Master AWS API Gateway Logging - A Detailed Guide
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Master AWS API Gateway Logging: A Detailed Guide

Welcome to our comprehensive guide on mastering AWS API Gateway logging. As businesses embrace digital transformation, APIs have become the vital connecting links enabling systems to interact and exchange information. AWS API Gateway is a fully managed service that streamlines these API operations, allowing developers to create, publish, maintain, monitor, and secure APIs at any scale.

An important part of managing APIs is having a robust logging mechanism in place, which we aim to delve into with this guide. AWS API Gateway logging is a powerful feature that provides insights into all API calls, helping debug issues, identify potential security risks, and understand your API’s users and usage patterns.

This guide will explore the various aspects of AWS API Gateway logging, including understanding the log formats and how to interpret them. We will also cover the integration of AWS API Gateway logs with other AWS services such as CloudWatch, and S3 and tools like Terraform, Splunk, Elasticsearch, and OpenSearch.

We will specifically use Terraform – a powerful Infrastructure as Code tool – to illustrate how to implement AWS API Gateway logging in a declarative and reproducible manner. This approach ensures our deployments are consistent, reducing the likelihood of runtime errors and making the process more efficient.

Further, we will showcase how Python, a versatile and widely-used programming language, can be employed to extract and analyze AWS API Gateway logs. The goal is to empower you with the knowledge and tools necessary to unlock the full potential of AWS API Gateway logging in your projects.

Whether you are an experienced DevOps professional or a beginner stepping into AWS, this guide has something for you. We look forward to helping you enhance your skills and invite you to join us as we explore AWS API Gateway logging in detail. Let’s get started!

Understanding AWS API Gateway Logging

AWS API Gateway logging provides a window into your API’s interactions with other services and users. It is an essential tool for monitoring and debugging your APIs and identifying and responding to potential security incidents.

API Gateway provides two types of logging – Access Logging and Execution Logging.

Access Logging is a feature that records all requests made to your API Gateway, providing information such as the caller’s IP address, request parameters, request latency, and response status. These logs are especially useful for analyzing user behavior and troubleshooting latency issues.

Execution Logging offers a more in-depth look at the API calls, capturing data about the stages of API Gateway request/response workflows. It includes logs from the API Gateway system itself as well as logs from AWS Lambda functions if used. Execution logs are useful for debugging and identifying issues in your API’s workflow.

To enable these logging features, we can use Terraform, an Infrastructure as Code (IaC) tool that makes it simple and efficient to manage your AWS resources. Here is an example of how to use Terraform to enable Access Logging on an AWS API Gateway:

resource "aws_api_gateway_stage" "example" {
  deployment_id = aws_api_gateway_deployment.example.id
  rest_api_id   = aws_api_gateway_rest_api.example.id
  stage_name    = "example"
  access_log_settings {
    destination_arn = aws_cloudwatch_log_group.example.arn
    format          = "$context.identity.sourceIp $context.identity.caller $context.identity.user [$context.requestTime] \"$context.httpMethod $context.resourcePath $context.protocol\" $context.status $context.responseLength $context.requestId"
  }
}

In this code snippet, we define an AWS API Gateway stage resource and include an access_log_settings block. This block specifies the CloudWatch Logs group where the logs will be sent and the format of the logs.

You can analyze these logs using Python with the Boto3 library, which provides a direct API to AWS services. You can write a Python script to pull logs from CloudWatch and perform analysis tasks, such as identifying frequently occurring status codes or detecting patterns that may indicate a security concern.

In the following sections, we will delve deeper into the format of AWS API Gateway logs and how to interpret them, along with practical examples of integrating AWS API Gateway logs with different AWS services and tools.

Importance of AWS API Gateway Logs

API Gateway Logs play a vital role in maintaining your APIs’ health, performance, and security. Their importance cannot be overstated, and here are some key reasons why:

1. Troubleshooting and Debugging: API Gateway Logs provide crucial insights into your API operations, which can help pinpoint the root cause of an issue. By analyzing these logs, you can find answers to questions like “Why did this request fail?” or “Why is this endpoint responding slowly?”

2. Security Monitoring: Logs are essential for identifying suspicious activities. You can monitor the request patterns, detect potential threats, and mitigate them before they cause significant harm.

