How EMonitor Outperforms CAT: Deep Dive into Modern Monitoring Architecture

What CAT (Open‑Source Version) Does

CAT abstracts four monitoring models: Transaction, Event, Heartbeat, and Metric.

Transaction records execution time and count of a code segment.

Event records the occurrence count of an event.

Heartbeat represents periodic statistics such as CPU utilization.

Metric records business indicators, supporting count and sum.

Both Transaction and Event have fixed dimensions

type

and

name

, which are aggregated per minute for reporting.

CAT also provides a simple metric dashboard and integrates with components such as MyBatis to collect SQL execution statistics.

Alerting in CAT is based on simple threshold rules for the above models.

Comparison Between Meituan’s EMonitor and CAT

EMonitor adopts the Transaction and Event concepts from CAT but improves data aggregation and processing.

Key differences:

EMonitor aggregates data every 10 seconds, while CAT aggregates per minute.

EMonitor stores metrics in the open‑source LinDB time‑series database and Kafka for real‑time alerts; CAT writes aggregated data to MySQL and files/HDFS.

EMonitor supports flexible, multi‑dimensional aggregation of

type

and

name

, allowing cross‑hour queries and historical curve comparison; CAT is limited to hourly views.

EMonitor provides richer dashboards comparable to Grafana, with SQL‑like configuration, tag filtering, and environment‑aware synchronization.

EMonitor’s real‑time streaming compute converts Transaction, Event, and Metric data into 10‑second pre‑aggregates, while its data writer indexes link data into HDFS, HBase, and Neo4j.

EMonitor also offers a broader set of metric types (Counter, Timer, Histogram, Payload, Gauge) with tag support, all stored in LinDB.

Both systems integrate with other components, but EMonitor’s integration spans IaaS, PaaS, and application layers, providing a unified monitoring view.

Evolution Stages of Monitoring Systems

1. Log Monitoring : Use logs stored in ELK for basic metric curves; troubleshooting relies on searching logs.

2. Trace Monitoring : Introduced by CAT, modeling Transaction and Metric to provide link analysis and simple reports.

3. Metric Monitoring : Enriches metric types, leverages time‑series databases, and integrates with dashboards like Grafana.

4. Platform Integration : Unifies system, container, middleware, and business monitoring into a single platform, eliminating fragmented tools.

5. Deep Analysis : Moves from data collection to proactive analysis, building application and business dashboards, root‑cause analysis, trend detection, and automated alerting.

Logging, Tracing, and Metrics

All three are essential, but Metrics dominate monitoring, followed by Tracing, with Logging last. Combining Metrics with Tracing’s dependency information enables deeper global analysis.

References:

CAT – https://github.com/dianping/cat

In‑depth analysis of open‑source distributed monitoring CAT – https://tech.meituan.com/2018/11/01/cat-in-depth-java-application-monitoring.html