MongoDB Tutorial

1. What is MongoDB?

MongoDB is a NoSQL document database that stores data in JSON-like BSON format.

Example: A document in MongoDB looks like:
{
  name: "John",
  age: 30,
  city: "New York"
}

2. Installing MongoDB

Install MongoDB Community Server on your machine using the official website or package manager.

Example (Ubuntu):
sudo apt-get install -y mongodb

3. MongoDB Shell Basics

Interact with MongoDB using the mongo shell to run commands.

Example:
> show dbs
> use myDatabase
> db.myCollection.find()

4. MongoDB Compass GUI

MongoDB Compass is a GUI tool to manage your databases visually.

Example:
Open Compass → Connect to localhost → View collections & documents

5. Understanding Databases and Collections

Databases hold collections, collections hold documents.

Example:
use testDB
db.createCollection("users")

6. BSON Format Explained

BSON is binary JSON format used internally by MongoDB to store documents efficiently.

Example:
{ "age": NumberInt(25), "name": "Alice" }

7. CRUD Overview

CRUD means Create, Read, Update, Delete operations on documents.

Example:
db.users.insertOne({name:"Alice"})
db.users.find()

8. MongoDB Drivers

Drivers enable programming languages to connect and interact with MongoDB.

Example: Node.js driver usage:
const { MongoClient } = require('mongodb');

9. MongoDB Atlas Overview

Atlas is MongoDB's cloud-hosted database platform.

Example:
Create cluster → Connect → Use connection string in your app

10. Basic Security Concepts

MongoDB supports authentication, authorization, and encryption to protect data.

Example:
Enable authentication in mongod.conf and create users with roles

1. Inserting Documents

Use insertOne() or insertMany() to add documents to a collection.

Example:
db.users.insertOne({name: "Bob", age: 28})

2. Querying Documents

Use find() to retrieve documents from a collection.

Example:
db.users.find({age: {$gt: 25}})

3. Updating Documents

Use updateOne(), updateMany() or replaceOne() to modify documents.

Example:
db.users.updateOne({name:"Bob"}, {$set: {age:29}})

4. Deleting Documents

Use deleteOne() or deleteMany() to remove documents.

Example:
db.users.deleteOne({name:"Bob"})

5. Upsert Operations

Update a document or insert it if it does not exist.

Example:
db.users.updateOne({name:"Eve"}, {$set:{age:26}}, {upsert:true})

6. Query Filters and Operators

Use comparison and logical operators in queries.

Example:
db.users.find({age: {$gte: 18, $lte: 30}})

7. Projection

Select only specific fields to return in query results.

Example:
db.users.find({}, {name:1, _id:0})

8. Sorting Results

Sort documents in ascending or descending order.

Example:
db.users.find().sort({age: -1})

9. Limiting and Skipping Results

Limit the number of documents returned or skip some documents.

Example:
db.users.find().limit(5).skip(10)

10. Bulk Write Operations

Perform multiple write operations in bulk for efficiency.

Example:
db.users.bulkWrite([
  { insertOne: { document: {name: "John"} } },
  { updateOne: { filter: {name: "Eve"}, update: {$set: {age: 30}} } }
])

1. What is Indexing?

Indexes improve query performance by creating data structures for fast lookup.

Example:
db.users.createIndex({name: 1})

2. Types of Indexes

Single field, compound, multikey, text, and hashed indexes.

Example:
db.users.createIndex({age: 1, name: -1})

3. Viewing Indexes

Check existing indexes on a collection.

Example:
db.users.getIndexes()

4. Dropping Indexes

Remove unwanted or unused indexes.

Example:
db.users.dropIndex("name_1")

5. Aggregation Framework Overview

Use aggregation pipelines for data processing and transformation.

Example:
db.orders.aggregate([{ $match: {status: "A"} }])

6. $match Stage

Filters documents in the pipeline.

Example:
{ $match: {age: {$gte: 18}} }

7. $group Stage

Groups documents and performs aggregations like sum or average.

Example:
{ $group: { _id: "$city", total: { $sum: 1 } } }

8. $project Stage

Reshapes documents by including or excluding fields.

Example:
{ $project: {name: 1, total: 1} }

9. $sort Stage

Sort documents in the pipeline.

Example:
{ $sort: {total: -1} }

10. Using Explain for Performance

Analyze query plans and index usage.

Example:
db.users.find({name: "John"}).explain()

1. Document vs Relational Model

MongoDB stores JSON documents unlike relational tables.

Example:
Document: {name:"Anna", hobbies:["reading", "travel"]}

2. Embedding vs Referencing

Embed related data or reference via ObjectIDs.

Example:
Embedding:
{
  name: "John",
  address: {street: "123 St", city:"NY"}
}

3. Normalization in MongoDB

Minimize duplication but sometimes denormalize for performance.

Example:
Separate collections for users and addresses linked by IDs

4. Schema Validation

Use JSON schema to enforce document structure.

Example:
db.createCollection("users", {
  validator: { $jsonSchema: {
    bsonType: "object",
    required: ["name", "email"],
    properties: {
      name: {bsonType: "string"},
      email: {bsonType: "string"}
    }
  }}
})

5. Using Mongoose (Node.js)

Mongoose provides schemas and models for MongoDB in Node.js.

Example:
const userSchema = new Schema({ name: String, age: Number });
const User = mongoose.model("User", userSchema);

6. Handling Relationships

Use populate() in Mongoose to reference related documents.

Example:
User.find().populate('posts').exec()

7. Best Practices for Schema Design

Design for query patterns, avoid large documents, index fields properly.

Example:
Keep documents <16MB, use arrays wisely

8. Time Series Data Modeling

Use capped collections or specialized schemas for time-based data.

Example:
Store sensor readings with timestamp fields

9. Versioning Documents

Maintain versions of documents for audit or rollback.

Example:
Add version field and archive old documents

10. Data Validation in Application Layer

Validate data before insertion/updating to maintain integrity.

Example:
Use Joi or Yup in Node.js before calling db

1. Replica Sets

Replica sets provide high availability and data redundancy.

Example:
rs.initiate()
rs.status()

2. Sharding

Distribute data across multiple servers for horizontal scaling.

Example:
sh.enableSharding("myDatabase")
sh.shardCollection("myDatabase.myCollection", { shardKey: 1 })

3. Backup and Restore

Use mongodump and mongorestore tools for backup and recovery.

Example:
mongodump --db myDB
mongorestore --db myDB ./backup/myDB

4. Performance Monitoring

Monitor database health with MongoDB Atlas or tools like mongotop, mongostat.

Example:
mongotop - shows read/write activity per collection

5. Transactions

Support multi-document ACID transactions in replica sets.

Example:
session.startTransaction()
db.collection.updateOne(...)
session.commitTransaction()

6. Aggregation Pipelines Optimization

Optimize pipelines using indexes and limiting stages early.

