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Sharding Architecture
Client -> mongos router -> Shard 1 (Replica Set)
-> Shard 2 (Replica Set)
-> Shard 3 (Replica Set)
Config Server Replica Set
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Shard Key Strategy
🇬🇧 English Definition
A shard key determines how data is distributed across cluster shards. Targeted queries including the shard key avoid scatter-gather broadcasts across all nodes.
🇮🇳 Hinglish Definition
Shard key choose karte waqt high cardinality aur query targeting ka dhyan rakhna chahiye taaki data evenly distribute ho.
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Replica Set Architecture
Application -> Primary (Writes) -> Replication -> Secondary 1 (Reads)
-> Secondary 2 (Reads)
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SQL → MongoDB Translation Cheat Sheet
| SQL Statement | MongoDB MQL Equivalent |
|---|---|
| SELECT * FROM users | db.users.find() |
| WHERE age > 18 | db.users.find({age: {$gt: 18}}) |
| SELECT name FROM users | db.users.find({}, {name:1, _id:0}) |
| ORDER BY age DESC | db.users.find().sort({age:-1}) |
| LIMIT 10 | db.users.find().limit(10) |
| GROUP BY city | db.users.aggregate([{$group:{_id:'$city',count:{$sum:1}}}]) |
| UPDATE users SET age=23 WHERE name='Aman' | db.users.updateOne({name:'Aman'},{$set:{age:23}}) |
| DELETE FROM users WHERE name='Aman' | db.users.deleteOne({name:'Aman'}) |
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20 Must-Know Commands — Quick Revision
show dbs
use college
db
show collections
db.createCollection("students")
db.students.insertOne({...})
db.students.insertMany([...])
db.students.find()
db.students.findOne({...})
db.students.updateOne({...}, {...})
db.students.updateMany({...}, {...})
db.students.replaceOne({...}, {...})
db.students.deleteOne({...})
db.students.deleteMany({...})
db.students.createIndex({...})
db.students.getIndexes()
db.students.aggregate([...])
db.stats()
db.runCommand({ping:1})
db.adminCommand({serverStatus:1})
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10 Practical Real-World Projects
🚀 Portfolio Projects Overview
- 1. Student Management System — CRUD + Schema Validation + Compound Indexes.
- 2. Employee Management System — Filters + Pagination + Department Aggregation.
- 3. E-commerce Backend — Products + Orders + Customers + Embedded Data Modeling.
- 4. Blog API — Users + Posts + Comments + Text Search Indexing.
- 5. Hospital Database — Patients + Appointments + Doctor Shift Reports.
- 6. Inventory API — Stock Updates + Multi-Document Transactions.
- 7. Job Portal API — Search + Filters + Cursor Pagination + Skill Match Aggregation.
- 8. Expense Tracker — Dynamic Categories + Monthly Aggregation Pipelines.
- 9. Real-Time Chat Backend — Messages + Indexes + Real-Time Change Streams.
- 10. Production FastAPI/Node API — MongoDB + Auth + Validation + Indexes + Monitoring.
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Scenario-Based Interview Questions
💡 Real-World Troubleshooting Scenarios
- Slow Query: Use `explain()` → check index usage → compare examined vs returned docs → review schema & projection.
- Duplicate Records: Verify unique business rules → clean duplicates → create unique index.
- High Memory Usage: Inspect working set size → evaluate index count → optimize query patterns.
- Primary Node Failure: Inspect replica set status & election logs → verify driver failover retry logic.
- Horizontal Scale Requirement: Measure performance → optimize indexes → evaluate sharding architecture.
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Interview Question Bank (35 Questions)
❓ Core Concept Interview Q&A
- Q: What is BSON? Binary JSON storing typed key-value pairs (e.g., Date, ObjectId, Int64).
- Q: `insertOne` vs `insertMany`? `insertOne` writes single doc; `insertMany` writes array batch with optional `{ordered: false}`.
- Q: Difference between `updateOne` and `replaceOne`? `updateOne` modifies specific fields via `$set`/`$inc`, while `replaceOne` overwrites entire matching document.
- Q: What is an index scan vs collection scan? IXSCAN uses index B-tree; COLLSCAN scans every document sequentially (slow).
- Q: How does `$lookup` work? Performs left outer join between local collection field and foreign collection field.
- Q: What are Change Streams? Real-time notification mechanism listening to collection changes via `watch()`.
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90-Day MongoDB Learning Roadmap
🇬🇧 Detailed Schedule
Days 1–15: Installation, shell, database/collection/document, BSON, CRUD basics.
Days 16–30: Filters, operators, arrays, embedded documents, updates, deletes, pagination.
Days 31–45: Schema design, embedding/reference, validation, indexes and explain.
Days 46–60: Aggregation stages and real reporting queries.
Days 61–75: Transactions, replication, security, backup/restore, monitoring.
Days 76–90: Sharding, Python/Node APIs, Atlas, projects, performance and interviews.
Days 16–30: Filters, operators, arrays, embedded documents, updates, deletes, pagination.
Days 31–45: Schema design, embedding/reference, validation, indexes and explain.
Days 46–60: Aggregation stages and real reporting queries.
Days 61–75: Transactions, replication, security, backup/restore, monitoring.
Days 76–90: Sharding, Python/Node APIs, Atlas, projects, performance and interviews.
🇮🇳 Hinglish Guidance
Step-by-step 90 days ka structured plan follow karke basic CRUD se lekar production sharding aur enterprise security tak master karein!
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Final Master Checklist & Golden Rule
🌟 The Golden Rule of MongoDB
Design for Access Patterns + Use Safe Queries + Index Real Workloads + Measure with Evidence + Secure and Back Up Production Data.
Hinglish: MongoDB flexible hai, par production DB ko properly design, secure, monitor aur backup karna equally important hai.
Hinglish: MongoDB flexible hai, par production DB ko properly design, secure, monitor aur backup karna equally important hai.