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Herman van Hovell updated SPARK-18388: -------------------------------------- Priority: Major (was: Critical) > 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: SQL > Affects Versions: 1.5.2, 1.6.2, 2.0.1 > Environment: PySpark 2.0.1, Jupyter > Reporter: Raviteja Lokineni > Attachments: spark-bug-jupyter.py, spark-bug-stacktrace.txt, > spark-bug.csv > > > Usecase: I am generating weekly aggregates of every column of data > {code} > 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} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org