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• Is there a maven 2 archetype for spring 3 MVC applications?

  - by gregor

  I'm experimenting with the spring 3 MVC framework. Since i use maven 2 to manage my project, i'm searching for a archetype to create a spring 3 MVC application.

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• Which of CouchDB or MongoDB suits my needs?

  - by vonconrad

  Where I work, we use Ruby on Rails to create both backend and frontend applications. Usually, these applications interact with the same MySQL database. It works great for a majority of our data, but we have one situation which I would like to move to a NoSQL environment. We have clients, and our clients have what we call "inventories"--one or more of them. An inventory can have many thousands of items. This is currently done through two relational database tables, inventories and inventory_items. The problems start when two different inventories have different parameters: # Inventory item from inventory 1, televisions { inventory_id: 1 sku: 12345 name: Samsung LCD 40 inches model: 582903-4 brand: Samsung screen_size: 40 type: LCD price: 999.95 } # Inventory item from inventory 2, accomodation { inventory_id: 2 sku: 48cab23fa name: New York Hilton accomodation_type: hotel star_rating: 5 price_per_night: 395 } Since we obviously can't use brand or star_rating as the column name in inventory_items, our solution so far has been to use generic column names such as text_a, text_b, float_a, int_a, etc, and introduce a third table, inventory_schemas. The tables now look like this: # Inventory schema for inventory 1, televisions { inventory_id: 1 int_a: sku text_a: name text_b: model text_c: brand int_b: screen_size text_d: type float_a: price } # Inventory item from inventory 1, televisions { inventory_id: 1 int_a: 12345 text_a: Samsung LCD 40 inches text_b: 582903-4 text_c: Samsung int_a: 40 text_d: LCD float_a: 999.95 } This has worked well... up to a point. It's clunky, it's unintuitive and it lacks scalability. We have to devote resources to set up inventory schemas. Using separate tables is not an option. Enter NoSQL. With it, we could let each and every item have their own parameters and still store them together. From the research I've done, it certainly seems like a great alterative for this situation. Specifically, I've looked at CouchDB and MongoDB. Both look great. However, there are a few other bits and pieces we need to be able to do with our inventory: We need to be able to select items from only one (or several) inventories. We need to be able to filter items based on its parameters (eg. get all items from inventory 2 where type is 'hotel'). We need to be able to group items based on parameters (eg. get the lowest price from items in inventory 1 where brand is 'Samsung'). We need to (potentially) be able to retrieve thousands of items at a time. We need to be able to access the data from multiple applications; both backend (to process data) and frontend (to display data). Rapid bulk insertion is desired, though not required. Based on the structure, and the requirements, are either CouchDB or MongoDB suitable for us? If so, which one will be the best fit? Thanks for reading, and thanks in advance for answers. EDIT: One of the reasons I like CouchDB is that it would be possible for us in the frontend application to request data via JavaScript directly from the server after page load, and display the results without having to use any backend code whatsoever. This would lead to better page load and less server strain, as the fetching/processing of the data would be done client-side.

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• Use spring tag in XSLT

  - by X-Pippes

  I have a XSL/XML parser to produce jsp/html code. Using MVC model I need to accees spring library in order to perform i18n translation. Thus, given the xml <a> ... <country>EN</country> ... </a> and using <spring:message code="table_country_code.EN"/> tag, choose based on the browser language, the transalation into England, Inglaterra, etc... However, the XSL do not support <spring:message> tag. The idea is to have a XSLT with something like this <spring:message code="table_country_code.><xsl:value-of select="country"/>"/>` I also tried to create the spring tag in Java when I make a parse to create the XML but I sill have the same error. ERROR [STDERR] (http-0.0.0.0-8080-1) file:///C:/Software/Jboss/jboss-soa-p-5/jboss-as/bin/jstl:; Line #5; Column #58; The prefix "spring" for element "spring:message" is not bound. How can I resolve?

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• How to create TestContext for Spring Test?

  - by HDave

  Newcomer to Spring here, so pardon me if this is a stupid question. I have a relatively small Java library that implements a few dozen beans (no database or GUI). I have created a Spring Bean configuration file that other Java projects use to inject my beans into their stuff. I am now for the first time trying to use Spring Test to inject some of these beans into my junit test classes (rather than simply instantiating them). I am doing this partly to learn Spring Test and partly to force the tests to use the same bean configuration file I provide for others. In the Spring documentation is says I need to create an application context using the "TestContext" class that comes with Spring. I believe this should be done in a spring XML file that I reference via the @ContextConfiguration annotation on my test class. @ContextConfiguration({"/test-applicationContext.xml"}) However, there is no hint as to what to put in the file! When I go to run my tests from within Eclipse it errors out saying "failed to load Application Context"....of course.

