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https://issues.apache.org/jira/browse/CALCITE-2040?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17820512#comment-17820512
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Alessandro Solimando commented on CALCITE-2040:
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[~hongyuguo], I have left a (partial) review, there is enough to be looked at
already I feel, I will finish the review sometime next week.
> 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: 1.5h
> 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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