Kubernetes Health Checks: 5 Critical Best Practices

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What Is a Kubernetes Health Check? 

A Kubernetes health check is a mechanism used to automatically monitor and determine the operational status of applications running within Kubernetes pods. These health checks enable Kubernetes to respond appropriately to various application states, such as restarting a failing container or preventing traffic from being directed to an unready container. 

By conducting regular checks, Kubernetes can ensure that applications are functioning correctly and are available to serve user requests. Health checks in Kubernetes are implemented using probes, which are configured to perform specific diagnostic tasks on containers. These tasks can range from simple actions like making an HTTP request to a container’s endpoint, to executing a custom script designed to assess the health of the application. Through these probes, Kubernetes gains the insight needed to manage application availability and reliability effectively.

The use of health checks in Kubernetes is critical for maintaining the resilience and stability of containerized applications. They play a key role in enabling self-healing application deployments, where Kubernetes can automatically detect and correct problems without human intervention.

This is part of a series of articles about Kubernetes monitoring

4 Reasons You Should Monitor Application Health in Kubernetes 

Here are a few reasons you should monitor the health of applications deployed in Kubernetes containers:

  1. Early problem detection: Monitoring application health enables the early identification of issues, allowing teams to address problems before they impact users or escalate into more significant outages.
  2. Efficient resource utilization: By ensuring only healthy instances receive traffic, Kubernetes maximizes the utilization of resources, avoiding waste on failing or underperforming containers.
  3. Improved deployment outcomes: Health checks verify that new deployments are ready and functioning correctly before they start serving traffic, reducing the risk of deployment-related errors.
  4. Enhanced security: Regular health monitoring can help detect anomalies that may indicate security breaches, such as unexpected performance degradation or unresponsive services, allowing for prompt remedial actions.

Related content: Read the detailed guide to software deployment

Kubernetes Health Check Best Practices

1. Choose the Right Protocol

When configuring health checks in Kubernetes, choosing the right protocol for probes is essential: 

  • HTTP probes are ideal for web applications where health can be determined via a specific endpoint. 
  • TCP probes are useful for checking the availability of services that listen on a port but do not use HTTP. 
  • Command probes execute a command inside the container and use its exit status to assess health. 

It’s important to match the probe type to the application’s characteristics and requirements. For instance, HTTP probes can leverage existing endpoints used for external health monitoring, while TCP probes are suitable for lower-level checks. Command probes offer the most flexibility, allowing for custom health checks that can consider application-specific metrics or states.

2. Use Appropriate Timeouts

Configuring appropriate timeouts for health check probes is crucial to avoid false positives or negatives that can lead to unnecessary restarts or traffic being sent to unhealthy instances. Timeouts should be carefully balanced to allow sufficient time for the application to respond under normal conditions but not so long that it delays the detection of failures. 

The timeoutSeconds setting determines how long the kubelet waits for a probe to succeed before considering it a failure. Setting this value too low might not give applications enough time to respond, especially under heavy load or during initialization. Conversely, too high a value may delay the reaction to real issues, affecting availability.

When determining the optimal timeout, consider the application’s typical response times and any external dependencies that might affect its performance. Monitoring and historical performance data can provide valuable insights into setting realistic timeouts. You should also account for variance in response times caused by factors such as network latency or resource contention.

3. Enable Connection Reuse

Enabling connection reuse for health check probes can significantly reduce the overhead associated with establishing new connections for each check, especially for HTTP and TCP probes. Reusing connections minimizes the time and resources required for probe execution, improving overall system efficiency. 

In high-traffic environments or services with frequent health checks, the impact of connection reuse on performance can be substantial. Kubernetes and the underlying container runtime or application must support connection reuse for it to be effective.

To leverage connection reuse, ensure that both the probe configuration and the application are optimized for persistent connections. For HTTP probes, this might involve configuring keep-alive headers or using connection pooling. For TCP probes, it may require tuning the application or the network stack to keep connections open for longer periods.

4. Implement Custom Scripts for Command Probes

Implementing custom scripts for command probes allows for tailored health checks that can consider complex application states or dependencies. These scripts can execute a series of checks within the container, aggregating various health indicators into a single pass/fail result. 

Custom scripts offer flexibility and precision, enabling health checks that are closely aligned with the application’s operational requirements. When writing custom probe scripts, ensure they are efficient and return quickly to avoid timing out. The script should exit with a status code of 0 for success and a non-zero code for failure, following Unix conventions.

Custom scripts should be kept lightweight and focused on essential checks to minimize their impact on container performance. It’s also important to handle error conditions gracefully, ensuring that the script does not hang or consume excessive resources. Incorporating logging within the script can aid in troubleshooting by providing insights into the health check’s execution and outcome.

5. Limit Resource Consumption

Limiting resource consumption for health checks is important to prevent them from adversely affecting the application’s performance. Health checks, especially custom command probes, can consume CPU, memory, and network resources, potentially impacting the application’s responsiveness or stability. 

To mitigate this risk, health checks should be designed to be as lightweight and efficient as possible. For instance, HTTP and TCP probes should target lightweight endpoints or ports, minimizing the processing required to respond. Command probes should execute quickly and avoid intensive computations or disk I/O operations.

Kubernetes allows configuring resource limits for pods, which can help manage the resource usage of health checks along with the application’s workload. However, it’s crucial to balance these limits to ensure that health checks can still perform their function effectively without being throttled or killed. Monitoring the resource usage of health checks and adjusting configurations as necessary can help optimize their performance impact.

Related content: Read our guide to Kubernetes monitoring best practices

Kubernetes Monitoring and Troubleshooting with Lumigo

Lumigo is a troubleshooting platform, purpose-built for microservice-based applications. Developers using Kubernetes to orchestrate their containerized applications can use Lumigo to monitor, trace, and troubleshoot issues fast. Deployed with zero-code changes and automated in one click, Lumigo stitches together every interaction between micro and managed service into end-to-end stack traces. These traces, served alongside request payload data, give developers complete visibility into their container environments. Using Lumigo, developers get:

End-to-end virtual stack traces across every micro and managed service that makes up a serverless application, in context

  • API visibility that makes all the data passed between services available and accessible, making it possible to perform root cause analysis without digging through logs 
  • Distributed tracing that is deployed with no code and automated in one click 
  • Unified platform to explore and query across microservices, see a real-time view of applications, and optimize performance

To try out more about Lumigo for Kubernetes, check out our Kubernetes operator on GitHub

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