[jira] [Updated] (ARROW-3245) [Python] Infer index and/or filtering from parquet column statistics

["Wes McKinney (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-3245?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Wes McKinney updated ARROW-3245:
--------------------------------
    Fix Version/s: 0.14.0

> [Python] Infer index and/or filtering from parquet column statistics
> --------------------------------------------------------------------
>
>                 Key: ARROW-3245
>                 URL: https://issues.apache.org/jira/browse/ARROW-3245
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: Python
>            Reporter: Martin Durant
>            Priority: Major
>              Labels: parquet
>             Fix For: 0.14.0
>
>
> The metadata included in parquet generally gives the min/max of data for each chunk of each column. This allows early filtering out of whole chunks if they do not meet some criterion, and can greatly reduce reading burden in some circumstances. In Dask, we care about this for setting an index and its "divisions" (start/stop values for each data partition) and for directly avoiding including some chunks in the graph of tasks to be processed. Similarly, filtering may be applied on the values of fields defined by the directory partitioning.
> Currently, dask using the fastparquet backend is able to infer possible columns to use as an index, perform filtering on that index and do general filtering on any column which has statistical or partitioning information. It would be very helpful to have such facilities via pyarrow also.
>  This is probably the most important of the requests from Dask.
> (please forgive that some of this has already been mentioned elsewhere; this is one of the entries in the list at [https://github.com/dask/fastparquet/issues/374] as a feature that is useful in fastparquet)



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