[jira] [Created] (SPARK-18388) Running aggregation on many columns throws SOE

["Raviteja Lokineni (JIRA)" <ji...@apache.org>]

Raviteja Lokineni created SPARK-18388:
-----------------------------------------

             Summary: Running aggregation on many columns throws SOE
                 Key: SPARK-18388
                 URL: https://issues.apache.org/jira/browse/SPARK-18388
             Project: Spark
          Issue Type: Bug
          Components: Spark Core
    Affects Versions: 2.0.1, 1.6.2, 1.5.2
         Environment: PySpark 2.0.1, Jupyter
            Reporter: Raviteja Lokineni
            Priority: Critical


Usecase: I am generating weekly aggregates of every column of data

{code:python}
from pyspark.sql.window import Window
from pyspark.sql.functions import *

timeSeries = sqlContext.read.option("header", "true").format("org.apache.spark.sql.execution.datasources.csv.CSVFileFormat").load("file:///tmp/spark-bug.csv")

# Hive timestamp is interpreted as UNIX timestamp in seconds*
days = lambda i: i * 86400

w = (Window()
     .partitionBy("id")
     .orderBy(col("dt").cast("timestamp").cast("long"))
     .rangeBetween(-days(6), 0))

cols = ["id", "dt"]
skipCols = ["id", "dt"]

for col in timeSeries.columns:
    if col in skipCols:
        continue
    cols.append(mean(col).over(w).alias("mean_7_"+col))
    cols.append(count(col).over(w).alias("count_7_"+col))
    cols.append(sum(col).over(w).alias("sum_7_"+col))
    cols.append(min(col).over(w).alias("min_7_"+col))
    cols.append(max(col).over(w).alias("max_7_"+col))

df = timeSeries.select(cols)
df.orderBy('id', 'dt').write\
    .format("org.apache.spark.sql.execution.datasources.csv.CSVFileFormat")\
    .save("file:///tmp/spark-bug-out.csv")
{code}



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