Example:
Place $match as first stage in pipeline

7. Security Best Practices

Enable authentication, use role-based access control, encrypt data at rest.

Example:
Create users with limited roles using db.createUser()

8. Using Change Streams

Real-time notification of data changes.

Example:
const changeStream = db.collection.watch()

9. MongoDB Stitch / Realm

Serverless platform for backend logic and mobile sync.

Example:
Trigger functions on database changes

10. Upgrading and Maintenance

Plan upgrades, maintenance windows, and patching carefully.

Example:
Follow official MongoDB upgrade guides

1. Enabling Authentication

Require users to authenticate before accessing the database.

Example:
Edit mongod.conf → security:
  authorization: "enabled"
Restart MongoDB server

2. Creating Users and Roles

Assign roles with specific permissions to users.

Example:
use admin
db.createUser({
  user: "appUser",
  pwd: "password123",
  roles: [{ role: "readWrite", db: "myDB" }]
})

3. Role-Based Access Control (RBAC)

Control access by assigning roles with fine-grained privileges.

Example:
Roles include read, readWrite, dbAdmin, clusterAdmin

4. LDAP Integration

Integrate MongoDB authentication with LDAP servers.

Example:
Configure mongod to authenticate via LDAP for centralized management

5. SCRAM Authentication

Default secure password-based authentication mechanism.

Example:
SCRAM-SHA-1 or SCRAM-SHA-256 methods for user authentication

6. Kerberos Authentication

Support for enterprise authentication using Kerberos protocol.

Example:
Setup Kerberos realm and configure MongoDB accordingly

7. TLS/SSL Encryption

Encrypt data in transit between client and server.

Example:
Configure mongod with SSL certificates for encrypted connections

8. Encrypted Storage Engines

Encrypt data at rest using MongoDB’s encrypted storage engine.

Example:
Enable WiredTiger encryption at storage level

9. Auditing

Track and log database activities for compliance.

Example:
Enable MongoDB auditing to log authentication and data access

10. Security Best Practices

Apply least privilege, secure configuration, regular updates.

Example:
Disable unused features and ports, use strong passwords

1. Understanding Query Plans

Use explain() to analyze how MongoDB executes queries.

Example:
db.users.find({age: 25}).explain("executionStats")

2. Index Optimization

Create appropriate indexes based on query patterns.

Example:
db.users.createIndex({lastName: 1, firstName: 1})

3. Avoiding Collection Scans

Use indexes to prevent full collection scans for queries.

Example:
Check explain output to avoid COLLSCAN

4. Using Covered Queries

Queries that can be answered entirely by indexes.

Example:
db.users.find({name: "John"}, {name:1, _id:0})

5. Caching and Working Set

Keep frequently accessed data in RAM for faster access.

Example:
Monitor cache hit ratio in mongostat or Atlas

6. Aggregation Pipeline Optimization

Place filtering stages early to reduce workload.

Example:
Start pipeline with $match before $group

7. Write Concern and Journaling

Balance between performance and data durability.

Example:
Use writeConcern: { w: 1, j: true }

8. Hardware Considerations

Use SSDs, enough RAM, and network speed for optimal performance.

Example:
Prefer NVMe SSDs over HDDs for database storage

9. Monitoring with Profiler

Enable database profiler to detect slow queries.

Example:
db.setProfilingLevel(1)

10. Shard Key Selection

Choose shard keys carefully to ensure even data distribution.

Example:
Use hashed shard key for uniform distribution

1. Replica Set Architecture

A group of MongoDB servers maintaining the same data set.

Example:
Primary node accepts writes; secondaries replicate data

2. Initiating a Replica Set

Use rs.initiate() to start a replica set configuration.

Example:
rs.initiate({
  _id: "rs0",
  members: [{ _id: 0, host: "localhost:27017" }]
})

3. Replica Set Members

Primary, secondary, arbiter, hidden, delayed members.

Example:
Add arbiters for voting without data storage

4. Failover and Election

Automatic failover elects new primary if current primary fails.

Example:
Secondaries monitor primary heartbeat for failover

5. Read Preferences

Control which members handle read operations.

Example:
Read from primary or secondary using readPreference option

6. Write Concerns in Replica Sets

Guarantee acknowledgement of writes from multiple members.

Example:
writeConcern: { w: "majority", wtimeout: 5000 }

7. Oplog Explained

Operation log that secondaries use to replicate changes.

Example:
rs.printReplicationInfo() shows oplog status

8. Replica Set Maintenance

Regularly check replication lag and member status.

Example:
Use rs.status() to monitor health

9. Backup in Replica Sets

Take backups from secondary members to reduce primary load.

Example:
mongodump --host secondaryHost

10. Read-Only Secondaries

Secondaries cannot accept writes; use for reads or backups.

Example:
Set secondary read preference to offload read traffic

1. What is Sharding?

Distributing data across multiple machines to scale horizontally.

Example:
Split large collections across shards

2. Shard Components

Shards, config servers, and mongos query routers.

Example:
mongos routes queries to appropriate shards

3. Enabling Sharding

Enable sharding on a database and shard collections.

Example:
sh.enableSharding("myDB")
sh.shardCollection("myDB.orders", {orderId: 1})

4. Choosing a Shard Key

Select a shard key that evenly distributes data.

Example:
Hashed shard key on userId

5. Balancer

Automatically redistributes data chunks across shards.

Example:
sh.startBalancer()

6. Chunk Migration

Chunks of data move between shards to balance load.

Example:
Check balancer status with sh.getBalancerState()

7. Query Routing

mongos routes queries based on shard keys for efficiency.

Example:
Queries with shard key go directly to relevant shard

8. Managing Sharded Clusters

Monitor health and performance of all components.

Example:
Use MongoDB Ops Manager or Atlas UI

9. Resharding

Change the shard key of a collection without downtime.

Example:
Use reshardCollection command

10. Limitations and Considerations

Sharding adds complexity; plan shard keys carefully.

Example:
Avoid shard keys with low cardinality

1. Using MongoDB with Node.js

Connect to MongoDB and perform operations using the native driver.

Example:
const client = new MongoClient(uri);
await client.connect();
const db = client.db("test");

2. Using Mongoose ODM

Define schemas and models for easier MongoDB interaction.

Example:
const userSchema = new Schema({ name: String });
const User = mongoose.model("User", userSchema);

3. Using MongoDB with Python

Use PyMongo to connect and work with MongoDB.

Example:
from pymongo import MongoClient
client = MongoClient()
db = client.test

4. Using MongoDB with Java

Use MongoDB Java driver for database operations.

Example:
MongoClient mongoClient = MongoClients.create();
MongoDatabase database = mongoClient.getDatabase("test");

5. Using MongoDB with Go

Use the official Go driver for MongoDB.

Example:
client, err := mongo.Connect(ctx, options.Client().ApplyURI(uri))

6. Using MongoDB with PHP

Use MongoDB PHP library for interaction.

