MongoDB

• Tuning
• MongoDB: average value of field
• MongoDB : sorting documents by multiple fields
• MongoDB: shell commands examples
• MongoDB: find documents matching regular expression

Tuning

This video provides an overview of the MongoDB architecture for query performance:

06:26

MongoDB

YouTube • 9 Jul 2025

1. Data Modeling & Indexing:
   • Embed vs. Reference: Model data for common access patterns, embedding related data for fewer reads, but referencing when data is shared or grows large (minimizing document size).
   • Index Strategically: Create indexes on fields used in find(), sort(), and aggregate() stages. Use compound indexes for multi-field queries.
   • Avoid Collection Scans: Use explain() to ensure queries use index scans, not full collection scans. 

This video explains the importance of indexes for performance:

58s

solarwindsinc

YouTube • 12 Oct 2021

2. Query & Aggregation Optimization:
   • Use the Profiler: Enable the database profiler to find slow queries exceeding a threshold.
   • explain() Plan: Analyze explain() output to see if indexes are used, how many documents are examined, and identify bottlenecks.
   • Projection: Use projection to return only needed fields, reducing network traffic and memory.
   • Aggregation Pipelines: Optimize stages, push filters down, and use appropriate operators for complex data processing. 

This video demonstrates how to use the profiler and analyze query plans:

1m

CodeLucky

YouTube • 30 Aug 2025

3. Server & Storage Engine Tuning:
   • WiredTiger Tickets: Adjust wiredTigerTicketValues for read/write concurrency if operations queue up (tickets hit 0).
   • Memory: Configure storage.wiredTiger.engineConfig.cacheSizeGB for optimal WiredTiger cache size.
   • Compression: Enable compression (e.g., Snappy, zlib) to reduce I/O and storage, often improving performance. 

This video discusses memory settings and their impact on performance:

47s

Percona

YouTube • 19 Jan 2021

4. Connection Management:
   • Connection Pooling: Tune minPoolSize, maxPoolSize, and socketTimeoutMS in your drivers to match application load and network conditions. 

This video explores patterns for tuning MongoDB performance and scalability:

1m

MongoDB

YouTube • 21 Nov 2022

5. Hardware & OS (Advanced):
   • NUMA Settings: Configure BIOS/OS settings (like iommu=pt) for optimal CPU/memory interaction on NUMA systems. 

• Monitor: Use MongoDB Atlas metrics or tools like mongostat, mongotop, and the profiler.
• Methodical Changes: Apply changes one at a time and measure the impact.
• Balance: Indexing speeds up reads but slows writes; find the right balance for your workload. 

MongoDB: average value of field

{ "_id": 1, "item": "A", "price": 10 }
{ "_id": 2, "item": "B", "price": 15 }
{ "_id": 3, "item": "C", "price": 20 }
{ "_id": 4, "item": "D", "price": 12 }

db.products.aggregate([
  {
    $group: {
      _id: null, // Group all documents into a single group
      averagePrice: { $avg: "$price" } // Calculate the average of the 'price' field
    }
  }
])

{ "_id": null, "averagePrice": 14.25 }

db.products.aggregate([
  {
    $group: {
      _id: "$item", // Group documents by the 'item' field
      averagePrice: { $avg: "$price" } // Calculate the average of 'price' for each group
    }
  }
])

{ "_id": 1, "item": "A", "price": 10 }
{ "_id": 2, "item": "B", "price": 15 }
{ "_id": 3, "item": "A", "price": 20 }
{ "_id": 4, "item": "B", "price": 12 }

[
  { "_id": "B", "averagePrice": 13.5 },
  { "_id": "A", "averagePrice": 15 }
]

MongoDB : sorting documents by multiple fields

{ "name": "Alice", "grade": "A", "age": 18 }
{ "name": "Bob", "grade": "B", "age": 19 }
{ "name": "Charlie", "grade": "A", "age": 17 }
{ "name": "David", "grade": "B", "age": 18 }

db.students.find().sort({ grade: 1, age: -1 });

{ "name": "Charlie", "grade": "A", "age": 17 }
{ "name": "Alice", "grade": "A", "age": 18 }
{ "name": "Bob", "grade": "B", "age": 19 }
{ "name": "David", "grade": "B", "age": 18 }

MongoDB: shell commands examples

    show dbs

    use myDatabase

    db

    show collections

    db.createCollection("myCollection")

    db.myCollection.insertOne({ name: "Alice", age: 30, city: "New York" })

    db.myCollection.insertMany([
        { name: "Bob", age: 25, city: "London" },
        { name: "Charlie", age: 35, city: "Paris" }
    ])

    db.myCollection.find()

    db.myCollection.find({ age: { $gt: 28 } }) // Find documents where age is greater than 28

    db.myCollection.find({}, { name: 1, city: 1, _id: 0 }) // Include name and city, exclude _id

    db.myCollection.updateOne({ name: "Alice" }, { $set: { age: 31 } })

    db.myCollection.updateMany({ city: "London" }, { $set: { status: "active" } })

    db.myCollection.deleteOne({ name: "Bob" })

    db.myCollection.deleteMany({ status: "inactive" })

    cls

    exit

MongoDB: find documents matching regular expression

db.collectionName.find({ fieldName: /pattern/options });

db.users.find({ name: /john/i });

db.collectionName.find({ fieldName: { $regex: "pattern", $options: "options" } });

db.products.find({ description: { $regex: "eco-friendly", $options: "i" } });