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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. Below is a transcript of our conversation. 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.

Code Monkeyism | The Blog for Developers by Stephan Schmidt 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. For your reference, you can find the complete documentation for the PHP driver on the PHP community site. 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. PHP driver tips & tricks Index builds can sometimes block new connections

Hadoop Has Promise but Also Problems... Show Me the Cheaper or Simpler Alternatives Jessica E. Vascellaro for WSJ: But some early adopters of Hadoop now say using the technology is challenging and rolling it out will take time.[…]Mr. Boroditsky says Hadoop is “immature” and comes with additional costs of hiring in-house expertise and consultants. “There is a very substantial cost to free software,” he says, declining to comment on dollar figures. I’m starting to believe that the “Hadoop has problems and is complex” chorus is a vendor reaction very similar to the reaction they had to open source in general. how many other tools can lead you to the same solution? It would be great if Hadoop administration would get simpler and operational costs would go down and if know-how would be easier to find. Original title and link: Hadoop Has Promise but Also Problems… Show Me the Cheaper or Simpler Alternatives (NoSQL database©myNoSQL)

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. We frequently find them wrestling with unnecessarily complex and slow methods of solving problems that the aggregation framework is purpose built to solve. 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. Variables and Explaining

Reflections on the New Database Revolution | The Database Revolution The recent Bloor Group roundtable discussion on The New Database Revolution covered a lot of ground. Both the benefits and challenges of the new database paradigms were discussed and a broad range of questions were raised by the audience. In thinking about how the discussion went, there are a number of themes that are worth further mention. The Need for History One important theme is the need to understand how we got where we are today. Today, we find architectures in enterprises that cannot be explained by conscious decisions based on a single current architecture. So we have an existing architecture, which is already very complex and may not be fully understood (or understandable) and we are going to have to add to it to support the new database platforms. Before and After the Relational Interlude Another historical topic that came up in the roundtable was the existence of databases in the long-forgotten world prior to the dominance of the relational paradigm.

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. 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. 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. Our Metrics Framework Architecture In our app, we’ve set up an AWS SQS queue and any data we want to track from the app goes immediately to this queue. We wrote simple client libraries for tracking data for every language that we use (PHP, Python, Java, NodeJS, Javascript, Objective-C). Tracking events Our event tracking is super simple. Result:

SOA Patterns - Partial State Deferral How can services be designed to optimize resource consumption while still remaining stateful? Problem Service capabilities may be required to store and manage large amounts of state data, resulting in increased memory consumption and reduced scalability. Solution Even when services are required to remain stateful, a subset of their state data can be temporarily deferred. Application Various state management deferral options exist, depending on the surrounding architecture. Impacts Partial state management deferral can add to design complexity and bind a service to the architecture. Architecture Inventory, Service Applying this pattern results in the same amount of concurrent service instances but less overall state-related memory consumption.

MongoDB - Wiki Un article de Wikipédia, l'encyclopédie libre. MongoDB (de l'anglais humongous qui peut être traduit par « énorme ») est un système de gestion de base de données orientée documents, répartissable sur un nombre quelconque d'ordinateurs, efficace pour les requêtes simples, et ne nécessitant pas de schéma prédéfini des données. Il est écrit en C++ et distribué sous licence AGPL. Il fait partie de la mouvance NoSQL. Historique[modifier | modifier le code] MongoDB est développé depuis 2007 par 10gen. Principales caractéristiques[modifier | modifier le code] Données manipulées[modifier | modifier le code] MongoDB permet de manipuler des objets structurés au format BSON (JSON binaire), sans schéma prédéterminé. Une collection MongoDB en revanche pourrait se présenter de la manière suivante : Manipulation des données[modifier | modifier le code] MongoDB est livré avec des pilotes pour les principaux langages de programmation : En tapant : . Le résultat s'imprime à l'écran :

ApplicationDatabase application integration · database tags: I use the term Application Database for a database that is controlled and accessed by a single application, (in contrast to an IntegrationDatabase). Since only a single application accesses the database, the database can be defined specifically to make that one application's needs easy to satisfy. This leads to a more concrete schema that is usually easier to understand and often less complex than that for an IntegrationDatabase. To share data with other applications the controlling application may provide services. One the great advantages of an application database is that it is easier to change since all its use is encapsulated by a single application. An application database schema is usually best designed and controlled by the application team themselves - often by having an experienced database professional as a member of the application team.

Aggregation Concepts — MongoDB Manual 2.6.1 MongoDB provides the three approaches to aggregation, each with its own strengths and purposes for a given situation. This section describes these approaches and also describes behaviors and limitations specific to each approach. See also the chart that compares the approaches. Aggregation Pipeline The aggregation pipeline is a framework for performing aggregation tasks, modeled on the concept of data processing pipelines. Map-Reduce Map-reduce is a generic multi-phase data aggregation modality for processing quantities of data. Single Purpose Aggregation Operations MongoDB provides a collection of specific data aggregation operations to support a number of common data aggregation functions. Aggregation Mechanics Details internal optimization operations, limits, support for sharded collections, and concurrency concerns.

DatabaseThaw database · noSQL tags: A few years ago I heard programming language people talk about the "Nuclear Winter" in languages caused by Java. Tim Bray's thought-provoking keynote talked about storage; including highlighting several alternatives to the conventional database world When I started in the software development profession, I worked with several people who had evangelized relational databases. At QCon last week, there was a strong thread of talks that questioned this assumption. As well as this talk, there was a whole track on alternative databases hosted by Kresten Krab Thorup. The natural question to ask about these products is why they should prevail when the ODBMSs failed. Kresten Krab Thorup does a great job as a leader of the technical content of the JAOO and QCon conferences. For many organizations today, the primary pattern for integration is Shared Database Integration - where multiple applications are integrated by all using a common database.