MongoSense Query Builder

MongoSense is a flexible and easy-to-use MongoDB aggregation pipeline builder. It supports chaining, conditional stage inclusion, logging for debugging purposes, and advanced query optimization via the IntelliOptimizer engine. Build complex pipelines easily with methods that map directly to MongoDB’s aggregation framework while optimizing performance.

Features

• Chaining: Chain multiple aggregation stages easily.
• Conditional Query: Skip stages when conditions aren't met (e.g., when parameters are null or undefined).
• Logging: Enable debugMode to log the pipeline construction process for debugging.
• MongoDB Aggregation Stages: Supports all major MongoDB aggregation stages.
• Intelli Optimization: The IntelliOptimizer engine provides performance enhancements, including:
  • Index Recommendation: Automatically suggests indexes for fields used in queries.
  • Index Creation: Optionally create indexes for recommended fields.
  • Pipeline Reordering: Optimizes the order of pipeline stages for better performance.

Installation

npm install mongosense

MongoSense Factory Function

The MongoSense() function is a factory that creates and returns an instance of the MongoSenseQueryBuilder class. This builder provides a flexible way to construct MongoDB aggregation pipelines using chained methods.

The MongoSenseQueryBuilder class provides the following key methods:

• collection(): Select one or more collections.
• match(): Add a $match stage to filter documents.
• sort(): Add a $sort stage to order documents.
• limit(): Add a $limit stage to restrict the number of documents.
• skip(): Add a $skip stage to skip a number of documents.
• lookup(): Add a $lookup stage for left outer joins.
• addFields(), $bucket(), $bucketAuto(), $count(), $facet(), $project(), $unwind(), $out(), $replaceRoot(), $merge(), $redact(), $sample().

Usage Example

import { MongoSense } from 'mongosense';

const builder = new MongoSense(true)  // Enable debug mode
  .collection('users')
  .match({ isActive: true })  // Add $match stage
  .addFields({ fullName: { $concat: ['$firstName', ' ', '$lastName'] } })  // Add $addFields stage
  .project({ firstName: 1, lastName: 1, fullName: 1 })  // Add $project stage
  .unwind('$orders')  // Add $unwind stage
  .count('orderCount')  // Add $count stage
  .sample(10)  // Add $sample stage
  .build();

console.log(builder);

Output:

{
  "pipeline": [
    { "$match": { "isActive": true } },
    { "$addFields": { "fullName": { "$concat": ["$firstName", " ", "$lastName"] } } },
    { "$project": { "firstName": 1, "lastName": 1, "fullName": 1 } },
    { "$unwind": "$orders" },
    { "$count": "orderCount" },
    { "$sample": { "size": 10 } }
  ],
  "collections": ["users"]
}

API Documentation

MongoSense(debugMode: boolean = false)

Creates a new instance of the MongoSenseQueryBuilder.

• Parameters:
  • debugMode: When set to true, enables logging of pipeline construction. Default is false.

Collection Selector

The collection() method allows you to specify one or more MongoDB collections that the query will target. This is useful for operations like $lookup or for multi-collection queries.

// Example: Single Collection
const builder = MongoSense().collection('users');

// Example: Multiple Collections
const builder = MongoSense().collection('users', 'orders');

You can also chain the collection selector with other methods, as shown:

const pipeline = MongoSense()
  .collection('users')
  .build();

$match Stage

The match() method is used to add a $match stage to the MongoDB aggregation pipeline. It allows you to filter documents based on a given set of criteria.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .match({ isActive: true })  // Filter for active users
  .build();

console.log(pipeline);

// Output:
// [
//   { $match: { isActive: true } }
// ]

• Parameters:
  • criteria: An object representing the filter criteria. This is similar to the MongoDB find() query.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$sort Stage

The sort() method is used to add a $sort stage to the MongoDB aggregation pipeline. It allows you to sort documents based on specific fields in either ascending or descending order.

Example:

const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .match({ isActive: true })  // Filter for active users
  .sort({ age: 1 })  // Sort by age in ascending order
  .build();

console.log(pipeline);
// Output:
// [
//   { $match: { isActive: true } },
//   { $sort: { age: 1 } }
// ]

• Parameters:
  • sortCriteria: An object specifying the field names as keys and the sort order as values. Use 1 for ascending and -1 for descending.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$limit and $skip Stages

The limit() and skip() methods are used to add $limit and $skip stages to the MongoDB aggregation pipeline. These stages are essential for pagination, where skip() is used to skip a certain number of documents and limit() is used to return a limited number of documents.

