[jira] [Updated] (SPARK-18388) Running aggregation on many columns throws SOE
[
https://issues.apache.org/jira/browse/SPARK-18388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wenchen Fan updated SPARK-18388:
Target Version/s: (was: 2.4.0)
> 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
>Priority: Major
> 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}
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[jira] [Updated] (SPARK-18388) Running aggregation on many columns throws SOE
[
https://issues.apache.org/jira/browse/SPARK-18388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sameer Agarwal updated SPARK-18388:
---
Target Version/s: 2.4.0 (was: 2.3.0)
> 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}
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[jira] [Updated] (SPARK-18388) Running aggregation on many columns throws SOE
[
https://issues.apache.org/jira/browse/SPARK-18388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Michael Armbrust updated SPARK-18388:
-
Target Version/s: 2.3.0 (was: 2.2.0)
> 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.csv, spark-bug-jupyter.py,
> spark-bug-stacktrace.txt
>
>
> 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}
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[jira] [Updated] (SPARK-18388) Running aggregation on many columns throws SOE
[
https://issues.apache.org/jira/browse/SPARK-18388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Herman van Hovell updated SPARK-18388:
--
Target Version/s: 2.2.0 (was: 2.1.0)
> 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}
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[jira] [Updated] (SPARK-18388) Running aggregation on many columns throws SOE
[
https://issues.apache.org/jira/browse/SPARK-18388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Herman van Hovell updated SPARK-18388:
--
Target Version/s: 2.1.0
> 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}
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[jira] [Updated] (SPARK-18388) Running aggregation on many columns throws SOE
[
https://issues.apache.org/jira/browse/SPARK-18388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
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}
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[jira] [Updated] (SPARK-18388) Running aggregation on many columns throws SOE
[
https://issues.apache.org/jira/browse/SPARK-18388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Michael Armbrust updated SPARK-18388:
-
Component/s: (was: Spark Core)
SQL
> 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
>Priority: Critical
> 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}
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[jira] [Updated] (SPARK-18388) Running aggregation on many columns throws SOE
[
https://issues.apache.org/jira/browse/SPARK-18388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Raviteja Lokineni updated SPARK-18388:
--
Description:
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}
was:
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}
> 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: 1.5.2, 1.6.2, 2.0.1
> Environment: PySpark 2.0.1, Jupyter
>Reporter: Raviteja Lokineni
>Priority: Critical
> 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}
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[jira] [Updated] (SPARK-18388) Running aggregation on many columns throws SOE
[
https://issues.apache.org/jira/browse/SPARK-18388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Raviteja Lokineni updated SPARK-18388:
--
Description:
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}
was:
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}
> 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: 1.5.2, 1.6.2, 2.0.1
> Environment: PySpark 2.0.1, Jupyter
>Reporter: Raviteja Lokineni
>Priority: Critical
> 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}
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[jira] [Updated] (SPARK-18388) Running aggregation on many columns throws SOE
[
https://issues.apache.org/jira/browse/SPARK-18388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Raviteja Lokineni updated SPARK-18388:
--
Attachment: spark-bug.csv
spark-bug-jupyter.py
spark-bug-stacktrace.txt
> 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: 1.5.2, 1.6.2, 2.0.1
> Environment: PySpark 2.0.1, Jupyter
>Reporter: Raviteja Lokineni
>Priority: Critical
> Attachments: spark-bug-jupyter.py, spark-bug-stacktrace.txt,
> spark-bug.csv
>
>
> 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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