On 1/15/2015 10:58 PM, Manohar Sripada wrote: > The reason I have created 64 Shards is there are 4 CPU cores on each VM; > while querying I can make use of all the CPU cores. On an average, Solr > QTime is around 500ms here. > > Last time to my other discussion, Erick suggested that I might be over > sharding, So, I tried reducing the number of shards to 32 and then 16. To > my surprise, it started performing better. It came down to 300 ms (for 32 > shards) and 100 ms (for 16 shards). I haven't tested with filters and > facets yet here. But, the simple search queries had shown lot of > improvement. > > So, how come the less number of shards performing better?? Is it because > there are less number of posting lists to search on OR less merges that are > happening? And how to determine the correct number of shards?
Daniel has replied with good information. One additional problem I can think of when there are too many shards: If your Solr server is busy enough to have any possibility of simultaneous requests, then you will find that it's NOT a good idea to create enough shards to use all your CPU cores. In that situation, when you do a single query, all your CPU cores will be in use. When multiple queries happen at the same time, they have to share the available CPU resources, slowing them down. With a smaller number of shards, the additional CPU cores can handle simultaneous queries. I have an index with nearly 100 million documents. I've divided it into six large cold shards and one very small hot shard. It's not SolrCloud. I put three large shards on each of two servers, and the small shard on one of those two servers. The distributed query normally happens on the server without the small shard. Each server has 8 CPU cores and 64GB of RAM. Solr requires a 6GB heap. My median QTime over the last 231836 queries is 25 milliseconds and my 95th percentile QTime is 376 milliseconds. My query rate is pretty low - I've never seen Solr's statistics for the 15 minute query rate go above a single digit per second. Thanks, Shawn
