ExprTk.js
https://exprtk.js.org
Node.js bindings for ExprTk (Github) by @ArashPartow
ExprTk.js
supports both synchronous and asynchronous background execution of thunks precompiled from a string including asynchronous and multithreaded versions of TypedArray.prototype.map
and TypedArray.prototype.reduce
and a synchronous multi-threaded version of TypedArray.prototype.map
.
Its main advantage is that it allows deferring of heavy computation for asynchronous execution in a background thread - something that Node.js/V8 does not allow without the very complex mechanisms of worker_threads
.
Even in single-threaded synchronous mode ExprTk.js
outperforms the native JS TypedArray.prototype.map
running in V8 by a significant margin for all types and array sizes and it comes very close to a direct iterative for
loop.
It also supports being directly called from native add-ons, including native threads, without any synchronization with V8, allowing JS code to drive multi-threaded mathematical transformations.
It has two main use cases:
- Performing heavy mathematical calculation in Express-like frameworks which are not allowed to block
- Speed-up of back-end calculation by parallelizing over multiple cores
Installation
ExprTk.js
uses node-pre-gyp
and it comes with pre-built binaries for x86-64 for Linux (baseline is Ubuntu 18.04), Windows and OS X.
Since version 2.0.1, the minimum required Node.js version is 10.16.0.
npm install exprtk.js
If your platform is not supported, you can rebuild the binaries:
npm install exprtk.js --build-from-source
If you do not use the integer types, you can obtain a significantly smaller binary by doing:
npm install exprtk.js --build-from-source --disable_int
However this won't have any effect on the startup times, since the addon uses lazy binding and does not load code that is not used.
Rebuilding requires a working C++17 environment. It has been tested with g++
, clang
and MSVC 2019
.
Usage
Different methods of traversal work better for different array sizes, you should probably run/adjust the benchmarks - npm run bench
- to see for yourself.
For very small array sizes, the fast setup of eval
/evalAsync
will be better. For large arrays, the tight internal loop of map()
/mapAsync()
will be better.
When launching a large number of parallel operations, unless the expression is very complex, the bottleneck will be the cache/memory bandwidth.
The original documentation of ExprTk
and the syntax used for the expressions is available here: https://github.com/ArashPartow/exprtk
When launching an asynchronous operation, the scalar arguments will be copied and any TypedArray
s will be locked in place and protected from the GC. The whole operation, including traversal and evaluation will happen entirely in a pre-existing background thread picked from a pool and won't solicit the main thread until completion.
Refer to the ExprTk manual for the full expression syntax.
Parallelism
1.0 (obsolete)
An Expression
is not reentrant so multiple concurrent evaluations of the same Expression
object will wait on one another. This is something that is taken care by the module itself - whether it is called from JS or from C/C++. Multiple evaluations on multiple Expression
objects will run in parallel up to the limit set by the Node.js environment variable UV_THREADPOOL_SIZE
. Mixing synchronous and asynchronous evaluations is supported, but a synchronous evaluation will block the event loop until all asynchronous evaluations on that same object are finished. If an evaluation of an Expression
object has to wait for a previous evaluation of the same object to complete, the two evaluations will use two thread pool slots. This means that starting UV_THREADPOOL_SIZE
evaluations on a single object can tie down the whole thread pool until the first one is completed.
2.0 (current)
A single Expression
object can contain multiple ExprTk
expression
instances that are compiled on-demand when needed up to a limit set by the maxParallel
instance property. The global number of available threads can be set by using the environment variable EXPRTKJS_THREADS
and it is independent of Node.js/libuv's own async work mechanism. It can be read from the maxParallel
static class property. The actual peak instances usage of an Expression
object can be checked by reading the maxActive
instance property.
Simple examples
const expr = require("exprtk.js").Float64;
const mean = new expr('(a + b) / 2');
const m = mean.eval({a: 2, b: 4});
Array traversal with map()
/mapAsync()
const inputArray = new Float64Array(n);
const clamp = new expr('clamp(minv, x, maxv)', ['minv', 'x', 'maxv']);
const resultingArray = clamp.map(inputArray, 'x', 5, 10);
const resultingArray = clamp.map(inputArray, 'x', {minv: 5, maxv: 10});
const resultingArray = await clamp.mapAsync(inputArray, 'x', 5, 10);
const resultingArray = await clamp.mapAsync(inputArray, 'x', {minv: 5, maxv: 10});
const resultingArray = clamp.map(4, inputArray, 'x', 5, 10);
const resultingArray = await clamp.mapAsync(4, inputArray, 'x', {minv: 5, maxv: 10});
Array traversal with reduce()
/reduceAsync()
const inputArray = new Float64Array(n);
const sumPow = new expr('a + pow(x, n)', ['a', 'x', 'n']);
const sumSquares = sumPow.reduce(inputArray, 'x', 'a', 0, 2);
const sumSquares = sumPow.reduce(inputArray, 'x', 'a', 0, {p: 2});
const sumSquares = await sumPow.reduceAsync(inputArray, 'x', 'a', 0, 2);
const sumSquares = await sumPow.reduceAsync(inputArray, 'x', 'a', 0, {p: 2});
Explicit traversal by using an ExprTk for
loop
The data type and the array size must be known when compiling (constructing) the expression. ExprTk
supports only fixed-size arrays.
