Metrics vs. Logs: When to Use Each in Your Telemetry Stack

Metrics vs. Logs: When to Use Each

When it comes to telemetry and observability, one of the most important questions is: metrics or logs? These two approaches offer very different ways of understanding the behavior of your systems. Knowing when to use each one is essential to maintaining a high-performing infrastructure and troubleshooting issues effectively.

In this post, we’ll explore the key differences between metrics and logs, highlight the “gotchas” to watch out for, and provide guidance on when to use one over the other.

Metrics: The Pulse of Your System

Metrics are quantitative measurements that capture system performance in an aggregated, easy-to-query format. Think of them as the heartbeat of your infrastructure, providing quick, real-time insights into how things are running. Whether you’re monitoring CPU utilization, memory consumption, or network latency, metrics give you a bird’s-eye view of the health of your services.

When Should You Use Metrics?

1. Performance Monitoring: Metrics provide a clear, real-time understanding of how well your system is performing. They allow you to monitor key indicators like request latency, memory usage, or database throughput.
2. Alerting: Metrics are ideal for setting up performance and SLO’s alerts. For example, you can trigger alerts if CPU usage crosses 90% or if request latency exceeds a specific threshold. This makes metrics invaluable for proactively managing system issues.
3. Capacity Planning: When it comes to scaling your infrastructure or optimizing resource allocation, metrics can help identify trends over time. If you notice a consistent rise in memory consumption during peak hours, metrics provide the data you need to plan for more capacity.
4. Quick Analysis: If you want to quickly understand whether something is off with your system, metrics are perfect. Instead of sifting through verbose logs, you can identify trends, spikes, or anomalies in real time.

Gotchas for Metrics:

• Limited Context: Metrics are great for telling you that something is wrong (e.g., CPU is at 100%), but they won’t tell you why. Without the context provided by logs, you may struggle to pinpoint the root cause of issues.
• Over-Aggregation: If metrics are too aggregated, you may lose important nuances. For example, an average latency metric might hide intermittent spikes that could cause intermittent user-facing issues.
• Predefined Metrics Only: If you didn’t set up a metric beforehand, you won’t have historical data on it. Unlike logs, which capture all events, metrics only track what you’ve told them to.
• High Cardinality and Too Many Labels: One of the most common pitfalls with metrics is high cardinality—when you create too many unique combinations of metric labels (dimensions). For example, if you track request counts by user ID or IP address, the number of unique metric series could explode.

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Impact of High Cardinality or Too Many Labels:

• System Performance: High cardinality metrics can put enormous strain on the telemetry system, especially when you're storing metrics in a time-series database. It can cause performance issues, slow down queries, and even lead to system instability.
• Cost: Storing large amounts of unique metrics data, especially with complex label combinations, can drastically increase storage costs. This is especially problematic in cloud-based environments, where you pay for both storage and query time.
• Availability: In extreme cases, excessive cardinality can overwhelm the monitoring infrastructure, leading to data loss, delayed alerts, or a complete outage of your observability system. This means your system might be down, and you won’t even know it because your monitoring system itself is overloaded.
• Metric Explosion: Defining too many metrics or tracking everything can lead to metric explosion, where the observability tool generates thousands (or millions) of individual time-series data points. This bloats storage, complicates analysis, and leads to noisy dashboards and ineffective alerting.

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Impact of Metric Explosion:

• Alert Fatigue: Too many irrelevant metrics can result in a flood of alerts, making it difficult for teams to focus on critical issues. This often leads to ignoring alerts altogether.
• Monitoring Blind Spots: With too much data, you might lose focus on the truly important metrics. Over-reliance on irrelevant or redundant metrics can obscure visibility into actual system health.

Logs: The Storytellers of Your System

While metrics are great for high-level performance monitoring, logs are the storytellers of your system. They provide a detailed, often human-readable record of specific events, like errors, transactions, and system operations. Each log entry typically includes a timestamp and information about what was happening at a specific moment in time.

When Should You Use Logs?

1. Detailed Event Tracing: Logs are essential when you need to dig deep into specific events. They capture every detail, from HTTP request bodies to stack traces, helping you trace a precise sequence of events.
2. Debugging and Troubleshooting: When your system breaks or behaves unexpectedly, logs provide the granular data needed for root cause analysis. They tell you why something is happening, which is crucial for debugging.
3. Audit and Compliance: Logs can provide a record of system actions, helping you track who did what and when. This is critical for organizations that need detailed records for security audits or compliance requirements.
4. Granular Data Capture: Logs capture complex data in raw form, which can be highly useful when diagnosing intricate problems. They are often used in combination with metrics to provide deeper insights.

Gotchas for Logs:

• Storage Costs: Logs can grow quickly, consuming large amounts of storage and potentially driving up costs, especially if you’re logging in high-traffic environments or storing data for long periods.
• Harder to Analyze at Scale: While logs provide detail, sifting through them can be overwhelming. Analyzing large volumes of logs can be slow and resource-intensive, especially when looking for specific events across a distributed system.
• Log Noise: Without proper filtering or structuring, logs can become "noisy" and cluttered with irrelevant data, making it harder to find the insights you actually need.

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Choosing the Right Tool for the Job

So, when should you use metrics, and when should you use logs? Below is a list of application types where one approach is typically better than the other:

Applications Where Metrics Are Better:

1. Microservices-Based Applications:
   • In microservices architectures, tracking the performance and availability of individual services through metrics can give you a high-level view of the system's health without needing to dive into the details of every service.
2. High-Traffic Web Applications:
   • Metrics are ideal for monitoring response times, throughput, and system resources in high-traffic environments. Real-time alerts based on metrics help in identifying issues quickly.
3. IoT and Sensor Networks:
   • For IoT applications, where you need to monitor the status and performance of multiple devices in real-time, metrics provide an efficient way to get insights into large-scale systems.
4. Cloud Infrastructure Monitoring:
   • Monitoring resource utilization (CPU, memory, disk, network) across cloud infrastructure is a common use case for metrics. This helps with auto-scaling, cost optimization, and ensuring service-level agreements (SLAs) are met.

Applications Where Logs Are Better:

1. E-Commerce or Financial Transactions:
   • Logs are crucial for auditing user actions and transaction details. If an issue arises with an order or a financial transaction, logs provide detailed records of every step in the process.
2. Security-Centric Applications:
   • Applications that require strong security need logs to track access control events, authorization failures, or unauthorized actions. Logs can be used to investigate potential security breaches and for compliance audits.
3. APIs and Backend Services:
   • When running APIs, logs help debug failed requests, trace request lifecycles, and identify bottlenecks in specific service calls. They provide the necessary context to figure out why a specific request failed or performed poorly.
4. DevOps Pipelines and CI/CD Systems:
   • Logs are essential in tracking build failures, deployment steps, and debugging issues in continuous integration/continuous delivery (CI/CD) pipelines.

Finding the Balance: Use Both

In most modern observability setups, you won’t be choosing between metrics and logs—you’ll be using both. Metrics give you a clear, real-time view of your system’s health, while logs provide the fine-grained details necessary to diagnose issues. Used together, they form a powerful toolkit for maintaining and optimizing system performance.

By embracing the strengths of both, you’ll be prepared for anything your system throws at you—whether it’s a sudden CPU spike or a mysterious 500 error in production.

How do you use metrics and logs in your telemetry stack? Let us know in the comments below or reach out if you have any questions!