Operations | Monitoring | ITSM | DevOps | Cloud

In the first part of this series, we outlined the different components of a Pivotal Cloud Foundry deployment and how they work together to host and run applications. In this article we will look at some of the most important metrics that PCF operators should monitor. These metrics provide information that can help you ensure that the deployment is running smoothly, that it has enough capacity to meet demand, and that the applications hosted on it are healthy.

Pivotal Cloud Foundry (PCF) is a multi-cloud platform for the deployment, management, and continuous delivery of applications, containers, and functions. PCF is a distribution of the open source Cloud Foundry developed and maintained by Pivotal Software, Inc. PCF is aimed at enterprise users and offers additional features and services—from Pivotal and from other third parties—for installing and operating Cloud Foundry as well as to expand its capabilities and make it easier to use.

In part three of this series, we showed you a number of methods and tools for accessing key metrics and logs from a Pivotal Cloud Foundry deployment. Some of these tools help PCF operators monitor the health and performance of the cluster, whereas others allow developers to view metrics, logs, and performance data from their applications running on the cluster.

So far in this series we’ve explored Pivotal Cloud Foundry’s architecture and looked at some of the most important metrics for monitoring each PCF component. In this post, we’ll show you how you can view these metrics, as well as application and system logs, in order to monitor your PCF cluster and the applications running on it.

Achieving optimal performance can be challenging when you depend on separate platforms to monitor service health and to manage your logs. When data about your systems is spread across multiple platforms, investigating issues—and ultimately resolving them—takes longer and requires expertise with more tools. It takes more effort to identify real customer impact, as well as to verify that your responses to an incident are having the desired effect.

Since we made Datadog APM generally available last year, we have continually added new features and support for new languages and frameworks to ensure that you can monitor every aspect of application performance. Datadog APM helps companies such as Airbnb, Square, and Zendesk to optimize application performance and deliver top-notch customer experiences.

Since 2006, Amazon Web Services (AWS) has spurred organizations to embrace Infrastructure-as-a-Service (IaaS) to build, automate, and scale their systems. Over the years, AWS has expanded beyond basic compute resources (such as EC2 and S3), to include tools like CloudWatch for AWS monitoring, and managed infrastructure services like Amazon RDS for database management.

Logging is an important part of understanding the behavior of your applications. Your logs contain essential records of application operations including database queries, server requests, and errors. With proper logging, you always have comprehensive, context-rich insights into application usage and performance. In this post, we’ll walk through logging options for Rails applications and look at some best practices for creating informative logs.

In a previous post, we walked through how you can configure logging for Rails applications, create custom logs, and use Lograge to convert the standard Rails log output into a more digestible JSON format. In this post, we will show how you can forward these application logs to Datadog and keep track of application behavior with faceted log search and analytics, custom processing pipelines, and log-based alerting.

Datadog makes it easy to correlate, compare, and visualize metrics from your infrastructure and applications. Some metrics, however, are inherently so noisy that the graphs become unreadable (the dreaded spaghettification problem), and you lose the ability to extract essential information about trends and large-scale deviations. For cases like these, we provide several smoothing functions that help you identify trends in your metrics.