// Example:

const pageSize = 10;
const pageNumber = 3;  // For page 3
const skip = (pageNumber - 1) * pageSize;

const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .skip(skip)  // Skip the first 20 documents (for page 3)
  .limit(pageSize)  // Return 10 documents
  .build();

console.log(pipeline);
// Output:
// [
//   { $skip: 20 },
//   { $limit: 10 }
// ]

• Parameters:
  • limit: The number of documents to return.
  • skip: The number of documents to skip.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

Pagination Logic:

When implementing pagination, you typically calculate how many documents to skip based on the current page number and the page size (number of items per page). Here's the formula:

• Skip Formula: skip = (pageNumber - 1) * pageSize
• Limit Formula: limit = pageSize With the skip() and limit() methods, you can easily create a paginated query.

$lookup Stage

The lookup() method is used to add a $lookup stage to the MongoDB aggregation pipeline. This stage performs a left outer join with another collection, allowing you to merge documents from two collections.

// Example:
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .lookup('orders', '_id', 'userId', 'userOrders')  // Join with the 'orders' collection
  .build();

console.log(pipeline);
// Output:
// [
//   {
//     $lookup: {
//       from: 'orders',
//       localField: '_id',
//       foreignField: 'userId',
//       as: 'userOrders'
//     }
//   }
// ]

• Parameters:
  • from: The target collection to join with.
  • localField: The field from the current collection to match with the foreignField.
  • foreignField: The field from the target collection to match with the localField.
  • as: The name of the field where the joined documents will be stored.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$group Stage

The group() method is used to add a $group stage to the MongoDB aggregation pipeline. This stage allows you to group documents by a specified key and perform various aggregation operations, such as $sum, $avg, $min, and $max.

// Example:
const pipeline = MongoSense()
  .collection('sales')  // Select the 'sales' collection
  .group({ category: "$category" }, { totalSales: { $sum: "$amount" } })  // Group by category and sum total sales
  .build();

console.log(pipeline);
// Output:
// [
//   {
//     $group: {
//       _id: { category: "$category" },
//       totalSales: { $sum: "$amount" }
//     }
//   }
// ]

• Parameters:
  • groupBy: Specifies the field (or fields) to group by.
  • accumulations: Defines the aggregation operations, such as $sum, $avg, $min, or $max.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$addFields Stage

The addFields() method is used to add new fields to documents in the MongoDB aggregation pipeline.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .addFields({ fullName: { $concat: ['$firstName', ' ', '$lastName'] } })  // Add a fullName field
  .build();

console.log(pipeline);

// Output:
// [
//   { $addFields: { fullName: { $concat: ['$firstName', ' ', '$lastName'] } } }
// ]

• Parameters:
  • fields: An object defining the new fields to add.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$bucket Stage

The bucket() method is used to group documents into user-defined buckets based on a specified field.

// Example
const pipeline = MongoSense()
  .collection('sales')  // Select the 'sales' collection
  .bucket({
    groupBy: "$amount",
    boundaries: [0, 100, 200, 300, 400],
    default: "Other",
    output: {
      count: { $sum: 1 },
      totalAmount: { $sum: "$amount" }
    }
  })  // Group sales by amount
  .build();

console.log(pipeline);

// Output:
// [
//   { $bucket: { groupBy: "$amount", boundaries: [0, 100, 200, 300, 400], default: "Other", output: { count: { $sum: 1 }, totalAmount: { $sum: "$amount" } } } }
// ]

• Parameters:
  • bucketSpec: The bucket specification object.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$bucketAuto Stage

The bucketAuto() method is used to automatically group documents into a specified number of buckets based on a field.

