问题
I'd like to be able to have Spark group by a step size, as opposed to just single values. Is there anything in spark similar to PySpark 2.x's window
function for numeric (non-date) values?
Something along the lines of:
sqlContext = SQLContext(sc)
df = sqlContext.createDataFrame([10, 11, 12, 13], "integer").toDF("foo")
res = df.groupBy(window("foo", step=2, start=10)).count()
回答1:
You can reuse timestamp one and express parameters in seconds. Tumbling:
from pyspark.sql.functions import col, window
df.withColumn(
"window",
window(
col("foo").cast("timestamp"),
windowDuration="2 seconds"
).cast("struct<start:bigint,end:bigint>")
).show()
# +---+-------+
# |foo| window|
# +---+-------+
# | 10|[10,12]|
# | 11|[10,12]|
# | 12|[12,14]|
# | 13|[12,14]|
# +---+-------+
Rolling one:
df.withColumn(
"window",
window(
col("foo").cast("timestamp"),
windowDuration="2 seconds", slideDuration="1 seconds"
).cast("struct<start:bigint,end:bigint>")
).show()
# +---+-------+
# |foo| window|
# +---+-------+
# | 10| [9,11]|
# | 10|[10,12]|
# | 11|[10,12]|
# | 11|[11,13]|
# | 12|[11,13]|
# | 12|[12,14]|
# | 13|[12,14]|
# | 13|[13,15]|
# +---+-------+
Using groupBy
and start
:
w = window(col("foo").cast("timestamp"), "2 seconds").cast("struct<start:bigint,end:bigint>")
start = w.start.alias("start")
df.groupBy(start).count().show()
+-----+-----+
|start|count|
+-----+-----+
| 10| 2|
| 12| 2|
+-----+-----+
来源:https://stackoverflow.com/questions/48467215/pyspark-numeric-window-group-by