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• Spring validation has error in XML document from ServletContext resource

  - by user1441404

  I applied spring validation in my registration page .but the follwing error are shown in my server log of my app engine server. javax.servlet.UnavailableException: org.springframework.beans.factory.xml.XmlBeanDefinitionStoreException: Line 22 in XML document from ServletContext resource [/WEB-INF/spring/appServlet/servlet-context.xml] is invalid; nested exception is org.xml.sax.SAXParseException; lineNumber: 22; columnNumber: 30; cvc-complex-type.2.4.c: The matching wildcard is strict, but no declaration can be found for element 'property'. My code is given below : <?xml version="1.0" encoding="UTF-8"?> <beans:beans xmlns="http://www.springframework.org/schema/mvc" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:beans="http://www.springframework.org/schema/beans" xmlns:context="http://www.springframework.org/schema/context" xsi:schemaLocation="http://www.springframework.org/schema/mvc http://www.springframework.org/schema/mvc/spring-mvc-3.0.xsd http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans-3.0.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context-3.0.xsd http://www.springframework.org/schema/aop http://www.springframework.org/schema/aop/spring-aop-3.0.xsd http://www.springframework.org/schema/jee http://www.springframework.org/schema/jee/spring-jee-3.0.xsd > <beans:bean name="/register" class="com.my.registration.NewUserRegistration"> <property name="validator"> <bean class="com.my.validation.UserValidator" /> </property> <beans:property name="formView" value="newuser"></beans:property> <beans:property name="successView" value="home"></beans:property> </beans:bean> <beans:bean class="org.springframework.web.servlet.view.InternalResourceViewResolver"> <beans:property name="prefix" value="/WEB-INF/views/" /> <beans:property name="suffix" value=".jsp" /> </beans:bean> </beans:beans>

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• Big GRC: Turning Data into Actionable GRC Intelligence

  - by Jenna Danko

  While it’s no longer headline news that Governments have carried out large scale data-mining programmes aimed at terrorism detection and identifying other patterns of interest across a wide range of digital data sources, the debate over the ethics and justification over this action, will clearly continue for some time to come. What is becoming clear is that these programmes are a framework for the collation and aggregation of massive amounts of unstructured data and from this, the creation of actionable intelligence from analyses that allowed the analysts to explore and extract a variety of patterns and then direct resources. This data included audio and video chats, phone calls, photographs, e-mails, documents, internet searches, social media posts and mobile phone logs and connections. Although Governance, Risk and Compliance (GRC) professionals are not looking at the implementation of such programmes, there are many similar GRC “Big data” challenges to be faced and potential lessons to be learned from these high profile government programmes that can be applied a lot closer to home. For example, how can GRC professionals collect, manage and analyze an enormous and disparate volume of data to create and manage their own actionable intelligence covering hidden signs and patterns of criminal activity, the early or retrospective, violation of regulations/laws/corporate policies and procedures, emerging risks and weakening controls etc. Not exactly the stuff of James Bond to be sure, but it is certainly more applicable to most GRC professional’s day to day challenges. So what is Big Data and how can it benefit the GRC process? Although it often varies, the definition of Big Data largely refers to the following types of data: Traditional Enterprise Data – includes customer information from CRM systems, transactional ERP data, web store transactions, and general ledger data. Machine-Generated /Sensor Data – includes Call Detail Records (“CDR”), weblogs and trading systems data. Social Data – includes customer feedback streams, micro-blogging sites like Twitter, and social media platforms like Facebook. The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020. But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, according to sources such as Forrester there are four key characteristics that define big data: Volume. Machine-generated data is produced in much larger quantities than non-traditional data. This is all the data generated by IT systems that power the enterprise. This includes live data from packaged and custom applications – for example, app servers, Web servers, databases, networks, virtual machines, telecom equipment, and much more. Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management as well as offering early insight into potential reputational risk issues. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day) need to be managed. Variety. Traditional data formats tend to be relatively well defined by a data schema and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. Without question, all GRC professionals work in a dynamic environment and as new services, new products, new business lines are added or new marketing campaigns executed for example, new data types are needed to capture the resultant information.  Value. The economic value of data varies significantly. Typically, there is good information hidden amongst a larger body of non-traditional data that GRC professionals can use to add real value to the organisation; the greater challenge is identifying what is valuable and then transforming and extracting that data for analysis and action. For example, customer service calls and emails have millions of useful data points and have long been a source of information to GRC professionals. Those calls and emails are critical in helping GRC professionals better identify hidden patterns and implement new policies that can reduce the amount of customer complaints.   