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From Jörn Franke <jornfra...@gmail.com>
Subject Re: Indexes in Hive
Date Wed, 06 Jan 2016 19:15:27 GMT
I am not sure how much performance one could gain in comparison to ORC or
Parquet. They work pretty well once you know how to use them. However,
there is still ways to optimize them. For instance, sorting of data is a
key factor for these formats to be efficient. Nevertheless, if you have a
lot of columns then sorting each column individually does not make sense.
Here one could explore a sorting algorithm that, for instance, identifies
certain groups of values that are often queried together and co-locates
them. Alternatively, you can create for each row a hash sum over often
queried columns and do pruning only based on this hashsum (=one column).
This can be already done in Hive. Another alternative is to create
redundant tables and each of them sorted differently. This may be
implemented in Hive automatically depending on query patterns.

I think there is sometimes also a wrong perception of what is possible with
Big Data. If you query a petabyte of data and the query processes the whole
amount then you need a lot of nodes or simply live with the fact that it
takes longer. However, in many of the cases users usually query more data
then they need. There are also many cases where they could just work with
samples from the table to define their models and later let them evaluate
over the whole set of data over night. Additionally, usually there is not
one user, but many.

Given this, column-orientation does not solve everything anyway. It is just
one little part of the big picture. For example, graph structures, such as
provided by TitanDB with interactive Gremlin queries, can be much faster
executed for certain scenarios than in a column store.
Interactive In-memory technologies, such as Apache Ignite, can speed up
Hive or even Spark if you have a lot of users or processes that share data.
I think TEZ+LLAP show also some interesting features for Hive related to
this.


In my blog you can find some discussion on how to optimize for big data
technologies in general.

On Wed, Jan 6, 2016 at 7:24 AM, Mich Talebzadeh <mich@peridale.co.uk> wrote:

> Hi,
>
> Thinking loudly.
>
> Ideally we should consider a totally columnar storage offering in which
> each
> column of table is stored as compressed value (I disregard for now how
> actually ORC does this but obviously it is not exactly a columnar storage).
>
> So each table can be considered as a loose federation of columnar storage
> and each column is effectively an index?
>
> As columns are far narrower than tables, each index block will be very
> higher density and all operations like aggregates can be done directly on
> index rather than table.
>
> This type of table offering will be in true nature of data warehouse
> storage. Of course row operations (get me all rows for this table) will be
> slower but that is the trade-off that we need to consider.
>
> Expecting users to write their own IndexHandler may be technically
> interesting but commercially not viable as Hive needs to be a product on
> its
> own merit not a development base. Writing your own storage attributes etc.
> requires skills that will put off people seeing Hive as an attractive
> proposition (requiring considerable investment in skill sets in order to
> maintain Hive).
>
> Thus my thinking on this is to offer true columnar storage in Hive to be a
> proper data warehouse. In addition, the development tools cab ne made
> available for those interested in tailoring their own specific Hive
> solutions.
>
>
> HTH
>
>
>
> Dr Mich Talebzadeh
>
> LinkedIn
>
> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUr
> V8Pw
>
> Sybase ASE 15 Gold Medal Award 2008
> A Winning Strategy: Running the most Critical Financial Data on ASE 15
>
> http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.
> pdf
> Author of the books "A Practitioner's Guide to Upgrading to Sybase ASE 15",
> ISBN 978-0-9563693-0-7.
> co-author "Sybase Transact SQL Guidelines Best Practices", ISBN
> 978-0-9759693-0-4
> Publications due shortly:
> Complex Event Processing in Heterogeneous Environments, ISBN:
> 978-0-9563693-3-8
> Oracle and Sybase, Concepts and Contrasts, ISBN: 978-0-9563693-1-4, volume
> one out shortly
>
> http://talebzadehmich.wordpress.com
>
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>
> -----Original Message-----
> From: Gopal Vijayaraghavan [mailto:gopal@hortonworks.com] On Behalf Of
> Gopal
> Vijayaraghavan
> Sent: 05 January 2016 23:55
> To: user@hive.apache.org
> Subject: Re: Is Hive Index officially not recommended?
>
>
> >So in a nutshell in Hive if "external" indexes are not used for
> >improving query response, what value they add and can we forget them for
> now?
>
> The builtin indexes - those that write data as smaller tables are only
> useful in a pre-columnar world, where the indexes offer a huge reduction in
> IO.
>
> Part #1 of using hive indexes effectively is to write your own
> HiveIndexHandler, with usesIndexTable=false;
>
> And then write a IndexPredicateAnalyzer, which lets you map arbitrary
> lookups into other range conditions.
>
> Not coincidentally - we're adding a "ANALYZE TABLE ... CACHE METADATA"
> which consolidates the "internal" index into an external store (HBase).
>
> Some of the index data now lives in the HBase metastore, so that the
> inclusion/exclusion of whole partitions can be done off the consolidated
> index.
>
> https://issues.apache.org/jira/browse/HIVE-11676
>
>
> The experience from BI workloads run by customers is that in general, the
> lookup to the right "slice" of data is more of a problem than the actual
> aggregate.
>
> And that for a workhorse data warehouse, this has to survive even if
> there's
> a non-stop stream of updates into it.
>
> Cheers,
> Gopal
>
>

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