Logging & Tracing

Why Logging Matters

Metrics tell you something is wrong. Logs tell you why. When error rates spike at 2 AM, metrics identify the problem exists. Logs show you the stack trace, the malformed input, the database timeout that started the cascade. Without logs, you’re debugging blind.

In distributed systems, the challenge multiplies. A single user request might touch an API gateway, authentication service, main application, cache, database, and payment processor. When that request fails, which service caused it? Without distributed tracing, you’re searching through separate log files hoping to find correlation. With tracing, you see the entire request flow in one view.

Good logging infrastructure pays for itself during the first serious incident. Instead of hours reconstructing what happened from fragmentary evidence, engineers pinpoint root causes in minutes. Customer complaints get resolved the same day instead of next week. Post-mortems reference actual data instead of speculation. The investment in logging returns every time something goes wrong.

What We Build

We implement logging and tracing that scales from startup to enterprise.

Structured Logging:

• JSON-formatted logs with consistent field names across all services
• Standard fields: timestamp, level, service name, request ID, user ID
• Contextual information: request paths, response codes, latency
• Error details: stack traces, error codes, upstream failure information
• Business context: order IDs, transaction amounts, feature flags

Centralized Log Collection:

• Log shipping from containers, servers, and serverless functions
• Real-time ingestion handling thousands of events per second
• Index management and retention policies
• Access controls and audit trails for sensitive data
• Search interfaces that make finding logs fast

Distributed Tracing:

• Trace context propagation across HTTP, gRPC, and message queues
• Automatic instrumentation for common frameworks
• Custom span creation for business-critical operations
• Trace sampling to control storage costs
• Service dependency mapping from trace data

Log Analysis:

• Dashboards showing error trends and patterns
• Anomaly detection for unusual log volumes
• Alerting on specific log patterns or error types
• Log-based metrics for scenarios where instrumentation isn’t possible

Our Experience Level

We’ve implemented logging infrastructure for applications generating gigabytes of logs daily and for simple services where a single log file sufficed. We understand where different solutions fit.

We’ve deployed the ELK stack (Elasticsearch, Logstash, Kibana) for teams that wanted full control and had operations capacity. We’ve implemented Loki for teams already using Prometheus and Grafana who wanted a lighter-weight solution. We’ve configured Datadog and New Relic for teams that preferred managed services. We’ve instrumented applications with OpenTelemetry when vendor neutrality mattered.

Specific things we’ve built:

• Multi-tenant logging — Separate log streams and access controls for different customers
• PII handling — Redaction and encryption for sensitive data in logs
• High-volume ingestion — Kafka-based pipelines handling hundreds of thousands of events per second
• Cost-optimized retention — Hot-warm-cold architectures that keep recent logs fast and old logs cheap
• Cross-service debugging — Trace visualization that shows exactly where requests slow down or fail

When to Use It (And When Not To)

Every production application needs logging. Console output isn’t enough once you have more than one server or container.

For simple applications, basic centralized logging might be sufficient. Ship logs somewhere searchable. Retain them for a few weeks. That’s the minimum.

For distributed systems with multiple services, distributed tracing becomes essential. Without trace context, correlating logs across services is manual detective work. The more services you have, the more tracing helps.

For applications handling sensitive data, logging requires additional care. You need redaction policies, access controls, and audit trails. Compliance requirements might dictate specific retention periods or encryption standards.

For high-traffic applications, logging infrastructure becomes a significant cost and operational concern. You need sampling strategies, retention policies, and architecture that can handle the volume without bankrupting you.

We assess your situation and recommend appropriate solutions. Not every application needs Elasticsearch. Not every team should manage their own logging infrastructure.

Common Challenges and How We Solve Them

Log volume that overwhelms storage. Applications log everything at debug level and storage costs explode. We implement log levels properly: debug for development, info for normal operations, warning and error for problems. We add sampling for high-volume, low-value logs. We set retention policies that match actual needs.

String soup that nobody can search. Log messages like “Processing user” without structure. We implement structured logging from day one with consistent fields. When inheriting unstructured logs, we add parsing rules to extract useful fields. Logs become queryable data, not text blobs.

Missing trace context across services. Request IDs exist but don’t flow through async operations or message queues. We instrument context propagation at every boundary. HTTP headers, message queue metadata, async task parameters — trace IDs follow requests everywhere.

Sensitive data appearing in logs. User passwords, API keys, or PII in log messages. We implement redaction at the source. We add scanning in the log pipeline as a safety net. We establish patterns for what should never be logged and enforce them in code review.

Slow log search when debugging urgent issues. Kibana queries take minutes during incidents when you need answers in seconds. We optimize index settings, implement query patterns that work, and pre-build dashboards for common investigation scenarios. Fast search during incidents isn’t optional.

Logs that don’t help debugging. Plenty of log volume but missing the context that would actually explain failures. We review logging coverage during development. Every error path should log enough context to understand what happened. Log reviews become part of code review.