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https://issues.apache.org/jira/browse/ORC-161?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16440373#comment-16440373
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ASF GitHub Bot commented on ORC-161:
------------------------------------

Github user wgtmac commented on a diff in the pull request:

    https://github.com/apache/orc/pull/245#discussion_r181950089
  
    --- Diff: site/_docs/file-tail.md ---
    @@ -249,12 +249,25 @@ For booleans, the statistics include the count of 
false and true values.
     }
     ```
     
    -For decimals, the minimum, maximum, and sum are stored.
    +For decimals, the minimum, maximum, and sum are stored. In ORC 2.0,
    +string representation is deprecated and DecimalStatistics uses integers
    +which have better performance.
     
     ```message DecimalStatistics {
      optional string minimum = 1;
      optional string maximum = 2;
      optional string sum = 3;
    +  message Int128 {
    --- End diff --
    
    Good suggestion!


> Create a new column type that run-length-encodes decimals
> ---------------------------------------------------------
>
>                 Key: ORC-161
>                 URL: https://issues.apache.org/jira/browse/ORC-161
>             Project: ORC
>          Issue Type: Wish
>          Components: encoding
>            Reporter: Douglas Drinka
>            Priority: Major
>
> I'm storing prices in ORC format, and have made the following observations 
> about the current decimal implementation:
> - The encoding is inefficient: my prices are a walking-random set, plus or 
> minus a few pennies per data point. This would encode beautifully with a 
> patched base encoding.  Instead I'm averaging 4 bytes per data point, after 
> Zlib.
> - Everyone acknowledges that it's nice to be able to store huge numbers in 
> decimal columns, but that you probably won't.  Presto, for instance, has a 
> fast-path which engages for precision of 18 or less, and decodes to 64-bit 
> longs, and then a slow path which uses BigInt.  I anticipate the majority of 
> implementations fit the decimal(18,6) use case.
> - The whole concept of precision/scale, along with a dedicated scale per data 
> point is messy.  Sometimes it's checked on data ingest, other times its an 
> error on reading, or else it's cast (and rounded?)
> I don't propose eliminating the current column type.  It's nice to know 
> there's a way to store really big numbers (or really accurate numbers) if I 
> need that in the future.
> But I'd like to see a new column that uses the existing Run Length Encoding 
> functionality, and is limited to 63+1 bit numbers, with a fixed precision and 
> scale for ingest and query.
> I think one could call this FixedPoint.  Every number is stored as a long, 
> and scaled by a column constant.  Ingest from decimal would scale and throw 
> or round, configurably.  Precision would be fixed at 18, or made configurable 
> and verified at ingest.  Stats would use longs (scaled with the column) 
> rather than strings.
> Anyone can opt in to faster, smaller data sets, if they're ok with 63+1 bits 
> of precision.  Or they can keep using decimal if they need 128 bits.  Win/win?



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