Example:
$client = new MongoDB\Client("mongodb://localhost:27017");
$db = $client->test;

7. Connection Pooling

Manage connections efficiently in applications.

Example:
Set maxPoolSize option in connection string

8. Handling Errors and Retries

Gracefully handle connection errors and retry operations.

Example:
Use try/catch blocks and retry logic in code

9. Environment Configuration

Use environment variables for sensitive info like connection URIs.

Example:
process.env.MONGODB_URI in Node.js

10. Using MongoDB Atlas with Applications

Connect your app securely to cloud-hosted MongoDB clusters.

Example:
Use connection string provided by Atlas dashboard

1. mongodump and mongorestore

Command-line tools to backup and restore MongoDB databases.

Example:
mongodump --db mydb
mongorestore --db mydb ./dump/mydb

2. File System Snapshots

Use storage-level snapshots for fast backup of data files.

Example:
LVM or EBS snapshots on server volumes

3. Cloud Backup Services

Use MongoDB Atlas cloud backup or third-party services.

Example:
Enable continuous backups in Atlas UI

4. Point-in-Time Recovery (PITR)

Restore database to a specific moment using oplog.

Example:
Use PITR in MongoDB Atlas for disaster recovery

5. Backup Frequency and Retention

Plan backup schedules and how long to keep backups.

Example:
Daily backups kept for 7 days, weekly backups for 4 weeks

6. Testing Backups

Regularly test restores to ensure backup integrity.

Example:
Restore backups on a test environment

7. Automating Backups

Use scripts and cron jobs to automate backup tasks.

Example:
cron job running mongodump every night

8. Backup Compression and Encryption

Compress and encrypt backups to save space and secure data.

Example:
Use gzip and OpenSSL for backup files

9. Backup in Sharded Clusters

Back up each shard and config servers correctly.

Example:
Backup all shards independently with consistent timestamps

10. Restore Strategies

Choose between full restore, partial restore, or point-in-time restore.

Example:
Restore selected collections using mongorestore --nsInclude

1. Introduction to MongoDB Atlas

MongoDB's fully-managed cloud database service.

Example:
Create free tier cluster at https://cloud.mongodb.com

2. Atlas Cluster Types

Shared, dedicated, and serverless clusters.

Example:
Use M0 for development, M10+ for production workloads

3. Network Access and Security

Configure IP whitelisting, VPC peering, and encryption.

Example:
Add your IP to Atlas IP Access List

4. Database Users Management

Create and manage users with roles in Atlas UI.

Example:
Add user with readWrite role for your app database

5. Backup and Restore in Atlas

Automatic backups with point-in-time recovery.

Example:
Enable backups on cluster and restore from snapshots

6. Atlas Performance Advisor

Suggestions to optimize indexes and queries.

Example:
Review recommendations in Atlas UI and apply as needed

7. Global Clusters and Multi-Region Deployment

Deploy clusters across geographic regions for low latency.

Example:
Enable multi-region clusters with zone sharding

8. Serverless Instances

Pay-as-you-go serverless MongoDB instances for small apps.

Example:
Use serverless instance for lightweight workloads

9. Data Federation

Query data across multiple sources from one interface.

Example:
Use Atlas Data Federation to combine S3 and MongoDB data

10. Atlas Triggers and Functions

Run serverless functions triggered by database events.

Example:
Create trigger to send notification on document insert

1. Aggregation Pipeline Basics

Process data through multiple stages for complex transformations.

Example:
db.orders.aggregate([ { $match: { status: "A" } } ])

2. $lookup for Joins

Join documents from multiple collections.

Example:
{
  $lookup: {
    from: "inventory",
    localField: "item",
    foreignField: "sku",
    as: "inventory_docs"
  }
}

3. $unwind Operator

Deconstruct arrays to output one document per element.

Example:
{ $unwind: "$items" }

4. $facet for Multiple Pipelines

Run multiple aggregation pipelines within a single stage.

Example:
{
  $facet: {
    categorizedByPrice: [ { $match: { price: { $exists: true } } }, { $bucket: { groupBy: "$price", boundaries: [0, 200, 400, 600] } } ],
    categorizedByYear: [ { $match: { year: { $exists: true } } }, { $bucketAuto: { groupBy: "$year", buckets: 4 } } ]
  }
}

5. $bucket and $bucketAuto

Group documents into buckets for range-based aggregation.

Example:
{ $bucket: { groupBy: "$age", boundaries: [0, 18, 65, 100], default: "Other" } }

6. $redact for Access Control

Filter documents based on user permissions.

Example:
{ $redact: { $cond: [ { $eq: ["$secret", true] }, "$$PRUNE", "$$DESCEND" ] } }

7. $graphLookup for Recursive Search

Perform recursive searches on hierarchical data.

Example:
{
  $graphLookup: {
    from: "employees",
    startWith: "$reportsTo",
    connectFromField: "reportsTo",
    connectToField: "employeeId",
    as: "reportingChain"
  }
}

8. $replaceRoot and $replaceWith

Replace the input document with a specified document.

Example:
{ $replaceRoot: { newRoot: "$mergedDocument" } }

9. Performance Considerations

Use indexes, limit documents early, and optimize pipeline stages.

Example:
Place $match stage as first in pipeline

10. Aggregation Expressions

Use expressions to compute new fields.

Example:
{ $addFields: { totalPrice: { $multiply: ["$price", "$quantity"] } } }

1. Introduction to Transactions

Execute multiple operations atomically.

Example:
Start session and transaction for multi-doc updates

2. Transaction API

Use startTransaction(), commitTransaction(), abortTransaction().

Example:
session.startTransaction()
db.collection.updateOne(...)
session.commitTransaction()

3. Transactions in Replica Sets

Transactions supported only on replica sets and sharded clusters.

Example:
Use replica set connection string to enable transactions

4. Limitations of Transactions

Transactions have performance overhead and size limits.

Example:
Maximum 16MB document size applies within transactions

5. Retryable Writes

Automatically retry certain write operations on transient failures.

Example:
Driver retries idempotent writes after network errors

6. Use Cases for Transactions

When strong consistency and atomicity across documents is needed.

Example:
Financial applications needing multi-document consistency

7. Session Management

Use sessions to scope transactions.

Example:
const session = client.startSession()

8. Handling Transaction Errors

Catch and handle errors, retry transactions when needed.

Example:
try { ... } catch (e) { if (e.hasErrorLabel('TransientTransactionError')) retry(); }

9. Transactions in Sharded Clusters

Transactions can span multiple shards but have performance costs.

Example:
Use transactions carefully in sharded setups

10. Best Practices

Keep transactions short, avoid long-running operations.

Example:
Commit or abort as soon as possible to release locks

1. Using mongotop

Monitor read/write activity on collections in real-time.

Example:
mongotop 5

2. Using mongostat

View server status like inserts, queries, connections.

