Why Ignoring Proper Logging Will Crash Your App—and How to Fix It

Writing an application without logs leaves you clueless when things break; online issues remain mysteries after incidents.

1. Even a simple print should output key data

Beginners often use print for debugging; on Linux, redirecting print output via nohup to a file provides a trace for locating data.

2. Use a mature logging framework

Do not rely on print for logging. Mature frameworks let you configure storage methods and log levels, making it easy to extract useful metrics such as request counts or slow endpoints.

Storing logs in a database (e.g., MySQL or MongoDB) enables simple API calls for statistics, avoiding cumbersome text parsing.

Common frameworks like Java’s Log4j or Python’s logging provide methods such as debug, info, warn, and error, allowing you to filter output by severity.

3. Log segmentation

Never keep all logs in a single file or table; old logs should be discarded to save space, and large volumes degrade query performance.

Two common segmentation strategies:

By time

When data volume reaches a threshold

Time‑based splitting is often simpler for analysis.

4. Where should logs be stored?

Typical storage options include files, MySQL, MongoDB, HBase, and PostgreSQL.

In early development, file storage is convenient—no complex configuration or dependencies, and Linux commands can quickly locate information.

As the application scales across multiple servers, a centralized database (e.g., MySQL) becomes necessary; MongoDB handles evolving schemas and high write throughput, while HBase suits massive data volumes.

5. Need a unified log platform

Managing logs for many applications with separate databases and dashboards is cumbersome; a unified interface simplifies log management. The author’s open‑source solution is available at https://github.com/yubang/ilog .

6. Real‑time log analysis with ELK

ELK consists of three components: a collector (Logstash) that streams logs to Elasticsearch, a search/analysis service (Elasticsearch), and a visualization layer (Kibana). Logstash can track the last read line and use seek for incremental, real‑time ingestion.

7. Business logs

Beyond debugging, business logs record critical domain events, such as user balance changes, timestamps, sources, amounts, and before/after states.

8. Log‑code coupling problem

Embedding logging statements throughout code can be intrusive. Two viable solutions are:

Hook functions (e.g., Flask before/after request hooks) Instrumentation‑free logging, injecting logging code at runtime (similar to Baidu analytics JS).