[HACKERS] GSOC Student Project Idea
Greetings, Hello, my name is Michael Schuh and I am a PhD student in Computer Science at Montana State University. I have never participated in GSOC before, but I am very excited to propose a project to PostgreSQL that I feel would be a great follow-up to last year's project by Alexander Korotkov ( http://www.google-melange.com/gsoc/project/google/gsoc2012/akorotkov/53002). I contacted Mr. Korotkov's mentor from last year, Mr. Heikki Linnakangas, and he suggested I email this mailing list with my idea. In brief, I would like to implement a state-of-the-art indexing algorithm (named iDistance) directly in PostgreSQL using GiST or SP-GiST trees and whatever means necessary. It is an ideal follow-up to last year's project with Mr. Korotkov, which implemented classical indexing structures for range queries. I strongly believe the community would greatly benefit from the inclusion of iDistance, which has been shown to be dramatically more effective than R-trees and KD-trees, especially for knn queries and above 10-20 dimensions. A major focus of my current PhD thesis is high-dimensional data indexing and retrieval, with an emphasis towards applied use in CBIR systems. Recently, I published work which introduced a new open source implementation of iDistance in C++ (and some Python), which I believe makes me highly qualified and motivated for this opportunity. I have been strongly considering a PostgreSQL implementation for an easy plug-and-play use in existing applications, but with academic grant funding, the priority is low. Below are links to my google code repository and recent publication. I am happy to discuss any of this in further detail if you'd like. https://code.google.com/p/idistance/ http://www.cs.montana.edu/~timothy.wylie/files/bncod13.pdf Although I do not have a lot of experience with PostgreSQL development, I am eager to learn and commit my summer to enabling another fantastic feature for the community. Since iDistance is a non-recursive, data-driven, space-based partitioning strategy which builds directly onto a B+-tree, I believe the implementation should be possible using only GiST support. Please let me know if this is of any interest, or if you have any additional questions. Unfortunately, I will be unavailable most of the day, but I plan to fill out the GSOC application later this evening. Thank you for your time, Mike Schuh
Re: [HACKERS] GSOC Student Project Idea
On Wed, Apr 24, 2013 at 5:31 AM, Florian Pflug wrote: > On Apr23, 2013, at 23:25 , Alexander Korotkov > wrote: > > I've taken a brief look on the paper and implementation. As I can see > iDistance implements some global building strategy. I mean, for example, it > selects some point, calculates distances from selected point to all points > in dataset etc. So, it uses the whole dataset at the same time. > > > > However you can try to implement global index building in GiST or > SP-GiST. In this case I think you should carefully estimate your > capabilities during single GSoC project. You would need to extend GiST or > SP-GiST interface and write completely new implementation of tree building > algorithm. Question of how to exactly extend GiST or SP-GiST interface for > this case could appear to be very hard even theoretically. > > +1. That seemed to be a major roadblock to me too when I read the paper. > > You could work around that by making partition identification a separate > step. You'd have a function > > idist_analyze(cfg name, table name, field name) > > which'd identify suitable partitions for the data distribution in > table.field and store them somewhere. Such a set of pre-identified > partitions would be akin to a tsearch configuration, i.e. all other parts > of the iDistance machinery would use it to map points to index keys and > queries to ranges of those keys. You'll want to look at how tsearch handles > that, and check if the method can indeed be applied to iDistance. > > In a first cut, you'd probably only allow inserts into index which don't > change the maximum distances from the partition centers that > idist_analyze() found. With that restriction in place, you might get away > without GiST or SP-GiSt, and simply use postgres' standard btree, if you > find a way to map queries of the form "field > idistance_knn_function(point, distance)" to "where (idstance_keys(field) > between P1_lowerbound AND P2_upperbound) OR (idistance_keys(field) between > P2_lowerbuild AND P2_upperbound) …" or something equivalent. Thank you both for the very helpful feedback. Perhaps the scope of this project (application's "completeness criteria") is better as a feasibility prototyping of the global/distance-based index strategy with B+-tree and/or GiST extension possibilities. Let me briefly elaborate on the current implementation details related to your comments. The public C++ version uses a stand-alone STX B+-tree implementation, and has only been used in the research context of one-time data loading and indexing and then KNN retrieval performance efficiency. This requires an upfront global partition building (sequential look at all data points) and a bit of overhead information about the partitions (such as reference point locations and maximum distances in each). Of course, there are a lot of improvements, modifications, variants, etc., that can be built from this basic setup... but that's mostly my in-progress thesis work yet published or defended :) A given query is broken down into range(s) of key values in the B+-tree, based on the negligible overhead info kept from partitioning. Only then does this small subset of pages need to be loaded from disk. where the partitions are located with respect to the query. Therefore it is necessary to have left/right leaf pointers within the B+-tree. While I think a DBMS-integrated implementation would be more ideal for general use by everyone, my naive assumption is that I could probably implement the idistance functionality in stored procedures and metadata tables (at the cost of performance and usability). Best regards, Mike Schuh
