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  • MongoDB – (No-SQL)

MongoDB – (No-SQL)

Curriculum

  • 10 Sections
  • 31 Lessons
  • 10 Weeks
Expand all sectionsCollapse all sections
  • Introduction to MongoDB
    MongoDB is a NoSQL database that is designed for handling large volumes of unstructured or semi-structured data. Unlike traditional relational databases (RDBMS) that use tables and rows to organize data, MongoDB stores data in a flexible document-oriented format using JSON-like documents (BSON - Binary JSON). This makes it highly scalable, flexible, and performant for applications that need to handle varying types of data with complex structures.
    5
    • 1.1
      What is MongoDB?
    • 1.2
      Why MongoDB?
    • 1.3
      When to use MongoDB?
    • 1.4
      Key Features of MongoDB
    • 1.5
      Installing MongoDB
  • MongoDB Basic Operations
    MongoDB provides a rich set of basic operations for interacting with the database, including creating, reading, updating, and deleting data (often abbreviated as CRUD operations). Below are the basic operations that you can perform with MongoDB.
    2
    • 2.0
      Database and Collection Basics
    • 2.1
      CRUD Operations
  • Advanced Querying Techniques
    MongoDB offers a rich set of querying capabilities, and as you work with larger datasets and more complex application requirements, you’ll often need to use advanced querying techniques. These techniques help you optimize performance, execute sophisticated queries, and leverage MongoDB’s powerful indexing and aggregation features.
    4
    • 3.1
      Query Filters and Operators
    • 3.2
      Advanced Querying
    • 3.3
      Sorting and Limiting Results
    • 3.4
      Aggregation Framework
  • Data Modeling and Schema Design
    Data modeling and schema design are critical when using MongoDB (or any NoSQL database) to ensure efficient data storage, fast queries, and scalability. Unlike relational databases, MongoDB is schema-less, which means you are not required to define a fixed schema upfront. However, making the right design decisions from the beginning is essential for maintaining performance and avoid complications as your data grows.
    4
    • 4.1
      Data Modeling
    • 4.2
      Document Structure
    • 4.3
      Schema Design Patterns
    • 4.4
      MongoDB and Relationships
  • Indexing and Performance Optimization
    In MongoDB, indexing is a critical part of performance optimization. Without proper indexes, MongoDB has to scan every document in a collection to satisfy queries, which can be very inefficient for large datasets. Indexes are used to quickly locate data without scanning every document, making reads faster and more efficient.
    3
    • 5.0
      Creating Indexes
    • 5.1
      Using Text Search
    • 5.2
      Performance Optimization
  • Integrating MongoDB with a Web Application (Node.js)
    Integrating MongoDB with a web application built using Node.js is a common and powerful combination for building scalable and efficient web apps. MongoDB’s flexibility with JSON-like data and Node.js's asynchronous event-driven architecture work well together. In this guide, I'll walk you through the steps for integrating MongoDB with a Node.js web application, covering the essentials of setting up the connection, performing CRUD operations, and using popular libraries.
    3
    • 6.0
      Setting Up MongoDB with Node.js
    • 6.1
      CRUD Operations with Mongoose
    • 6.2
      Error Handling and Validation
  • Security in MongoDB
    Security is an essential aspect when working with MongoDB, especially when handling sensitive data in production environments. MongoDB provides a variety of security features to help protect your data against unauthorized access, injection attacks, and other vulnerabilities. Here’s a guide on securing MongoDB and your Node.js application when interacting with MongoDB.
    2
    • 7.0
      Authentication and Authorization
    • 7.1
      Data Encryption
  • Working with MongoDB in Production
    3
    • 8.0
      MongoDB Backup and Restore
    • 8.1
      MongoDB Scaling and Sharding
    • 8.2
      MongoDB Replication
  • Deploying and Monitoring MongoDB
    Working with MongoDB in a production environment requires careful planning, attention to detail, and best practices to ensure optimal performance, security, reliability, and scalability.
    3
    • 9.0
      Deploying MongoDB to Production
    • 9.1
      Monitoring and Management
    • 9.2
      Summary for MongoDB deployment on Production
  • Building a Web App with MongoDB (Final Project)
    Demo Project (OneStopShop)
    2
    • 10.0
      Building the Application
    • 10.1
      Final Project Features

Deploying MongoDB to Production

Deploying MongoDB to a production environment requires careful attention to architecture, configuration, security, monitoring, and maintenance practices. In this guide, we’ll cover the essential steps to deploy MongoDB in production, from initial setup and scaling strategies to ensuring high availability, performance, and security.

