udpate results

Author: MDA2AV

frameworks/flask/gunicorn_conf.py

@@ -13,8 +13,9 @@
 workers = _WRK_COUNT
 keepalive = 120
 loglevel = 'critical'
-accesslog = "-"
+accesslog = None
 errorlog = "-"
+disable_redirect_access_to_syslog = True
 pidfile = "gunicorn.pid"
 worker_class = "sync"
 

frameworks/flask/gunicorn_conf_ssl.py

@@ -13,8 +13,9 @@
 workers = _WRK_COUNT
 keepalive = 120
 loglevel = 'critical'
-accesslog = "-"
+accesslog = None
 errorlog = "-"
+disable_redirect_access_to_syslog = True
 pidfile = "gunicorn_ssl.pid"
 worker_class = "sync"
 

site/content/docs/scoring/composite-score.md

@@ -111,9 +111,11 @@ CPU efficiency was intentionally dropped: a framework that leaves CPU on the tab
 For each profile, compute the efficiency ratio for every framework:
 
 ```
-memEfficiency = rps / memoryMB
+memEfficiency = sqrt(rps) / memoryMB
 ```
 
+Why `sqrt(rps)` instead of `rps`? A plain `rps / MB` ratio double-counts throughput: high-rps frameworks would win both `rpsScore` *and* `memScore` because `rps` dominates the ratio. Taking the square root dampens rps to log-scale — it still matters (a dead framework shouldn't win "efficiency"), but memory can now actually move the needle.
+
 Normalize against the best efficiency in that profile:
 
 ```
@@ -130,19 +132,19 @@ With the toggle on, per-profile scores range 0–150 (up to 100 from throughput,
 
 ### Example
 
-| Framework | RPS | Mem (MB) | rps/MB |
+| Framework | RPS | Mem (MB) | sqrt(rps)/MB |
 |---|---|---|---|
-| A | 500,000 | 50 | 10,000 |
-| B | 100,000 | 20 | 5,000 |
+| A | 500,000 | 50 | 14.14 |
+| B | 100,000 | 20 | 15.81 |
 
 - RPS scores: A = 100, B = 20
-- Memory efficiency scores: A = 100 (best), B = 50
+- Memory efficiency scores: A = 89.4, B = 100 (best)
 
 With the memory toggle on:
-- A: `100 + 0.5 × 100 = 150.0`
-- B: `20 + 0.5 × 50 = 45.0`
+- A: `100 + 0.5 × 89.4 = 144.7`
+- B: `20 + 0.5 × 100 = 70.0`
 
-A leads on both axes and pulls the maximum bonus; B picks up a partial bonus for its lower absolute memory footprint but its 5× throughput deficit still dominates.
+B actually wins the memory term despite A's 5× throughput advantage, because `sqrt(rps)` only gives A a √5 ≈ 2.24× boost in the numerator — not enough to beat B's 2.5× memory savings. A still wins overall thanks to its raw throughput lead, but B's lean memory footprint is now rewarded meaningfully instead of being drowned out.
 
 ## Engine-level implementations
 

site/data/api-16-1024.json

@@ -152,6 +152,32 @@
     "tpl_static": 0,
     "tpl_async_db": 333101
   },
+  {
+    "framework": "fastapi",
+    "language": "Python",
+    "rps": 18677,
+    "avg_latency": "54.63ms",
+    "p99_latency": "190.40ms",
+    "cpu": "1585.1%",
+    "memory": "1.5GiB",
+    "connections": 1024,
+    "threads": 64,
+    "duration": "5s",
+    "pipeline": 1,
+    "bandwidth": "95.74MB/s",
+    "input_bw": "1.05MB/s",
+    "reconnects": 55832,
+    "status_2xx": 280158,
+    "status_3xx": 0,
+    "status_4xx": 0,
+    "status_5xx": 0,
+    "tpl_baseline": 104850,
+    "tpl_json": 104941,
+    "tpl_db": 0,
+    "tpl_upload": 0,
+    "tpl_static": 0,