Now on a scale and depth far beyond those in place today, all that unstructured call and email data can be captured, stored and analyzed to reveal the reasons for the contact, perhaps with the aggregated customer results cross referenced against what is being said about the organization or a similar peer organization on social media. The organization can then take positive actions, communicating to the market in advance of issues reaching the press, strengthening controls, adjusting risk profiles, changing policy and procedures and completely minimizing, if not eliminating, complaints and compensation for that specific reason in the future. In this one example of many similar ones, the GRC team(s) has demonstrated real and tangible business value. Big Challenges - Big Opportunities As pointed out by recent Forrester research, high performing companies (those that are growing 15% or more year-on-year compared to their peers) are taking a selective approach to investing in Big Data.  "Tomorrow's winners understand this, and they are making selective investments aimed at specific opportunities with tangible benefits where big data offers a more economical solution to meet a need." (Forrsights Strategy Spotlight: Business Intelligence and Big Data, Q4 2012) As pointed out earlier, with the ever increasing volume of regulatory demands and fines for getting it wrong, limited resource availability and out of date or inadequate GRC systems all contributing to a higher cost of compliance and/or higher risk profile than desired – a big data investment in GRC clearly falls into this category. However, to make the most of big data organizations must evolve both their business and IT procedures, processes, people and infrastructures to handle these new high-volume, high-velocity, high-variety sources of data and be able integrate them with the pre-existing company data to be analyzed. GRC big data clearly allows the organization access to and management over a huge amount of often very sensitive information that although can help create a more risk intelligent organization, also presents numerous data governance challenges, including regulatory compliance and information security. In addition to client and regulatory demands over better information security and data protection the sheer amount of information organizations deal with the need to quickly access, classify, protect and manage that information can quickly become a key issue  from a legal, as well as technical or operational standpoint. However, by making information governance processes a bigger part of everyday operations, organizations can make sure data remains readily available and protected. The Right GRC & Big Data Partnership Becomes Key  The "getting it right first time" mantra used in so many companies remains essential for any GRC team that is sponsoring, helping kick start, or even overseeing a big data project. To make a big data GRC initiative work and get the desired value, partnerships with companies, who have a long history of success in delivering successful GRC solutions as well as being at the very forefront of technology innovation, becomes key. Clearly solutions can be built in-house more cheaply than through vendor, but as has been proven time and time again, when it comes to self built solutions covering AML and Fraud for example, few have able to scale or adapt appropriately to meet the changing regulations or challenges that the GRC teams face on a daily basis. This has led to the creation of GRC silo’s that are causing so many headaches today. The solutions that stand out and should be explored are the ones that can seamlessly merge the traditional world of well-known data, analytics and visualization with the new world of seemingly innumerable data sources, utilizing Big Data technologies to generate new GRC insights right across the enterprise.Ultimately, Big Data is here to stay, and organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be the ones that are well positioned to make the most of it. A Blueprint and Roadmap Service for Big Data Big data adoption is first and foremost a business decision. As such it is essential that your partner can align your strategies, goals, and objectives with an architecture vision and roadmap to accelerate adoption of big data for your environment, as well as establish practical, effective governance that will maintain a well managed environment going forward. Key Activities: While your initiatives will clearly vary, there are some generic starting points the team and organization will need to complete: Clearly define your drivers, strategies, goals, objectives and requirements as it relates to big data Conduct a big data readiness and Information Architecture maturity assessment Develop future state big data architecture, including views across all relevant architecture domains; business, applications, information, and technology Provide initial guidance on big data candidate selection for migrations or implementation Develop a strategic roadmap and implementation plan that reflects a prioritization of initiatives based on business impact and technology dependency, and an incremental integration approach for evolving your current state to the target future state in a manner that represents the least amount of risk and impact of change on the business Provide recommendations for practical, effective Data Governance, Data Quality Management, and Information Lifecycle Management to maintain a well-managed environment Conduct an executive workshop with recommendations and next steps There is little debate that managing risk and data are the two biggest obstacles encountered by financial institutions.  Big data is here to stay and risk management certainly is not going anywhere, and ultimately financial services industry organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be best positioned to make the most of it. Matthew Long is a Financial Crime Specialist for Oracle Financial Services. He can be reached at matthew.long AT oracle.com.

