Thanks, Jack. I'd like to stay away from a terminology debate, since it is clear you know what I am talking about. But just to give my opinion, I prefer the term 'cloud' because it differentiates it from the term 'cluster', which refers to the Hadoop environment which I am running it on. I would also refrain from using the term 'node' when talking about Solr for the same reason. I already have this setup and running as described in my original email, with over 300 billion total records so far and counting, so changing my hardware configuration is really not an option( except adding more memory ). My issue is specific to keeping up with the volume of new documents, as my ingest rate is barely able to keep up, and I fear will eventually be perpetually latent as the amount of documents in the cloud/cluster continues to grow.
I now have a few things to try, thanks to all of your comments. I am very appreciative. Thanks, Scott -----Original Message----- From: Jack Krupansky [mailto:j...@basetechnology.com] Sent: Thursday, August 14, 2014 8:31 AM To: solr-user@lucene.apache.org Subject: Re: Solr cloud performance degradation with billions of documents You're using the term "cloud" again. Maybe that's the cause of your misunderstanding - SolrCloud probably should have been named SolrCluster since that's what it really is, a cluster rather than a "cloud". The term "cloud" conjures up images of vast, unlimited numbers of nodes, thousands, tens of thousands of machines, but SolrCloud is much more modest than that. Again, start with a model of 100 million documents on a fairly commodity box (say, 32GB as opposed to expensive 16-core 256GB machines). So, 1 billion docs means 10 servers, times replication - I assume you want to serve a healthy query load. So, 5 billion docs needs 50 servers, times replication. 100 billion docs would require 1,000 servers. 500 billion documents would require 5,000 servers, times replication. Not quite Google class, but not a typical SolrCloud "cluster" either. You will have to test for yourself whether that 100 million number is achievable for your particular hardware and data. Maybe you can double it... or maybe only half of that. And, once again, make sure your index for each node fits in the OS system memory available for file caching. I haven't heard of any specific experiences of SolrCloud beyond dozens of nodes, but 64 nodes is probably a reasonable expectation for a SolrCloud cluster. How much bigger than that a SolrCloud cluster could grow is unknown. Whatever the actual practical limit, based on your own hardware, I/O, and network, and your own data schema and data patterns, which you will have to test for yourself, you will probably need to use an application layer to "shard" your 100s of billions to specific SolrCloud clusters. -- Jack Krupansky -----Original Message----- From: Wilburn, Scott Sent: Thursday, August 14, 2014 11:05 AM To: solr-user@lucene.apache.org Subject: RE: Solr cloud performance degradation with billions of documents Erick, Thanks for your suggestion to look into MapReduceIndexerTool, I'm looking into that now. I agree what I am trying to do is a tall order, and the more I hear from all of your comments, the more I am convinced that lack of memory is my biggest problem. I'm going to work on increasing the memory now, but was wondering if there are any configuration or other techniques that could also increase ingest performance? Does anyone know if a cloud of this size( hundreds of billions ) with an ingest rate of 5 billion new each day, has ever been attempted before? Thanks, Scott -----Original Message----- From: Erick Erickson [mailto:erickerick...@gmail.com] Sent: Wednesday, August 13, 2014 4:48 PM To: solr-user@lucene.apache.org Subject: Re: Solr cloud performance degradation with billions of documents Several points: 1> Have you considered using the MapReduceIndexerTool for your ingestion? Assuming you don't have duplicate IDs, i.e. each doc is new, you can spread your indexing across as many nodes as you have in your cluster. That said, it's not entirely clear that you'll gain throughput since you have as many nodes as you do. 2> Uhhhhm, fitting this many documents into 6G of memory is ambitious. 2> Very ambitious. Actually it's impossible. By my calculations: bq: 4 separate and individual clouds of 32 shards each so 128 shards in aggregate bq: inserting into these clouds per day is 5 Billion each in two clouds, 3 Billion into the third, and 2 Billion into the fourth so we're talking 15B docs/day bq: the plan is to keep up to 60 days... So were talking 900B documents. It just won't work. 900B/128 docs/shard is over 7B documents/shard on average. Your two larger collections will have more than that, the two smaller ones less. But it doesn't matter because: 1: Lucene has a limit of 2B docs per core(shard), positive signed int. 2: It ain't gonna fit in 6G of memory even without this limit I'm pretty sure. 3: I've rarely heard of a single shard coping with over 300M docs without performance issues. I usually start getting nervous around 100M and insist on stress testing. Of course it depends lots on your query profile. So you're going to need a LOT more shards. You might be able to squeeze some more from your hardware by hosting multiple shards on for each collection on each machine, but I'm pretty sure your present setup is inadequate for your projected load. Of course I may be misinterpreting what you're saying hugely, but from what I understand this system just won't work. Best, Erick On Wed, Aug 13, 2014 at 2:39 PM, Markus Jelsma <markus.jel...