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Dashboards

Dashboards in Anypoint Monitoring provide visibility into Mule apps that are deployed to your environments (for example, Production, Sandbox, or Design).

Built-In Dashboards

Built-in dashboards contain a set of time-series graphs that plot current and historical data collected over a given time and date period. There is a built-in dashboard for each Mule app in each environment.

Built-in Dashboard Example
Figure 1. Example: Built-in Dashboard

Built-in dashboards display deployment information about your app at the top of the screen and provide access to a number of different graphs, for example:

Deployment Info on an App in a Built-In Dashboard

The graphs and tables in built-in dashboards support a number of metrics, including:

  • Application metrics, including overall measures through the Overview tab and more granular application metrics through the Inbound, Outbound, and Failure tabs.

    Important use cases are end-to-end tracing of API calls and dependency analyses to help you isolate the source of issues. For example, you can track response times at each endpoint (outbound or inbound).

    Average Response Time Grouped By Endpoint Outbound
    Figure 2. Example: Average Response Time Grouped by Endpoint Outbound
  • Infrastructure-level and JVM metrics.

  • Performance metrics that break down inbound and outbound response times into averages and percentiles.

Built-In Dashboards Charts

Overview

  • Total Inbound Requests

  • Average Response Time Inbound

  • Total Outbound Requests

  • Average Response Time Outbound

  • CPU Utilization

  • Memory Utilization

  • Thread Count - Server

Inbound

  • Total Inbound Requests

  • Average Response Time Inbound

  • Average Response Time by Endpoint

  • Response Time graphs (99, 90, 75, and 50 Percentile Inbound): Measured in milliseconds (ms).

  • Total Inbound Calls - Failed

  • Slow Requests: Total calls where Response Time > 1000ms.

Outbound

  • Total Outbound Requests

  • Average Response Time Outbound

  • Average Response Time Grouped by Endpoint Outbound

  • Response Time graphs (99, 90, 75, and 50 Percentile Outbound): Measured in milliseconds (ms).

  • Total Outbound Calls - Failed

Performance

  • Average Response Time Inbound

  • Average Response Time Grouped by Endpoint Inbound

  • Response Time graphs: 99, 90, 75, and 50 Percentile Inbound

  • Average Response Time Outbound

  • Average Response Time Grouped by Endpoint Outbound

  • Response Time graphs: 99, 90, 75, and 50 Percentile Outbound

Failures

  • Total Failed Inbound Requests

  • Total Failed Outbound Requests

  • All Inbound Grouped By Response Type

  • All Outbound Grouped By Response Type

  • Total Failed Inbound Grouped By Endpoint

  • Total Failed Outbound Grouped By Endpoint

JVM

  • Garbage Collection Count, Garbage Collection Time

  • Par New Collection Count, Par New Collection Time

  • Classes: Loaded, Loaded Total, Unloaded

  • Heap: Committed, Used

  • Thread Count - Server

  • JVM Uptime

  • Par Eden graphs (Used, Max, Init, and Committed): Usage metrics for the Par Eden Space pool.

  • Par Survivor graphs (Used, Max, Init, and Committed) Usage metrics for the Par Eden Space pool.

  • Metaspace graphs (Used, Max, Init, Committed):

  • Code Cache graphs (Used, Max, Init, Committed): Usage metrics for the Code Cache pool.

  • Compressed Class Space graphs (Used, Max, Init, Committed):

Infrastructure

  • CPU Utilization %: Percentage of CPU used by each worker over time.

  • Memory Utilization: Amount of memory used by each worker over time.

  • Total System Processors: Number of system processors for workers available over time.

  • Total System Memory: Amount of system memory available for workers over time.

  • Thread Count - Server: Number of simultaneous requests for a worker over time.

Example: Infrastructure Dashboard

Example: Infrastructure Dashboard

Custom Dashboards

Custom dashboards in Anypoint Monitoring can bring together important metrics and data points that you need to see on one screen. You can specify the resources and metrics that you want to monitor, allowing you to:

  • Correlate diverse metrics

  • Perform comparative analysis

  • Differentiate between regular trends and anomalies

  • Isolate issues quickly

For example, you can compare live data with historic data to detect anomalies and expedite the troubleshooting process.

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