OTel Observability: Traces, Metrics, Logs & Dashboards

by Admin 55 views
OTel Observability: Traces, Metrics, Logs & Dashboards

This document outlines the plan to implement OpenTelemetry (OTel) for enhanced observability across our web applications, APIs, and worker processes. We'll cover traces, metrics, and logs, along with the creation of informative dashboards. Let's dive in!

Present Starter Patterns

Currently, we have some foundational patterns in place, but we need to expand and formalize our observability strategy. These existing patterns serve as a good starting point, which we will build upon to achieve a more comprehensive and insightful view of our system's behavior. We aim to enhance our existing tools and methodologies, ensuring they are aligned with industry best practices for monitoring and diagnostics. We'll start by identifying and leveraging the successful elements of our current approach, then strategically incorporate OTel to address the gaps and improve the overall observability coverage.

Our strategy includes the integration of OTel SDK into our web applications, APIs, and worker processes, which are the critical components of our system. This integration enables us to gather detailed telemetry data, providing a more accurate picture of how these components are performing. By analyzing this data, we can identify bottlenecks, performance issues, and areas for optimization. This proactive approach allows us to improve system efficiency and reliability. Furthermore, we will establish Grafana and Prometheus panels specifically designed for monitoring Service Level Objectives (SLOs). These panels will provide real-time insights into our system's adherence to these objectives, enabling us to detect and address any deviations promptly. Finally, we will implement PII filters to safeguard sensitive information in our logs. These filters ensure that no personally identifiable information is exposed, enhancing our compliance with data protection regulations. These initiatives are geared toward delivering a more robust and secure system that meets the highest standards of performance and reliability.

Needed Traces + Metrics

We need to focus on capturing specific traces and metrics to gain deeper insights into critical aspects of our system's performance. Specifically, we need traces and metrics related to: ingestion lag, calculation runtime, and queue depth. These elements are crucial for understanding the efficiency and responsiveness of our system. By tracking these key metrics, we can quickly identify and address potential bottlenecks, ensuring optimal performance and reliability.

Ingestion Lag

Ingestion lag refers to the time it takes for data to be processed and made available after it is initially ingested into the system. Monitoring this metric is essential for ensuring data freshness and timely processing. High ingestion lag can indicate issues with data pipelines, network congestion, or processing bottlenecks, which can lead to delays in critical operations. To address this, we need to implement real-time monitoring and alerting mechanisms that trigger when ingestion lag exceeds predefined thresholds. This will enable us to proactively identify and resolve problems before they impact system performance. Additionally, we can optimize our data processing workflows and infrastructure to minimize lag and improve overall efficiency. By keeping a close watch on ingestion lag, we can maintain data accuracy and reliability, ensuring that our system operates smoothly and effectively.

Calculation Runtime

Calculation runtime is the time it takes to complete specific computations within the system. Monitoring this metric helps in identifying performance bottlenecks and areas where calculations can be optimized. Prolonged calculation times can indicate inefficient algorithms, resource constraints, or other underlying issues that need to be addressed. To mitigate these problems, we need to implement detailed profiling and tracing of calculation processes, allowing us to pinpoint specific areas where performance improvements can be made. We can also optimize our code, upgrade hardware, or distribute the workload across multiple resources to reduce calculation times. By closely monitoring and optimizing calculation runtime, we can improve the overall speed and efficiency of our system, ensuring that it can handle increasing workloads without compromising performance.

Queue Depth

Queue depth refers to the number of items waiting to be processed in a queue. Monitoring queue depth is crucial for preventing system overload and ensuring that tasks are processed in a timely manner. A consistently high queue depth can indicate that the system is unable to keep up with the incoming workload, leading to delays and potential system instability. To address this issue, we need to implement real-time monitoring and alerting mechanisms that trigger when queue depths exceed predefined thresholds. This will enable us to proactively identify and resolve bottlenecks, ensuring that our system remains responsive and stable. Additionally, we can optimize our queue management strategies, scale up resources, or improve the efficiency of our processing workflows to reduce queue depths. By closely monitoring and managing queue depths, we can maintain system performance and prevent disruptions caused by excessive workload.

Definition of Done (DoD)

Here's what needs to be completed to consider this initiative done:

  • [ ] OTel SDK wired into web, API, workers: The OpenTelemetry SDK must be integrated into our web applications, APIs, and worker processes. This ensures that we can collect the necessary telemetry data from all critical components of our system.
  • [ ] Grafana/Prom panels for SLOs: Grafana and Prometheus panels should be set up to monitor our Service Level Objectives (SLOs). These panels will provide real-time insights into how well our system is meeting its performance goals.
  • [ ] Log PII filters: Implement filters to remove Personally Identifiable Information (PII) from our logs. This ensures that we comply with data protection regulations and protect sensitive information.

Acceptance Tests

These tests will validate that our OTel implementation is working as expected:

  • Given a failed job, then trace shows span with error & correlation ID: When a job fails, the trace should include a span that indicates the error and provides a correlation ID for easy debugging.
  • Given ingestion lag > threshold, then alert fires: If the ingestion lag exceeds a predefined threshold, an alert should be triggered to notify the operations team.

By implementing OTel traces, metrics, and logs, and creating informative dashboards, we'll gain unprecedented visibility into our system's behavior. This will enable us to proactively identify and resolve issues, ensuring optimal performance and reliability. Let's get started, folks! We're set to level up our observability game!