Distributed tracing is a technique used to monitor and observe requests as they flow through different services or components in a distributed system, such as microservices architectures. It helps to track the journey of a single request as it propagates through multiple interconnected services, offering insights into the performance, latency, and potential bottlenecks.
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Context Propagation: To maintain visibility into the flow of requests, distributed tracing uses context propagation to pass tracing information (like trace ID and span ID) between services. This allows subsequent services to continue the trace.
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Service Dependency Mapping or Topology Mapping visual overview of the relationships between different services and components in a distributed system. They help with understanding dependencies, identifying bottlenecks, and diagnosing performance issues across the architecture.
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Trace Snapshot or Trace Detail: captures detailed information about a specific transaction or request at a granular level, including all the interactions and spans within a distributed trace. It essentially provides a "snapshot" of all the steps involved in processing a request for diagnostic purposes.
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Error or Exception Tracking refers to the process of detecting, collecting, and analyzing errors that occur in an application or system. This includes capturing exceptions, failures, or unexpected behaviors in real-time, helping developers and operations teams understand the root causes of issues. Effective error tracking tools provide detailed insights such as stack traces, error rates, and service dependencies, enabling faster debugging and resolution of application problems.
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Alerts or Threshold-Based Monitoring is the capability of observability tools to monitor predefined metrics or conditions and automatically trigger notifications when thresholds are breached or anomalies are detected. This allows operations teams to respond quickly to critical issues (e.g., high error rates, performance degradation) before they impact users. Real-time alerts can be sent via various channels, such as email, SMS, or incident management systems, and are crucial for proactive system management and minimizing downtime.
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SLOs and SLIs (Service-Level Objectives and Indicators) are specific, measurable goals set for the performance and reliability of a service, typically defined in terms of availability, latency, or error rates. Service-Level Indicators (SLIs) are the metrics used to measure whether a service is meeting those objectives. Together, SLOs and SLIs help organizations ensure that they are delivering the expected level of service to users and can be used to monitor compliance with Service-Level Agreements (SLAs). These metrics provide clear targets for maintaining system reliability and user satisfaction.
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Anomaly Detection is the process of identifying unusual patterns or behaviors in system performance metrics or logs that deviate from the expected norm. By using machine learning or statistical models, observability tools can detect performance issues, failures, or security breaches in real-time, even if predefined thresholds aren’t breached. This helps organizations catch issues early, often before they escalate into larger problems affecting users.
Log Management involves the aggregation, storage, and analysis of log data generated by applications, services, and infrastructure. Logs capture detailed, timestamped records of events occurring within a system, such as error messages, database queries, and transaction failures. Effective log management allows operators to search, analyze, and correlate log data in real-time to diagnose issues, monitor security, and track application behavior. Logs are critical for root cause analysis and troubleshooting.
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Root Cause Analysis (RCA) is the process of identifying the fundamental cause of a problem or performance issue in a system. By correlating metrics, logs, traces, and events, observability tools help engineers pinpoint the exact component or service responsible for an issue, enabling faster resolution. RCA is essential for reducing the time to identify and fix critical issues, ensuring system reliability and preventing recurring problems.
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