Example:
mongostat 2

3. Database Profiler

Log slow queries and operations for analysis.

Example:
db.setProfilingLevel(1, {slowms: 100})

4. Analyzing Logs

Inspect MongoDB logs for errors and warnings.

Example:
tail -f /var/log/mongodb/mongod.log

5. Using MongoDB Atlas Monitoring

Track performance metrics and alerts in cloud UI.

Example:
Setup alerts for CPU and memory usage

6. Diagnosing Replication Lag

Monitor delays between primary and secondary nodes.

Example:
rs.printSlaveReplicationInfo()

7. Common Performance Issues

Identify causes like missing indexes or slow queries.

Example:
Use explain() to optimize queries

8. Connection Pool Troubleshooting

Diagnose issues with connection limits and timeouts.

Example:
Increase maxPoolSize or check network latency

9. Data Corruption and Repair

Use repairDatabase command to fix corrupt data.

Example:
mongod --repair --dbpath /data/db

10. Monitoring Disk Usage

Track database size and free disk space to avoid crashes.

Example:
db.stats() shows storageSize and dataSize

1. Understanding Data Relationships

Decide between embedding and referencing related data.

Example:
Embed addresses inside user documents or reference address IDs

2. Embedding vs Referencing

Embed for one-to-few, reference for one-to-many or many-to-many.

Example:
Embed comments for a blog post vs reference user profiles

3. Using Schemas and Validation

Enforce data structure with schema validation.

Example:
db.createCollection("users", {
  validator: { $jsonSchema: { bsonType: "object", required: ["name", "email"] } }
})

4. Designing for Query Patterns

Optimize schema based on how data will be queried.

Example:
Denormalize data for frequently accessed joins

5. Avoiding Large Documents

Keep documents under 16MB limit and avoid large arrays.

Example:
Split large logs into separate collections

6. Using Discriminators

Handle different types of documents in a collection.

Example:
Add a "type" field to distinguish document subtypes

7. Time-Series Data Modeling

Design collections optimized for time-stamped data.

Example:
Use buckets to store multiple time entries per document

8. Handling Relationships in Aggregation

Use $lookup for joining data in aggregation pipelines.

Example:
Join orders with customers for reports

9. Versioning Documents

Keep track of document changes with versions or history.

Example:
Add a version field or store snapshots in separate collection

10. Schema Evolution Strategies

Plan for evolving schemas without downtime.

Example:
Use optional fields and migration scripts

1. Using Regular Expressions in Queries

Perform pattern matching on string fields.

Example:
db.users.find({ name: { $regex: /^A/, $options: 'i' } })

2. Text Search

Search text fields with text indexes.

Example:
db.articles.createIndex({ content: "text" })
db.articles.find({ $text: { $search: "mongodb" } })

3. Geospatial Queries

Query data by location using 2d or 2dsphere indexes.

Example:
db.places.find({ location: { $near: { $geometry: { type: "Point", coordinates: [ -73.9667, 40.78 ] }, $maxDistance: 5000 } } })

4. Array Queries

Match documents with array fields and conditions.

Example:
db.orders.find({ items: { $elemMatch: { price: { $gt: 100 } } } })

5. Aggregation Expressions in Queries

Use expressions for computed filtering.

Example:
db.sales.aggregate([{ $match: { $expr: { $gt: ["$price", 100] } } }])

6. Projection with Computed Fields

Include new fields calculated on the fly.

Example:
db.users.find({}, { fullName: { $concat: ["$firstName", " ", "$lastName"] } })

7. Using $expr for Complex Conditions

Compare fields within the same document.

Example:
db.orders.find({ $expr: { $gt: ["$quantity", "$threshold"] } })

8. Query Optimization with Indexes

Use explain() to tune queries and indexes.

Example:
db.collection.find(query).explain("executionStats")

9. Using $text Score for Sorting

Sort search results by relevance score.

Example:
db.articles.find({ $text: { $search: "mongodb" } }, { score: { $meta: "textScore" } }).sort({ score: { $meta: "textScore" } })

10. Complex Updates Using Aggregation Pipeline

Update documents using aggregation expressions.

Example:
db.collection.updateMany({}, [{ $set: { total: { $sum: ["$price", "$tax"] } } }])

1. Introduction to Change Streams

Listen to real-time data changes in collections or databases.

Example:
const changeStream = db.collection.watch()

2. Watch Specific Operations

Filter change streams for insert, update, delete.

Example:
db.collection.watch([{ $match: { operationType: "insert" } }])

3. Resume Tokens

Resume watching changes from last seen event.

Example:
const resumeToken = changeStream.resumeToken

4. Using Change Streams with Node.js

Implement real-time features like notifications.

Example:
changeStream.on("change", (next) => { console.log(next); })

5. Scalability Considerations

Handle large volumes of change events efficiently.

Example:
Use batch processing or debounce events

6. Change Streams on Replica Sets

Supported only on replica sets and sharded clusters.

Example:
Ensure deployment supports change streams

7. Integration with Messaging Systems

Pipe change stream events into Kafka, RabbitMQ, etc.

Example:
Forward changes for further processing or analytics

8. Error Handling in Change Streams

Handle errors and reconnect on failures.

Example:
Reconnect on network errors using try/catch

9. Change Streams for Audit Logging

Use streams to log changes for compliance and audit.

Example:
Store change events in audit collection

10. Real-Time Dashboards

Use change streams to power live data dashboards.

Example:
Update UI in real-time as data changes

1. Overview of Atlas Search

Full-text search engine integrated with MongoDB Atlas.

Example:
Create search index via Atlas UI

2. Creating Search Indexes

Define custom analyzers and mappings for text fields.

Example:
Configure index with standard or custom analyzers

3. Basic Search Queries

Use $search stage in aggregation to perform text search.

Example:
db.collection.aggregate([{ $search: { text: { query: "mongodb", path: "content" } } }])

4. Faceted Search

Provide filtered search results by categories or ranges.

Example:
Use $facet to return counts by category

5. Autocomplete Search

Enable search-as-you-type with autocomplete indexes.

Example:
Configure autocomplete analyzer in search index

6. Highlighting Results

Return snippets showing matched text in results.

Example:
Use highlight operator in $search stage

7. Synonyms and Stemming

Improve search relevance with synonyms and stemming.

Example:
Add synonym mappings in search index definition

8. Search Score and Sorting

Sort results by relevance score or other fields.

Example:
Sort by { score: { $meta: "searchScore" } }

9. Combining Search with Filters

Combine $search with $match and other pipeline stages.

Example:
Filter search results by date or category

10. Monitoring and Analytics

Track usage and performance of Atlas Search.

Example:
Review Atlas Search analytics in dashboard

1. Integrating MongoDB with Apache Spark

Use Spark Connector for big data processing with MongoDB.

Example:
Load MongoDB data into Spark DataFrame

2. Using MongoDB with Hadoop

Store and access MongoDB data within Hadoop workflows.

