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Microservices and other distributed software architectures need distributed tracing to be monitored, debugged, and effectively optimised. Identify performance bottlenecks and events over distributed architectures with your observability data in the full context by using Logit.io. Easily manage trace ingestion, retention, and costs with full visibility of your billing.

Identify problems and correlate events with the full context of data by monitoring service dependencies and health metrics throughout your systems.

Experience real-time monitoring, analysis, and reporting of high-granularity data and custom metrics, displaying throughput, availability, reliability, and error rates by using our all-in-one observability platform.


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distributed tracing platform

What Is Distributed Tracing?

In distributed tracing, a set of traces is collected for the purpose of identifying operations by tying them together. This set of traces is called a distributed transaction. As one of the main pillars of observability, tracing is vital for monitoring transaction level tracking.

Because of the limitations of manual debugging for analysing traces, it is beneficial to use a robust analytics platform for analysing these massive amounts of data. Logit.io aims to help you discover patterns versus outliers, establish correlations, and derive insights that would otherwise be buried within individual traces.

Common analytics use-cases include service dependency graphs, critical path analysis, error analysis, latency analysis and anomaly detection. Using distributed tracing, application requests can be tracked from frontend devices to backend services and databases. In order to troubleshoot high latency or error requests, distributed tracing can be used by engineers. Additionally, distributed tracing can determine the root cause of code issues by breaking down time spent on CPU, garbage collection, lock contention, and improving CPU utilization.

While logs and metrics remain significant, ultimately it's going to be tracing that will solve the transaction processing issues that your system is encountering.

Companies Feel The Difference When They Use Logit.io

Youredi testimonial

"Internally, Logit.io has made it easier for us to provide better support for our customers, since finding individual messages based on various data in the payload has become easier.

At Youredi, pretty much everyone from our technical support teams through to our professional services teams uses Logit.io."

Mats von Weissenberg, CTO @ Youredi

Distributed Tracing With Logit.io

Using Trace Analytics with Logit.io, you can automatically generate a service map showing how your systems are connected. The purpose of trace groups is to enable users to group similar traces, monitor performance, and identify issues sooner.

Using trace groups, you can group traces that are similar to look at, for example, what is happening with my login system. To see how they are performing, you can group these into one group.

With Logit.io's deployment tracking features, releasing code is safer and more efficient. Improve code-level visibility across web, mobile, and cloud-native applications, including microservices, serverless, and other modern cloud-native technologies.

Anomalies and outliers can also be flagged in our platform using daily, weekly, and as they happen alerts.

Logit.io distributed tracing
pinpoint and troubleshoot

Pinpoint issues with ease

A trace tells a complete story about a request or transaction, whereas metrics provide a high-level view of whether the system works as expected without explaining why. In contrast, logs provide fine-grained details about events such as timestamps and ordering of events.

Using traces over logs is often said to be more sophisticated because you are often able to work with more added context against your events.

Trace data can be filtered and visualized based on application, subsystem, service, and action. In addition, detect the most resource-consuming methods or classes in your applications in seconds with a low-overhead continuous profiler that's easy to use.

Analyse tracing data grouped by service to pinpoint exactly where a problem originated by filtering for traces exceeding a given latency.

Data flow and dependency visualisations

The dynamic tags object and related logs can be seen by clicking on a trace to drill down and investigate a specific span. By switching to the dependency view, you can also see how the data flows across the different services.

As long as there are changes being made to the application, changes being deployed at a fast pace, and changes in user behaviour, there will always be production issues. Logit.io can help you resolve these issues. Regardless of your role, even if you don't know much about the application, Logit.io can provide insight into what's causing performance issues.

logit.io for OTellogit.io for OTel

Logit.io For OpenTelemetry

OpenTelemetry (OTel) combined with Logit.io makes end-to-end observability easy. Using OpenTelemetry, engineers can essentially standardize any data coming from any source.

A secure, compliant, and production-ready distribution of the OpenTelemetry project, Logit.io for OpenTelemetry provides unified analysis and complete centralization of any kind of telemetry data.

Since all major observability vendors are required to support the OpenTelemetry protocol, choosing an analysis service that is already OpenTelemetry-compliant is crucial to future-proofing your operations.

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