// Example
const pipeline = MongoSense()
  .collection('sales')  // Select the 'sales' collection
  .bucketAuto({
    groupBy: "$amount",
    buckets: 4,
    output: {
      count: { $sum: 1 },
      totalAmount: { $sum: "$amount" }
    }
  })  // Automatically group sales into 4 buckets
  .build();

console.log(pipeline);

// Output:
// [
//   { $bucketAuto: { groupBy: "$amount", buckets: 4, output: { count: { $sum: 1 }, totalAmount: { $sum: "$amount" } } } }
// ]

• Parameters:
  • bucketAutoSpec: The auto bucket specification object.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$count Stage

The count() method is used to count the number of documents that pass through the pipeline.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .count('userCount')  // Count the number of users
  .build();

console.log(pipeline);

// Output:
// [
//   { $count: "userCount" }
// ]

• Parameters:
  • field: The name of the field where the count will be stored.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$facet Stage

The facet() method is used to run multiple aggregation pipelines in parallel and merge the results.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .facet({
    ageFacet: [
      { $match: { age: { $gte: 18 } } },
      { $count: "adultCount" }
    ],
    locationFacet: [
      { $match: { location: { $exists: true } } },
      { $count: "locationCount" }
    ]
  })  // Run two pipelines: one to count adults, one to count users with locations
  .build();

console.log(pipeline);

// Output:
// [
//   { $facet: { ageFacet: [{ $match: { age: { $gte: 18 } } }, { $count: "adultCount" }], locationFacet: [{ $match: { location: { $exists: true } } }, { $count: "locationCount" }] } }
// ]

• Parameters:
  • facetSpec: An object containing multiple pipelines to run in parallel.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$project Stage

The project() method is used to include, exclude, or add new fields to documents in the MongoDB aggregation pipeline.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .project({ firstName: 1, lastName: 1, fullName: { $concat: ['$firstName', ' ', '$lastName'] } })  // Include fullName field
  .build();

console.log(pipeline);

// Output:
// [
//   { $project: { firstName: 1, lastName: 1, fullName: { $concat: ['$firstName', ' ', '$lastName'] } } }
// ]

• Parameters:
  • projection: An object specifying the fields to include, exclude, or compute.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$unwind Stage

The unwind() method is used to deconstruct an array field into separate documents.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .unwind('$orders')  // Unwind the 'orders' array field
  .build();

console.log(pipeline);

// Output:
// [
//   { $unwind: "$orders" }
// ]

• Parameters:
  • path: The path to the array field to unwind.
  • options: Additional unwind options (optional).
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$out Stage

The out() method is used to write the results of the pipeline to a specified collection.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .out('usersArchive')  // Write the output to the 'usersArchive' collection
  .build();

console.log(pipeline);

// Output:
// [
//   { $out: "usersArchive" }
// ]

• Parameters:
  • collection: The name of the collection to output the results to.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$replaceRoot Stage

The replaceRoot() method is used to replace the root document with a new document.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .replaceRoot({ newRoot: "$contactInfo" })  // Replace root with the 'contactInfo' document
  .build();

console.log(pipeline);

// Output:
// [
//   { $replaceRoot: { newRoot: "$contactInfo" } }
// ]

• Parameters:
  • newRoot: The document that will replace the root.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$merge Stage

The merge() method is used to merge the pipeline output into an existing collection.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .merge({
    into: "archivedUsers",
    whenMatched: "merge",
    whenNotMatched: "insert"
  })  // Merge output into the 'archivedUsers' collection
  .build();

console.log(pipeline);

// Output:
// [
//   { $merge: { into: "archivedUsers", whenMatched: "merge", whenNotMatched: "insert" } }
// ]

• Parameters:
  • mergeSpec: The merge specification.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$redact Stage

The redact() method is used to restrict the content of documents based on some criteria.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .redact({
    $cond: {
      if: { $eq: ['$role', 'admin'] },
      then: "$$DESCEND",
      else: "$$PRUNE"
    }
  })  // Restrict access to admin documents
  .build();

console.log(pipeline);

// Output:
// [
//   { $redact: { $cond: { if: { $eq: ['$role', 'admin'] }, then: "$$DESCEND", else: "$$PRUNE" } } }
// ]

• Parameters:
  • redactExpr: The redact expression object.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

$sample Stage

The sample() method is used to randomly select a specified number of documents from the collection.

