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https://issues.apache.org/jira/browse/SPARK-8380?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14588196#comment-14588196
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Shivaram Venkataraman commented on SPARK-8380:
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Thanks for the update. I'm going to mark this issue as resolved. BTW if there
are documentation changes that you think will be helpful feel free to create
JIRAs / PRs for them
> SparkR mis-counts
> -----------------
>
> Key: SPARK-8380
> URL: https://issues.apache.org/jira/browse/SPARK-8380
> Project: Spark
> Issue Type: Bug
> Components: SparkR
> Affects Versions: 1.4.0
> Reporter: Rick Moritz
>
> On my dataset of ~9 Million rows x 30 columns, queried via Hive, I can
> perform count operations on the entirety of the dataset and get the correct
> value, as double checked against the same code in scala.
> When I start to add conditions or even do a simple partial ascending
> histogram, I get discrepancies.
> In particular, there are missing values in SparkR, and massively so:
> A top 6 count of a certain feature in my dataset results in an order of
> magnitude smaller numbers, than I get via scala.
> The following logic, which I consider equivalent is the basis for this report:
> counts<-summarize(groupBy(df, df$col_name), count = n(tdf$col_name))
> head(arrange(counts, desc(counts$count)))
> versus:
> val table = sql("SELECT col_name, count(col_name) as value from df group by
> col_name order by value desc")
> The first, in particular, is taken directly from the SparkR programming
> guide. Since summarize isn't documented from what I can see, I'd hope it does
> what the programming guide indicates. In that case this would be a pretty
> serious logic bug (no errors are thrown). Otherwise, there's the possibility
> of a lack of documentation and badly worded example in the guide being behind
> my misperception of SparkRs functionality.
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