Building Highly Available Java Clusters

The core of a highly available Java cluster is to "disperse risks and automatically recover." 1. Use load balancing and service registration discovery to implement service registration through Consul, Zookeeper, or Eureka, and dynamically forward requests to healthy instances in combination with Nginx, HAProxy, or Ribbon. 2. Multi-replica deployment plus failover mechanism to ensure that each service is at least two instances and distributed on different nodes. The database uses MHA or Patroni to achieve master-slave switching, and the Java service is degraded through Hystrix or Resilience4j circuit breaking and downgrading to prevent avalanches. 3. Use the Kubernetes container orchestration platform to achieve automated operation and maintenance, define the Deployment and Service to automatically maintain the number of pods, and ensure service stability with probes and resource restrictions.

The core of a highly available Java cluster is to "disperse risks and automatically recover." Simply put, it means not allowing any node or service to become a single point of failure, and at the same time, it can quickly take over and repair problems when there is a problem.

1. Use load balancing and service registration discovery

In Java cluster, the most feared thing is that no one knows if a service is disabled. At this time, load balancing and service registration discovery mechanisms are needed to help.

A common practice is to use tools like Consul , Zookeeper or Eureka to register services. After each Java instance is started, it will register itself and tell the cluster "I'm still alive". Load balancers (such as Nginx, HAProxy, or Ribbon) will dynamically forward requests to healthy instances based on this information.

For example: you deployed three instances of the order service, and one of them suddenly lapsed. If no service discovery is found, the front-end may also send a request to this instance, resulting in failure; and with the registration center, it will automatically mark the instance as offline and cut the traffic away.

suggestion:

• Select the right service registration component, combined with your technology stack
• Set reasonable health check frequency and timeout time
• Load balancing policy is usually safe to choose polling or minimum number of connections.

2. Multi-replica deployment failover mechanism

It is a hidden danger that a Java application runs on a node sooner or later. Therefore, multiple replica deployments must be done, which is often called "multiple instances".

But having multiple instances is not enough, and there is also a failover mechanism . When there is a problem with the primary node, the system must be able to automatically switch to the backup node. For example, when using a database, you can use MySQL's MHA or PostgreSQL's Patroni to achieve automatic master-slave switching.

The Java service itself can also be fuse-down downgraded through Spring Cloud's Hystrix or Resilience4j. For example, if a call fails too much, it temporarily "trips" to prevent the avalanche effect.

Key points of operation:

• At least two instances per service, distributed in different servers or availability zones
• Middleware such as databases must also support master-slave or cluster mode
• The circuit breaker strategy should set the threshold reasonably. Too sensitive affects the experience, and too slow will not work.

3. Use container orchestration platform to automate operation and maintenance

Now that Java cluster is deployed, more and more people use container orchestration platforms such as Kubernetes (K8s). Its advantage is its high degree of automation, which can automatically restart failed containers, schedule traffic, and expand capacity.

You can define the Deployment and Service, and then K8s will help you maintain a specified number of pods (i.e. Java instances) and automatically perform rolling updates or rollbacks. Coupled with the Ingress controller, it can also achieve unified external entrance.

For example, if your application memory overflows, K8s can detect and automatically restart the pod instead of waiting for you to manually intervene.

suggestion:

• Package Java applications into Docker image deployment
• Configure the liveness/readiness probe to let K8s correctly judge the service status
• Set resource limits (CPU/memory) reasonably to avoid resource scrambling

Basically that's it. By doing a good job in service discovery, multiple replicas plus failover, and with container platform automation, the availability of Java clusters can be greatly improved. It is not complicated, but it is easy to ignore details, especially probe configuration and circuit breaking strategy, so you have to spend more time.

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