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  • How should calculations be handled in a document database

    - by Morten
    Ok, so I have a program that basically logs errors into a nosql database. Right now there is just a single model for an error and its stored as a document in the nosql database. Basically I want to summarize across different errors and produce a summary of the "types" of errors that occured. Traditionally in a SQL database the this normalization would work with groupings, sums and averages but in a NoSQL database I assume I need to use mapreduce. My current model seems unfit for the task, how should I change the way I store "models" in order to make statistical analysis easy? Would a NoSQL database even be the right tool for this type of problem? I'm storing things in Google AppEngine's BigTable, so there are some limitations to think of as well.

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  • [News] Le mouvement NoSQL et MongoDB

    Le mouvement "NoSQL" est une communaut? cr??e dans le but de promouvoir les bases de donn?es non relationnelles (d'o? le terme NoSQL). Dans cet hymne ? ce mouvement, Michael Kennedy, instructeur pour DevelopMentor, d?crit les pr?ceptes de NoSQL en l'illustrant ? travers un moteur finalement peu connu du grand public, MongoDB. A lire, tr?s int?ressant m?me si on ne partage pas l'opinion de cette communaut? : "(...) A basic SQL Server cluster might run you $100,000 just to get it up and running on decent hardware. Rather than leveraging crazy scaling-up options, the NoSQL databases let you scale-out. They make this possible (dare I say easy?) by dropping the relational aspects of a database (...) "

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  • Oracle NoSQL Database: Cleaner Performance

    - by Charles Lamb
    In an earlier post I noted that Berkeley DB Java Edition cleaner performance had improved significantly in release 5.x. From an Oracle NoSQL Database point of view, this is important because Berkeley DB Java Edition is the core storage engine for Oracle NoSQL Database. Many contemporary NoSQL Databases utilize log based (i.e. append-only) storage systems and it is well-understood that these architectures also require a "cleaning" or "compaction" mechanism (effectively a garbage collector) to free up unused space. 10 years ago when we set out to write a new Berkeley DB storage architecture for the BDB Java Edition ("JE") we knew that the corresponding compaction mechanism would take years to perfect. "Cleaning", or GC, is a hard problem to solve and it has taken all of those years of experience, bug fixes, tuning exercises, user deployment, and user feedback to bring it to the mature point it is at today. Reports like Vinoth Chandar's where he observes a 20x improvement validate the maturity of JE's cleaner. Cleaner performance has a direct impact on predictability and throughput in Oracle NoSQL Database. A cleaner that is too aggressive will consume too many resources and negatively affect system throughput. A cleaner that is not aggressive enough will allow the disk storage to become inefficient over time. It has to Work well out of the box, and Needs to be configurable so that customers can tune it for their specific workloads and requirements. The JE Cleaner has been field tested in production for many years managing instances with hundreds of GBs to TBs of data. The maturity of the cleaner and the entire underlying JE storage system is one of the key advantages that Oracle NoSQL Database brings to the table -- we haven't had to reinvent the wheel.

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  • Passoker Online Betting Use of Oracle NoSQL Database

    - by Charles Lamb
    Here's an Oracle NoSQL Database customer success story for Passoker, an online betting house. http://www.oracle.com/us/corporate/customers/customersearch/passoker-1-nosql-ss-1863507.html There are a lot of great points made in the Solutions section, but as a developer the one I like the most is this one: Eliminated daily maintenance related to single-node points-of-failure by moving to Oracle NoSQL Database, which is designed to be resilient and hands-off, thus minimizing IT support costs

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  • NoSQL For The Rest Of Us

    No one would blame you for strictly associating NoSQL with performance. Most of the back and forth about NoSQL - an umbrella term given for non-relational storage mechanisms - has squarely put the focus on performance, sites with massive traffic, and server farms. It’s an interesting conversation, but one that risks alienating NoSQL from the majority of developers. The Problem Does NoSQL provide us simple developers with any tangible benefit? As a matter of fact, it can - one as significant...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Yes to NoSQL

    There seems to be some backlash building up against NoSQL with posts like Ted Dziuba I Can't Wait for NoSQL to Die or Dennis Forbes The Impact of SSDs on Database Performance and the Performance Paradox of Data Explodification (aka Fighting the NoSQL mindset). These are interesting articles to read and yes RDBMSs are not going the way of the dodo yet (I even said that in The RDBMS is dead, which by the way, was written before NoSQL was coined, but I digress ). Nevertheless,...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • What scalability problems have you solved using a NoSQL data store?

