Real-world examples of excessive logging and system performance issues

Concrete examples of excessive logging and system performance issues

Let’s start where it actually hurts: real outages and slowdowns. The best examples of excessive logging and system performance issues usually share the same pattern: a small config change, a spike in log volume, and a system that suddenly feels much slower even though the core business logic hasn’t changed.

Here are several real examples pulled from patterns I see repeatedly in engineering teams.

1. API latency spike after enabling debug logging in production

A payment API was running comfortably at ~120 ms p95 latency. During an incident investigation, an engineer flipped the global log level from INFO to DEBUG in production and forgot to flip it back.

Over the next hour:

• p95 latency climbed from ~120 ms to ~450 ms
• CPU usage on the app nodes jumped by ~40%
• Disk I/O wait time doubled

Root cause: every request now produced dozens of additional log lines, including large serialized objects. The volume of logs going to stdout and then to the centralized log collector saturated I/O and increased garbage collection pressure.

This is a textbook example of excessive logging and system performance issues: nothing in the business logic changed, but the logging configuration alone turned a fast API into a sluggish one.

2. Microservices flooding the log pipeline during traffic peaks

In a microservices architecture, each service was configured to log every outbound HTTP call at INFO level with full request and response bodies.

Under normal traffic, the system coped. But during a Black Friday sale:

• Log volume increased by ~15x
• The shared log collector cluster started backpressuring
• Application pods blocked while trying to write to stdout
• Overall error rate increased as timeouts cascaded

The services themselves weren’t CPU-bound; they were blocked on I/O to the logging subsystem. Here, the best examples of excessive logging and system performance issues come from “chatty” microservices that treat logs as a debug stream instead of a carefully curated signal.

3. Synchronous file logging on spinning disks

A legacy Java application wrote logs synchronously to local files on spinning disks using a pattern like:

logger.info("User {} performed action {}", userId, action);

During a batch job window, the app logged millions of lines per hour. Because the logging framework was configured with synchronous file appenders:

• Threads frequently blocked on disk writes
• Response times became highly variable
• The OS showed high I/O wait despite low CPU utilization

Switching to asynchronous logging and buffering, plus moving logs to SSD-backed storage, immediately stabilized latency. This is a classic example of excessive logging and system performance issues caused not just by volume, but by how logs are written.

4. Chatty debug logs in mobile backends

A mobile backend team added detailed debug logs for every push notification send attempt, including payloads and third-party provider responses.

When a popular app feature rolled out:

• Push notification volume spiked
• Log ingestion costs jumped by thousands of dollars per month
• The logging agent on each node consumed significant CPU compressing and shipping logs

Performance profiling showed that 10–20% of CPU time on some nodes was spent inside the logging agent process. This is a quieter example of excessive logging and system performance issues: no visible outage, but real capacity loss and higher cloud bills.

5. Data pipeline slowed by verbose transformation logs

A nightly ETL pipeline logged every row-level transformation at INFO:

• INFO: Input row: {...}
• INFO: Transformed row: {...}

On a dataset of 10 million rows, this produced tens of millions of log lines. The pipeline:

• Took 3–4x longer than expected
• Generated hundreds of gigabytes of logs
• Struggled to keep up with backfill jobs

Once logging was reduced to batch-level summaries and error samples, throughput improved dramatically. This is another clear example of excessive logging and system performance issues where logging overwhelmed the actual work.

6. Security audits gone wild: logging full payloads

A team, under pressure from security auditors, started logging full HTTP payloads for all authentication and authorization requests at INFO level “for traceability.”

Side effects:

• Requests with large JSON bodies produced huge log entries
• Log-based metrics queries became painfully slow due to massive index sizes
• The logging backend needed frequent scaling, adding cost and operational overhead

This is one of the more dangerous examples of excessive logging and system performance issues because it also introduces compliance and privacy risk, not just performance pain.

7. Kubernetes cluster drowning in sidecar logs

In a Kubernetes environment, each pod had a sidecar container that logged health checks and internal metrics every second at INFO level.

Across hundreds of pods:

• The cluster generated millions of log lines per minute
• The centralized logging stack (e.g., Elasticsearch or OpenSearch) became a bottleneck
• Nodes sometimes experienced high CPU due to log parsing and shipping

The applications looked fine in isolation, but the platform team saw cluster-wide degradation. This is a modern example of excessive logging and system performance issues at the infrastructure layer rather than within a single app.

How excessive logging translates into system performance problems

Across all these real examples of excessive logging and system performance issues, the same technical forces show up repeatedly.

I/O saturation and blocking behavior

Logging is I/O-heavy. When you log aggressively:

• Disk writes can saturate local storage
• Network bandwidth can be consumed by log shipping
• Log pipelines can apply backpressure, causing application threads to block

Synchronous logging amplifies this: every log call can become a potential pause in request handling. As log volume grows, those tiny pauses add up to noticeable latency.

CPU overhead from formatting, serialization, and compression

Every log line involves some amount of CPU:

• String formatting and interpolation
• JSON serialization for structured logs
• Compression and encryption in log shippers

In high-throughput systems, even modest per-request logging can eat into CPU headroom. When you add verbose debug logs, stack traces, or large payload dumps, you get textbook examples of excessive logging and system performance issues: CPU charts spike without any change in business logic.

