07. Window Operators ($setWindowFields) & Accumulator Functions • MongoDB & NoSQL Architecture: CRUD to Sharding & WiredTiger • Rishabh Ki Kaksha

MongoDB & NoSQL Architecture: CRUD to Sharding & WiredTiger › 07. Window Operators ($setWindowFields) & Accumulator Functions

45 mins

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Course Syllabus

Module 1: MongoDB Foundations, BSON & CRUD Mechanics

01. NoSQL vs SQL: CAP Theorem & Document-Oriented Architecture 02. BSON Data Types & Document Schema Design (Embedding vs Referencing) 03. High-Performance CRUD Operations, BulkWrite & Write Concerns 04. Schema Validation with JSON Schema ($jsonSchema)

Module 2: Advanced Aggregation Framework & Data Pipelines

05. Aggregation Pipeline Core: $match, $project, $group, $sort & $limit 06. Complex Transformations: $unwind, $lookup (Joins) & $facet (Multi-Facet Analytics) 07. Window Operators ($setWindowFields) & Accumulator Functions

Module 3: Indexing Strategies & Query Optimization

08. B-Tree Indexes in MongoDB: Single Field, Compound & Partial Indexes 09. Special Indexes: TTL (Time-To-Live), Geospatial (2dsphere) & Text Indexes 10. Query Profiling, explain("executionStats") & Index Covering Analysis

Module 4: Storage Engine Internals (WiredTiger) & ACID Transactions

11. WiredTiger Storage Engine Architecture: Cache, Compression & Checkpoints 12. Read Concerns (local, majority, linearizable) & Write Concerns (w:majority, j:true) 13. Multi-Document Distributed ACID Transactions with Sessions

Module 5: High Availability, Replica Sets, Sharding & Backups

14. Replica Sets Architecture: Primary Election, Oplog & Automatic Failover 15. Sharding Architecture: mongos Router, Config Servers & Shard Key Selection 16. Range vs Hash Sharding, Chunk Balancing & Jumbo Chunks Mitigation 17. Change Streams: Building Real-Time Event-Driven Pipelines with MongoDB 18. Production Backup Strategies: mongodump/mongorestore, Point-in-Time & Atlas CLI

Interactive Lesson Studio

# 07. Window Operators ($setWindowFields) & Accumulator Functions

> MongoDB Notes by Rishabh - Topic 07

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### 1. Window Functions Kya Hain? (MongoDB 5.0+ Analytical Power 🪟)

Normal `$group` saare documents ko collapse karke summary rows bana deta hai (Individual rows lose ho jaati hain).  
**`$setWindowFields` (Window Function)** har individual document ko intact rakhte huye **uske aas-paas ke data window (Running Total, Moving Average, Rank) ka calculated value append karta hai!**

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### 2. Real-World Example: Running Sales Total & Department Salary Ranking

```javascript
await db.collection('employees').aggregate([
    {
        $setWindowFields: {
            // Partition by Department (Sales, Engineering, Marketing):
            partitionBy: "$department",
            // Sort within partition by Salary Descending:
            sortBy: { salary: -1 },
            output: {
                // 1. Department Rank (1, 2, 3...):
                deptRank: { $denseRank: {} },
                // 2. Cumulative Running Salary Sum:
                runningTotalSalary: {
                    $sum: "$salary",
                    window: { documents: ["unbounded", "current"] }
                },
                // 3. Difference from Next Employee (Lead/Lag):
                nextPersonSalary: {
                    $shift: { output: "$salary", by: 1, default: 0 }
                }
            }
        }
    }
]);
```

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### 3. Master Comparison Matrix

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### 4. Interview Ke 3 Sawal Is Topic Se

1. **`$rank` aur `$denseRank` me kya difference hai?** -> Agar 2 employees ki salary same ho aur dono ko Rank 1 mile, toh `$rank` agle person ko Rank 3 dega (gaps allow karta hai); `$denseRank` agle person ko Rank 2 dega (zero gaps).
2. **`$shift` operator ka practical use case kya hota hai?** -> Stock market price trends me current day ke price ko previous day (`by: -1`) ke price se compare karke daily percentage gain calculate karna.
3. **Window boundaries me `"unbounded"` ka kya matlab hai?** -> Partition ke pehle document se lekar current document tak ka running frame calculate karna.

---
**Next Topic:** 08. B-Tree Indexes in MongoDB: Single Field, Compound & Partial Indexes

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