1. Deployment Architecture

a. Choosing a Deployment Model

Depending on your application’s needs, MongoDB can be deployed in different configurations. Your choice will depend on factors such as availability, scalability, and fault tolerance.

  1. Standalone Deployment:
    • Suitable for development or small-scale production environments where fault tolerance and high availability are not critical.
    • Not recommended for production due to lack of redundancy and failover.
  2. Replica Sets:
    • Recommended for high availability and data redundancy. A replica set consists of a primary node and one or more secondary nodes. If the primary node goes down, one of the secondaries is promoted to primary.
    • A minimum of 3 nodes is recommended for production environments to ensure quorum and automatic failover.
    • Replica sets also enable read scaling, where read queries can be directed to secondary nodes.
  3. Sharded Clusters:
    • For large-scale applications that require horizontal scalability, a sharded cluster distributes data across multiple shards (servers). Each shard contains a subset of the data.
    • A mongos router directs client requests to the appropriate shard, and config servers maintain metadata.
    • Sharded clusters are suitable when your data set exceeds the capacity of a single machine.
  4. MongoDB Atlas:
    • MongoDB Atlas is the fully managed cloud version of MongoDB. Atlas handles backups, monitoring, scaling, and security, freeing you from manual management of your database infrastructure.
    • Atlas provides an easy-to-use platform for creating replica sets, sharded clusters, and automatic scaling.

2. Setting Up the MongoDB Servers

a. Installation

You can deploy MongoDB on-premise or use cloud providers like AWS, Google Cloud, or Azure. For manual installations, follow these steps to install MongoDB on Linux, macOS, or Windows.

  1. Install MongoDB:
    • On Linux:bashCopy code# Ubuntu/Debian sudo apt-get install -y mongodb # CentOS/RHEL sudo yum install -y mongodb-org
    • On macOS (using Homebrew):bashCopy codebrew tap mongodb/brew brew install mongodb-community@6.0
    • On Windows: Download the MongoDB installer from MongoDB Downloads and follow the installation instructions.
  2. Start MongoDB:
    • Start the mongod service:bashCopy codesudo systemctl start mongod # For Ubuntu brew services start mongodb # For macOS
  3. Verify the Installation:
    • Check if MongoDB is running by connecting to the shell:bashCopy codemongo

b. Configuring MongoDB for Production

  1. Replication (Replica Set): A replica set provides high availability. Configure each mongod instance to participate in a replica set.
    1. Edit the mongod.conf file on each node and enable replication:yamlCopy codereplication: replSetName: "rs0"
    2. Start each mongod instance with the appropriate dbpath and port:bashCopy codemongod --config /etc/mongod.conf --port 27017 --dbpath /data/db --replSet rs0
    3. Initialize the replica set on the primary node:bashCopy codemongo --port 27017 rs.initiate()
    4. Add secondary nodes:bashCopy coders.add("secondary_host:27017")
  2. Sharding: Sharded clusters are used when horizontal scaling is required. A sharded MongoDB setup involves three components:
    • Shards: Each shard holds part of the data.
    • Config Servers: Store metadata about the cluster.
    • Mongos Routers: Direct client queries to the appropriate shard.
    1. Enable sharding on the database:bashCopy codesh.enableSharding("mydb")
    2. Shard a collection by specifying the shard key:bashCopy codesh.shardCollection("mydb.mycollection", { "shardKey": 1 })
    3. MongoDB uses a hash-based or range-based sharding strategy, depending on the data.

3. Security Configuration

Securing MongoDB is essential to prevent unauthorized access and ensure that your data is protected from potential vulnerabilities.

