Tutorial: AWS Certified Developer - Associate (DVA-C02)

Structured logging

Concepts

Structured logging is a practice of capturing and formatting logs in a consistent and machine-readable format. Unlike traditional logging, which often involves unstructured text messages, structured logging organizes data into predefined fields, making it easier to search, analyze, and automate response actions.

For developers preparing for the AWS Certified Developer – Associate exam, understanding structured logging is essential as it is a key part of developing, deploying, and maintaining applications in the AWS cloud in a scalable and manageable way.

Importance of Structured Logging in AWS

Structured logging is particularly important in AWS due to the distributed nature of cloud applications. AWS provides various services for managing logs, such as Amazon CloudWatch, AWS CloudTrail, and the Amazon Elasticsearch Service, which can ingest, store, and analyze structured logs. This allows developers to:

Monitor applications and automatically respond to specific events.
Troubleshoot issues by quickly searching and filtering logs based on specific criteria.
Aggregate logs from multiple sources to gain insights into application performance and usage patterns.
Implement compliance and security auditing more effectively.

Implementing Structured Logging in AWS

When implementing structured logging within AWS, you’ll likely be using the AWS SDK within your application. Here’s an example of how you might structure logs using JSON, one of the most common formats for structured logging:

{
“timestamp”: “2023-03-20T15:00:00Z”,
“logLevel”: “INFO”,
“message”: “User login successful”,
“userId”: “abcd1234”,
“sessionId”: “efgh5678”,
“ip”: “192.0.2.1”,
“additionalInfo”: {
“userAgent”: “Mozilla/5.0”,
“screenResolution”: “1920×1080”
}
}

Every log entry includes a set of predefined fields which can include a timestamp, log level, and a message among others. Additional context-specific information is nested within the additionalInfo field.

In the AWS context, it’s common to use libraries or frameworks that support structured logging, such as Log4j for Java, Serilog for .NET, or Zap for Go. Additionally, AWS Lambda functions can be configured to log to CloudWatch in a structured format.

Best Practices for Structured Logging with AWS

Consistency: Ensure that the log format is consistent across all components of your application. Define a schema for your logs to maintain uniformity.
Meaningful Data: Include data that is meaningful for troubleshooting and analysis, such as unique identifiers for requests or users.
Security: Be careful not to log sensitive data. AWS also provides the means to encrypt log data at rest and in transit.
Integration with AWS Services: Take advantage of the integration between AWS services. For instance, logs from Amazon EC2 or AWS Lambda can be directly sent to CloudWatch.
Automation: Use AWS services to set alarms, trigger events, and automatically take corrective actions based on specific log entries.
Retention: Configure retention policies for your logs in CloudWatch Logs, ensuring you comply with company policies or regulatory requirements.

Comparison Between Structured and Unstructured Logging

Feature	Structured Logging	Unstructured Logging
Format	Organized, machine-readable (JSON, XML)	Free-form text
Searchability	High, with the ability to filter on fields	Limited, often requires full-text search
Analysis	Easier to process with tools and services	More challenging to extract meaningful metrics
Monitoring and Alerting	Can be automated based on log content	May require pattern matching, less precise
Overhead	Potentially higher due to the structured format	Usually lower due to simple text
Tool Integration	Easily integrated with log management tools	Requires custom parsing logic

Conclusion

Structured logging is a pivotal concept for AWS Certified Developer – Associate candidates to master, as it directly affects the ability to monitor, analyze, and operate applications effectively in AWS. By following best practices and utilizing AWS services that support structured logs, developers can build robust, scalable, and maintainable applications that are well-prepared for production environments.

Answer the Questions in Comment Section

True or False: Structured logging refers to a format where logs are written as unstructured text data.

False
True

Answer: False

Explanation: Structured logging refers to the practice of writing logs in a structured format, often in key-value pairs or as JSON objects, which makes them easier to search and analyze.

In AWS, which service can you use to manage and analyze structured log data?

Amazon CloudFront
Amazon CloudSearch
Amazon CloudWatch Logs
Amazon S3

Answer: Amazon CloudWatch Logs

Explanation: Amazon CloudWatch Logs allows you to monitor, store, and access your log files from EC2 instances, AWS CloudTrail, and other sources, making it suitable for managing and analyzing structured log data.

How can you enrich log data with metadata in a structured logging approach?

By manually adding metadata via the AWS Management Console
By including key contextual information within each log statement
By contacting AWS Support to enable metadata enrichment
By using IP filtering rules

Answer: By including key contextual information within each log statement

Explanation: Structured logging allows you to enrich log data by including key contextual information within each log statement, such as user IDs, session IDs, and request IDs.

True or False: When using AWS Lambda, you must manually configure structured logging using third-party libraries.

True
False

Answer: False

Explanation: While you can use third-party libraries to enable structured logging in AWS Lambda, AWS Lambda automatically captures output from standard output and standard error streams and can directly write structured logs if the output is formatted correctly.

Which log message format is commonly used for structured logging?

XML
JSON
Plain Text
HTML

Answer: JSON

Explanation: JSON (JavaScript Object Notation) is a common format used for structured logging, as it is easily read by humans and machines, and can be parsed by various log analysis tools.

True or False: Structured logs cannot be queried.

True
False

Answer: False

Explanation: One of the main benefits of structured logging is the ability to query logs due to the structured nature of the data, making it easy to filter and search for specific information.

Which AWS service provides a managed Elasticsearch environment that can be used for analyzing log data?

Amazon ES
Amazon MSK
Amazon RDS
Amazon Kinesis

Answer: Amazon ES

Explanation: Amazon Elasticsearch Service (Amazon ES) provides a fully managed environment to deploy, secure, and run Elasticsearch at scale, which is commonly used for log analytics.

What is the primary benefit of structured logging over unstructured logging?

Lower storage costs
Enhanced security
Higher performance of the logging system
Improved searchability and analysis

Answer: Improved searchability and analysis

Explanation: The primary benefit of structured logging is improved searchability and analysis because structured data is more organized and easier to query.

Which feature of Amazon CloudWatch logs allows for real-time processing of log data?

Log groups
Log streams
Metric filters
Subscription filters

Answer: Subscription filters

Explanation: Subscription filters in CloudWatch Logs allow for real-time processing of log data by enabling you to stream your log data to other services such as Amazon Kinesis Data Firehose, Amazon Kinesis Data Streams, and AWS Lambda.

True or False: You can use AWS CloudFormation to create logging solutions that implement structured logging.

True
False

Answer: True

Explanation: AWS CloudFormation enables you to automate the deployment of AWS resources, including those used for implementing structured logging solutions, by defining them in a YAML or JSON template.