[jira] [Assigned] (SPARK-20356) Spark sql group by returns incorrect results after join + distinct transformations

2017-04-19 Thread Wenchen Fan (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-20356?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Wenchen Fan reassigned SPARK-20356:
---

Assignee: Liang-Chi Hsieh

> Spark sql group by returns incorrect results after join + distinct 
> transformations
> --
>
> Key: SPARK-20356
> URL: https://issues.apache.org/jira/browse/SPARK-20356
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.2.0
> Environment: Linux mint 18
> Python 3.5
>Reporter: Chris Kipers
>Assignee: Liang-Chi Hsieh
> Fix For: 2.2.0, 2.3.0
>
>
> I'm experiencing a bug with the head version of spark as of 4/17/2017. After 
> joining to dataframes, renaming a column and invoking distinct, the results 
> of the aggregation is incorrect after caching the dataframe. The following 
> code snippet consistently reproduces the error.
> from pyspark.sql import SparkSession
> import pyspark.sql.functions as sf
> import pandas as pd
> spark = SparkSession.builder.master("local").appName("Word 
> Count").getOrCreate()
> mapping_sdf = spark.createDataFrame(pd.DataFrame([
> {"ITEM": "a", "GROUP": 1},
> {"ITEM": "b", "GROUP": 1},
> {"ITEM": "c", "GROUP": 2}
> ]))
> items_sdf = spark.createDataFrame(pd.DataFrame([
> {"ITEM": "a", "ID": 1},
> {"ITEM": "b", "ID": 2},
> {"ITEM": "c", "ID": 3}
> ]))
> mapped_sdf = \
> items_sdf.join(mapping_sdf, on='ITEM').select("ID", 
> sf.col("GROUP").alias('ITEM')).distinct()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> mapped_sdf.cache()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 3, incorrect
> The next code snippet is almost the same after the first except I don't call 
> distinct on the dataframe. This snippet performs as expected:
> mapped_sdf = \
> items_sdf.join(mapping_sdf, on='ITEM').select("ID", 
> sf.col("GROUP").alias('ITEM'))
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> mapped_sdf.cache()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> I don't experience this bug with spark 2.1 or event earlier versions for 2.2



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[jira] [Assigned] (SPARK-20356) Spark sql group by returns incorrect results after join + distinct transformations

2017-04-18 Thread Apache Spark (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-20356?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-20356:


Assignee: Apache Spark

> Spark sql group by returns incorrect results after join + distinct 
> transformations
> --
>
> Key: SPARK-20356
> URL: https://issues.apache.org/jira/browse/SPARK-20356
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.2.0
> Environment: Linux mint 18
> Python 3.5
>Reporter: Chris Kipers
>Assignee: Apache Spark
>
> I'm experiencing a bug with the head version of spark as of 4/17/2017. After 
> joining to dataframes, renaming a column and invoking distinct, the results 
> of the aggregation is incorrect after caching the dataframe. The following 
> code snippet consistently reproduces the error.
> from pyspark.sql import SparkSession
> import pyspark.sql.functions as sf
> import pandas as pd
> spark = SparkSession.builder.master("local").appName("Word 
> Count").getOrCreate()
> mapping_sdf = spark.createDataFrame(pd.DataFrame([
> {"ITEM": "a", "GROUP": 1},
> {"ITEM": "b", "GROUP": 1},
> {"ITEM": "c", "GROUP": 2}
> ]))
> items_sdf = spark.createDataFrame(pd.DataFrame([
> {"ITEM": "a", "ID": 1},
> {"ITEM": "b", "ID": 2},
> {"ITEM": "c", "ID": 3}
> ]))
> mapped_sdf = \
> items_sdf.join(mapping_sdf, on='ITEM').select("ID", 
> sf.col("GROUP").alias('ITEM')).distinct()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> mapped_sdf.cache()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 3, incorrect
> The next code snippet is almost the same after the first except I don't call 
> distinct on the dataframe. This snippet performs as expected:
> mapped_sdf = \
> items_sdf.join(mapping_sdf, on='ITEM').select("ID", 
> sf.col("GROUP").alias('ITEM'))
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> mapped_sdf.cache()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> I don't experience this bug with spark 2.1 or event earlier versions for 2.2



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[jira] [Assigned] (SPARK-20356) Spark sql group by returns incorrect results after join + distinct transformations

2017-04-18 Thread Apache Spark (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-20356?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Apache Spark reassigned SPARK-20356:


Assignee: (was: Apache Spark)

> Spark sql group by returns incorrect results after join + distinct 
> transformations
> --
>
> Key: SPARK-20356
> URL: https://issues.apache.org/jira/browse/SPARK-20356
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.2.0
> Environment: Linux mint 18
> Python 3.5
>Reporter: Chris Kipers
>
> I'm experiencing a bug with the head version of spark as of 4/17/2017. After 
> joining to dataframes, renaming a column and invoking distinct, the results 
> of the aggregation is incorrect after caching the dataframe. The following 
> code snippet consistently reproduces the error.
> from pyspark.sql import SparkSession
> import pyspark.sql.functions as sf
> import pandas as pd
> spark = SparkSession.builder.master("local").appName("Word 
> Count").getOrCreate()
> mapping_sdf = spark.createDataFrame(pd.DataFrame([
> {"ITEM": "a", "GROUP": 1},
> {"ITEM": "b", "GROUP": 1},
> {"ITEM": "c", "GROUP": 2}
> ]))
> items_sdf = spark.createDataFrame(pd.DataFrame([
> {"ITEM": "a", "ID": 1},
> {"ITEM": "b", "ID": 2},
> {"ITEM": "c", "ID": 3}
> ]))
> mapped_sdf = \
> items_sdf.join(mapping_sdf, on='ITEM').select("ID", 
> sf.col("GROUP").alias('ITEM')).distinct()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> mapped_sdf.cache()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 3, incorrect
> The next code snippet is almost the same after the first except I don't call 
> distinct on the dataframe. This snippet performs as expected:
> mapped_sdf = \
> items_sdf.join(mapping_sdf, on='ITEM').select("ID", 
> sf.col("GROUP").alias('ITEM'))
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> mapped_sdf.cache()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> I don't experience this bug with spark 2.1 or event earlier versions for 2.2



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