3. Performance Optimization: By monitoring logs, you can observe the performance of your API over time and identify bottlenecks. This data can help you optimize your APIs for better latency and throughput.

4. Compliance Auditing: In some sectors, logging is beneficial and mandatory. AWS API Gateway logs can help meet compliance requirements by providing an audit trail of all API activities.

5. User Behavior Analysis: The logs can give you an idea about the consumers of your API. It can provide information such as which endpoints are frequently used, the peak usage time, etc.

To gain these benefits, it is essential to configure your AWS API Gateway logging correctly. Terraform can automate this process, ensuring all your API stages have the necessary logging settings. Here’s an example of a Terraform configuration to enable execution logs:

resource "aws_api_gateway_account" "example" {
  cloudwatch_role_arn = aws_iam_role.example.arn
}
resource "aws_iam_role" "example" {
  name = "example"
  assume_role_policy = <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Action": "sts:AssumeRole",
      "Principal": {
        "Service": "apigateway.amazonaws.com"
      },
      "Effect": "Allow",
      "Sid": ""
    }
  ]
}
EOF
}
resource "aws_api_gateway_stage" "example" {
  deployment_id = aws_api_gateway_deployment.example.id
  rest_api_id   = aws_api_gateway_rest_api.example.id
  stage_name    = "example"
  settings {
    metrics_enabled = true
    logging_level   = "INFO"
    data_trace_enabled = true
  }
}

In the above code snippet, an IAM role is created first, which API Gateway will assume to write logs to CloudWatch. Then, in the API Gateway Stage resource, we enable detailed metrics and set the logging level to INFO.

Next, you might want to analyze these logs for actionable insights. For this, Python comes in handy. You can leverage AWS SDK for Python (Boto3) to fetch the logs and use Python’s powerful data analysis libraries, such as pandas or PySpark, for analysis. We will dive deeper into this in the upcoming sections.

The next section will review the AWS API Gateway Logs format and how to interpret it for effective debugging and analysis.

AWS API Gateway Logs Format

The format of AWS API Gateway logs is fundamental to understand, as it’s the key to extracting valuable information from the logs. Understanding the log format is the first step when debugging an issue, monitoring performance, or analyzing user behavior.

AWS API Gateway provides two types of logs – Access Logs and Execution Logs, each having a distinct format.

Understanding Log Entries

Access Logs: These logs contain detailed information about each access request, such as the requester’s IP address, user agent, request URL, response size, and latency. An example of an access log entry is as follows:

{
  "requestId": "abcdef12-3456-789a-bcde-f123456789ab",
  "ip": "203.0.113.0",
  "caller": "ABCDEFGH1234567",
  "user": "ABCDEFGHIJ1234567",
  "requestTime": "29/Nov/2020:13:58:38 +0000",
  "httpMethod": "GET",
  "resourcePath": "/my/path",
  "status": 200,
  "protocol": "HTTP/1.1",
  "responseLength": 1024
}

Execution Logs: These logs contain information about the internal execution of the API Gateway, including the setup of the request and the delivery of the response. An example of an execution log entry is as follows:

(abcdef12-3456-789a-bcde-f123456789ab) Method request path: {my_param=123}
(abcdef12-3456-789a-bcde-f123456789ab) Method request query string: {my_query=456}

In both examples, you see an ID at the beginning of the log entry. This is the request ID, which is unique for each request and is the same in both the access logs and execution logs for the corresponding request. This ID can be used to trace a request through all the logs.

Decoding Log Data

Decoding the data in AWS API Gateway logs involves understanding what each field in the log entries signifies. For example, in access logs, the requestId field is the unique ID of the request, the ip field is the IP address of the requester, and the status field is the HTTP status code of the response.

In execution logs, the information is more about the internal workings of the API Gateway. The lines usually start with the request ID, followed by a description of a particular stage in handling the request.

Once you understand the log format, you can use Python’s rich set of libraries to analyze this data. For instance, you could load the log data into a pandas DataFrame and perform various analyses, such as calculating the average response size or identifying the most frequently accessed endpoints.

As for Terraform, while it doesn’t directly interact with log data, it plays a crucial role in setting up the logging configuration, ensuring that all necessary data is captured in the logs.

In the following sections, we will delve deeper into integrating AWS API Gateway logs with various services and tools and how to implement logging with Terraform and extract actionable insights with Python.

