[jira] [Updated] (CALCITE-2040) Create adapter for Apache Arrow

["Michael Mior (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/CALCITE-2040?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Michael Mior updated CALCITE-2040:
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    Attachment: arrow_data.py

> Create adapter for Apache Arrow
> -------------------------------
>
>                 Key: CALCITE-2040
>                 URL: https://issues.apache.org/jira/browse/CALCITE-2040
>             Project: Calcite
>          Issue Type: Bug
>            Reporter: Julian Hyde
>            Assignee: Julian Hyde
>            Priority: Major
>              Labels: pull-request-available
>         Attachments: arrow_data.py
>
>          Time Spent: 50m
>  Remaining Estimate: 0h
>
> Create an adapter for [Apache Arrow|http://arrow.apache.org/]. This would allow people to execute SQL statements, via JDBC or ODBC, on data stored in Arrow in-memory format.
> Since Arrow is an in-memory format, it is not as straightforward as reading, say, CSV files using the file adapter: an Arrow data set does not have a URL. (Unless we use Arrow's [Feather|https://blog.cloudera.com/blog/2016/03/feather-a-fast-on-disk-format-for-data-frames-for-r-and-python-powered-by-apache-arrow/] format, or use an in-memory file system such as Alluxio.) So we would need to devise a way of addressing Arrow data sets.
> Also, since Arrow is an extremely efficient format for processing data, it would also be good to have Arrow as a calling convention. That is, implementations of relational operators such as Filter, Project, Aggregate in addition to just TableScan.
> Lastly, when we have an Arrow convention, if we build adapters for file formats (for instance the bioinformatics formats SAM, VCF, FASTQ discussed in CALCITE-2025) it would make a lot of sense to translate those formats directly into Arrow (applying simple projects and filters first if applicable). Those adapters would belong as a "contrib" module in the Arrow project better than in Calcite.



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