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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

Summary for MongoDB deployment on Production

Working with MongoDB in a production environment requires careful planning, attention to detail, and best practices to ensure optimal performance, security, reliability, and scalability. Here’s a comprehensive guide to working with MongoDB in production:

1. Choosing the Right MongoDB Deployment Architecture

Depending on your use case, MongoDB offers different deployment models, each with its own strengths. Consider these options for production deployments:

a. Standalone Deployment

  • Use Case: Small-scale applications or development environments.
  • Description: A single MongoDB instance. Not recommended for production due to lack of fault tolerance and high availability.
  • Drawbacks: Single point of failure, no automatic failover or redundancy.

b. Replica Sets

  • Use Case: High availability (HA) and fault tolerance for critical applications.
  • Description: A replica set consists of a primary node and one or more secondary nodes that replicate the data. If the primary node fails, one of the secondary nodes automatically takes over as the primary (automatic failover).
  • Key Features:
    • Automatic Failover: Ensures continuous availability of the database.
    • Data Redundancy: Data is replicated across multiple nodes.
    • Read Scaling: Secondary nodes can be used for read operations to distribute the load.

c. Sharded Cluster

  • Use Case: Horizontal scalability for large-scale applications with high throughput and large datasets.
  • Description: Data is distributed across multiple shard servers, each holding a portion of the data. The cluster is managed by a mongos router and a config server that maintains metadata about the sharded data.
  • Key Features:
    • Horizontal Scaling: Distributes data across multiple machines, allowing MongoDB to scale with data growth.
    • Automatic Balancing: Shards are automatically balanced based on data distribution and load.

d. MongoDB Atlas

  • Use Case: Managed MongoDB service with automated scaling, monitoring, and backups.
  • Description: MongoDB Atlas is a fully managed database service provided by MongoDB, Inc. It runs on cloud platforms like AWS, Azure, and Google Cloud.
  • Key Features:
    • Managed Service: No need to handle server maintenance, backups, or scaling.
    • Cloud Integration: Seamless integration with cloud services like AWS S3, Google Cloud Storage, and more.
    • Automated Backups, Monitoring, and Security: Out-of-the-box tools for scaling, backups, and alerts.

2. Hardware and System Considerations for Production

The hardware and system setup for your MongoDB deployment plays a critical role in performance and reliability.

a. CPU and Memory

  • CPU: MongoDB is CPU-intensive, especially for write-heavy operations. Opt for multi-core processors for better performance. MongoDB can take advantage of multiple cores during read/write operations, aggregation queries, and index building.
  • Memory: MongoDB’s performance heavily depends on memory, as the working set (frequently accessed data) should ideally fit in memory. MongoDB uses memory-mapped files, so more RAM allows the database to cache more data.
    • Recommended: At least 8 GB of RAM for production workloads.
    • Best Practice: Aim for the size of your working set to be smaller than the available RAM to reduce disk I/O.

b. Disk I/O

  • MongoDB uses the WiredTiger storage engine by default, which is optimized for modern hardware. It performs well on SSD disks for low latency and high throughput.
    • SSD vs HDD: SSDs are highly recommended because MongoDB’s performance depends on fast disk reads and writes. Traditional hard disk drives (HDDs) may not perform well in production environments, especially for write-heavy workloads.

c. Network Considerations

  • Network Latency: MongoDB’s replica set and sharded clusters require efficient communication between nodes. Ensure low-latency, high-speed networking between your MongoDB nodes and between your application servers and MongoDB.
  • SSL/TLS: If you’re transmitting sensitive data, configure SSL/TLS encryption for secure communication.

3. Configuration Best Practices for Production

a. Configuring MongoDB for High Availability

To ensure high availability in production, you need to set up a replica set with at least three nodes (one primary and two secondaries). A minimum of three nodes ensures that the replica set can elect a new primary if one fails.

  1. Replica Set Setup: Start the mongod instances with the --replSet option to indicate that they should join a replica set.
    • Example:bashCopy codemongod --port 27017 --dbpath /data/db --replSet rs0 --bind_ip_all
  2. Initiating the Replica Set: On the primary node, initiate the replica set.bashCopy codemongo --port 27017 rs.initiate()
  3. Add Secondary Nodes: On the primary node, add secondary nodes to the replica set.bashCopy coders.add("secondary_host:27017")
  4. Write Concern and Read Preference:
    • Write Concern: Configure appropriate write concern to ensure data durability. For example, { w: 2 } requires acknowledgment from at least two nodes before acknowledging the write.
    • Read Preference: If you want to offload read operations to secondary nodes, set up the readPreference to secondary or nearest.

b. Configuring Sharded Clusters

A sharded cluster in MongoDB enables horizontal scaling by splitting your data across multiple shards. This is useful when your dataset is too large to fit on a single server.

