The three pillars of observability
Do you feel you’re always playing catch-up with incidents? If so, you’re not alone. As IT environments become more complex, alerts keep piling up, and finding the root cause feels like searching for a needle in a haystack. And ITOps and incident responders are left scratching their heads and wondering: what went wrong?
It can be frustrating when you don’t have end-to-end visibility into your systems. This is where observability comes in. It helps you move from constantly reacting to incidents and proactively preventing them. However, observability only works well when it covers three crucial pillars: logs, metrics, and traces. Each provides unique insights, creating a complete picture of what’s happening across your infrastructure.
What are the three pillars of observability?
The three pillars of observability—logs, metrics, and traces—give ITOps and DevOps teams a holistic view of system health. Logs show what happened, metrics reveal performance, and traces reveal where things are breaking down in real-time. Together, they allow teams to transition from reactive firefighting to proactive performance issue resolution for smoother, more efficient IT operations.
Let’s explore each pillar of a robust observability strategy below.
1. Logs
Logs are detailed records of system and application events. Each log data entry is a time-stamped message that can include anything from user actions to internal system errors. Think of them as raw data—whether in plain text, binary, or structured formats with metadata—that tells the story of what happened and when.
Purpose
Event logs serve as the foundation of incident diagnosis and troubleshooting. They provide a detailed sequence of events to help teams find the exact cause of an issue. For example, they can show whether a failed API call was due to invalid input or a connection timeout.
Specific use cases
- Root cause analysis: With effective log management, you can pinpoint where and why the system failed during a service outage.
- Debugging microservices: Logs from different microservices architectures can be correlated to see how one service failure might cascade into others.
- Security audits: When investigating suspicious behavior or breaches, security teams rely on logs to find unauthorized access or unusual activity.
2. Metrics
Observability metrics are numerical data points that measure system performance over time, such as CPU usage or successful API requests per minute. Metrics give you clear, quantifiable insights into system behavior.
Purpose
Metrics offer real-time insights for tracking performance and capacity planning. For example, if CPU usage stays above 80% during peak hours, you might need to allocate more resources or optimize workloads to avoid crashes.
Specific use cases
- Auto-scaling in cloud environments: If metrics show a spike in execution time, cloud infrastructure can automatically add resources based on predefined metrics thresholds.
- Network and application performance tuning: Tracking key metrics, like response times and error rates, allows teams to refine applications and ensure systems can handle demand.
- Anomaly detection: Metrics help spot problems early, such as unexpected increases in network latency or drops in database performance, allowing teams to take action before they become major issues.
3. Traces
Traces follow a single request as it moves through the system, showing each step and time taken.
Purpose
In today’s distributed architectures, especially with microservices, traces are crucial for finding bottlenecks and identifying where requests slow down. For example, in a microservices app, a user’s request may go through several steps, such as authentication, product catalog, and payment processing. If there’s a delay, distributed tracing shows which service caused it and why.
Specific use cases
- Latency tracking in microservices: If users report slow product searches on your website, traces can pinpoint the problem. For example, they might show delays caused by inefficient database queries.
- Service dependency mapping: In complex distributed systems, a failure in one service can impact others. Traces map these dependencies so you can see how failures affect the entire system.
- End-to-end request monitoring: For high-stakes applications like online banking, traces ensure that every transaction—logging in, transferring money, or checking balances—happens without delays or errors.
Why is end-to-end observability important?
Managing IT systems without full observability is like solving a puzzle with missing pieces; it’s frustrating. Full context observability combines logs, metrics, and traces for a complete picture of your network topology, helping your team stay ahead of issues and maintain smooth operations.
Key benefits of end-to-end observability include:
- Faster incident resolution: Full observability means no more scrambling for answers when things break. Metrics highlight performance deviations, while traces and logs help pinpoint the service or event causing the disruption. This drastically reduces downtime and improves recovery times.
- Proactive monitoring: Observability helps catch early warning signs, such as abnormal CPU spikes or latency increases, before they snowball into major outages.
- Improved system performance: Real-time insights into system behavior help ITOps teams optimize resources and spot bottlenecks to keep systems running smoothly for better user experiences—even at peak times.
Challenges in implementing full observability
Implementing full observability can be tricky, especially as systems grow in scale and complexity. Here are some common obstacles.
- Complex and distributed systems: With so many services and components interacting, getting a clear, unified view is challenging. Each part generates data, which makes correlating logs, metrics, and traces difficult without a solid strategy.
- Tool and data silos: IT teams often use multiple monitoring tools, each with a different focus. Even though they provide valuable insights, they are frequently siloed, which limits visibility and slows down troubleshooting.
- High data volume: Observability tools generate a massive amount of data. Without effective data aggregation and filtering strategies, teams are flooded with logs, metrics, and traces, making it harder to identify the root cause of performance issues.
- Alert fatigue: Too many alerts from observability tools make it hard to know what’s critical, leading to alert fatigue, slower response times, or missed issues.
How BigPanda improves observability
BigPanda unifies observability data from various monitoring and IT management tools into a single platform, giving you a real-time, complete view of your IT environment. Real-time Topology Mesh pulls data from sources like configuration management and cloud-native services, showing how all parts of your infrastructure connect. This visibility helps your team quickly assess incidents and prioritize responses, making it easier to keep systems running smoothly.
In addition, BigPanda AI-driven event correlation reduces noise by grouping related alerts and highlighting what’s important. This can reduce the number of alerts by up to 90%, helping your team avoid alert fatigue and focus on critical issues. With real-time root cause analysis, BigPanda enables quick identification of incident sources, whether it’s a recent change or a malfunctioning system component.
Learn more about how you can maximize your observability strategy with AIOps from BigPanda.
Here are more resources to help you understand how BigPanda enables you to cut through noise and improve observability.