Storage, indexing, and query performance in log backends

Centralized logging systems like Elasticsearch, OpenSearch, or cloud logging services face their own performance challenges:

• High ingest rates require more nodes or higher tiers
• Large indices slow down queries and dashboards
• Retention policies need constant tuning to avoid storage explosions

Modern observability platforms increasingly warn about this. For example, the U.S. National Institute of Standards and Technology (NIST) has guidance on logging and monitoring as part of security controls, emphasizing the need for appropriate logging rather than “log everything forever” (NIST SP 800-53). Too much unfiltered data can be as problematic as too little.

Patterns that create excessive logging and system performance issues

When you look for the best examples of excessive logging and system performance issues, you see the same anti-patterns again and again.

Logging at the wrong level by default

Teams often:

• Set the global level to DEBUG or TRACE “temporarily” and never revert
• Use INFO for noisy events that should be DEBUG
• Log errors at multiple layers, turning a single failure into a flood of stack traces

The result is log noise that hides real problems and drags down performance.

Logging inside tight loops and hot paths

Common mistakes include:

• Logging every iteration of a loop processing thousands of items
• Logging every cache hit and miss at INFO
• Logging per-row operations in data pipelines

These patterns are frequent sources of excessive logging and system performance issues because they multiply log volume by the hottest parts of your code.

Logging large objects and payloads

Serializing entire request bodies, database rows, or in-memory objects can be expensive. It also bloats log entries, which:

• Increases network traffic to log collectors
• Slows indexing in log storage
• Makes queries more expensive and less responsive

Security and privacy teams increasingly warn against this as well. While not performance-focused, organizations like the U.S. Department of Health and Human Services (HHS) highlight data minimization in logging for HIPAA-covered entities (HHS guidance), which aligns nicely with reducing log volume.

Overlapping observability tools

You might have:

• Application logs
• Web server logs
• APM traces
• Metrics with high-cardinality labels

If each layer logs similar information at high volume, you end up with redundant data and higher overhead. Some of the worst examples of excessive logging and system performance issues come from stacks where every layer is “just being safe” and logging everything.

Better practices to avoid excessive logging and system performance issues

You don’t need to choose between observability and performance. You need intentional logging.

Design log levels with real policies

Instead of ad-hoc choices:

• Use ERROR for actionable failures
• Use WARN for unusual but non-fatal conditions
• Use INFO for high-level business events and key lifecycle events
• Use DEBUG for detailed diagnostics, but keep it off in normal production

Document these conventions and enforce them in code review. This alone prevents many examples of excessive logging and system performance issues.

Use sampling and rate limiting

For high-volume events:

• Sample debug logs (e.g., 1 out of N requests)
• Rate-limit repetitive warnings
• Aggregate repetitive errors into counters and periodic summaries

This approach is increasingly common in modern observability tools and aligns with the idea of collecting representative data instead of everything.

Make logging asynchronous and non-blocking

Where possible:

• Use async appenders or non-blocking loggers
• Buffer logs in memory with backpressure strategies
• Offload heavy formatting or serialization off the main request thread

This doesn’t give you a free pass to log endlessly, but it reduces the direct coupling between log volume and request latency.

Monitor your logging system as a first-class citizen

If you care about performance, treat the logging pipeline as part of your production system:

• Track log volume per service
• Alert on sudden spikes in log rate
• Watch CPU and memory usage of log shippers and collectors

Some organizations even include logging metrics in their SLOs. That’s a smart response to years of real examples of excessive logging and system performance issues that began in the observability stack itself.

Redact and trim logs for privacy and performance

Redacting sensitive data (PII, secrets, health information) is often required for compliance, as highlighted in HIPAA and other regulations (HHS HIPAA Security Rule). But it also helps performance:

• Shorter log lines
• Less serialization overhead
• Smaller index sizes

Data minimization is good security and good performance hygiene.

FAQ: examples of excessive logging and system performance issues

Q: What is a simple example of excessive logging causing slow response times?
A: A common example of excessive logging and system performance issues is enabling DEBUG logging for all HTTP requests in a production API. Each request now generates multiple detailed log lines, often with serialized payloads. The extra I/O and CPU overhead raises average latency and can push p95 and p99 response times far beyond your SLOs, even though the core business logic hasn’t changed.

Q: How can I tell if my logging is affecting performance?
A: Look for correlations between log volume and system metrics. If CPU, disk I/O, or latency spikes line up with increased log rates, you may be facing one of those classic examples of excessive logging and system performance issues. Profiling tools, APM, and even simple experiments where you temporarily reduce logging levels can confirm the impact.

Q: Are there safe examples of detailed logging in production?
A: Yes. A good example of detailed logging that doesn’t hurt performance is using structured logs for key business events (like order creation or payment failures) at INFO level, while keeping verbose debug logs sampled or disabled. You get high-value signals without drowning the system in noise.

Q: What are examples of logging patterns I should avoid in high-traffic systems?
A: Examples include logging every cache hit at INFO, dumping full request and response bodies for all API calls, logging inside tight loops over large datasets, and writing logs synchronously to slow disks. These patterns regularly show up in real examples of excessive logging and system performance issues across web apps, microservices, and data pipelines.

Q: How does excessive logging interact with security and compliance?
A: Over-logging can leak sensitive data and create massive audit trails that are hard to manage. Security frameworks and regulations generally encourage logging what you need, not everything you could possibly capture. This aligns with avoiding excessive logging and system performance issues: less unnecessary data means less performance overhead and lower risk.

If you recognize your own systems in these examples of excessive logging and system performance issues, you’re not alone. Logging is one of the easiest things to change and one of the easiest to accidentally weaponize against your own performance. Treat it like any other production dependency: measured, monitored, and intentionally designed.