+    "tpl_async_db": 70367
+  },
   {
     "framework": "fastpysgi-asgi",
     "language": "Python",
@@ -204,6 +230,32 @@
     "tpl_static": 0,
     "tpl_async_db": 208403
   },
+  {
+    "framework": "flask",
+    "language": "Python",
+    "rps": 17096,
+    "avg_latency": "59.63ms",
+    "p99_latency": "64.10ms",
+    "cpu": "1501.4%",
+    "memory": "916MiB",
+    "connections": 1024,
+    "threads": 64,
+    "duration": "5s",
+    "pipeline": 1,
+    "bandwidth": "87.25MB/s",
+    "input_bw": "985.02KB/s",
+    "reconnects": 256454,
+    "status_2xx": 256445,
+    "status_3xx": 0,
+    "status_4xx": 0,
+    "status_5xx": 0,
+    "tpl_baseline": 96111,
+    "tpl_json": 96224,
+    "tpl_db": 0,
+    "tpl_upload": 0,
+    "tpl_static": 0,
+    "tpl_async_db": 64109
+  },
   {
     "framework": "frankenphp-trueasync",
     "language": "PHP",
@@ -516,28 +568,28 @@
   {
     "framework": "ring-http-exchange",
     "language": "Clojure",
-    "rps": 74549,
+    "rps": 74450,
     "avg_latency": "13.59ms",
-    "p99_latency": "308.50ms",
-    "cpu": "1570.0%",
-    "memory": "5.2GiB",
+    "p99_latency": "69.20ms",
+    "cpu": "1445.9%",
+    "memory": "4.4GiB",
     "connections": 1024,
     "threads": 64,
     "duration": "5s",
     "pipeline": 1,
-    "bandwidth": "377.91MB/s",
+    "bandwidth": "376.37MB/s",
     "input_bw": "4.19MB/s",
-    "reconnects": 223418,
-    "status_2xx": 1118236,
+    "reconnects": 223419,
+    "status_2xx": 1116762,
     "status_3xx": 0,
     "status_4xx": 0,
     "status_5xx": 0,
-    "tpl_baseline": 416836,
-    "tpl_json": 420907,
+    "tpl_baseline": 418263,
+    "tpl_json": 419115,
     "tpl_db": 0,
     "tpl_upload": 0,
     "tpl_static": 0,
-    "tpl_async_db": 280490
+    "tpl_async_db": 279382
   },
   {
     "framework": "roda",

site/data/api-4-256.json

@@ -152,6 +152,32 @@
     "tpl_static": 0,
     "tpl_async_db": 138968
   },
+  {
+    "framework": "fastapi",
+    "language": "Python",
+    "rps": 7634,
+    "avg_latency": "33.51ms",
+    "p99_latency": "116.40ms",
+    "cpu": "399.8%",
+    "memory": "495MiB",
+    "connections": 256,
+    "threads": 64,
+    "duration": "5s",
+    "pipeline": 1,
+    "bandwidth": "39.13MB/s",
+    "input_bw": "439.85KB/s",
+    "reconnects": 22896,
+    "status_2xx": 114523,
+    "status_3xx": 0,
+    "status_4xx": 0,
+    "status_5xx": 0,
+    "tpl_baseline": 42884,
+    "tpl_json": 42988,
+    "tpl_db": 0,
+    "tpl_upload": 0,
+    "tpl_static": 0,
+    "tpl_async_db": 28651
+  },
   {
     "framework": "fastpysgi-asgi",
     "language": "Python",
@@ -204,6 +230,32 @@
     "tpl_static": 0,
     "tpl_async_db": 73673
   },
+  {
+    "framework": "flask",
+    "language": "Python",
+    "rps": 6328,
+    "avg_latency": "40.32ms",
+    "p99_latency": "64.00ms",
+    "cpu": "366.7%",
+    "memory": "268MiB",
+    "connections": 256,
+    "threads": 64,
+    "duration": "5s",
+    "pipeline": 1,
+    "bandwidth": "32.29MB/s",
+    "input_bw": "364.60KB/s",
+    "reconnects": 94931,
+    "status_2xx": 94932,
+    "status_3xx": 0,
+    "status_4xx": 0,
+    "status_5xx": 0,
+    "tpl_baseline": 35597,
+    "tpl_json": 35620,
