Structured logging is the method of having a consistent log format for your application logs so that they can be easily searched and analyzed. Having structured logs allows for more efficient searching, filtering, and aggregation of log data. It enables users to extract meaningful information from log data easily.
Logging is an essential aspect of system administration and monitoring. Logging allows you to record information data about the application's activity. The primary purpose of obtaining logs is to streamline the debugging, troubleshooting, or auditing practices.
Logs include system events, user actions, and error messages. They can be stored in various forms, such as text files and databases. This allows them to analyze and visualize using log management platforms such as SigNoz. The structure of these logs falls on a large spectrum. A log can be structured, semi-structured or unstructured. In this blog, we will learn about structured logging and its benefits.
What are Structured Logs?
A structured log is a log record with a well-defined structure and format. The structured logging practice creates easily human-readable textual log data. The same data can be easily integrated with other systems to automate log analyzing, querying, and aggregation.
Semi-structured and unstructured logging usually provide text data that are easy to read by humans but can be challenging for machines to extract. It is harder to query such data structures for helpful information. Unstructured log data makes it harder to analyze log data.
Structured logging solves these challenges and creates room for additional analytic techniques. Structure logging aims to create machine-readable data for advanced functionalities. Structured logs ensure each log entry is composed of a set of fields or key-value pairs in a well-structured and consistent format that can be easily parsed and analyzed.
This structure allows for more efficient searching, filtering, and aggregation of log data. With the help of logging tools, you can automatically extract meaningful information from the logs. It becomes more straightforward to transform your data efficiently. Due to their format, structured logs also allow effortless integration with other systems, such as monitoring and alerting tools.
Consider the following simple example of an unstructured log message:
[info] [Friday, 20-Jan-23 11:17:55 UTC] The application has started.
[Error] [Friday, 20-Jan-23 11:17:55 UTC] An error occurred while processing this request.
The above log example contains log information. While it is a straightforward log, having a huge list of such records that includes complex examples can take time to draw a valid conclusion. Imagine having thousands of such logs that you need to analyze for insights.
It can be hard to diagnose and troubleshoot how an issue occurred, as the log message needs descriptive log information. Using the same log message, you can use structured log formats to describe the message with payload:
{
"timestamp": "Friday, 20-Jan-23 11:17:55 UTC",
"level": "info",
"message": "The application has started."
}
Both the unstructured and structured payloads contain the same information. However, the structured log message has key attributes that can be paired with any corresponding values. This means an analysis system can analyze the data using these attributes to perform searches and filter results. On top of that, you can detect patterns in the data and have a concrete conclusion based on your log messages.
With key-value pairs, you can add more meaningful information to your logs. Here is an example of log using a structured format with more details on error type:
{
"timestamp": "Friday, 20-Jan-23 11:17:55 UTC",
"level": "error",
"message": "An error occurred while processing this request",
"error": {
"code": "500",
"message": "Internal server error",
"details": "Error occurred when sending POST data query."
}
}
Anatomy of a Structured Log Entry
A structured log entry typically consists of several essential fields:
- Timestamp: When the event occurred
- Log level: Severity of the event (e.g., INFO, WARN, ERROR)
- Message: A human-readable description of the event
- Context: Additional metadata about the event
Here's an example of a structured log entry in JSON format:
{
"timestamp": "2023-05-09T14:32:10.123Z",
"level": "ERROR",
"message": "Failed to process payment",
"context": {
"user_id": "12345",
"transaction_id": "tx-98765",
"error_code": "INSUFFICIENT_FUNDS"
}
}
This format allows for easy parsing and analysis, enabling you to quickly extract relevant information when troubleshooting issues.
Why is Structured Logging Needed?
There are numerous benefits to using structured logs. Below are some points for why structured logging is needed:
- With structured logs, it is accessible to search, filter, and aggregate log data.
- Its format is friendly with observability and logging tools like SigNoz. This makes it easier to analyze your log data faster and more efficiently to troubleshoot any application issues.
- Structured logs improve automation. You can automatically extract any relevant information, such as error messages and performance metrics, and create aggregates.
- Structured logs are easily integrated with alert systems. You can get notified if the software system starts sending logs of a certain type.
- With structured logs, it is easier to identify trends and patterns that might be missed with unstructured logs.
- Structured logs open room for broader integration with logs from different sources and with monitoring tools.
- Structured logs reduce costs on tools you can choose to integrate and analyze data. The data is consistent. This makes storing in long-term retention easier with reduced storage cost.
- It is easier to visualize structured logs data with which you can drive insights faster. Log analytics tool like SigNoz can help you create charts from structured logs data easily.
Best Practices for Structured Logging
Structured logging has many benefits. Based on this guide, you have understood the importance in detail. However, when using structured logs, it's good to follow the best practices to ensure you get the optimal benefits structured logs offer. Some of the best practices are:
- Always use a consistent format if you choose JSON, for example. You should stick to that one format across your application. This makes it easier to parse the collected data, analyze logs and provide timely feedback.
- Remember to include relevant information in your log entries. Fields such as timestamps, logging levels, and context-specific data (user information and request details) make it easier to understand your application history.
- Use the standard log levels for your log entries. This includes
info,warning,error,debugging, etc. Such practices make it possible to have filtering features for your log data. - Remember to add unique keys in log entries. This makes it easy to search and filter log data using tools like SigNoz.
- Create security-logging practices to monitor security breaches, suspicious activity, and other potential threats.
- Before rolling your application to production, always test and validate your logs. This will help you ensure your application generates logs correctly and in the expected format.
