Hi, I made change in the model by making the LTRScoringModel as immutable and cache hashCode calculation. The response time improved a lot after the change.
http://lucene.472066.n3.nabble.com/jira-Updated-SOLR-12688-LTR-Multiple-performance-fixes-pure-DocValues-support-for-FieldValueFeature-td4404254.html On Sat, Apr 6, 2019 at 12:22 PM Jörn Franke <jornfra...@gmail.com> wrote: > It is a little bit difficult to say, because it could be also the business > logic in the query execution. What is your performance baseline, ie if you > just execute one query for each of the models? > How fast should it be? Do you have really 10 or more concurrent users, or > users that fire up queries at exactly the same time? > > Can you please monitor CPU and memory? > > > Am 05.04.2019 um 21:42 schrieb Kamal Kishore Aggarwal < > kkroyal....@gmail.com>: > > > > Hi, > > > > Any update on this? > > Is this model running in multi threaded mode or is there is any scope to > do > > this. Please let me know. > > > > Regards > > Kamal > > > > On Sat, Mar 23, 2019 at 10:35 AM Kamal Kishore Aggarwal < > > kkroyal....@gmail.com> wrote: > > > >> HI Jörn Franke, > >> > >> Thanks for the quick reply. > >> > >> I have performed the jmeter load testing on one of the server for Linear > >> vs Multipleadditive tree model. We are using lucidworks fusion. > >> There is some business logic in the query pipeline followed by main solr > >> ltr query. This is the total time taken by query pipeline. > >> Below are the response time: > >> > >> # of Threads Ramup Period Loop Count Type Total Requests Average > Response > >> Time (ms) > >> Iteration 1 Iteration 2 Iteration 3 > >> 10 1 10 Linear Model 100 2038 1998 1975 > >> 25 1 10 Linear Model 250 4329 3961 3726 > >> > >> 10 1 10 MultiAdditive Model 100 12721 12631 12567 > >> 25 1 10 MultiAdditive Model 250 27924 31420 30758 > >> # of docs: 500K and Indexing size is 10 GB. > >> > >> As of now, I did not checked the CPU or memory usage, but did not > observed > >> any errors during jmeter load test. > >> > >> Let me know if any other information is required. > >> > >> Regards > >> Kamal > >> > >> > >> < > https://www.avast.com/en-in/recommend?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=default3&tag=d4ef6ef9-b8d1-40b8-96ac-2354fd69483b> > I’m > >> protected online with Avast Free Antivirus. Get it here — it’s free > >> forever. > >> < > https://www.avast.com/en-in/recommend?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=default3&tag=d4ef6ef9-b8d1-40b8-96ac-2354fd69483b > > > >> <#m_-1438210790161476832_DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> > >> > >>> On Fri, Mar 22, 2019 at 5:13 PM Jörn Franke <jornfra...@gmail.com> > wrote: > >>> > >>> Can you share the time needed of the two models? How many documents? > What > >>> is your loading pipeline? Have you observed cpu/memory? > >>> > >>>> Am 22.03.2019 um 12:01 schrieb Kamal Kishore Aggarwal < > >>> kkroyal....@gmail.com>: > >>>> > >>>> Hi, > >>>> > >>>> I am trying to use LTR with solr 6.6.2.There are different types of > >>> model > >>>> like Linear Model, Multiple Additive Trees Model and Neural Network > >>> Model. > >>>> > >>>> I have tried using Linear & Multiadditive model and compared the > >>>> performance of results. There is a major difference in response time > >>>> between the 2 models. I am observing that Multiadditive model is > taking > >>> way > >>>> higher time than linear model. > >>>> > >>>> Is there a way we can improve the performance here. > >>>> > >>>> Note: The size of Multiadditive model is 136 MB. > >>>> > >>>> Regards > >>>> Kamal Kishore > >>>> > >>>> < > >>> > https://www.avast.com/en-in/recommend?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=default3&tag=d4ef6ef9-b8d1-40b8-96ac-2354fd69483b > >>>> > >>>> I’m > >>>> protected online with Avast Free Antivirus. Get it here — it’s free > >>> forever. > >>>> < > >>> > https://www.avast.com/en-in/recommend?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail&utm_term=default3&tag=d4ef6ef9-b8d1-40b8-96ac-2354fd69483b > >>>> > >>>> <#DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2> > >>> > >> >