Example:
Use Mongo-Hadoop connector for MapReduce jobs

3. MongoDB and Kafka Integration

Stream MongoDB changes into Kafka topics.

Example:
Use Kafka Connector for MongoDB to stream change events

4. BI Connector

Enable SQL-based BI tools to query MongoDB data.

Example:
Connect Tableau or PowerBI using MongoDB BI Connector

5. ETL Pipelines

Extract, Transform, Load workflows integrating MongoDB.

Example:
Use tools like Apache NiFi to move data to/from MongoDB

6. Integration with Elasticsearch

Sync MongoDB data with Elasticsearch for advanced search.

Example:
Use connectors or custom scripts for syncing data

7. Data Lakes with MongoDB Atlas

Combine MongoDB data with cloud object stores like S3.

Example:
Use Atlas Data Lake to query S3 data via MongoDB interface

8. Graph Processing

Use MongoDB with graph processing frameworks.

Example:
Export data to Neo4j or use $graphLookup

9. Machine Learning Integration

Use MongoDB data as input for ML models.

Example:
Export datasets for TensorFlow or PyTorch training

10. Streaming Analytics

Real-time analytics combining MongoDB and streaming tools.

Example:
Use MongoDB change streams with Apache Flink

1. Enabling Authentication

Require users to authenticate before accessing the database.

Example:
Set security.authorization: enabled in mongod.conf

2. Role-Based Access Control (RBAC)

Assign users roles with limited privileges.

Example:
db.createUser({user: "appUser", roles: ["readWrite"]})

3. Enabling TLS/SSL

Encrypt network traffic between clients and servers.

Example:
Configure mongod with sslMode: requireSSL

4. IP Whitelisting

Restrict network access to trusted IP addresses only.

Example:
Configure IP Access List in MongoDB Atlas

5. Encryption at Rest

Encrypt stored data on disk to protect against theft.

Example:
Enable WiredTiger encryption at rest

6. Auditing Database Activity

Log database events for monitoring and compliance.

Example:
Enable auditLog in mongod.conf

7. Preventing Injection Attacks

Sanitize user inputs to avoid injection vulnerabilities.

Example:
Validate and escape inputs in your application code

8. Password Management

Use strong passwords and rotate credentials regularly.

Example:
Use SCRAM-SHA-256 authentication mechanism

9. Securing Backup Data

Encrypt and protect backups to avoid data leaks.

Example:
Store backups encrypted with tools like GPG

10. Monitoring and Alerting

Setup alerts for suspicious activity or security events.

Example:
Use MongoDB Atlas alerts for failed login attempts

1. Analyzing Query Performance

Use explain() to understand query execution details.

Example:
db.collection.find({}).explain("executionStats")

2. Index Usage and Optimization

Create indexes that support your query patterns efficiently.

Example:
db.collection.createIndex({ email: 1 })

3. Avoiding Collection Scans

Ensure queries use indexes to prevent full scans.

Example:
Check explain plan for COLLSCAN

4. Using Covered Queries

Query only indexed fields to speed up performance.

Example:
db.collection.find({ email: "test@example.com" }, { email: 1, _id: 0 })

5. Sharding for Scalability

Distribute data across multiple servers using shard keys.

Example:
Shard collection on user_id field

6. Caching Strategies

Use in-memory caches to reduce database load.

Example:
Use Redis or application-level caching

7. Monitoring Resource Usage

Track CPU, memory, and disk usage on database servers.

Example:
Use mongostat and mongotop commands

8. Connection Pooling

Manage database connections efficiently to improve throughput.

Example:
Configure maxPoolSize in your MongoDB driver

9. Aggregation Pipeline Optimization

Place filtering stages early and minimize processed documents.

Example:
Start pipeline with $match stage

10. Avoiding Large Documents

Keep document sizes reasonable to avoid performance hits.

Example:
Split large embedded arrays into separate collections

1. Introduction to Sharding

Distribute data across multiple machines to scale horizontally.

Example:
Shard collections to handle large datasets

2. Shard Key Selection

Choose appropriate shard key for data distribution and query efficiency.

Example:
Use hashed or ranged shard keys based on workload

3. Setting Up a Sharded Cluster

Configure config servers, shard servers, and mongos routers.

Example:
Deploy replica sets as shards with config servers

4. Balancer Process

Automatically redistribute data chunks across shards.

Example:
Balancer moves chunks to balance load

5. Query Routing with mongos

mongos directs queries to correct shards transparently.

Example:
Application connects to mongos router endpoint

6. Impact on Aggregation

Sharding affects aggregation pipelines and performance.

Example:
$lookup across shards can be slower

7. Transactions on Sharded Clusters

Support for multi-shard transactions with some limitations.

Example:
Use sessions and transactions spanning shards

8. Monitoring Sharded Clusters

Track balancer activity, chunk migrations, and shard health.

Example:
Use sh.status() and Atlas monitoring tools

9. Resharding

Change shard key without downtime using resharding.

Example:
Use reshardCollection command in MongoDB 5.0+

10. Best Practices for Sharding

Plan shard key and cluster topology carefully for workload.

Example:
Avoid monotonically increasing shard keys like timestamps

1. Introduction to Replica Sets

Replicate data across multiple nodes for redundancy and failover.

Example:
Primary-secondary architecture with automatic failover

2. Setting up Replica Sets

Configure mongod instances to join a replica set.

Example:
rs.initiate() to start a replica set

3. Read and Write Concerns

Configure acknowledgment levels for reliability.

Example:
Write concern "majority" ensures durability

4. Failover and Election Process

Replica set automatically elects a new primary on failure.

Example:
Secondary becomes primary if primary goes down

5. Secondary Reads

Configure applications to read from secondaries to reduce load.

Example:
Use readPreference: secondary in drivers

6. Arbiter Nodes

Lightweight members participate in elections without data.

Example:
Add arbiter to break voting ties

7. Oplog and Replication Lag

Operation log drives replication; lag impacts read freshness.

Example:
rs.printSlaveReplicationInfo() to check lag

8. Backup Strategies with Replica Sets

Use secondaries for backup to avoid primary load.

Example:
Run mongodump on secondary nodes

9. Maintenance and Upgrades

Upgrade replica set members with minimal downtime.

Example:
Step down primary and upgrade secondaries first

10. Monitoring Replica Sets

Track health, replication lag, and election events.

Example:
Use MongoDB Cloud or Ops Manager monitoring tools

1. Connection Pooling

Manage multiple simultaneous connections efficiently.

Example:
Configure maxPoolSize option in connection string

2. Bulk Write Operations

Perform multiple write operations in a single request.

Example:
collection.bulkWrite([{ insertOne: {...} }, { updateOne: {...} }])

3. Retryable Writes

Automatically retry transient write errors.

Example:
Enable retryWrites=true in connection URI

4. Sessions and Transactions

Use multi-document ACID transactions.

