https://github.com/kuzzleio/koncorde
Supersonic reverse-search engine, featuring a full-fledged DSL, including geofencing capabilities
https://github.com/kuzzleio/koncorde
filter geofencing kuzzle realtime reverse-search
Last synced: about 2 months ago
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Supersonic reverse-search engine, featuring a full-fledged DSL, including geofencing capabilities
- Host: GitHub
- URL: https://github.com/kuzzleio/koncorde
- Owner: kuzzleio
- License: apache-2.0
- Created: 2017-09-05T15:23:45.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2025-01-15T14:27:04.000Z (5 months ago)
- Last Synced: 2025-04-02T10:13:13.151Z (2 months ago)
- Topics: filter, geofencing, kuzzle, realtime, reverse-search
- Language: JavaScript
- Homepage:
- Size: 1.52 MB
- Stars: 27
- Watchers: 9
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.md
Awesome Lists containing this project
README
[](http://codecov.io/github/kuzzleio/koncorde?branch=master)
[](https://lgtm.com/projects/g/kuzzleio/koncorde/context:javascript)
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# Koncorde
This module is a reverse-search engine.
Instead of indexing data and searching for them using filters, Koncorde does the opposite: it indexes search filters, and returns the corresponding ones when presented with data.
* an arbitrary large number of filters can be registered and indexed;
* whenever data are submitted to Koncorde, it returns the list of indexed filters matching them;
* Koncorde's [filter syntax](https://github.com/kuzzleio/koncorde/wiki/Filter-Syntax) supports a variety of different matchers, and ways to combine them.
Koncorde can be used in a variety of ways. For instance:
* as a base of a notification system, where indexed filters are used as user subscriptions: Koncorde tells which JSON objects verify what subscriptions, making it easy to send events to listening users;
* to verify if JSON objects comply to filters used as validation rules.
Check our [full documentation](https://github.com/kuzzleio/koncorde/wiki) to know more about Koncorde's API, filter syntax, and more.
# Quick start example
In the following example, we'll listen to objects containing a `position` property, describing a geopoint. We want that geopoint to be 500 meters around a pre-defined starting position.
This can be described by the following Koncorde filter:
```json
{
"geoDistance": {
"position": {
"lat": 43.6073913,
"lon": 3.9109057
},
"distance": "500m"
}
}
```
All you need to do now is to register this filter to the engine, and use it to test data:
```js
import { Koncorde } from 'koncorde';
const engine = new Koncorde();
const filter = {
geoDistance: {
position: {
lat: 43.6073913,
lon: 3.9109057
},
distance: "500m"
}
};
const filterId = engine.register(filter);
// Filter Identifiers are seeded. Given the same seed (to be provided to the
// constructor), then the filter IDs stay stable.
console.log(`Filter identifier: ${filterId}`);
// No match found, returns an empty array (distance is greater than 500m)
console.log(engine.test({ position: { lat: 43.6073913, lon: 5.7 } }));
// Point within the filter's scope: returns the list of matched filters
// Here we registered just one of them, so the array contains only 1 filter ID
console.log(engine.test({ position: { lat: 43.608, lon: 3.905 } }));
// No match found, returns an empty array
// (the geopoint in the provided data is not stored in the tested field)
console.log(engine.test({ not_position: { lat: 43.608, lon: 3.905 } }));
```
# Install
This library is compatible with Node.js version 12.x or higher.
Both a C and a C++ compilers are needed to install its dependencies: Koncorde cannot be used in a browser.
Koncorde is compatible with either Javascript or Typescript projects.
To install:
```
npm install koncorde
```
## Benchmarks
The following results are obtained running `node benchmark.js` at the root of the projet.
```
Filter count per tested keyword: 10000
> Benchmarking keyword: equals
Indexation: time = 0.255s, mem = +39MB
Matching x 10,320,209 ops/sec ±0.70% (95 runs sampled)
Filters removal: time = 0.018s
> Benchmarking keyword: exists
Indexation: time = 0.285s, mem = +20MB
Matching x 5,047,932 ops/sec ±0.23% (97 runs sampled)
Filters removal: time = 0.021s
> Benchmarking keyword: geoBoundingBox
Indexation: time = 0.685s, mem = +-8MB
Matching x 1,322,528 ops/sec ±0.52% (94 runs sampled)
Filters removal: time = 0.092s
> Benchmarking keyword: geoDistance
Indexation: time = 1.052s, mem = +3MB
Matching x 1,656,882 ops/sec ±0.65% (96 runs sampled)
Filters removal: time = 0.094s
> Benchmarking keyword: geoDistanceRange
Indexation: time = 1.551s, mem = +20MB
Matching x 1,344,257 ops/sec ±2.83% (90 runs sampled)
Filters removal: time = 0.101s
> Benchmarking keyword: geoPolygon (5 vertices)
Indexation: time = 0.818s, mem = +-74MB
Matching x 112,091 ops/sec ±0.54% (97 runs sampled)
Filters removal: time = 0.098s
> Benchmarking keyword: in (5 random values)
Indexation: time = 0.974s, mem = +90MB
Matching x 3,579,507 ops/sec ±2.93% (92 runs sampled)
Filters removal: time = 0.058s
> Benchmarking keyword: range (random bounds)
Indexation: time = 0.276s, mem = +-72MB
Matching x 122,311 ops/sec ±1.28% (96 runs sampled)
Filters removal: time = 0.074s
```
_(results obtained with node v16.2.0)_