Is your custom query parser multithreaded and leverages all cores?
> Am 16.08.2019 um 13:12 schrieb Vignan Malyala <dsmsvig...@gmail.com>: > > I want response time below 3 seconds. > And fyi I'm already using 32 cores. > My cache is already full too and obviously same requests don't occur in my > case. > > >> On Fri 16 Aug, 2019, 11:47 AM Jörn Franke, <jornfra...@gmail.com> wrote: >> >> How much response time do you require? >> I think you have to solve the issue in your code by introducing higher >> parallelism during calculation and potentially more cores. >> >> Maybe you can also precalculate what you do, cache it and use during >> request the precalculated values. >> >>> Am 16.08.2019 um 05:08 schrieb Vignan Malyala <dsmsvig...@gmail.com>: >>> >>> Hi >>> Any solution for this? Taking around 50 seconds to get response. >>> >>>> On Mon 12 Aug, 2019, 3:28 PM Vignan Malyala, <dsmsvig...@gmail.com> >> wrote: >>>> >>>> Hi Doug / Walter, >>>> >>>> I'm just using this methodology. >>>> PFB link of my sample code. >>>> https://github.com/saaay71/solr-vector-scoring >>>> >>>> The only issue is speed of response for 1M records. >>>> >>>> On Mon, Aug 12, 2019 at 12:24 AM Walter Underwood < >> wun...@wunderwood.org> >>>> wrote: >>>> >>>>> tf.idf was invented because cosine similarity is too much computation. >>>>> tf.idf gives similar results much, much faster than cosine distance. >>>>> >>>>> I would expect cosine similarity to be slow. I would also expect >>>>> retrieving 1 million records to be slow. Doing both of those in one >> minute >>>>> is pretty good. >>>>> >>>>> As Kernighan and Paugher said in 1978, "Don’t diddle code to make it >>>>> faster—find a better algorithm.” >>>>> >>>>> https://en.wikipedia.org/wiki/The_Elements_of_Programming_Style >>>>> >>>>> wunder >>>>> Walter Underwood >>>>> wun...@wunderwood.org >>>>> http://observer.wunderwood.org/ (my blog) >>>>> >>>>>> On Aug 11, 2019, at 10:40 AM, Doug Turnbull < >>>>> dturnb...@opensourceconnections.com> wrote: >>>>>> >>>>>> Hi Vignan, >>>>>> >>>>>> We need to see more details / code of what your query parser plugin >> does >>>>>> exactly with term vectors, we can't really help you without more >>>>> details. >>>>>> Is it open source? Can you share a minimal example that recreates the >>>>>> problem? >>>>>> >>>>>> On Sun, Aug 11, 2019 at 1:19 PM Vignan Malyala <dsmsvig...@gmail.com> >>>>> wrote: >>>>>> >>>>>>> Hi guys, >>>>>>> >>>>>>> I made my custom qparser plugin in Solr for scoring. The plugin only >>>>> does >>>>>>> cosine similarity of vectors for each record. I use term vectors >> here. >>>>>>> Results are fine! >>>>>>> >>>>>>> BUT, Solr response is very slow with term vectors. It takes around 55 >>>>>>> seconds for each request for 1000000 records. >>>>>>> How do I make it faster to get my results in ms ? >>>>>>> Please respond soon as its lil urgent. >>>>>>> >>>>>>> Note: All my values are stored and indexed. I am not using Solr >> Cloud. >>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> *Doug Turnbull **| CTO* | OpenSource Connections >>>>>> <http://opensourceconnections.com>, LLC | 240.476.9983 >>>>>> Author: Relevant Search <http://manning.com/turnbull> >>>>>> This e-mail and all contents, including attachments, is considered to >> be >>>>>> Company Confidential unless explicitly stated otherwise, regardless >>>>>> of whether attachments are marked as such. >>>>> >>>>> >>
