Hey Daniel, Thanks for the response.
After playing around for a bit, it looks like it's probably the something similar to the first situation you mentioned, with the Parquet format causing issues. Both programmatically created dataset and a dataset pulled off the internet (rather than out of S3 and put into HDFS/Hive) acted with DataFrames as one would expect (printed out everything, grouped properly, etc.) It looks like there is more than likely an outstanding bug that causes issues with data coming from S3 and is converted in the parquet format (found an article here highlighting it was around in 1.4, and I guess it wouldn't be out of the realm of things for it still to exist. Link to article: https://www.appsflyer.com/blog/the-bleeding-edge-spark-parquet-and-s3/ Hopefully a little more stability will come out with the upcoming Spark 1.6 release on EMR (I think that is happening sometime soon). Thanks again for the advice on where to dig further into. Much appreciated. Andrew On Tue, Jan 26, 2016 at 9:18 AM, Daniel Darabos < daniel.dara...@lynxanalytics.com> wrote: > Have you tried setting spark.emr.dropCharacters to a lower value? (It > defaults to 8.) > > :) Just joking, sorry! Fantastic bug. > > What data source do you have for this DataFrame? I could imagine for > example that it's a Parquet file and on EMR you are running with two wrong > version of the Parquet library and it messes up strings. It should be easy > enough to try a different data format. You could also try what happens if > you just create the DataFrame programmatically, e.g. > sc.parallelize(Seq("asdfasdfasdf")).toDF. > > To understand better at which point the characters are lost you could try > grouping by a string attribute. I see "education" ends up either as "" > (empty string) or "y" in the printed output. But are the characters already > lost when you try grouping by the attribute? Will there be a single "" > category, or will you have separate categories for "primary" and "tertiary"? > > I think the correct output through the RDD suggests that the issue happens > at the very end. So it will probably happen also with different data > sources, and grouping will create separate groups for "primary" and > "tertiary" even though they are printed as the same string at the end. You > should also check the data from "take(10)" to rule out any issues with > printing. You could try the same "groupBy" trick after "take(10)". Or you > could print the lengths of the strings. > > Good luck! > > On Tue, Jan 26, 2016 at 3:53 AM, awzurn <awz...@gmail.com> wrote: > >> Sorry for the bump, but wondering if anyone else has seen this before. >> We're >> hoping to either resolve this soon, or move on with further steps to move >> this into an issue. >> >> Thanks in advance, >> >> Andrew Zurn >> >> >> >> -- >> View this message in context: >> http://apache-spark-user-list.1001560.n3.nabble.com/Dataframe-Spark-SQL-Drops-First-8-Characters-of-String-on-Amazon-EMR-tp26022p26065.html >> Sent from the Apache Spark User List mailing list archive at Nabble.com. >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> For additional commands, e-mail: user-h...@spark.apache.org >> >> >