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• Introducing Data Annotations Extensions

  - by srkirkland

  Validation of user input is integral to building a modern web application, and ASP.NET MVC offers us a way to enforce business rules on both the client and server using Model Validation.  The recent release of ASP.NET MVC 3 has improved these offerings on the client side by introducing an unobtrusive validation library built on top of jquery.validation.  Out of the box MVC comes with support for Data Annotations (that is, System.ComponentModel.DataAnnotations) and can be extended to support other frameworks.  Data Annotations Validation is becoming more popular and is being baked in to many other Microsoft offerings, including Entity Framework, though with MVC it only contains four validators: Range, Required, StringLength and Regular Expression.  The Data Annotations Extensions project attempts to augment these validators with additional attributes while maintaining the clean integration Data Annotations provides. A Quick Word About Data Annotations Extensions The Data Annotations Extensions project can be found at http://dataannotationsextensions.org/, and currently provides 11 additional validation attributes (ex: Email, EqualTo, Min/Max) on top of Data Annotations’ original 4.  You can find a current list of the validation attributes on the afore mentioned website. The core library provides server-side validation attributes that can be used in any .NET 4.0 project (no MVC dependency). There is also an easily pluggable client-side validation library which can be used in ASP.NET MVC 3 projects using unobtrusive jquery validation (only MVC3 included javascript files are required). On to the Preview Let’s say you had the following “Customer” domain model (or view model, depending on your project structure) in an MVC 3 project: public class Customer { public string Email { get; set; } public int Age { get; set; } public string ProfilePictureLocation { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } When it comes time to create/edit this Customer, you will probably have a CustomerController and a simple form that just uses one of the Html.EditorFor() methods that the ASP.NET MVC tooling generates for you (or you can write yourself).  It should look something like this: With no validation, the customer can enter nonsense for an email address, and then can even report their age as a negative number!  With the built-in Data Annotations validation, I could do a bit better by adding a Range to the age, adding a RegularExpression for email (yuck!), and adding some required attributes.  However, I’d still be able to report my age as 10.75 years old, and my profile picture could still be any string.  Let’s use Data Annotations along with this project, Data Annotations Extensions, and see what we can get: public class Customer { [Email] [Required] public string Email { get; set; }   [Integer] [Min(1, ErrorMessage="Unless you are benjamin button you are lying.")] [Required] public int Age { get; set; }   [FileExtensions("png|jpg|jpeg|gif")] public string ProfilePictureLocation { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now let’s try to put in some invalid values and see what happens: That is very nice validation, all done on the client side (will also be validated on the server).  Also, the Customer class validation attributes are very easy to read and understand. Another bonus: Since Data Annotations Extensions can integrate with MVC 3’s unobtrusive validation, no additional scripts are required! Now that we’ve seen our target, let’s take a look at how to get there within a new MVC 3 project. Adding Data Annotations Extensions To Your Project First we will File->New Project and create an ASP.NET MVC 3 project.  I am going to use Razor for these examples, but any view engine can be used in practice.  Now go into the NuGet Extension Manager (right click on references and select add Library Package Reference) and search for “DataAnnotationsExtensions.”  You should see the following two packages: The first package is for server-side validation scenarios, but since we are using MVC 3 and would like comprehensive sever and client validation support, click on the DataAnnotationsExtensions.MVC3 project and then click Install.  This will install the Data Annotations Extensions server and client validation DLLs along with David Ebbo’s web activator (which enables the validation attributes to be registered with MVC 3). Now that Data Annotations Extensions is installed you have all you need to start doing advanced model validation.  If you are already using Data Annotations in your project, just making use of the additional validation attributes will provide client and server validation automatically.  However, assuming you are starting with a blank project I’ll walk you through setting up a controller and model to test with. Creating Your Model In the Models folder, create a new User.cs file with a User class that you can use as a model.  To start with, I’ll use the following class: public class User { public string Email { get; set; } public string Password { get; set; } public string PasswordConfirm { get; set; } public string HomePage { get; set; } public int Age { get; set; } } Next, create a simple controller with at least a Create method, and then a matching Create view (note, you can do all of this via the MVC built-in tooling).  Your files will look something like this: UserController.cs: public class UserController : Controller { public ActionResult Create() { return View(new User()); }   [HttpPost] public ActionResult Create(User user) { if (!ModelState.IsValid) { return View(user); }   return Content("User valid!"); } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Create.cshtml: @model NuGetValidationTester.Models.User   @{ ViewBag.Title = "Create"; }   <h2>Create</h2>   <script src="@Url.Content("~/Scripts/jquery.validate.min.js")" type="text/javascript"></script> <script src="@Url.Content("~/Scripts/jquery.validate.unobtrusive.min.js")" type="text/javascript"></script>   @using (Html.BeginForm()) { @Html.ValidationSummary(true) <fieldset> <legend>User</legend> @Html.EditorForModel() <p> <input type="submit" value="Create" /> </p> </fieldset> } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } In the Create.cshtml view, note that we are referencing jquery validation and jquery unobtrusive (jquery is referenced in the layout page).  These MVC 3 included scripts are the only ones you need to enjoy both the basic Data Annotations validation as well as the validation additions available in Data Annotations Extensions.  These references are added by default when you use the MVC 3 “Add View” dialog on a modification template type. Now when we go to /User/Create we should see a form for editing a User Since we haven’t yet added any validation attributes, this form is valid as shown (including no password, email and an age of 0).  With the built-in Data Annotations attributes we can make some of the fields required, and we could use a range validator of maybe 1 to 110 on Age (of course we don’t want to leave out supercentenarians) but let’s go further and validate our input comprehensively using Data Annotations Extensions.  The new and improved User.cs model class. { [Required] [Email] public string Email { get; set; }   [Required] public string Password { get; set; }   [Required] [EqualTo("Password")] public string PasswordConfirm { get; set; }   [Url] public string HomePage { get; set; }   [Integer] [Min(1)] public int Age { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now let’s re-run our form and try to use some invalid values: All of the validation errors you see above occurred on the client, without ever even hitting submit.  The validation is also checked on the server, which is a good practice since client validation is easily bypassed. That’s all you need to do to start a new project and include Data Annotations Extensions, and of course you can integrate it into an existing project just as easily. Nitpickers Corner ASP.NET MVC 3 futures defines four new data annotations attributes which this project has as well: CreditCard, Email, Url and EqualTo.  Unfortunately referencing MVC 3 futures necessitates taking an dependency on MVC 3 in your model layer, which may be unadvisable in a multi-tiered project.  Data Annotations Extensions keeps the server and client side libraries separate so using the project’s validation attributes don’t require you to take any additional dependencies in your model layer which still allowing for the rich client validation experience if you are using MVC 3. Custom Error Message and Globalization: Since the Data Annotations Extensions are build on top of Data Annotations, you have the ability to define your own static error messages and even to use resource files for very customizable error messages. Available Validators: Please see the project site at http://dataannotationsextensions.org/ for an up-to-date list of the new validators included in this project.  As of this post, the following validators are available: CreditCard Date Digits Email EqualTo FileExtensions Integer Max Min Numeric Url Conclusion Hopefully I’ve illustrated how easy it is to add server and client validation to your MVC 3 projects, and how to easily you can extend the available validation options to meet real world needs. The Data Annotations Extensions project is fully open source under the BSD license.  Any feedback would be greatly appreciated.  More information than you require, along with links to the source code, is available at http://dataannotationsextensions.org/. Enjoy!

