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nano-memoize

1.2.0 • Public • Published

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Faster than fast, smaller than micro ... nano-memoizer.

Introduction

The devs caiogondim and planttheidea have produced great memoizers. We analyzed their code to see if we could build something faster than fast-memoize and smaller than micro-memoize while adding back some of the functionality of moize removed in micro-memoize. We think we have done it ... but credit to them ... we just merged the best ideas in both and eliminated excess code.

During development we also discovered that despite its popularity and goal to be the fastest possible memoizer, fast-memoize is actually one of the slowest out-of-the-box when it comes to multiple argument functions because it uses JSON.stringify to generate a single key generator for all arguments. It also only memoizes out to 3 arguments, which may cause issues. This is not to say it should not be used, it also seems to have the cleanest software architecture and it may be theoretically possible to write a high-speed multi-argument plugin. And, MANY people are very happy with it.

Special appreciation to @titoBouzout and @popbee who spent a good bit of time reviewing code for optimization and making recommendations. See Issue 4 for the conversation.

The minified/brotli size is 789 bytes for nano-memoize v1.1.5 vs 1,356 bytes for micro-memoize v4.08. And, nano-memoize has slightly more functionality.

The speed tests are below. At the time of testing the most recent version of fast-memoize 2.5.1 was a year old. The most recent version of micro-memoize 4.0.8 was 3 months old.

  • For single primitive argument functions nano-memoize is typically 20% faster than fast-memoize and is almost 2x faster than micro-memoize.

  • For single object argument functions nano-memoize is typically 30% faster than fast-memoize and 2x faster than micro-memoize.

  • For multiple primitive argument functions nano-memoize is typically 10% faster than micro-memoize. They are 40x faster than fast-memoize.

  • For multiple object argument functions nano-memoize is typically 10% faster than micro-memoize. They are 40x faster than fast-memoize.

  • When deepEquals tests are used, nano-memoize is typically 5-10% faster than micro-memoize.

We have found that benchmarks can vary dramatically from O/S to O/S or Node version to Node version. These tests were run on a Windows 10 Pro 64bit 1.8ghz i7 machine with 16GB RAM and Node v12.7.0. Also, even with multiple samplings, garbage collection can have a substative impact and multiple runs in different orders are really required for apples-to-apples comparisons.

Functions with a single primitive parameter...

+----------------------------------------------------------------------+
� Name          � Ops / sec   � Relative margin of error � Sample size �
+----------------------------------------------------------------------+
� nano-memoize  � 111,079,216 � � 2.37%                  � 78          �
+----------------------------------------------------------------------+
� fast-memoize  � 65,479,705  � � 3.34%                  � 75          �
+----------------------------------------------------------------------+
� iMemoized     � 62,291,787  � � 2.52%                  � 77          �
+----------------------------------------------------------------------+
� lru-memoize   � 60,937,690  � � 2.60%                  � 82          �
+----------------------------------------------------------------------+
� micro-memoize � 55,728,952  � � 2.81%                  � 77          �
+----------------------------------------------------------------------+
� moize         � 53,819,146  � � 2.37%                  � 78          �
+----------------------------------------------------------------------+
� lodash        � 33,465,668  � � 1.62%                  � 83          �
+----------------------------------------------------------------------+
� underscore    � 29,056,353  � � 1.88%                  � 79          �
+----------------------------------------------------------------------+
� memoizee      � 26,065,006  � � 1.84%                  � 82          �
+----------------------------------------------------------------------+
� addy-osmani   � 13,832,042  � � 1.60%                  � 85          �
+----------------------------------------------------------------------+
� memoizerific  � 8,427,361   � � 1.91%                  � 82          �
+----------------------------------------------------------------------+

Functions with a single object parameter...

+---------------------------------------------------------------------+
� Name          � Ops / sec  � Relative margin of error � Sample size �
+---------------------------------------------------------------------+
� nano-memoize  � 46,495,146 � � 1.97%                  � 80          �
+---------------------------------------------------------------------+
� micro-memoize � 43,077,944 � � 2.09%                  � 81          �
+---------------------------------------------------------------------+
� moize         � 42,777,883 � � 2.78%                  � 77          �
+---------------------------------------------------------------------+
� lru-memoize   � 37,611,410 � � 1.79%                  � 82          �
+---------------------------------------------------------------------+
� memoizee      � 17,154,216 � � 2.01%                  � 79          �
+---------------------------------------------------------------------+
� iMemoized     � 10,634,931 � � 1.49%                  � 87          �
+---------------------------------------------------------------------+
� memoizerific  � 6,097,165  � � 1.87%                  � 79          �
+---------------------------------------------------------------------+
� addy-osmani   � 5,582,986  � � 2.31%                  � 79          �
+---------------------------------------------------------------------+
� fast-memoize  � 1,218,211  � � 1.75%                  � 84          �
+----------------------------------------------------------------------+

Functions with multiple parameters that contain only primitives...

