API Observability refers to the comprehensive real-time monitoring and analysis of its operational status, performance, and health. This capability encompasses three key components: metrics monitoring, log analysis, and tracing analysis. In the previous installment, we delved into metrics monitoring. In this article, we will focus on how to enhance API observability from the perspective of log analysis.
Key Aspects of Log Analysis
API Log Characteristics
Different types of information may be contained within API logs, crucial for monitoring and issue resolution, including:
1. Structured and Unstructured Data
- Structured Data: Typically follows a fixed format and includes fields such as timestamps of API calls, request methods (GET, POST, etc.), request paths, status codes, etc. This data facilitates searching and analysis through query languages like SQL.
- Unstructured Data: May encompass specific content within request and response bodies, often in text or JSON format with varying content. Analyzing unstructured data typically requires text processing, regular expression matching, or natural language processing techniques.
2. Real-time and Historical Data
- Real-time: API logs often require real-time analysis to promptly detect and address anomalies such as excessive error requests or performance degradation.
- Historical Data: Analyzing historical data allows for understanding long-term performance trends of APIs, identifying periodic issues, or performing capacity planning.
3. Error and Performance Data
- Error Data: Includes abnormal status codes, error messages, or stack traces, crucial for identifying and resolving API issues.
- Performance Data: Such as response time, throughput, etc., can aid in evaluating API performance, identifying bottlenecks, and optimizing.
Methods of API Log Collection
- Automated Collection of Log Files: Regular scanning and collection of log files, transferring them to centralized storage and analysis systems.
- Real-time Log Stream Processing: Real-time pushing of logs to specific endpoints or streams such as Kafka, Flume, etc., for real-time analysis and handling of anomalies.