AWS X-Ray enables trace analysis to identify performance bottlenecks by providing detailed insights into each component's performance and identifying the root causes of slowdowns.

AWS X-Ray provides service maps and insights to visualize the architecture of an application and identify performance bottlenecks and areas for optimization.

AWS X-Ray implements distributed tracing to track and analyze requests as they travel through various services in a microservices architecture, providing insights into request flow and performance.

AWS X-Ray provides benefits such as tracing requests through distributed systems, identifying performance bottlenecks, and offering insights into application performance, which are essential for debugging and performance optimization.

AWS X-Ray integrates with AWS Lambda functions through automatic instrumentation, capturing traces without requiring manual code changes.

The primary components of AWS X-Ray include the Tracing SDK, which instruments your application, the X-Ray daemon, which collects and sends tracing data to X-Ray, and the X-Ray console, which provides a visual representation of your application's performance.

AWS X-Ray visualizes the flow of requests through distributed applications, showing how requests are processed and which components are involved, aiding in understanding application architecture and performance.

AWS X-Ray provides insights into latency and errors by tracing requests and capturing data such as response times and error rates, helping you identify performance bottlenecks.

By creating retention policies in CloudWatch Logs, you can specify the retention period for log data, ensuring that it is retained for the required duration of seven years to comply with regulatory requirements.

By setting up log metric filters and alarms in CloudWatch Logs to extract specific patterns from log events related to performance degradation and alerting when these metrics exceed thresholds, you can identify and mitigate issues caused by excessive logging.

Examples of custom metrics that can be collected in AWS include application latency, API response time, and custom error rates, allowing you to monitor and optimize various aspects of your applications or services.

Amazon CloudWatch is commonly used for storing and analyzing custom metrics in AWS, providing dashboards, alarms, and insights into system performance and behavior.