nano-memoize

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.

The minified/gzipped size is 592 bytes for nano-memoize vs 959 bytes for micro-memoize. And, nano-memoize has slightly more functionality.

The speed tests are below. In most cases nano-memoize is the fastest.

• For single primitive argument functions it is comparable to, but slightly and probably un-importantly faster that fast-memoize.

• For single object argument functions it is typically 2x faster than anything else.

• For multiple primitive argument functionsnano-memoize is slightly and probably un-importantly faster than fast-memoize.

• For multiple object argument functions micro-memoize is slightly and probably un-importantly faster.

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 64bit 2.4ghz machine with 8GB RAM and Node v11.6.0. 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  � 277,174,954 � � 0.39%                  � 94          �
+---------------+-------------+--------------------------+-------------�
� fast-memoize  � 243,829,313 � � 4.97%                  � 81          �
+---------------+-------------+--------------------------+-------------�
� iMemoized     � 49,406,719  � � 3.90%                  � 82          �
+---------------+-------------+--------------------------+-------------�
� micro-memoize � 48,245,239  � � 2.19%                  � 89          �
+---------------+-------------+--------------------------+-------------�
� moize         � 47,380,879  � � 0.59%                  � 88          �
+---------------+-------------+--------------------------+-------------�
� lru-memoize   � 39,284,232  � � 4.35%                  � 87          �
+---------------+-------------+--------------------------+-------------�
� lodash        � 31,464,058  � � 2.91%                  � 91          �
+---------------+-------------+--------------------------+-------------�
� memoizee      � 19,406,111  � � 4.90%                  � 79          �
+---------------+-------------+--------------------------+-------------�
� underscore    � 16,986,840  � � 5.83%                  � 75          �
+---------------+-------------+--------------------------+-------------�
� addy-osmani   � 4,496,619   � � 0.98%                  � 92          �
+---------------+-------------+--------------------------+-------------�
� memoizerific  � 2,394,952   � � 6.96%                  � 49          �
+---------------+-------------+--------------------------+-------------�
� ramda         � 1,095,063   � � 2.10%                  � 86          �
+----------------------------------------------------------------------+

Functions with a single object parameter...

+----------------------------------------------------------------------+
� Name          � Ops / sec   � Relative margin of error � Sample size �
+---------------+-------------+--------------------------+-------------�
� nano-memoize  � 271,647,146 �   0.74%                  � 90          �
+---------------+-------------+--------------------------+-------------�
� micro-memoize � 44,126,430  �   4.22%                  � 81          �
+---------------+-------------+--------------------------+-------------�
� fast-memoize  � 44,125,722  �   2.14%                  � 82          �
+---------------+-------------+--------------------------+-------------�
� iMemoized     � 43,981,304  �   1.61%                  � 89          �
+---------------+-------------+--------------------------+-------------�
� moize         � 32,603,505  �   3.19%                  � 14          �
+---------------+-------------+--------------------------+-------------�
� lodash        � 31,277,037  �   1.16%                  � 88          �
+---------------+-------------+--------------------------+-------------�
� underscore    � 20,293,644  �   1.02%                  � 88          �
+---------------+-------------+--------------------------+-------------�
� memoizee      � 11,533,134  �   1.35%                  � 89          �
+---------------+-------------+--------------------------+-------------�

Functions with multiple parameters that contain only primitives...