    - by knorv
    NoSQL refers to non-relational data stores that break with the history of relational databases and ACID guarantees. Popular open source NoSQL data stores include: Cassandra (tabular, written in Java, used by Facebook, Twitter, Digg, Rackspace, Mahalo and Reddit) CouchDB (document, written in Erlang, used by Engine Yard and BBC) Dynomite (key-value, written in C++, used by Powerset) HBase (key-value, written in Java, used by Bing) Hypertable (tabular, written in C++, used by Baidu) Kai (key-value, written in Erlang) MemcacheDB (key-value, written in C, used by Reddit) MongoDB (document, written in C++, used by Sourceforge, Github, Electronic Arts and NY Times) Neo4j (graph, written in Java, used by Swedish Universities) Project Voldemort (key-value, written in Java, used by LinkedIn) Redis (key-value, written in C, used by Engine Yard, Github and Craigslist) Riak (key-value, written in Erlang, used by Comcast and Mochi Media) Ringo (key-value, written in Erlang, used by Nokia) Scalaris (key-value, written in Erlang, used by OnScale) ThruDB (document, written in C++, used by JunkDepot.com) Tokyo Cabinet/Tokyo Tyrant (key-value, written in C, used by Mixi.jp (Japanese social networking site)) I'd like to know about specific problems you - the SO reader - have solved using data stores and what NoSQL data store you used. Questions: What scalability problems have you used NoSQL data stores to solve? What NoSQL data store did you use? What database did you use before switching to a NoSQL data store? I'm looking for first-hand experiences, so please do not answer unless you have that.

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  • noSQL/SQL/RoR: Trying to build scalable ratings table for the game

    - by alexeypro
    I am trying to solve complex thing (as it looks to me). I have next entities: PLAYER (few of them, with names like "John", "Peter", etc.). Each has unique ID. For simplicity let's think it's their name. GAME (few of them, say named "Hide and Seek", "Jump and Run", etc.). Same - each has unique ID. For simplicity of the case let it be it's name for now. SCORE (it's numeric). So, how it works. Each PLAYER can play in multiple GAMES. He gets some SCORE in every GAME. I need to build rating table -- and not one! Table #1: most played GAMES Table #2: best PLAYERS in all games (say the total SCORE in every GAME). Table #3: best PLAYERS per GAME (by SCORE in particularly that GAME). I could be build something straight right away, but that will not work. I will have more than 10,000 players; and 15 games, which will grow for sure. Score can be as low as 0, and as high as 1,000,000 (not sure if higher is possible at this moment) for player in the game. So I really need some relative data. Any suggestions? I am planning to do it with SQL, but may be just using it for key-value storage; anything -- any ideas are welcome. Thank you!

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  • Embedded non-relational (nosql) data store

    - by Igor Brejc
    I'm thinking about using/implementing some kind of an embedded key-value (or document) store for my Windows desktop application. I want to be able to store various types of data (GPS tracks would be one example) and of course be able to query this data. The amount of data would be such that it couldn't all be loaded into memory at the same time. I'm thinking about using sqlite as a storage engine for a key-value store, something like y-serial, but written in .NET. I've also read about FriendFeed's usage of MySQL to store schema-less data, which is a good pointer on how to use RDBMS for non-relational data. sqlite seems to be a good option because of its simplicity, portability and library size. My question is whether there are any other options for an embedded non-relational store? It doesn't need to be distributable and it doesn't have to support transactions, but it does have to be accessible from .NET and it should have a small download size. UPDATE: I've found an article titled SQLite as a Key-Value Database which compares sqlite with Berkeley DB, which is an embedded key-value store library.

