index

📄️ Systems

A business system refers to a collection of applications that together complete a specific type of business (such as e-commerce systems, OA systems, email systems, etc.). A business system can contain one to multiple applications, and each application can only belong to one business system.

📄️ Applications

The Applications module provides comprehensive analysis of applications from aspects such as metrics, Topology, events, logs, traces, and security.

📄️ Requests

The Requests module provides comprehensive analysis of all monitored requests from metrics, traces, relationships, problems, and other aspects. Requests are divided into web requests and background tasks.

📄️ Database

The Database List page allows users to view the list of all database components within the statistical period. Users can click the Instance Name to enter the details page to view detailed information about the database's relationships, performance, exceptions, etc.

📄️ Error Analysis

The Error Analysis module aggregates and analyzes errors and exceptions that occur in the current business system, application, or instance.

📄️ MQ

MQ List

📄️ Distributed Tracing

Distributed Tracing is used to record detailed information about all business systems, applications, and related service components that a request passes through and accesses during a single request process, including business data, performance data, code stacks, detailed operations of service components, as well as error and exception information, etc. When the response time of a request exceeds a predefined traces threshold, the system will record the process of that request in detail.

📄️ Configuration

Applications & microservices provide four levels of configuration options. The configuration levels (i.e., the order of precedence) from highest to lowest are

📄️ GenAI Observability

GenAI refers to the various large language models called by an application. GenAI observability provides an overview of the overall runtime performance of these AI model calls, including key metrics such as execution time, first token time, input token count, output token count, and also the GenAI span information. This helps users quickly understand the current state of GenAI and identify performance bottlenecks.