A nice way to visualize and interact with actuator endpoints is to incorporate the Spring Boot Admin client library into the Spring Cloud Data Flow server. You can create the Spring Boot Admin application by following a few simple steps.

A simple way to have the Spring Cloud Data Flow server be a client to the Spring Boot Admin Server is by adding a dependency to the Data Flow server’s Maven pom.xml file and an additional configuration property as documented in Registering Client Applications. You will need to clone the github repository for the Spring Cloud Data Flow server in order to modify the Maven pom. There are tags in the repository for each release.

Adding this dependency will result in a UI with tabs for each of the actuator endpoints.

Figure 18.1. Spring Boot Admin UI

Additional configuration is required to interact with JMX beans and logging levels. Refer to the Spring Boot admin documentation for more information. As only the info and health endpoints are available to unauthenticated users, you should enable security on the Data Flow Server and also configure Spring Boot Admin server’s security so that it can securely access the actuator endpoints.

The applications that are deployed by Spring Cloud Data Flow are based on Spring Boot which contains several features for monitoring your application in production. Each deployed application contains several web endpoints for monitoring and interacting with Stream and Task applications.

In particular, the /metrics endpoint contains counters and gauges for HTTP requests, System Metrics (such as JVM stats), DataSource Metrics and Message Channel Metrics (such as message rates). Spring Boot lets you add your own metrics to the /metrics endpoint either by registering an implementation of the PublicMetrics interface or through it’s integration with Dropwizard.

The Spring Boot interfaces MetricWriter and Exporter are used to send the metrics data to a place where they can be displayed and analyzed. There are implementations in Spring Boot to export metrics to Redis, Open TSDB, Statsd, and JMX.

There are a few additional Spring projects that provide support for sending metrics data to external systems.

The Spring Cloud Stream Emitter is used by the Spring Cloud Stream App Starters project that provides the most commonly used applications when creating Data Flow Streams.

The architecture when using Spring Cloud Stream’s Emitter, the Data Flow Metrics Collector, and the Data Flow server is shown below.

Figure 18.2. Spring Cloud Data Flow Metrics Architecture

As with the App Starters, there is a Spring Boot uber jar artifact of the Metrics Collector for all of the supported binders. You can find more information on building and running the Metrics Collector on its project page.

The dataflow server now accepts an optional property spring.cloud.dataflow.metrics.collector.uri, this property should point to the URI of your deployed metrics collector app. For example, if you are running the metrics collector locally on port 8080 then start the server (local example) with the following command:

$ java -jar spring-cloud-dataflow-server-local-1.2.1.RELEASE.jar --spring.cloud.dataflow.metrics.collector.uri=http://localhost:8080

The Metrics Collector can be secured with 'basic' authentication that requires a username and password. To set the username and password, use the properties spring.cloud.dataflow.metrics.collector.username and spring.cloud.dataflow.metrics.collector.password.

The metrics for each application are published when the property spring.cloud.stream.bindings.applicationMetrics.destination is set. This can be set as any other application property when deploying an application in Data Flow. Since it is quite common to want all applications in a stream to emit metrics, setting it at the Data Flow server level is a good way to achieve that.

spring.cloud.dataflow.applicationProperties.stream.spring.cloud.stream.bindings.applicationMetrics.destination=metrics

Using the destination name metrics is a good choice as the Metrics Collector subscribes to that name by default.

The next most common way to configure the metrics destination is using deployment properties. Here is an example for the ticktock stream that uses the App Starters time and log applications.

app register --name time --type source --uri maven://org.springframework.cloud.stream.app:time-source-rabbit:1.2.0.RELEASE

app register --name log --type sink --uri maven://org.springframework.cloud.stream.app:log-sink-rabbit:1.2.0.RELEASE

stream create --name foostream --definition "time | log"

stream deploy --name foostream --properties "app.*.spring.cloud.stream.bindings.applicationMetrics.destination=metrics,deployer.*.count=2"

The Metrics Collector exposes aggregated metrics under the HTTP endpoint /collector/metrics in JSON format. The Data Flow server accesses this endpoint in two distinct ways. The first is by exposing a /metrics/streams HTTP endpoint that acts as a proxy to the Metrics Collector endpoint. This is accessed by the UI when overlaying message rates on the Flo diagrams for each stream. It is also accessed to enrich the Data Flow /runtime/apps endpoint that is exposed in the UI via the Runtime tab and in the shell through the runtime apps command with message rates.

Figure 18.3. Stream Message Rates

By default, Data Flow will set the property

spring.cloud.stream.metrics.properties=spring.application.name,spring.application.index,spring.cloud.application.*,spring.cloud.dataflow.*

Which is the set of application properties values needed to perform aggregation. It will also set the property

spring.metrics.export.triggers.application.includes=integration**`

since Data Flow will only display instantaneous input and output channel message rates. By default, all metric values in the /metric endpoint are sent so restricting it reduces the size of the message payload without impacting the functionality. Data Flow also exposes a guid property when displaying metric data which is used track back to the specific application instance that generated the metric. The guid value is platform dependent.

Note that you can override these defaults by setting then as you would any application property value.

Data Flow will not provide it’s own implementation to store and visualize historical metrics data. We will integrate with existing ALM system by providing an Exporter application that consumes messages from the same destination as the Metrics Collector and writes them to an existing ALM system. Which specific ALM system we will support is driven by user demand. However, to serve as an example, we will develop an Elastic Search exporter with a Grafana front end since it is open source.

If you prefer to have deployed applications bypass the centralized collection of metrics via the Metrics Collector, you can use the MetricWriters in Spring Cloud Data Flow Metrics and Spring Cloud Data Flow Metrics Datadog Metrics.

The Data Flow Metrics project provides the foundation for exporting Spring Boot metrics via MetricWriters. It provides Spring Boots AutoConfiguration to setup the writing process and common functionality such as defining a metric name prefix appropriate for your environement. For example, you may want to includes the region where the application is running in addition to the application’s name and stream/task to which it belongs. It also includes a LogMetricWriter so that metrics can be stored into the log file. While very simple in approach, log files are often ingested into application monitoring tools (such as Splunk) where they can be further processed to create dashboards of an application’s performance.

To make use of this functionality, you will need to add additional dependencies into your Stream and Task applications. To customize the "out of the box" Task and Stream applications you can use the Data Flow Initializr to generate a project and then add to the generated Maven pom file the MetricWriter implementation you want to use. The documentation on the Data Flow Metrics project pages provides the additional information you need to get started.