+---------------------------------------------------------------------+
� Name          � Ops / sec  � Relative margin of error � Sample size �
+---------------+------------+--------------------------+-------------�
� nano-memoize  � 24,739,433 �   0.98%                  � 85          �
+---------------+------------+--------------------------+-------------�
� micro-memoize � 23,131,341 �   3.33%                  � 74          �
+---------------+------------+--------------------------+-------------�
� moize         � 20,241,359 �   2.45%                  � 81          �
+---------------+------------+--------------------------+-------------�
� memoizee      � 9,917,821  �   2.58%                  � 85          �
+---------------+------------+--------------------------+-------------�
� lru-memoize   � 7,582,999  �   2.85%                  � 82          �
+---------------+------------+--------------------------+-------------�
� iMemoized     � 4,765,891  �   12.92%                 � 68          �
+---------------+------------+--------------------------+-------------�
� memoizerific  � 3,200,253  �   3.02%                  � 84          �
+---------------+------------+--------------------------+-------------�
� addy-osmani   � 2,240,692  �   2.28%                  � 87          �
+---------------+------------+--------------------------+-------------�
� fast-memoize  � 885,271    �   3.99%                  � 82          �
+---------------------------------------------------------------------+

Functions with multiple parameters that contain objects...

+---------------------------------------------------------------------+
� Name          � Ops / sec  � Relative margin of error � Sample size �
+---------------+------------+--------------------------+-------------�
� micro-memoize � 23,846,343 �   3.35%                  � 84          �
+---------------+------------+--------------------------+-------------�
� nano-memoize  � 17,861,879 �   2.49%                  � 84          �
+---------------+------------+--------------------------+-------------�
� moize         � 17,147,054 �   5.62%                  � 63          �
+---------------+------------+--------------------------+-------------�
� lru-memoize   � 7,247,819  �   3.85%                  � 81          �
+---------------+------------+--------------------------+-------------�
� memoizee      � 6,860,227  �   1.17%                  � 88          �
+---------------+------------+--------------------------+-------------�
� memoizerific  � 3,399,423  �   2.60%                  � 85          �
+---------------+------------+--------------------------+-------------�
� addy-osmani   � 795,071    �   1.43%                  � 85          �
+---------------+------------+--------------------------+-------------�
� fast-memoize  � 715,841    �   1.05%                  � 86          �
+---------------------------------------------------------------------+

Deep equals ...

+---------------------------------------------------------------------------------------------------------+
� Name                                              � Ops / sec  � Relative margin of error � Sample size �
+---------------------------------------------------+------------+--------------------------+-------------�
� micro-memoize deep equals (lodash isEqual)        � 37,582,028 �   1.87%                  � 83          �
+---------------------------------------------------+------------+--------------------------+-------------�
� micro-memoize deep equals (fast-equals deepEqual) � 21,181,692 �   6.75%                  � 66          �
+---------------------------------------------------+------------+--------------------------+-------------�
� nanomemoize deep equals (fast-equals deepEqual)   � 17,186,548 �   3.22%                  � 80          �
+---------------------------------------------------+------------+--------------------------+-------------�
� nanomemoize deep equals (lodash isEqual)          � 14,995,992 �   3.49%                  � 75          �
+---------------------------------------------------+------------+--------------------------+-------------�
� micro-memoize deep equals (hash-it isEqual)       � 14,376,860 �   3.27%                  � 78          �
+---------------------------------------------------------------------------------------------------------+

We were puzzled about the multiple argument performance on fast-memoize given its stated goal of being the "fastest possible". We discovered that the default caching and serialization approach used by fast-memoize only performs well for single argument functions for two reasons:

1. It uses JSON.stringify to create a key for an entire argument list. This can be slow.

2. Because a single key is generated for all arguments when perhaps only the first argument differs in a call, a lot of extra work is done. The moize and micro-memoize approach adopted by nano-memoize is far faster for multiple arguments.

Usage

npm install nano-memoize

Use the code in the browser directory for the browser

Since most devs are running a build pipeline, the code is not transpiled, although it is browserified

API

The API is a subset of the moize API.

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

memoized(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, 
  // number of milliseconds to cache a result, set to `Infinity` to never create timers or expire
  maxAge: number, 
  // the serializer/key generator to use for single argument functions (multi-argument functionsuse equals)
  serializer: function,
  // the equals function to use for multi-argument functions, e.g. deepEquals for objects (single-argument functions serializer)
  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 
}

Release History (reverse chronological order)

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.