+---------------------------------------------------------------------+
� Name          � Ops / sec  � Relative margin of error � Sample size �
+---------------------------------------------------------------------+
� nano-memoize  � 46,495,146 � � 1.97%                  � 80          �
+---------------------------------------------------------------------+
� micro-memoize � 43,077,944 � � 2.09%                  � 81          �
+---------------------------------------------------------------------+
� moize         � 42,777,883 � � 2.78%                  � 77          �
+---------------------------------------------------------------------+
� lru-memoize   � 37,611,410 � � 1.79%                  � 82          �
+---------------------------------------------------------------------+
� memoizee      � 17,154,216 � � 2.01%                  � 79          �
+---------------------------------------------------------------------+
� iMemoized     � 10,634,931 � � 1.49%                  � 87          �
+---------------------------------------------------------------------+
� memoizerific  � 6,097,165  � � 1.87%                  � 79          �
+---------------------------------------------------------------------+
� addy-osmani   � 5,582,986  � � 2.31%                  � 79          �
+---------------------------------------------------------------------+
� fast-memoize  � 1,218,211  � � 1.75%                  � 84          �
+---------------------------------------------------------------------+

Functions with multiple parameters that contain objects...

+---------------------------------------------------------------------+
� Name          � Ops / sec  � Relative margin of error � Sample size �
+---------------------------------------------------------------------+
� nano-memoize  � 48,155,435 � � 2.54%                  � 78          �
+---------------------------------------------------------------------+
� moize         � 40,315,112 � � 2.07%                  � 78          �
+---------------------------------------------------------------------+
� micro-memoize � 39,886,911 � � 2.40%                  � 80          �
+---------------------------------------------------------------------+
� lru-memoize   � 36,058,456 � � 2.57%                  � 79          �
+---------------------------------------------------------------------+
� memoizee      � 15,785,666 � � 3.43%                  � 81          �
+---------------------------------------------------------------------+
� memoizerific  � 6,107,157  � � 2.99%                  � 81          �
+---------------------------------------------------------------------+
� addy-osmani   � 1,712,749  � � 3.02%                  � 83          �
+---------------------------------------------------------------------+
� fast-memoize  � 775,548    � � 3.24%                  � 80          �
+---------------------------------------------------------------------+

Deep equals ...

+---------------------------------------------------------------------------------------------------------+
� Name                                              � Ops / sec  � Relative margin of error � Sample size �
+---------------------------------------------------------------------------------------------------------+
� nanomemoize deep equals (lodash isEqual)          � 61,286,418 � � 2.01%                  � 80          �
+---------------------------------------------------------------------------------------------------------+
� nanomemoize deep equals (hash-it isEqual)         � 61,085,147 � � 4.10%                  � 77          �
+---------------------------------------------------------------------------------------------------------+
� micro-memoize deep equals (lodash isEqual)        � 57,944,413 � � 3.21%                  � 79          �
+---------------------------------------------------------------------------------------------------------+
� micro-memoize deep equals (hash-it isEqual)       � 54,368,987 � � 2.66%                  � 81          �
+---------------------------------------------------------------------------------------------------------+
� nanomemoize deep equals (fast-equals deepEqual)   � 50,030,425 � � 3.39%                  � 76          �
+---------------------------------------------------------------------------------------------------------+
� micro-memoize deep equals (fast-equals deepEqual) � 41,445,170 � � 2.84%                  � 76          �
+---------------------------------------------------------------------------------------------------------+

Usage

npm install nano-memoize

Use the code in the browser directory for the browser

The code is hand-crafted to run across all browsers all the way back to IE 11. No transpiling is necessary.

API

The API is a subset of the moize API.

const memoized = nanomemoize(sum(a,b) => a + b);
memoized(1,2); // 3
memoized(1,2); // pulled from cache

nanomemoize(function,options) returns function

The shape of options is:

{
  // only use the provided maxArgs for cache look-up, useful for ignoring final callback arguments
  maxArgs: number,
  // go ahead and call memoized multi-args functions after a number of milliseconds via a timeout after the 
  // cached result has been returned, perhaps to ensure that callbacks are invoked, does not cache the timemout result
  // e.g. nanomemoize(function(a,b,cb) { var result = a + b; cb(result); return result; },{maxArgs:2,callTimeout:0});
  callTimeout: number,
  // number of milliseconds to cache a result, set to `Infinity` or `-1` to never create timers or expire
  maxAge: number, 
  // the serializer/key generator to use for single argument functions (optional, not recommended)
  // must be able to serialize objects and functions, by default a WeakMap is used internally without serializing
  serializer: function,
  // the equals function to use for multi-argument functions (optional, try to avoid) e.g. deepEquals for objects
  equals: function
  // forces the use of multi-argument paradigm, auto set if function has a spread argument or uses `arguments` in its body.
  vargs: boolean 
}

To clear the cache you can call .clear() on the function returned my nanomemoize.

