[jira] [Commented] (SPARK-17908) Column names Corrupted in pysaprk dataframe groupBy
[
https://issues.apache.org/jira/browse/SPARK-17908?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15572478#comment-15572478
]
Harish commented on SPARK-17908:
Yes. You are code structure is same as mine.. But i have 70M records with 1000
columns. It works with simple joins as above. But when you try to modify the DF
multiple times this will happen, as i was getting this error from 1.6.0 but i
didn't raise because i cant prove this with working use case. But it happens
frequently with my code so i tried with rename
Here my steps:
df = df.select('key1', 'key2', 'key3', 'val','total') -70Million records
df =df.withColumn('key2', 'ABC')
df1= df.groupBy('key1', 'key2',
'key3').agg(func.count(func.col('val')).alias('total'))
df1 = df1.columnRenamed('key2', 'key2')
df3 =df.join(df1, ['key1', 'key2', 'key3'])\
.withcolumn('newcol', func.col('val')/func.col('total'))
I just wanted to see if any one else observed this behavior, I will try to find
the code sample to proof this issue. If not in another 1-2 days i will mark it
not reproducible.
> Column names Corrupted in pysaprk dataframe groupBy
> ---
>
> Key: SPARK-17908
> URL: https://issues.apache.org/jira/browse/SPARK-17908
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 1.6.0, 1.6.1, 1.6.2, 2.0.0, 2.0.1
>Reporter: Harish
>Priority: Minor
>
> I have DF say df
> df1= df.groupBy('key1', 'key2',
> 'key3').agg(func.count(func.col('val')).alias('total'))
> df3 =df.join(df1, ['key1', 'key2', 'key3'])\
> .withcolumn('newcol', func.col('val')/func.col('total'))
> I am getting key2 is not present in df1, which is not truw becuase df1.show
> () is having the data with the key2.
> Then i added this code before join-- df1 = df1.columnRenamed('key2', 'key2')
> renamed with same name. Then it works.
> Stack trace will say column missing, but it is npt.
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[jira] [Commented] (SPARK-17908) Column names Corrupted in pysaprk dataframe groupBy
[
https://issues.apache.org/jira/browse/SPARK-17908?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15572421#comment-15572421
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Sean Owen commented on SPARK-17908:
---
You must be doing something different than what you show, because what you show
doesn't even quite compile. Here I adapted your example from the Python example
in the docs and ran it succesfully on 2.0.1:
{code}
import pyspark.sql.functions as func
from pyspark.sql.types import *
sc = spark.sparkContext
lines = sc.textFile("examples/src/main/resources/people.txt")
parts = lines.map(lambda l: l.split(","))
people = parts.map(lambda p: (p[0], p[1].strip()))
schemaString = "name age"
fields = [StructField(field_name, StringType(), True) for field_name in
schemaString.split()]
schema = StructType(fields)
df = spark.createDataFrame(people, schema)
df1 = df.groupBy('name', 'age').agg(func.count(func.col('age')).alias('total'))
df3 = df.join(df1, ['name', 'age']).withColumn('newcol',
func.col('age')/func.col('total'))
{code}
> Column names Corrupted in pysaprk dataframe groupBy
> ---
>
> Key: SPARK-17908
> URL: https://issues.apache.org/jira/browse/SPARK-17908
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 1.6.0, 1.6.1, 1.6.2, 2.0.0, 2.0.1
>Reporter: Harish
>Priority: Minor
>
> I have DF say df
> df1= df.groupBy('key1', 'key2',
> 'key3').agg(func.count(func.col('val')).alias('total'))
> df3 =df.join(df1, ['key1', 'key2', 'key3'])\
> .withcolumn('newcol', func.col('val')/func.col('total'))
> I am getting key2 is not present in df1, which is not truw becuase df1.show
> () is having the data with the key2.
> Then i added this code before join-- df1 = df1.columnRenamed('key2', 'key2')
> renamed with same name. Then it works.
> Stack trace will say column missing, but it is npt.
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[jira] [Commented] (SPARK-17908) Column names Corrupted in pysaprk dataframe groupBy
[
https://issues.apache.org/jira/browse/SPARK-17908?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15572373#comment-15572373
]
Harish commented on SPARK-17908:
Sorry.. I didnt put the actual column names of my code in stack trace, i have
modified the first line of stack trace for the column names.
