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The Smartest IDE for MongoDB
NoSQLBooster for MongoDB (formerly MongoBooster) is a shell-centric cross-platform GUI tool for MongoDB v2.6-4.0, which provides fluent query builder, SQL query, update-in-place, ES2017 syntax support and true intellisense experience.
Embedded Shell
NoSQLBooster for MongoDB embeds V8 JavaScript engine. No external MongoDB command line tools dependence.
MongoDB 2.6 to 4.0
NoSQLBooster for MongoDB can connect MongoDB server and fully support its mongo shell commands from v2.6 to v4.0.
Enterprise Edition
Support MongoDB Enterprise Edition by adding authentication mechanisms: Kerberos(GSSAPI) & LDAP(PLAIN).
Why Choose NoSQLBooster for MongoDB?
True IntelliSense
NoSQLBooster for MongoDB offers true IntelliSense experience. The build-in language service knows all possible completions, methods, properties, variables, key words, even the MongoDB collection names, field names and operators. The IntelliSense suggestions will pop up as you type. You can always manually trigger it with Ctrl-Shift-Space. Out of the box, Ctrl-Space, Alt-Space are acceptable triggers.
• In the script editor, parameter hints will pop up as you're typing a method invocation.
• We offer tons of build-in snippets, Handy date range snippets, SQL to MongoDB conversion snippets...
• The mouse hover will show many useful information, such as types of symbols, function definition, type information and document.
• Matching brackets will be highlighted as soon as the cursor is near one of them.
Query MongoDB with SQL
With NoSQLBooster for MongoDB, you can run SQL SELECT Query against MongoDB. SQL support includes functions, expressions, aggregation for collections with nested objects and arrays.
Let's look at how to use the GROUP BY clause with the SUM function in SQL.
Instead of writing the MongoDB query which is represented as a JSON-like structure
db.employees.aggregate([{
$group: { _id: "$department", total: { $sum: "$salary" }}
}])
You can query MongoDB by using old SQL which you probably already know
SELECT department, SUM(salary) AS total FROM employees GROUP BY department
SQL Query Features
• Access data via SQL including WHERE filters, ORDER BY, GROUP BY, HAVING, DISTINCT, LIMIT
• SQL Functions (COUNT, SUM, MAX, MIN, AVG)
• Aggregation Pipeline Operators as SQL Functions (dateToString, toUpper, split, substr...)
• Provide a programming interface (mb.runSQLQuery) that can be integrated into your script
• Autocomplete for key words, MongoDB collection names, field names and SQL functions
Please note that SQL features are not natively supported by MongoDB. The SQL query is validated and translated into a MongoDB query and executed by NoSQLBooster for MongoDB. The Equivalent MongoDB Query can be viewed in console.log tab.
Click here to learn how to run SQL SELECT Query against MongoDB
Two-Way Query Builder for MongoDB
NoSQLBooster for MongoDB comes with visual query builder. The two-way query builder could help you construct and display complex MongoDB find statements even without the knowledge of the MongoDB shell commands syntax.
Fluent MongoDB Query builder (Unique Feature)
NoSQLBooster for MongoDB supports mongoose-like fluent query builder API. A Query enables you to build up a query using chaining syntax, rather than specifying a JSON object. The aggregation framework is now fluent as well. You can use it as currently documented or via the chainable methods.
// instead of writing:
db.user.find({age:{$gte:18,$lte:65}},{name:1,age:1,_id:0}).sort({age:-1, name:1});
// we can write:
db.user.where('age').gte(1 .lte(65).select('name age -_id').sort("-age name" ;
// passing query conditions is permitted too
db.collection.find().where({ name: 'nosqlbooster' })
// chaining
db.collection
.where('age').gte(1.lte(65)
.where({ 'name': /^nosqlbooster/i })
.where('friends').slice(10)
// aggregation
db.companies.aggregate()
.match(qb.where('founded_year').gte(2000).lte(2010)) //qb:querybuilder
.group({_id:"$category_code",count:{$sum:1}})
.sort('-count')
.limit(100)
Using Node Modules in your script (Unique Feature)
You can assemble npm packages like building blocks in your MongoDB shell script. The npm registry hosts almost half a million packages of free, reusable code — the largest software registry in the world.
Launch NoSQLBooster for MongoDB. - Execute Main Menu -> Help -> Open User Data Directory - New Terminal at this folder
npm i axios # run it in NoSQLBooster for MongoDB user data directory
After successfully installing this package in the NoSQLBooster for MongoDB User Data Directory, you can require and access it in the NoSQLBooster for MongoDB script.
const axios=require("axios";
let rst=await (axios.get('https://api.github.com/users/github'));//await promise
console.log(rst.data);
The "await" is a build-in method in NoSQLBooster for MongoDB. It's a common js method, not a keyword. It can await a promise or a promise array. Note this await function is different from es7 await, this await function may be used in functions without the async keyword marked.
Run it and got result.
{
"avatar_url" : "https://avatars2.githubusercontent.com/u/9919?v=3",
"bio" : "How people build software.",
"blog" : "https://github.com/about",
"company" : null,
"created_at" : "2008-05-11T04:37:31Z",
...
...
}
Schema Analyzer
Schema Analyzer is a very useful build-in tool. Due to schema-less feature, collections in MongoDB do not have a schema document to describe field's datatype, collection structure and validations. With our brand new Schema Analyzer Tool, you will get a document to describe the schema of certain collection from sampled(random, first, last) N or all records.
The document shows the probability of sampled objects , different types percentage, you could get a brief of certain collection's schema. If you want more accurate result, you could sample more records or analyze whole collection, but it may took a long time to finish if the collection has millions records or thousands fields.
It also shows document validation of the collection, which is a new feature in MongoDB 3.2. There will be a validator window showing below the document. If you click the link, field will be highlighted in the window.
You could export this document to most popular document file types, like MS Word, PDF, HTML, along with JSON, TXT and CSV. Mongoose.js schema file supported as well. Click here to view sample schema file
Shell Extensions - Lodash, Shelljs, Moment.js and Mathjs
NoSQLBooster for MongoDB includes a few useful MongoDB shell extensions to make the life inside of the MongoDB shell a little bit easier.
ES2016 (formally ES7) is a fantastic step forward for the JavaScript language. It lets us write dramatically more concise and readable MongoDB shell script, thanks to built-in support for block variable scoping, arrow functions, template strings, and numerous other improvements to the language.
NoSQLBooster for MongoDB integrates lodash, shelljs, momentjs and mathjs utility library. You can directly use lodash , shelljs, moment and mathjs in your shell script.
• Lodash is a toolkit of Javascript functions that provides clean, performant methods for manipulating objects and collections.
• ShellJs ShellJS is a portable (Windows/Linux/OS X) implementation of Unix shell commands on top of the Node.js API.
• Moment.js is a full featured date library for parsing, validating, manipulating, and formatting dates.
• Mathjs is an extensive math library for JavaScript and Node.js.
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Installation/Activation Instruction is Included in the folder!
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