Structured logging is an approach to logging that utilizes a consistent, predictable format to make it easier for software and systems to parse and exploit log data. This approach contrasts with traditional unstructured logging, where log events are generally represented as plain text messages with little or no pre-defined structure at all. It also greatly assists in AWS Cloud environment to handle important processes and provide accurate tracking on applications.
Structured Logging in AWS Certified Developer – Associate (DVA-C02) exam
In the context of the AWS Certified Developer – Associate (DVA-C02) exam, a good understanding of structured logging is key, especially when it comes to developing efficient, scalable, and reliable AWS cloud applications.
Structured logs are generally represented as JSON objects, making them easier for systems to parse and interpret. They can include things like timestamps, event levels, messages, and any other contextual information relevant to the event. Below is an example of how a structured log might look in JSON:
{
“timestamp”:”2022-01-01T12:00:00Z”,
“level”:”INFO”,
“message”:”User login successful”,
“context”:{
“userId”:”1234″,
“ipAddress”:”192.168.0.10″,
“source”:”authenticationService”
}
}
AWS Services and Structured Logging
When developing applications to run in the AWS Cloud environment, you can utilize various AWS services, like Amazon CloudWatch, AWS X-Ray, and AWS CloudTrail, to leverage structured logging and improve the traceability and visibility of your applications. For instance, if you are using a service like AWS Lambda, your Lambda function can emit structured logs to CloudWatch Logs, where they can be searched, monitored, and even turned into operational insights using CloudWatch Insights.
console.log(JSON.stringify({
time: new Date(),
level: ‘ERROR’,
message: ‘An error occurred’,
service: ‘user-service’,
context: {
userId: 123,
action: ‘login’,
metadata: {
ip: ‘192.168.1.1’,
}
}
}));
This is a typical example of how to emit structured JSON logs from an AWS Lambda function implemented in JavaScript.
Notably, an additional benefit of structured logging is that the data can be more easily queried and manipulated. Using Amazon Athena, a serverless interactive query service, you can run SQL-like queries on your structured log data that’s stored in an Amazon S3 bucket. This allows you to analyze your application behavior or identify trends in your log efficiently.
Unstructured logging versus Structured logging:
| Unstructured Logging | Structured Logging | |
|---|---|---|
| Log Format | Plain text messages | JSON objects |
| Readability | Readable by humans, hard for machines | Easily parsed by machines |
| Data Extraction | Difficult, needs text processing | Easy, as it is structured |
| Scalability | Scaling becomes difficult due to the lack of structure | High scalability due to structure involved |
| Usage | Traditional legacy systems | Modern distributed systems |
Every AWS Certified Developer – Associate (DVA-C02) exam taker should understand the importance of structured logging. Whether it’s making your system easier to debug, or providing valuable data for system use, structured logging is a crucial concept for developing and operating software systems effectively on the AWS Cloud.
Practice Test
Structured logging allows for the addition of arbitrary attributes such as error codes or user ids to structured data.
- a) True
- b) False
Answer: a) True
Explanation: Structured logging is where you write your log lines in a structured, predictable and machine-readable format. This version of logging allows you to add and structure any arbitrary attribute, such as error codes or user IDs.
Structured logging data often includes the time of the event, the source of the event, the severity of the event, and the message related to the event.
- a) True
- b) False
Answer: a) True
Explanation: Structured logging uses a consistent, predictable, and machine-readable format which can include a timestamp, log level, message, and other context-specific structured data.
Structured logging is limited to storing only log messages.
- a) True
- b) False
Answer: b) False
Explanation: In addition to log messages, structured logging also allows storing other structured data like the time of the event, severity, source, and other user-defined attributes.
AWS does not support structured logging in their applications.
- a) True
- b) False
Answer: b) False
Explanation: AWS actively encourages the use of structured logging. Services such as AWS CloudTrail and AWS CloudWatch logs can be used for structured logging.
Structured logging does not support easy searching and filtering of logs.
- a) True
- b) False
Answer: b) False
Explanation: One of the major advantages of structured logging is that it supports easy searching, filtering and aggregation of logs due to their structured nature.