const inputArray = new Float64Array(n);
const expr = require("exprtk.js").Float64;
const mean = new expr(
'var r := 0;' +
'for (var i := 0; i < x[]; i += 1)' +
'{ r += x[i]; };' +
'r / x[];',
[], { 'x': 6 });
const r = mean.eval(inputArray);
const r = await mean.evalAsync(inputArray);
Types
ExprTk.js
supports the following types:
JS | C/C++ |
---|
Float64 | double |
Float32 | float |
Uint32 | uint32_t (unsigned long) |
Int32 | int32_t (long) |
Uint16 | uint16_t (unsigned short) |
Int16 | int16_t (short) |
Uint8 | uint8_t (unsigned char) |
Int8 | int8_t (char) |
Strided arrays
Starting from version 2.1, ExprTk.js
supports strided N-dimensional arrays. Both the scijs/ndarray
and @stdlib/ndarray
forms are supported.
An ndarray
can be used in place of a normal linear array in cwise
/cwiseAsync
. If more than one ndarray
is passed, all ndarray
s must have the same shape. The arrays are always traversed in a positive row-major order, meaning that other orders incur a performance penalty when the array does not fit in the CPU L1 cache.
API
Table of Contents
Expression
Parameters
expression
string functionvariables
Array<string>? An array containing all the scalar variables' names, will be determined automatically if omitted, however order won't be guaranteed, scalars are passed by valuevectors
Record<string, number>? An object containing all the vector variables' names and their sizes, vector size must be known at compilation (object construction), vectors are passed by reference and can be modified by the ExprTk expression
Examples
const expr = require("exprtk.js").Float64;
const mean = new Expression('(a+b)/2', ['a', 'b']);
const stddev = new Expression(
'var sum := 0; var sumsq := 0; ' +
'for (var i := 0; i < x[]; i += 1) { sum += x[i]; sumsq += x[i] * x[i] }; ' +
'(sumsq - (sum*sum) / x[]) / (x[] - 1);',
[], {x: 1024})
Returns Expression The Expression
represents an expression compiled to an AST from a string. Expressions come in different flavors depending on the internal type used.
toString
Get a string representation of this object
Returns string
cwise
Generic vector operation with implicit traversal.
Supports automatic type conversions, multiple inputs, strided N-dimensional arrays and writing into a pre-existing array.
If using N-dimensional arrays, all arrays must have the same shape. The result is always in positive row-major order.
When mixing linear vectors and N-dimensional arrays, the linear vectors are considered to be in positive row-major order
in relation to the N-dimensional arrays.
Parameters
threads
number? arguments
Record<string, (number | TypedArray<any> | ndarray.NdArray<any> | stdlib.ndarray)> target
TypedArray<T>?
Examples
const R = 0.0831446;
const Md = 0.0289652;
const Mv = 0.018016;
const phi = new Float32Array([0, 0.2, 0.5, 0.9, 0.5]);
const P = new Uint16Array([1013, 1013, 1013, 1013, 995]);
const T = new Uint16Array([25, 25, 25, 25, 25]);
const density = new Float64Expression(
'Pv := ( phi * 6.1078 * pow(10, (7.5 * T / (T + 237.3))) ); ' +
'( (P - Pv) * Md + Pv * Mv ) / ( R * (T + 273.13) )',
['P', 'T', 'phi', 'R', 'Md', 'Mv']
);
const result = new Float32Array(P.length);
density.cwise({phi, T, P, R, Md, Mv}, result);
density.cwise(os.cpus().length, {phi, T, P, R, Md, Mv}, result);
await density.cwiseAsync({phi, T, P, R, Md, Mv}, result);
await density.cwiseAsync(os.cpus().length, {phi, T, P, R, Md, Mv}, result);
Returns TypedArray<T>
eval
Evaluate the expression.
All arrays must match the internal data type.
Parameters
arguments
...(Array<(number | TypedArray<T>)> | Record<string, (number | TypedArray<T>)>) of the function
Examples
const r1 = expr.eval({a: 2, b: 5});
const r2 = expr.eval(2, 5);
expr.evalAsync({a: 2, b: 5}, (e,r) => console.log(e, r));
expr.evalAsync(2, 5, (e,r) => console.log(e, r));
Returns number
map
Evaluate the expression for every element of a TypedArray.
Evaluation and traversal happens entirely in C++ so this will be much
faster than calling array.map(expr.eval)
.
All arrays must match the internal data type.
If target is specified, it will write the data into a preallocated array.
This can be used when multiple operations are chained to avoid reallocating a new array at every step.
Otherwise it will return a new array.
