Increasing user traffic and API requests can mean your business is headed in the right direction. It can also mean you need to scale your API gateway. Let us take you through the benefits, challenges and practicalities of doing so.
Benefits of scaling an API gateway
Scaling an API gateway means it will be able to handle more concurrent incoming requests. The benefits include enhanced performance, reduced latency and response times, and high availability, as you can distribute the load across multiple instances to protect against any one instance failing.
API gateway scaling also protects against performance bottlenecks as you increase throughput capacity to accommodate your growth in traffic.
There are also cost benefits to be gained when you look at how to scale your API gateway to best support fluctuating traffic volumes. With the gateway architecture dynamically adjusting resources in response to incoming traffic volumes, you have the ability to maintain optimal performance during busy periods while keeping your costs at a minimum when demand is lower.
What are the challenges?
Load balancing, service discovery, monitoring, debugging, managing shared states and ensuring data consistency can all be a challenge when scaling an API gateway. Below, we’ll look at how designing an effective gateway architecture can minimise these challenges while providing some troubleshooting tips.
Choosing the right platform for your needs
Different API gateways deliver different benefits. Your choice of which platform to use can also impact the cost of scaling. Let’s look at some of the key considerations in relation to choosing the right API gateway.
Remember that whichever option best suits you, using Tyk’s open source API gateway can deliver added benefits, such as no vendor lock-in and superb community support.
Cloud vs on-premise solutions
An on-premise API gateway delivers total control, flexibility and scalability. Installed on your own servers, it can be a great way to meet the requirements of stringent regulators. A cloud gateway solution, meanwhile, which you can install on your own servers, the public cloud or as a multi-cloud software-as-a-service (SaaS) solution, delivers the ability to flex and scale, minus the infrastructure headaches.
Security and authorisation
Clearly, whichever platform you choose, your API gateway needs to deliver robust security and authorisation mechanisms. One of the benefits of Tyk is that it provides this right out of the box. You can implement security at the platform level so that every API in your portfolio enjoys the same security features. And not only does Tyk support all major types of security, you can also create custom implementations to suit your needs. Make sure that, whichever platform you choose, it puts powerful security like this at your fingertips as you scale.
Designing an effective architecture
Part of approaching how to scale an API gateway effectively is designing an effective architecture. This means implementing everything from synchronisation mechanisms for managing shared states to caching mechanisms and data storage solutions for ensuring data consistency across multiple instances. The latter is particularly important if you’re using an API gateway for microservices, with the gateway interacting with multiple backend services.
Service discovery and client-side load balancing
Service discovery and load balancing are cornerstones of effective scaling. Distributing incoming traffic efficiently across multiple instances can be complex, but this is essential to smooth operation. As such, you must implement load-balancing mechanisms to share the workload evenly across each instance.
This increasing number of instances means that robust service discovery mechanisms are also essential. Service discovery supports the dynamic location of and connection to available instances.
Logging and monitoring
API gateway scaling can result in increased complexity. As such, it’s essential to log and monitor performance and resource utilisation. Doing so can ensure you are alerted swiftly to any potential issues so that you can troubleshoot them.
It’s worth noting that automation can play a key role in designing an effective architecture for scaling. Automation tools can help to manage and streamline the scaling process, providing time and cost savings. They can help provision and deploy infrastructure resources, configure and manage load balancers, implement monitoring and alerting mechanisms, and more.
Strategies for scaling an API gateway
The approach you take to API gateway scaling will depend on your use case and needs. Three common strategies include database sharding, caching strategies and asynchronous processing.
This involves horizontally partitioning data across multiple databases or shards, each containing a subset of the data. If you aim to reduce the load on a single database as you scale, this can be an effective way to deliver improved performance and increased throughput. As the data volume grows, you can add more shards, enabling scalable storage and higher availability (as each shard can have its failover mechanism or be replicated).
By caching frequently accessed data, you can reduce the load on backend systems and deliver performance improvements, thanks to reduced response times and lower resource utilisation. Reducing the computational load on backend systems through caching can support effective scaling while delivering a superior user experience.
Asynchronous processing involves queueing or scheduling tasks for later processing instead of processing everything synchronously and waiting for immediate completion. This operational task independence can handle more requests concurrently, thus supporting effective scaling while delivering greater resilience, fault tolerance, and reduced response times.
As mentioned above, scaling an API gateway can not only increase the volume of traffic it can handle, it provides the chance to optimise performance. You can do this in numerous ways.
