Optimize Cloud Foundry with Advanced Monitoring Tools

Optimizing Cloud Foundry requires more than just setting it up and letting it run. To truly get the most out of this powerful platform, you need to be able to monitor it closely.

Advanced monitoring tools can help you identify performance bottlenecks and optimize your Cloud Foundry setup for better results. As we'll see, these tools can also help you troubleshoot issues and ensure your application is running smoothly.

By using advanced monitoring tools, you can gain a deeper understanding of your Cloud Foundry environment and make data-driven decisions to improve its performance. This is especially important for large-scale applications where even small issues can have significant impacts.

Cloud Foundry's built-in monitoring features, such as the CF CLI and the Cloud Foundry Dashboard, can provide some basic insights into your application's performance. However, for more detailed analysis, you'll want to turn to third-party monitoring tools like New Relic, Datadog, or Splunk.

Dynatrace is a great tool for monitoring Cloud Foundry environments, automatically scaling to 100k+ hosts and providing real-time auto discovery and OneAgent injection of PAS and PKS containers without code or image changes.

It supports the broadest range of DevOps tool chain technologies, including wide integration with existing CI/CD tooling and AI-powered monitoring of key technical metrics throughout the development lifecycle.

Dynatrace also helps customers automate quality by building AI-powered quality gates along their pipeline to remediate problems early and allow release confidence that no bad code changes reach production.

There are several other tools available for monitoring Cloud Foundry, including Datadog. Datadog's Cluster Monitoring tile can be imported and configured to monitor PCF infrastructure, reporting metrics and tags for easy organization and grouping.

Datadog dashboards allow for visualization and correlation of any metrics coming from the Firehose, making it easy to identify hot spots in the cluster and track performance indicators.

Here's a list of products and tools for viewing Cloud Foundry logs and metrics:

Datadog's Application Monitoring tile enables developers to collect custom metrics, distributed traces, and logs from their applications running in PCF.

Dynatrace Configuration makes full-stack, automated monitoring a platform feature. This means that every app and microservice you deploy is always monitored, eliminating the need to deploy, configure, and update agents.

With Dynatrace, you can scale across hundreds or thousands of nodes and apps with ease. This is especially useful for large-scale applications that require monitoring.

Dynatrace integrates with Cloud Foundry resources, providing a range of benefits, including:

Dynatrace also includes features such as automatic detection of related organizations in Cloud Foundry foundations, ensuring that all relevant data is captured and monitored.

Deploying Dynatrace with BOSH makes full-stack, automated monitoring a platform feature. This approach ensures every app and microservice you deploy is always monitored.

By using the BOSH add-on, you can eliminate the need to deploy, configure, and update agents. This simplifies the process and reduces the administrative burden.

Dynatrace + Cloud Foundry resources are available to help you get started. You can read customer stories, download eBooks, and watch webinars to learn more about how Dynatrace can benefit your organization.

Here are some key benefits of deploying Dynatrace via the BOSH add-on:

To configure Dynatrace using environment variables, you'll want to follow a similar approach to setting up Datadog. You can either use the cf set-env command to set environment variables or add them to a manifest file for your application.

At minimum, you need to set an environment variable to provide your Dynatrace API key so that your application data will appear in your Dynatrace account. This is similar to setting the DD_SERVICE_NAME variable, which tags all traces from your application with the service name.

The service name is important because it lets you focus on performance data from individual services in Dynatrace and correlate request traces with other monitoring data from that same service. This is exactly what the DD_SERVICE_NAME variable does for Datadog.

You can also use a manifest file to set these variables when pushing your application, similar to how you would with a Datadog manifest file. A manifest file can include other requirements specific to your application, such as the dd-java-agent.jar for tracing requests to Java applications.

Creating a logging service on Cloud Foundry is a straightforward process, and you can create one with a command that includes the IP of your Logstash server.

To bind the new logging service to your application, you'll need to use a specific command that references the service name. This process typically takes a minute or two to complete, and you'll be able to see logs in Kibana once it's done.

After binding the service, you can define the logstash-* index pattern in Kibana and start analyzing and visualizing your logs.

You can define the logstash-* index pattern in Kibana after setting up the logging service.

Slicing and dicing data in Kibana is an art unto itself, and the way you analyze logs will depend on your application and logging setup.

After a short delay, logs begin to flow automatically, and you can refresh mapping from the Setting page to see the new logs.

With Datadog, you can ship all your logs without worrying about gaps or missing data, and use it to filter or retain them on the fly.

You can customize your processing pipelines and filters to exclude unnecessary logs, giving you full visibility when you need it for troubleshooting and analysis.

By default, an external syslog server will treat incoming system logs like its own and write them to its syslog file, but you can configure rsyslog to write these incoming logs to a separate file.

To target logs from a specific cluster, you can use the hostname or IP address of the log's source, as it's included in the syslog-format message.

You can create a custom rsyslog configuration file to segregate logs as you see fit, and adjust the rules to forward only the cluster's system logs to Datadog.

Cloud Foundry attaches a log type to each log message depending on its origin. For example, HTTP requests going through the router will get the RTR log type.

Application logs will be assigned the APP log type. You can read more about these types in the Cloud Foundry documentation.

A basic pie chart visualization using the syslog5424_proc field will give you a nice breakdown of the different logs. This can help you quickly see which types of logs are dominating your system.

You can build a bar chart visualization depicting the same, which can be useful for comparing the frequency of different log types.