Uh uh. 6 instances per node all pointing to the same index? Yes, this can increase performance, but only because it essentially gives you 6 separate searchers (SolrIndexSearchers). This clearly uses more RAM, especially if you sort on fields and especially if you are not omiting norms where you can.
Is this a dual or quad-core box and how big is your index, Alexander? Otis -- Sematext -- http://sematext.com/ -- Lucene - Solr - Nutch ----- Original Message ---- > From: Alexander Ramos Jardim <[EMAIL PROTECTED]> > To: solr-user@lucene.apache.org > Sent: Wednesday, August 20, 2008 9:49:04 AM > Subject: Re: shards and performance > > Another thing to consider on your sharding is the access rate you want to > guarantee. > > In the project I am working, I need to guarantee at least 200hits/second > with various facets in all queries. > > I am not using sharding, but I have 6 Solr instances per cluster node, and I > have 3 nodes, to a total of 18 solr instances. Each node has only one index, > so I keep the 6 instance pointing to the same the index in a given node. > What made a huge diference in my performance was the removal of the lock. > > I expect that helps you out. > > 2008/8/20 Ian Connor > > > I have based my machines on bare bones servers (I call them ghetto > > servers). I essentially have motherboards in a rack sitting on > > catering trays (heat resistance is key). > > > > http://web.mac.com/iconnor/iWeb/Site/ghetto-servers.html > > > > Motherboards: GIGABYTE GA-G33M-S2L (these are small mATX with 4 RAM > > slots - allows as much cheap RAM as possible) > > CPU: Intel Q6600 (quad core 2.4GHz - but I might try AMD next to see > > if the different RAM approach works better and they are greener) > > Memory: 8GB (4 x 2GB DDR2 - best price per GB) > > HDD: SATA Disk (between 200 to 500GB - I had these from another project) > > > > I have HAProxy between the App servers and Solr so that I get failover > > if one of these goes down (expect failure). > > > > Having only 1M documents but more data per document will mean your > > situation is different. I am having particular performance issues with > > facets and trying to get my head around all the issues involved there. > > > > I see Mike has only 2 shards per box as he was "squeezing" > > performance. I didn't see any significant gain in performance but that > > is not to say there isn't one. Just for me, I had a level of > > performance in mind and stopped when that was met. It took almost a > > month of testing to get to that point so I was ready to move on to > > other problems - I might revisit it later. > > > > Also, my ghetto servers are getting similar reliability to the Dell > > Servers I have - but I have built the system with the expectations > > they will fail often although that has not happened yet. > > > > On Tue, Aug 19, 2008 at 4:40 PM, Alexander Ramos Jardim > > wrote: > > > As long as Solr/Lucene makes smart use from memory (and they from my > > > experiences), it is really easy to calculate how long a huge query/update > > > will take when you know how much the smaller ones will take. Just keep in > > > mind that the resource consumption of memory and disk space is almost > > always > > > proportional. > > > > > > 2008/8/19 Mike Klaas > > > > > >> > > >> On 19-Aug-08, at 12:58 PM, Phillip Farber wrote: > > >> > > >>> > > >>> So you experience differs from Mike's. Obviously it's an important > > >>> decision as to whether to buy more machines. Can you (or Mike) weigh > > in on > > >>> what factors led to your different take on local shards vs. shards > > >>> distributed across machines? > > >>> > > >> > > >> I do both; the only reason I have two shards on each machine is to > > squeeze > > >> maximum performance out of an equipment budget. Err on the side of > > multiple > > >> machines. > > >> > > >> At least for building the index, the number of shards really does > > >>>> help. To index Medline (1.6e7 docs which is 60Gb in XML text) on a > > >>>> single machine starts at about 100doc/s but slows down to 10doc/s when > > >>>> the index grows. It seems as though the limit is reached once you run > > >>>> out of RAM and it gets slower and slower in a linear fashion the > > >>>> larger the index you get. > > >>>> My sweet spot was 5 machines with 8GB RAM for indexing about 60GB of > > >>>> data. > > >>>> > > >>> > > >>> Can you say what the specs were for these machines? Given that I have > > more > > >>> like 1TB of data over 1M docs how do you think my machine requirements > > might > > >>> be affected as compared to yours? > > >>> > > >> > > >> You are in a much better position to determine this than we are. See > > how > > >> big an index you can put on a single machine while maintaining > > acceptible > > >> performance using a typical query load. It's relatively safe to > > extrapolate > > >> linearly from that. > > >> > > >> -Mike > > >> > > > > > > > > > > > > -- > > > Alexander Ramos Jardim > > > > > > > > > > > -- > > Regards, > > > > Ian Connor > > 1 Leighton St #605 > > Cambridge, MA 02141 > > Direct Line: +1 (978) 6333372 > > Call Center Phone: +1 (714) 239 3875 (24 hrs) > > Mobile Phone: +1 (312) 218 3209 > > Fax: +1(770) 818 5697 > > Suisse Phone: +41 (0) 22 548 1664 > > Skype: ian.connor > > > > > > -- > Alexander Ramos Jardim
