Search Results

Search found 8893 results on 356 pages for 'stored'.

Page 113/356 | < Previous Page | 109 110 111 112 113 114 115 116 117 118 119 120  | Next Page >

  • MySQL Workbench will not open on my Ubuntu 12.04

    - by Voidcode
    I have install mysql-workbench version 5.2.38+dfsg-3 via Ubuntu Software Center on my Ubuntu 12.04 laptop for some week ago, This work fine until now! Now when I press in the mysql-workbench icon in the Unity lanuncher, It just start opening and then nothing happens :( If I try start it via the terminal: I get this: http://paste.ubuntu.com/1004428/ UPDATE: I can open it via: sudo mysql-workbench But then is can save my passwords.. it says: voidcode@voidcode-Aspire-5750:~$ sudo mysql-workbench [sudo] password for voidcode: ** Message: Gnome keyring daemon seems to not be available. Stored passwords will be lost once quit Ready.

    Read the article

  • Programmatically reuse Dynamics CRM 4 icons

    - by gperera
    The team that wrote the dynamics crm sdk help rocks! I wanted to display the same crm icons on our time tracking application for consistency, so I opened up the sdk help file, searched for 'icon', ignored all the sitemap/isv config entries since I know I want to get these icons programatically, about half way down the search results I see 'organizationui', sure enough that contains the 16x16 (gridicon), 32x32 (outlookshortcuticon) and 66x48 (largeentityicon) icons!To get all the entities, execute a retrieve multiple request. RetrieveMultipleRequest request = new RetrieveMultipleRequest{    Query = new QueryExpression    {        EntityName = "organizationui",        ColumnSet = new ColumnSet(new[] { "objecttypecode", "formxml", "gridicon" }),    }}; var response = sdk.Execute(request) as RetrieveMultipleResponse;Now you have all the entities and icons, here's the tricky part, all the custom entities in crm store the icons inside gridicon, outlookshortcuticon and largeentityicon attributes, the built-in entity icons are stored inside the /_imgs/ folder with the format of /_imgs/ico_16_xxxx.gif (gridicon), with xxxx being the entity type code. The entity type code is not stored inside an attribute of organizationui, however you can get it by looking at the formxml attribute objecttypecode xml attribute. response.BusinessEntityCollection.BusinessEntities.ToList()    .Cast<organizationui>().ToList()    .ForEach(a =>    {        try        {            // easy way to check if it's a custom entity            if (!string.IsNullOrEmpty(a.gridicon))            {                byte[] gif = Convert.FromBase64String(a.gridicon);            }            else            {                // built-in entity                if (!string.IsNullOrEmpty(a.formxml))                {                    int start = a.formxml.IndexOf("objecttypecode=\"") + 16;                    int end = a.formxml.IndexOf("\"", start);                     // found the entity type code                    string code = a.formxml.Substring(start, end - start);                    string url = string.Format("/_imgs/ico_16_{0}.gif", code);Enjoy!

    Read the article

  • Coding standards

    - by Piotr Rodak
    This post will be about coding standards. There are countless articles and blog posts related to this topic, so I know this post will not be too revealing. Yet I would like to mention a few things I came across during my work with the T-SQL code. Naming convention - there are many of them obviously. Too bad if all of them are used in the same database, and sometimes even in the same stored procedure. It is not uncommon to see something like create procedure dbo . Proc1 ( @ParamId int ) as begin declare...(read more)

    Read the article

  • Windows Azure Use Case: Hybrid Applications

    - by BuckWoody
    This is one in a series of posts on when and where to use a distributed architecture design in your organization's computing needs. You can find the main post here: http://blogs.msdn.com/b/buckwoody/archive/2011/01/18/windows-azure-and-sql-azure-use-cases.aspx  Description: Organizations see the need for computing infrastructures that they can “rent” or pay for only when they need them. They also understand the benefits of distributed computing, but do not want to create this infrastructure themselves. However, they may have considerations that prevent them from moving all of their current IT investment to a distributed environment: Private data (do not want to send or store sensitive data off-site) High dollar investment in current infrastructure Applications currently running well, but may need additional periodic capacity Current applications not designed in a stateless fashion In these situations, a “hybrid” approach works best. In fact, with Windows Azure, a hybrid approach is an optimal way to implement distributed computing even when the stipulations above do not apply. Keeping a majority of the computing function in an organization local while exploring and expanding that footprint into Windows and SQL Azure is a good migration or expansion strategy. A “hybrid” architecture merely means that part of a computing cycle is shared between two architectures. For instance, some level of computing might be done in a Windows Azure web-based application, while the data is stored locally at the organization. Implementation: There are multiple methods for implementing a hybrid architecture, in a spectrum from very little interaction from the local infrastructure to Windows or SQL Azure. The patterns fall into two broad schemas, and even these can be mixed. 1. Client-Centric Hybrid Patterns In this pattern, programs are coded such that the client system sends queries or compute requests to multiple systems. The “client” in this case might be a web-based codeset actually stored on another system (which acts as a client, the user’s device serving as the presentation layer) or a compiled program. In either case, the code on the client requestor carries the burden of defining the layout of the requests. While this pattern is often the easiest to code, it’s the most brittle. Any change in the architecture must be reflected on each client, but this can be mitigated by using a centralized system as the client such as in the web scenario. 2. System-Centric Hybrid Patterns Another approach is to create a distributed architecture by turning on-site systems into “services” that can be called from Windows Azure using the service Bus or the Access Control Services (ACS) capabilities. Code calls from a series of in-process client application. In this pattern you move the “client” interface into the server application logic. If you do not wish to change the application itself, you can “layer” the results of the code return using a product (such as Microsoft BizTalk) that exposes a Web Services Definition Language (WSDL) endpoint to Windows Azure using the Application Fabric. In effect, this is similar to creating a Service Oriented Architecture (SOA) environment, and has the advantage of de-coupling your computing architecture. If each system offers a “service” of the results of some software processing, the operating system or platform becomes immaterial, assuming it adheres to a service contract. There are important considerations when you federate a system, whether to Windows or SQL Azure or any other distributed architecture. While these considerations are consistent with coding any application for distributed computing, they are especially important for a hybrid application. Connection resiliency - Applications on-premise normally have low-latency and good connection properties, something you’re not always guaranteed in a distributed and hybrid application. Whether a centralized client or a distributed one, the code should be able to handle extended retry logic. Authorization and Access - In a single authorization environment like a Active Directory domain, security is handled at a user-password level. In a distributed computing environment, you have more options. You can mitigate this with  using The Windows Azure Application Fabric feature of ACS to make the Azure application aware of the App Fabric as an ADFS provider. However, a claims-based authentication structure is often a superior choice.  Consistency and Concurrency - When you have a Relational Database Management System (RDBMS), Consistency and Concurrency are part of the design. In a Service Architecture, you need to plan for sequential message handling and lifecycle. Resources: How to Build a Hybrid On-Premise/In Cloud Application: http://blogs.msdn.com/b/ignitionshowcase/archive/2010/11/09/how-to-build-a-hybrid-on-premise-in-cloud-application.aspx  General Architecture guidance: http://blogs.msdn.com/b/buckwoody/archive/2010/12/21/windows-azure-learning-plan-architecture.aspx   

    Read the article

  • JPA/EclipseLink multitenancy screencast

    - by alexismp
    I find JPA and in particular EclipseLink 2.3 to be particularly well suited to illustrate the concept of multitenancy, one of the key PaaS features en route for Java EE 7. Here's a short (5-minute) screencast showing GlassFish 3.1.1 (due out real soon now) and its EclipseLink 2.3 JPA provider showing multitenancy in action. In short, it adds EclipseLink annotations to a JPA entity and deploys two identical applications with different tenant-id properties defined in the persistence.xml descriptor. Each application only sees its own data, yet everything is stored in the same table which was augmented with a discriminator column. For more advanced uses such as tenant property being set on the @PersistenceContext, XML configuration of multitenant JPA entities, and more check out the nicely written wiki page.

