A forced merge might improve speed 20%. Going from spinning disk to SSD
will improve speed 20X or more. Don’t waste your time even thinking about
forced merges.

You need to get SSDs.

The even bigger speedup is to get enough RAM that the OS can keep the 
Solr index files in file system buffers. Check how much space is used by
your indexes, then make sure that there is that much available RAM that
is not used by the OS or Solr JVM.

Some people make the mistake of giving a huge heap to the JVM, thinking
this will improve caching. This almost always makes things worse, by 
using RAM that could be use for caching files. 8GB of heap is usually enough.

wunder
Walter Underwood
wun...@wunderwood.org
http://observer.wunderwood.org/  (my blog)

> On Feb 21, 2021, at 11:52 PM, Danilo Tomasoni <tomas...@cosbi.eu> wrote:
> 
> Hello all,
> we are running a solr instance with around 41 MLN documents on a SATA class 
> 10 disk with around 10.000 rpm.
> We are experiencing very slow query responses (in the order of hours..) with 
> an average of 205 segments.
> We made a test with a normal pc and an SSD disk, and there the same solr 
> instance with the same data and the same number of segments was around 45 
> times faster.
> Force optimize was also tried to improve the performances, but it was very 
> slow, so we abandoned it.
> 
> Since we still don't have enterprise server ssd disks, we are now wondering 
> if in the meanwhile defragmenting the solrdata folder can help.
> The idea is that due to many updates, each segment file is fragmented across 
> different phisical blocks.
> Put in another way, each segment file is non-contiguous on disk, and this can 
> slow-down the solr response.
> 
> What do you suggest?
> Is this somewhat equivalent to force-optimize or it can be faster?
> 
> Thank you.
> Danilo
> 
> Danilo Tomasoni
> 
> Fondazione The Microsoft Research - University of Trento Centre for 
> Computational and Systems Biology (COSBI)
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