AWS API Gateway Logs Integration

AWS API Gateway logging doesn’t exist in isolation. It is designed to integrate smoothly with various other services, enhancing the power and flexibility of your logging and analysis capabilities.

Integration with CloudWatch

AWS CloudWatch is a monitoring and observability service. By default, API Gateway integrates with CloudWatch, allowing you to send your logs for storage, monitoring, and analysis.

You can create a new CloudWatch Log group and Log stream where your API Gateway logs will be delivered. This can be accomplished using the AWS Management Console, AWS CLI, or an Infrastructure as Code (IaC) tool such as Terraform. Here is an example Terraform code snippet to create a CloudWatch log group for API Gateway:

provider "aws" {
  region = "us-west-2"
}
resource "aws_api_gateway_rest_api" "example" {
  name        = "example_api"
  description = "Example REST API"
}
resource "aws_api_gateway_deployment" "example" {
  rest_api_id = aws_api_gateway_rest_api.example.id
  stage_name  = "prod"
  depends_on  = [aws_api_gateway_integration.example]
}
resource "aws_api_gateway_resource" "example" {
  rest_api_id = aws_api_gateway_rest_api.example.id
  parent_id   = aws_api_gateway_rest_api.example.root_resource_id
  path_part   = "example"
}
resource "aws_api_gateway_method" "example" {
  rest_api_id   = aws_api_gateway_rest_api.example.id
  resource_id   = aws_api_gateway_resource.example.id
  http_method   = "GET"
  authorization = "NONE"
}
resource "aws_api_gateway_integration" "example" {
  rest_api_id = aws_api_gateway_rest_api.example.id
  resource_id = aws_api_gateway_resource.example.id
  http_method = aws_api_gateway_method.example.http_method
  type        = "MOCK"
}
resource "aws_cloudwatch_log_group" "example" {
  name = "/aws/apigateway/${aws_api_gateway_rest_api.example.name}"
}
resource "aws_api_gateway_account" "example" {
  cloudwatch_role_arn = aws_iam_role.example.arn
}
resource "aws_iam_role" "example" {
  name = "api-gateway-cloudwatch-global"
  assume_role_policy = <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Action": "sts:AssumeRole",
      "Principal": {
        "Service": "apigateway.amazonaws.com"
      },
      "Effect": "Allow",
      "Sid": ""
    }
  ]
}
EOF
}
resource "aws_iam_role_policy" "example" {
  name = "api-gateway-cloudwatch-global"
  role = aws_iam_role.example.id
  policy = <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "logs:CreateLogGroup",
        "logs:CreateLogStream",
        "logs:DescribeLogGroups",
        "logs:DescribeLogStreams",
        "logs:PutLogEvents",
        "logs:GetLogEvents",
        "logs:FilterLogEvents"
      ],
      "Resource": "*"
    }
  ]
}
EOF
}
resource "aws_api_gateway_stage" "example" {
  deployment_id = aws_api_gateway_deployment.example.id
  rest_api_id   = aws_api_gateway_rest_api.example.id
  stage_name    = "prod"
  access_log_settings {
    destination_arn = aws_cloudwatch_log_group.example.arn
    format          = "$context.identity.sourceIp - - [$context.requestTime] \"$context.httpMethod $context.routeKey $context.protocol\" $context.status $context.responseLength $context.requestId"
  }
  depends_on = [aws_api_gateway_account.example]
}

With CloudWatch, you can create dashboards, set alarms, and even automate responses to particular events by using CloudWatch Events. For more information, check the following articles:

detailed CloudWatch metrics

As mentioned earlier, Terraform is a powerful tool for managing your infrastructure, including your API Gateway logging setup. With Terraform, you can write a configuration file that sets up your API Gateway, enables logging, and connects it with the necessary services, such as CloudWatch or S3.

