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1. Database Scalability

Database scalability refers to the ability of a database system to handle increasing amounts of data and growing numbers of users without sacrificing performance. Scalability is crucial for applications that experience rapid growth in data volume or user base.

  • Horizontal Scaling

Horizontal scaling, also known as scale-out or sharding, involves distributing the database workload across multiple servers or nodes. Each server contains a subset of the data, and requests are distributed among these servers. Horizontal scaling increases capacity by adding more servers to the system.

Example:

Consider a social media platform where user data is stored in a database. As the number of users grows, the database becomes overloaded, leading to performance issues. By horizontally scaling the database, user data can be distributed across multiple servers based on user IDs or other criteria. Each server handles a portion of the user base, improving performance and scalability.

Resources:

  • Vertical Scaling

Vertical scaling, also known as scale-up, involves increasing the capacity of a single server by adding more resources such as CPU, memory, or storage. This approach requires upgrading the hardware of the server to handle increased workload.

Example:

In a vertical scaling scenario, a company may upgrade its database server by adding more CPU cores and increasing memory capacity to accommodate growing data and user load. This allows the server to handle more concurrent requests and process larger datasets without the need for distributing the workload across multiple servers.

Resources:

  • Distributed Systems

Distributed systems are collections of interconnected computers that work together to achieve a common goal. In the context of databases, distributed systems distribute data and processing across multiple nodes or servers, often located in different geographical locations.

Example:

A distributed database system replicates data across multiple nodes in different data centers to ensure high availability and fault tolerance. Each node stores a copy of the data, and changes made to one node are propagated to other nodes asynchronously or synchronously. This architecture improves resilience and reduces the risk of data loss in case of hardware failures or network issues.

Resources:

2. Introduction to Sharding

  • Definition

  • Purpose

  • Benefits and Challenges

3. Types of Sharding

  • Horizontal Sharding

  • Vertical Sharding

  • Key-based Sharding

  • Range-based Sharding

4. Sharding Architecture

  • Sharding Key

  • Shard Key Selection

  • Data Distribution

  • Shard Placement

  • Shard Management

5. Sharding Strategies

  • Hash Sharding

  • List Sharding

  • Range Sharding

  • Composite Sharding

6. Sharding Implementation

  • Sharding Middleware

  • Sharding Proxy

  • Sharding Router

  • Sharding Algorithm

7. Data Consistency

  • ACID Properties

  • CAP Theorem

  • BASE Principles

  • Eventual Consistency

8. Query Routing

  • Query Analysis

  • Shard Selection

  • Query Parallelization

9. Data Distribution

  • Data Partitioning

  • Data Replication

  • Consistent Hashing

  • Virtual Nodes

10. Monitoring and Management

  • Shard Health Monitoring

  • Load Balancing

  • Automatic Failover

  • Scaling Operations

11. Data Migration

  • Shard Splitting

  • Shard Merging

  • Shard Rebalancing

  • Zero Downtime Migration

12. Performance Optimization

  • Indexing Strategies

  • Query Optimization

  • Caching Techniques

  • Compression Techniques

13. Failure Handling

  • Node Failures

  • Network Failures

  • Shard Failures

  • Disaster Recovery

14. Security

  • Access Control

  • Encryption

  • Authentication

  • Authorization

15. Case Studies and Best Practices

  • Real-world Examples

  • Lessons Learned

  • Best Practices and Anti-patterns

16. Advanced Topics

  • Multi-shard Transactions

  • Cross-shard Joins

  • Time-based Sharding

  • Geo-sharding