Mastering the Elastic stack on Kubernetes enables you to store, search, and visualize a wide range of data. This includes logs shipped by your infrastructure and managed services.
Structured logging uses a predefined format to structure log data, making it easier to analyze. It typically involves deploying an open-source logging agent such as Fluentd or Logstash.
Ensure Your Cluster Has Enough Resources
Many modern services are stateful, meaning they hold data that cannot be lost and must work in a distributed manner. This is why having highly available, redundant clusters spanning multiple zones is essential.
Kubernetes provides a great way to scale and manage stateful services. It offers built-in scaling, health checks, and auto-healing mechanisms to help support application performance. It also allows for multiple replicas of an application to be deployed and scaled in tandem. This helps provide a high level of availability, even during demand peaks and outages.
For stateful applications like Elasticsearch, you must ensure that your cluster has enough resources. This includes both memory and CPU. One way to do this is to set LimitRanges at the Namespace level. This will enforce that any Pod in the Namespace has a set of resource requirements, which will be used when scheduling Pods. Set these limits to be lower than needed, as over-provisioning can result in CPU throttling and OOM.
Another way to ensure that your Elasticsearch Pods have enough resources is by using readiness probes. These are a great way to monitor CPU and memory usage on your Pods. However, it’s important to note that not all Kubernetes platforms have readiness probes enabled by default.
Deploy a Headless Service
Elasticsearch uses sharding to process large data sets and deliver optimal performance. A shard is a complete copy of an index that can be searched and queried independently. Each chip has its own memory and disk space requirements. To prevent data loss, it is essential to configure at least one replica per index in your cluster.
Kubernetes offers several features to manage stateful workloads like databases and search engines, including PersistentVolumes (PVs) and StatefulSets. To deploy a headless service on Kubernetes, add the “clusterIP: None” attribute to the service definition in your YAML file. When clients look up a service by name, they will receive a list of IP addresses of the Pods that make up the service. Clients can then connect to any of those Pods.
In addition to ensuring enough disk space for your database, you should also configure a high watermark for each node shard to protect against out-of-memory conditions. When a bit hits the watermark, the cluster will try to reassign shards off that Node onto other fragments.
Another way to improve your Elasticsearch on Kubernetes best practices is to increase the number of replicas in your cluster. This will allow you to run a more stable search query and reduce the time it takes for your group to recover after outages.
Optimize Your Cluster
Elasticsearch is a distributed, scalable, real-time search engine that stores and searches indices. It supports full-text and structured search as well as analytics.
To run the application effectively, it must have adequate memory and CPU resources. This is where Kubernetes comes in. Kubernetes can optimize the cluster by adjusting its Pod CPU and memory requests based on actual utilization metrics. This allows organizations to avoid overprovisioning their Pods and reduce the chance of out-of-memory exceptions.
The Kubernetes cluster must run the Elasticsearch API to take advantage of this feature. The group must also be configured to route all traffic through the service. This will ensure that all incoming traffic is evenly distributed across the Pods. In addition, the cluster must be set up with dedicated data and client Pods instead of general-purpose Pods.
In most cases, it’s best to use three master Pods in your cluster to avoid “split-brain” failures. This will prevent a single node from becoming disconnected from the rest of the group and allow the remaining Pods to elect a new master. Also, its a good idea because the latter doesn’t allow for maintaining the state. You can use Affinity and Anti-Affinity rules to determine the placement of your Pods on each Node in the cluster.
Optimize Your Servers
Many services that make up an Elasticsearch cluster are stateful, storing data that must be recovered. These include the master, ingest, and Kibana pods. To ensure that the data in these services is not lost if one or more nodes go down, they are deployed with PersistentVolumeClaims, which store persistent storage on the Kubernetes cloud.
Another way to increase the reliability of stateful applications is by using a deployment controller like the Anthos Policy Controller. This allows you to set constraints for your deployments, which the cluster enforces. This includes things like the minimum CPU and memory requirements of a container and how many copies of a pod are required to be online.
If you want to improve the performance of your cluster further, you can use other pod configurations, such as pod priority and pod affinity. Pod priority lets you specify how important your pods are to the collection and allows Kubernetes to prioritize evicting higher-priority pods from a node before expelling lower-priority ones. Pod affinity lets you determine that certain pods should be scheduled closely together, decreasing network latency.
Finally, a good practice is to run a cron job that cleans up old indices regularly. This helps to keep your indices from filling up and improves search performance.