This Terraform code snippet shows how to enable detailed CloudWatch metrics for an API Gateway stage:

resource "aws_api_gateway_stage" "example" {
  deployment_id = aws_api_gateway_deployment.example.id
  rest_api_id   = aws_api_gateway_rest_api.example.id
  stage_name    = "example"
  settings {
    metrics_enabled = true
    logging_level   = "INFO"
    data_trace_enabled = true
  }
}

AWS API Gateway logs can also be integrated with various other services for more specialized use cases:

  • S3: You might want to archive your logs in S3 for long-term storage. This can be accomplished by creating a subscription filter on your CloudWatch log group that forwards log events to an S3 bucket.
  • Splunk: If you’re using Splunk for log analysis and visualization, you can send your API Gateway logs to Splunk via Kinesis Data Firehose.
  • Elasticsearch and OpenSearch: These powerful search and analytics engines can provide more sophisticated analysis capabilities for your logs. API Gateway logs can be sent to an Elasticsearch or OpenSearch cluster via Kinesis Data Firehose.

In the next sections, we’ll delve into implementing API Gateway logging using Terraform and extracting and analyzing logs with Python.

Implementing AWS API Gateway Logging using Terraform

Terraform is a powerful tool for managing infrastructure as code. With Terraform, you can create a reproducible plan for your AWS resources, including AWS API Gateway and its logging capabilities.

Prerequisites

Before we dive into the step-by-step guide, ensure you have the following:

  • AWS Account: You’ll need an AWS account with the necessary permissions to create and manage API Gateway, CloudWatch Logs, and IAM roles.
  • Terraform Installed: Make sure you have Terraform installed on your machine. You can download it from the official HashiCorp downloads page.
  • AWS CLI: The AWS Command Line Interface is a unified tool to manage your AWS services. While not strictly necessary for running Terraform, it is useful for setting up your AWS credentials.
  • An API to work with: This guide assumes that you have an existing API on AWS API Gateway that you wish to enable logging for.

Step-by-Step Guide

  1. Set Up AWS Credentials: First, ensure Terraform can access your AWS account. One way to do this is by setting your AWS access key and secret key as environment variables:
export AWS_ACCESS_KEY_ID="your_access_key"
export AWS_SECRET_ACCESS_KEY="your_secret_key"
  1. Write the Terraform Configuration: Create a file with a .tf extension, and start by configuring the AWS provider:
provider "aws" {
  region = "us-west-2"
}

Next, add the necessary resources for API Gateway logging. Here’s a basic example:

resource "aws_api_gateway_rest_api" "example" {
  name = "example"
}
resource "aws_api_gateway_deployment" "example" {
  rest_api_id = aws_api_gateway_rest_api.example.id
  stage_name  = "example"
}
resource "aws_cloudwatch_log_group" "example" {
  name = "/aws/apigateway/example"
}
resource "aws_api_gateway_stage" "example" {
  rest_api_id   = aws_api_gateway_rest_api.example.id
  deployment_id = aws_api_gateway_deployment.example.id
  stage_name    = "example"
  access_log_settings {
    destination_arn = aws_cloudwatch_log_group.example.arn
    format          = "$context.identity.sourceIp $context.identity.caller $context.identity.user [$context.requestTime] \"$context.httpMethod $context.resourcePath $context.protocol\" $context.status $context.responseLength $context.requestId"
  }
}

This configuration creates an API Gateway, a deployment, a CloudWatch log group, and a stage with access logging enabled.

  1. Run Terraform: Apply your configuration by running terraform init to initialize your working directory and then terraform apply to create your resources.

Troubleshooting Tips

If you encounter issues while setting up AWS API Gateway logging with Terraform, here are a few tips:

  • Check the Terraform output: Terraform will print error messages if it fails to apply your configuration. These messages often provide clues about what went wrong.
  • Check your AWS permissions: Ensure that the AWS credentials you’re using have sufficient permissions to create and configure the necessary resources.
  • Check your Terraform version: Make sure you’re using a version of Terraform that’s compatible with your AWS provider version.

Remember, while Terraform is a great tool for setting up your AWS API Gateway logging, analyzing the logs often requires other tools or scripts, like Python with Boto3, which we will discuss in the following section.

Extracting and Analyzing AWS API Gateway Logs with Python

With its extensive set of libraries, Python is a popular choice for log extraction and analysis. AWS provides the Boto3 SDK, which allows Python to interact directly with AWS services, including CloudWatch, for log extraction. Once you’ve extracted the logs, you can analyze them using a variety of Python libraries.