// Example
const pipeline = MongoSense()
  .collection('users')  // Select the 'users' collection
  .sample(10)  // Randomly select 10 documents
  .build();

console.log(pipeline);

// Output:
// [
//   { $sample: { size: 10 } }
// ]

• Parameters:
  • size: The number of documents to randomly select.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

Conditional Query Construction

MongoSense allows for dynamic and flexible query building with conditional stages. You can add stages like $match, $sort, $limit, $skip, $lookup, $group, $addFields, $bucket, $bucketAuto, $count, $facet, $project, $unwind, $out, $replaceRoot, $merge, $redact, and $sample only if the input is provided. If null or undefined is passed, the stage is skipped.

// Example:
const pipeline = MongoSense()
  .collection('users')
  .match({ isActive: true })  // Add $match stage if criteria is provided
  .sort(null)  // Skip $sort stage if no sorting is needed
  .limit(10)  // Add $limit stage if provided
  .addFields(null)  // Skip $addFields stage if no fields are provided
  .sample(10)  // Add $sample stage if size is provided
  .build();

console.log(pipeline);
// Output:
// [
//   { $match: { isActive: true } },
//   { $limit: 10 },
//   { $sample: { size: 10 } }
// ]

• Conditional Stages:
  • If you pass null or undefined to any method, the corresponding stage will be skipped.
• Supported Stages:
  • $match, $sort, $limit, $skip, $lookup, $group, $addFields, $bucket, $bucketAuto, $count, $facet, $project, $unwind, $out, $replaceRoot, $merge, $redact, and $sample.
• Returns: The instance of the MongoSenseQueryBuilder for method chaining.

Intelli Optimization Engine

The IntelliOptimizer is an optional performance-first engine for query optimization, index recommendations, and index creation in MongoDB. This can be enabled by passing the IntelliOptimizer instance to the MongoSenseQueryBuilder. It helps reorder query stages for performance and suggests or automatically creates indexes for optimized querying.

Setup

To use the Intelli optimizer, you need to initialize the IntelliOptimizer class with a MongoDB connection and pass it to the MongoSenseQueryBuilder factory.

import { MongoClient } from 'mongodb';
import { MongoSense } from './queryBuilder';
import IntelliOptimizer from './intelli';

async function main() {
  const client = new MongoClient('mongodb://localhost:27017');
  await client.connect();

  // Initialize IntelliOptimizer with the MongoDB client
  const intelli = new IntelliOptimizer(client);

  const builder = MongoSense(true, intelli)
    .collection('users')
    .match({ isActive: true })  // $match stage
    .sort({ createdAt: -1 })    // $sort stage
    .limit(10);

  await builder.optimize();  // Perform optimizations (reordering and index recommendations)

  const pipeline = builder.build();
  console.log(pipeline);

  // Automatically create indexes if necessary
  await builder.createIndexes();

  await client.close();
}

main();

Intelli Features

1. Index Recommendation
   The Intelli engine can analyze query patterns (such as fields used in $match and $sort stages) and recommend indexes for those fields if they are not already indexed.

   const recommendations = await optimizer.analyzeAndRecommendIndexes('users', ['isActive', 'createdAt']);
   console.log('Recommended indexes:', recommendations);

2. Automatic Index Creation
   After recommendations are made, Intelli can automatically create indexes for those fields to optimize MongoDB queries.

   const createdIndexes = await optimizer.createIndexes('users', ['isActive', 'createdAt']);
   console.log('Created indexes:', createdIndexes);

3. Pipeline Optimization
   Intelli can reorder the pipeline stages, ensuring that $match and $sort stages appear earlier in the pipeline for better performance.

   await builder.optimize();  // Automatically reorders stages for performance

4. Debug Mode
   When debugMode is enabled, Intelli logs its internal operations (such as pipeline optimization and index recommendations) for inspection.

   const builder = MongoSense(true, intelli);  // Enable debug mode

Contributing

We welcome contributions! If you find a bug or have a feature request, please open an issue. Pull requests are also welcome.

To contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add new feature')
4. Push to your branch (git push origin feature/your-feature)
5. Open a pull request

License

This repository is licensed under the MIT License. See the LICENSE file for more details.

Additional License Considerations:

• Open Source Licensing: Your project is under the MIT License, which is one of the most permissive open-source licenses. It allows others to freely use, modify, and distribute your code as long as they include the original license and copyright notice.
• Attribution: The only requirement is attribution, meaning users must keep your copyright notice and the license terms in any distributed version of your code.
• Warranty Disclaimer: The license disclaims any warranties, protecting you from legal liability if the software doesn’t work as intended.