High availability (HA) clusters ensure that business applications and services stay online, even during unexpected failures. Also known as failover clusters, these systems help maintain continuous operation and minimize downtime by seamlessly handling hardware or software disruptions.
Here’s an inside look at how HA clusters work, why they’re essential, and what you need to consider when implementing them.
What is a High Availability Cluster?
A high availability cluster is a group of servers that work together as a single system to ensure that critical applications and services are always available. In some cases, they are also known as failover clusters.
How High Availability Clusters Work
HA clusters rely on multiple servers, called nodes, working together to keep systems running smoothly. These nodes collaborate to share resources, distribute workloads, and provide backup when issues arise.
Here’s how a HA cluster works:
Nodes That Work Together
Each node in an HA cluster connects with others to share storage, processing power, and critical data. This collaboration ensures that if one node fails, another steps in. Every node plays a role in keeping the system strong and resilient.
Load Balancing for Performance
Load balancing is the process of distributing workloads across multiple places to prevent any one resource from becoming overloaded. In HA clusters, using load balancing to distribute across multiple nodes prevents a single node from being overwhelmed. This helps to maintain operations, delivers enterprise-grade reliability, and enhances performance.
Immediate Failover Action
When a server in a cluster fails, another server immediately takes over the tasks to keep performance and service running smoothly. This setup, called redundancy, ensures there is always a node available to handle the workload in the event of a failure.
However, as systems grow more complex, maintaining HA can become harder because the number of potential failure points increases. The right technology will instantly shift workloads to a functioning node, maintaining service without interruption. Failover actions keep your operations running no matter what happens behind the scenes.
Regular Testing with Failure Scenarios
Regular testing ensures the cluster is prepared to handle failure scenarios at any moment. The right software regularly tests for failure scenarios to ensure uptime and seamless server availability. Testing simulated failures flags any vulnerabilities and confirms that every node can take over when needed.
Active-active vs. Active-passive High Availability Clusters
Active-active HA Clusters
Active-active HA clusters distribute workloads evenly across all nodes. This enables load balancing for performance. This configuration is most suitable for systems that require full redundancy and real-time performance. Active-active handles peak traffic flows better than active-passive. However, the active-active HA cluster is more complex to design and may need extra configuration.
Active-passive HA Clusters
Active-passive HA clusters keep nodes on standby, activating them only when the primary fails. This configuration is simpler to design, troubleshoot, and generally costs less than active-active. However, it can also lead to delays because the standby nodes need to be manually activated.
Why High Availability Clusters Matter
HA clusters protect your business from the risks of downtime, data loss, and service disruption. In an era of large, complex, and data-dependant enterprise operations, businesses prioritize uptime as a mission-critical need, especially in industries like e-commerce and financial services that operate 24/7. HA clusters keep critical services running, prevent data loss, and reduce errors by maintaining system activity and ensuring data accessibility.
Additionally, in remote or edge environments, where onsite IT support often remains unavailable, problems can take hours or days to resolve. These delays can lead to significant productivity and revenue losses. By deploying HA solutions, businesses keep their IT systems resilient, ensure smooth operations, and avoid costly interruptions.
Challenges in High Availability Clusters
Split-brain
Split-brain in a HA cluster occurs when nodes lose communication with each other and incorrectly assume they are the active node. This leads to multiple nodes managing shared resources independently, which can cause data corruption, inconsistencies, and service disruptions. Multi-node server clusters usually require at least three nodes to avoid ‘split-brain’ issues.
How to Solve Split-brain in HA Clusters
To tackle this challenge, clusters are sized to ensure there is always a majority of available nodes when one node is offline. This majority, or quorum allows the cluster to establish which node(s) are the leader when offline nodes are brought back online.
However, in certain cases, a majority cannot be established. This is most often seen with 2-node clusters where, if one node goes offline and is then re-introduced to the cluster, there is no way of establishing which of the two nodes should be leader.
In these cases, a witness node can help, and provide the quorum necessary. As well as being the ‘tiebreaker’ to ensure the correct leader is established, it regularly checks the state of each node in the cluster. It only sends and receives small “heartbeat” signals, so it’s not involved in data transfers. This design means it can work even with high latency and low bandwidth, allowing it to be located far from the clusters it supports, even in remote or challenging environments. This also makes it a cost-effective solution.
Scalability
As demand and complexity grow in HA systems, so do the challenges of scaling them. Things like hardware, software, bandwidth, and the physical energy of running the systems can impact costs. Additionally, greater complexity also adds more pressure to IT teams, who must design, test, and update systems, which can be difficult to manage in-house.
How to Solve Scalability in HA Clusters
Choosing the right software with HA built-in can radically simplify how your business achieves HA. Rather than having to maintain HA internally, the right software will offer HA as a key feature of its service.
To find software with HA built in, check for high availability service level agreements (SLAs). High availability in an SLA is a percentage of uptime agreed upon by a service vendor. This is what they’re expected to provide for their customers. Although HA metrics can sometimes be subjective, availability metrics should be defined within SLAs. Some IT teams might choose to measure other availability metrics, such as:
- Mean time between failures (MTBF)
- Mean downtime (MDT)
- Recovery time objectives (RTO)
- Recovery point objectives (RPO)
You can learn more about high availability, how it works, and how it’s measured in our High Availability Beginner’s Guide.