Prerequisites

Before starting, make sure you have:

  • Python Installed: Ensure you have Python 3.8 or later installed on your machine.
  • AWS Credentials Configured: The Boto3 library needs access to your AWS account. You can configure this by setting your AWS access and secret keys as environment variables or using the AWS CLI aws configure command. Alternatively, you can use aws-vault to manage your AWS credentials more securely.
  • Boto3 and Other Necessary Libraries Installed: You can install Boto3 and other necessary libraries like Pandas for data analysis and Matplotlib for plotting by using pip:
pip install boto3 pandas matplotlib

Python Script for Log Extraction

Here is a simple Python script to extract logs from CloudWatch:

import boto3
def get_logs(log_group, start_time, end_time):
    client = boto3.client('logs')
    response = client.filter_log_events(
        logGroupName=log_group,
        startTime=start_time,
        endTime=end_time,
    )
    return response['events']
log_group = '/aws/apigateway/example'
start_time = 1617235200000  # Timestamp in milliseconds
end_time = 1617321600000    # Timestamp in milliseconds
logs = get_logs(log_group, start_time, end_time)

This script fetches logs from a specific log group between the provided start and end times.

Python Script for Log Analysis

Once you’ve extracted the logs, you can load them into a Pandas DataFrame for analysis. Here’s a simple example of how to calculate the average response time from your logs:

import pandas as pd
def analyze_logs(logs):
    df = pd.DataFrame(logs)
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
    df.set_index('timestamp', inplace=True)
    df['responseTime'] = df['message'].str.extract('(\d+) ms')
    df['responseTime'] = pd.to_numeric(df['responseTime'])
    avg_response_time = df['responseTime'].mean()
    return avg_response_time
average_time = analyze_logs(logs)
print(f'Average Response Time: {average_time} ms')

This script creates a Pandas DataFrame from the logs, extracts the response time from each log message, and then calculates the average response time.

Remember, these scripts are just examples, and your logs might need a different approach depending on their specific format and the type of analysis you want to perform. But with Python and its rich set of libraries, you have a lot of flexibility to analyze your logs in a way that suits your needs.

Best Practices for AWS API Gateway Logging

Proper logging is a crucial part of managing and maintaining healthy applications. Regarding AWS API Gateway Logging, following a few best practices can make your life much easier.

Enable Detailed Logging

Detailed logging should be enabled for your APIs. This includes both access logging and execution logging. Access logs are essential for auditing and analyzing the traffic to your API, while execution logs provide valuable insight into the execution flow and performance of your API.

Here’s a Terraform snippet to enable detailed logging for an API Gateway stage:

resource "aws_api_gateway_stage" "example" {
  deployment_id = aws_api_gateway_deployment.example.id
  rest_api_id   = aws_api_gateway_rest_api.example.id
  stage_name    = "example"
  settings {
    metrics_enabled = true
    logging_level   = "INFO"
    data_trace_enabled = true
  }
}

Regularly Analyze Logs

Regularly analyzing your logs can help you detect problems early before they become serious. Python can be a powerful tool for this, as it can automate the process of extracting logs, analyzing them, and even sending alerts when something unusual is detected.

Archive Old Logs

Old logs can be archived to S3 for cost-effective storage. AWS provides lifecycle policies that automatically move old CloudWatch log data to S3 and Glacier for long-term storage. This can be configured in Terraform with the aws_s3_bucket_lifecycle_configuration resource.

Use a Centralized Logging Solution

You should consider using a centralized logging solution if you’re managing multiple APIs across different stages and regions. This could be CloudWatch Logs, or it could be a third-party solution like Splunk or Elasticsearch. Centralized logging makes searching and analyzing your log data easier in one place.

Monitor and Alert

Create CloudWatch Alarms for specific log events or trends that could indicate a problem with your API. For instance, you could create an alarm for a sudden increase in 5XX error status codes. This can also be set up using Terraform via the aws_cloudwatch_metric_alarm resource.

Following these best practices will help ensure that your AWS API Gateway logging setup is robust and reliable and provides the information you need to monitor and manage your APIs effectively.

Conclusion

Efficient logging in AWS API Gateway is not just about recording data; it’s about optimizing the data capture, storage, analysis, and visualization process to better understand your APIs’ behavior and performance. When appropriately implemented and analyzed, logs from AWS API Gateway can be a treasure trove of insights and opportunities for performance optimization.

