Understanding and implementing API gateway clusters

An API gateway cluster can deliver the high availability, reliability and supercharged application performance you’ve always dreamed of. Read on to discover all you need to know.

Understanding API gateway clustering

Setting up an API gateway cluster is a way to achieve a high availability (HA) gateway that can handle everything from traffic spikes to hardware failures – all without impacting performance. An HA gateway is, essentially, a means of spreading your risk and removing a single point of failure.

When you use an API gateway, you have a single entry point between your clients and your backend services. It’s hosted on a server with a data store for configuration details. While this is a delightfully neat and tidy arrangement, it does raise the spectre of the API gateway service becoming a single point of failure.

With an API gateway cluster, you can implement an HA gateway that removes this risk. A cluster consists of multiple instances of your API gateway. You can run these instances on separate servers (called nodes), with the nodes connecting to a data store. Depending on your setup, the data store can be sharded across multiple nodes to further support high availability. Using Tyk with Redis Cluster is an example of this clustered API gateway service in action.

Note that a gateway cluster is not the same as having multiple API gateways. With a cluster, you have multiple instances of one gateway. You can centrally manage them and only need one dashboard/portal, making it easy and efficient to roll out changes. If you use multiple API gateways and want to ensure high availability, you’ll need to implement a cluster for each gateway.

Benefits of API gateway clusters

In our always-on digital world, downtime means lost revenue and irritated customers. API gateway clusters guard against this, enabling businesses to benefit from ensuring the high availability of their services. Customers enjoy reliable, consistently available services while the business reaps the financial and reputational rewards. This remains the case even in the event of a hardware failure or cloud service outage that could bring a single instance of the gateway crashing down. Spreading nodes across different regions and cloud services is particularly helpful in terms of maintaining reliable and consistent availability.

An API gateway cluster can also deliver performance benefits. With requests spread across multiple nodes, both large traffic volumes and spikes can be dealt with comfortably without end users noticing any impact on service performance. It’s one of multiple performance fine-tuning measures you can implement.

This underpins the ability to scale seamlessly, as well. With an HA gateway cluster in place, you can scale a microservices architecture without worrying about the impact on the reliability, consistency or performance of your services. It means that your API gateway for microservices can handle whatever you throw at it while you concentrate on scaling.

Components of an API gateway cluster

We mentioned above that each API gateway needs a server (node) and a data store. This is the same for each instance within the gateway cluster. This gives rise to the need for additional components or functionality to ensure the cluster operates effectively.

Top of the list is the need for a load balancer. Load balancing is important for a range of reasons, from the ability to scale your API gateway to rotating requests to distribute traffic effectively across multiple nodes in a gateway cluster. The load balancer sits in front of the HA gateway setup (we’ll look at load-balancing strategies in more depth in a moment).

Another API gateway resource you’ll need to think about is caching. Caching commonly requested data is a handy way to improve response times. When you have multiple instances of your API gateway as part of a cluster, you’ll need to keep all of your caching in sync to keep everything running effectively.

API gateway clustering strategies

API gateway clustering strategies can be used to meet a range of needs. Let’s consider some of these.

Vertical scaling vs horizontal scaling

Demand for your services will vary over time. This means you’ll need to think about scaling. An API gateway cluster can help with both vertical and horizontal scaling.

In API terms, vertical scaling means increasing the resources of an individual server. You can do this within your cluster, increasing the resources available in individual machines to enable the cluster to handle higher loads.

For horizontal scaling within a cluster, you can add more nodes. This enables the cluster to cope with increased demand.

Geographic distribution

Within an HA gateway cluster, you can spread your nodes as widely as you wish in geographic terms. This is an important strategy in terms of ensuring availability, as it mitigates the risk of a failure in one region or of a cloud provider going down. With a geographically dispersed cluster, even if that happens, other regions and clouds can pick up the slack, meaning your users won’t notice any impact on the performance or reliability of your services.

Traffic management and load balancing

With a load balancer sitting in front of your HA gateway, you can choose how you manage traffic within the cluster. A common way to do so is to use a round-robin approach, which is what Tyk offers natively for load balancing. This rotates requests through a list of target hosts.

There are other options. For example, you could use a weighted load-balancing approach that factors in different nodes’ response times. The strategy you choose will depend upon your particular setup and needs.

Security considerations for API gateway clusters

Many security mechanisms you need to secure a gateway cluster mirror those required to secure a single instance of your gateway. Let’s look at some security considerations these approaches share and the notable differences.

Security protocols and standards

Securing your API gateway cluster means implementing robust authentication and authorisation mechanisms, encrypting data in transit, implementing rate limiting and validating and sanitising input data. Other important security measures include:

  • Implementing comprehensive logging and monitoring.
  • Using firewalls.
  • Managing tokens effectively.
  • Utilising security headers and taking a carefully controlled approach to versioning.

These apply whether you’re using a single gateway instance or a cluster. 

Best practices for securing an API gateway cluster

The added complexity of implementing a clustered HA gateway means adhering to additional security best practices. For example, you will need to secure communications between the nodes in the cluster and between clients and your API gateway. You’ll also need to ensure that your security mechanisms account for the dynamic nature of using load balancing across distributed systems. All while ensuring data consistency across multiple nodes when dynamically updating security configurations.

When implementing API gateway cluster security, there are other considerations, too. One example is ensuring consistent security and availability in failover scenarios. Another is ensuring that token management remains secure when validation and synchronisation occur across multiple nodes. Logging and monitoring will also need to aggregate information from all the different nodes.

All of these are key considerations when it comes to securing your API gateway clusters.

Monitoring and maintaining API gateway clusters

Close monitoring of your gateway clusters is essential to ensure they remain healthy, secure, and performant (and why wouldn’t you, given you’ve gone to the trouble of implementing them?!). With that in mind, let’s dive into monitoring and maintenance matters.

Key performance indicators (KPIs)

You can use key performance indicators to ensure your gateway cluster is delivering optimal reliability, performance and security. Important KPIs to measure include:

  • Latency – to ensure rapid response times are ensuring a positive user experience
  • Uptime – so you can meet your high availability goals
  • Throughput – to ensure your cluster can handle the usual volume of traffic, plus any spikes
  • Error rate – so that you can spot any abnormal spikes and investigate them
  • Resource utilisation – to ensure resources are appropriately allocated and avoid performance bottlenecks
  • Security metrics – so you can spot trends such as an increasing number of unauthorised access attempts

It is also helpful to monitor traffic patterns so you can understand when peak usage usually occurs and ensure your scaling strategy accounts for this. Other useful KPIs include monitoring rate limiting, scaling mechanism efficiency, API version usage and external dependency health/responsiveness.

Tools and strategies for monitoring

One of the benefits of Tyk is that it brings a new level of simplicity and ease to API monitoring. It means you can spot security incidents and unusual behaviour at an early stage while also keeping an eye on the health of your gateway cluster. You can use Tyk Pump to export analytics to the business intelligence tool of your choice if you want additional insights – we flex around what works best for you.

Maintenance best practices

To ensure your API gateway cluster remains reliable, consistent, secure and performant, make sure you regularly update and patch software, in addition to monitoring and analysing the KPIs discussed above. This will help you identify issues swiftly and proactively. It’s also a good idea to undertake security audits periodically.

API gateway clusters: further reading

There’s plenty to think about when implementing an API gateway cluster. The Tyk team is always happy to chat through the details. You might also like to think about clustering in terms of API management architectural and deployment patterns to consider which would work best for supercharging your application performance.