An API log is a comprehensive record generated by an API that documents all the requests sent to and responses received from the API. It includes information such as the endpoint accessed, the request method, timestamps, headers, body content, and often, the status codes of responses.

API logs allow you to capture the interactions between different services and offer comprehensive insight into the workings of APIs. You can use the information from API logs to debug issues with precision, monitor the performance of APIs to ensure they meet service level agreements (SLAs), and understand how your users interact with your application.

You can read on to learn how API logs work, the types of API logs that exist, and the tools used for collecting and processing API logs.

How API logs work

To fully understand API logs, you'll need to know how they're generated, collected, and how to read their output.

How API logs are generated

When you visit a website, the browser makes an API call to the s server with your request for the server to process and respond with the information you requested.

While the server processes the requests and their corresponding responses, it automatically captures relevant data. This data typically includes the request URL, the method (GET, POST, PUT, DELETE, etc.), headers, body content, your IP address, the timestamp when you made the request, the server response, and the response code, along with any headers sent back to the browser.

All this captured data is formatted into a log entry according to the system’s logging standards or configuration. This involves structuring the data in a specific format (e.g., JSON, XML) and appending it with additional metadata.

How API Logs Are Collected

One way to collect API logs is locally in the API server.

However, if you aggregate API logs in a central logging system instead, you can manage the logs more effectively. It also ensures that the logs are easily accessible for analysis. Central logging systems, such as ELK Stack and Splunk, or cloud-native solutions, such as Amazon CloudWatch Logs, Azure Monitor Logs, and Google Cloud Logging, provide the infrastructure for indexing large volumes of log data and offer features for searching, visualizing, and analyzing the logs.

The Components of an API Log

Let's dissect the components of an API log using a specific example.

{
  "timestamp": "2024-02-22T15:30:00Z",
  "httpMethod": "GET",
  "endpoint": "/api/v1/weather/forecast",
  "request": {
    "headers": {
      "Content-Type": "application/json",
      "Authorization": "Bearer someAuthToken"
    },
    "queryParams": {
      "city": "Paris",
      "country": "France",
      "date": "2024-02-22"
    }
  },
  "response": {
    "status": 200,
    "headers": {
      "Content-Type": "application/json"
    },
    "body": {
      "city": "Paris",
      "country": "France",
      "date": "2024-02-22",
      "forecast": {
        "temperature": "18°C",
        "wind": "5 km/h E",
        "humidity": "77%",
        "condition": "Partly cloudy"
      }
    }
  },
  "timeConsumed": "200ms"
}

‍The timestamp records the exact date and time when the API request was made and when the response was sent. The timestamp is needed to identify when specific events occur, which helps trace API interactions chronologically.

The HTTP method and endpoint specify the HTTP method used (GET, POST, PUT, DELETE, etc.) and the API endpoint being accessed. This component indicates the type of operation requested and the resource involved.

The request details include the full request URL, headers (such as Content-Type and uthentication tokens), and the body content, if applicable. It provides a comprehensive view of what was requested from the API.

The response comprises the status code (e.g., 200 OK), headers (e.g., Content-Type, Set-Cookie), and the body of the response. It details what the API returned in reaction to the request.

And lastly, timeConsumed measures the duration taken to process the API request and generate a response. This metric helps identify slow endpoints or potential bottlenecks as part of performance monitoring.

Related: API logging best practices

Types of API logs

API logs can be categorized into different types based on their purpose and the information they capture. Each type has a role in monitoring and maintaining the health, security, and efficiency of APIs.

Here's an overview of the different types of API logs with examples.

Access logs

Access logs record every request made to the API, including successful and failed attempts. They typically contain information like the timestamp, HTTP method, endpoint accessed, IP address of the requester, response status code, and the size of the response data.

The example in the previous section that explains the components of an API log is an access log.

Error logs

Error logs specifically capture information about requests that resulted in errors. They include error messages, stack traces, request IDs, and sometimes, user IDs to help you diagnose and fix issues.

For instance, an error log might capture a 404 Not Found error when a request is made to a nonexistent endpoint. The log entry would include the timestamp, the requested URL, and possibly a message indicating that the endpoint does not exist.

Security logs

Security logs focus on recording events that impact the API's security, such as authentication attempts, access control violations, and other potentially malicious activities. They often include details about the requester, the action attempted, and whether the attempt was successful or blocked.

For instance, a security log might record several failed authentication attempts to access a premium endpoint.

The log details could include timestamps, the IP address of the requester, and the type of authentication failure, alerting you or the admin to possible unauthorized access attempts.

Performance logs

Performance logs capture metrics, such as response times, throughput, and system resource usage, to identify performance bottlenecks and optimize the API.

For instance, a performance log  note that the average response time was 250 milliseconds on a particular day, with a peak load of 1,500 requests per hour. This information can help you assess whether the API's performance meets the expected SLAs. It's also useful for planning capacity or optimization efforts.

Related: A guide to API error handling

Tools for collecting and processing API logs

Several tools and platforms are available to help you collect and process API logs effectively. Here's an overview of some of the most popular ones.

ELK Stack

ELK Stack comprises of the following:

• Elasticsearch, a highly scalable search and analytics engine designed for speed, horizontal scalability, and reliability
• Logstash, a server-side data processing pipeline capable of simultaneously receiving data from multiple sources that it transforms and forwards to a storage solution, such as Elasticsearch
• Kibana, a visualization layer that works on top of Elasticsearch to provide a user-friendly interface for viewing, searching, and interacting with data stored in Elasticsearch

The ELK Stack is a powerful and flexible solution for logging management that can scale as your needs grow. Its speed and efficiency, schema flexibility, and scalability features make it particularly useful for searching and analyzing large volumes of log data.

Datadog

Datadog is a cloud-based monitoring and analytics platform that integrates and automates infrastructure monitoring, application performance monitoring (APM), and log management. It offers real-time log processing and visualization, which allows you to monitor and troubleshoot your stack at any scale.

Datadog is a good option if you're looking for an all-in-one solution that not only handles logs but also offers extensive monitoring capabilities across your entire tech stack. It offers intuitive dashboards and AI-powered alerts to help you proactively identify and resolve issues.

Sentry

Sentry is an error-tracking and performance-monitoring tool that helps you monitor and fix crashes in real time. While it's more focused on code-level issues, it can capture exceptions and errors that occur in API calls to give you insights into the health and performance of your applications.

Axiom

Axiom is a newer, cloud-native log management solution. It provides fast, scalable log management and analytics without the operational overhead of managing the infrastructure. Axiom supports real-time log aggregation, analysis, and visualization.

Axiom is a good option if you prefer a managed service for log management, offering ease of use and minimal setup. It's particularly attractive for teams looking for scalable solutions without the complexity of managing the logging infrastructure.

Related: API integration support drawbacks

Final thoughts

Merge, the leading unified API solution, not only lets you add hundreds of integrations to your product through a single unified API but also access comprehensive, fully-searchable logs across your API requests. You'll also receive automated alerts for specific issues, along with suggested remediations.

Taken together, these features help your team spend less time and money on building and maintaining your logging infrastructure and focus more on building a best-in-class product.

Learn more about how you can analyze your logs with Merge by scheduling a demo with one of our integration experts.