NoSQL Malgré l’appellation NoSQL, ce n’est pas tant le langage SQL en lui même qui est inadapté mais les grands principes sur lesquels il a été construit : le modèle relationnel et transactionnel. En effet, les bases de données relationnelles mettent à la disposition des développeurs un certains nombre d’opérations de relations entre les tables : un système de jointure entre les tables permettant de construire des requêtes complexes faisant intervenir plusieurs entités (les tables en l'occurrence) un système d’intégrité référentielle permettant de s’assurer que les liens entre les entités sont valides La mise en œuvre de tels mécanismes a un coût considérable dés lors que l’on se trouve dans le contexte d’un système distribué. Sur la plupart des SGBD relationnels, il convient de s’assurer en permanence que les données liées entre elles sont placées sur le même noeud du serveur.
Getting Started with NoSQL « myNoSQL Couple of weeks ago, I had the pleasure to sit down with Mathias Meyer, Chief Visionary at Scalarium, a Berlin startup and discuss NoSQL adoption. Like myself, Mathias is really excited about NoSQL and he uses every opportunity to introduce more people to the NoSQL space. Recently he gave quite a few presentations around the Europe about NoSQL databases. The discussion has focused on how would someone start learning and using NoSQL databases and the path to follow in this new ecosystem. Alex: How does one get started with NoSQL? Mathias: Well, that’s a question I get quite a lot, but it is not that easy to answer. From a business perspective, you are probably going to find some use cases where storing your data in a relational database doesn’t make too much sense and you’ll start looking for ways to get it out of the database. Alex: So, as a developer you should just give yourself a chance to play with the new shiny toys. Mathias: Indeed. You can’t really give a universal answer here.
Big Data Right Now: Five Trendy Open Source Technologies Big Data is on every CIO’s mind this quarter, and for good reason. Companies will have spent $4.3 billion on Big Data technologies by the end of 2012. But here’s where it gets interesting. Those initial investments will in turn trigger a domino effect of upgrades and new initiatives that are valued at $34 billion for 2013, per Gartner. Over a 5 year period, spend is estimated at $232 billion. What you’re seeing right now is only the tip of a gigantic iceberg. Big Data is presently synonymous with technologies like Hadoop, and the “NoSQL” class of databases including Mongo (document stores) and Cassandra (key-values). But there are new, untapped advantages and non-trivially large opportunities beyond these usual suspects. Did you know that there are over 250K viable open source technologies on the market today? We have a lot of…choices, to say the least. What’s on our own radar, and what’s coming down the pipe for Fortune 2000 companies? Storm and Kafka Why should you care? Drill and Dremel
PHP MongoDB driver examples & tips A large proportion of support requests to MongoLab are questions about how to properly configure and use a particular MongoDB driver. This blog post is the third of a series where we are covering each of the major MongoDB drivers in depth. The driver we’ll be covering here is the PHP driver, developed and maintained by the MongoDB, Inc. team (primarily @derickr, @bjori and @jmikola). In this post: This post aims to help you understand how to configure and use the PHP driver effectively in your MongoDB application. A simple PHP example You can find a straightforward example on connecting, inserting, updating and querying using the PHP driver in MongoLab’s Language Center. Production-ready connection settings We often see incorrect configurations of the driver, particularly around timeouts and replica set connections. Additional connection options that are supported by the PHP driver can be found here. PHP driver tips & tricks Index builds can sometimes block new connections connectionTimeoutMS
The time for NoSQL standards is now | Application Development A decline for Oracle over the next 15 years is inevitable. It will be impossible to sustain the RDBMS-only paradigm against all logic as the new wave of databases lumped in under NoSQL and big data takes over. Oracle is responding with partnerships, and it already has a NoSQL database, but it's difficult to imagine a transition that leaves Oracle's revenue stream intact -- smells almost like Novell, circa 1996. Yet the RDBMS will take its time to fade. [ Harness the power of Hadoop with InfoWorld's 7 top tools for taming big data. | Find out which database works best for you in InfoWorld Test Center's survey, "NoSQL standouts: New databases for new applications." | Follow the latest issues in software development with InfoWorld's Developer World newsletter. ] It may surprise you that I don't consider "transactions" to be one of those advantages. The key advantage of the RDBMS, rather, is standardization. Standards, anyone?
