Raviteja Lokineni created SPARK-18388:
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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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