Example:
const session = client.startSession();
session.withTransaction(async () => { ... });

5. Change Streams in Drivers

Listen for data changes using driver APIs.

Example:
collection.watch().on("change", (data) => { ... });

6. GridFS API

Upload and download large files.

Example:
const bucket = new mongodb.GridFSBucket(db); bucket.uploadFromStream(...)

7. Aggregation Framework Support

Build aggregation pipelines with driver helpers.

Example:
collection.aggregate([{ $match: {...} }, { $group: {...} }])

8. Custom Serialization

Customize BSON serialization/deserialization.

Example:
Use BSON types and serializers in application

9. Change Stream Resume Tokens

Resume listening from last event after interruption.

Example:
watch({ resumeAfter: lastToken })

10. Connection Monitoring and Events

Listen for driver connection state changes.

Example:
client.on('serverDescriptionChanged', (event) => { ... });

1. mongodump and mongorestore

Use command line tools for manual backups and restores.

Example:
mongodump --db=mydb --out=/backup/dir
mongorestore --db=mydb /backup/dir/mydb

2. MongoDB Atlas Automated Backups

Configure continuous backups in Atlas cloud service.

Example:
Enable daily snapshots via Atlas UI

3. Backup Consistency

Ensure backups are consistent, use filesystem snapshots or oplog.

Example:
Use --oplog option with mongodump

4. Point-in-Time Recovery

Restore database state at a specific timestamp.

Example:
Use oplog replay with backup files

5. Backup Compression and Encryption

Compress and encrypt backup files for storage efficiency and security.

Example:
Use gzip or openssl with mongodump output

6. Backup Scheduling

Automate backup jobs using cron or cloud scheduler.

Example:
Setup cron job to run mongodump nightly

7. Restore Strategies for Large Datasets

Restore efficiently by restoring collections in parallel.

Example:
Restore multiple collections simultaneously

8. Backup Validation

Verify backups integrity before relying on them.

Example:
Test restore on staging environment

9. Disaster Recovery Planning

Define procedures for data loss scenarios and recovery.

Example:
Maintain offsite backups and tested recovery process

10. Backup Monitoring and Alerts

Setup notifications for backup success or failures.

Example:
Use monitoring tools with email alerts

1. Introduction to Transactions

Perform multiple operations atomically across documents.

Example:
Use session.startTransaction()

2. Multi-Document Transactions

Support for ACID transactions across multiple collections.

Example:
session.withTransaction(async () => { ... });

3. Transaction Isolation Levels

MongoDB provides snapshot isolation to prevent dirty reads.

Example:
Read operations inside transactions see a consistent snapshot

4. Commit and Abort

Commit changes or abort to rollback within a transaction.

Example:
await session.commitTransaction()
await session.abortTransaction()

5. Performance Considerations

Transactions can impact performance and lock contention.

Example:
Keep transactions short to reduce lock duration

6. Retryable Transactions

Handle transient errors by retrying transactions.

Example:
Retry transaction on network error or write conflict

7. Using Transactions with Sharded Clusters

Transactions support multi-shard writes with limitations.

Example:
Ensure all shards support transactions (MongoDB 4.2+)

8. Transaction Timeouts

Transactions automatically abort after a time limit.

Example:
Default timeout is 60 seconds

9. Monitoring Transactions

Track slow or failed transactions for optimization.

Example:
Use profiler and logs to analyze transaction performance

10. Use Cases for Transactions

Examples where atomic multi-document operations are necessary.

Example:
Bank transfers, inventory reservations, order processing

1. What is GridFS?

Store and retrieve files larger than 16MB using GridFS.

Example:
GridFS splits files into chunks and stores metadata

2. Uploading Files

Upload files into GridFS buckets programmatically.

Example:
bucket.uploadFromStream("file.txt", fs.createReadStream("file.txt"))

3. Downloading Files

Retrieve files stored in GridFS and stream to clients.

Example:
bucket.openDownloadStreamByName("file.txt").pipe(res)

4. Metadata Management

Store custom metadata with files for indexing or retrieval.

Example:
Add metadata on upload with upload options

5. File Deletion

Remove files and associated chunks from GridFS.

Example:
bucket.delete(fileId)

6. File Versioning

Store multiple versions of the same file using naming conventions.

Example:
Use different filenames or metadata version fields

7. Performance Considerations

GridFS is optimized for large files but not for small objects.

Example:
Use normal collections for small blobs

8. Using GridFS with Drivers

GridFS APIs available in official MongoDB drivers.

Example:
Use mongodb.GridFSBucket class in Node.js driver

9. Integrations with Web Frameworks

Serve files stored in GridFS via web applications.

Example:
Express.js route streams file from GridFS to response

10. Backup and Restore of GridFS Data

Include GridFS collections in backup processes.

Example:
Backup fs.files and fs.chunks collections

1. Overview of Aggregation Pipeline

Process data through a sequence of pipeline stages.

Example:
db.collection.aggregate([{ $match: {...} }, { $group: {...} }])

2. Common Pipeline Stages

$match, $group, $project, $sort, $limit, $skip etc.

Example:
Use $match to filter documents early

3. Using $group for Aggregation

Group documents by key and calculate aggregates.

Example:
{ $group: { _id: "$category", total: { $sum: "$amount" } } }

4. $project for Shaping Data

Include, exclude, or compute new fields.

Example:
{ $project: { name: 1, total: { $multiply: ["$price", "$quantity"] } } }

5. $lookup for Joins

Perform left outer join with another collection.

Example:
{ $lookup: { from: "orders", localField: "_id", foreignField: "userId", as: "orders" } }

6. Pipeline Optimization Tips

Place $match and $limit early to reduce data processed.

Example:
Filtering first improves performance

7. Using Expressions in Pipelines

Calculate fields with expressions like $add, $subtract, $cond.

Example:
{ $project: { discountPrice: { $cond: [ { $gt: ["$discount", 0] }, { $subtract: ["$price", "$discount"] }, "$price" ] } } }

8. Working with Arrays in Aggregation

Use $unwind to deconstruct arrays for individual processing.

Example:
{ $unwind: "$items" }

9. Faceted Aggregation

Perform multiple aggregations in parallel within one pipeline.

Example:
{ $facet: { counts: [...], averages: [...] } }

10. Using $merge and $out

Output aggregation results to collections.

Example:
{ $merge: { into: "results" } }

1. Global Clusters

Deploy clusters across multiple regions for low latency.

Example:
Use zone sharding to direct users to nearest region

2. Atlas Data Lake

Query data in cloud object storage as if it were MongoDB collections.

Example:
Define S3 buckets as virtual collections

3. Atlas Search

Integrated full-text search powered by Apache Lucene.

Example:
Create search indexes and query with $search

4. Triggers

Run server-side functions in response to database events.

Example:
Create a trigger to send notification on insert

5. Realm Application Services

Build serverless apps integrated with Atlas and MongoDB.