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  • SQL (MySQL) vs NoSQL (CouchDB)

    - by christopher-mccann
    I am in the middle of designing a highly-scalable application which must store a lot of data. Just for example it will store lots about users and then things like a lot of their messages, comments etc. I have always used MySQL before but now I am minded to try something new like couchdb or similar which is not SQL. Does anyone have any thoughts or guidance on this?

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  • Schema-less design guidelines for Google App Engine Datastore and other NoSQL DBs

    - by jamesaharvey
    Coming from a relational database background, as I'm sure many others are, I'm looking for some solid guidelines for setting up / designing my datastore on Google App Engine. Are there any good rules of thumb people have for setting up these kinds of schema-less data stores? I understand some of the basics such as denormalizing since you can't do joins, but I was wondering what other recommendations people had. The particular simple example I am working with concerns storing searches and their results. For example I have the following 2 models defined in my Google App Engine app using Python: class Search(db.Model): who = db.StringProperty() what = db.StringProperty() where = db.StringProperty() createDate = db.DateTimeProperty(auto_now_add=True) class SearchResult(db.Model): title = db.StringProperty() content = db.StringProperty() who = db.StringProperty() what = db.StringProperty() where = db.StringProperty() createDate = db.DateTimeProperty(auto_now_add=True) I'm duplicating a bunch of properties between the models for the sake of denormalization since I can't join Search and SearchResult together. Does this make sense? Or should I store a search ID in the SearchResult model and effectively 'join' the 2 models in code when I retrieve them from the datastore? Please keep in mind that this is a simple example. Both models will have a lot more properties and the way I'm approaching this right now, I would put any property I put in the Search model in the SearchResult model as well.

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  • noSQL AMazon ec2 (Any suggestions?)

    - by terence410
    There are a lot of discussion on this but I still don't have clear idea what is the best solution. I am currently considering MongoDB. Do you think it's good? What about Cassandra? Besides, ThruDB looks good but seems there is no official release.

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  • Tutorial: Getting Started with the NoSQL JavaScript / Node.js API for MySQL Cluster