    Read the article

  • Replication Services in a BI environment

    - by jorg
    In this blog post I will explain the principles of SQL Server Replication Services without too much detail and I will take a look on the BI capabilities that Replication Services could offer in my opinion. SQL Server Replication Services provides tools to copy and distribute database objects from one database system to another and maintain consistency afterwards. These tools basically copy or synchronize data with little or no transformations, they do not offer capabilities to transform data or apply business rules, like ETL tools do. The only “transformations” Replication Services offers is to filter records or columns out of your data set. You can achieve this by selecting the desired columns of a table and/or by using WHERE statements like this: SELECT <published_columns> FROM [Table] WHERE [DateTime] >= getdate() - 60 There are three types of replication: Transactional Replication This type replicates data on a transactional level. The Log Reader Agent reads directly on the transaction log of the source database (Publisher) and clones the transactions to the Distribution Database (Distributor), this database acts as a queue for the destination database (Subscriber). Next, the Distribution Agent moves the cloned transactions that are stored in the Distribution Database to the Subscriber. The Distribution Agent can either run at scheduled intervals or continuously which offers near real-time replication of data! So for example when a user executes an UPDATE statement on one or multiple records in the publisher database, this transaction (not the data itself) is copied to the distribution database and is then also executed on the subscriber. When the Distribution Agent is set to run continuously this process runs all the time and transactions on the publisher are replicated in small batches (near real-time), when it runs on scheduled intervals it executes larger batches of transactions, but the idea is the same. Snapshot Replication This type of replication makes an initial copy of database objects that need to be replicated, this includes the schemas and the data itself. All types of replication must start with a snapshot of the database objects from the Publisher to initialize the Subscriber. Transactional replication need an initial snapshot of the replicated publisher tables/objects to run its cloned transactions on and maintain consistency. The Snapshot Agent copies the schemas of the tables that will be replicated to files that will be stored in the Snapshot Folder which is a normal folder on the file system. When all the schemas are ready, the data itself will be copied from the Publisher to the snapshot folder. The snapshot is generated as a set of bulk copy program (BCP) files. Next, the Distribution Agent moves the snapshot to the Subscriber, if necessary it applies schema changes first and copies the data itself afterwards. The application of schema changes to the Subscriber is a nice feature, when you change the schema of the Publisher with, for example, an ALTER TABLE statement, that change is propagated by default to the Subscriber(s). Merge Replication Merge replication is typically used in server-to-client environments, for example when subscribers need to receive data, make changes offline, and later synchronize changes with the Publisher and other Subscribers, like with mobile devices that need to synchronize one in a while. Because I don’t really see BI capabilities here, I will not explain this type of replication any further. Replication Services in a BI environment Transactional Replication can be very useful in BI environments. In my opinion you never want to see users to run custom (SSRS) reports or PowerPivot solutions directly on your production database, it can slow down the system and can cause deadlocks in the database which can cause errors. Transactional Replication can offer a read-only, near real-time database for reporting purposes with minimal overhead on the source system. Snapshot Replication can also be useful in BI environments, if you don’t need a near real-time copy of the database, you can choose to use this form of replication. Next to an alternative for Transactional Replication it can be used to stage data so it can be transformed and moved into the data warehousing environment afterwards. In many solutions I have seen developers create multiple SSIS packages that simply copies data from one or more source systems to a staging database that figures as source for the ETL process. The creation of these packages takes a lot of (boring) time, while Replication Services can do the same in minutes. It is possible to filter out columns and/or records and it can even apply schema changes automatically so I think it offers enough features here. I don’t know how the performance will be and if it really works as good for this purpose as I expect, but I want to try this out soon!