Example:
Use Realm functions and rules for app logic

6. Backup and Restore in Atlas

Use built-in continuous backups and point-in-time restores.

Example:
Restore cluster from snapshot in Atlas UI

7. Performance Advisor

Recommendations to improve query performance and indexing.

Example:
Use Atlas Performance Advisor alerts

8. Data Federation

Query data across multiple sources including on-prem and cloud.

Example:
Create federated queries spanning MongoDB and S3

9. VPC Peering and Private Endpoints

Secure connectivity options between Atlas and your network.

Example:
Configure AWS VPC peering for private network access

10. Atlas API

Automate cluster and project management using REST API.

Example:
Use Atlas API to create clusters programmatically

1. Using mongostat

Real-time overview of server status and performance metrics.

Example:
mongostat --host localhost --port 27017

2. Using mongotop

Monitor time spent reading and writing data per collection.

Example:
mongotop --host localhost

3. MongoDB Profiler

Collect detailed information about database operations.

Example:
db.setProfilingLevel(2)

4. Analyzing Profiler Data

Review slow queries and optimize accordingly.

Example:
db.system.profile.find().sort({ ts: -1 }).limit(5)

5. Server Logs

Check logs for errors, warnings, and other events.

Example:
tail -f /var/log/mongodb/mongod.log

6. Monitoring via MongoDB Cloud

Use Atlas or Ops Manager for visual monitoring dashboards.

Example:
View real-time metrics and alerts in Atlas UI

7. Setting up Alerts

Configure notifications for critical events or thresholds.

Example:
Set alerts for high CPU or replication lag

8. Diagnostic Commands

Use commands like serverStatus() to get detailed metrics.

Example:
db.serverStatus()

9. Performance Advisor

Get automated index and performance improvement suggestions.

Example:
Review advisor recommendations in Atlas

10. Third-Party Monitoring Tools

Integrate with Prometheus, Grafana, or Datadog.

Example:
Export MongoDB metrics to Prometheus exporter

1. LDAP Authentication Integration

Use LDAP for centralized authentication.

Example:
Configure mongod.conf with ldap configuration

2. Kerberos Authentication

Integrate MongoDB with Kerberos for secure auth.

Example:
Setup keytab files and configure mongod

3. Client-Side Field Level Encryption (FLE)

Encrypt specific fields before sending data to MongoDB.

Example:
Use MongoDB drivers with FLE enabled

4. Auditing with Fine-Grained Control

Track detailed database operations for compliance.

Example:
Configure audit filters in mongod.conf

5. TLS Mutual Authentication

Use client certificates for two-way TLS authentication.

Example:
Enable sslMode requireSSL and configure client certs

6. IP Access List and Network Restrictions

Configure firewall rules and IP whitelists.

Example:
Set CIDR blocks in Atlas IP Access List

7. Role Customization

Create custom roles with specific privileges.

Example:
db.createRole({role: "appRead", privileges: [...], roles: []})

8. Password Policies

Enforce password complexity and expiration.

Example:
Use external tools or enforce via LDAP

9. Security Best Practices Checklist

Regularly review and audit MongoDB security posture.

Example:
Disable unused features and update regularly

10. Incident Response

Steps to handle security breaches or suspicious activity.

Example:
Isolate compromised nodes and review logs

1. Compound Indexes

Index on multiple fields to support complex queries.

Example:
db.collection.createIndex({ firstName: 1, lastName: 1 })

2. Multikey Indexes

Index fields containing arrays.

Example:
db.collection.createIndex({ tags: 1 })

3. Text Indexes

Enable text search over string content.

Example:
db.collection.createIndex({ description: "text" })

4. Hashed Indexes

Hash values for uniform distribution in sharding.

Example:
db.collection.createIndex({ userId: "hashed" })

5. Sparse Indexes

Index only documents with the indexed field.

Example:
db.collection.createIndex({ optionalField: 1 }, { sparse: true })

6. TTL Indexes

Expire documents automatically after a set time.

Example:
db.collection.createIndex({ createdAt: 1 }, { expireAfterSeconds: 3600 })

7. Partial Indexes

Index only documents matching a filter.

Example:
db.collection.createIndex({ status: 1 }, { partialFilterExpression: { status: "active" } })

8. Wildcard Indexes

Index all fields under a specified path.

Example:
db.collection.createIndex({ "$**": 1 })

9. Index Collation

Customize string comparison rules (case, locale).

Example:
db.collection.createIndex({ name: 1 }, { collation: { locale: "en", strength: 2 } })

10. Index Usage and Maintenance

Analyze index usage and rebuild as needed.

Example:
Use db.collection.getIndexes() and compact command

1. Bucketing Data with $bucket

Group documents into buckets based on ranges.

Example:
{ $bucket: { groupBy: "$age", boundaries: [0, 20, 40, 60], default: "Other" } }

2. Dynamic Bucketing with $bucketAuto

Automatically divide data into equal buckets.

Example:
{ $bucketAuto: { groupBy: "$score", buckets: 5 } }

3. Using $redact for Document-Level Access Control

Filter fields and documents dynamically in pipeline.

Example:
Use $cond and $$DESCEND or $$PRUNE in $redact

4. Faceted Search with $facet

Run multiple aggregation pipelines in parallel.

Example:
{ $facet: { categoryCounts: [...], priceStats: [...] } }

5. Graph Lookup for Recursive Relationships

Traverse linked documents recursively.

Example:
{ $graphLookup: { from: "employees", startWith: "$managerId", connectFromField: "managerId", connectToField: "_id", as: "hierarchy" } }

6. Using $function for Custom Expressions

Write custom JavaScript functions in aggregation.

Example:
{ $addFields: { customValue: { $function: { body: function(x) { return x*2; }, args: ["$field"], lang: "js" } } } }

7. Pipeline Variables with let and $expr

Use variables inside pipeline stages for dynamic queries.

Example:
{ $match: { $expr: { $gt: ["$field", "$$var"] } } }

8. Advanced Sorting and Pagination

Implement efficient pagination with $sort and $skip.

Example:
db.collection.aggregate([{ $sort: { date: -1 } }, { $skip: 20 }, { $limit: 10 }])

9. Combining Pipelines with $unionWith

Merge results from multiple collections.

Example:
{ $unionWith: "otherCollection" }

10. Using $merge for Materialized Views

Store aggregation results into a collection.

Example:
{ $merge: { into: "materializedView" } }

1. Polymorphic Schemas

Store different document types in a single collection.

Example:
Use a "type" field to distinguish document variants

2. Bucket Pattern

Group many related items into fewer documents.

Example:
Store logs in documents with an array of entries

3. Outlier Pattern

Store frequently accessed fields separately from infrequently used ones.

Example:
Main document stores summary, details stored in another collection

4. Attribute Pattern

Model dynamic attributes as key-value pairs.

Example:
Store attributes array with name and value fields

5. Tree Structures

Represent hierarchical data with parent references or nested sets.

