We’ve looked at best practices for building scalable applications. We turn our attention now to the technical issues your development teams must address. This guide introduces ten software design principles for building scalable applications.
Start With Scalable Databases
What’s the first thing to consider when designing for scale? Many organizations start by focusing on APIs or processing services. However, this would overlook one of the most critical system components: databases.
A single database store is a bottleneck to scaling the rest of your application, so you most likely need multiple databases – but scaling and replicating databases is challenging. Databases are usually large and complex to start with. Synchronizing multiple databases in a distributed environment multiplies the challenge. You also need to ensure appropriate coverage, performance, and redundancy.
Designing a scalable distributed database requires focus from your development team and is a foundational element for everything else you will do on the path to creating a scalable application. So start with the database. Get it right, and the rest will be much easier.
If an application running at a small scale requires a bit of storage, then the same app running at a large scale would need lots of storage, right? And the bigger the storage requirement, the higher the cost and the potential for performance problems.
Suppose you intend to use serverless architecture or rapidly scaling systems. In that case, storage can also constrain your agility, preventing you from taking full advantage of the most agile parts of your application.
Avoid storage wherever possible. Rely on storage only for the most critical components.
Design your application to leverage existing data structures to configure and deploy new systems without recalling data from storage. Following this approach, you will significantly reduce your need to store, process, and replicate data, overcoming one of the most common limits to scaling up.
Build Stateless Applications
Many applications store or transfer data (or their “state”) between user sessions or services. Saving state helps provide a smooth user experience even if a session gets disrupted. However, it also makes scaling complicated because your application needs to replicate the stored state as it scales up. It also creates dependencies between services and processes, which makes scaling difficult.
Instead, build stateless applications. A stateless application does not store any session data to use in another session. Stateless applications allow processes, services, and microservices to operate independently of the other with fewer dependencies, making it easier to scale up granularly.
Stateless applications also enhance security, which makes scaling safer.
Use Asynchronous Communication
Any process that can only happen in series (one after another) and not in parallel is a big bottleneck on scale. When your application scales up, the number of requests will also grow. If your application only responds to one request at a time, your application will quickly become unresponsive. When your application scales up, the number of requests will also grow. When a request kicks off a long-running task, your application could be unresponsive for a while.
Asynchronous communication enables an application to respond to multiple requests and start multiple tasks in parallel. This technique helps your application to remain responsive even as the volume of requests grows.
If you apply asynchronous design to your services and overall architecture, your application will quickly process I/O and CPU-bound requests. Asynchronous practices like reactive programming will also allow services to be scalable, interactive, resilient, and event-driven.
Queue Automation Tasks
Asynchronous communications rely on extensive queueing systems for the automated flow of tasks and operations. You need to design your queue automation to scale up with the rest of your application services; otherwise, it will become a bottleneck.
Use Read Replicas
The primary instance of your application can come under heavy load from read requests, for example, for traffic analytics. This load can mean that scaling up needs more compute resources for your primary instance.
You can reduce this load on your primary instance by replicating data from the primary to other instances or a sidecar service. This technique allows you to offload your read requests from your primary instance.
Read replicas allow your monitoring service to send requests to one of your replica instances, adding zero load to your primary instance.
Read replicas also improve your resilience and disaster recovery. A terminated or unresponsive service can restart quickly by reading data from a read replica. You can use this technique and also have a stateless application by ensuring that the recovery process only transmits critical performance information and not any client or session data.
Reduce Write Requests
It’s probably clear by now that an application with independent services and asynchronous communications generates a lot of messages. The volume of messages could become overwhelming when operating at scale and make it complex and impractical for teams to scale up effectively.
To reduce the messaging load, try to reduce the number of write requests your application makes. Monitor your application performance to understand the volume of write requests and the size of different types of write requests. Try to find the right balance between size and volume for optimal performance.
More and bigger messages also mean an overall slower experience for users. Reducing the messaging load benefits the user experience too.
Scale Your Servers Appropriately
When you scale up your application, you will need to scale up your application servers. Despite the adoption of serverless computing, where services operate independently of the underlying servers, many parts of your application will still need to run on various servers or images.
Application teams make two common mistakes when scaling servers.
- If your server scaling is too conservative, you might not provide enough server capacity for your application traffic, or you might not be able to quickly spin up new servers in response to demand.
- If your server scaling is too aggressive, you might spin up new servers when you don’t need to and spend too much money on server capacity.
Make sure you understand how your application behaves on particular servers or images and with different amounts of headroom in server capacity. Monitor application traffic and understand how traffic and server capacity affect performance. Set appropriate thresholds for scale-up and scale-down that are not too aggressive or conservative.
Use A Robust Caching Engine and a Good CDN Provider
Caching reduces the time traffic takes to flow to all parts of your distributed application and end-users. Multi-location caching can reduce latency, especially in global deployments, because geographic distance affects latency.
A content delivery network (CDN) or edge computing platform can provide multi-location caching. Alternatively, you can build your own caching system using multi-location storage, replication, load balancing, and content acceleration.
Integrate Performance and Load Testing In Your Build Pipelines
Test early and test often. If you test too late in your development, fixing problems with performance under load could be very expensive.
Integrate testing as early as possible in your development cycle. By testing in development, you can identify weaknesses and bottlenecks early, giving you time to correct your design and ensure your application will be able to meet its scaling demands.
If you hope that your business will make it big, you need to prepare by building scalable applications. This requires more than just deploying your application in the cloud. You must follow specific design principles throughout your development process.
By following the guidance in this article, you will be ready to take advantage of the opportunities that come with scale.