Krzysztof Skulski created SPARK-24426:
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Summary: Unexpected combination of cache and join on DataFrame
Key: SPARK-24426
URL: https://issues.apache.org/jira/browse/SPARK-24426
Project: Spark
Issue Type: Bug
Components: Spark Core
Affects Versions: 2.3.0
Reporter: Krzysztof Skulski
I have unexpected results, when I cache DataFrame and try to do another
grouping on it. New DataFrames based on cached groupBy DataFrame works ok, but
when i try join it to anohter DataFrame it seems like second join is adding new
column but the data is copy from first joined DataFrame. Example below
(userAgentType - is ok,
userChannelType - is ok, userOrigin - is not ok).
When I remove cache from aggregated DataFrame it works ok.
{code}
val aggregated = dataFrame.cache()
val userAgentType = aggregated.groupBy("id", "agentType").count()
.orderBy(asc("id"),
desc("count")).groupBy("id").agg(first("agentType").as("agentType"))
val userChannelType = aggregated.groupBy("id", "channelType").count()
.orderBy(asc("id"),
desc("count")).groupBy("id").agg(first("channelType").as("channelType"))
val userOrigin = userInfo
.join(userAgentType, Seq("id"), "left")
.join(userChannelType, Seq("id"), "left")
{code}
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