Example:
Use parentId field or $graphLookup for hierarchy

6. References vs Embedding

Decide when to embed documents vs use references.

Example:
Embed for tightly coupled data, reference for large or shared data

7. Time Series Data Modeling

Efficiently model time-series data for analytics.

Example:
Use bucketing and TTL indexes

8. Schema Versioning

Handle changes in schema over time.

Example:
Add version fields and migration scripts

9. Using Views for Abstraction

Create read-only views to simplify queries.

Example:
db.createView("activeUsers", "users", [ { $match: { status: "active" } } ])

10. Data Modeling Best Practices

Design for scalability, maintainability, and query performance.

Example:
Analyze query patterns and index needs before modeling

1. Index Optimization

Create and maintain indexes tailored to query patterns.

Example:
Use explain() to check index usage

2. Query Optimization

Rewrite queries to use indexes effectively.

Example:
Avoid $ne or $nin when possible

3. Use of Projection

Return only necessary fields to reduce network load.

Example:
db.collection.find({}, { name: 1, email: 1 })

4. Connection Pooling

Reuse database connections for efficiency.

Example:
Configure maxPoolSize in connection string

5. Caching Strategies

Use in-memory caches for frequently accessed data.

Example:
Integrate Redis or Memcached with app

6. Write Concern and Journaling

Tune write durability settings for performance.

Example:
Set writeConcern: { w: 1, j: false }

7. Sharding and Load Balancing

Distribute data to balance load across servers.

Example:
Choose shard keys carefully for even distribution

8. Compression

Enable data compression to reduce storage and IO.

Example:
Use WiredTiger storage engine with compression enabled

9. Hardware Considerations

Use SSDs and sufficient RAM for better performance.

Example:
Deploy on machines optimized for I/O intensive workloads

10. Monitoring and Continuous Improvement

Track performance metrics and iteratively tune.

Example:
Set up alerts for slow queries and resource spikes

1. Automating mongodump Backups

Schedule regular dumps using cron or task schedulers.

Example:
0 2 * * * mongodump --out /backups/$(date +\%F)

2. Incremental Backups Using Oplog

Capture only changes since last backup to save space.

Example:
Use --oplog with mongodump

3. Backup Retention Policies

Define how long backups are stored and when deleted.

Example:
Keep last 7 days and delete older backups

4. Cloud Storage Integration

Store backups in AWS S3, Azure Blob, or Google Cloud Storage.

Example:
Upload backup files using AWS CLI after dump

5. Verifying Backup Integrity

Periodically test backups by restoring to staging.

Example:
Restore a backup on a test server

6. Automating Backup Monitoring

Set up alerts for backup failures or missed schedules.

Example:
Use monitoring tools like Nagios or Datadog

7. Securing Backup Data

Encrypt backups and restrict access permissions.

Example:
Use GPG encryption on backup archives

8. Using MongoDB Cloud Backup Services

Leverage Atlas or Ops Manager backup features.

Example:
Enable continuous backup and point-in-time restore

9. Automating Restore Processes

Create scripts to quickly restore backups when needed.

Example:
Shell script with mongorestore and error handling

10. Documentation and SOPs

Maintain clear procedures for backup and restore.

Example:
Write step-by-step guides for team use

1. Introduction to Change Streams

Listen to changes in collections, databases, or clusters in real time.

Example:
collection.watch()

2. Watching Specific Operations

Filter change streams for inserts, updates, deletes.

Example:
collection.watch([{ $match: { operationType: "insert" } }])

3. Resume Tokens

Resume change streams after interruptions using tokens.

Example:
watch({ resumeAfter: lastResumeToken })

4. Change Stream Events Structure

Understand the data format of change events.

Example:
event.documentKey, event.updateDescription

5. Using Change Streams in Drivers

Implement change streams with official MongoDB drivers.

Example:
collection.watch().on("change", callback)

6. Integrating Change Streams with Message Queues

Forward changes to Kafka, RabbitMQ, or other systems.

Example:
Publish change events to Kafka topic

7. Use Cases for Change Streams

Real-time analytics, cache invalidation, notifications.

Example:
Update UI instantly on data changes

8. Limitations and Considerations

Watch for oplog size, latency, and memory usage.

Example:
Ensure oplog is large enough for your workload

9. Security and Permissions

Users require proper privileges to use change streams.

Example:
Grant changeStream role

10. Scaling Change Stream Applications

Handle multiple listeners and high throughput.

Example:
Shard-aware listeners and load balancing

1. Atlas Security Overview

Built-in security features and compliance certifications.

Example:
Encryption at rest and in transit

2. Network Security Controls

IP whitelisting, VPC peering, and private endpoints.

Example:
Configure IP Access List in Atlas

3. User and Role Management

Fine-grained access controls with built-in and custom roles.

Example:
Assign roles per project or cluster

4. Data Encryption

Support for customer-managed encryption keys.

Example:
Use AWS KMS integration

5. Audit Logging

Track user actions and system changes for compliance.

Example:
Enable audit logs in Atlas UI

6. Compliance Certifications

Atlas meets GDPR, HIPAA, SOC2, and more.

Example:
Review MongoDB compliance documentation

7. Security Best Practices

Enforce MFA, rotate credentials, and limit permissions.

Example:
Require MFA for all users

8. Incident Response in Atlas

Plan and respond to security incidents.

Example:
Use Atlas alerts and logging to investigate

9. Integration with SIEM Tools

Connect audit logs to Security Information and Event Management.

Example:
Export logs to Splunk or Elastic SIEM

10. Atlas Security Automation

Use APIs and scripts to enforce policies automatically.

Example:
Automate role assignments via Atlas API

1. Introduction to Realm

Serverless platform to build mobile and web apps backed by MongoDB.

Example:
Create a Realm app in Atlas UI

2. Syncing Data with Realm Sync

Real-time sync between client and server data.

Example:
Configure sync rules in Realm app

3. Realm Functions

Write serverless functions triggered by HTTP or database events.

Example:
Create a function to validate user input

4. Authentication Providers

Support for email/password, OAuth, API keys, and anonymous auth.

Example:
Enable Google authentication in Realm

5. Data Access Rules

Define fine-grained read/write permissions for collections.

Example:
Set roles with read-only or write access

6. Triggers in Realm

Automate workflows with triggers on data changes or schedules.

Example:
Trigger function on document insert

7. Integrating Realm with Frontend Frameworks

Use SDKs for React, Swift, Kotlin, and more.

Example:
Use Realm Web SDK in React app

8. Offline First and Conflict Resolution

Support for offline data and automatic conflict handling.

Example:
Configure sync conflict strategies

9. Logging and Monitoring Realm Apps

Track function executions, errors, and usage.

Example:
Use Realm logs in Atlas UI

10. Deploying and Managing Realm Apps

Manage app versions, environments, and deployments.

Example:
Promote changes from staging to production