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• Outgrew MongoDB … now what?

  - by samsmith

  We dump debug and transaction logs into mongodb. We really like mongodb because: Blazing insert perf document oriented Ability to let the engine drop inserts when needed for performance But there is this big problem with mongodb: The index must fit in physical RAM. In practice, this limits us to 80-150gb of raw data (we currently run on a system with 16gb RAM). Sooooo, for us to have 500gb or a tb of data, we would need 50gb or 80gb of RAM. Yes, I know this is possible. We can add servers and use mongo sharding. We can buy a special server box that can take 100 or 200 gb of RAM, but this is the tail wagging the dog! We could spend boucoup $$$ on hardware to run FOSS, when SQL Server Express can handle WAY more data on WAY less hardware than Mongo (SQL Server does not meet our architectural desires, or we would use it!) We are not going to spend huge $ on hardware here, because it is necessary only because of the Mongo architecture, not because of the inherent processing/storage needs. (And sharding? Please! Cost aside, who needs the ongoing complexity of three, five, or more servers to manage a relatively small load?) Bottom line: MongoDB is FOSS, but we gotta spend $$$$$$$ on hardware to run it? We sould rather buy commercial SW! I am sure we are not the first to hit this issue, so we ask the community: Where do we go next? (We already run Mongo v2) Thanks!!

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• MongoDb vs Ehcache caching advise for speeding up read only mysql Database

  - by paddydub

  I'm building a Route Planner Webapp using Spring/Hibernate/Tomcat and a mysql database, I have a database containing read only data, such as Bus Stop Coordinates, Bus times which is never updated. I'm trying to make the app run faster, each time the application is run it will preform approx 1000 reads to the database to calculate a route. I have setup a Ehcache which greatly improves the read from database times. I'm now setting terracotta + Ehcache distributed caching to share the cache with multiple Tomcat JVMs. This seems a bit complicated. I've tried memcached but it was not performing as fast as ehcache. I'm wondering if a MongoDb would be better suited. I have no experience with nosql but I would appreciate if anyone has any ideas. All i need is quick access to the read only database.

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• Middleware for MongoDB or CouchDB with jQuery Ajax/JSON frontend

  - by Tauren

  I've been using the following web development stack for a few years: java/spring/hibernate/mysql/jetty/wicket/jquery For certain requirements, I'm considering switching to a NoSQL datastore with an AJAX frontend. I would probably build the frontend with jQuery and communicate with the web application middleware using JSON. I'm leaning toward MongoDB because of more dynamic query capabilities, but am still considering CouchDB. I'm not sure what to use in the middle. Probably something RESTful? My preference is to stick with Java (or maybe Scala or Groovy) since I'm using tools like Drools for rules and Shiro for security. But then again, I want to pick something that is quick an easy to work with, so I'm open to other solutions. If you are building ajax/json/nosql solutions, I'd like to hear details about what tools you are using and any pros/cons you've found to using them. Thanks!

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• How to get the ObjectId value from MongoDB?

  - by LVarayut

  I'm using Jongo with Play framework 2, java. I added some data into my MongoDB. {"_id" : ObjectId("538dafffbf6b562617252178"), ... } However, when I fetched the ObjectId from the database, it gave me like: de.undercouch.bson4jackson.types.ObjectId@484431ff instead of 538dafffbf6b562617252178. I don't quite understand how can I get the ObjectId value. My class is defined as following: public class Product { @JsonProperty("_id") protected String id; ... public Product() { } public String getId() { return id; } public void setId(String id) { this.id = id; } } EDIT In order to fetch the data, I simply use find() function provided by Jongo as following: public static Iterable<Product> findAll(){ return products().find().as(Product.class); }

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• How to "defragment" MongoDB index effectively in production?

  - by dfrankow

  I've been looking at MongoDB. Feels good. I added some indexes to a collection, uploaded a bunch of data, then removed all the data, and I noticed the indexes did not change size, similar to the behavior reported here. If I call db.repairDatabase() the indexes are then squashed to near-zero. Similarly if I don't remove all the data, but call repairDatabase(), the indexes are squashed somewhat (perhaps because unused extends are truncated?). I am getting index size from "totalIndexSize" of db.collection.stats(). However, that takes a long time (I've read it could be hours on a large database). It's unclear to me how available the database is for reads or writes while it is running. I am guessing not so available. Since I want to run as few instances of mongod as possible, I want to understand more about how indexes are managed after deletes. Can anyone point me to anything or give any advice?

