Support sampling and polling intervals

Appears to be a way to significantly reduce performance impact of a frequent polling interval when combined with an infrequent sampling interval while maintaining an accurate GIL contention metric.
milesgranger · Mar 15, 2023 · 2f01324 · 2f01324
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diff --git a/Cargo.lock b/Cargo.lock
diff --git a/Cargo.toml b/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "gil-knocker"
-version = "0.2.2"
+version = "0.3.0"
 edition = "2021"
 authors = ["Miles Granger <[email protected]>"]
 license = "MIT"

diff --git a/README.md b/README.md
@@ -32,7 +32,7 @@ to align with what is reported by `py-spy` when running the same [test case](./t
 This works by spawning a thread which, at regular intervals, re-acquires the GIL and checks 
 how long it took for the GIL to answer.
 
-Note, the interval (`interval_micros`) is configurable. The lower the value, the 
+Note, the `polling_interval_micros` and `sampling_interval_micros` are configurable. The lower the value, the 
 more accurate the metric, but will be more likely to slow your
 program down.. because it will play a larger role in competing for the GIL's attention.
 
@@ -44,7 +44,7 @@ Look at the [tests](./tests)
 
 from gilknocker import KnockKnock
 
-knocker = KnockKnock(interval_micros=1000, timeout_secs=1)
+knocker = KnockKnock(polling_interval_micros=1_000, sampling_interval_micros=10_000, timeout_secs=1)
 knocker.start()
 
 ... smart code here ...
@@ -55,42 +55,55 @@ knocker.reset_contention_metric()  # reset timers and meteric calculation
 ... some more smart code ...
 
 knocker.stop()
+knocker.stop()  # Idempodent stopping behavior
 
 knocker.contention_metric  # will stay the same after `stop()` is called.
 
+knocker.is_running  # If you're ever in doubt
+
 ```
 
 ### How will this impact my program?
 
 Short answer, it depends, but probably not much. As stated above, the more frequent the 
-monitoring interval, the more likely non-GIL bound programs will be affected, since there is 
+polling and sampling interval, the more likely non-GIL bound programs will be affected, since there is 
 more room for contention. In GIL heavy programs, the monitoring thread will spend most of its 
 time simply waiting for a lock. This is demonstrated in the [benchmarks](./benchmarks) testing.
 
+In general, it appears that `polling_interval_micros=1_000` is a good tradeoff in terms of accurate
+GIL contention metric and the resulting `sampling_interval_micros=10_000` (defaults to 10x polling interval)
+is high enough to relax performance impact a bit.
+
 Below is a summary of benchmarking two different 
 functions, one which uses the GIL, and one which releases it. For `interval=None` this means 
 no polling was used, effectively just running the function without `gilknocker`. Otherwise, 
-the interval represents the value passed to `KnockKnock(interval_micros=interval)`
+the interval represents the value passed to `KnockKnock(polling_interval_micros=interval)`
 
 `python -m pytest -v --benchmark-only benchmarks/ --benchmark-histogram`
 
