System Design - Scalability: Sharding Part 2

Advanced Sharding Strategies

• Composite shard keys use multiple columns to determine the shard, providing more granularity and control over data distribution.
• Examples of composite shard keys include combining user_id and region_id to distribute data based on both user identification and geographic location.

Dynamic Sharding

• Dynamic sharding adjusts the number of shards based on the current load and data volume.
• This approach requires monitoring and dynamically reallocating data as needed to maintain balanced shards.

Consistent Hashing

• Consistent hashing distributes data evenly across shards and minimizes data movement when adding or removing shards.
• Benefit: Reduces the impact of shard changes on the overall system by ensuring only a small portion of the data is redistributed.

Re-sharding

• Reasons for re-sharding include uneven data distribution leading to hotspots, changes in data volume or application requirements, and adding or removing shards to scale the system.
• The re-sharding process involves planning, data migration, updating metadata, and testing.

Global Distribution

• Geo-sharding distributes data based on geographic regions to reduce latency and improve user experience.
• Data locality ensures that related data is kept within the same geographic region, reducing cross-region data access and improving performance.

Consistency Models

• Strong consistency guarantees that all reads return the most recent write, suitable for applications requiring strict data accuracy.
• Eventual consistency guarantees that, given enough time, all replicas will converge to the same value, suitable for applications where data can tolerate temporary inconsistencies.
• Causal consistency ensures that operations that are causally related are seen by all nodes in the same order, providing a middle ground between strong and eventual consistency.

Operational Considerations

• Implement comprehensive monitoring to track the performance and health of each shard, and set up alerts for potential issues.
• Regularly back up each shard to ensure data can be recovered in case of failure, and implement a robust disaster recovery plan to handle data loss scenarios.
• Ensure each shard adheres to security best practices, such as encryption at rest and in transit, and comply with regulatory requirements.

Case Study: Implementing Sharding in a Real-World Application

• Identify shard keys, such as user_id for user data and order_id for order data, and consider composite keys for more granularity.
• Choose a sharding strategy, such as hash-based sharding for even distribution and geo-sharding for minimizing latency.
• Set up shards in multiple regions, configure multi-master replication for high availability and fault tolerance, and implement re-sharding to handle hotspots.
• Choose a consistency model based on application requirements, and operationalize with monitoring, alerting, automated backups, and compliance with data protection regulations.