In this article, we’ve covered how to enable logging on AWS API Gateway, explored the format of logs generated, and discussed the importance of these logs. We also delved into integrating API Gateway logs with other AWS services, demonstrated how to set up AWS API Gateway logging using Terraform, and illustrated how to extract and analyze these logs using Python. Finally, we listed some best practices for AWS API Gateway logging.

Remember, Terraform is a potent tool for infrastructure management, including setting up your logging system on AWS. With its powerful data manipulation libraries, Python is ideal for automating the extraction and analysis of these logs.

The journey to a well-logged API doesn’t stop here, though. As your applications grow and change, so too will your logging needs. Always be ready to iterate and improve upon your existing systems, and keep up to date with AWS updates and best practices.

By implementing and investing in robust logging practices, you’re not just adding a feature to your APIs but building a foundation for greater reliability, observability, and performance.

Additional Resources

To further enhance your understanding and skills with AWS API Gateway logging, Terraform, and Python, here are some additional resources:

  • AWS Documentation: The AWS Documentation is a treasure trove of information about AWS services, including API Gateway and CloudWatch Logs.
  • Terraform Documentation: The Terraform Documentation provides in-depth details about using Terraform with AWS and many other providers.
  • Python AWS SDK (Boto3) Documentation: The Boto3 Documentation provides a detailed guide to using Python for AWS services.
  • HashiCorp Learn: The HashiCorp Learn platform offers a collection of tutorials and guides on using Terraform with a variety of cloud providers, including AWS.
  • AWS API Gateway Logging: This AWS Blog post specifically discusses troubleshooting Amazon API Gateway with CloudWatch Logs.
  • Python for Data Analysis: This book by Wes McKinney provides a great introduction to data analysis with Python.
  • Coursera AWS Fundamentals: This specialization on Coursera provides a comprehensive understanding of AWS fundamentals, including networking, storage, databases, and more.
  • Medium Blog: Numerous Medium blogs are dedicated to Python, Terraform, and AWS, which share real-world experiences, gotchas, and best practices.

The key to mastering any tool or technology is consistent learning and practice. Don’t be afraid to experiment, make mistakes, and learn from them.

FAQ

How do I get logs from AWS API Gateway?

You must first enable logging at the stage level to get logs from AWS API Gateway. Log into AWS Console, navigate to API Gateway, select your API, then choose the stage. In the ‘Logs/Tracing’ tab, you can turn on ‘CloudWatch Logs.’ Choose the desired log level, decide whether to log the full API call request/response body and then specify a CloudWatch log group where the logs will be delivered. Alternatively, if you prefer infrastructure as code, you can use AWS CloudFormation or Terraform to enable and configure AWS API Gateway logging. You can then view and analyze your logs in the CloudWatch console.

How do I enable access logging for API Gateway?

To enable access logging for API Gateway, you must navigate to your API in the API Gateway console. Select the stage you want to enable access logging and then go to the ‘Logs/Tracing’ tab. There, you will find the ‘Access Logging’ setting. Turn it on and provide the ARN (Amazon Resource Name) of a CloudWatch Logs Log Group where the access logs will be stored. You can customize the access log format to suit your needs. If you prefer to use infrastructure as code, AWS CloudFormation or Terraform can be used to automate this process by defining the necessary resources and properties in a script.

Where does API Gateway log to?

AWS API Gateway can be configured to log to Amazon CloudWatch Logs, a service that collects, processes, and stores log data. By enabling access and execution logging at the API Gateway stage level, you can direct logs to a specified CloudWatch Logs Log Group. Additionally, you can also integrate API Gateway with other AWS services such as S3, Kinesis, or even third-party services like Splunk, Elasticsearch, and OpenSearch for customized log analysis and storage needs, providing you with a wide range of options for log data management.

How do I debug AWS API Gateway?

Debugging AWS API Gateway primarily involves using the built-in logging and tracing features. To start debugging, you must first enable access and execution logging in the API Gateway settings. These logs will be sent to Amazon CloudWatch Logs, where you can view, search, and analyze them. Additionally, you can enable AWS X-Ray for your APIs which provides insights into the behavior of your APIs and helps in tracing the requests from start to end. By analyzing the logs and traces, you can identify patterns, detect anomalies, and troubleshoot the issues affecting your API performance or causing failures. AWS CloudFormation or Terraform can also automate and manage the logging setup process.

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