Rick Moritz created SPARK-8380:
----------------------------------
Summary: 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
