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https://issues.apache.org/jira/browse/SPARK-20356?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15973929#comment-15973929
]
Dilip Biswal commented on SPARK-20356:
--------------------------------------
[~viirya] Did you try from spark-shell or from one of our query suites ? I
could reproduce it from spark-shell fine. From our query suites i had to force
the number of shuffle partitions to reproduce it.
{code}
test("cache defect") {
withSQLConf("spark.sql.shuffle.partitions" -> "200") {
val df1 = Seq(("a", 1), ("b", 1), ("c", 2)).toDF("item", "group")
val df2 = Seq(("a", 1), ("b", 2), ("c", 3)).toDF("item", "id")
val df3 = df1.join(df2, Seq("item")).select($"id",
$"group".as("item")).distinct()
df3.explain(true)
df3.unpersist()
val agg_without_cache = df3.groupBy($"item").count()
agg_without_cache.show()
df3.cache()
val agg_with_cache = df3.groupBy($"item").count()
agg_with_cache.explain(true)
agg_with_cache.show()
}
}
{code}
> 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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