[jira] [Commented] (PARQUET-1946) Parquet File not readable by Google big query (works with Spark)

["Gabor Szadovszky (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/PARQUET-1946?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17237422#comment-17237422 ] 

Gabor Szadovszky commented on PARQUET-1946:
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There are no statistics/metadata in the parquet specification that is related to unique values. The value displayed by parquet-tools (after VC) is the value count (the number of values in the related page). So, depending on the actual data both 547 value count and 421 distinct value count can be valid in the same time.

I don't know what parquet implementation does Big Query use. If Spark can read the data from the same file properly I would suggest creating an issue for Big Query to investigate why they cannot read that parquet file.

> Parquet File not readable by Google big query (works with Spark)
> ----------------------------------------------------------------
>
>                 Key: PARQUET-1946
>                 URL: https://issues.apache.org/jira/browse/PARQUET-1946
>             Project: Parquet
>          Issue Type: Bug
>          Components: parquet-avro
>    Affects Versions: 1.11.0
>         Environment: [secor|https://github.com/pinterest/secor]
> GCP 
> Big Query google cloud
> Parquet writer 1.11
>  
>  
>            Reporter: Richard Grossman
>            Priority: Blocker
>
> Hi
> I'm trying to write Avro message to parquet on GCS. These parquet should be query by big query engine who support now parquet.
> To do this I'm using Secor a kafka log persister tools from pinterest.
> First I didn't notice any problem using Spark the same file can be read without any problem all is working perfect.
> Now using Big query bring and error like this :
> Error while reading table: , error message: Read less values than expected: Actual: 29333, Expected: 33827. Row group: 0, Column: , File:
> After investigation using parquet-tools I figured out that in parquet there is metadata regarding number total of unique values for each columns eg from parquet-tools
> page 0: DLE:BIT_PACKED RLE:BIT_PACKED [more]... CRC:[PAGE CORRUPT] VC:547
> So the VC value indicate that the total number of unique value in the file is 547.
> Now when make a spark SQL like SELECT DISTINCT COUNT(column) FROM ... I get 421 mean this number in the metadata is incorrect.
> So what is not a problem for Spark to read is a blocking problem for Big data because it relies on these values and found it incorrect.
> Is there any configuration of the writer that can prevent these errors in the metadata ? Or is it a normal behavior that should be a problem ?
> Thanks



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