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• MongoDB query to return only embedded document

  - by Matt

  assume that i have a BlogPost model with zero-to-many embedded Comment documents. can i query for and have MongoDB return only Comment objects matching my query spec? eg, db.blog_posts.find({"comment.submitter": "some_name"}) returns only a list of comments. edit: an example: import pymongo connection = pymongo.Connection() db = connection['dvds'] db['dvds'].insert({'title': "The Hitchhikers Guide to the Galaxy", 'episodes': [{'title': "Episode 1", 'desc': "..."}, {'title': "Episode 2", 'desc': "..."}, {'title': "Episode 3", 'desc': "..."}, {'title': "Episode 4", 'desc': "..."}, {'title': "Episode 5", 'desc': "..."}, {'title': "Episode 6", 'desc': "..."}]}) episode = db['dvds'].find_one({'episodes.title': "Episode 1"}, fields=['episodes']) in this example, episode is: {u'_id': ObjectId('...'), u'episodes': [{u'desc': u'...', u'title': u'Episode 1'}, {u'desc': u'...', u'title': u'Episode 2'}, {u'desc': u'...', u'title': u'Episode 3'}, {u'desc': u'...', u'title': u'Episode 4'}, {u'desc': u'...', u'title': u'Episode 5'}, {u'desc': u'...', u'title': u'Episode 6'}]} but i just want: {u'desc': u'...', u'title': u'Episode 1'}

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• MongoDB map/reduce counts

  - by ibz

  The output from MongoDB's map/reduce includes something like 'counts': {'input': I, 'emit': E, 'output': O}. I thought I clearly understand what those mean, until I hit a weird case which I can't explain. According to my understanding, counts.input is the number of rows that match the condition (as specified in query). If so, how is it possible that the following two queries have different results? db.mycollection.find({MY_CONDITION}).count() db.mycollection.mapReduce(SOME_MAP, SOME_REDUCE, {'query': {MY_CONDITION}}).counts.input I thought the two should always give the same result, independent of the map and reduce functions, as long as the same condition is used.

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• Adding a MongoDB collection to Netbeans

  - by Saif Bechan

  In Netbeans I have an option to add my mysql databases to netbeans. This way I can easily browse and so small queries. Now I am working on a MongoDB project, and I want to know if it is possible to use the same functionality. I see that on the website of mongo there is a list of drivers, and I see that you can add drivers in netbeans. I do not know if the same thing, or if this can be used. I have tried google, but no luck. Anyone have an idea?

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• Are MongoDB and CouchDB perfect substitutes?

  - by raoulsson

  I haven't got my hands dirty yet with neither CouchDB nor MongoDB but I would like to do so soon... I also have read a bit about both systems and it looks to me like they cover the same cases... Or am I missing a key distinguishing feature? I would like to use a document based storage instead of a traditional RDBMS in my next project. I also need the datastore to handle large binary objects (images and videos) automatically replicate itself to physically separate nodes rendering the need of an additional RDBMS superfluous Are both equally well suited for these requirements? Thanks!

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• How to organise a many to many relationship in MongoDB

  - by Gareth Elms

  I have two tables/collections; Users and Groups. A user can be a member of any number of groups and a user can also be an owner of any number of groups. In a relational database I'd probably have a third table called UserGroups with a UserID column, a GroupID column and an IsOwner column. I'm using MongoDB and I'm sure there is a different approach for this kind of relationship in a document database. Should I embed the list of groups and groups-as-owner inside the Users table as two arrays of ObjectIDs? Should I also store the list of members and owners in the Groups table as two arrays, effectively mirroring the relationship causing a duplication of relationship information? Or is a bridging UserGroups table a legitimate concept in document databases for many to many relationships? Thanks

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• Get averages from pre-aggregated reports in mongodb

  - by Chris

  I've got a database with pre-aggregated metrics similar to the one outlined in this use case: http://docs.mongodb.org/manual/use-cases/pre-aggregated-reports/ I have a daily collection with a subdocument for hour and minute metrics, and a 'metadata.date' entry for midnight on the day it represents. I also have a monthly collection with a day subdocument for each day. If I want to get an average of a metric over the past eight or so days how can I do that with the aggregation framework? Is the aggregation framework not the right tool for this since it's already pre-aggregated?

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• Find objects between two dates MongoDB

  - by Tom

  I've been playing around storing tweets inside mongodb, each object looks like this: { "_id" : ObjectId("4c02c58de500fe1be1000005"), "contributors" : null, "text" : "Hello world", "user" : { "following" : null, "followers_count" : 5, "utc_offset" : null, "location" : "", "profile_text_color" : "000000", "friends_count" : 11, "profile_link_color" : "0000ff", "verified" : false, "protected" : false, "url" : null, "contributors_enabled" : false, "created_at" : "Sun May 30 18:47:06 +0000 2010", "geo_enabled" : false, "profile_sidebar_border_color" : "87bc44", "statuses_count" : 13, "favourites_count" : 0, "description" : "", "notifications" : null, "profile_background_tile" : false, "lang" : "en", "id" : 149978111, "time_zone" : null, "profile_sidebar_fill_color" : "e0ff92" }, "geo" : null, "coordinates" : null, "in_reply_to_user_id" : 149183152, "place" : null, "created_at" : "Sun May 30 20:07:35 +0000 2010", "source" : "web", "in_reply_to_status_id" : { "floatApprox" : 15061797850 }, "truncated" : false, "favorited" : false, "id" : { "floatApprox" : 15061838001 } How would I write a query which checks the *created_at* and finds all objects between 18:47 and 19:00? Do I need to update my documents so the dates are stored in a specific format? Thanks

