I figured I was trying to pull a coup here, but this is a temporary configuration while we only run a few users through an early beta. The performance is perfectly good for most terms, it's just this books term. I'm curious how adding RAM will solve that. I can see how deploying solr cloud and sharding should affect it, but would simply giving Solr 16GB of ram improve query time with this one term that is common to 90M of the 300M documents?
In due time I do plan to implement solr cloud and run the whole thing through proper load testing. Right now I'm just trying to get it to "work" for a few users. If you could elaborate a bit on your thinking I'd be quite grateful. David -----Original Message----- From: Jan Høydahl [mailto:jan....@cominvent.com] Sent: Thursday, March 21, 2013 8:01 PM To: solr-user@lucene.apache.org Subject: Re: Slow queries for common terms Hi, If you say that you try to index 300M docs in ONE single Solr server, with "a few gigs" of RAM, then that's the reason for some bad performance right there. You should benchmark to find the sweet-spot of how many documents you want to fit per node/shard and still have acceptable indexing/query performance. -- Jan Høydahl, search solution architect Cominvent AS - www.cominvent.com Solr Training - www.solrtraining.com 21. mars 2013 kl. 12:43 skrev David Parks <davidpark...@yahoo.com>: > We have 300M documents, each about a paragraph of text on average. The > index is 140GB in size. I'm not sure how to find the IDF score, was > that in the debug query below? > > It seems that any query with the word "book" in it triggers a 15 sec > response time (unless it's the 2nd time we run the same query). > Looking at terms, 'book' is the 2nd highest term with 90M documents in the index. > > Calling 'book' a stop word doesn't seem reasonable, and while that > article on bigrams and common grams is fascinating, I wonder if it > addresses this situation, in which we aren't really likely to manage a > bi-gram phrase match between the search "book sales improvement", and the terms in the document: > "category book marketing and sales today the real guide to improving" right? > I think this is what's happening here, everything with a common phrase > "category book" is getting included, which seems logical and correct. > > > > -----Original Message----- > From: Jan Høydahl [mailto:jan....@cominvent.com] > Sent: Thursday, March 21, 2013 5:43 PM > To: solr-user@lucene.apache.org > Subject: Re: Slow queries for common terms > > Hi, > > I think you can start by reading this blog > http://www.hathitrust.org/blogs/large-scale-search/slow-queries-and-co > mmon-w > ords-part-2 and try out the approach using a dictionary of the most > common words in your index. > > You don't say how many documents, avg. doc size, the IDF value of > "book", how much RAM, whether you utilize disk caching well enough and > many other things which could affect this situation. But the pure fact > that only a few common search words trigger such a delay would suggest > commongrams as a possible way forward. > > -- > Jan Høydahl, search solution architect Cominvent AS - > www.cominvent.com Solr Training - www.solrtraining.com > > 21. mars 2013 kl. 11:09 skrev David Parks <davidpark...@yahoo.com>: > >> I've got a query that takes 15 seconds to return whenever I have the >> term "book" in a query that isn't cached. That's a pretty common term >> in our search index. We're indexing about 120 GB of text data. We >> only store terms and IDs, no document data, and the disk is virtually >> unused, it's all CPU time. >> >> >> >> I haven't done much yet to optimizing and scale solr, as we're only >> trying to support a small number of users in a private beta. I >> currently only have a couple of gigs of ram dedicated to Solr (we've >> ordered more hardware for it, but it's not in yet). >> >> >> >> I wonder if there's something I can do in the short term to alleviate >> the problem. Many searches work great, but these ones that take 15+ >> sec are a black eye. I'd be happy with a short term fix followed in >> the near future by a more proper long-term fix. >> >> >> >> If I were to take a stab at this I'd say the following two are the >> short and long term solutions: >> >> . Short: Configure solr to short-circuit quries, thus reducing > query >> quality, but guaranteeing a certain response time (I'm ok with this >> tradeoff, but is this possible or contains risks I need to consider >> more?) >> >> . Long: Implement sharding, get more hardware resources for these >> boxes and split up the index across multiple servers. >> >> >> >> Am I on track in my thinking here? >> >> Thanks, >> >> David >> >> >> >> My long query: >> >> <?xml version="1.0" encoding="UTF-8"?