@openindex.io> wrote: > Hi - You are running mapred jobs on the same nodes as Solr runs right? > The first thing i would think of is that your OS file buffer cache is > abused. > The mappers read all data, presumably residing on the same node. The > mapper output and shuffling part would take place on the same node, > only the reducer output is sent to your nodes, which i assume are on > the same machines. Those same machines have a large Lucene index. All > this data, written to and read from the same disk, competes for a nice > spot in the OS buffer cache. > > Forget it if i misread anything, but when you're using serious figures > of size, then do not abuse your caches. Have a separate mapred and > Solr cluster, because they both eat cache space. I assume you can see > serious IO WAIT times. > > Split the stuff and maybe even use smaller hardware, but more. > > M > > -----Original message----- > > From:Wilburn, Scott <scott.wilb...@verizonwireless.com.INVALID> > > Sent: Wednesday 13th August 2014 23:09 > > To: solr-user@lucene.apache.org > > Subject: Solr cloud performance degradation with billions of > > documents > > > > Hello everyone, > > I am trying to use SolrCloud to index a very large number of simple > documents and have run into some performance and scalability > limitations and was wondering what can be done about it. > > > > Hardware wise, I have a 32-node Hadoop cluster that I use to run all > > of > the Solr shards and each node has 128GB of memory. The current > SolrCloud setup is split into 4 separate and individual clouds of 32 > shards each thereby giving four running shards per cloud or one cloud > per eight nodes. > Each shard is currently assigned a 6GB heap size. I’d prefer to avoid > increasing heap memory for Solr shards to have enough to run other > MapReduce jobs on the cluster. > > > > The rate of documents that I am currently inserting into these > > clouds > per day is 5 Billion each in two clouds, 3 Billion into the third, and > 2 Billion into the fourth ; however to account for capacity, the aim > is to scale the solution to support double that amount of documents. > To index these documents, there are MapReduce jobs that run that > generate the Solr XML documents and will then submit these documents > via SolrJ's CloudSolrServer interface. In testing, I have found that > limiting the number of active parallel inserts to 80 per cloud gave > the best performance as anything higher gave diminishing returns, most > likely due to the constant shuffling of documents internally to > SolrCloud. From an index perspective, dated collections are being > created to hold an entire day's of documents and generally the > inserting happens primarily on the current day (the previous days are > only to allow for searching) and the plan is to keep up to 60 days (or > collections) in each cloud. A single shar d index in one collection > in the busiest cloud currently takes up 30G disk space or 960G for the > entire collection. The documents are being auto committed with a hard > commit time of 4 minutes (opensearcher = false) and soft commit time > of 8 minutes. > > > > From a search perspective, the use case is fairly generic and simple > searches of the type :, so there is no need to tune the system to use > any of the more advanced querying features. Therefore, the most > important thing for me is to have the indexing performance be able to > keep up with the rate of input. > > > > In the initial load testing, I was able to achieve a projected > > indexing > rate of 10 Billion documents per cloud per day for a grand total of 40 > Billion per day. However, the initial load testing was done on fairly > empty clouds with just a few small collections. Now that there have > been several days of documents being indexed, I am starting to see a > fairly steep drop-off in indexing performance once the clouds reached > about 15 full collections (or about 80-100 Billion documents per > cloud) in the two biggest clouds. Based on current application logging > I’m seeing a 40% drop off in indexing performance. Because of this, I > have concerns on how performance will hold as more collections are added. > > > > My question to the community is if anyone else has had any > > experience in > using Solr at this scale (hundreds of Billions) and if anyone has > observed such a decline in indexing performance as the number of > collections increases. My understanding is that each collection is a > separate index and therefore the inserting rate should remain > constant. Aside from that, what other tweaks or changes can be done in > the SolrCloud configuration to increase the rate of indexing > performance? Am I hitting a hard limitation of what Solr can handle? > > > > Thanks, > > Scott > > > > >