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• Squid logs on mongodb

  - by user306241

  Hi, I'm planning to log my squid instances to a mongodb, but the actual problem is that we have a huge traffic to be logged, every access authenticated with user/pass. Eventually we have to make some reports based on logs. I was thinking to insert the logs distributed by months and by users, so my collection will look like this: {month: 'april', users: [{user: 'loop0', logs: [{timestamp: 12345678.9, url: 'http://stackoverflow.com/question/ask', ... }]}] So if I want to generate my reports based on the month of april I just have to get the right month instead of looking in zillions of lines to fetch the lines that timestamp match between April, 1 and April, 30. Of course this type of insert will be slower than just insert the log line directly. So my question is: is there a best way to do this? Nowadays we have around 12 million lines of log by day.

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• Cleaning a dataset of song data - what sort of problem is this?

  - by Rob Lourens

  I have a set of data about songs. Each entry is a line of text which includes the artist name, song title, and some extra text. Some entries are only "extra text". My goal is to resolve as many of these as possible to songs on Spotify using their web API. My strategy so far has been to search for the entry via the API - if there are no results, apply a transformation such as "remove all text between ( )" and search again. I have a list of heuristics and I've had reasonable success with this but as the code gets more and more convoluted I keep thinking there must be a more generic and consistent way. I don't know where to look - any suggestions for what to try, topics to study, buzzwords to google?

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• How to use SQLErrorCodeSQLExceptionTranslator and DAO class with @Repository in Spring?

  - by GuidoMB

  I'm using Spring 3.0.2 and I have a class called MovieDAO that uses JDBC to handle the db. I have set the @Repository annotations and I want to convert the SQLException to the Spring's DataAccessException I have the following example: @Repository public class JDBCCommentDAO implements CommentDAO { static JDBCCommentDAO instance; ConnectionManager connectionManager; private JDBCCommentDAO() { connectionManager = new ConnectionManager("org.postgresql.Driver", "postgres", "postgres"); } static public synchronized JDBCCommentDAO getInstance() { if (instance == null) instance = new JDBCCommentDAO(); return instance; } @Override public Collection<Comment> getComments(User user) throws DAOException { Collection<Comment> comments = new ArrayList<Comment>(); try { String query = "SELECT * FROM Comments WHERE Comments.userId = ?"; Connection conn = connectionManager.getConnection(); PreparedStatement stmt = conn.prepareStatement(query); stmt = conn.prepareStatement(query); stmt.setInt(1, user.getId()); ResultSet result = stmt.executeQuery(); while (result.next()) { Movie movie = JDBCMovieDAO.getInstance().getLightMovie(result.getInt("movie")); comments.add(new Comment(result.getString("text"), result.getInt("score"), user, result.getDate("date"), movie)); } connectionManager.closeConnection(conn); } catch (SQLException e) { e.printStackTrace(); //CONVERT TO DATAACCESSEXCEPTION } return comments; } } I Don't know how to get the Translator and I don't want to extends any Spring class, because that is why I'm using the @Repository annotation

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• Augmenting your Social Efforts via Data as a Service (DaaS)