+    "tpl_db": 0,
+    "tpl_upload": 0,
+    "tpl_static": 0,
+    "tpl_async_db": 23715
+  },
   {
     "framework": "frankenphp-trueasync",
     "language": "PHP",
@@ -516,28 +568,28 @@
   {
     "framework": "ring-http-exchange",
     "language": "Clojure",
-    "rps": 31785,
-    "avg_latency": "7.93ms",
-    "p99_latency": "118.10ms",
-    "cpu": "393.4%",
-    "memory": "1.6GiB",
+    "rps": 32506,
+    "avg_latency": "7.49ms",
+    "p99_latency": "28.60ms",
+    "cpu": "385.3%",
+    "memory": "874MiB",
     "connections": 256,
     "threads": 64,
     "duration": "5s",
     "pipeline": 1,
-    "bandwidth": "160.66MB/s",
-    "input_bw": "1.79MB/s",
-    "reconnects": 95316,
-    "status_2xx": 476775,
+    "bandwidth": "164.22MB/s",
+    "input_bw": "1.83MB/s",
+    "reconnects": 97568,
+    "status_2xx": 487594,
     "status_3xx": 0,
     "status_4xx": 0,
     "status_5xx": 0,
-    "tpl_baseline": 178603,
-    "tpl_json": 178540,
+    "tpl_baseline": 182840,
+    "tpl_json": 182943,
     "tpl_db": 0,
     "tpl_upload": 0,
     "tpl_static": 0,
-    "tpl_async_db": 119632
+    "tpl_async_db": 121808
   },
   {
     "framework": "roda",

site/data/async-db-1024.json

@@ -116,6 +116,26 @@
     "status_4xx": 0,
     "status_5xx": 0
   },
+  {
+    "framework": "fastapi",
+    "language": "Python",
+    "rps": 54572,
+    "avg_latency": "18.66ms",
+    "p99_latency": "152.90ms",
+    "cpu": "6230.0%",
+    "memory": "5.0GiB",
+    "connections": 1024,
+    "threads": 64,
+    "duration": "5s",
+    "pipeline": 1,
+    "bandwidth": "205.07MB/s",
+    "input_bw": "3.64MB/s",
+    "reconnects": 21345,
+    "status_2xx": 545720,
+    "status_3xx": 0,
+    "status_4xx": 0,
+    "status_5xx": 0
+  },
   {
     "framework": "fastpysgi-asgi",
     "language": "Python",
@@ -156,6 +176,26 @@
     "status_4xx": 0,
     "status_5xx": 0
   },
+  {
+    "framework": "flask",
+    "language": "Python",
+    "rps": 42384,
+    "avg_latency": "23.88ms",
+    "p99_latency": "25.70ms",
+    "cpu": "5346.6%",
+    "memory": "3.3GiB",
+    "connections": 1024,
+    "threads": 64,
+    "duration": "5s",
+    "pipeline": 1,
+    "bandwidth": "163.19MB/s",
+    "input_bw": "2.83MB/s",
+    "reconnects": 423841,
+    "status_2xx": 423842,
+    "status_3xx": 0,
+    "status_4xx": 0,
+    "status_5xx": 0
+  },
   {
     "framework": "Fletch",
     "language": "Dart",
@@ -416,19 +456,19 @@
   {
     "framework": "ring-http-exchange",
     "language": "Clojure",
-    "rps": 69314,
-    "avg_latency": "14.55ms",
-    "p99_latency": "84.10ms",
-    "cpu": "6126.6%",
-    "memory": "13.1GiB",
+    "rps": 70611,
+    "avg_latency": "14.14ms",
+    "p99_latency": "133.40ms",
+    "cpu": "6348.1%",
+    "memory": "20.3GiB",
     "connections": 1024,
     "threads": 64,
     "duration": "5s",
     "pipeline": 1,
-    "bandwidth": "266.01MB/s",
-    "input_bw": "4.63MB/s",
-    "reconnects": 27495,
-    "status_2xx": 693145,
+    "bandwidth": "271.02MB/s",
+    "input_bw": "4.71MB/s",
+    "reconnects": 27854,
+    "status_2xx": 706119,
     "status_3xx": 0,
     "status_4xx": 0,
     "status_5xx": 0