This article explores the logging mechanism of an application, detailing its importance for monitoring, troubleshooting, and various storage approaches.
In this article, we explore the logging mechanism used by our application and how it handles various output events like server startup, port listening, HTTP requests, and error reporting. Logs are crucial for monitoring system activities and troubleshooting issues.When the application starts, it produces logs detailing the server startup sequence, including server addresses and port numbers. Every HTTP request served is recorded, as illustrated in the example below.
These logs not only capture standard operations of the server but also record errors and other significant events, making them indispensable for diagnosing issues when failures occur.
Traditionally, applications write logs to local files. However, in containerized environments, this method presents challenges:
Volatility: A container may terminate at any time, causing the loss of local log files.
Inflexibility: Tying your logging system to a specific file system location restricts scalability and portability.
An alternative is to send logs to a centralized logging server using systems such as Fluentd, the ELK Stack, or Splunk. While centralized logging enhances management and analysis, directly integrating your application with a specific logging provider is not recommended.
Always design your application so that it remains agnostic to any logging backend, which improves flexibility, scalability, and ease of maintenance.
The following Python code demonstrates how logs can be sent to a Fluentd logging server. Note, however, that this pattern directly couples your application to Fluentd, which is discouraged:
Copy
Ask AI
from fluent import sender# Configure logger for remote logging via Fluentdlogger = sender.FluentSender('app', host='host', port=24224)# Emit a log event with detailslogger.emit('follow', {'from': 'userA', 'to': 'userB'})
According to the 11th principle of the 12 Factor App methodology, applications should not be responsible for log storage or routing. Instead, all logs should be directed to standard output or written as structured JSON to a local file. This practice allows an external agent to collect and forward logs to a centralized repository, where they can be queried and analyzed efficiently.Centralized logging solutions like the ELK Stack and Splunk are designed to ingest and process structured log data, making log analysis faster and more effective.By decoupling the logging mechanism from your application, you ensure that your system remains agile and well-suited for cloud-native and containerized environments.
