jkschneider

#!/bin/sh
#
# /etc/init.d/mysystem
# Subsystem file for "Atlas" server
#
# chkconfig: 2345 95 05  (1)
# description: Atlas server daemon

ATLAS_VERSION=1.5.3
RETVAL=0

jkschneider

package io.micrometer.core.samples;

import com.sun.net.httpserver.HttpServer;

import java.io.IOException;
import java.io.OutputStream;
import java.net.InetSocketAddress;
import java.util.Map;
import java.util.TreeMap;
import java.util.concurrent.atomic.DoubleAdder;

jkschneider

/**
 * Copyright 2017 Pivotal Software, Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software

jkschneider

package io.micrometer.core.instrument.config;

import java.time.Duration;

/**
 * Filter properties that are common to both distribution summaries and timers.
 */
public abstract class DistributionFilterProperties extends MeterFilterProperties {
    /**
     * Controls whether to publish a histogram structure for those monitoring systems that support

jkschneider

management.metrics.enabled.my.timer=false
management.metrics.summaries.minimum-expected-value.my.summary=10
management.metrics.summaries.maximum-expected-value.my.summary=10
management.metrics.summaries.percentile-histogram.my.summary=true

# Map<String, Long[]>
management.metrics.summaries.sla.my.summary=10

# Map<String, Duration>
management.metrics.summaries.histogram-expiry.my.summary=10s

jkschneider

package io.micrometer.core.samples;

import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.Tags;
import io.micrometer.core.instrument.binder.jvm.JvmMemoryMetrics;
import io.micrometer.core.samples.utils.SampleRegistries;
import reactor.core.publisher.Flux;

import java.time.Duration;
import java.util.concurrent.atomic.AtomicLong;

jkschneider

package io.micrometer.core.samples;

import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.Tags;
import io.micrometer.core.samples.utils.SampleConfig;

import java.util.Collections;
import java.util.List;

public class GaugeCollection {

jkschneider

public class Slack320 {
    public static void main(String[] args) {
        MeterRegistry registry = SampleRegistries.graphite();
        Counter counter = registry.counter("serviceName", "client",
                "token", "statistic", "timeout_connect");

        RandomEngine r = new MersenneTwister64(0);
        Normal dist = new Normal(0, 1, r);

        Flux.interval(Duration.ofMillis(10))

jkschneider

Repave and repair your applications with confidence

If a security vulnerability is reported in a common java library today, would you be able to identify affected applications and repair your whole organization before dinner time?

In this talk, we're going to stitch together a continuous delivery platform, application monitoring, automated canary analysis, and organization-wide code search to show you how you can identify affected applications in minutes and repair your organization's code. We'll start with the assumption that we are working with a complex multi-platform (some K8S, some GCP, multiple Cloud Foundry foundations over private VSphere) environment like those found in many organizations.

Some of the tools we'll demonstrate include Spinnaker for continuous delivery, Kayenta for automated canary analysis, Micrometer for metrics collection, Prometheus for monitoring, and Rewrite for code search.

Bio

jkschneider

package io.micrometer.spectator;

import com.netflix.spectator.api.*;
import io.micrometer.core.instrument.MeterRegistry;

import java.util.Collections;
import java.util.stream.Collectors;
import java.util.stream.StreamSupport;

public class MicrometerRegistry extends AbstractRegistry {