Microservice Observability

Demystifying Microservice Observability

Seeing Inside Your Distributed Systems

Microservices offer incredible flexibility and scalability, but they also introduce complexity. Gone are the days of monolithic applications where tracing a request was relatively straightforward. In a distributed microservice architecture, understanding system behavior, diagnosing issues, and ensuring performance requires a dedicated strategy: Observability.

But what is observability? It's more than just monitoring; it's the ability to infer the internal state of your system based on the external signals of its applications instrumented to do so.

This blog explores the key facets of building an effective observability strategy for your microservices.

Why Bother? The Motivation for Monitoring System Health

Before diving into the "how," let's establish the "why." Why invest time and resources into monitoring and observability?

• Proactive Problem Solving: Catch issues before they impact users.
• Faster Incident Resolution: Quickly pinpoint the root cause of failures in a distributed environment.
• Performance Optimization: Identify bottlenecks and understand resource utilization.
• Informed Decision Making: Base architectural and scaling decisions on real data, not guesswork.
• Understanding User Experience: See how system performance directly affects users.
• Building Confidence: Know your system is behaving as expected. Without observability, navigating microservice issues is like flying blind in a storm.

The Basic Pillars of Observability

At its heart, observability relies on collecting performance-relevant data. This data generally falls into key categories often referred to as the "pillars" of observability:

1. Metrics: These are numerical representations of data measured over time intervals. They are great for dashboards, alerting, and understanding trends. Key types include:

• Counters: Values that only increase (e.g., number of requests served, errors encountered).
• Gauges: Values that can go up or down (e.g., current CPU usage, queue length).
• Histograms/Distributions: Track the statistical distribution of values over time (e.g., request latency percentiles).

2. Logging: These are timestamped records of discrete events. Logs provide detailed, context-rich information about specific occurrences, which are invaluable for debugging specific transactions or errors. Metrics are cheap so you can store them forever. Storing logs are expensive so you will most probably set a retention policy where older logs are archived then purged. Effective logging involves:

• Structured Formatting: Formats like JSON (per message format) make logs machine-readable and easier to query.
• Aggregation: Collecting logs from all services into a central system.
• Analysis: Often involves counting specific log messages or types (counts) and potentially summarizing (rolling up to the minute and zooming out to the hour, day, or week).

3. Tracing: Traces track a single request as it propagates through multiple services. Such traces are crucial for debugging microservices, showing the entire journey, timings at each step, and dependencies.

• Standards: OpenTelemetry is becoming the standard for instrumenting applications to generate trace data.
• APM Integration: Tracing is often a core component of Application Performance Management (APM) tools.

Expanding Beyond the Basics

Let's dive a little deeper into how we package metrics, logging, and tracing together into an engineering friendly experience and supplement with even more performance and debugging relevant data.

• APM (Application Performance Management): Tools often bundle metrics, tracing, and sometimes logging, providing specific insights like requests per second and latency (usually measured in milliseconds). You should break this request data down for synchronous API services by endpoint (i.e., path pattern and HTTP method) and for asynchronous (a.k.a. reactive) services by messages produced or consumed per topic. This data is frequently aggregated (rolled up to the minute and zoomed out to the hour, day, week, or month). Smoothing is when you use a moving average algorithm for the aggregation.
• Resource Utilization Metrics: Software still requires hardware to run on and will stop working if it runs out. Be prepared to monitor CPU utilization, available RAM, free disk space, and network bandwidth per VM or pod to track and avoid destabilization due to overutilization.
• Business Metrics: Don't just monitor system health; connect it to business outcomes. Track user-centric metrics and engagement funnel conversion to understand the real-world impact.
• Synthetics: Proactively simulate user journeys or API calls, especially just after deploying changes.
• Deployments: Track deployment events and correlate with other metrics to immediately see the effect (positive or negative) of new code releases.

Making Sense of It All: Reporting and Action

We have covered collecting performance-related data so far, but how do we turn that data into actionable insights?

• Monitoring: Real-time dashboards provide an at-a-glance view of system health.
• Alerting: Automated DoC notifications (Developer-on-Call) triggered by predefined conditions on SLO violations (Service Level Objective).
• Statistics: Trends can be analyzed using mean, median, and especially percentiles (e.g., p95, p99 latency), which give a more complete picture than simple averages.
• Anomaly Detection: Using algorithms to identify unusual patterns that might indicate emerging problems automatically.

Effective reporting ensures that the collected data provides insight on how to improve system stability, performance, and faster incident response.

The Right Tools for the Job

You need tools to collect, store, visualize, and alert on observability data. The landscape is vast, but common examples include:

• Monitoring & Visualization: The open-source stack of Prometheus with Grafana is a popular choice for lean, early-stage startups. Datadog is the go-to choice for younger SMB (Small and Midsize Business) organizations. Larger and older corporations tend to use either App Dynamics or New Relic.
• Log Aggregation: Splunk is the leader in this category but it is pricey. You can use the open-source ELK Stack (Elasticsearch, Logstash, Kibana). Both Datadog and New Relic also handle this capability. ELK can also be used for monitoring and visualization. Each cloud provider also offers solutions in this space.
• Alerting: When notifying the DoC about out-of-SLO situations with escalations to managers if needed, PagerDuty is the clear winner.
• Communication: Monitors send alerts to PagerDuty and/or Slack, depending on how critical the events are. If desired, integrations with other enterprise chat applications are usually available.

There are other types of software that consume observability data and facilitate the software management process. Examples include issue tracking, incident tracking, service catalog, and operational playbook management. A lot of these categories overlap and may not even be all that necessary. Which tools to choose depends on your stack, scale, budget, and team expertise.

Measuring What Matters: Key Performance Indicators (KPIs)

Collecting data is useless if you don't know what signifies success or failure. KPIs provide that focus. The DORA metrics are widely accepted industry standards for measuring DevOps performance, directly benefiting from good observability:

• Deployment Frequency: How often do you successfully release to production? Lead Time for Changes: How long does it take to get committed code into production?
• Change Failure Rate: What percentage of changes result in degraded service or require remediation?
• Mean Time to Restore (MTTR): How long does it typically take to recover from a failure? With the right metrics collection, tooling, reporting, and actions afforded by a robust observability setup, you can measure KPIs and track them over time to improve your systems and support the overall business strategy.

Conclusion: Observability as a Necessity

In microservices, observability isn't a luxury; it's a fundamental requirement for building, running, and evolving reliable systems. By strategically collecting the right metrics, logs, and traces, utilizing appropriate tooling, focusing on KPIs like the DORA metrics, and implementing effective reporting and alerting, you can gain the visibility needed to manage complexity and deliver robust, high-performing applications.

Does your microservice observability strategy provide the insights you need? Let's talk if you're struggling to see inside your distributed systems or want to optimize your monitoring and reporting.