    - by Mat Keep
    Tutorial authored by Craig Russell and JD Duncan  The MySQL Cluster team are working on a new NoSQL JavaScript connector for MySQL. The objectives are simplicity and high performance for JavaScript users: - allows end-to-end JavaScript development, from the browser to the server and now to the world's most popular open source database - native "NoSQL" access to the storage layer without going first through SQL transformations and parsing. Node.js is a complete web platform built around JavaScript designed to deliver millions of client connections on commodity hardware. With the MySQL NoSQL Connector for JavaScript, Node.js users can easily add data access and persistence to their web, cloud, social and mobile applications. While the initial implementation is designed to plug and play with Node.js, the actual implementation doesn't depend heavily on Node, potentially enabling wider platform support in the future. Implementation The architecture and user interface of this connector are very different from other MySQL connectors in a major way: it is an asynchronous interface that follows the event model built into Node.js. To make it as easy as possible, we decided to use a domain object model to store the data. This allows for users to query data from the database and have a fully-instantiated object to work with, instead of having to deal with rows and columns of the database. The domain object model can have any user behavior that is desired, with the NoSQL connector providing the data from the database. To make it as fast as possible, we use a direct connection from the user's address space to the database. This approach means that no SQL (pun intended) is needed to get to the data, and no SQL server is between the user and the data. The connector is being developed to be extensible to multiple underlying database technologies, including direct, native access to both the MySQL Cluster "ndb" and InnoDB storage engines. The connector integrates the MySQL Cluster native API library directly within the Node.js platform itself, enabling developers to seamlessly couple their high performance, distributed applications with a high performance, distributed, persistence layer delivering 99.999% availability. The following sections take you through how to connect to MySQL, query the data and how to get started. Connecting to the database A Session is the main user access path to the database. You can get a Session object directly from the connector using the openSession function: var nosql = require("mysql-js"); var dbProperties = {     "implementation" : "ndb",     "database" : "test" }; nosql.openSession(dbProperties, null, onSession); The openSession function calls back into the application upon creating a Session. The Session is then used to create, delete, update, and read objects. Reading data The Session can read data from the database in a number of ways. If you simply want the data from the database, you provide a table name and the key of the row that you want. For example, consider this schema: create table employee (   id int not null primary key,   name varchar(32),   salary float ) ENGINE=ndbcluster; Since the primary key is a number, you can provide the key as a number to the find function. function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find('employee', 0, onData); }; function onData = function(err, data) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(data));   ... use data in application }; If you want to have the data stored in your own domain model, you tell the connector which table your domain model uses, by specifying an annotation, and pass your domain model to the find function. var annotations = new nosql.Annotations(); function Employee = function(id, name, salary) {   this.id = id;   this.name = name;   this.salary = salary;   this.giveRaise = function(percent) {     this.salary *= percent;   } }; annotations.mapClass(Employee, {'table' : 'employee'}); function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find(Employee, 0, onData); }; Updating data You can update the emp instance in memory, but to make the raise persistent, you need to write it back to the database, using the update function. function onData = function(err, emp) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(emp));   emp.giveRaise(0.12); // gee, thanks!   session.update(emp); // oops, session is out of scope here }; Using JavaScript can be tricky because it does not have the concept of block scope for variables. You can create a closure to handle these variables, or use a feature of the connector to remember your variables. The connector api takes a fixed number of parameters and returns a fixed number of result parameters to the callback function. But the connector will keep track of variables for you and return them to the callback. So in the above example, change the onSession function to remember the session variable, and you can refer to it in the onData function: function onSession = function(err, session) {   if (err) {     console.log(err);     ... error handling   }   session.find(Employee, 0, onData, session); }; function onData = function(err, emp, session) {   if (err) {     console.log(err);     ... error handling   }   console.log('Found: ', JSON.stringify(emp));   emp.giveRaise(0.12); // gee, thanks!   session.update(emp, onUpdate); // session is now in scope }; function onUpdate = function(err, emp) {   if (err) {     console.log(err);     ... error handling   } Inserting data Inserting data requires a mapped JavaScript user function (constructor) and a session. Create a variable and persist it: function onSession = function(err, session) {   var data = new Employee(999, 'Mat Keep', 20000000);   session.persist(data, onInsert);   } }; Deleting data To remove data from the database, use the session remove function. You use an instance of the domain object to identify the row you want to remove. Only the key field is relevant. function onSession = function(err, session) {   var key = new Employee(999);   session.remove(Employee, onDelete);   } }; More extensive queries We are working on the implementation of more extensive queries along the lines of the criteria query api. Stay tuned. How to evaluate The MySQL Connector for JavaScript is available for download from labs.mysql.com. Select the build: MySQL-Cluster-NoSQL-Connector-for-Node-js You can also clone the project on GitHub Since it is still early in development, feedback is especially valuable (so don't hesitate to leave comments on this blog, or head to the MySQL Cluster forum). Try it out and see how easy (and fast) it is to integrate MySQL Cluster into your Node.js platforms. You can learn more about other previewed functionality of MySQL Cluster 7.3 here

