hive-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Lefty Leverenz <leftylever...@gmail.com>
Subject Re: Request for feedback on work intent for non-equijoin support
Date Wed, 01 Apr 2015 04:48:14 GMT
Hello Andres, the link to your paper is missing:

In our preliminary work, which you can find here (pointer to the paper) ...


You can find general information about contributing to Hive in the
wiki:  Resources
for Contributors
<https://cwiki.apache.org/confluence/display/Hive/Home#Home-ResourcesforContributors>
, How to Contribute
<https://cwiki.apache.org/confluence/display/Hive/HowToContribute>.

-- Lefty

On Tue, Mar 31, 2015 at 10:42 PM, <Andres.Quiroz@parc.com> wrote:

>  Dear Hive development community members,
>
>
>
> I am interested in learning more about the current support for
> non-equijoins in Hive and/or other Hadoop SQL engines, and in getting
> feedback about community interest in more extensive support for such a
> feature. I intend to work on this challenge, assuming people find it
> compelling, and I intend to contribute results to the community. Where
> possible, it would be great to receive feedback and engage in
> collaborations along the way (for a bit more context, see the postscript of
> this message).
>
>
>
> My initial goal is to support query conditions such as the following:
>
>
>
> A.x < B.y
>
> A.x in_range [B.y, B.z]
>
> distance(A.x, B.y) < D
>
>
>
> where A and B are distinct tables/files. It is my understanding that
> current support for performing non-equijoins like those above is quite
> limited, and where some forms are supported (like in Cloudera's Impala),
> this support is based on doing a potentially expensive cross product join.
> Depending on the data types involved, I believe that joins with these
> conditions can be made to be tractable (at least on the average) with join
> algorithms that exploit properties of the data types, possibly with some
> pre-scanning of the data.
>
>
>
> I am asking for feedback on the interest & need in the community for this
> work, as well as any pointers to similar work. In particular, I would
> appreciate any answers people could give on the following questions:
>
>
>
> - Is my understanding of the state of the art in Hive and similar tools
> accurate? Are there groups currently working on similar or related issues,
> or tools that already accomplish some or all of what I have proposed?
>
> - Is there significant value to the community in the support of such a
> feature? In other words, are the manual workarounds necessary because of
> the absence of non-equijoins such as these enough of a pain to justify the
> work I propose?
>
> - Being aware that the potential pre-scanning adds to the cost of the
> join, and that data could still blow-up in the worst case, am I missing any
> other important considerations and tradeoffs for this problem?
>
> - What would be a good avenue to contribute this feature to the community
> (e.g. as a standalone tool on top of Hadoop, or as a Hive extension or
> plugin)?
>
> - What is the best way to get started in working with the community?
>
>
>
> Thanks for your attention and any info you can provide!
>
>
>
> Andres Quiroz
>
>
>
> P.S. If you are interested in some context, and why/how I am proposing to
> do this work, please read on.
>
>
>
> I am part of a small project team at PARC working on the general problems
> of data integration and automated ETL. We have proposed a tool called
> HiperFuse that is designed to accept declarative, high-level queries in
> order to produce joined (fused) data sets from multiple heterogeneous raw
> data sources. In our preliminary work, which you can find here (pointer to
> the paper), we designed the architecture of the tool and obtained some
> results separately on the problems of automated data cleansing, data type
> inference, and query planning. One of the planned prototype implementations
> of HiperFuse relies on Hadoop MR, and because the declarative language we
> proposed was closely related to SQL, we thought that we could exploit the
> existing work in Hive and/or other open-source tools for handling the SQL
> part and layer our work on top of that. For example, the query given in the
> paper could easily be expressed in SQL-like form with a non-equijoin
> condition:
>
>
>
> SELECT web_access_log.ip, census.income
>
> FROM web_access_log, ip2zip, census
>
> WHERE web_access_log.ip in_range [ip2zip.ip_low, ip2zip.ip_high]
>
> AND ip2zip.zip = census.zip
>
>
>
> As you can see, the first impasse that we hit in order to bring the
> elements together to solve this query end-to-end was the realization and
> performance of the non-equality join in the query. The intent now is to
> tackle this problem in a general sense and provide a solution for a wide
> range of queries.
>
>
>
> The work I propose to do would be based on three main components within
> HiperFuse:
>
>
>
> - Enhancements to the extensible data type framework in HiperFuse that
> would categorize data types based on the properties needed to support the
> join algorithms, in order to write join-ready domain-specific data type
> libraries.
>
> - The join algorithms themselves, based on Hive or directly on Hadoop MR.
>
> - A query planner, which would determine the right algorithm to apply and
> automatically schedule any necessary pre-scanning of the data.
>
>
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message