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https://issues.apache.org/jira/browse/DRILL-2849?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14527558#comment-14527558
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Steven Phillips commented on DRILL-2849:
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The problem is that we don't really have a good concept of null in Text files.
In the original text file, some rows have 7 entries, and some have 8. So when
selecting columns[7], for the rows where it is missing, we return null.
The CSV writer, on the other hand, write the string "null" when it sees a null
value. But since there is no concept of null in text files as far as the reader
is concerned, this is simply returned as a string "null".
Also of significance, is the fact that the original csv file doesn't appear to
be valid. I see lines like this scattered throughout:
1348755809001,/user/ovguide,1363510469000,/user/turtlewax_bot,/m/0jkdvpx,/common/topic/description,"Guests:
Columnist Thomas Friedman, writer David Frum, singer Natalie Mames, Gen.
Wesley Clark and Sen. Barbara Boxer.
Note how there is no closing quotation mark for the string beginning with
"Guests". This is causing unexpected behavior because the reader currently
treats the quotations mark as part of the string, and thus faithfully writes it
to the new file when doing CTAS. The null value that comes after is then
assumed by the reader to part of the same string.
> Difference in query results over CSV file created by CTAS, compared to
> results over original CSV file
> ------------------------------------------------------------------------------------------------------
>
> Key: DRILL-2849
> URL: https://issues.apache.org/jira/browse/DRILL-2849
> Project: Apache Drill
> Issue Type: Bug
> Components: Storage - Text & CSV
> Affects Versions: 0.9.0
> Environment: 64e3ec52b93e9331aa5179e040eca19afece8317 | DRILL-2611:
> value vectors should report valid value count | 16.04.2015 @ 13:53:34 EDT
> Reporter: Khurram Faraaz
> Assignee: Steven Phillips
> Priority: Critical
> Fix For: 1.0.0
>
>
> Different results are seen for the same query over CSV data file and another
> CSV data file created by CTAS using the same CSV file.
> Tests were executed on 4 node cluster on CentOS.
> I got rid of the header information that is written by CTAS into the new CSV
> file that CTAS creates, and then ran my queries over CTAS' CSV file.
> query over uncompressed CSV file, deletions/deletions-00000-of-00020.csv
> {code}
> > select count(cast(columns[0] as double)),max(cast(columns[0] as
> > double)),min(cast(columns[0] as double)),avg(cast(columns[0] as double)),
> > columns[7] from `deletions/deletions-00000-of-00020.csv` group by
> > columns[7];
> 88 rows selected (6.893 seconds)
> =================================================
> {code}
> query over CSV file that was created by CTAS. (input to CTAS was
> deletions/deletions-00000-of-00020.csv)
> Notice there is one more record returned.
> {code}
> > select count(cast(columns[0] as double)),max(cast(columns[0] as
> > double)),min(cast(columns[0] as double)),avg(cast(columns[0] as double)),
> > columns[7] from `csvToCSV_00000_of_00020/0_0_0.csv` group by columns[7];
>
> 89 rows selected (6.623 seconds)
> ==================================================
> {code}
> query over compressed CSV file
> {code}
> > select count(cast(columns[0] as double)),max(cast(columns[0] as
> > double)),min(cast(columns[0] as double)),avg(cast(columns[0] as double)),
> > columns[7] from `deletions-00000-of-00020.csv.gz` group by columns[7];
> 88 rows selected (10.526 seconds)
> ==================================================
> {code}
> In the below cases, the count and sum results are different when query is
> executed over CSV file that was created by CTAS. ( this may explain why we
> see the difference in results in the above queries ? )
> {code}
> 0: jdbc:drill:> select count(cast(columns[0] as double)),max(cast(columns[0]
> as double)),min(cast(columns[0] as double)),avg(cast(columns[0] as double)),
> columns[7] from `deletions/deletions-00000-of-00020.csv` where columns[7] is
> null group by columns[7];
> +------------+------------+------------+------------+------------+
> | EXPR$0 | EXPR$1 | EXPR$2 | EXPR$3 | EXPR$4 |
> +------------+------------+------------+------------+------------+
> | 252 | 1.362983396001E12 | 1.165768779027E12 | 1.293794515595635E12 |
> null |
> +------------+------------+------------+------------+------------+
> 1 row selected (6.013 seconds)
> 0: jdbc:drill:> select count(cast(columns[0] as double)),max(cast(columns[0]
> as double)),min(cast(columns[0] as double)),avg(cast(columns[0] as double)),
> columns[7] from `deletions-00000-of-00020.csv.gz` where columns[7] is null
> group by columns[7];
> +------------+------------+------------+------------+------------+
> | EXPR$0 | EXPR$1 | EXPR$2 | EXPR$3 | EXPR$4 |
> +------------+------------+------------+------------+------------+
> | 252 | 1.362983396001E12 | 1.165768779027E12 | 1.293794515595635E12 |
> null |
> +------------+------------+------------+------------+------------+
> 1 row selected (8.899 seconds)
> {code}
> Notice that count and sum results are different (from those above) when query
> is executed over the CSV file created by CTAS.
> {code}
> 0: jdbc:drill:> select count(cast(columns[0] as double)),max(cast(columns[0]
> as double)),min(cast(columns[0] as double)),avg(cast(columns[0] as double)),
> columns[7] from `csvToCSV_00000_of_00020/0_0_0.csv` where columns[7] is null
> group by columns[7];
> +------------+------------+------------+------------+------------+
> | EXPR$0 | EXPR$1 | EXPR$2 | EXPR$3 | EXPR$4 |
> +------------+------------+------------+------------+------------+
> | 245 | 1.349670663E12 | 1.165768779027E12 | 1.2930281335065144E12 |
> null |
> +------------+------------+------------+------------+------------+
> 1 row selected (5.736 seconds)
> {code}
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