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  • NoSQL

    - by NoReasoning
    Last night, (Tuesday, June 28), at the KC .NET User group meeting, George Westwater gave a terrific presentation on NoSQL. The best way to define it (the best way is to see George explain it, and he says he will record his presentation and make it available through his blog – link above)  is databases  that does not use relational technology. And his point, and this is true – I have been around awhile – is that non-relational databases have been used for over 50 years in the business. He points out that Wall Street firms have been using non-relational technology ever since they started using computers. IBM still fully supports IMS, now in version 11 (12 is in beta), because these firms are still using this product and will continue to do so for a long time. Of course, like a lot of computer business technology, there are a lot of new NoSQL products available these days, simply as a reaction to the problems of scaling relational databases for internet use. As a result, it almost looks as though NoSQL is something new. And there are a lot, I mean a LOT, I mean a L-O-T , of new products out there for this technology. The best resource to cover all of these products is http://nosql-database.org/, which has a huge listing of what is available. My interest in the subject is primarily due to my interest in Windows Azure and the fact that Windows Azure storage is all non-relational, even the table storage. It is very fascinating and most of all, far cheaper than using SQL Azure for storage in the “cloud."

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  • Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the Cloud in the Big Data Story. In this article we will understand the role of Operational Databases Supporting Big Data Story. Even though we keep on talking about Big Data architecture, it is extremely crucial to understand that Big Data system can’t just exist in the isolation of itself. There are many needs of the business can only be fully filled with the help of the operational databases. Just having a system which can analysis big data may not solve every single data problem. Real World Example Think about this way, you are using Facebook and you have just updated your information about the current relationship status. In the next few seconds the same information is also reflected in the timeline of your partner as well as a few of the immediate friends. After a while you will notice that the same information is now also available to your remote friends. Later on when someone searches for all the relationship changes with their friends your change of the relationship will also show up in the same list. Now here is the question – do you think Big Data architecture is doing every single of these changes? Do you think that the immediate reflection of your relationship changes with your family member is also because of the technology used in Big Data. Actually the answer is Facebook uses MySQL to do various updates in the timeline as well as various events we do on their homepage. It is really difficult to part from the operational databases in any real world business. Now we will see a few of the examples of the operational databases. Relational Databases (This blog post) NoSQL Databases (This blog post) Key-Value Pair Databases (Tomorrow’s post) Document Databases (Tomorrow’s post) Columnar Databases (The Day After’s post) Graph Databases (The Day After’s post) Spatial Databases (The Day After’s post) Relational Databases We have earlier discussed about the RDBMS role in the Big Data’s story in detail so we will not cover it extensively over here. Relational Database is pretty much everywhere in most of the businesses which are here for many years. The importance and existence of the relational database are always going to be there as long as there are meaningful structured data around. There are many different kinds of relational databases for example Oracle, SQL Server, MySQL and many others. If you are looking for Open Source and widely accepted database, I suggest to try MySQL as that has been very popular in the last few years. I also suggest you to try out PostgreSQL as well. Besides many other essential qualities PostgreeSQL have very interesting licensing policies. PostgreSQL licenses allow modifications and distribution of the application in open or closed (source) form. One can make any modifications and can keep it private as well as well contribute to the community. I believe this one quality makes it much more interesting to use as well it will play very important role in future. Nonrelational Databases (NOSQL) We have also covered Nonrelational Dabases in earlier blog posts. NoSQL actually stands for Not Only SQL Databases. There are plenty of NoSQL databases out in the market and selecting the right one is always very challenging. Here are few of the properties which are very essential to consider when selecting the right NoSQL database for operational purpose. Data and Query Model Persistence of Data and Design Eventual Consistency Scalability Though above all of the properties are interesting to have in any NoSQL database but the one which most attracts to me is Eventual Consistency. Eventual Consistency RDBMS uses ACID (Atomicity, Consistency, Isolation, Durability) as a key mechanism for ensuring the data consistency, whereas NonRelational DBMS uses BASE for the same purpose. Base stands for Basically Available, Soft state and Eventual consistency. Eventual consistency is widely deployed in distributed systems. It is a consistency model used in distributed computing which expects unexpected often. In large distributed system, there are always various nodes joining and various nodes being removed as they are often using commodity servers. This happens either intentionally or accidentally. Even though one or more nodes are down, it is expected that entire system still functions normally. Applications should be able to do various updates as well as retrieval of the data successfully without any issue. Additionally, this also means that system is expected to return the same updated data anytime from all the functioning nodes. Irrespective of when any node is joining the system, if it is marked to hold some data it should contain the same updated data eventually. As per Wikipedia - Eventual consistency is a consistency model used in distributed computing that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. In other words -  Informally, if no additional updates are made to a given data item, all reads to that item will eventually return the same value. Tomorrow In tomorrow’s blog post we will discuss about various other Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • NoSQL Java API for MySQL Cluster: Questions & Answers

