5:38 a.m.
New Hawkular blog post from noreply(a)hawkular.org (Gary Brown): http://ift.tt/2rX1NbW
This article will show how OpenTracing instrumentation can be used to collect Application
Metrics, in addition to (but independent from) reported tracing data, from services
deployed within Kubernetes. These Application Metrics can then be displayed in your
monitoring dashboard and used to trigger alerts.
The example application
In a recent article we showed how a Spring Boot application could easily be instrumented
using OpenTracing.
The example we are going to use in this article uses the same approach to create two
services, ordermgr and accountmgr.
accountmgr presents a single REST endpoint (/getAccount) for internal use by ordermgr. The
code for this endpoint is:
Account Managers’s Controller:
@RequestMapping("/account")
public String getAccount() throws InterruptedException {
Thread.sleep(1 + (long)(Math.random()*500)); (1)
if (Math.random() > 0.8) { (2)
throw new RuntimeException("Failed to find account");
}
return "Account details";
}
1
This line simply introduces a random delay, to make the collected metrics more
interesting.
2
These three lines randomly cause an exception which will result in the span (associated
with the REST endpoint invocation) being tagged as an error with associated log events
identifying the error details.
ordermgr presents three REST endpoints for use by an end user. These are:
Order Manager’s Controller:
@Autowired
private io.opentracing.Tracer tracer; (1)
@RequestMapping("/buy")
public String buy() throws InterruptedException {
Thread.sleep(1 + (long)(Math.random()*500)); (2)
tracer.activeSpan().setBaggageItem("transaction",
"buy"); (3)
ResponseEntity<String> response =
restTemplate.getForEntity(accountMgrUrl + "/account", String.class);
return "BUY + " + response.getBody();
}
@RequestMapping("/sell")
public String sell() throws InterruptedException {
Thread.sleep(1 + (long)(Math.random()*500)); (2)
tracer.activeSpan().setBaggageItem("transaction",
"sell"); (3)
ResponseEntity<String> response =
restTemplate.getForEntity(accountMgrUrl + "/account", String.class);
return "SELL + " + response.getBody();
}
@RequestMapping("/fail")
public String fail() throws InterruptedException {
Thread.sleep(1 + (long)(Math.random()*500)); (2)
ResponseEntity<String> response =
restTemplate.getForEntity(accountMgrUrl + "/missing", String.class);
(4)
return "FAIL + " + response.getBody();
}
1
The service injects the OpenTracing Tracer to enable access to the active span.
2
All three methods introduce a random delay.
3
The buy and sell methods additionally set a baggage item transaction with the name of the
business transaction being performed (i.e. buy or sell). For those not familiar with
OpenTracing, the baggage concept allows information to be carried in band with the trace
context between invoked services. We will show you how a baggage item can be used to
isolate the metrics relevant only for a particular business transaction.
4
Invoking a non-existent endpoint on accountmgr will lead to an error being reported in the
trace and metric data.
Adding Metrics Reporting to the OpenTracing instrumentation
The OpenTracing API defines the concept of a Span which represents a unit of work
performed by a service, e.g. to receive a service invocation, perform some internal task
(e.g. accessing a database) or invoking an external service. They provide an ideal basis
upon which to report metrics (count and duration) regarding these points within a
service.
Therefore a new OpenTracing contrib project has been established (initially just for Java)
to intercept the finished spans, and create the relevant metrics. These metrics are then
submitted to a MetricsReporter for recording - the initial implementation of this
interface being for Prometheus.
The first step is to expose an endpoint for collecting the Prometheus metrics. Each
service has the following configuration:
@Configuration
@ConditionalOnClass(CollectorRegistry.class)
public class PrometheusConfiguration {
@Bean
@ConditionalOnMissingBean
CollectorRegistry metricRegistry() {
return CollectorRegistry.defaultRegistry;
}
@Bean
ServletRegistrationBean registerPrometheusExporterServlet(CollectorRegistry
metricRegistry) {
return new ServletRegistrationBean(new MetricsServlet(metricRegistry),
"/metrics");
}
}
This will allow the Prometheus metrics to be obtained from the service’s /metrics REST
endpoint.
Each service then requires a configuration to obtain the io.opentracing.Tracer:
@Configuration
public class TracerConfiguration implements javax.servlet.ServletContextListener {
@Bean
public io.opentracing.Tracer tracer() {
return io.opentracing.contrib.metrics.Metrics.decorate(
io.opentracing.contrib.tracerresolver.TracerResolver.resolveTracer(),
PrometheusMetricsReporter.newMetricsReporter()
.withBaggageLabel("transaction","n/a")
.build());
}
@Override
public void contextInitialized(javax.servlet.ServletContextEvent sce) {
sce.getServletContext().setAttribute(io.opentracing.contrib.web.servlet.filter.TracingFilter.SKIP_PATTERN,
Pattern.compile("/metrics"));
}
...