    Read the article

  • NoSQL Memcached API for MySQL: Latest Updates

    - by Mat Keep
    With data volumes exploding, it is vital to be able to ingest and query data at high speed. For this reason, MySQL has implemented NoSQL interfaces directly to the InnoDB and MySQL Cluster (NDB) storage engines, which bypass the SQL layer completely. Without SQL parsing and optimization, Key-Value data can be written directly to MySQL tables up to 9x faster, while maintaining ACID guarantees. In addition, users can continue to run complex queries with SQL across the same data set, providing real-time analytics to the business or anonymizing sensitive data before loading to big data platforms such as Hadoop, while still maintaining all of the advantages of their existing relational database infrastructure. This and more is discussed in the latest Guide to MySQL and NoSQL where you can learn more about using the APIs to scale new generations of web, cloud, mobile and social applications on the world's most widely deployed open source database The native Memcached API is part of the MySQL 5.6 Release Candidate, and is already available in the GA release of MySQL Cluster. By using the ubiquitous Memcached API for writing and reading data, developers can preserve their investments in Memcached infrastructure by re-using existing Memcached clients, while also eliminating the need for application changes. Speed, when combined with flexibility, is essential in the world of growing data volumes and variability. Complementing NoSQL access, support for on-line DDL (Data Definition Language) operations in MySQL 5.6 and MySQL Cluster enables DevOps teams to dynamically update their database schema to accommodate rapidly changing requirements, such as the need to capture additional data generated by their applications. These changes can be made without database downtime. Using the Memcached interface, developers do not need to define a schema at all when using MySQL Cluster. Lets look a little more closely at the Memcached implementations for both InnoDB and MySQL Cluster. Memcached Implementation for InnoDB The Memcached API for InnoDB is previewed as part of the MySQL 5.6 Release Candidate. As illustrated in the following figure, Memcached for InnoDB is implemented via a Memcached daemon plug-in to the mysqld process, with the Memcached protocol mapped to the native InnoDB API. Figure 1: Memcached API Implementation for InnoDB With the Memcached daemon running in the same process space, users get very low latency access to their data while also leveraging the scalability enhancements delivered with InnoDB and a simple deployment and management model. Multiple web / application servers can remotely access the Memcached / InnoDB server to get direct access to a shared data set. With simultaneous SQL access, users can maintain all the advanced functionality offered by InnoDB including support for Foreign Keys, XA transactions and complex JOIN operations. Benchmarks demonstrate that the NoSQL Memcached API for InnoDB delivers up to 9x higher performance than the SQL interface when inserting new key/value pairs, with a single low-end commodity server supporting nearly 70,000 Transactions per Second. Figure 2: Over 9x Faster INSERT Operations The delivered performance demonstrates MySQL with the native Memcached NoSQL interface is well suited for high-speed inserts with the added assurance of transactional guarantees. You can check out the latest Memcached / InnoDB developments and benchmarks here You can learn how to configure the Memcached API for InnoDB here Memcached Implementation for MySQL Cluster Memcached API support for MySQL Cluster was introduced with General Availability (GA) of the 7.2 release, and joins an extensive range of NoSQL interfaces that are already available for MySQL Cluster Like Memcached, MySQL Cluster provides a distributed hash table with in-memory performance. MySQL Cluster extends Memcached functionality by adding support for write-intensive workloads, a full relational model with ACID compliance (including persistence), rich query support, auto-sharding and 99.999% availability, with extensive management and monitoring capabilities. All writes are committed directly to MySQL Cluster, eliminating cache invalidation and the overhead of data consistency checking to ensure complete synchronization between the database and cache. Figure 3: Memcached API Implementation with MySQL Cluster Implementation is simple: 1. The application sends reads and writes to the Memcached process (using the standard Memcached API). 2. This invokes the Memcached Driver for NDB (which is part of the same process) 3. The NDB API is called, providing for very quick access to the data held in MySQL Cluster’s data nodes. The solution has been designed to be very flexible, allowing the application architect to find a configuration that best fits their needs. It is possible to co-locate the Memcached API in either the data nodes or application nodes, or alternatively within a dedicated Memcached layer. The benefit of this flexible approach to deployment is that users can configure behavior on a per-key-prefix basis (through tables in MySQL Cluster) and the application doesn’t have to care – it just uses the Memcached API and relies on the software to store data in the right place(s) and to keep everything synchronized. Using Memcached for Schema-less Data By default, every Key / Value is written to the same table with each Key / Value pair stored in a single row – thus allowing schema-less data storage. Alternatively, the developer can define a key-prefix so that each value is linked to a pre-defined column in a specific table. Of course if the application needs to access the same data through SQL then developers can map key prefixes to existing table columns, enabling Memcached access to schema-structured data already stored in MySQL Cluster. Conclusion Download the Guide to MySQL and NoSQL to learn more about NoSQL APIs and how you can use them to scale new generations of web, cloud, mobile and social applications on the world's most widely deployed open source database See how to build a social app with MySQL Cluster and the Memcached API from our on-demand webinar or take a look at the docs Don't hesitate to use the comments section below for any questions you may have 

    Read the article

  • Edubuntu boots in low graphics mode. with an Intel HD Graphics system

    - by user63957
    I have a HD Intel graphics card in my laptop. It was working fine the first few days with the new version Edubuntu. Now when you start, just before it goes to the part asking for the login password I think the OP means lightdm it sends me to a low graphics mode. Things I've tried: I tried Ctl+Alt+F1. Updated and installed fglrx from the terminal. All my work is all stored there. Please, if anyone knows how to fix this, tell me. Original version: hola tengo una tarjeta intel hd graphics en mi laptop estuve trabajando los primeros dias bien con la nueva version edubuntu solo que ahora cuando inicia y justo antes de que pase a la parte que me pide la contraseña me manda low graphic mode no se que hacer ya entre y le di ctr alt f1 y actualice tmb instale fglrx necesito obtener toda miinformacion todo mi trabajo esta ahi guardado, por favor si alguien sabe como solucionar este bug digame como, gracias, ciao.

    Read the article

  • Visualising data a different way with Pivot collections

    - by Rob Farley
    Roger’s been doing a great job extending PivotViewer recently, and you can find the list of LobsterPot pivots at http://pivot.lobsterpot.com.au Many months back, the TED Talk that Gary Flake did about Pivot caught my imagination, and I did some research into it. At the time, most of what we did with Pivot was geared towards what we could do for clients, including making Pivot collections based on students at a school, and using it to browse PDF invoices by their various properties. We had actual commercial work based on Pivot collections back then, and it was all kinds of fun. Later, we made some collections for events that were happening, and even got featured in the TechEd Australia keynote. But I’m getting ahead of myself... let me explain the concept. A Pivot collection is an XML file (with .cxml extension) which lists Items, each linking to an image that’s stored in a Deep Zoom format (this means that it contains tiles like Bing Maps, so that the browser can request only the ones of interest according to the zoom level). This collection can be shown in a Silverlight application that uses the PivotViewer control, or in the Pivot Browser that’s available from getpivot.com. Filtering and sorting the items according to their facets (attributes, such as size, age, category, etc), the PivotViewer rearranges the way that these are shown in a very dynamic way. To quote Gary Flake, this lets us “see patterns which are otherwise hidden”. This browsing mechanism is very suited to a number of different methods, because it’s just that – browsing. It’s not searching, it’s more akin to window-shopping than doing an internet search. When we decided to put something together for the conferences such as TechEd Australia 2010 and the PASS Summit 2010, we did some screen-scraping to provide a different view of data that was already available online. Nick Hodge and Michael Kordahi from Microsoft liked the idea a lot, and after a bit of tweaking, we produced one that Michael used in the TechEd Australia keynote to show the variety of talks on offer. It’s interesting to see a pattern in this data: The Office track has the most sessions, but if the Interactive Sessions and Instructor-Led Labs are removed, it drops down to only the sixth most popular track, with Cloud Computing taking over. This is something which just isn’t obvious when you look an ordinary search tool. You get a much better feel for the data when moving around it like this. The more observant amongst you will have noticed some difference in the collection that Michael is demonstrating in the picture above with the screenshots I’ve shown. That’s because it’s been extended some more. At the SQLBits conference in the UK this year, I had some interesting discussions with the guys from Xpert360, particularly Phil Carter, who I’d met in 2009 at an earlier SQLBits conference. They had got around to producing a Pivot collection based on the SQLBits data, which we had been planning to do but ran out of time. We discussed some of ways that Pivot could be used, including the ways that my old friend Howard Dierking had extended it for the MSDN Magazine. I’m not suggesting I influenced Xpert360 at all, but they certainly inspired us with some of their posts on the matter So with LobsterPot guys David Gardiner and Roger Noble both having dabbled in Pivot collections (and Dave doing some for clients), I set Roger to work on extending it some more. He’s used various events and so on to be able to make an environment that allows us to do quick deployment of new collections, as well as showing the data in a grid view which behaves as if it were simply a third view of the data (the other two being the array of images and the ‘histogram’ view). I see PivotViewer as being a significant step in data visualisation – so much so that I feature it when I deliver talks on Spatial Data Visualisation methods. Any time when there is information that can be conveyed through an image, you have to ask yourself how best to show that image, and whether that image is the focal point. For Spatial data, the image is most often a map, and the map becomes the central mode for navigation. I show Pivot with postcode areas, since I can browse the postcodes based on their data, and many of the images are recognisable (to locals of South Australia). Naturally, the images could link through to the map itself, and so on, but generally people think of Spatial data in terms of navigating a map, which doesn’t always gel with the information you’re trying to extract. Roger’s even looking into ways to hook PivotViewer into the Bing Maps API, in a similar way to the Deep Earth project, displaying different levels of map detail according to how ‘zoomed in’ the images are. Some of the work that Dave did with one of the schools was generating the Deep Zoom tiles “on the fly”, based on images stored in a database, and Roger has produced a collection which uses images from flickr, that lets you move from one search term to another. Pulling the images down from flickr.com isn’t particularly ideal from a performance aspect, and flickr doesn’t store images in a small-enough format to really lend itself to this use, but you might agree that it’s an interesting concept which compares nicely to using Maps. I’m looking forward to future versions of the PivotViewer control, and hope they provide many more events that can be used, and even more hooks into it. Naturally, LobsterPot could help provide your business with a PivotViewer experience, but you can probably do a lot of it yourself too. There’s a thorough guide at getpivot.com, which is how we got into it. For some examples of what we’ve done, have a look at http://pivot.lobsterpot.com.au. I’d like to see PivotViewer really catch on a data visualisation tool.

