Kibana

Collectd How to set up Semantic Logging: part one with Logstash, Kibana, ElasticSearch and Puppet, Logging today is mostly done too unstructured; each application developer has his own syntax for the logs, optimized for his personal requirements and when it is time to deploy, ops consider themselves lucky if there is even some logging in the application, and even luckier if that logging can be used to find problems as they occur by being able to adjust verbosity where needed. I’ve come to the point where I want a really awesome piece of logging from the get-go – something I can pick up and install in a couple of minutes when I come to a new customer site without proper operations support. I want to be able to be able to search, drill down into, filter out patterns and have good tooling that allow me to let logging be an obvious support as the application is brought through its life cycle, from development to production. That’s where semantic logging comes in – my applications should be broadcasting log data in a manner that allow code to route, filter and index it. Virtually Awesome or

Premiers pas avec ElasticSearch (Partie 1) Malgré une prise en main rapide et une documentation officielle très complète, l'utilisation d'ElasticSearch peut devenir rapidement complexe pour qui n'a jamais utilisé de moteur de recherche. C'est pourquoi nous avons choisi de démarrer une nouvelle série d'articles, dans laquelle nous allons essayer de présenter les notions de base d'ElasticSearch et les fonctionnalités les plus utilisées de ce fantastique outil. Dans ce premier article, nous verrons comment installer et configurer ElasticSearch. Nous en profiterons pour introduire les concepts de nœud et de cluster, puis nous effectuerons nos premières indexations de documents et recherches par mots clés. ElasticSearch : cool, bonsaï, cool ! ElasticSearch est un projet open source développé en Java sous licence Apache2. La première version a été mise à disposition du public en février 2010. La version actuelle est 0.19.11, mais ne vous fiez pas à ce numéro! Au cœur du projet, Apache Lucene Les fonctionnalités clés d'ElasticSearch Avec

Apache Logs Viewer | Analyze & View Apache/IIS Log Files Fold In functional programming, fold (or reduce) is a family of higher order functions that process a data structure in some order and build a return value. This is as opposed to the family of unfold functions which take a starting value and apply it to a function to generate a data structure. 1 Overview fold (+) [1,2,3,4,5] which would result in 1 + 2 + 3 + 4 + 5, which is 15. However, in the general case, functions of two parameters are not associative, so the order in which one carries out the combination of the elements matters. -- if the list is empty, the result is the initial value z; else-- apply f to the first element and the result of folding the restfoldr f z [] = z foldr f z (x:xs) = f x (foldr f z xs) -- if the list is empty, the result is the initial value; else-- we recurse immediately, making the new initial value the result-- of combining the old initial value with the first element.foldl f z [] = z foldl f z (x:xs) = foldl f (f z x) xs 2 Special folds for nonempty lists

Centreon - Open Source Network, Systems and Application monitoring solution Jeroen Reijn: Real-time visitor analysis with Couchbase, Elasticsearch and Kibana At Hippo we recently started using Couchbase as the storage solution for our targeting/relevance module. Couchbase is a really high performant NoSQL database, which since version 2.0 can be used as a (JSON) document database. Couchbase is really fast when it comes to simple CRUD operations, but does lack some search capabilities like Geo-spatial search (still 'experimental' mode) and free text search, which you might find in other document oriented NoSQL databases like MongoDB. However the lack of these search capabilities can be overcome quite easily by combining Couchbase with Elasticsearch by using the Couchbase-Elasticsearch transport plugin. The plugin uses the Couchbase built-in cross data center replication mechanism (XDCR), which can be used for replicating data between Couchbase clusters. In this post we will go through all the necessary steps to setup Couchbase, Elasticsearch and Kibana for doing 'real-time' visitor analysis. Setting up Couchbase Elasticsearch You should see:

Getting Started with elasticsearch and AngularJS: Part1 - Searching The ability to deliver sophisticated client-side JavaScript applications is an important aspect of data discovery and visualization. It’s no secret that elasticsearch is phenomenal at extracting meaning from enormous data sets in near real-time. Exposing that power to end users requires equally impressive applications. Elasticsearch has made search more approachable by exposing Web friendly APIs (REST + JSON) that reduce the impedance mismatch associated with relational models, at no sacrifice to query capability. The goal here is to write a series of articles that help folks gain some insight into how these technologies fit together. Getting Started Loading Data Throughout the series, I’ll be using the StackOverflow data that Matt used in this post, which also describes how to aquire and load the data. Application Module Angular provides its own module system for loading and bootstrapping applications. // app.jsangular.module('demo', []); Creating a Search Controller Executing Searches <! <!

crawling and indexing Webmaster Level: Intermediate to Advanced Including a rel=canonical link in your webpage is a strong hint to search engines your about preferred version to index among duplicate pages on the web. It’s supported by several search engines, including Yahoo!, Bing, and Google. While the webmaster sees the “red velvet” page on the left in their browser, search engines notice on the webmaster’s unintended “blue velvet” rel=canonical on the right. We recommend the following best practices for using rel=canonical:A large portion of the duplicate page’s content should be present on the canonical version. One test is to imagine you don’t understand the language of the content—if you placed the duplicate side-by-side with the canonical, does a very large percentage of the words of the duplicate page appear on the canonical page? Imagine that you have an article that spans several pages: example.com/article? rel=canonical from component pages to the view-all page Mistake 5: rel=canonical in the <body>

LaTeX:Symbols From AoPSWiki This article will provide a short list of commonly used LaTeX symbols. Operators Relations Negations of many of these relations can be formed by just putting \not before the symbol, or by slipping an n between the \ and the word. To use other relations not listed here, such as =, >, and <, in LaTeX, you may just use the symbols on your keyboard. Greek Letters Headline text Arrows (For those of you who hate typing long strings of letters, \iff and \implies can be used in place of \Longleftrightarrow and \Longrightarrow respectively.) Dots (The '2's after \ldots and \cdots are only present to make the distinction between the two clear.) Accents When applying accents to i and j, you can use \imath and \jmath to keep the dots from interfering with the accents: \tilde and \hat have wide versions that allow you to accent an expression: Others Command Symbols Some symbols are used in commands so they need to be treated in a special way. (Warning: Using \$ for will result in . Bracketing Symbols

The OpenNMS Project Sneak Peek: HuffPost Brings Real Time Collaboration to the Newsroom | John Pavley Trigger warnings: computer jargon, monster movie references If you’re a regular reader of the Huffington Post you might not have given much thought to the technology behind the news articles that you read, share, and comment upon on our site. Since 2005 the tech team at HuffPost has been working hand-in-glove with our editors to create the ultimate digital content delivery system. We call this system “MT” because it was originally based on Movable Type. Over the last eight years we have enhanced MT and its features to the point that they have morphed into a tool that puts every feature a modern tech-savvy journalist needs at her finger tips. Eight years is a long time for an Internet application to live. Earlier this year HuffPost assembled a team of battle hardened software developers and product managers to work with our editors on the next generation of MT — codenamed Athena. MySQL is a database, one of the best. I hope you enjoyed this “sneak peak” behind the curtain at HuffPost.