4 window functions in pyspark 1.4.1
but getting mostly errors or unexpected results. Here is a very simple example that I think should work:
from pyspark.sql.window import Window
import pyspark.sql.functions as func
l = [(1,101),(2,202),(3,303),(4,404),(5,505)]
df = sqlContext.createDataFrame(l,["a","b"])
wSpec = Window.orderBy(df.a).rowsBetween(-1,1)
df.select(df.a, func.rank().over(wSpec).alias("rank"))
==> Failure org.apache.spark.sql.AnalysisException: Window function rank does not take a frame specification.
df.select(df.a, func.lag(df.b,1).over(wSpec).alias("prev"), df.b, func.lead(df.b,1).over(wSpec).alias("next"))
===> org.apache.spark.sql.AnalysisException: Window function lag does not take a frame specification.;
wSpec = Window.orderBy(df.a)
df.select(df.a, func.rank().over(wSpec).alias("rank"))
===> org.apache.hadoop.hive.ql.exec.UDFArgumentTypeException: One or more arguments are expected.
df.select(df.a, func.lag(df.b,1).over(wSpec).alias("prev"), df.b, func.lead(df.b,1).over(wSpec).alias("next")).collect()
[Row(a=1, prev=None, b=101, next=None), Row(a=2, prev=None, b=202, next=None), Row(a=3, prev=None, b=303, next=None)]
As you can see, if I add rowsBetween frame specification, neither rank() nor lag/lead() window functions recognize it: "Window function does not take a frame specification".
If I omit the rowsBetween frame specification at leas lag/lead() do not throw exceptions but return unexpected (for me) result: always None. And the rank() still doesn't work with different exception.
Can anybody help me to get my window functions right?
UPDATE
All right, that starts to look as a pyspark bug. I have prepared the same test in pure Spark (Scala, spark-shell):
import sqlContext.implicits._
import org.apache.spark.sql._
import org.apache.spark.sql.types._
val l: List[Tuple2[Int,Int]] = List((1,101),(2,202),(3,303),(4,404),(5,505))
val rdd = sc.parallelize(l).map(i => Row(i._1,i._2))
val schemaString = "a b"
val schema = StructType(schemaString.split(" ").map(fieldName => StructField(fieldName, IntegerType, true)))
val df = sqlContext.createDataFrame(rdd, schema)
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
val wSpec = Window.orderBy("a").rowsBetween(-1,1)
df.select(df("a"), rank().over(wSpec).alias("rank"))
==> org.apache.spark.sql.AnalysisException: Window function rank does not take a frame specification.;
df.select(df("a"), lag(df("b"),1).over(wSpec).alias("prev"), df("b"), lead(df("b"),1).over(wSpec).alias("next"))
===> org.apache.spark.sql.AnalysisException: Window function lag does not take a frame specification.;
val wSpec = Window.orderBy("a")
df.select(df("a"), rank().over(wSpec).alias("rank")).collect()
====> res10: Array[org.apache.spark.sql.Row] = Array([1,1], [2,2], [3,3], [4,4], [5,5])
df.select(df("a"), lag(df("b"),1).over(wSpec).alias("prev"), df("b"), lead(df("b"),1).over(wSpec).alias("next"))
====> res12: Array[org.apache.spark.sql.Row] = Array([1,null,101,202], [2,101,202,303], [3,202,303,404], [4,303,404,505], [5,404,505,null])
Even though the rowsBetween cannot be applied in Scala, both rank() and lag()/lead() work as I expect when rowsBetween is omitted.