Database Sharding

Database Sharding

Introduction

Databases are one of the most critical components of any application but can be a source of pain when it comes time to scale. Sharding is one of the essential components of any application, but it can also be a source of pain when it comes time to scale.

Sharding is a common scaling strategy when your application's dataset (the data stored in your database) and traffic (the number of queries sent to your database) have grown beyond the capacity of a single database server.

Sharding is a common scaling strategy when your application's dataset (the data stored in your database) and traffic (the number of queries sent to your database) have grown beyond the capacity of a single database server.

In sharded databases, you divide the data so that each node contains a subset of the total number of rows; each node will only be responsible for answering queries related to its subset. This has two advantages: it reduces contention between nodes because they're storing separate subsets of rows and allows you to take advantage of parallel processing capabilities on modern hardware.

The benefit here is clear—sharding allows you to scale out horizontally while maintaining ACID guarantees—but there are also drawbacks: it can complicate joins between different tables or require costly cross-shard joins if queries need access across shards; additional indexes may be required on read-only tables; and there are some restrictions on how individual columns can be accessed from each fragment due to transaction isolation concerns.

The term "Shard" comes from broken glass or pottery. In a sharded environment, the overall dataset is broken into smaller pieces called "shards," each stored on its database server instance, separate from the others. A shard can be hosted on the same physical hardware as other shards, or it can be hosted on physically different machines.

Sharding is breaking up the data into smaller pieces called "shards," which are stored on separate database server instances, each known as a shard. The term "shard" comes from broken glass or pottery. In a sharded environment, the data is broken into smaller pieces called "shards, " each stored on its database server instance. A shard can be hosted on the same physical hardware as other shards or physically separate machines.

To implement sharding, you'll need an additional layer in your application to target queries to the appropriate shards and to combine data from multiple shards for queries that join data from different shards.

To implement sharding, you'll need an additional layer in your application to target queries to the appropriate shards and to combine data from multiple shards for queries that join data from different shards. This is sometimes called a "sharding layer." The sharding layer is a separate system you deploy as part of your application.

The sharding layer needs to know how to target queries to the appropriate shards and how to join data from multiple shards. It may also have other responsibilities, such as aggregating data from multiple shards.

If you're using Third-Party Hosted databases, many providers offer a managed service for sharding (such as AWS Database Migration Service).

If you're using Third-party Hosted databases, many providers offer a managed service for sharding (such as AWS Database Migration Service).

If you have your hardware, it’s possible to set up sharding yourself by installing the right software and configuring it. If this is the case, be aware of some limitations with self-hosting because of how shards work with some database engines (e.g., PostgreSQL).

After you've decided where to partition your data (horizontal vs. vertical), you'll need to decide what kind of query router to use. Query routers can direct client requests to the right shards and rewrite queries to work in a distributed environment.

Once you've decided where to partition your data (horizontal vs. vertical), you'll need to decide what kind of query router to use. Query routers can direct client requests to the right shards and rewrite queries to work in a distributed environment.

Horizontal sharding (aka Key Sharding) is partitioning data by key. It's excellent for large datasets because it allows you to scale out horizontally by adding more machines or nodes that store only a portion of your total dataset so that each node has its subset of keys, which reduces its memory footprint as well as improves performance for reads as it doesn't have all the records in its cache at any given time. It only makes sense if your dataset is small enough that all shards simultaneously fit into one machine's memory. However, this could be an option if you have elasticity on demand from cloud providers like AWS EC2 Spot Instances—you can spin up another instance instead!

Vertical sharding (aka table sharding) is when tables are split into multiple pieces based on their primary fundamental values and placed on distinct machines within a single database instance; this type may seem similar, but there are some important distinctions: vertical partitioning always involves splitting tables, while horizontal partitioning involves splitting both tables AND columns within those tables (and thus requires more complex logic); these distinctions mean we can achieve more excellent concurrency with vertical partitions since we don't need specialized locking mechanisms; this means our read/write throughput will likely be lower than horizontal partitions due.

Sharding is a way to scale beyond a single server.

In this section, you’ll learn how to:

• Decide what kind of query router to use.
• Select a sharding strategy.
• Select a query router.
• Create a sharded environment.

You can use this knowledge to build your cluster or use a framework that provides all the infrastructure for you, such as Kubernetes or OpenShift.

Conclusion

Sharding is a common way to scale beyond a single server. When done right, sharding can improve query performance, enable more data to be stored on each machine and make your application fault tolerant. However, there are some limitations associated with sharding that you should be aware of before implementing this strategy in your app.