  1. Sharded Cluster Components:
    • Shards: Each shard contains a portion of your dataset.
    • Config Servers: Store metadata about the cluster’s sharding setup.
    • Mongos: Acts as a routing service to direct client requests to the correct shard.
  2. Sharding Key: Choose an appropriate sharding key for your data. This key should allow for even distribution of data across shards. Picking the right sharding key is critical for performance.
    • Avoid Hotspots: Ensure the key is evenly distributed (e.g., using a range-based or hashed sharding strategy).
    • Avoid Monolithic Shards: If the data distribution is uneven, some shards may receive too many requests while others are underutilized.
  3. Enable Sharding on a Collection:bashCopy codesh.enableSharding("mydb") sh.shardCollection("mydb.mycollection", { "shardKey": 1 })

c. Indexing and Query Optimization

Proper indexing and query optimization are key for maintaining good performance in a production environment.

  1. Create Indexes: Indexes allow MongoDB to quickly locate documents, improving the performance of queries.
    • Example: Create an index on a commonly queried field.javascriptCopy codedb.users.createIndex({ "email": 1 })
  2. Monitor Query Performance: Use the explain() method to analyze query execution plans and make optimizations.
  3. Use Compound Indexes: For multi-field queries, use compound indexes. For example, if you query by both age and name fields frequently, create a compound index:javascriptCopy codedb.users.createIndex({ "age": 1, "name": 1 })
  4. TTL Indexes: For expiring data (e.g., sessions or logs), use a TTL (Time-To-Live) index to automatically delete documents after a certain period.javascriptCopy codedb.sessions.createIndex({ "createdAt": 1 }, { expireAfterSeconds: 3600 })

d. Monitoring and Alerts

MongoDB provides several tools and metrics for monitoring the health and performance of your database in production.

  1. MongoDB Atlas Monitoring: If using MongoDB Atlas, it provides a rich monitoring dashboard with metrics like CPU usage, disk I/O, memory usage, replication lag, and more.
  2. MongoDB Ops Manager: For self-hosted MongoDB, use Ops Manager to manage backups, monitoring, and automation.
  3. Mongostat and Mongotop:
    • mongostat: Provides real-time statistics about MongoDB server operations.
    • mongotop: Tracks the time MongoDB spends reading and writing data for each collection.
  4. Set Up Alerts: Create alerts based on specific conditions (e.g., high CPU usage, replication lag, or disk space). You can use MongoDB Atlas or integrate with tools like Prometheus, Grafana, or Datadog for comprehensive monitoring and alerting.
  5. Check Server Logs: Regularly check the MongoDB logs for any warnings, errors, or issues that need to be addressed.

e. Backups

  1. Regular Backups: Implement regular backups to ensure data recovery in case of failure.
    • Mongodump: Create backups using mongodump, which allows you to dump data from MongoDB into a BSON file:bashCopy codemongodump --uri="mongodb://youruser:yourpassword@localhost:27017" --out=/path/to/backup
  2. Continuous Backup (Ops Manager/Atlas): Use MongoDB’s Ops Manager or Atlas to set up continuous backups for production data.
  3. Point-in-Time Recovery: Use WiredTiger’s journaling feature to enable point-in-time recovery.

4. Security Considerations for MongoDB in Production

Security is a critical concern in any production environment. Here are some essential steps:

  1. Authentication: Enable authentication for your MongoDB deployment. Use SCRAM (Salted Challenge Response Authentication Mechanism) or x.509 certificates for authentication.
  2. Authorization: Use role-based access control (RBAC) to limit the permissions of users. Create roles that grant only the minimum permissions necessary for each user or application.
  3. Encryption:
    • Encryption at Rest: Use encryption to protect stored data (enabled by default in MongoDB Enterprise).
    • Encryption in Transit: Use SSL/TLS to encrypt data transmitted between MongoDB and clients.
  4. Network Security:
    • Firewall: Only allow trusted IP addresses to access MongoDB.
    • VPN/Private Network: Use a VPN or a private cloud network to restrict access to your MongoDB instance.
  5. Auditing: Enable auditing to track administrative actions and user activities for compliance purposes.
Monitoring and Management
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