Application Dependency Topology Automatic Discovery
APM relies on the model of distributed call link tracing to automatically discover application logic topologies, and take the application as the basic unit to draw global topology relationships. It visually displays the dependencies between complex applications, enables real-time data drill-down, and quickly identifies key factors affecting business (bottleneck applications or components) through intelligent application state analysis. Also, it clearly shows upstream and downstream dependencies of an application dimension, including upstream load and downstream impact, to comprehensively analyze the health status and performance metrics of the application.
Multidimensional Trace Query
APM supports filtering call links by application name, API name, response time, sampling time, error status, and excessive duration. It can further help users pinpoint links with specific exception information and database query links, focus on critical links among vast amounts of link data, quickly locate abnormal links, and troubleshoot.
One-Stop Call Trace Analysis
APM's tracing feature can automatically build the complete path of each request across services in a microservice architecture. It collects diverse information from request parameters, transaction data, errors, and exceptions to method stacks and underlying instance environment information, enabling full-trace analysis from one platform and improving the troubleshooting efficiency. This solves troubleshooting challenges like difficult aggregation of scattered logs in non-standard formats as well as difficulties in associating upstream/downstream service logs.
API Analysis
On the basis of covering the three golden metrics of application monitoring, APM adds Apdex score to scientifically evaluate user satisfaction. Inherit rich experience in visual reports from Tencent Cloud Observability Platform, support users in flexibly switching comparison baseline, and accurately determine application dynamics and trends over time. Meanwhile, intelligently monitor TOP5 duration and TOP5 error rate APIs, proactively surface issues, accelerate user focus acceleration, and achieve precise application performance monitoring.
Database Call Monitoring
For calls to commonly used relational databases (such as MySQL, PostgreSQL) and NoSQL databases (such as Redis), APM provides slow SQL analysis capability and automatically collects relevant database performance metrics for your business system, enabling real-time insights into slow SQL, call conditions, read performance, etc., and accurately locating database performance problems.
Multidimensional Drill-Down Analysis
APM provides continuous observation of the call conditions of each service, API, and service instance, and can also detect call data to middleware within the system.
APM provides drill-down analysis capabilities based on key monitoring indicators (throughput, response time, error rate), helping you efficiently learn about the running state of each dimension of the system.
Application Performance Diagnosis
APM provides advanced performance diagnostic capabilities such as memory analysis, application performance profiling, and resource pool analysis. Based on extremely low performance overhead, it can online diagnose difficult-to-solve problems in the field of application performance, such as high CPU consumption, memory overflow, and connection pool issues.
Customize Dashboards
Through the Tencent Cloud APM Grafana Plugin, you can integrate with the APM-Grafana data source, obtain commonly used APM metrics and customize extensions, and achieve flexible visualization through Grafana dashboards.
Alarm Management
Based on the alert management function provided by TCOP, you can set alarm triggering rules for key performance indicators of the application and notify through various channels (phone, SMS, email, WeChat, WeCom, DingTalk, Lark, Slack, etc.). When a monitoring metric is abnormal, users can receive exception alarm notifications immediately, respond to and handle faults in a timely manner, and avoid business losses caused by untimely exception detection.