> Column names Corrupted in pysaprk dataframe groupBy
> ---
>
> Key: SPARK-17908
> URL: https://issues.apache.org/jira/browse/SPARK-17908
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 1.6.0, 1.6.1, 1.6.2, 2.0.0, 2.0.1
>Reporter: Harish
>Priority: Minor
>
> I have DF say df
> df1= df.groupBy('key1', 'key2',
> 'key3').agg(func.count(func.col('val')).alias('total'))
> df3 =df.join(df1, ['key1', 'key2', 'key3'])\
> .withcolumn('newcol', func.col('val')/func.col('total'))
> I am getting key2 is not present in df1, which is not truw becuase df1.show
> () is having the data with the key2.
> Then i added this code before join-- df1 = df1.columnRenamed('key2', 'key2')
> renamed with same name. Then it works.
> Stack trace will say column missing, but it is npt.
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[jira] [Commented] (SPARK-17908) Column names Corrupted in pysaprk dataframe groupBy
[
https://issues.apache.org/jira/browse/SPARK-17908?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15572330#comment-15572330
]
Sean Owen commented on SPARK-17908:
---
Yes what's your code? This says one DF has just 'columns' as column.
> Column names Corrupted in pysaprk dataframe groupBy
> ---
>
> Key: SPARK-17908
> URL: https://issues.apache.org/jira/browse/SPARK-17908
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 1.6.0, 1.6.1, 1.6.2, 2.0.0, 2.0.1
>Reporter: Harish
>Priority: Minor
>
> I have DF say df
> df1= df.groupBy('key1', 'key2',
> 'key3').agg(func.count(func.col('val')).alias('total'))
> df3 =df.join(df1, ['key1', 'key2', 'key3'])\
> .withcolumn('newcol', func.col('val')/func.col('total'))
> I am getting key2 is not present in df1, which is not truw becuase df1.show
> () is having the data with the key2.
> Then i added this code before join-- df1 = df1.columnRenamed('key2', 'key2')
> renamed with same name. Then it works.
> Stack trace will say column missing, but it is npt.
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[jira] [Commented] (SPARK-17908) Column names Corrupted in pysaprk dataframe groupBy
[
https://issues.apache.org/jira/browse/SPARK-17908?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15572278#comment-15572278
]
Harish commented on SPARK-17908:
Traceback (most recent call last):
File
"/home/hpcuser/iri/spark-2.0.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/sql/utils.py",
line 63, in deco
File
"/home/hpcuser/iri/spark-2.0.1-bin-hadoop2.7/python/lib/py4j-0.10.3-src.zip/py4j/protocol.py",
line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o376.select.
: org.apache.spark.sql.AnalysisException: cannot resolve '`key2`' given input
columns: [columns];
at
org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:77)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:300)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:191)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:201)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:205)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:205)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$5.apply(QueryPlan.scala:210)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:210)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
at
org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
at
org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
at
org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
at
org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2603)
at org.apache.spark.sql.Dataset.select(Dataset.scala:969)
at sun.reflect.GeneratedMethodAccessor52.invoke(Unknown Source)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:745)
> Column names Corrupted in pysaprk dataframe groupBy
> ---
>
> Key: SPARK-17908
> URL: https://issues.apache.org/jira/browse/SPARK-17908
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 1.6.0, 1.6.1, 1.6.2, 2.0.0, 2.0.1
>Reporter: Harish
>Priority: Minor
>
> I have DF say df
> df1= df.groupBy('key1', 'key2',
> 'key3').agg(func.count(func.col('val')).alias('total'))
> df3 =df.join(df1, ['key1', 'key2', 'key3'])\
>
[jira] [Commented] (SPARK-17908) Column names Corrupted in pysaprk dataframe groupBy
[
https://issues.apache.org/jira/browse/SPARK-17908?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15572189#comment-15572189
]
Sean Owen commented on SPARK-17908:
---
Can you provide the error and a self-contained reproduction? I can't reproduce
that on similar code but there's probably something different in what you're
actually doing.
> Column names Corrupted in pysaprk dataframe groupBy
> ---
>
> Key: SPARK-17908
> URL: https://issues.apache.org/jira/browse/SPARK-17908
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 1.6.0, 1.6.1, 1.6.2, 2.0.0, 2.0.1
>Reporter: Harish
>Priority: Minor
>
> I have DF say df
> df1= df.groupBy('key1', 'key2',
> 'key3').agg(func.count(func.col('val')).alias('total'))
> df3 =df.join(df1, ['key1', 'key2', 'key3'])\
> .withcolumn('newcol', func.col('val')/func.col('total'))
> I am getting key2 is not present in df1, which is not truw becuase df1.show
> () is having the data with the key2.
> Then i added this code before join-- df1 = df1.columnRenamed('key2', 'key2')
> renamed with same name. Then it works.
> Stack trace will say column missing, but it is npt.
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