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 shard 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?