The first method uses the TracerResolver to provide a vendor neutral approach for
accessing a Tracer. This tracer is then enhanced with the metrics capability using a
PrometheusMetricsReporter. This metrics reporter is further configured to add a special
label related to the baggage key transaction (discussed later).
By default, the Servlet OpenTracing integration will trace all REST endpoints. Therefore
in the second method above we add an attribute that will inform the instrumentation to
ignore the /metrics endpoint. Otherwise we will have tracing data reported each time
Prometheus reads the metrics for the service.
Deploying on Kubernetes
The steps to set up an environment on Kubernetes is discussed in the example codebase. A
summary of the steps is:
Start minikube
minikube start
minikube dashboard
Deploy Prometheus - using the Prometheus Operator project to capture metrics from the
services
kubectl create -f http://ift.tt/2rXa67N
# Wait until pods are green, then add configuration to locate service monitors based on
label "team: frontend":
kubectl create -f http://ift.tt/2tgfO8q
# Wait until these pods are green, then get the URL from the following command and open in
browser:
minikube service prometheus --url
Deploy Jaeger - an OpenTracing compatible tracing system
kubectl create -f http://ift.tt/2tfYiRY
# Once pods are green, then get the Jaeger dashboard URL from the following command and
open in a browser
minikube service jaeger-all-in-one --url
For this article, we also deployed Grafana to display the metrics, although the Prometheus
dashboard could be used. Once Grafana is installed:
Obtain the Prometheus server URL using minikube service prometheus --url
Configure a new Datasource named Prometheus of type Prometheus and specify the URL
obtained from the previous command
Download the example dashboard using the following command and import it into Grafana
wget http://ift.tt/2tfRq7b
Once they are all running, then the simple example with the two services can be deployed.
For this you will need to clone the example code repo, and follow these instructions.
At this stage the Kubernetes dashboard would look like this:
Figure 1: Kubernetes dashboard
The example code includes a script that loops, randomly invoking the three REST endpoints
provided by ordermgr. Once some example requests have been created, you can view the
tracing dashboard:
Figure 2: Jaeger tracing dashboard
Then you can select a specific trace instance and see further details:
Figure 3: Jaeger trace instance view
This shows that the trace instance has three spans, the first representing the receipt of
the /buy request on ordermgr, the second where ordermgr is invoking accountmgr, and
finally the accountmgr receiving the /hello request. In this particular trace instance,
the accountmgr invocation has reported an error, indicated by the error=true tag.
Now we will look at the Grafana dashboard to see what metrics have been reported from the
OpenTracing instrumentation within the two services:
Figure 4: Grafana dashboard
This dashboard includes three graphs, the first showing the number of spans created (i.e.
span count) by our sell() method, and we can use it to track how many times this business
operation has been executed. The second showing the average duration of the spans, and
third showing the ratio between successful and erronous spans.
The metrics reported by Prometheus are based on a range of labels - a metric exists for
each unique combination of those labels.
The standard labels included with the OpenTracing java-metrics project are: operation,
span.kind and error.
With this particular example, we also included the transaction label.
However when the services are deployed to Kubernetes, the following additional labels are
included for free: pod, instance, service, job and namespace.
In our example Prometheus queries, we have ignored most of the Kubernetes added labels
(except service) so that the metrics are aggregated across the specific pods, namespaces,
etc. However, having these labels available means it is possible to segment the metrics in
whatever way is required to analyse the data.
When using the java-metrics project outside of Kubernetes, it is still possible to include
the service label, however you would configure this when setting up the tracer.
We can also filter the data, to focus on specific areas of interest:
Figure 5: Customized Grafana graph focusing on metrics for transaction 'sell' and
service 'accountmgr'
In this image we have filtered the metrics based on the transaction='sell' and
service='accountmgr'. This is where using the metric label based on the baggage
item transaction can be useful, to understand the usage of a particular shared service by
a business transaction. With further work it would be possible to show the distribution of
requests for a service across the various business transactions.
Video
Conclusion
This article has shown how a service can be instrumented once (using OpenTracing) and
generate both tracing and application metrics.
When deployed to a Kubernetes environment, the metrics also benefit from an additional set
of labels automatically added by the infrastructure, describing the service, pod,
namespace, etc. This makes it easy to isolate specific metrics of interest, or view high
level aggregated metrics to gain an overview of your applications performance.
Links
OpenTracing: http://opentracing.io
Github repository with demo: http://ift.tt/2rX5MoK
OpenTracing java metrics: http://ift.tt/2rWUNvF
Kubernetes: https://kubernetes.io
Jaeger: http://ift.tt/2eOSqHE
Prometheus: https://prometheus.io
from Hawkular Blog