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Edit: benchmarks.md
--- id: benchmarks title: Benchmarks --- Benchmarks have been ran with the following versions of modules. ``` ├── async@1.5.2 ├── babel@5.8.35 ├── davy@1.1.0 ├── deferred@0.7.5 ├── kew@0.7.0 ├── lie@3.0.2 ├── neo-async@1.7.3 ├── optimist@0.6.1 ├── promise@7.1.1 ├── q@1.4.1 ├── rsvp@3.2.1 ├── streamline@2.0.16 ├── streamline-runtime@1.0.38 ├── text-table@0.2.0 ├── vow@0.4.12 └── when@3.7.7 ``` ###1\. DoxBee sequential This is Gorki Kosev's benchmark used in the article [Analysis of generators and other async patterns in node](http://spion.github.io/posts/analysis-generators-and-other-async-patterns-node.html). The benchmark emulates a situation where N=10000 requests are being made concurrently to execute some mixed async/sync action with fast I/O response times. This is a throughput benchmark. Every implementation runs in a freshly created isolated process which is warmed up to the benchmark code before timing it. The memory column represents the highest snapshotted RSS memory (as reported by `process.memoryUsage().rss`) during processing. Command: `./bench doxbee` (<a href="{{ "/docs/contribute.html#benchmarking" | prepend: site.baseurl }}">needs cloned repository</a>) The implementations for this benchmark are found in [`benchmark/doxbee-sequential`](https://github.com/petkaantonov/bluebird/tree/master/benchmark/doxbee-sequential) directory. ``` results for 10000 parallel executions, 1 ms per I/O op file time(ms) memory(MB) callbacks-baseline.js 116 33.98 callbacks-suguru03-neo-async-waterfall.js 145 43.81 promises-bluebird-generator.js 183 42.35 promises-bluebird.js 214 43.41 promises-cujojs-when.js 312 64.37 promises-then-promise.js 396 74.33 promises-tildeio-rsvp.js 414 84.80 promises-native-async-await.js 422 104.23 promises-ecmascript6-native.js 424 92.12 generators-tj-co.js 444 90.98 promises-lvivski-davy.js 480 114.46 callbacks-caolan-async-waterfall.js 520 109.01 promises-dfilatov-vow.js 612 134.38 promises-obvious-kew.js 725 208.63 promises-calvinmetcalf-lie.js 730 164.96 streamline-generators.js 809 154.36 promises-medikoo-deferred.js 913 178.51 observables-pozadi-kefir.js 991 194.00 streamline-callbacks.js 1127 196.54 observables-Reactive-Extensions-RxJS.js 1906 268.41 observables-caolan-highland.js 6887 662.08 promises-kriskowal-q.js 8533 435.51 observables-baconjs-bacon.js.js 21282 882.61 Platform info: Linux 4.4.0-79-generic x64 Node.JS 8.6.0 V8 6.0.287.53 Intel(R) Core(TM) i5-6600K CPU @ 3.50GHz × 4 ``` ###2\. Parallel This made-up scenario runs 25 shimmed queries in parallel per each request (N=10000) with fast I/O response times. This is a throughput benchmark. Every implementation runs in a freshly created isolated process which is warmed up to the benchmark code before timing it. The memory column represents the highest snapshotted RSS memory (as reported by `process.memoryUsage().rss`) during processing. Command: `./bench parallel` (<a href="{{ "/docs/contribute.html#benchmarking" | prepend: site.baseurl }}">needs cloned repository</a>) The implementations for this benchmark are found in [`benchmark/madeup-parallel`](https://github.com/petkaantonov/bluebird/tree/master/benchmark/madeup-parallel) directory. ``` results for 10000 parallel executions, 1 ms per I/O op file time(ms) memory(MB) callbacks-baseline.js 274 75.11 callbacks-suguru03-neo-async-parallel.js 320 88.84 promises-bluebird.js 407 107.25 promises-bluebird-generator.js 432 113.19 callbacks-caolan-async-parallel.js 550 154.27 promises-cujojs-when.js 648 168.65 promises-ecmascript6-native.js 1145 308.87 promises-lvivski-davy.js 1153 257.36 promises-native-async-await.js 1260 323.68 promises-then-promise.js 1372 313.24 promises-tildeio-rsvp.js 1435 398.73 promises-medikoo-deferred.js 1626 306.02 promises-calvinmetcalf-lie.js 1805 351.21 promises-dfilatov-vow.js 2492 558.25 promises-obvious-kew.js 3403 784.61 streamline-generators.js 13068 919.24 streamline-callbacks.js 25509 1141.57 Platform info: Linux 4.4.0-79-generic x64 Node.JS 8.6.0 V8 6.0.287.53 Intel(R) Core(TM) i5-6600K CPU @ 3.50GHz × 4 ``` ###3\. Latency benchmarks For reasonably fast promise implementations latency is going to be fully determined by the scheduler being used and is therefore not interesting to benchmark. [JSPerfs](https://jsperf.com/) that benchmark promises tend to benchmark latency.
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