 ```
----------------------------------------------------------------------------------------------- benchmark: 12 tests ----------------------------------------------------------------------------------------------
-Name (time in ms)                          Min                   Max                  Mean             StdDev                Median                 IQR            Outliers     OPS            Rounds  Iterations
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-test_bench[a_lotta_gil-None]          697.6828 (1.0)        804.5402 (1.11)       755.6981 (1.06)     53.0970 (61.53)      777.1266 (1.09)     101.6509 (83.91)         2;0  1.3233 (0.95)          5           1
-test_bench[a_lotta_gil-10]            707.0513 (1.01)       724.3552 (1.0)        714.4783 (1.0)       6.8460 (7.93)       715.2083 (1.0)       10.0545 (8.30)          2;0  1.3996 (1.0)           5           1
-test_bench[a_lotta_gil-1000]          708.0325 (1.01)       742.4564 (1.02)       722.2247 (1.01)     12.6517 (14.66)      721.7707 (1.01)      12.5343 (10.35)         2;0  1.3846 (0.99)          5           1
-test_bench[a_lotta_gil-10000]         716.1168 (1.03)       791.8905 (1.09)       733.0825 (1.03)     32.9744 (38.21)      717.7345 (1.00)      23.2516 (19.19)         1;1  1.3641 (0.97)          5           1
-test_bench[a_lotta_gil-100000]        758.2248 (1.09)       760.4424 (1.05)       759.2441 (1.06)      0.8629 (1.0)        758.9144 (1.06)       1.2114 (1.0)           2;0  1.3171 (0.94)          5           1
-test_bench[a_lotta_gil-100]           760.8787 (1.09)       839.1526 (1.16)       777.9811 (1.09)     34.2144 (39.65)      763.4823 (1.07)      20.4199 (16.86)         1;1  1.2854 (0.92)          5           1
-test_bench[a_little_gil-None]       1,505.1989 (2.16)     1,510.2234 (2.08)     1,508.0564 (2.11)      1.8985 (2.20)     1,508.2229 (2.11)       2.5074 (2.07)          2;0  0.6631 (0.47)          5           1
-test_bench[a_little_gil-100000]     1,506.0053 (2.16)     1,559.4051 (2.15)     1,531.3341 (2.14)     22.6875 (26.29)    1,524.5321 (2.13)      38.7802 (32.01)         2;0  0.6530 (0.47)          5           1
-test_bench[a_little_gil-10000]      1,508.9686 (2.16)     1,521.0912 (2.10)     1,515.0701 (2.12)      5.5128 (6.39)     1,514.7033 (2.12)      10.3673 (8.56)          2;0  0.6600 (0.47)          5           1
-test_bench[a_little_gil-1000]       1,534.0449 (2.20)     1,540.6296 (2.13)     1,537.8621 (2.15)      2.5307 (2.93)     1,538.5808 (2.15)       3.4261 (2.83)          2;0  0.6503 (0.46)          5           1
-test_bench[a_little_gil-100]        1,566.4128 (2.25)     1,576.2634 (2.18)     1,569.6245 (2.20)      4.0978 (4.75)     1,567.4297 (2.19)       5.3087 (4.38)          1;0  0.6371 (0.46)          5           1
-test_bench[a_little_gil-10]         1,587.1471 (2.27)     1,597.2920 (2.21)     1,592.0651 (2.23)      3.7001 (4.29)     1,591.2409 (2.22)       4.1942 (3.46)          2;0  0.6281 (0.45)          5           1
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+------------------------------------------------------------------------------------ benchmark: 18 tests -------------------------------------------------------------------------------------
+Name (time in s)                       Min               Max              Mean            StdDev            Median               IQR            Outliers     OPS            Rounds  Iterations
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+test_bench[a_little_gil-100000]     1.5368 (2.07)     1.6596 (2.23)     1.5968 (2.14)     0.0476 (130.12)   1.5943 (2.14)     0.0719 (140.14)        2;0  0.6262 (0.47)          5           1
+test_bench[a_little_gil-10000]      1.5321 (2.06)     1.5989 (2.14)     1.5618 (2.09)     0.0289 (78.95)    1.5610 (2.09)     0.0510 (99.52)         2;0  0.6403 (0.48)          5           1
+test_bench[a_little_gil-1000]       1.5246 (2.05)     1.5298 (2.05)     1.5271 (2.05)     0.0019 (5.12)     1.5269 (2.05)     0.0021 (4.00)          2;0  0.6549 (0.49)          5           1
+test_bench[a_little_gil-100]        1.5505 (2.09)     1.5543 (2.08)     1.5528 (2.08)     0.0014 (3.96)     1.5533 (2.08)     0.0018 (3.60)          2;0  0.6440 (0.48)          5           1
+test_bench[a_little_gil-10]         1.5863 (2.13)     1.6074 (2.16)     1.5928 (2.14)     0.0088 (23.94)    1.5896 (2.13)     0.0111 (21.60)         1;0  0.6278 (0.47)          5           1
+test_bench[a_little_gil-None]       1.5043 (2.02)     1.5067 (2.02)     1.5051 (2.02)     0.0011 (2.95)     1.5044 (2.02)     0.0016 (3.17)          1;0  0.6644 (0.50)          5           1
+test_bench[a_lotta_gil-100000]      0.7450 (1.00)     0.7458 (1.0)      0.7455 (1.0)      0.0004 (1.0)      0.7457 (1.0)      0.0005 (1.0)           1;0  1.3413 (1.0)           5           1
+test_bench[a_lotta_gil-10000]       0.7471 (1.00)     0.8104 (1.09)     0.7601 (1.02)     0.0281 (76.94)    0.7472 (1.00)     0.0168 (32.82)         1;1  1.3156 (0.98)          5           1
+test_bench[a_lotta_gil-1000]        0.7436 (1.0)      0.7472 (1.00)     0.7463 (1.00)     0.0015 (4.11)     0.7470 (1.00)     0.0013 (2.54)          1;1  1.3400 (1.00)          5           1
+test_bench[a_lotta_gil-100]         0.7558 (1.02)     0.7680 (1.03)     0.7640 (1.02)     0.0050 (13.56)    0.7644 (1.03)     0.0061 (11.97)         1;0  1.3089 (0.98)          5           1
+test_bench[a_lotta_gil-10]          0.7542 (1.01)     0.7734 (1.04)     0.7649 (1.03)     0.0084 (23.05)    0.7669 (1.03)     0.0151 (29.45)         2;0  1.3074 (0.97)          5           1
+test_bench[a_lotta_gil-None]        0.7437 (1.00)     0.8490 (1.14)     0.8006 (1.07)     0.0501 (137.15)   0.8074 (1.08)     0.0969 (189.03)        1;0  1.2490 (0.93)          5           1
+test_bench[some_gil-100000]         1.4114 (1.90)     1.4131 (1.89)     1.4122 (1.89)     0.0007 (1.81)     1.4121 (1.89)     0.0010 (2.00)          2;0  0.7081 (0.53)          5           1
+test_bench[some_gil-10000]          1.4115 (1.90)     1.4258 (1.91)     1.4167 (1.90)     0.0059 (16.03)    1.4141 (1.90)     0.0083 (16.19)         1;0  0.7058 (0.53)          5           1
+test_bench[some_gil-1000]           1.4169 (1.91)     1.5793 (2.12)     1.4618 (1.96)     0.0690 (188.82)   1.4232 (1.91)     0.0769 (150.04)        1;0  0.6841 (0.51)          5           1
+test_bench[some_gil-100]            1.4468 (1.95)     1.6261 (2.18)     1.5701 (2.11)     0.0752 (205.83)   1.5998 (2.15)     0.1004 (195.70)        1;0  0.6369 (0.47)          5           1
+test_bench[some_gil-10]             1.5269 (2.05)     1.9894 (2.67)     1.7037 (2.29)     0.1895 (518.49)   1.7301 (2.32)     0.2692 (524.96)        1;0  0.5870 (0.44)          5           1
+test_bench[some_gil-None]           1.4115 (1.90)     1.4267 (1.91)     1.4155 (1.90)     0.0063 (17.33)    1.4136 (1.90)     0.0053 (10.24)         1;1  0.7065 (0.53)          5           1
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 ```
 
 ![](./benchmarks/histogram.svg)