Parameters
threads
TypedArray<T>? number of threads to use, 1 if not specifiedtarget
TypedArray<T>? array in which the data is to be written, will allocate a new array if none is specifiedarray
TypedArray<T> for the expression to be iterated overiterator
string variable namearguments
...(Array<(number | TypedArray<T>)> | Record<string, (number | TypedArray<T>)>) of the function, iterator removed
Examples
const expr = new Expression('clamp(f, x, c)', ['f', 'x', 'c']);
const r = new array.constructor(array.length);
expr.map(r, array, 'x', 0, 1000);
expr.map(r, array, 'x', {f: 0, c: 0});
expr.mapAsync(r, array, 'x', 0, 1000, (e,r) => console.log(e, r));
expr.mapAsync(r, array, 'x', {f: 0, c: 0}, (e,r) => console.log(e, r));
const r1 = expr.map(array, 'x', 0, 1000);
const r2 = expr.map(array, 'x', {f: 0, c: 0});
expr.mapAsync(array, 'x', 0, 1000, (e,r) => console.log(e, r));
expr.mapAsync(array, 'x', {f: 0, c: 0}, (e,r) => console.log(e, r));
const r1 = expr.map(4, array, 'x', 0, 1000);
const r2 = await expr.mapAsync(4, array, 'x', {f: 0, c: 0});
Returns TypedArray<T>
reduce
Evaluate the expression for every element of a TypedArray
passing a scalar accumulator to every evaluation.
Evaluation and traversal happens entirely in C++ so this will be much
faster than calling array.reduce(expr.eval)
.
All arrays must match the internal data type.
Parameters
array
TypedArray<T> for the expression to be iterated overiterator
string variable nameaccumulator
string variable nameinitializer
number for the accumulatorarguments
...(Array<(number | TypedArray<T>)> | Record<string, (number | TypedArray<T>)>) of the function, iterator removed
Examples
const sum = new Expression('a + pow(x, p)', ['a', 'x', 'p']);
const sumSq = sum.reduce(array, 'x', 'a', 0, {'p': 2});
const sumSq = sum.reduce(array, 'x', 'a', 0, 2);
sum.reduceAsync(array, 'x', {'a' : 0}, (e,r) => console.log(e, r));
const sumSq = await sum.reduceAsync(array, 'x', {'a' : 0}, (e,r) => console.log(e, r));
Returns number
allocator
Return the data type constructor
Type: TypedArrayConstructor
expression
Return the expression as a string
Type: string
maxActive
Get the currently reached peak of simultaneously running instances for this Expression
Type: number
maxParallel
Get/set the maximum allowed parallel instances for this Expression
Type: number
scalars
Return the scalar arguments as an array
Type: Array<string>
type
Return the type as a string
Type: string
type
Return the type as a string
Type: string
vectors
Return the vector arguments as an object
Type: Record<string, Array<number>>
allocator
Return the data type constructor
Type: TypedArrayConstructor
maxParallel
Get the number of threads available for evaluating expressions.
Set by the EXPRTKJS_THREADS
environment variable.
Type: number
Notes
Integer types
Originally, ExprTk
supports only floating point types. The version bundled with ExprTk.js
has working integer support, but one should be extra careful as it internally uses NaN
values and most built-in mathematical functions - like sin
, cos
, pow
or exp
- won't work correctly with integer types. Using unsigned types will further aggravate this. Always check the result of your function when using anything but basic arithmetic. Also, do not forget that the internal data type also applies to all eventual index variables - using an ExprTk
for
loop in an eval()
over a large Int8
array is not possible as the index variable won't be able to hold the index. Implicit map()
, reduce()
and cwise()
loops are not affected by this as they use internal C++ variables that are not affected by the Expression
type.
Evaluating expressions from another addon
One of the main features of ExprTk.js
is allowing native C++ addons to accept expressions constructed in JS and then to evaluate them independently of the main V8 context.
You can check test/addon.test.cc
for different examples of processing an Expression
object received from JS and evaluating the expression. The calling JS code can be found in test/capi.test.js
.
You will need ExprTk.js
as a development dependency and you will need to include node_modules/exprtk.js/include/exprtkjs.h
in your C/C++ code. You don't need to link against anything and your package won't require ExprTk.js
as a production dependency. If the package is installed and the JS code sends you an Expression
object, everything that will be needed to decode it and to evaluate the expression will be contained in it.
You will need to be much more careful when using the C-API which is much less strict on checking its input arguments. Passing dangling pointers or arrays of incorrect sizes or types will result in a Node.js crash.
Be also advised that, while being completely independent of V8, the C-API can still block if all Expression
instances are busy running asynchronous operations. The module is always safe to call and it will take care of waiting for all other operations to complete.
Invocations from C++ follow the synchronous call semantics as it is expected that a C++ module will manage its own threads. Normally, one should avoid calling the module from the main thread - as this will block the event loop until the evaluation completes.
Build time and binary size
ExprTk
is a C++ template-based engine and it contains an exceptionally high number of symbols that are multiplied by the number of supported types. The final binary contains more than 250000 symbols which is the reason for the huge binary size and the slow build process. This has no effect on its performance or even its initial loading time as the symbols are not exported through the dynamic linker.
Security
ExprTk
is a Turing-complete evaluator that is not properly sandboxed from a security point of view, so untrusted user input is not to be used in an Expression
.