Implementing application performance monitoring (COP409)

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Observability and the Full Picture of Application Monitoring

Lessons from a 1986 Commercial

  • In 1986, a commercial that won a Golden Lion advertisement award depicted a scenario where a young man was initially perceived as a criminal but was actually saving an elderly man's life.
  • The commercial conveyed the message that without a full picture, one can draw the wrong conclusions from partial perspectives.

Building a Full Picture of Application Observability at AWS

  • At AWS, builders take pride in operational excellence, which is a big part of their business results.
  • To achieve this, AWS engineers have been building a "full picture" of application observability, allowing them to make the right decisions about their services.

Cloud-First Observability

  • The observability built by AWS must be:
    1. Resilient and available when other services are down.
    2. Efficient at scale to support a variety of scales.

Application Signals in Amazon CloudWatch

  • Last year, AWS launched a "full picture" tool in CloudWatch called Application Signals.
  • This tool embodies operational practices to help engineers be efficient and productive.

Understanding the Full Picture

Application Signals provides three entry points to understand the full picture:

  1. Service Level Objectives (SLOs): Allows you to define a set of things that matter to your users, such as API latency or object creation ability.
  2. Service Dashboard: Provides a full overview across all your services, including information about dependencies.
  3. Service Map: Allows you to see the full topology of your application and any health issues.

Diagnosing Anomalies

Once an anomaly is spotted, you can use Application Signals to:

  1. Identify the anomaly, such as a spike in P99 metrics.
  2. Get the precise transactions causing the anomaly.
  3. Determine the root cause of the high-latency transactions.

Incorporating Additional Perspectives

Application Signals allows you to incorporate other perspectives, such as:

  1. Synthetic, outside-in monitoring to model customer workflows.
  2. Real user monitoring to understand issues experienced by end-users.
  3. Resource-level information (e.g., container or Lambda metrics) to identify resource-level stresses.

New Capabilities in Application Signals

  • Transaction Analytics: Captures 100% of application transactions for analysis and to identify unique anomalies.
  • Integration with Amazon DevOps Guru: Quickly identifies the root cause of service-level objective breaches.
  • Expanded support for open-source observability with OpenTelemetry.
  • Enhancements for volume-based SLOs, runtime metrics, language coverage, database support, and generative AI analytics.

Demonstration of Application Signals Features

The presentation includes a live demonstration showcasing various use cases and capabilities of Application Signals, such as:

  1. Troubleshooting issues in a Lambda-based appointment service.
  2. Correlating application-level and database-level performance.
  3. Monitoring the usage of generative AI models in an application.
  4. Providing efficient customer support by quickly identifying the root cause of issues.
  5. Automating the root cause analysis using Amazon DevOps Guru.

Testimonial from PBS

  • Brian Link, the Director of Technical Operations at PBS, shared how Application Signals and CloudWatch have helped PBS improve their application observability and business metrics.
  • Examples include monitoring the performance of their Station Video Portal, tracking localization issues, and gaining visibility into their recommendation engine and donation tracking.

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