  - by Mike Stiles

  The following is the 3rd in a series of posts on the value of leveraging social data across your enterprise by Oracle VP Product Development Don Springer and Oracle Cloud Data and Insight Service Sr. Director Product Management Niraj Deo. In this post, we will discuss the approach and value of integrating additional “public” data via a cloud-based Data-as-as-Service platform (or DaaS) to augment your Socially Enabled Big Data Analytics and CX Management. Let’s assume you have a functional Social-CRM platform in place. You are now successfully and continuously listening and learning from your customers and key constituents in Social Media, you are identifying relevant posts and following up with direct engagement where warranted (both 1:1, 1:community, 1:all), and you are starting to integrate signals for communication into your appropriate Customer Experience (CX) Management systems as well as insights for analysis in your business intelligence application. What is the next step? Augmenting Social Data with other Public Data for More Advanced Analytics When we say advanced analytics, we are talking about understanding causality and correlation from a wide variety, volume and velocity of data to Key Performance Indicators (KPI) to achieve and optimize business value. And in some cases, to predict future performance to make appropriate course corrections and change the outcome to your advantage while you can. The data to acquire, process and analyze this is very nuanced: It can vary across structured, semi-structured, and unstructured data It can span across content, profile, and communities of profiles data It is increasingly public, curated and user generated The key is not just getting the data, but making it value-added data and using it to help discover the insights to connect to and improve your KPIs. As we spend time working with our larger customers on advanced analytics, we have seen a need arise for more business applications to have the ability to ingest and use “quality” curated, social, transactional reference data and corresponding insights. The challenge for the enterprise has been getting this data inline into an easily accessible system and providing the contextual integration of the underlying data enriched with insights to be exported into the enterprise’s business applications. The following diagram shows the requirements for this next generation data and insights service or (DaaS): Some quick points on these requirements: Public Data, which in this context is about Common Business Entities, such as - Customers, Suppliers, Partners, Competitors (all are organizations) Contacts, Consumers, Employees (all are people) Products, Brands This data can be broadly categorized incrementally as - Base Utility data (address, industry classification) Public Master Reference data (trade style, hierarchy) Social/Web data (News, Feeds, Graph) Transactional Data generated by enterprise process, workflows etc. This Data has traits of high-volume, variety, velocity etc., and the technology needed to efficiently integrate this data for your needs includes - Change management of Public Reference Data across all categories Applied Big Data to extract statics as well as real-time insights Knowledge Diagnostics and Data Mining As you consider how to deploy this solution, many of our customers will be using an online “cloud” service that provides quality data and insights uniformly to all their necessary applications. In addition, they are requesting a service that is: Agile and Easy to Use: Applications integrated with the service can obtain data on-demand, quickly and simply Cost-effective: Pre-integrated into applications so customers don’t have to Has High Data Quality: Single point access to reference data for data quality and linkages to transactional, curated and social data Supports Data Governance: Becomes more manageable and cost-effective since control of data privacy and compliance can be enforced in a centralized place Data-as-a-Service (DaaS) Just as the cloud has transformed and now offers a better path for how an enterprise manages its IT from their infrastructure, platform, and software (IaaS, PaaS, and SaaS), the next step is data (DaaS). Over the last 3 years, we have seen the market begin to offer a cloud-based data service and gain initial traction. On one side of the DaaS continuum, we see an “appliance” type of service that provides a single, reliable source of accurate business data plus social information about accounts, leads, contacts, etc. On the other side of the continuum we see more of an online market “exchange” approach where ISVs and Data Publishers can publish and sell premium datasets within the exchange, with the exchange providing a rich set of web interfaces to improve the ease of data integration. Why the difference? It depends on the provider’s philosophy on how fast the rate of commoditization of certain data types will occur. How do you decide the best approach? Our perspective, as shown in the diagram below, is that the enterprise should develop an elastic schema to support multi-domain applicability. This allows the enterprise to take the most flexible approach to harness the speed and breadth of public data to achieve value. The key tenet of the proposed approach is that an enterprise carefully federates common utility, master reference data end points, mobility considerations and content processing, so that they are pervasively available. One way you may already be familiar with this approach is in how you do Address Verification treatments for accounts, contacts etc. If you design and revise this service in such a way that it is also easily available to social analytic needs, you could extend this to launch geo-location based social use cases (marketing, sales etc.). Our fundamental belief is that value-added data achieved through enrichment with specialized algorithms, as well as applying business “know-how” to weight-factor KPIs based on innovative combinations across an ever-increasing variety, volume and velocity of data, will be where real value is achieved. Essentially, Data-as-a-Service becomes a single entry point for the ever-increasing richness and volume of public data, with enrichment and combined capabilities to extract and integrate the right data from the right sources with the right factoring at the right time for faster decision-making and action within your core business applications. As more data becomes available (and in many cases commoditized), this value-added data processing approach will provide you with ongoing competitive advantage. Let’s look at a quick example of creating a master reference relationship that could be used as an input for a variety of your already existing business applications. In phase 1, a simple master relationship is achieved between a company (e.g. General Motors) and a variety of car brands’ social insights. The reference data allows for easy sort, export and integration into a set of CRM use cases for analytics, sales and marketing CRM. In phase 2, as you create more data relationships (e.g. competitors, contacts, other brands) to have broader and deeper references (social profiles, social meta-data) for more use cases across CRM, HCM, SRM, etc. This is just the tip of the iceberg, as the amount of master reference relationships is constrained only by your imagination and the availability of quality curated data you have to work with. DaaS is just now emerging onto the marketplace as the next step in cloud transformation. For some of you, this may be the first you have heard about it. Let us know if you have questions, or perspectives. In the meantime, we will continue to share insights as we can.Photo: Erik Araujo, stock.xchng

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• Working with data and meta data that are separated on different servers

  - by afuzzyllama

  While developing a product, I've come across a situation where my group wants to store meta data for data entry forms (questions, layout, etc) in a different database then the database where the collected data is stored. This is mostly for security because we want to be able to have our meta data public facing, while keeping collected data as secure as possible. I was thinking about writing a web service that provides the meta information that the data collection program could access. The only issue I see with this approach is the front end is going to have to match the meta data with the collected data, which would be more efficient as a join on the back end. Currently, this system is slated to run on .NET and MSSQL. I haven't played around with .NET libraries running in SQL, but I'm considering trying to create logic that would pull from the web service, convert the meta data into a table that SQL can join on, and return the combined data and meta data that way. Is this solution the wrong way to approach the problem? Is there a pattern or "industry standard" way of bringing together two datasets that don't live in the same database?

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• spring classloader isolation problem on jboss

  - by mkosm

  Hi, i have two ears deployed on jboss with proper classloaders isolation settings. When seam bean call spring beans which make some queries on database everything works fine, but when spring quartz job bean is invoked and execute tries to execute database queries then there is a problem because spring tries too use hibernate jar from other ear and exception is thrown! It is clearily spring classloader isolation problem. Did anyone meet such a problem? How to ensure isolation?

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