- You can also integrate your logs with traces. Having correlated telemetry signals can help you drive contextual insights faster.
The Developer's Guide to Implementing Structured Logging
Implementing structured logging in your applications requires careful planning and execution. Here are some key steps to get you started:
- Choose the right logging library:
Select a library that supports structured logging in your programming language. Popular options include:
- Define your log structure:
Establish a consistent format for your logs across all services. This might include standard fields like timestamp, log level, and message, as well as custom fields specific to your application.
- Implement correlation IDs:
Use unique identifiers to trace requests across multiple services. This is especially important in microservices architectures.
- Handle sensitive information:
Be cautious about logging personally identifiable information (PII) or other sensitive data. Implement masking or encryption for sensitive fields.
- Choose a log management tool: The next step is to choose a log analytics tool. When choosing a log analytics tool, three things must be kept in mind - ingestion, query, and storage. Based on these parameters and your use case, you can choose a log analytics tool that suits you. SigNoz is an open-source log management tool that you can use for structured logging.
It uses OpenTelemetry to collect logs and uses ClickHouse which is a columnar database for storage. You can check out the logs performance benchmark of SigNoz to see if it fits your needs.
SigNoz cloud is the easiest way to run SigNoz. Sign up for a free account and get 30 days of unlimited access to all features.
You can also install and self-host SigNoz yourself since it is open-source. With 19,000+ GitHub stars, open-source SigNoz is loved by developers. Find the instructions to self-host SigNoz.
SigNoz allows you to:
- Correlate logs with distributed traces and metrics
- Visualize log data alongside other telemetry
- Set up alerts based on complex log patterns
- Gain end-to-end visibility into your applications
Common Pitfalls and How to Avoid Them
As you implement structured logging, be aware of these common mistakes:
- Overlogging: Don't log everything—focus on meaningful events and data.
- Inconsistent formatting: Maintain a consistent log structure across all services.
- Performance issues: Be mindful of the performance impact of logging, especially in high-throughput systems.
- Invalid structures: Implement validation to ensure your logs always conform to the expected structure.
Leveraging Structured Logs for Advanced Analysis
With structured logs in place, you can take advantage of powerful log management tools to gain deeper insights into your systems. These tools can help you:
- Parse and analyze logs at scale
- Create custom dashboards and visualizations
- Set up alerts based on specific log patterns
- Correlate logs with metrics and traces for full observability
Structured Logging in Cloud Environments
Cloud environments present unique challenges and opportunities for structured logging:
- Containerized applications: Adapt your logging practices for ephemeral containers and orchestration platforms like Kubernetes.
- Cloud-native logging services: Utilize services like AWS CloudWatch or Google Cloud Logging for seamless integration with your cloud infrastructure.
- Microservices architectures: Implement centralized log aggregation to maintain visibility across distributed systems.
- Compliance and security: Ensure your logging practices meet regulatory requirements and protect sensitive information.
Conclusion
Logging is an integral part of all software systems. You can understand what's happening and where problems might be. Generally, if something goes wrong in production, you have a reference to trace your logs. Structured logs make it easier to search, query, and analyze logs data at scale.
A log analysis tool like SigNoz can help you derive insights from logs data quickly while ensuring efficient management and storage. You can check out the SigNoz GitHub repo here.
FAQs
What are the main differences between structured and unstructured logging?
Structured logging uses a consistent, predefined format (often JSON or XML) with key-value pairs, making logs easily parseable and searchable. Unstructured logging typically consists of free-form text, which is harder to analyze programmatically.
How does structured logging impact application performance?
While structured logging can have a slight performance overhead due to the additional formatting, modern logging libraries are highly optimized. The benefits in terms of improved troubleshooting and analysis usually outweigh the minimal performance impact.
Can I convert existing unstructured logs to structured format?
Yes, it's possible to convert unstructured logs to a structured format using log parsing tools or custom scripts. However, it's generally more effective to implement structured logging directly in your application code.
What are some popular structured logging libraries for different programming languages?
- Java: Logback with Logstash encoder
- Python: structlog
- JavaScript: Winston
- Go: Zap or Logrus
- .NET: Serilog
What are structured logs?
Structured logs are log records with a well-defined structure and format. They create easily human-readable textual log data that can also be easily integrated with other systems for automated analyzing, querying, and aggregation.
Why is structured logging important?
Structured logging is important because it allows for more efficient searching, filtering, and aggregation of log data. It enables users to extract meaningful information from log data easily, improves automation, facilitates integration with alert systems, and makes it easier to identify trends and patterns.
What are the benefits of using structured logs?
The benefits of using structured logs include:
- Easier searching, filtering, and aggregation of log data
- Better compatibility with observability and logging tools
- Improved automation for extracting relevant information
- Easier integration with alert systems
- Ability to identify trends and patterns more easily
- Reduced storage costs due to consistent data format
- Easier visualization of log data for faster insights
What are some best practices for structured logging?
Some best practices for structured logging include:
- Using a consistent format across your application
- Including relevant information like timestamps, logging levels, and context-specific data
- Using standard log levels (info, warning, error, debugging, etc.)
- Adding unique keys in log entries for easy searching and filtering
- Creating security-logging practices
- Testing and validating logs before production deployment
- Integrating logs with traces for correlated telemetry signals
How can I get started with structured logging?
To get started with structured logging:
- Choose a logging library that supports structured logging (e.g., winston for Node.js)
- Select a log analytics tool like SigNoz for ingestion, querying, and storage
- Implement structured logging in your application following best practices
- Use the log analytics tool to analyze and visualize your structured log data
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