    Read the article

  • Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21

    - by Pinal Dave
    Data is forever. Think about it – it is indeed true. Are you using any application as it is which was built 10 years ago? Are you using any piece of hardware which was built 10 years ago? The answer is most certainly No. However, if I ask you – are you using any data which were captured 50 years ago, the answer is most certainly Yes. For example, look at the history of our nation. I am from India and we have documented history which goes back as over 1000s of year. Well, just look at our birthday data – atleast we are using it till today. Data never gets old and it is going to stay there forever.  Application which interprets and analysis data got changed but the data remained in its purest format in most cases. As organizations have grown the data associated with them also grew exponentially and today there are lots of complexity to their data. Most of the big organizations have data in multiple applications and in different formats. The data is also spread out so much that it is hard to categorize with a single algorithm or logic. The mobile revolution which we are experimenting right now has completely changed how we capture the data and build intelligent systems.  Big organizations are indeed facing challenges to keep all the data on a platform which give them a  single consistent view of their data. This unique challenge to make sense of all the data coming in from different sources and deriving the useful actionable information out of is the revolution Big Data world is facing. Defining Big Data The 3Vs that define Big Data are Variety, Velocity and Volume. Volume We currently see the exponential growth in the data storage as the data is now more than text data. We can find data in the format of videos, musics and large images on our social media channels. It is very common to have Terabytes and Petabytes of the storage system for enterprises. As the database grows the applications and architecture built to support the data needs to be reevaluated quite often. Sometimes the same data is re-evaluated with multiple angles and even though the original data is the same the new found intelligence creates explosion of the data. The big volume indeed represents Big Data. Velocity The data growth and social media explosion have changed how we look at the data. There was a time when we used to believe that data of yesterday is recent. The matter of the fact newspapers is still following that logic. However, news channels and radios have changed how fast we receive the news. Today, people reply on social media to update them with the latest happening. On social media sometimes a few seconds old messages (a tweet, status updates etc.) is not something interests users. They often discard old messages and pay attention to recent updates. The data movement is now almost real time and the update window has reduced to fractions of the seconds. This high velocity data represent Big Data. Variety Data can be stored in multiple format. For example database, excel, csv, access or for the matter of the fact, it can be stored in a simple text file. Sometimes the data is not even in the traditional format as we assume, it may be in the form of video, SMS, pdf or something we might have not thought about it. It is the need of the organization to arrange it and make it meaningful. It will be easy to do so if we have data in the same format, however it is not the case most of the time. The real world have data in many different formats and that is the challenge we need to overcome with the Big Data. This variety of the data represent  represent Big Data. Big Data in Simple Words Big Data is not just about lots of data, it is actually a concept providing an opportunity to find new insight into your existing data as well guidelines to capture and analysis your future data. It makes any business more agile and robust so it can adapt and overcome business challenges. Tomorrow In tomorrow’s blog post we will try to answer discuss Evolution of 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

    Read the article

  • I made a 2D ENGINE for Android, looking for cooperation.

    - by Roger Travis
    My name is Robert, I am an Android programmer and wanted to show off my latest project - a 2d game engine. You can see it in action here - https://play.google.com/store/apps/details?id=engineDemo.com My engine's main advantage is its ease of use. To have your level up and running, you'll need only 3 lines of code. ABoxView aboxView = new ABoxView(this); setContentView(aboxView); aboxView.loadLevel("level/level02"); Level are created in a special level constructor and object physical properties are stored in a corresponding XML file. I am looking to cooperate with those, who might be interesting in using my engine in their games. You can email me at [email protected] or post here. Thanks, Robert

    Read the article

  • Hello With Oracle Identity Manager Architecture

    - by mustafakaya
    Hi, my name is Mustafa! I'm a Senior Consultant in Fusion Middleware Team and living in Istanbul,Turkey. I worked many various Java based software development projects such as end-to-end web applications, CRM , Telco VAS and integration projects.I want to share my experiences and research about Fusion Middleware Products in this column. Customer always wants best solution from software consultants or developers. Solution will be a code snippet or change complete architecture. We faced different requests according to the case of customer. In my posts i want to discuss Fusion Middleware Products Architecture or how can extend usability with apis or UI customization and more and I look forward to engaging with you on your experiences and thoughts on this.  In my first post, i will be discussing Oracle Identity Manager architecture  and i plan to discuss Oracle Identity Manager 11g features in next posts. Oracle Identity Manager System Architecture Oracle Identity Governance includes Oracle Identity Manager,Oracle Identity Analytics and Oracle Privileged Account Manager. I will discuss Oracle Identity Manager architecture in this post.  In basically, Oracle Identity Manager is a n-tier standard  Java EE application that is deployed on Oracle WebLogic Server and uses  a database .  Oracle Identity Manager presentation tier has three different screen and two different client. Identity Self Service and Identity System Administration are web-based thin client. Design Console is a Java Swing Client that communicates directly with the Business Service Tier.  Identity Self Service provides end-user operations and delegated administration features. System Administration provides system administration functions. And Design Console mostly use for development management operations such as  create and manage adapter and process form,notification , workflow desing, reconciliation rules etc. Business service tier is implemented as an Enterprise JavaBeans(EJB) application. So you can extense Oracle Identity Manager capabilities.  -The SMPL and EJB APIs allow develop custom plug-ins such as management roles or identities.  -Identity Services allow use core business capabilites of Oracle Identity Manager such as The User provisioning or reconciliation service. -Integration Services allow develop custom connectors or adapters for various deployment needs. -Platform Services allow use Entitlement Servers, Scheduler or SOA composites. The Middleware tier allows you using capabilites ADF Faces,SOA Suites, Scheduler, Entitlement Server and BI Publisher Reports. So OIM allows you to configure workflows uses Oracle SOA Suite or define authorization policies use with Oracle Entitlement Server. Also you can customization of OIM UI without need to write code and using ADF Business Editor  you can extend custom attributes to user,role,catalog and other objects. Data tiers; Oracle Identity Manager is driven by data and metadata which provides flexibility and adaptability to Oracle Identity Manager functionlities.  -Database has five schemas these are OIM,SOA,MDS,OPSS and OES. Oracle Identity Manager uses database to store runtime and configuration data. And all of entity, transactional and audit datas are stored in database. -Metadata Store; customizations and personalizations are stored in file-based repository or database-based repository.And Oracle Identity Manager architecture,the metadata is in Oracle Identity Manager database to take advantage of some of the advanced performance and availability features that this mode provides. -Identity Store; Oracle Identity Manager provides the ability to integrate an LDAP-based identity store into Oracle Identity Manager architecture.  Oracle Identity Manager uses the human workflow module of Oracle Service Oriented Architecture Suite. OIM connects to SOA using the T3 URL which is front-end URL for the SOA server.Oracle Identity Manager uses embedded Oracle Entitlement Server for authorization checks in OIM engine.  Several Oracle Identity Manager modules use JMS queues. Each queue is processed by a separate Message Driven Bean (MDB), which is also part of the Oracle Identity Manager application. Message producers are also part of the Oracle Identity Manager application. Oracle Identity Manager uses a scheduled jobs for some activities in the background.Some of scheduled jobs come with Out-Of-Box such as the disable users after the end date of the users or you can define your custom schedule jobs with Oracle Identity Manager APIs. You can use Oracle BI Publisher for reporting Oracle Identity Manager transactions or audit data which are in database. About me: Mustafa Kaya is a Senior Consultant in Oracle Fusion Middleware Team, living in Istanbul. Before coming to Oracle, he worked in teams developing web applications and backend services at a telco company. He is a Java technology enthusiast, software engineer and addicted to learn new technologies,develop new ideas. Follow Mustafa on Twitter,Connect on LinkedIn, and visit his site for Oracle Fusion Middleware related tips.

    Read the article

  • SQL SERVER – Fundamentals of Columnstore Index

    - by pinaldave
    There are two kind of storage in database. Row Store and Column Store. Row store does exactly as the name suggests – stores rows of data on a page – and column store stores all the data in a column on the same page. These columns are much easier to search – instead of a query searching all the data in an entire row whether the data is relevant or not, column store queries need only to search much lesser number of the columns. This means major increases in search speed and hard drive use. Additionally, the column store indexes are heavily compressed, which translates to even greater memory and faster searches. I am sure this looks very exciting and it does not mean that you convert every single index from row store to column store index. One has to understand the proper places where to use row store or column store indexes. Let us understand in this article what is the difference in Columnstore type of index. Column store indexes are run by Microsoft’s VertiPaq technology. However, all you really need to know is that this method of storing data is columns on a single page is much faster and more efficient. Creating a column store index is very easy, and you don’t have to learn new syntax to create them. You just need to specify the keyword “COLUMNSTORE” and enter the data as you normally would. Keep in mind that once you add a column store to a table, though, you cannot delete, insert or update the data – it is READ ONLY. However, since column store will be mainly used for data warehousing, this should not be a big problem. You can always use partitioning to avoid rebuilding the index. A columnstore index stores each column in a separate set of disk pages, rather than storing multiple rows per page as data traditionally has been stored. The difference between column store and row store approaches is illustrated below: In case of the row store indexes multiple pages will contain multiple rows of the columns spanning across multiple pages. In case of column store indexes multiple pages will contain multiple single columns. This will lead only the columns needed to solve a query will be fetched from disk. Additionally there is good chance that there will be redundant data in a single column which will further help to compress the data, this will have positive effect on buffer hit rate as most of the data will be in memory and due to same it will not need to be retrieved. Let us see small example of how columnstore index improves the performance of the query on a large table. As a first step let us create databaseset which is large enough to show performance impact of columnstore index. The time taken to create sample database may vary on different computer based on the resources. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 Now let us do quick performance test. I have kept STATISTICS IO ON for measuring how much IO following queries take. In my test first I will run query which will use regular index. We will note the IO usage of the query. After that we will create columnstore index and will measure the IO of the same. -- Performance Test -- Comparing Regular Index with ColumnStore Index USE AdventureWorks GO SET STATISTICS IO ON GO -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO -- Table 'MySalesOrderDetail'. Scan count 1, logical reads 342261, physical reads 0, read-ahead reads 0. -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO -- Select Table with Columnstore Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO It is very clear from the results that query is performance extremely fast after creating ColumnStore Index. The amount of the pages it has to read to run query is drastically reduced as the column which are needed in the query are stored in the same page and query does not have to go through every single page to read those columns. If we enable execution plan and compare we can see that column store index performance way better than regular index in this case. Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO In future posts we will see cases where Columnstore index is not appropriate solution as well few other tricks and tips of the columnstore index. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • Benefits of PerformancePoint Services Using SharePoint Server 2010

    - by Wayne
    What is PerformancePoint Services? Most of the time it happens that the metrics that make up your key performance indicators are not simple values from a data source. In SharePoint Server 2007 PerformancePoint Services, you could create two kinds of KPI metrics: Simple single value metrics from any supported data source or Complex multiple value metrics from a single Analysis Services data source using MDX. Now things are even easier with Performance Point Services in SharePoint 2010. Let us check what is it? PerformancePoint Services in SharePoint Server 2010 is a performance management service that you can use to monitor and analyze your business. By providing flexible, easy-to-use tools for building dashboards, scorecards, reports, and key performance indicators (KPIs), PerformancePoint Services can help everyone across an organization make informed business decisions that align with companywide objectives and strategy. Scorecards, dashboards, and KPIs help drive accountability. Integrated analytics help employees move quickly from monitoring information to analyzing it and, when appropriate, sharing it throughout the organization. Prior to the addition of PerformancePoint Services to SharePoint Server, Microsoft Office PerformancePoint Server 2007 functioned as a standalone server. Now PerformancePoint functionality is available as an integrated part of the SharePoint Server Enterprise license, as is the case with Excel Services in Microsoft SharePoint Server 2010. The popular features of earlier versions of PerformancePoint Services are preserved along with numerous enhancements and additional functionality. New PerformancePoint Services features PerformancePoint Services now can utilize SharePoint Server scalability, collaboration, backup and recovery, and disaster recovery capabilities. Dashboards and dashboard items are stored and secured within SharePoint lists and libraries, providing you with a single security and repository framework. New features and enhancements of SharePoint 2010 PerformancePoint Services • With PerformancePoint Services, functioning as a service in SharePoint Server, dashboards and dashboard items are stored and secured within SharePoint lists and libraries, providing you with a single security and repository framework. The new architecture also takes advantage of SharePoint Server scalability, collaboration, backup and recovery, and disaster recovery capabilities. You also can include and link PerformancePoint Services Web Parts with other SharePoint Server Web Parts on the same page. The new architecture also streamlines security models that simplify access to report data. • The Decomposition Tree is a new visualization report type available in PerformancePoint Services. You can use it to quickly and visually break down higher-level data values from a multi-dimensional data set to understand the driving forces behind those values. The Decomposition Tree is available in scorecards and analytic reports and ultimately in dashboards. • You can access more detailed business information with improved scorecards. Scorecards have been enhanced to make it easy for you to drill down and quickly access more detailed information. PerformancePoint scorecards also offer more flexible layout options, dynamic hierarchies, and calculated KPI features. Using this enhanced functionality, you can now create custom metrics that use multiple data sources. You can also sort, filter, and view variances between actual and target values to help you identify concerns or risks. • Better Time Intelligence filtering capabilities that you can use to create and use dynamic time filters that are always up to date. Other improved filters improve the ability for dashboard users to quickly focus in on information that is most relevant. • Ability to include and link PerformancePoint Services Web Parts together with other PerformancePoint Services Web parts on the same page. • Easier to author and publish dashboard items by using Dashboard Designer. • SQL Server Analysis Services 2008 support. • Increased support for accessibility compliance in individual reports and scorecards. • The KPI Details report is a new report type that displays contextually relevant information about KPIs, metrics, rows, columns, and cells within a scorecard. The KPI Details report works as a Web part that links to a scorecard or individual KPI to show relevant metadata to the end user in SharePoint Server. This Web part can be added to PerformancePoint dashboards or any SharePoint Server page. • Create analytics reports to better understand underlying business forces behind the results. Analytic reports have been enhanced to support value filtering, new chart types, and server-based conditional formatting. To conclude, PerformancePoint Services, by becoming tightly integrated with SharePoint Server 2010, takes advantage of many enterprise-level SharePoint Server 2010 features. Unfortunately, SharePoint Foundation 2010 doesn’t include this feature. There are still many choices in SharePoint family of products that include SharePoint Server 2010, SharePoint Foundation, SharePoint Server 2007 and associated free SharePoint web parts and templates.

    Read the article

  • dovecot can't compact mail folder /var/mail/username

    - by G. He
    ubuntu 11.10 32bit. Setup a dovecot imap server. Using Thunderbird on a different ubuntu machine (64bit) to access imap server. Everything else is fine, except I can not compact the deleted email in inbox, which is stored at /var/mail/username. Checking mail.log and I see this error message: Apr 3 00:10:11 autumn dovecot: imap(username): Error: file_dotlock_create(/var/mail/username) failed: Permission denied (euid=1000(username) egid=1000(username) missing +w perm: /var/mail, euid is not dir owner) (set mail_privileged_group=mail) what is wrong with the permission? Here are the permissions for the relevant files: $ ls -ld /var/mail drwxrwsr-x 2 mail mail 4096 2012-04-02 23:36 /var/mail $ ls -l /var/mail/username -rw------- 1 username mail 417 2012-04-02 23:36 /var/mail/username Anyone knows what's going on here?

    Read the article

  • SQL SERVER – Parsing SSIS Catalog Messages – Notes from the Field #030

    - by Pinal Dave
    [Note from Pinal]: This is a new episode of Notes from the Field series. SQL Server Integration Service (SSIS) is one of the most key essential part of the entire Business Intelligence (BI) story. It is a platform for data integration and workflow applications. The tool may also be used to automate maintenance of SQL Server databases and updates to multidimensional cube data. In this episode of the Notes from the Field series I requested SSIS Expert Andy Leonard to discuss one of the most interesting concepts of SSIS Catalog Messages. There are plenty of interesting and useful information captured in the SSIS catalog and we will learn together how to explore the same. The SSIS Catalog captures a lot of cool information by default. Here’s a query I use to parse messages from the catalog.operation_messages table in the SSISDB database, where the logged messages are stored. This query is set up to parse a default message transmitted by the Lookup Transformation. It’s one of my favorite messages in the SSIS log because it gives me excellent information when I’m tuning SSIS data flows. The message reads similar to: Data Flow Task:Information: The Lookup processed 4485 rows in the cache. The processing time was 0.015 seconds. The cache used 1376895 bytes of memory. The query: USE SSISDB GO DECLARE @MessageSourceType INT = 60 DECLARE @StartOfIDString VARCHAR(100) = 'The Lookup processed ' DECLARE @ProcessingTimeString VARCHAR(100) = 'The processing time was ' DECLARE @CacheUsedString VARCHAR(100) = 'The cache used ' DECLARE @StartOfIDSearchString VARCHAR(100) = '%' + @StartOfIDString + '%' DECLARE @ProcessingTimeSearchString VARCHAR(100) = '%' + @ProcessingTimeString + '%' DECLARE @CacheUsedSearchString VARCHAR(100) = '%' + @CacheUsedString + '%' SELECT operation_id , SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1))) AS LookupRowsCount , SUBSTRING(MESSAGE, (PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1)) - (PATINDEX(@ProcessingTimeSearchString, MESSAGE) + LEN(@ProcessingTimeString) + 1))) AS LookupProcessingTime , CASE WHEN (CONVERT(numeric(3,3),SUBSTRING(MESSAGE, (PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1)) - (PATINDEX(@ProcessingTimeSearchString, MESSAGE) + LEN(@ProcessingTimeString) + 1))))) = 0 THEN 0 ELSE CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1)))) / CONVERT(numeric(3,3),SUBSTRING(MESSAGE, (PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1)) - (PATINDEX(@ProcessingTimeSearchString, MESSAGE) + LEN(@ProcessingTimeString) + 1)))) END AS LookupRowsPerSecond , SUBSTRING(MESSAGE, (PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1)) - (PATINDEX(@CacheUsedSearchString, MESSAGE) + LEN(@CacheUsedString) + 1))) AS LookupBytesUsed ,CASE WHEN (CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1)))))= 0 THEN 0 ELSE CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1)) - (PATINDEX(@CacheUsedSearchString, MESSAGE) + LEN(@CacheUsedString) + 1)))) / CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1)))) END AS LookupBytesPerRow FROM [catalog].[operation_messages] WHERE message_source_type = @MessageSourceType AND MESSAGE LIKE @StartOfIDSearchString GO Note that you have to set some parameter values: @MessageSourceType [int] – represents the message source type value from the following results: Value     Description 10           Entry APIs, such as T-SQL and CLR Stored procedures 20           External process used to run package (ISServerExec.exe) 30           Package-level objects 40           Control Flow tasks 50           Control Flow containers 60           Data Flow task 70           Custom execution message Note: Taken from Reza Rad’s (excellent!) helper.MessageSourceType table found here. @StartOfIDString [VarChar(100)] – use this to uniquely identify the message field value you wish to parse. In this case, the string ‘The Lookup processed ‘ identifies all the Lookup Transformation messages I desire to parse. @ProcessingTimeString [VarChar(100)] – this parameter is message-specific. I use this parameter to specifically search the message field value for the beginning of the Lookup Processing Time value. For this execution, I use the string ‘The processing time was ‘. @CacheUsedString [VarChar(100)] – this parameter is also message-specific. I use this parameter to specifically search the message field value for the beginning of the Lookup Cache  Used value. It returns the memory used, in bytes. For this execution, I use the string ‘The cache used ‘. The other parameters are built from variations of the parameters listed above. The query parses the values into text. The string values are converted to numeric values for ratio calculations; LookupRowsPerSecond and LookupBytesPerRow. Since ratios involve division, CASE statements check for denominators that equal 0. Here are the results in an SSMS grid: This is not the only way to retrieve this information. And much of the code lends itself to conversion to functions. If there is interest, I will share the functions in an upcoming post. If you want to get started with SSIS with the help of experts, read more over at Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Backup and Restore, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SSIS

    Read the article

  • Item 2, Scott Myers Effective C++ question

    - by user619818
    In Item2 on page 16, (Prefer consts, enums, and inlines to #defines), Scott says: 'Also, though good compilers won't set aside storage for const objects of integer types'. I don't understand this. If I define a const object, eg const int myval = 5; then surely the compiler must set aside some memory (of int size) to store the value 5? Or is const data stored in some special way? This is more a question of computer storage I suppose. Basically, how does the computer store const objects so that no storage is set aside?

    Read the article

  • Optimizing collision engine bottleneck

    - by Vittorio Romeo
    Foreword: I'm aware that optimizing this bottleneck is not a necessity - the engine is already very fast. I, however, for fun and educational purposes, would love to find a way to make the engine even faster. I'm creating a general-purpose C++ 2D collision detection/response engine, with an emphasis on flexibility and speed. Here's a very basic diagram of its architecture: Basically, the main class is World, which owns (manages memory) of a ResolverBase*, a SpatialBase* and a vector<Body*>. SpatialBase is a pure virtual class which deals with broad-phase collision detection. ResolverBase is a pure virtual class which deals with collision resolution. The bodies communicate to the World::SpatialBase* with SpatialInfo objects, owned by the bodies themselves. There currenly is one spatial class: Grid : SpatialBase, which is a basic fixed 2D grid. It has it's own info class, GridInfo : SpatialInfo. Here's how its architecture looks: The Grid class owns a 2D array of Cell*. The Cell class contains two collection of (not owned) Body*: a vector<Body*> which contains all the bodies that are in the cell, and a map<int, vector<Body*>> which contains all the bodies that are in the cell, divided in groups. Bodies, in fact, have a groupId int that is used for collision groups. GridInfo objects also contain non-owning pointers to the cells the body is in. As I previously said, the engine is based on groups. Body::getGroups() returns a vector<int> of all the groups the body is part of. Body::getGroupsToCheck() returns a vector<int> of all the groups the body has to check collision against. Bodies can occupy more than a single cell. GridInfo always stores non-owning pointers to the occupied cells. After the bodies move, collision detection happens. We assume that all bodies are axis-aligned bounding boxes. How broad-phase collision detection works: Part 1: spatial info update For each Body body: Top-leftmost occupied cell and bottom-rightmost occupied cells are calculated. If they differ from the previous cells, body.gridInfo.cells is cleared, and filled with all the cells the body occupies (2D for loop from the top-leftmost cell to the bottom-rightmost cell). body is now guaranteed to know what cells it occupies. For a performance boost, it stores a pointer to every map<int, vector<Body*>> of every cell it occupies where the int is a group of body->getGroupsToCheck(). These pointers get stored in gridInfo->queries, which is simply a vector<map<int, vector<Body*>>*>. body is now guaranteed to have a pointer to every vector<Body*> of bodies of groups it needs to check collision against. These pointers are stored in gridInfo->queries. Part 2: actual collision checks For each Body body: body clears and fills a vector<Body*> bodiesToCheck, which contains all the bodies it needs to check against. Duplicates are avoided (bodies can belong to more than one group) by checking if bodiesToCheck already contains the body we're trying to add. const vector<Body*>& GridInfo::getBodiesToCheck() { bodiesToCheck.clear(); for(const auto& q : queries) for(const auto& b : *q) if(!contains(bodiesToCheck, b)) bodiesToCheck.push_back(b); return bodiesToCheck; } The GridInfo::getBodiesToCheck() method IS THE BOTTLENECK. The bodiesToCheck vector must be filled for every body update because bodies could have moved meanwhile. It also needs to prevent duplicate collision checks. The contains function simply checks if the vector already contains a body with std::find. Collision is checked and resolved for every body in bodiesToCheck. That's it. So, I've been trying to optimize this broad-phase collision detection for quite a while now. Every time I try something else than the current architecture/setup, something doesn't go as planned or I make assumption about the simulation that later are proven to be false. My question is: how can I optimize the broad-phase of my collision engine maintaining the grouped bodies approach? Is there some kind of magic C++ optimization that can be applied here? Can the architecture be redesigned in order to allow for more performance? Actual implementation: SSVSCollsion Body.h, Body.cpp World.h, World.cpp Grid.h, Grid.cpp Cell.h, Cell.cpp GridInfo.h, GridInfo.cpp

    Read the article

  • Entity Framework Batch Update and Future Queries

    - by pwelter34
    Entity Framework Extended Library A library the extends the functionality of Entity Framework. Features Batch Update and Delete Future Queries Audit Log Project Package and Source NuGet Package PM> Install-Package EntityFramework.Extended NuGet: http://nuget.org/List/Packages/EntityFramework.Extended Source: http://github.com/loresoft/EntityFramework.Extended Batch Update and Delete A current limitations of the Entity Framework is that in order to update or delete an entity you have to first retrieve it into memory. Now in most scenarios this is just fine. There are however some senerios where performance would suffer. Also, for single deletes, the object must be retrieved before it can be deleted requiring two calls to the database. Batch update and delete eliminates the need to retrieve and load an entity before modifying it. Deleting //delete all users where FirstName matches context.Users.Delete(u => u.FirstName == "firstname"); Update //update all tasks with status of 1 to status of 2 context.Tasks.Update( t => t.StatusId == 1, t => new Task {StatusId = 2}); //example of using an IQueryable as the filter for the update var users = context.Users .Where(u => u.FirstName == "firstname"); context.Users.Update( users, u => new User {FirstName = "newfirstname"}); Future Queries Build up a list of queries for the data that you need and the first time any of the results are accessed, all the data will retrieved in one round trip to the database server. Reducing the number of trips to the database is a great. Using this feature is as simple as appending .Future() to the end of your queries. To use the Future Queries, make sure to import the EntityFramework.Extensions namespace. Future queries are created with the following extension methods... Future() FutureFirstOrDefault() FutureCount() Sample // build up queries var q1 = db.Users .Where(t => t.EmailAddress == "[email protected]") .Future(); var q2 = db.Tasks .Where(t => t.Summary == "Test") .Future(); // this triggers the loading of all the future queries var users = q1.ToList(); In the example above, there are 2 queries built up, as soon as one of the queries is enumerated, it triggers the batch load of both queries. // base query var q = db.Tasks.Where(t => t.Priority == 2); // get total count var q1 = q.FutureCount(); // get page var q2 = q.Skip(pageIndex).Take(pageSize).Future(); // triggers execute as a batch int total = q1.Value; var tasks = q2.ToList(); In this example, we have a common senerio where you want to page a list of tasks. In order for the GUI to setup the paging control, you need a total count. With Future, we can batch together the queries to get all the data in one database call. Future queries work by creating the appropriate IFutureQuery object that keeps the IQuerable. The IFutureQuery object is then stored in IFutureContext.FutureQueries list. Then, when one of the IFutureQuery objects is enumerated, it calls back to IFutureContext.ExecuteFutureQueries() via the LoadAction delegate. ExecuteFutureQueries builds a batch query from all the stored IFutureQuery objects. Finally, all the IFutureQuery objects are updated with the results from the query. Audit Log The Audit Log feature will capture the changes to entities anytime they are submitted to the database. The Audit Log captures only the entities that are changed and only the properties on those entities that were changed. The before and after values are recorded. AuditLogger.LastAudit is where this information is held and there is a ToXml() method that makes it easy to turn the AuditLog into xml for easy storage. The AuditLog can be customized via attributes on the entities or via a Fluent Configuration API. Fluent Configuration // config audit when your application is starting up... var auditConfiguration = AuditConfiguration.Default; auditConfiguration.IncludeRelationships = true; auditConfiguration.LoadRelationships = true; auditConfiguration.DefaultAuditable = true; // customize the audit for Task entity auditConfiguration.IsAuditable<Task>() .NotAudited(t => t.TaskExtended) .FormatWith(t => t.Status, v => FormatStatus(v)); // set the display member when status is a foreign key auditConfiguration.IsAuditable<Status>() .DisplayMember(t => t.Name); Create an Audit Log var db = new TrackerContext(); var audit = db.BeginAudit(); // make some updates ... db.SaveChanges(); var log = audit.LastLog;