Re: [HACKERS] GSOC Student Project Idea
On Wed, May 8, 2013 at 1:48 PM, Jim Nasby wrote: > On 5/8/13 3:54 AM, Heikki Linnakangas wrote: > >> On 24.04.2013 14:31, Florian Pflug wrote: >> >>> On Apr23, 2013, at 23:25 , Alexander Korotkov >>> wrote: >>> I've taken a brief look on the paper and implementation. As I can see iDistance implements some global building strategy. I mean, for example, it selects some point, calculates distances from selected point to all points in dataset etc. So, it uses the whole dataset at the same time. However you can try to implement global index building in GiST or SP-GiST. In this case I think you should carefully estimate your capabilities during single GSoC project. You would need to extend GiST or SP-GiST interface and write completely new implementation of tree building algorithm. Question of how to exactly extend GiST or SP-GiST interface for this case could appear to be very hard even theoretically. >>> >>> +1. That seemed to be a major roadblock to me too when I read the >>> paper. >>> >>> You could work around that by making partition identification a >>> separate step. You'd have a function >>> >>> idist_analyze(cfg name, table name, field name) >>> >>> which'd identify suitable partitions for the data distribution in >>> table.field and store them somewhere. Such a set of pre-identified >>> partitions would be akin to a tsearch configuration, i.e. all other >>> parts of the iDistance machinery would use it to map points to index >>> keys and queries to ranges of those keys. You'll want to look at how >>> tsearch handles that, and check if the method can indeed be applied >>> to iDistance. >>> >> >> You could perform that step as part of the index build. Before the index >> build starts to add tuples to the index, it could scan a random sample of >> the heap and identify the partitions based on that. >> >> If you need to store the metadata, like a map of partitions, it becomes >> difficult to cajole this into a normal GiST or SP-GiST opclass. The API >> doesn't have any support for storing such metadata. >> >> In a first cut, you'd probably only allow inserts into index which >>> don't change the maximum distances from the partition centers that >>> idist_analyze() found. >>> >> >> That seems like a pretty serious restriction. I'd try to write it so that >> you can insert any value, but if the new values are very different from any >> existing values, it would be OK for the index quality to degrade. For >> example, you could simply add any out-of-bounds values to a separate branch >> in the index, which would have no particular structure and would just have >> to be scanned on every query. You can probably do better than that, but >> that would be a trivial way to deal with it. >> > > Or you could use the new insert to start a new partition. > > Heck, maybe the focus should actually be on partitions and not individual > records/points. ISTM the entire challenge here is figuring out a way to > maintain a set of partitions that: > > - Are limited enough in number that you can quickly perform > operations/searches across all partitions > - Yet small enough that once you've narrowed down a set of partitions you > don't have a ton of raw records to still look at > > Before we had range types I experimented with representing time ranges as > rectangles of varying size (ie: for (start, end), create > rectangle(point(start,start), point(end,end)). The problem with that is you > had to convert timestamp into a float, which was not exact. So when > querying you could use a GiST index on all the rectangles to narrow your > scope, but you still needed a set of exact clauses (ie: start >= now() - '1 > year' AND end <= now()). Partitions would be similar in that they wouldn't > be exact but could greatly narrow the search space (of course we'd want to > handle the secondary exact checking internally instead of exposing the user > to that). I appreciate all the responses, and I think everyone has more-or-less confirmed the scope of the project proposal I submitted. It was hard to find time during the final weeks of the semester to greatly explore the (SP-)GiST interfaces, but given the responses here, it seems the integrated implementation is clearly beyond scope for a summer project, which I agree with. Instead, I proposed my original plan that can surely be accomplished over the summer. Coincidentally enough, it is in essence, what Florian Pflug and the rest have discussed here. In short, I will use only the btree in postgresql to store single dimensional values mapped to multi-dimensional point data, and then query ranges of these values in the btree based on partition information stored separately. The information can be gathered upfront and periodically updated as needed, which done properly will not require downtime or reshuffling of the btree. This will result in a fully useable index, and the project will also include a performance and u
