Hi Matthias- I'm trying to understand how you have your data indexed so we can give reasonable direction.
What field type are you using for your locations? Is it using the solr spatial field types? What do you see when you look at the debug information from &debugQuery=true? >From my experience, there is no single best practice for spatial queries -- it will depend on your data density and distribution if. You may also want to look at: http://code.google.com/p/lucene-spatial-playground/ but note this is off lucene trunk -- the geohash queries are super fast though ryan 2012/2/8 Matthias Käppler <matth...@qype.com>: > Hi Erick, > > if we're not doing geo searches, we filter by "location tags" that we > attach to places. This is simply a hierachical regional id, which is > simple to filter for, but much less flexible. We use that on Web a > lot, but not on mobile, where we want to performance searches in > arbitrary radii around arbitrary positions. For those location tag > kind of queries, the average time spent in SOLR is 43msec (I'm looking > at the New Relic snapshot of the last 12 hours). I have disabled our > "optimization" again just yesterday, so for the bbox queries we're now > at an avg of 220ms (same time window). That's a 5 fold increase in > response time, and in peak hours it's worse than that. > > I've also found a blog post from 3 years ago which outlines the inner > workings of the SOLR spatial indexing and searching: > http://www.searchworkings.org/blog/-/blogs/23842 > From that it seems as if SOLR already performs a similar optimization > we had in mind during the index step, so if I understand correctly, it > doesn't even search over all records, only those that were mapped to > the grid box identified during indexing. > > What I would love to see is what the suggested way is to perform a geo > query on SOLR, considering that they're so difficult to cache and > expensive to run. Is the best approach to restrict the candidate set > as much as possible using cheap filter queries, so that SOLR merely > has to do the geo search against these subsets? How does the query > planner work here? I see there's a cost attached to a filter query, > but one can only set it when cache is set to false? Are cached geo > queries executed last when there are cheaper filter queries to cut > down on documents? If you have a real world practical setup to share, > one that performs well in a production environment that serves > requests in the Millions per day, that would be great. > > I'd love to contribute documentation by the way, if you knew me you'd > know I'm an avid open source contributor and actually run several open > source projects myself. But tell me, how can I possibly contribute > answer to questions I don't have an answer to? That's why I'm here, > remember :) So please, these kinds of snippy replies are not helping > anyone. > > Thanks > -Matthias > > On Tue, Feb 7, 2012 at 3:06 PM, Erick Erickson <erickerick...@gmail.com> > wrote: >> So the obvious question is "what is your >> performance like without the distance filters?" >> >> Without that knowledge, we have no clue whether >> the modifications you've made had any hope of >> speeding up your response times.... >> >> As for the docs, any improvements you'd like to >> contribute would be happily received >> >> Best >> Erick >> >> 2012/2/6 Matthias Käppler <matth...@qype.com>: >>> Hi, >>> >>> we need to perform fast geo lookups on an index of ~13M places, and >>> were running into performance problems here with SOLR. We haven't done >>> a lot of query optimization / SOLR tuning up until now so there's >>> probably a lot of things we're missing. I was wondering if you could >>> give me some feedback on the way we do things, whether they make >>> sense, and especially why a supposed optimization we implemented >>> recently seems to have no effect, when we actually thought it would >>> help a lot. >>> >>> What we do is this: our API is built on a Rails stack and talks to >>> SOLR via a Ruby wrapper. We have a few filters that almost always >>> apply, which we put in filter queries. Filter cache hit rate is >>> excellent, about 97%, and cache size caps at 10k filters (max size is >>> 32k, but it never seems to reach that many, probably because we >>> replicate / delta update every few minutes). Still, geo queries are >>> slow, about 250-500msec on average. We send them with cache=false, so >>> as to not flood the fq cache and cause undesirable evictions. >>> >>> Now our idea was this: while the actual geo queries are poorly >>> cacheable, we could clearly identify geographical regions which are >>> more often queried than others (naturally, since we're a user driven >>> service). Therefore, we dynamically partition Earth into a static grid >>> of overlapping boxes, where the grid size (the distance of the nodes) >>> depends on the maximum allowed search radius. That way, for every user >>> query, we would always be able to identify a single bounding box that >>> covers it. This larger bounding box (200km edge length) we would send >>> to SOLR as a cached filter query, along with the actual user query >>> which would still be sent uncached. Ex: >>> >>> User asks for places in 10km around 49.14839,8.5691, then what we will >>> send to SOLR is something like this: >>> >>> fq={!bbox cache=false d=10 sfield=location_ll pt=49.14839,8.5691} >>> fq={!bbox cache=true d=100.0 sfield=location_ll >>> pt=49.4684836290799,8.31165802979391} <-- this one we derive >>> automatically >>> >>> That way SOLR would intersect the two filters and return the same >>> results as when only looking at the smaller bounding box, but keep the >>> larger box in cache and speed up subsequent geo queries in the same >>> regions. Or so we thought; unfortunately this approach did not help >>> query execution times get better, at all. >>> >>> Question is: why does it not help? Shouldn't it be faster to search on >>> a cached bbox with only a few hundred thousand places? Is it a good >>> idea to make these kinds of optimizations in the app layer (we do this >>> as part of resolving the SOLR query in Ruby), and does it make sense >>> at all? We're not sure what kind of optimizations SOLR already does in >>> its query planner. The documentation is (sorry) miserable, and >>> debugQuery yields no insight into which optimizations are performed. >>> So this has been a hit and miss game for us, which is very ineffective >>> considering that it takes considerable time to build these kinds of >>> optimizations in the app layer. >>> >>> Would be glad to hear your opinions / experience around this. >>> >>> Thanks! >>> >>> -- >>> Matthias Käppler >>> Lead Developer API & Mobile >>> >>> Qype GmbH >>> Großer Burstah 50-52 >>> 20457 Hamburg >>> Telephone: +49 (0)40 - 219 019 2 - 160 >>> Skype: m_kaeppler >>> Email: matth...@qype.com >>> >>> Managing Director: Ian Brotherston >>> Amtsgericht Hamburg >>> HRB 95913 >>> >>> This e-mail and its attachments may contain confidential and/or >>> privileged information. If you are not the intended recipient (or have >>> received this e-mail in error) please notify the sender immediately >>> and destroy this e-mail and its attachments. Any unauthorized copying, >>> disclosure or distribution of this e-mail and its attachments is >>> strictly forbidden. This notice also applies to future messages. > > > > -- > Matthias Käppler > Lead Developer API & Mobile > > Qype GmbH > Großer Burstah 50-52 > 20457 Hamburg > Telephone: +49 (0)40 - 219 019 2 - 160 > Skype: m_kaeppler > Email: matth...@qype.com > > Managing Director: Ian Brotherston > Amtsgericht Hamburg > HRB 95913 > > This e-mail and its attachments may contain confidential and/or > privileged information. If you are not the intended recipient (or have > received this e-mail in error) please notify the sender immediately > and destroy this e-mail and its attachments. Any unauthorized copying, > disclosure or distribution of this e-mail and its attachments is > strictly forbidden. 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