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From Gmail <xunzhang...@gmail.com>
Subject Re: [Propose] More data skipping technology for IO intensive performance enhancement
Date Wed, 06 Jul 2016 11:23:20 GMT
BTW, could you create some related issues in JIRA? 


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> 在 2016年7月2日,23:19,Ming Li <mli@pivotal.io> 写道:
> Data skipping technology can extremely avoiding unnecessary IO,  so it can
> extremely enhance performance for IO intensive query. Including eliminating
> query on unnecessary table partition according to the partition key range ,
> I think more options are available now:
> (1) Parquet / ORC format introduce a lightweight meta data info like
> Min/Max/Bloom filter for each block, such meta data can be exploited when
> predicate/filter info can be fetched before executing scan.
> However now in HAWQ, all data in parquet need to be scanned into memory
> before processing predicate/filter. We don't generate the meta info when
> INSERT into parquet table, the scan executor doesn't utilize the meta info
> neither. Maybe some scan API need to be refactored so that we can get
> predicate/filter
> info before executing base relation scan.
> (2) Base on (1) technology,  especially with Bloom filter, more optimizer
> technology can be explored furthur. E.g. Impala implemented Runtime
> filtering(*https://www.cloudera.com/documentation/enterprise/latest/topics/impala_runtime_filtering.html
> <https://www.cloudera.com/documentation/enterprise/latest/topics/impala_runtime_filtering.html>*
> ),  which can be used at
> - dynamic partition pruning
> - converting join predicate to base relation predicate
> It tell the executor to wait for one moment(the interval time can be set in
> guc) before executing base relation scan, if the interested values(e.g. the
> column in join predicate only have very small set) arrived in time, it can
> use these value to filter this scan, if doesn't arrived in time, it scan
> without this filter, which doesn't impact result correctness.
> Unlike (1) technology, this technology cannot be used in any case, it only
> outperform in some cases. So it just add some more query plan
> choices/paths, and the optimizer need based on statistics info to calculate
> the cost, and apply it when cost down.
> All in one, maybe more similar technology can be adoptable for HAWQ now,
> let's start to think about performance related technology, moreover we need
> to instigate how these technology can be implemented in HAWQ.
> Any ideas or suggestions are welcomed? Thanks.

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