Release History (reverse chronological order)

2020-06-18 v1.2.0 Enhanced multi-arg handling so that arg lengths must match precisely (thanks @amazingmarvin), unless maxArgs is passed as an option.

2020-05-26 v1.1.11 [Fixed Issue 17]Fixed https://github.com/anywhichway/nano-memoize/issues/17. It is not known when this bug made its way into the code.

2020-02-30 v1.1.10 Moved growl to dev dependency.

2020-01-30 v1.1.9 Code style improvements.

2019-11-29 v1.1.8 Corrected typos in documentation.

2019-09-25 v1.1.7 Manually created an IE 11 compatible version by removing arrow functions and replacing Object.assign with a custom function. Minor size increase from 660 to 780 Brotli bytes. Also eliminated complex bind approach in favor of closures. The latest v8 engine handles closures as fast as bound arguments for our case and we are not as concerned with older browser performace (particularly given how fast the code is anyway). Converted for loops to run in reverse, which for our case is faster (but is not always guranteed to be faster).

2019-09-17 v1.1.6 Added a manually transpiled es5_ie11.html file with an Object.assign polyfill to the test directory to verify compatibility with IE11. Modified unit tests so they are ES5 compatible. All tests pass. Added sideEffects=false to package.json.

2019-06-28 v1.1.5 Improved documentation. Updated version of micro-memoize used for benchmark testing. No code changes.

2019-05-31 v1.1.4 Fixed Issue 7.

2019-04-09 v1.1.3 Fixed Issue 6. Minor speed and size improvements.

2019-04-02 v1.1.2 Speed improvements for multiple arguments. Now consistently faster than fast-memoize and nano-memoize across multiple test runs. Benchmarks run in a new test environment. The benchmarks for v1.1.1 although correct from a relative perspective, grossly understated actual performance due to a corrupt testing environment.

2019-03-25 v1.1.1 Pushed incorrect version with v1.1.0. This corrects the version push.

2019-03-25 v1.1.0 Added use of WeakMap for high-speed caching of single argument functions when passed objects. The serializer option no longer defaults to (value) => JSON.stringify(value) so if you want to treat objects that have the same string representation as the same, you will have to provide a serializer.

2019-03-24 v1.0.8 Updated/corrected documentation.

2019-03-24 v1.0.7 Made smaller and faster. Renamed sngl to sng and mltpl to mlt. Converted all functions to arrow functions except sng and mlt. Simplified and optimized mlt. Removed () around args to single argument arrow function definitions, e.g. (a) => ... became a => .... Replaced the arg signature () in arrow functions with _, which is shorter. Eliminated multiple character variables except for those used in options to request memoization. Collapsed setTimeout into a single location. Defined const I = Infinity. Eliminated () around ? : condition expressions. Changed {} to Object.create(null). Documented all variables. Moved some variables around for clarity. Moved options into a destructing argument.

2019-03-20 v1.0.6 Updated documentation.

2019-03-11 v1.0.5 Now supports setting maxAge to Infinity and no timers will be created to expire caches.

2019-02-26 v1.0.4 Further optimized cache expiration. See Issue 4

2019-02-16 v1.0.3 Fixed README formatting

2019-02-16 v1.0.2 Further optimizations to deal with Issue 4. expireInterval introduced in v1.0.1 removed since it is no longer needed. Also, 25% reduction in size. Code no longer thrashes when memoizing a large number of functions.

2019-02-16 v1.0.1 Memo expiration optimization. Issue 4 addressed.

2018-04-13 v1.0.0 Code style improvements.

2018-02-07 v0.1.2 Documentation updates

2018-02-07 v0.1.1 Documentationand benchmark updates

2018-02-01 v0.1.0 Documentation updates. 50 byte decrease.

2018-01-27 v0.0.7b BETA Documentation updates.

2018-01-27 v0.0.6b BETA Minor size and speed improvements.

2018-01-27 v0.0.5b BETA Fixed edge case where multi-arg key may be shorter than current args.

2018-01-27 v0.0.4b BETA Fixed benchmarks. Removed maxSize. More unit tests. Fixed maxAge.

2018-01-27 v0.0.3b BETA More unit tests. Documentation. Benchmark code in repository not yet running.

2018-01-24 v0.0.2a ALPHA Minor speed enhancements. Benchmark code in repository not yet running.

2018=01-24 v0.0.1a ALPHA First public release. Benchmark code in repository not yet running.

Install

npm i nano-memoize

DownloadsWeekly Downloads

4,610

Version

1.2.0

License

MIT

Unpacked Size

76.7 kB

Total Files

21

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