mORMot Open Source

Well the test run without error just that it stopped at all thrown exception. I must have some settings wrong or..?

Well the test run without error just that it stopped at all thrown exception. I must have some settings wrong or..?

Sorry, merged, thanks

Sorry, merged, thanks

Should be fine with https://github.com/synopse/mORMot2/commit/2c1daf90d Note that a patch file also works great. With no GT from Micro$oft account needed. Thanks for the feedback!

Should be fine with https://github.com/synopse/mORMot2/commit/2c1daf90d Note that a patch file also works great. With no GT from Micro$oft account needed. Thanks for the feedback!

Tested on Windows 7, 10, 11 Pro, 11 Home, and 11 Pro ARM. We deployed an update to our main app with commit f674c4858 added, no complaints until now. Problem solved. Keep up the excellent work ab.

Tested on Windows 7, 10, 11 Pro, 11 Home, and 11 Pro ARM. We deployed an update to our main app with commit f674c4858 added, no complaints until now. Problem solved. Keep up the excellent work ab.

Thanks to Landrix feedback and pull requests, our MM unit for x86_64 and FPC did have some new default settings, and new algorithms. Some reference about the context: https://github.com/synopse/mORMot2/pull … 5398319192 I made some tests with our TFB sample project, to have some ideas. The server was always run as: $ ./raw -s 1 -t 10 AI-generated content begins here Updated comparison on current mORMot 2 master. All runs on the same machine with identical parameters. Test setup Linux x86_64 ulimit -n 100000 wrk parameters: 512 connections, 10 threads, 5-second runs Endpoints from the official techempower-bench The database here is SQlite3 because we wanted to be CPU and memory constrained for the tests /fortunes = ORM data context + TSynMustache.RenderDataArray (higher allocation pressure, and a giant lock around the ORM layer) /rawfortunes = direct DB output + per-connection TSynMustacheContextData (much lighter) Typical commands used: ulimit -n 100000 wrk -c 512 -t 10 -d 5 http://localhost:8080/plaintext wrk -c 512 -t 10 -d 5 http://localhost:8080/json wrk -c 512 -t 10 -d 5 http://localhost:8080/fortunes wrk -c 512 -t 10 -d 5 http://localhost:8080/rawfortunes Requests/sec summary Endpoint SERVER BOOSTER libc (fpclibcmm) -------------------------------------------------------------------- /plaintext ~1.23 M 1.234 M 1.277 M /json ~1.16 M 1.191 M 1.217 M /rawfortunes ~1.07 M 1.086 M 1.107 M /fortunes 103-105 k 117.2 k 117.2 k (The two independent SERVER passes were very close,

Thanks to Landrix feedback and pull requests, our MM unit for x86_64 and FPC did have some new default settings, and new algorithms. Some reference about the context: https://github.com/synopse/mORMot2/pull … 5398319192 I made some tests with our TFB sample project, to have some ideas. The server was always run as: $ ./raw -s 1 -t 10 AI-generated content begins here Updated comparison on current mORMot 2 master. All runs on the same machine with identical parameters. Test setup Linux x86_64 ulimit -n 100000 wrk parameters: 512 connections, 10 threads, 5-second runs Endpoints from the official techempower-bench The database here is SQlite3 because we wanted to be CPU and memory constrained for the tests /fortunes = ORM data context + TSynMustache.RenderDataArray (higher allocation pressure, and a giant lock around the ORM layer) /rawfortunes = direct DB output + per-connection TSynMustacheContextData (much lighter) Typical commands used: ulimit -n 100000 wrk -c 512 -t 10 -d 5 http://localhost:8080/plaintext wrk -c 512 -t 10 -d 5 http://localhost:8080/json wrk -c 512 -t 10 -d 5 http://localhost:8080/fortunes wrk -c 512 -t 10 -d 5 http://localhost:8080/rawfortunes Requests/sec summary Endpoint SERVER BOOSTER libc (fpclibcmm) -------------------------------------------------------------------- /plaintext ~1.23 M 1.234 M 1.277 M /json ~1.16 M 1.191 M 1.217 M /rawfortunes ~1.07 M 1.086 M 1.107 M /fortunes 103-105 k 117.2 k 117.2 k (The two independent SERVER passes were very close,

Thanks for the explanation on #2. I've never had to write software that cared about that level of optimization, so it's a cool trick to me.

Thanks for the explanation on #2. I've never had to write software that cared about that level of optimization, so it's a cool trick to me.