Running Elasticsearch on Kubernetes lets developers focus on their jobs, letting the platform handle infrastructure operations like updates, scale, and restarts. However, it’s essential to understand the best practices for maximizing performance.
Optimizing persistent volume storage is one of the most important ways to boost Elasticsearch performance on Kubernetes. This is because Elasticsearch performs many write operations and relies on a reliable storage and data management layer. In this article, we will discuss four key steps for optimizing PVS:
- Ensuring data durability and redundancy
- Optimizing storage performance
- Managing storage resources efficiently
- Deploying and configuring the right security controls
To ensure that Elasticsearch has the resources it needs, it’s important to deploy it in a cluster. In Kubernetes, a cluster is composed of Pods, which are groups of containers that run on a single host. An Elasticsearch Pod is the equivalent of an Elasticsearch node, and it’s important to ensure that each Pod has adequate memory and storage capacity.
It’s also important to monitor Pod resource usage over time to identify potential areas for optimization. By monitoring resource use, you can proactively address resource bloat and prevent unused resources from driving up your infrastructure costs.
Finally, it’s essential to configure your cluster with a horizontal Pod autoscaler to keep your Elasticsearch deployment running smoothly and at peak performance. A horizontal Pod autoscaler automatically scales your Pods by adding or removing low-priority Pods to match the CPU, memory, and storage each Pod needs.
Optimize Thread Pools
In a world of ever-increasing data, scalability and performance are key. The ability to search, analyze, and gain insights from this information can make or break a business. However, a business can only do that effectively when the data is highly available. To ensure that Elasticsearch and Kibana are always ready to provide insight, organizations must be able to access the data quickly and reliably.
To do so, they must deploy their apps on a Kubernetes platform that provides high availability and performance. Kubernetes supports using multiple nodes to store data, providing redundancy against single points of failure. It also allows a load balancer to evenly distribute traffic between nodes to enhance reliability further and ensure performance.
For example, a database can support a limited number of concurrent connections before reaching its limits. Therefore, a high-performance system must optimize thread pools to increase throughput by enabling more simultaneous requests.
Several algorithmic heuristics can improve performance by adapting to workloads and the overall environment. One such method is hill climbing, which focuses on the amount of work done by each thread. It can reduce CPU consumption by removing threads that are not working and thus increasing the overall throughput. To implement this approach, you need to know the amount of work per thread, available CPUs, and the overall environment throughput.
Logging is an important part of monitoring your Kubernetes infrastructure. It can help you troubleshoot issues, identify bottlenecks, and make data-driven resource allocation and application scaling decisions. To maximize the effectiveness of your logging pipeline, you should optimize both the number of logs generated and the number of resources used to store them.
By default, Kubernetes writes container logs to standard output and standard error streams—but this method can be limited in scope for DevOps teams that want to monitor many applications across a cluster. In addition, a lack of consistency in log formats and contextual information can be problematic, especially when aggregating them with other tools.
A better option is to deploy a logging agent—a sidecar container that captures logs from a main app container—to direct them to a central logging backend without modifying the main application container. With this approach, you can minimize the impact on a node’s CPU and disk resources while capturing ephemeral logs before they disappear or get cleaned up by Kubernetes.
A centralized logging backend allows you to use more advanced features like structured logs. These provide a richer context for analyzing and troubleshooting errors and performance problems and can be configured to automatically send alerts when a certain pattern or anomaly is detected.
Kubernetes provides built-in autoscaling that adjusts available computing resources automatically to match your application’s needs. This enables you to handle demand peaks and ensure your application is available quickly.
To maximize the value of this feature, it’s important to monitor your computing costs and understand what drives them. This will help you identify the most costly components of your cluster and take steps to reduce them. Keeping your cluster secure is another key to efficiency. This includes ensuring encrypted communication between nodes and securing the foot filesystem. Limiting access to your Elasticsearch cluster, including enabling role-based authentication (RBAC) with Kibana, is important. When it comes to high availability, deploy multiple master Pods for redundancy and use a load balancer to distribute traffic evenly across them. You can improve availability by leveraging Elasticsearch’s data replication features, applying pod anti-affinity rules, and using a dedicated cluster IP address for your Elasticsearch cluster.