Load testing and benchmarking
Load testing and benchmarking allow you to establish the maximum volume of concurrent requests that your API gateway can handle before performance is impacted. You can also measure request response time and latency to ensure that these are improved as you distribute the load across multiple instances as you scale.
Optimising data structures
Optimising data structures such as hash tables, trees or caches means you can enhance performance in areas such as request routing, data lookup and response processing. Doing so means that you can deliver more efficient data retrieval and faster response times as you scale.
Optimising code quality
When you optimise code quality, you can ensure system resources such as memory, CPU and network bandwidth are used more efficiently. This enables the API gateway to deliver optimised performance as well as handle higher loads.
Troubleshooting issues with your API gateway
Optimising performance as you scale doesn’t make you immune to issues cropping up. Indeed, the increased complexity that API gateway scaling encompasses means that you need to be ready to troubleshoot!
Monitoring tools and services
Monitoring tools and services can ensure you are fully abreast of what’s happening. Use powerful observability tools to track and collect system health data and to provide you with early alerts when something unexpected occurs. Doing so means you can start troubleshooting any issues as soon as they come to light, hopefully before your users are impacted.
Error handling and debugging
As you scale the API gateway, you can use error handling and debugging to identify performance issues and isolate faulty components, resolving issues in real-time as they arise. Graceful error handling means that the gateway can deliver meaningful and informative error responses rather than generic, unhelpful messages. This contributes to more effective troubleshooting and debugging as you scale.
Best practices for scaling an API gateway
When it comes to scaling effectively, you can use various designs for your API gateway pattern, including using multiple patterns in combination with one another. Your use case, traffic volume, performance objectives and fault tolerance considerations will all play into which pattern(s) you use.
Design patterns for scalability
Various design patterns are well-suited to API gateway scaling. These include:
- A shared-nothing architecture gateway pattern, where each gateway instance operates independently for easy horizontal scaling
- An event-driven architecture which supports asynchronous processing
- A circuit breaker pattern, which improves resilience by preventing cascading failures
Automated deployment strategies
Using automated deployment strategies (examples include infrastructure-as-code tools and cloud-specific deployment services) means you can provision and configure API gateway instances as required as you scale.
Security needs to be top of mind as you scale your API gateway. That means focusing on robust authentication and authorisation mechanisms, employing secure communication protocols, implementing thorough input validation and data sanitisation techniques and applying rate limiting and throttling – just as you did when first implementing your gateway. These elements remain just as important as you scale securely.
You’ll also need to pay attention to secure configuration management as you scale and ensure that your monitoring and logging systems can detect and analyse any suspicious activities or anomalies to provide an early warning system for any potential security incidents.
Be sure to put security auditing and penetration testing arrangements in place to verify that everything is working as it should be.
Integrating third-party services
As well as working beautifully with microservices behind the scenes, an API gateway can help with third-party service integration as you scale. Remember, however, that you must ensure those integrations are secure. That means verifying the third-party providers’ security measures, from data encryption to their authentication mechanisms.
Leveraging serverless computing
Leveraging serverless computing as you scale means you can focus on growth without worrying about infrastructure management – your cloud provider can worry about that instead. The elasticity of serverless platforms means you can focus on handling your increased traffic efficiently and optimally, delivering high availability and performance while minimising resource consumption.
Managing multiple environments
When managing multiple environments, you can scale efficiently using the guidance and best practices above. This means that you can optimise your development, staging and production environments so that scaling efficiency applies across the board.
To manage your costs carefully, keep your usage patterns firmly in mind as you scale your API gateway. This will mean it is data driving your changes, ensuring your evolving infrastructure meets your usage needs without wasting resources.
Remember, too, that automation can help you manage your costs, enabling dynamic scaling in response to traffic volumes and ensuring you don’t waste resources. You can further reduce costs by optimising your gateway to minimise unnecessary usage.
Other methods of managing your costs when scaling include using caching and content delivery networks to reduce your data transfer costs, implementing cost-effective storage (such as object storage services) and using your cloud provider’s tagging and resource grouping capabilities to track and categorise resources and identify areas for optimisation.
Your usage trends will evolve over time, so ensure you analyse your usage and costs regularly to identify changes you can make to manage your costs more efficiently as you scale.
The API gateway solution you choose will, of course, also have an impact. Pricing plans that grow with your business and are based on fixed monthly amounts (rather than per-request or per-user rates) will help you scale while carefully managing your budget.
Heard enough? Then ready, set, scale! Or why not find out more about the benefits of full lifecycle API management with Tyk?