What Is Database Sharding?
Database sharding is a strategy for scaling a database by breaking it into smaller, more manageable pieces, or “shards”. Each shard is a separate database, stored on a different server, and only contains a portion of the total data. This allows for horizontal scaling, as more shards can be added on new servers when needed.
Sharding is done based on a shard key, which determines how the data is distributed across the shards. The goal of database sharding is to enhance performance, scalability, and manageability of large databases.
What are the benefits of database sharding?
Scalability and performance
The primary benefit and purpose behind sharding a database is to improve the scalability and performance of your database. A sharded database is easier to scale horizontally as users and traffic to your application grows over time.
Reliability and availability
By sharding your database you can have multiple instances of your data across multiple shards on different machines. This means that even if a shard fails, users can still get their data because your application can get it from the active shard. This prevents single points of failure in your app.
A database can be sharded based on geographic region, which reduces latency for users by making sure their data is served from a database located closer to them. Another benefit is for regulatory compliance, with some countries like those in Europe requiring data to stay within the nation the users are located for privacy reasons. By having geographic sharding implemented, it’s easier to comply with these regulations.
What are the challenges of database sharding?
The primary challenge of database sharding is that the process of sharding your monolith database will itself be a challenge. Once done, there will be additional management overhead in your architecture when it comes to handling data integrity across shards.
Potential latency issues
Latency can become a factor in situations where queries require data that is stored on multiple shards rather than a single machine. This not only slows down performance, but could result in failures if the hardware holding data from one shard fails during a query.
There are a number of different strategies for choosing a shard key to breakup your data, with none of them being perfect. A common problem with sharding is when one specific shard holds data that is accessed more frequently than others. This results in a “hotspot” where that shard can cause performance issues due to handling more traffic than others. This can be solved by re-sharding your data or designing your database so shards can be scaled across machines independently depending on traffic.
Sharding will result in higher infrastructure costs because you will need to create duplicates of each shard to maintain availability as well as durability for your data. You will also most likely need to invest more in engineers to monitor and maintain the more complicated architecture of a sharded database.
Types of database sharding
Range sharding involves dividing a database into rows and distributing them across different shards. Each shard holds different data but with the same schema. For example, customers with ID 1-1000 may be on one shard, while customers with ID 1001-2000 are on another.
This type of sharding uses a shard key, which could be any column in a database, to determine where data is stored. The key is run through a hash function, and the resulting value determines the shard. This approach can distribute data evenly but reshuffling the shards can be challenging.
As mentioned earlier, geographic sharding is when you break down your data by some level of geographic location. This could be done at nation, city, or some other level depending on your use case.
Vertical sharding works like range sharding but breaks up the columns of the database instead of rows. Each shard has a subset of the data and a different schema. For instance, one shard may store user profile information, while another stores their transaction history.
What are the alternatives to database sharding?
There are a number of alternatives to database sharding and in general sharding should only be used as a last resort when it comes to scaling your database due to the complexity involved.
The simplest alternative to sharding is to upgrade the hardware your database is running on for as long as possible. This means faster CPUs, more RAM, and bigger disks. There is a physical limit for a single machine and vertical scaling eventually becomes far more expensive compared to scaling horizontally across commodity servers. Having a single huge server also creates a single point of failure for your application.
You can improve read performance by denormalizing your data so that the same data will be located across multiple tables, which reduces the need for costly join operations.
Another way to scale read performance for a database is to implement a caching layer using something like Redis. A cache sits in front of your database and stores frequently accessed data in memory to return it fast without the request ever hitting your database. One potential issue with caching is that data can become stale if a user updates something and the cache still has the old data stored.
Scaling writes is often the hardest part of scaling a database, with fewer options compared to scaling read performance like those listed above. One option is to create read replicas of your database, which handle all queries for data so that the primary database hardware only has to be used for handling write requests. One potential issue with read replicas is that they may return stale data because they have to wait to receive updated data from the primary database.
Another way to scale writes is to log the write operations before they are committed to the database. This can help scaling because the database won’t immediately have to perform write requests as they are received. The downside for this is again the fact that users won’t see their changes to the database reflected as quickly as they’d expect
What Is the Difference between Sharding and Partitioning?
Database Sharding refers to dividing a database into smaller pieces called “shards” and distributing them across separate database servers. Each shard holds a portion of the data and operates independently. This is a form of horizontal scaling, as you can add more servers to handle more data. Sharding is primarily used to increase performance and support larger databases, but it introduces significant complexity.
Partitioning is dividing a database into smaller, manageable parts within the same database system and often on the same server. Partitions can be based on criteria like rows or hashing. Partitioning can improve performance and manageability but doesn’t provide the same scalability as sharding since you’re not distributing data across multiple servers.
In short, sharding is a type of partitioning but the key difference lies in the fact that sharding involves separate database servers, offering superior scalability at the cost of added complexity.