Mingo is a JavaScript library that provides MongoDB-like query and aggregation capabilities for in-memory data processing. It allows you to filter, sort, group, and transform data collections using a syntax similar to MongoDB queries.
Querying
This feature allows you to perform complex queries on your data collections. The example demonstrates how to filter a list of people to find those older than 26.
const mingo = require('mingo');
const data = [
{ name: 'John', age: 25 },
{ name: 'Jane', age: 30 },
{ name: 'Jim', age: 27 }
];
const query = new mingo.Query({ age: { $gt: 26 } });
const result = query.find(data).all();
console.log(result); // Output: [ { name: 'Jane', age: 30 }, { name: 'Jim', age: 27 } ]Aggregation
This feature allows you to perform aggregation operations on your data collections. The example demonstrates how to calculate the average salary of people older than 26.
const mingo = require('mingo');
const data = [
{ name: 'John', age: 25, salary: 50000 },
{ name: 'Jane', age: 30, salary: 60000 },
{ name: 'Jim', age: 27, salary: 55000 }
];
const pipeline = [
{ $match: { age: { $gt: 26 } } },
{ $group: { _id: null, averageSalary: { $avg: '$salary' } } }
];
const result = mingo.aggregate(data, pipeline);
console.log(result); // Output: [ { _id: null, averageSalary: 57500 } ]Projection
This feature allows you to project specific fields from your data collections. The example demonstrates how to retrieve only the 'name' and 'age' fields from a list of people.
const mingo = require('mingo');
const data = [
{ name: 'John', age: 25, salary: 50000 },
{ name: 'Jane', age: 30, salary: 60000 },
{ name: 'Jim', age: 27, salary: 55000 }
];
const query = new mingo.Query({});
const result = query.find(data).project({ name: 1, age: 1 }).all();
console.log(result); // Output: [ { name: 'John', age: 25 }, { name: 'Jane', age: 30 }, { name: 'Jim', age: 27 } ]Lodash is a JavaScript utility library that provides a wide range of functions for manipulating arrays, objects, and other data structures. While it does not offer MongoDB-like query syntax, it provides similar functionalities for filtering, sorting, and transforming data collections.
Underscore is another JavaScript utility library similar to Lodash. It provides a set of utility functions for working with arrays, objects, and other data types. Like Lodash, it does not offer MongoDB-like query syntax but provides similar data manipulation capabilities.
json-query is a library for querying JSON data structures using a simple query language. It provides functionalities for filtering and transforming JSON data, similar to Mingo, but with a different query syntax.
JavaScript implementation of MongoDB query language
Mingo harnesses the power of MongoDB-style queries and allows direct querying of in-memory javascript objects in both client and server-side environments.
$ npm install mingo
In browser
<!-- DO NOT FORGET Underscore -->
<script type="text/javascript" src="./underscore-min.js"></script>
<script type="text/javascript" src="./mingo.min.js"></script>
$all, $elemMatch, $size)$gt, $gte, $lt, $lte, $ne, $nin, $in)$exists, $type)$regex, $mod, $where)$and, $or, $nor, $not)$group, $match, $project, $sort, $limit, $unwind, $skip)$addToSet, $sum, $max, $min, $avg, $push, $first, $last)$elemMatch, $slice)$add, $divide, $mod, $multiply, $subtract)$size)$and, $or, $not)$cmp, $gt, $gte, $lt, $lte, $ne, $nin, $in)$cond, $ifNull)$dayOfYear, $dayOfMonth, $dayOfWeek, $year, $month, $week, $hour, $minute, $second, $millisecond, $dateToString)$literal)$setEquals, $setIntersection, $setDifference, $setUnion, $setIsSubset, $anyElementTrue, $allElementsTrue)$strcasecmp, $concat, $substr, $toLower, $toUpper)$map, $let)For documentation on using query operators see mongodb
var Mingo = require('mingo');
// or just access *Mingo* global in browser
// setup the key field for your collection
Mingo.setup({
key: '_id' // default
});
// create a query with criteria
// find all grades for homework with score >= 50
var query = new Mingo.Query({
type: "homework",
score: { $gte: 50 }
});
// filter collection with find()
var cursor = query.find(collection);
// shorthand with query criteria
// cursor = Mingo.find(collection, criteria);
// sort, skip and limit by chaining
cursor.sort({student_id: 1, score: -1})
.skip(100)
.limit(100);
// count matches
cursor.count();
// iterate cursor
// iteration is forward only
while (cursor.hasNext()) {
console.log(cursor.next());
}
// use first(), last() and all() to retrieve matched objects
cursor.first();
cursor.last();
cursor.all();
// Filter non-matched objects (
var result = query.remove(collection);
var agg = new Mingo.Aggregator([
{'$match': { "type": "homework"}},
{'$group':{'_id':'$student_id', 'score':{$min:'$score'}}},
{'$sort':{'_id': 1, 'score': 1}}
]);
var result = agg.run(collection);
// shorthand
result = Mingo.aggregate(
collection,
[
{'$match': { "type": "homework"}},
{'$group':{'_id':'$student_id', 'score':{$min:'$score'}}},
{'$sort':{'_id': 1, 'score': 1}}
]
);
var JSONStream = require('JSONStream'),
fs = require('fs'),
Mingo = require('mingo');
var query = new Mingo.Query({
scores: { $elemMatch: {type: "exam", score: {$gt: 90}} }
}, {name: 1});
file = fs.createReadStream('./students.json');
var qs = query.stream();
qs.on('data', function (data) {
console.log(data); // log filtered outputs
// ex. { name: 'Dinah Sauve', _id: 49 }
});
file.pipe(JSONStream.parse("*")).pipe(qs);
// using with Backbone
var Grades = Backbone.Collection.extend(Mingo.CollectionMixin);
var grades = new Grades(collection);
// find students with grades less than 50 in homework or quiz
// sort by score ascending and type descending
cursor = grades.query({
$or: [{type: "quiz", score: {$lt: 50}}, {type: "homework", score: {$lt: 50}}]
}).sort({score: 1, type: -1}).limit(10);
// print grade with the lowest score
cursor.first();
$geoWithin, $geoIntersects, $near, $nearSphere)$geometry, $maxDistance, $center, $centerSphere, $box, $polygon)MIT
v0.6.5 / 2016-07-04
FAQs
MongoDB query language for in-memory objects
We found that mingo demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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