How do you scale your database horizontally?

3 How to scale horizontally?

There are various approaches to horizontally scaling your database, depending on your data model, query patterns, and scalability goals. Range-based sharding is a simple and straightforward method that involves dividing your data into shards based on a range of values. For example, you can shard your database by date ranges like January 2020 or February 2020. However, this approach can lead to an uneven distribution of data and load if some ranges are more popular or larger than others. Key-based sharding is more efficient and balanced than range-based sharding since it assigns a unique key to each record in the database and uses a hash function or formula to map the key to a shard. Additionally, directory-based sharding is more flexible and dynamic than key-based sharding but introduces an extra layer of complexity and overhead since it requires maintaining a separate table or service that stores the mapping of each record to its shard.

4 What are the challenges of horizontal scaling?

Horizontal scaling can bring many benefits to your database, but it can also pose some challenges and trade-offs that you need to be aware of. Data consistency and integrity can be difficult to achieve, requiring distributed transactions, locking mechanisms, or eventual consistency models that may affect performance and availability. Query complexity and optimization may require distributed query engines, caching systems, or denormalization techniques that introduce maintenance and overhead. Finally, sharding management and maintenance involves tasks such as adding or removing nodes, rebalancing or resharding data, backing up or restoring data, and troubleshooting issues. Automation tools, frameworks, or platforms may help with these tasks but also add dependencies and limitations.

5 What are the best practices for horizontal scaling?

Horizontal scaling can be a powerful and effective way to scale your database, but it requires careful planning and execution. To make your horizontal scaling successful, you should first understand your data and workload. This will help you choose the right sharding method, criterion, and strategy for your database. Additionally, you should test and benchmark your scaling solution's performance, availability, and reliability before implementation. Lastly, you should monitor and adjust your scaling solution after implementation to detect and resolve any problems or anomalies, as well as fine-tune it to meet changing needs and goals.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?