    - by Mat Keep
    The MySQL Cluster engineering team recently ran a live webinar, available now on-demand demonstrating the ClusterJ and ClusterJPA NoSQL APIs for MySQL Cluster, and how these can be used in building real-time, high scale Java-based services that require continuous availability. Attendees asked a number of great questions during the webinar, and I thought it would be useful to share those here, so others are also able to learn more about the Java NoSQL APIs. First, a little bit about why we developed these APIs and why they are interesting to Java developers. ClusterJ and Cluster JPA ClusterJ is a Java interface to MySQL Cluster that provides either a static or dynamic domain object model, similar to the data model used by JDO, JPA, and Hibernate. A simple API gives users extremely high performance for common operations: insert, delete, update, and query. ClusterJPA works with ClusterJ to extend functionality, including - Persistent classes - Relationships - Joins in queries - Lazy loading - Table and index creation from object model By eliminating data transformations via SQL, users get lower data access latency and higher throughput. In addition, Java developers have a more natural programming method to directly manage their data, with a complete, feature-rich solution for Object/Relational Mapping. As a result, the development of Java applications is simplified with faster development cycles resulting in accelerated time to market for new services. MySQL Cluster offers multiple NoSQL APIs alongside Java: - Memcached for a persistent, high performance, write-scalable Key/Value store, - HTTP/REST via an Apache module - C++ via the NDB API for the lowest absolute latency. Developers can use SQL as well as NoSQL APIs for access to the same data set via multiple query patterns – from simple Primary Key lookups or inserts to complex cross-shard JOINs using Adaptive Query Localization Marrying NoSQL and SQL access to an ACID-compliant database offers developers a number of benefits. MySQL Cluster’s distributed, shared-nothing architecture with auto-sharding and real time performance makes it a great fit for workloads requiring high volume OLTP. Users also get the added flexibility of being able to run real-time analytics across the same OLTP data set for real-time business insight. OK – hopefully you now have a better idea of why ClusterJ and JPA are available. Now, for the Q&A. Q & A Q. Why would I use Connector/J vs. ClusterJ? A. Partly it's a question of whether you prefer to work with SQL (Connector/J) or objects (ClusterJ). Performance of ClusterJ will be better as there is no need to pass through the MySQL Server. A ClusterJ operation can only act on a single table (e.g. no joins) - ClusterJPA extends that capability Q. Can I mix different APIs (ie ClusterJ, Connector/J) in our application for different query types? A. Yes. You can mix and match all of the API types, SQL, JDBC, ODBC, ClusterJ, Memcached, REST, C++. They all access the exact same data in the data nodes. Update through one API and new data is instantly visible to all of the others. Q. How many TCP connections would a SessionFactory instance create for a cluster of 8 data nodes? A. SessionFactory has a connection to the mgmd (management node) but otherwise is just a vehicle to create Sessions. Without using connection pooling, a SessionFactory will have one connection open with each data node. Using optional connection pooling allows multiple connections from the SessionFactory to increase throughput. Q. Can you give details of how Cluster J optimizes sharding to enhance performance of distributed query processing? A. Each data node in a cluster runs a Transaction Coordinator (TC), which begins and ends the transaction, but also serves as a resource to operate on the result rows. While an API node (such as a ClusterJ process) can send queries to any TC/data node, there are performance gains if the TC is where most of the result data is stored. ClusterJ computes the shard (partition) key to choose the data node where the row resides as the TC. Q. What happens if we perform two primary key lookups within the same transaction? Are they sent to the data node in one transaction? A. ClusterJ will send identical PK lookups to the same data node. Q. How is distributed query processing handled by MySQL Cluster ? A. If the data is split between data nodes then all of the information will be transparently combined and passed back to the application. The session will connect to a data node - typically by hashing the primary key - which then interacts with its neighboring nodes to collect the data needed to fulfil the query. Q. Can I use Foreign Keys with MySQL Cluster A. Support for Foreign Keys is included in the MySQL Cluster 7.3 Early Access release Summary The NoSQL Java APIs are packaged with MySQL Cluster, available for download here so feel free to take them for a spin today! Key Resources MySQL Cluster on-line demo  MySQL ClusterJ and JPA On-demand webinar  MySQL ClusterJ and JPA documentation MySQL ClusterJ and JPA whitepaper and tutorial

