self-tuning histograms - Mailing list pgsql-hackers
From | Neil Conway |
---|---|
Subject | self-tuning histograms |
Date | |
Msg-id | 20020529230518.28305191.nconway@klamath.dyndns.org Whole thread Raw |
Responses |
Re: self-tuning histograms
Re: self-tuning histograms |
List | pgsql-hackers |
What does everyone think about adding self-tuning histograms to PostgreSQL? Briefly, a self-tuning histogram is one that is constructed without looking at the data in the attribute; it uses the information provided by the query executor to adjust a default set of histograms that are created when the table is defined. Thus, the histograms automatically adapt to the data that is stored in the table -- as the data distribution changes, so do the histograms. Histogram refinement can take place in two possible ways: online (as queries are executed, the histograms are updated immediately), or offline (the necessary data is written to a log after every query, which is processed on a regular basis to refine the histograms). The paper I've looked at on this topic is "Self-tuning Histograms: Building Histograms Without Looking at Data", by Aboulnaga and Shaudhuri (1999), which you can find here: http://citeseer.nj.nec.com/255752.html -- please refer to it for lots more information on this technique. I think that ST histograms would be useful because: (1) It would make it easier for us to implement multi-dimensional histograms (for more info, see the Aboulnaga and Shaudhuri). Since no commercial system currently implements them, I think this would be a neat thing to have. (2) I'm unsure of the accuracy of building histograms through statistical sampling. My guess would be that ST histograms would achieve better accuracy when it matters most -- i.e. on those tables accessed the most often (since thoseare the tables for which the most histogram refinement is done). (3) The need for manual DB maintainence through VACUUM and ANALYZE is problematic. This technique would be a step in the direction of removing that requirement. Self-tuning databases are something a lot of industry players (IBM, Microsoft,others) are working toward. (4) They scale well -- refining histograms on a 100 million tuple table is no different than on a 100 tuple table. There are some disadvantages, however: (1) Reproduceability: At the moment, the system's performance only changes when the data is changed, or the DBA makes a configuration change. With this (and other "self-tuning" techniques, which are becoming very popular among commercialdatabases), the system can change the state of the system without the intervention of the DBA. While I'd hopethat those changes are for the better (i.e. histograms eventually converging toward "perfect" accuracy), that won'talways be the case. I don't really see a way around this, other than letting the DBA disable ST histograms whendebugging problems. (2) Performance: As Aboulnaga and Shaudhuri point out, online histogram refinement can become a point of contention. Obviously, we want to avoid that. I think online refinement is still possible as long as we: (a) don't block waiting for locks: try to acquire the necessary locks to refine the histograms, immediately give up if not possible (b) delay histogram refinement so it doesn't interfere with the user: for example, store histogram data locally and only update the system catalogs when the backend is idle (c) only update the histogram when major changes can be applied: skip trivial refinements (or store those in the offline log for later processing) (d) allow the DBA to choose between offline and online histogram refinement (assuming we choose to implement both) Any comments? Cheers, Neil -- Neil Conway <neilconway@rogers.com> PGP Key ID: DB3C29FC
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