Andrés, I followed that link and got the dread 404 Not Found: "The requested URI /pub/torres/Hiperfuse/extended_hiperfuse.pdf was not found on this server."
-- Lefty On Wed, Apr 1, 2015 at 7:23 PM, <andres.qui...@parc.com> wrote: > Dear Lefty, > > Thank you very much for pointing that out and for your initial pointers. > Here is the missing link: > > ftp.parc.com/pub/torres/Hiperfuse/extended_hiperfuse.pdf > > Regards, > > Andrés > > -----Original Message----- > From: Lefty Leverenz [mailto:leftylever...@gmail.com] > Sent: Wednesday, April 01, 2015 12:48 AM > To: dev@hive.apache.org > Subject: Re: Request for feedback on work intent for non-equijoin support > > 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.qui...@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. > > > > > > >
