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https://issues.apache.org/jira/browse/HIVE-15147?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15704543#comment-15704543
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Matt McCline commented on HIVE-15147:
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#1 Ok, we can take the core logic of VectorMapOperator that handles vectorizing
any input format and make it available as a shared class.
#2 I bumped into the LLAP stage being after vectorization when I was trying to
understand anomalies in EXPLAIN VECTORIZATION. I thought Vectorization was the
last stage -- but it is not. And, the Vectorizer is a little dangerous because
it starts modifying the operators when there is a small chance even after the
Vectorizer validation stage that the vertex cannot be vectorized. So, it might
make sense to build the vectorized operator tree separate from the original
operator input tree. The reason that wasn't done originally I suspect is all
the OperatorDesc objects would need to be clone-able.
If the vectorized operator tree was separate, you could change your mind later.
I'm not sure this is a good idea.
> LLAP: use LLAP cache for non-columnar formats in a somewhat general way
> -----------------------------------------------------------------------
>
> Key: HIVE-15147
> URL: https://issues.apache.org/jira/browse/HIVE-15147
> Project: Hive
> Issue Type: New Feature
> Reporter: Sergey Shelukhin
> Assignee: Sergey Shelukhin
> Attachments: HIVE-15147.WIP.noout.patch
>
>
> The primary goal for the first pass is caching text files. Nothing would
> prevent other formats from using the same path, in principle, although, as
> was originally done with ORC, it may be better to have native caching support
> optimized for each particular format.
> Given that caching pure text is not smart, and we already have ORC-encoded
> cache that is columnar due to ORC file structure, we will transform data into
> columnar ORC.
> The general idea is to treat all the data in the world as merely ORC that was
> compressed with some poor compression codec, such as csv. Using the original
> IF and serde, as well as an ORC writer (with some heavyweight optimizations
> disabled, potentially), we can "uncompress" the csv/whatever data into its
> "original" ORC representation, then cache it efficiently, by column, and also
> reuse a lot of the existing code.
> Various other points:
> 1) Caching granularity will have to be somehow determined (i.e. how do we
> slice the file horizontally, to avoid caching entire columns). As with ORC
> uncompressed files, the specific offsets don't really matter as long as they
> are consistent between reads. The problem is that the file offsets will
> actually need to be propagated to the new reader from the original
> inputformat. Row counts are easier to use but there's a problem of how to
> actually map them to missing ranges to read from disk.
> 2) Obviously, for row-based formats, if any one column that is to be read has
> been evicted or is otherwise missing, "all the columns" have to be read for
> the corresponding slice to cache and read that one column. The vague plan is
> to handle this implicitly, similarly to how ORC reader handles CB-RG overlaps
> - it will just so happen that a missing column in disk range list to retrieve
> will expand the disk-range-to-read into the whole horizontal slice of the
> file.
> 3) Granularity/etc. won't work for gzipped text. If anything at all is
> evicted, the entire file has to be re-read. Gzipped text is a ridiculous
> feature, so this is by design.
> 4) In future, it would be possible to also build some form or
> metadata/indexes for this cached data to do PPD, etc. This is out of the
> scope for now.
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