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Sajeeb Lohani
Global Technical Information Security Officer (TISO), Bugcrowd
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Glossary

Distributed tracing


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Table of contents

    Distributed tracing is an observability practice used to track a single request as it travels across multiple services in a distributed system. In modern cloud environments powered by microservices architecture, containers, and APIs, a single user action may trigger dozens of service calls. Distributed tracing provides visibility into how those services interact, where latency occurs, and where performance bottlenecks emerge.

    By generating and analyzing trace data, distributed tracing allows teams to monitor application performance, troubleshoot errors, and improve overall system reliability.

    Let’s look at some of the benefits and drawbacks of distributed tracing and some relevant alternatives in observation and monitoring.

    Benefits of distributed tracing:

    • Helps teams understand application performance issues quicker
    • Teams can effectively identify the causes of issues and resolve them
    • Monitoring and observability allow teams to predict when microservices are susceptible to bottlenecks and other problems within your infrastructure
    • Improves the user experience and helps companies abide by defined SLA compliances
    • It helps teams collectively understand issues, which improves communication and collaboration
    • Increase competitive edge for companies by allowing them to get new products and services to market much more quickly

    Some common issues:

    • Requires you to generate trace data, which can be difficult early on
    • Different software might not be structured to accept the instrumentation code that is a prerequisite for emitting tracing data
    • Distributed tracing activities are often obsolete on arrival because of challenges surrounding an existing codebase.
    • It is often difficult to parse through hundreds or thousands of services and decide which data is stored for analysis and which data to let go, as well as for how long you should store data
    • Once data is retrieved, it is difficult to efficiently translate raw data into concrete insights and actionable strategies

    Logging vs. distributed tracing

    Logging is a method for tracking error reports and related data using logs generated by an app. Logging focuses on what happens within the application, and administrators can use logging to ensure that their applications run smoothly. They can do this by collecting and storing data logs, tracking events, and utilizing that data to audit various processes within your network or application.

    The impact of distributed tracing

    Distributed tracing offers real-time insights into your application and system’s health and is a reliable source for tracking a request through the many components throughout separate systems. Distributed tracing will allow your IT, SRE teams, and DevOps teams to:

    • Accurately report on the status and health of microservices and applications to prevent failures
    • Alert you before errors or failures occur from automated scaling
    • Provide analytical reporting on end-user interactions, allowing you to stay on top of things like response times, user experience, errors, and other metrics relevant to DCX
    • Identify and alienate bottlenecks, resolve code-related problems, and help in the debugging process

    Distributed tracingSearch for traces of transactions with Sumo Logic

    How Sumo Logic can help

    Sumo logic’s transaction tracing provides cloud-native continuous intelligence for distributed business workflows by enriching and analyzing traces, logs, and metrics in real time. Sumo Logic provides a seamless end-to-end experience managing and responding to production incidents and reducing downtime by streamlining root cause analysis.

    Click here to learn more about how Sumo Logic can help you monitor your systems and resolve issues efficiently today.

    FAQs

    The duration of root cause analysis can vary depending on the issue’s complexity. It can range from a few hours for simpler problems to several weeks for more intricate issues.

    • Fault Tree analysis visually represents the various factors contributing to an issue.
    • Effect analysis examines the consequences or effects of an event or problem to identify the root cause that led to the outcome.
    • Causal factor analysis focuses on investigating specific factors or events that directly contribute to the occurrence of a problem or event.
    • Scatter diagrams show the relationship between two variables, helping to identify patterns or correlations that may reveal the underlying cause of a problem.
    • Pareto analysis, also known as the 80/20 rule, is a technique used to prioritize potential causes by identifying the most significant factors responsible for most problems or issues.
    • Effect diagrams, also called Ishikawa or fishbone diagrams, categorize the potential cause of a problem into different branches or categories, making it easier to identify the root cause.

    Sumo Logic provides an end-to-end approach to monitoring and troubleshooting. Quickly detect anomalous events via pre-set alerts, then enable rapid root cause analysis through machine learning-aided technology and robust querying capabilities for your logs and metrics. Beyond getting to the root cause of issues in the moment, capabilities like our predict operator for querying logs or metrics can also help you plan for the future — preempting bottlenecks and informing infrastructure capacity planning.

    Compared to other infrastructure monitoring solutions, Sumo Logic supports log data with a professional-grade query language and standard security for all users, including encryption-at-rest and security attestations (PCI, HIPAA, FISMA, SOC2, GDPR, etc.) and FedRAMP — at no additional charge.