Which of the following AWS services is used for monitoring, storing and accessing log files?
- a) AWS S3
- b) AWS CloudWatch
- c) AWS EBS
- d) AWS Lambda
Answer: b) AWS CloudWatch
Explanation: AWS CloudWatch is a monitoring service that allows you to collect and track metrics, collect and monitor log files, and react to changes in your AWS resources.
You cannot use structured logging with Lambda functions in AWS.
- a) True
- b) False
Answer: b) False
Explanation: You can use structured logging with AWS Lambda. AWS Lambda writes function activity to CloudWatch logs, which can be structured and logged.
In terms of logging, what does structured data often refer to?
- a) Storing each log message as a separate file.
- b) Storing log messages in a structured, machine-readable format.
- c) Writing repetitive log messages.
- d) Writing log messages in human-readable format.
Answer: b) Storing log messages in a structured, machine-readable format.
Explanation: Structured logging is the process of formatting and storing log messages in a structured, predictable, and machine-readable format.
In structured logging, the log data can be targeted and processed by metrics and alerting systems.
- a) True
- b) False
Answer: a) True
Explanation: Since the data in structured logging is in a machine-readable format, it can easily be processed and utilized by metrics and alerting systems.
The AWS service CloudTrail is not suitable for structured logging.
- a) True
- b) False
Answer: b) False
Explanation: On the contrary, AWS CloudTrail is designed to record and provide event history of your AWS account’s activity, making it suitable for structured logging.
Interview Questions
What is structured logging in AWS?
Structured logging is a method of logging where messages are logged in a predefined format or template, usually as machine-readable files such as JSON, allowing easier processing and analysis.
How can structured logging help when working with AWS services?
Structured logging makes it much easier to query and analyze logs, especially when using services such as AWS CloudWatch Logs and AWS X-Ray.
What AWS service provides structured logging out of the box?
AWS CloudTrail provides structured logging of API calls made in your AWS environment out of the box.
How does structured logging make it easier to troubleshoot application issues?
Structured logging allows for precise querying across multiple log files, making it easier to identify issues related to specific instances, API calls, or events within an application.
What AWS service would you use for gathering application logs in a structured format?
AWS CloudWatch Logs is the main service used for gathering application logs in a structured format.
How can you implement structured logging in your AWS Lambda functions?
Structured logging in AWS Lambda functions can be implemented by logging the output in a structured format, like JSON, along with any other necessary context about the function invocation.
Can structured logging improve system observability?
Yes. Structured logging can significantly enhance system observability by allowing you to quickly detect, understand, and troubleshoot issues that may occur in your application or system.
How can you visualize structured logs in AWS?
You can visualize structured logs using AWS services like Amazon CloudWatch Logs Insights, which allows you to query, aggregate, and visualize log data.
How does AWS X-Ray make use of structured logging?
AWS X-Ray uses structured logging to record detailed information about requests that an application serves, allowing developers to analyze and debug production applications.
What format is commonly used for structured logging in AWS?
The JSON format is commonly used for structured logging in AWS because it is easy to parse, widely supported and it allows complex data structures to be logged.
What programming practice is associated with structured logging in AWS?
Structured logging in AWS is associated with best programming practices because it improves the clarity, predictability, and usefulness of log data.
How can you analyze structured logs in AWS?
You can analyze structured logs in AWS with Amazon CloudWatch Logs Insights by writing queries to extract, filter, and visualize log data.
How does AWS CloudTrail use structured logging?
AWS CloudTrail uses structured logging to capture detailed event information for AWS account activity, including actions taken through the AWS Management Console, AWS SDKs, command line tools, and other AWS services.
Is structured logging supported by AWS SDKs?
Yes, most AWS SDKs support structured logging. The specifics may vary between different SDKs and languages, so it’s always best to check the official documentation.
Can you modify the structure of logs in AWS CloudTrail?
No, you cannot modify the structure of logs in AWS CloudTrail as they are predefined by AWS. However, you can handle and organize these logs based on your needs using services like AWS CloudWatch or Amazon S3.
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