  1. Enable Authentication: By default, MongoDB allows unauthenticated access. To secure MongoDB, enable authentication.
    • Modify the mongod.conf file:yamlCopy codesecurity: authorization: "enabled"
    • Restart the MongoDB server:bashCopy codesudo systemctl restart mongod
  2. Create Users and Roles: MongoDB uses role-based access control (RBAC) to assign specific permissions to users.
    • Create an admin user:bashCopy codeuse admin db.createUser({ user: "admin", pwd: "admin_password", roles: [{ role: "userAdminAnyDatabase", db: "admin" }] })
    • Create application-specific users with custom roles.
  3. Enable Encryption:
    • Encryption at Rest: Enable WiredTiger for storage-level encryption.
    • Encryption in Transit: Enable SSL/TLS to encrypt data transmitted between MongoDB and clients.
      • Modify mongod.conf to enable SSL:yamlCopy codenet: ssl: mode: requireSSL PEMKeyFile: /path/to/mongodb.crt PEMKeyPassword: your_password CAFile: /path/to/ca.pem
  4. Network Security:
    • Use firewalls to restrict access to MongoDB servers from untrusted IP addresses.
    • Ensure MongoDB is bound to only private IPs or specific IP ranges in the bindIp setting.yamlCopy codenet: bindIp: 127.0.0.1,192.168.0.10
  5. Audit Logging: MongoDB provides auditing to track access to your database and monitor user activities. This can help ensure compliance and investigate any suspicious actions.

4. Backup and Restore Strategy

A solid backup strategy is essential for any production database. MongoDB provides various methods for backups.

  1. Mongodump (Manual Backups): Use mongodump to back up a MongoDB database.bashCopy codemongodump --uri="mongodb://localhost:27017" --out=/path/to/backup
  2. Mongorestore (Restoring Backups): Use mongorestore to restore data from backups.bashCopy codemongorestore /path/to/backup
  3. Continuous Backup: For continuous backups, use MongoDB Atlas or Ops Manager for automated backups, incremental backups, and point-in-time recovery.
  4. Snapshot Backups: For larger deployments, consider using storage snapshots or cloud-based backup solutions (e.g., AWS EBS snapshots).

5. Monitoring and Performance Tuning

Monitoring is crucial to ensure your MongoDB deployment runs smoothly, especially in production environments. MongoDB provides built-in tools for monitoring, and there are also external solutions.

  1. MongoDB Logs:
    • MongoDB logs information about errors, slow queries, and other important events. Review the logs regularly for potential issues.
    • Log locations are typically found in /var/log/mongodb/mongod.log.
  2. Monitoring with MongoDB Atlas: If you’re using MongoDB Atlas, it offers built-in monitoring for database performance, replication lag, and query performance.
  3. Third-Party Monitoring Tools: Use tools like Prometheus, Grafana, Datadog, or New Relic to monitor the performance of your MongoDB deployment and set up custom alerts based on CPU, memory, disk I/O, and other critical metrics.
  4. Performance Optimization:
    • Indexes: Proper indexing can significantly improve query performance.
    • Aggregation Framework: Optimize aggregation queries by using indexed fields for grouping and sorting.
    • Write Concern and Read Preference: Tune the write concern for durability and the read preference for load balancing between primary and secondary nodes.

6. Scaling MongoDB

As your application grows, you might need to scale your MongoDB deployment. There are two ways to scale MongoDB:

  1. Vertical Scaling: Increase the resources (CPU, RAM, storage) of your existing MongoDB server. This may work well for small to medium workloads.
  2. Horizontal Scaling (Sharding): When data grows beyond the capacity of a single server, use sharded clusters to distribute the data across multiple servers. This is necessary for applications that need to handle very large datasets and high throughput.

7. Disaster Recovery

Ensure your MongoDB deployment is resilient to failure:

  1. Replication: Use replica sets for high availability and automatic failover.
  2. Regular Backups: Implement a robust backup and recovery plan.
  3. Monitoring: Set up alerts for critical metrics such as disk usage, CPU load, replication lag, etc.

Conclusion

Deploying MongoDB to production requires careful planning and configuration. By following best practices in architecture, security, scaling, monitoring, and backup strategies, you can ensure that your MongoDB deployment is robust, highly available, and secure. Whether using a self-hosted solution or MongoDB Atlas, it’s essential to continuously monitor and optimize your deployment to meet the demands of your growing application.

MongoDB Replication
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