After you install an Application Real-Time Monitoring Service (ARMS) agent for a Python application, ARMS starts to monitor the application. You can view information about application instances, such as basic metrics, garbage collection (GC), and heap memory on the Instance Monitoring tab of the application details page.
Prerequisite
An ARMS agent is installed for the application. For more information, see Application Monitoring overview.
Go to the Instance Monitoring tab
Log on to the ARMS console. In the left-side navigation pane, choose .
On the Application List page, select a region in the top navigation bar and click the name of the application that you want to manage.
NoteIcons displayed in the Language column indicate languages in which applications are written.
: Java application
: Go application
: Python applicationHyphen (-): application monitored in Managed Service for OpenTelemetry.
In the top navigation bar, click the Instance Monitoring tab.
Instance Monitoring tab
Instance monitoring is available only for Python applications deployed in a Container Service for Kubernetes (ACK) cluster. You need to integrate the Python applications with Managed Service for Prometheus. For more information, see Monitor an ACK cluster.
In the Quick Filter section (icon 1), you can query charts and instances by cluster or instance ID.
In the trend charts section (icon 2), you can view the trend charts of CPU/memory usage and GCs for the application in a specified time period.
Instance base monitoring: shows the trend chart of CPU and memory usage in the specified time period.
Instance GC: shows the trend chart of GCs in the specified time period.
Click the
icon. In the dialog box that appears, you can view the metric data in a specific time period or compare the metric data in the same time period on different dates. You can switch between the 
icons to display the data in a column chart or a trend chart. In the instance list section (icon 3), you can view instance information, such as the following:
IP address
Used CPU, memory, and disk
Number of CPU and memory requests
CPU, memory, and disk limits and usage rates (if the limits are not set, the CPU utilization column displays -)
Number of GCs
Key metrics of each instance defined by the RED Method, including rate, errors, and duration
In the instance list, you can perform the following operations:
Click an instance IP address or click Details in the Actions column to view the instance details. For more information, see the Instance Details section.
Click Traces in the Actions column to view the trace details of an instance. For more information, see Trace Explorer.
Instance details
Overview
On the Overview tab, you can view the number of requests, number of errors, and average duration.
Runtime Monitoring
On the Runtime Monitoring tab, you can view the following metrics of the instance:
Number of threads
Number of context switches
Number of GC objects, including allocated and released objects, which are respectively displayed as positive and negative numbers
Generation 2 GC count: The number of times the Python garbage collector collects garbage in generation 2.
Generation 2 GC time: The total time spent by the Python garbage collector collecting garbage in generation 2.
You can learn more about the Python GC mechanism and collection by generation in the official Python website.
Container monitoring
On the Container monitoring tab, you can view the time series of CPU, memory, and disk usage, load, traffic, and packets.
Trace Explorer
Trace Explorer allows you to combine filter conditions and aggregation dimensions for real-time analysis based on the stored full trace data. This can meet the custom diagnostics requirements in various scenarios. For more information, see Trace Explorer.
Reference
For information about Application Monitoring, see Application monitoring metrics.