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https://issues.apache.org/jira/browse/SPARK-15678?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15883035#comment-15883035
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Kazuaki Ishizaki commented on SPARK-15678:
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Sorry for being late to reply.
According to the comment of {{refreshByPath()}}, I think that it should work by
calling {{refreshByPath()}} once.
{quote}
Invalidate and refresh all the cached data (and the associated metadata) for
any dataframe that
contains the given data source path. Path matching is by prefix, i.e. "/"
would invalidate
everything that is cached.
{quote}
I think that it is better to create a new JIRA entry. This JIRA entry is not
for reporting issues of {{refreshByPath()}}.
What do you think?
> Not use cache on appends and overwrites
> ---------------------------------------
>
> Key: SPARK-15678
> URL: https://issues.apache.org/jira/browse/SPARK-15678
> Project: Spark
> Issue Type: Bug
> Affects Versions: 2.0.0
> Reporter: Sameer Agarwal
> Assignee: Sameer Agarwal
> Fix For: 2.0.0
>
>
> SparkSQL currently doesn't drop caches if the underlying data is overwritten.
> {code}
> val dir = "/tmp/test"
> sqlContext.range(1000).write.mode("overwrite").parquet(dir)
> val df = sqlContext.read.parquet(dir).cache()
> df.count() // outputs 1000
> sqlContext.range(10).write.mode("overwrite").parquet(dir)
> sqlContext.read.parquet(dir).count() // outputs 1000 instead of 10 <---- We
> are still using the cached dataset
> {code}
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