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Ticketfly/pillage Ganglia Monitoring System Hosted Graphite - Graphite as a service, with StatsD and Grafana Dashboards Start page – collectd – The system statistics collection daemon Start page – collectd – The system statistics collection daemon Getting Started | Metrics Getting Started will guide you through the process of adding Metrics to an existing application. We’ll go through the various measuring instruments that Metrics provides, how to use them, and when they’ll come in handy. Setting Up Maven You need the metrics-core library as a dependency: <dependencies><dependency><groupId>io.dropwizard.metrics</groupId><artifactId>metrics-core</artifactId><version>${metrics.version}</version></dependency></dependencies> Note Make sure you have a metrics.version property declared in your POM with the current version, which is 3.1.0. Now it’s time to add some metrics to your application! Meters A meter measures the rate of events over time (e.g., “requests per second”). private final Meter requests = metrics.meter("requests"); public void handleRequest(Request request, Response response) { requests.mark(); // etc} This meter will measure the rate of requests in requests per second. Console Reporter Complete getting started So the complete Getting Started is <? To run

Analyzing the Analyzers mozilla/crontabber Monitoring at Spotify: The Story So Far | Labs This is the first in a two-part series about Monitoring at Spotify. In this, I’ll be discussing our history, the challenges we faced, and how they were approached. Operational monitoring at Spotify started its life as a combination of two systems. In late 2013, we were starting to put more emphasis on self service and distributed operational responsibility. We tried to bandage up what we could: our Chief Architect hacked together an in-memory sitemon replacement that could hold roughly one month worth of metrics under the current load. Alerting as a service Alerting was the first problem we took a stab at. We considered developing Zabbix further. We found inspiration from attending Monitorama EU where we stumbled upon Riemann. We built a library on top of Riemann called Lyceum. Graphing We went a few rounds here. It became desirable to switch to a push-based approach to lower the barriers of entry for our engineers. The difficulties in sharding and rebalancing Graphite became prohibitive.

Welcome to Apache Flume — Apache Flume Composite Design Pattern Intent Compose objects into tree structures to represent whole-part hierarchies. Composite lets clients treat individual objects and compositions of objects uniformly.Recursive composition"Directories contain entries, each of which could be a directory."1-to-many "has a" up the "is a" hierarchy Problem Application needs to manipulate a hierarchical collection of "primitive" and "composite" objects. Discussion Define an abstract base class (Component) that specifies the behavior that needs to be exercised uniformly across all primitive and composite objects. Use this pattern whenever you have "composites that contain components, each of which could be a composite". Child management methods [e.g. addChild(), removeChild()] should normally be defined in the Composite class. Structure Composites that contain Components, each of which could be a Composite. Menus that contain menu items, each of which could be a menu. Directories that contain files, each of which could be a directory. Example Opinions

Mining Time-series with Trillions of Points: Dynamic Time Warping at scale Take a similarity measure that's already well-known to researchers who work with time-series, and devise an algorithm to compute it efficiently at scale. Suddenly intractable problems become tractable, and Big Data mining applications that use the metric are within reach. The classification, clustering, and searching through time series have important applications in many domains. In medicine EEG and ECG readings translate to time-series data collections with billions (even trillions) of points. The problem is that existing algorithms don't scale1 to sequences with hundreds of billions or trillions of points. Recently a team of researchers led by Eamonn Keogh of UC Riverside introduced a set of tools for mining time-series with trillions of points. What is Dynamic Time Warping? SQRT[ Σ (xi - yi)2 ] While ED is easy to define, it performs poorly as a similarity score. There are an exponential number of paths (from one time series to the other) through the warping matrix. 1. 1. 1.

Using monitoring and metrics to learn in development