> >> >> <response> >> >> >> >> <lst name="responseHeader"> >> >> <int name="status">0</int> >> >> <int name="QTime">15464</int> >> >> <lst name="params"> >> >> <str name="debugQuery">true</str> >> >> <str name="indent">true</str> >> >> <str name="q">cook book fourth baby >> >> </str> >> >> <str name="wt">xml</str> >> >> </lst> >> >> </lst> >> >> <result name="response" numFound="91695127" start="0"> >> >> <doc> >> >> <str name="id">1004505170125</str> >> >> <long name="facetime">0</long> >> >> <long name="_version_">1428753459934396420</long></doc> >> >> <doc> >> >> <str name="id">1005401904542</str> >> >> <long name="facetime">0</long> >> >> <long name="_version_">1428760446538612739</long></doc> >> >> <doc> >> >> <str 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idf(docFreq=90917737, maxDocs=128248074) >> >> 0.091944434 = queryNorm >> >> 2.4691064 = fieldWeight in 3352532, product of: >> >> 2.4494898 = tf(freq=6.0), with freq of: >> >> 6.0 = termFreq=6.0 >> >> 1.3440113 = idf(docFreq=90917737, maxDocs=128248074) >> >> 0.75 = fieldNorm(doc=3352532) >> >> 4.8760324 = (MATCH) weight(all:babi in 3352532) >> [DefaultSimilarity], result of: >> >> 4.8760324 = score(doc=3352532,freq=4.0 = termFreq=4.0 >> >> ), product of: >> >> 0.54670167 = queryWeight, product of: >> >> 5.9460006 = idf(docFreq=912073, maxDocs=128248074) >> >> 0.091944434 = queryNorm >> >> 8.919001 = fieldWeight in 3352532, product of: >> >> 2.0 = tf(freq=4.0), with freq of: >> >> 4.0 = termFreq=4.0 >> >> 5.9460006 = idf(docFreq=912073, maxDocs=128248074) >> >> 0.75 = fieldNorm(doc=3352532) >> >> 0.75 = coord(3/4) >> >> </str> >> >> <str name="1001607999738"> >> >> 8.979621 = (MATCH) product of: >> >> 11.972828 = (MATCH) sum of: >> >> 6.847668 = (MATCH) weight(all:cook in 524047) [DefaultSimilarity], >> result of: >> >> 6.847668 = score(doc=524047,freq=2.0 = termFreq=2.0 >> >> ), product of: >> >> 0.5447923 = queryWeight, product of: >> >> 5.925234 = idf(docFreq=931212, maxDocs=128248074) >> >> 0.091944434 = queryNorm >> >> 12.569319 = fieldWeight in 524047, product of: >> >> 1.4142135 = tf(freq=2.0), with freq of: >> >> 2.0 = termFreq=2.0 >> >> 5.925234 = idf(docFreq=931212, maxDocs=128248074) >> >> 1.5 = fieldNorm(doc=524047) >> >> 0.24912801 = (MATCH) weight(all:book in 524047) [DefaultSimilarity], >> result of: >> >> 0.24912801 = score(doc=524047,freq=1.0 = termFreq=1.0 >> >> ), product of: >> >> 0.12357436 = queryWeight, product of: >> >> 1.3440113 = idf(docFreq=90917737, maxDocs=128248074) >> >> 0.091944434 = queryNorm >> >> 2.016017 = fieldWeight in 524047, product of: >> >> 1.0 = tf(freq=1.0), with freq of: >> >> 1.0 = termFreq=1.0 >> >> 1.3440113 = idf(docFreq=90917737, maxDocs=128248074) >> >> 1.5 = fieldNorm(doc=524047) >> >> 4.8760324 = (MATCH) weight(all:babi in 524047) [DefaultSimilarity], >> result of: >> >> 4.8760324 = score(doc=524047,freq=1.0 = termFreq=1.0 >> >> ), product of: >> >> 0.54670167 = queryWeight, product of: >> >> 5.9460006 = idf(docFreq=912073, maxDocs=128248074) >> >> 0.091944434 = queryNorm >> >> 8.919001 = fieldWeight in 524047, product of: >> >> 1.0 = tf(freq=1.0), with freq of: >> >> 1.0 = termFreq=1.0 >> >> 5.9460006 = idf(docFreq=912073, maxDocs=128248074) >> >> 1.5 = fieldNorm(doc=524047) >> >> 0.75 = coord(3/4) >> >> </str> >> >> </lst> >> >> <str name="QParser">LuceneQParser</str> >> >> <lst name="timing"> >> >> <double name="time">15464.0</double> >> >> <lst name="prepare"> >> >> <double name="time">1.0</double> >> >> <lst name="query"> >> >> <double name="time">1.0</double> >> >> </lst> >> >> <lst name="facet"> >> >> <double name="time">0.0</double> >> >> </lst> >> >> <lst name="mlt"> >> >> <double name="time">0.0</double> >> >> </lst> >> >> <lst name="highlight"> >> >> <double name="time">0.0</double> >> >> </lst> >> >> <lst name="stats"> >> >> <double name="time">0.0</double> >> >> </lst> >> >> <lst name="debug"> >> >> <double name="time">0.0</double> >> >> </lst> >> >> </lst> >> >> <lst name="process"> >> >> <double name="time">15463.0</double> >> >> <lst name="query"> >> >> <double name="time">15411.0</double> >> >> </lst> >> >> <lst name="facet"> >> >> <double name="time">0.0</double> >> >> </lst> >> >> <lst name="mlt"> >> >> <double name="time">0.0</double> >> >> </lst> >> >> <lst name="highlight"> >> >> <double name="time">0.0</double> >> >> </lst> >> >> <lst name="stats"> >> >> <double name="time">0.0</double> >> >> </lst> >> >> <lst name="debug"> >> >> <double name="time">52.0</double> >> >> </lst> >> >> </lst> >> >> </lst> >> >> </lst> >> >> </response> >> >