The Apache Cassandra Project Drill Speed is Key Leveraging an efficient columnar storage format, an optimistic execution engine and a cache-conscious memory layout, Apache Drill is blazing fast. Coordination, query planning, optimization, scheduling, and execution are all distributed throughout nodes in a system to maximize parallelization. Liberate Nested Data Perform interactive analysis on all of your data, including nested and schema-less. Flexibility Strongly defined tiers and APIs for straightforward integration with a wide array of technologies. Disclaimer Apache Drill is an effort undergoing incubation at The Apache Software Foundation sponsored by the Apache Incubator PMC. Aggregation in MongoDB 2.6: Things Worth Knowing TL;DR: The powerful aggregation framework in MongoDB is even more powerful in MongoDB 2.6 The MongoDB 2.6 release improved aggregation framework (one of MongoDB's best features) considerably. We often hear from customers who are unaware of the aggregation framework, or unsure exactly why they should be using it. Introducing aggregation The aggregation framework in MongoDB has become the go-to tool for a range of problems which would traditionally have been solved with the map-reduce engine. Step by step We prefer to show rather than tell, so lets look at a worked example. If you don't have a collection like that try this Node.js program which will make you a million documents: (The program uses the Faker.js library to mock up records. What we want to know is how many of those records belong to the same zip code. Now we want to use the aggregate's $group operator as the first element of the pipeline. We add this as the next element in the pipeline array and we put that into the shell...
Big Data : Cassandra 1.2, base de données NoSQL open source Cassandra 1.2 s’annonce comme une version majeure de cette base de données NoSQL open source. Cette seconde génération de la base apporte son lot de nouvelles fonctionnalités validées par l’ASF (Apache Software Foundation). Cassandra 1.2 est tout d’abord une base de données haute performance, capable de traiter simultanément des milliers de requêtes, destinée au Big Data, NoSQL pour traiter la diversité des informations non structurées – par opposition aux données SQL structurées -, scalable pour évoluer sans interruption et à tolérance de panne. Cette nouvelle version a densifié le support des clusters avec le clustering au travers de nœuds virtuels (vnode), chaque nœud pouvant supporter plusieurs téraoctets de données, la virtualisation des nodes simplifiant la gestion des clusters et réduisant la charge des machines virtuelles Java. Atomic batches et CQL3 Enfin Cassandra 1.2 intègre la version 3 de CQL (Cassandra Query Language). Une communauté enrichie Voir aussi
CouchDB: Technical Overview A Database for the Web CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents with your web browser, via HTTP. CouchDB comes with a suite of features, such as on-the-fly document transformation and real-time change notifications, that makes web app development a breeze. See the introduction , technical overview , or one of the guides for more information. Want to Contribute? CouchDB is an open source project. One of the first things you should do is actually use CouchDB, and get to know it, read about it, evangelise it, and engage with the wider community. Why don’t you check out JIRA and help us triage some of those issues? Do you want to contribute code?
Marriage of Hadoop and OLAP: Best of both worlds to make sense of 200 Terabytes of data | zooskdev Like many other companies in the social networking world, Zoosk inherits a vast amount of data every day from user interactions, web logs, financial transactions, as well as standard business metric data. Making sense of the data and turning it into actionable intelligence is of utmost importance to Zoosk, where we are constantly trying to optimize our product offerings and business processes. The question is: how do we most effectively leverage our data, and turn it into business intelligence? There are a few typical approaches to answer this question. Traditionally to gain business intelligence, one can leverage a star schema data warehouse with a multi-dimensional OLAP engine, to provide the business with a user-friendly toolset to quickly “slice and dice” data to identify trends and patterns. These toolsets can be something that users are familiar with, such as Microsoft Excel and web dashboards. So what do we do at Zoosk? 8 OLAP cubes 20+ Fact tables 150+ cube dimensions
MongoDB Blog By Sunil Sadasivin, CTO at Buffer Buffer, powered by experiments and metrics At Buffer, every product decision we make is driven by quantitative metrics. We have always sought to be lean in our decision making, and one of the core tenants of being lean is launching experimental features early and measuring their impact. Buffer is a social media tool to help you schedule and space out your posts on social media networks like Twitter, Facebook, Google+ and Linkedin. We started in late 2010 and thanks to a keen focus on analytical data, we have now grown to over 1.5 million users and 155k unique active users per month. When I started at Buffer in September 2012 we were using a mixture of Google Analytics, Kissmetrics and an internal tool to track our app usage and analytics. We took the plunge in April 2013 to build our own metrics framework using MongoDB. Why we chose MongoDB At the time we were evaluating datastores, we had no idea what our data would look like. Tracking events Result: