$ cat services.json

MongoDB Schema Design

Design efficient MongoDB schemas for your use case.

Deliverables:

Document modeling
Embedding vs referencing decisions
Index strategy
Sharding design
Migration planning

Performance Optimization

Optimize slow MongoDB queries and operations.

Deliverables:

Query analysis
Index optimization
Aggregation pipeline tuning
Memory optimization
Monitoring setup

MongoDB Integration

Integrate MongoDB with your application stack.

Deliverables:

ODM setup (Mongoose, Motor)
Connection pooling
Transaction implementation
Change streams
Backup strategy

$ man mongodb

MongoDB vs PostgreSQL

MongoDB - Document database

Best for: Flexible schemas, rapid iteration
Pros: Schema flexibility, horizontal scaling
Cons: No ACID by default, eventual consistency

PostgreSQL - Relational database

Best for: Structured data, complex queries
Pros: ACID compliance, advanced SQL
Cons: Less flexible schema

I help you choose based on your specific needs.

MongoDB Patterns

Embedding - Nested documents

Use for: One-to-one, one-to-few
Pros: Single query, atomic updates

Referencing - Document references

Use for: One-to-many, many-to-many
Pros: Normalized, smaller documents

Bucket Pattern - Time series data Outlier Pattern - Handle edge cases Computed Pattern - Pre-computed aggregations

$ cat README.md

MongoDB Aggregation Pipeline

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// Complex Analytics Query
db.events.aggregate([
  // Filter by date range
  {
    $match: {
      createdAt: {
        $gte: ISODate("2024-01-01"),
        $lt: ISODate("2024-02-01")
      },
      type: "page_view"
    }
  },
  // Group by user and page
  {
    $group: {
      _id: {
        userId: "$userId",
        page: "$page"
      },
      viewCount: { $sum: 1 },
      avgDuration: { $avg: "$duration" }
    }
  },
  // Reshape output
  {
    $project: {
      _id: 0,
      userId: "$_id.userId",
      page: "$_id.page",
      viewCount: 1,
      avgDuration: { $round: ["$avgDuration", 2] }
    }
  },
  // Sort by views
  { $sort: { viewCount: -1 } },
  // Limit results
  { $limit: 100 }
]);

MongoDB Schema Design

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// Embedded Pattern for User Profile
const userSchema = {
  _id: ObjectId,
  email: String,
  profile: {
    firstName: String,
    lastName: String,
    avatar: String,
    preferences: {
      theme: String,
      notifications: Boolean
    }
  },
  // Embedded for small arrays
  recentActivity: [
    {
      action: String,
      timestamp: Date,
      metadata: Object
    }
  ]
};

// Reference Pattern for Large Collections
const orderSchema = {
  _id: ObjectId,
  userId: ObjectId,  // Reference to users
  items: [
    {
      productId: ObjectId,  // Reference to products
      quantity: Number,
      price: Number
    }
  ],
  total: Number,
  status: String,
  createdAt: Date
};

$ ls -la projects/

Analytics Storage

@ Flowrite

Challenge:

Store and query user analytics with flexible schema.

Solution:

MongoDB with time-series optimization, aggregation pipelines for dashboards.

Result:

Fast analytics queries, easy schema evolution.

FinTech Transaction System

@ Sutraq Technologies

Challenge:

Store financial transactions with audit trails.

Solution:

MongoDB with transactions, change streams for audit, and proper indexing.

Result:

99.9% uptime, reliable transaction processing.

Recruitment Platform

@ Crowdbotics (RecruitBot)

Challenge:

Store 600M+ candidate profiles with flexible attributes.

Solution:

MongoDB sharded cluster with optimized indexes and aggregation pipelines.

Result:

Fast candidate search and matching.

🍃 MongoDB

$ cat services.json

MongoDB Schema Design

Performance Optimization

MongoDB Integration

$ man mongodb

MongoDB vs PostgreSQL

MongoDB Patterns

$ cat README.md

MongoDB Aggregation Pipeline

MongoDB Schema Design

$ ls -la projects/

Analytics Storage

FinTech Transaction System

Recruitment Platform

$ diff me competitors/

Optimize Your MongoDB