Metrics by coda hale : to know your app’ health

Metrics by Coda Haleto know your app’ health
Izzet Mustafayev@EPAM Systems
@webdizz webdizz izzetmustafaiev
http://webdizz.name

this is me
● SA at EPAM Systems
● primary skill is Java
● JUG member/speaker
● hands-on-coding with Ruby, Groovy and
some Scala
● passionate about agile, clean code
practices and devops movement

agenda
● introduction
● why should i care
● features
● reporting
● recipes
● alternatives
● q&a

● does my app work?
● will my app keep working?

● does my app work?
● will my app keep working?
● what happened that
made my app stop
working?

Registry.
..- is the heart of metrics framework
MetricRegistry registry = new MetricRegistry();
// or
@Bean
public MetricRegistry metricRegistry(){
return new MetricRegistry();
}

Gauge...
- is the simplest metric type that just returns a value
registry.register(
name(Persistence.class, "entities-cached"),
new Gauge<Integer>() {
public Integer getValue() {
return cache.getItems();
}
}
);

Counter...
- is a simple incrementing and decrementing integer value
Counter onSiteUsers = registry.counter(
name(User.class, "users-logged-in"));
// on login increase
onSiteUsers.inc();
// on logout/session expiration decrease
onSiteUsers.dec();

Histogram.
..- measures the distribution of values in a stream of data
Histogram requestTimes = registry.histogram(
name(Request.class, "request-processed")
);
requestTimes.update(request.getTime());

Meter...
- measures the rate at which a set of events occur
Meter timedOutRequests = registry.meter(
name(Request.class, "request-timed-out")
);
// in finally block on time-out
timedOutRequests.mark();

Timer...
- is basically a histogram of the duration of a type of event
and a meter of the rate of its occurrence
Timer requests = registry.timer(
name(Request.class, "request-processed")
);
Timer.Context time = requests.time();
// in finally block of request processing
time.stop();

Health Check..
- is a unified way of performing application health checks
public class BackendHealthCheck extends HealthCheck {
private Backend backend;
protected Result check() {
if (backend.ping()) {
return Result.healthy();
}
return Result.unhealthy("Can't ping backend");
}
}

JMX
// somewhere in your application
JmxReporter reporter =
JmxReporter.forRegistry(registry).build();
reporter.start();

HTTP
● AdminServlet
○ MetricsServlet
○ ThreadDumpServlet
○ PingServlet
○ HealthCheckServlet

Console
ConsoleReporter reporter = ConsoleReporter.forRegistry
(registry)
.convertRatesTo(TimeUnit.SECONDS)
.convertDurationsTo(TimeUnit.MILLISECONDS)
.build();
reporter.start(1, TimeUnit.MINUTES);
Slf4jReporter reporter = Slf4jReporter.forRegistry(registry)
.outputTo(LoggerFactory.getLogger("com.app.metrics"))
.convertRatesTo(TimeUnit.SECONDS)
.convertDurationsTo(TimeUnit.MILLISECONDS)
.build();
reporter.start(1, TimeUnit.MINUTES);

Naming by Matt Aimonetti
<namespace>.<instrumented section>.<target (noun)>.
<action (past tense verb)>
Example
● customers.registration.validation.failed
Nesting
● customers.registration.validation.failure.
similar_email_found
● customers.registration.validation.failure.
email_verification_failed

AOP
There is an easy way to introduce metrics using AOP.
<aop:config proxy-target-class="false">
<aop:aspect id="controllerProfilerAspect"
ref="executionMonitor">
<aop:pointcut id="controllerMethods"
expression="execution(*
com.app.controller.*Controller.*(..))" />
<aop:around pointcut-ref="controllerMethods"
method="logExecutionTime" />
</aop:aspect>
</aop:config>

public Object logExecutionTime(ProceedingJoinPoint joinPoint)
throws Throwable {
String namePfx = metricNamePrefix(joinPoint);
Timer.Context time = registry.timer(
name(namePfx, "timed")).time();
try {
// execute joint point ...
} catch(Throwable throwable) {
registry.counter(
name(namePfx, "exception-counted")).inc();
throw throwable;
} finally {
time.stop()
}
}

metrics-spring
@Configuration
@EnableMetrics
public class ApplicationConfiguration {
@Bean
public MetricRegistry metricRegistry(){
return new MetricRegistry();
}
// your other beans here ...
}
// annotations
@Timed, @Metered, @ExceptionMetered, @Counted, @Gauge,
@CachedGauge and @Metric

instrumentation
● JVM
● Jetty
● Jersey
● InstrumentedFilter
● InstrumentedEhcache
● InstrumentedAppender (Log4j and Logback)
● InstrumentedHttpClient

https://github.com/twitter/zipkin
Zipkin is a distributed tracing system that helps
Twitter gather timing data for all the disparate
services.
Zipkin provides three services:
● to collect data: bin/collector
● to extract data: bin/query
● to display data: bin/web

http://www.moskito.org/
MoSKito is a
● Multi-purpose: collects any possible type of
performance data, including business-related
● Non-invasive: does not require any code
change
● Interval-based: works simultaneously with
short & long time intervals, allowing instant
comparison
● Data privacy: keeps collected data locally,
with no 3rd party resources involved.
● Analysis tools: displays accumulated
performance data in charts. Live profiling:
records user actions as system calls.

https://code.google.com/p/javasimon/
Java Simon is a
simple monitoring API that allows you to
follow and better understand your application.
Monitors (familiarly called Simons) are
placed directly into your code and you can
choose whether you want to count something or
measure time/duration.

https://github.com/Netflix/servo
Servo goal is to provide a simple interface for
exposing and publishing application metrics in
Java.
● JMX: JMX is the standard monitoring
interface for Java
● Simple: It is trivial to expose and publish
metrics
● Flexible publishing: Once metrics are
exposed, it is easy to regularly poll the
metrics and make them available for
reporting systems, logs, and services like
Amazon’s CloudWatch.

getting started
● http://metrics.codahale.com/
● http://matt.aimonetti.
net/posts/2013/06/26/practical-guide-to-
graphite-monitoring/
● http://www.ryantenney.com/metrics-spring/

ThanksIzzet Mustafayev@EPAM Systems
@webdizz webdizz izzetmustafaiev
http://webdizz.name

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