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  • EclipseLink 2.4 Released: RESTful Persistence, Tenant Isolation, NoSQL, and JSON

    - by arungupta
    EclipseLink 2.4 is released as part of Eclipse Juno release train. In addition to providing the Reference Implementation for JPA 2.0, the key features in the release are: RESTful Persistence - Expose Java Persistence units over REST using either JSON or XML Tenant Isolation - Manage entities for multiple tenants in the same application NoSQL - NoSQL support for MongoDB and Oracle NoSQL JSON - Marshaling and unmarshaling of JSON object Here is the complete list of bugs fixed in this release. The landing page provide the complete list of documentation and examples. Read Doug Clarke's blog for a color commentary as well. This release is already integrated in the latest GlassFish 4.0 promoted build. Try the functionality and give us feedback at GlassFish Forum or EclipseLink Forum.

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  • NoSQL : JSON, indexation distribuée et géoréplication débarquent dans Couchbase, le concurrent de MongoDB

    Base de données NoSQL : documents JSON, indexation distribuée et géoréplication débarquent dans Couchbase Le concurrent de MongoDB Couchbase Server, le système de gestion de bases de données NoSQL, vient de subir une mise à jour assez importante. La version 2.0 de Couchbase introduit un modèle de stockage de documents et un magasin clé-valeur (key-value), permettant à l'outil de faire un grand pas dans le support du Big Data (gros volumes de données). Pour rappel, CouchBase est un projet initialement basé sur le système noSQL Apache CouchDB, à la différence que le code Erlang de CouchDB a été entièrement réécrit en C++, avec des ajustements et ajouts en tirant profit du système de ...

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  • Google I/O 2012 - SQL vs NoSQL: Battle of the Backends

    Google I/O 2012 - SQL vs NoSQL: Battle of the Backends Ken Ashcraft, Alfred Fuller Google App Engine now offers both SQL and NoSQL data storage -- but which is right for your application? Advocates of each try to settle the issue once and for all, and show some of the tricks for getting the most out of each. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 2394 38 ratings Time: 43:09 More in Science & Technology

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  • NoSQL: How to retrieve a 'house' based on lat & long?

    - by Tedk
    I have a NoSQL system for storing real estate houses. One piece of information I have in my key-value store for each house is the longitude and latitude. If I wanted to retrieve all houses within a geo-lat/long box, like the SQL below: SELECT * from houses WHERE latitude IS BETWEEN xxx AND yyy AND longitude IS BETWEEN www AND zzz Question: How would I do this type of retrival with NoSQL ... using just a key-value store system? Even if I could do this with NoSQL, would it even be efficient or would simply going back to using a tradition database retrieve this type of information faster?

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  • Is there is something like stored procedures in NOSQL databases?

    - by Amr ElGarhy
    I am new to NOSQL world and still comparing between nosql and sql databases, I Just tried making few samples using mongodb. I am asking about stored procedures when we send few parameters to one stored procedure and this procedure execute number of other stored procedures in the database, will get data from stored procedures and send data to others. In other words, will make the logic happen on the database side using sequence of functions and stored procedures. Is that behavior or something the same already exist on NOSQL databases, or its completely different and i am thinking in the wrong way?

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