Data partitioning, or sharding, involves dividing a large database into smaller pieces. This helps improve how the database is managed, its performance, availability, and how the application distributes its workload. The idea behind sharding is that once a database reaches a certain size, it’s more efficient and cost-effective to add more machines horizontally (meaning, adding more machines) than to invest in bigger, more powerful servers vertically. In nutshell, it is the process of splitting up a DB/table across multiple machines.
Taking a user’s database as an example, As the number of users grows, we add more “shards” to our system, which are like compartments for storing data. One common method for splitting up user data is hash-based partitioning. Here’s how it works: we use a special math function (called a hash function) on certain pieces of information about each user. This function gives us a number, which tells us which shard to store or find that user’s data in. For instance, if we have 100 database servers and our user IDs are just numbers that go up by one each time we add a new user, we could use a function like ‘ID % 100’ to figure out which server holds each user’s information. This way, we spread the data evenly across all our servers. But there’s a snag: if we want to add more servers later on, we’d have to change our hash function, which means moving data around and causing downtime. To avoid this issue, we can use something called Consistent Hashing.
Challenges with sharding
When you’re dealing with a sharded database, there are some special constraints that affect what you can do. A big reason for this is that when you shard a database, operations involving multiple tables or rows might not all happen on the same server anymore. Here are extra things you need to think about when you’re working with sharded databases:
a. Joins and Denormalization: Joining data in a database that’s all on one server is pretty easy. But when you split up your database across lots of machines, it gets trickier. Joining data from different parts of the database, or “shards,” isn’t efficient because you have to gather it from all those machines. One way to work around this is to simplify the database by combining related data into a single table. This speeds up queries because everything you need is in one place. But be careful! Simplifying the database like this can lead to inconsistencies in your data, which might cause problems for your service.
b. Referential integrity: Just like it’s tough to do queries that involve different parts of a split-up database, enforcing rules about how data relates (like foreign keys) can be a real challenge in a sharded database.
Most relational databases don’t handle foreign keys that link data across different servers. So, if your app needs to make sure data stays consistent across these shards, you’ll probably have to do it in your app’s code. Often, this means running special jobs in your app to fix any mistakes or “dangling” references that might pop up.
c. Rebalancing: Sometimes, we need to change how we split up our data across different parts of our database, here are a couple of reasons why:
- Sometimes, our data isn’t spread out evenly. For example, there might be too many people with the same ZIP code to fit into one part of the database.
- Other times, one part of the database might be getting a lot of action. For instance, if there are tons of requests for user photos, the shard holding those photos might get overloaded.
When this happens, we might need to add more parts to our database or move stuff around to even out the load. But doing this without causing any downtime is really tough. Some methods, like directory-based partitioning, can help make the process smoother, but they also make the system more complicated and introduce new risks, like having one spot that, if it fails, takes down the whole system.
d. You’ll need to update your application logic to work with shards, which could result in complex SQL queries.
e. Sharding adds more hardware and additional complexity.