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Apigee hybrid makes debug, analytics, and deployment status data available to you. This data iscollected by a data collection pod which sends it to the management plane so that you can view andanalyze it and set up monitoring and alerts.

About the data

All Message Processor (MP) services in hybrid stream debug (when initiated), analytics,and deployment status data viaTCP to a data collection pod in the cluster. The data collection pod stores the streamed data on thepod's file system via a fluentd service.

The UDCA (Universal Data Collection Agent) periodically extracts the stored data and sends it tothe UAP (Unified Analytics Platform) service in the management plane. The UAP processes the incominganalytics and deployment status data and makes it available to you via the hybrid UI or theApigee APIs.

Apigee hybrid implements the data collection pod as aReplicaSet with a minimum of tworeplicas.

The following image shows the debug, analytics, and deployment status data collection process:

Note that the debug, analytics, and deployment status data is not stored in thesame location or accessed in the same way as the logging and metrics data:

• Logging and metrics data are stored on your Google Cloud Project and accessed via a tool such as Cloud Operations or whatever you choose to use.
• Debug, analytics and deployment status data, on the other hand, is stored in the hybrid management plane and you access it via Apigee services such as the hybrid UI orApigee APIs.

The following table summarizes the data collected by the data collection pod:

Type of Data	Dataset Name	Description	Update Frequency	APIs
Analytics	api	API usage data including transactions per second, cache usage, errors, latencies, request/response sizes, and traffic counts. For more information, seeApigee Analytics overview.	Up to 30 second delay	Analytics admin API
Deployment Status	event	The current deployment status of the API proxy. For more information about how you can see this information, seeView deployment status.	Immediately	Deployments API
Debug	debug	Debug session data for API proxies. This data includes the request/response parameters along with transformations applied to them at policy execution time. Because of its size, debug data—unlike analytics and deployment status data—is not collected all the time. Instead, debug data is collected when you initiate a debug session. For more information, seeDebug overview.	Immediately	Debug session API Debug session data API

TIP: Thedataset name is the name by which this type of data is referred to in the UDCA configuration properties. For example, analytics data is configured with the dataset nameapi. As a result, the configuration property that sets the location of the analytics data files is calledapiDataSubDir.

You can access this data through the Apigee hybrid UI orApigee APIs on the management plane.

View the data in the hybrid UI

This section describes how to view the debug, analytics, and deployment status data in the Apigeehybrid UI.

Debug

Debug data for hybrid services is accessible in the same way as Edge debug data, with somedifferences such as increased filter support. For moreinformation, seeDebug overview.

Analytics

Analytics data for hybrid services is accessible in the same wayas Edge analytics data. For more information, seeUsing the analytics dashboards inthe Edge documentation.

Proxy deployment status

For information on viewing deployment status, seeView deployment status.

Configure the data collection

To set how and where debug, analytics, and deployment status data iscollected on the data collection pod, you configure the UDCAservice via its configuration properties. The UDCA propertiesinclude properties that are general to the UDCA as well asproperties that are specific to each dataset.

To configure the UDCA:

1. Open theoverrides.yaml file for editing on your Kubernetes administration machine, as described inManage runtime plane components.
2. Set the values of the UDCA configuration settings. For the UDCA, you can set custom values for properties such as:
   • Polling interval
   • Number of replicas (min and max)
   • Target CPU percentage (that triggers additional replicas)

   For a complete list of UDCA properties that you can customize, seeudca.

3. Save your changes to the overrides.yaml file.
4. Apply your changes to your cluster by executing theapigeectl apply command, as the following example shows:

   apigeectl apply -f my-overrides.yaml --org --envenv-name

   TIP: To see the default configuration properties, the UDCA service (underudca:) in theConfiguration property reference.

   For more information about theapply command, seeApply the cluster configuration.