    Read the article

  • Why do you need float/double?

    - by acidzombie24
    I was watching http://www.joelonsoftware.com/items/2011/06/27.html and laughed at Jon Skeet joke about 0.3 not being 0.3. I personally never had problems with floats/decimals/doubles but then I remember I learned 6502 very early and never needed floats in most of my programs. The only time I used it was for graphics and math where inaccurate numbers were ok and the output was for the screen and not to be stored (in a db, file) or dependent on. My question is, where are places were you typically use floats/decimals/double? So I know to watch out for these gotchas. With money I use longs and store values by the cent, for speed of an object in a game I add ints and divide (or bitshift) the value to know if I need to move a pixel or not. (I made object move in the 6502 days, we had no divide nor floats but had shifts). So I was mostly curious.

    Read the article

  • Java default Integer value is int

    - by Chris Okyen
    My code looks like this import java.util.Scanner; public class StudentGrades { public static void main(String[] argv) { Scanner keyboard = new Scanner(System.in); byte q1 = keyboard.nextByte() * 10; } } It gives me an error "Type mismatch: cannot convert from int to byte." Why the heck would Java store a literal operand that is small enough to fit in a byte,. into a int type? Do literals get stored in variables/registers before the ALU performs arithmatic operations.

    Read the article

  • How to keep menu in a single place without using frames

    - by TJ Ellis
    This is probably a duplicate, but I can't find the answer anywhere (maybe I'm searching for the wrong thing?) and so I'm going to go ahead and ask. What is the accepted standard practice for creating a menu that is stored in a single file, but is included on every page across a site? Back in the day, one used frames, but this seems to be taboo now. I can get things layed out just the way I want, but copy/pasting across every page is a pain. I have seen php-based solutions, but my cheap-o free hosting doesn't support php (which is admittedly a pain, but it's a fairly simple webpage...). Any ideas for doing this that does not require server-side scripting?

    Read the article

  • Universal navigation menu across domains - would it be considered duplicate content?

    - by Jon Harley
    Across different sites on different second-level domains exists a universal navigation bar with a collection of roughly 30 links. This universal bar is exactly the same for every page on each domain. The bar's HTML, CSS and JavaScript are all stored in a subfolder for each domain and the HTML is embedded upon serving the page and is not being injected on the client side. None of the links use any rel directives and are as vanilla as can be. My question is about Google's duplicate content rule. Would something like this be considered duplicate content? Matt Cutt's blog post about duplicate content mentions boilerplate repetition, but then he mentions lengthy legalese. Since the text in this universal bar is brief and uses common terms, I wonder if this same rule applies. If this is considered duplicate content, what would be a good way to correct the problem?

    Read the article

  • July, the 31 Days of SQL Server DMO’s – Day 30 (sys.dm_server_registry)

    - by Tamarick Hill
    The sys.dm_server_registry DMV is used to provide SQL Server configuration and installation information that is currently stored in your Windows Registry. It is a very simple DMV that returns only three columns. The first column returned is the registry_key. The second column returned is the value_name which is the name of the actual registry key value. The third and final column returned is the value_data which is the value of the registry key data. Lets have a look at the information this DMV returns as well as some key values from the Windows Registy. SELECT * FROM sys.dm_server_registry View using RegEdit to view the registy: This DMV provides you with a quick and easy way to view SQL Server Instance registry values. For more information about this DMV, please see the below Books Online link: http://msdn.microsoft.com/en-us/library/hh204561.aspx Follow me on Twitter @PrimeTimeDBA

    Read the article

  • Retrieve Performance Data from SOA Infrastructure Database

    - by fip
    My earlier blog posting shows how to enable, retrieve and interpret BPEL engine performance statistics to aid performance troubleshooting. The strength of BPEL engine statistics at EM is its break down per request. But there are some limitations with the BPEL performance statistics mentioned in that blog posting: The statistics were stored in memory instead of being persisted. To avoid memory overflow, the data are stored to a buffer with limited size. When the statistic entries exceed the limitation, old data will be flushed out to give ways to new statistics. Therefore it can only keep the last X number of entries of data. The statistics 5 hour ago may not be there anymore. The BPEL engine performance statistics only includes latencies. It does not provide throughputs. Fortunately, Oracle SOA Suite runs with the SOA Infrastructure database and a lot of performance data are naturally persisted there. It is at a more coarse grain than the in-memory BPEL Statistics, but it does have its own strengths as it is persisted. Here I would like offer examples of some basic SQL queries you can run against the infrastructure database of Oracle SOA Suite 11G to acquire the performance statistics for a given period of time. You can run it immediately after you modify the date range to match your actual system. 1. Asynchronous/one-way messages incoming rates The following query will show number of messages sent to one-way/async BPEL processes during a given time period, organized by process names and states select composite_name composite, state, count(*) Count from dlv_message where receive_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and receive_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, state order by Count; 2. Throughput of BPEL process instances The following query shows the number of synchronous and asynchronous process instances created during a given time period. It list instances of all states, including the unfinished and faulted ones. The results will include all composites cross all SOA partitions select state, count(*) Count, composite_name composite, component_name,componenttype from cube_instance where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype order by count(*) desc; 3. Throughput and latencies of BPEL process instances This query is augmented on the previous one, providing more comprehensive information. It gives not only throughput but also the maximum, minimum and average elapse time BPEL process instances. select composite_name Composite, component_name Process, componenttype, state, count(*) Count, trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MaxTime, trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MinTime, trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) AvgTime from cube_instance where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype, state order by count(*) desc;   4. Combine all together Now let's combine all of these 3 queries together, and parameterize the start and end time stamps to make the script a bit more robust. The following script will prompt for the start and end time before querying against the database: accept startTime prompt 'Enter start time (YYYY-MM-DD HH24:MI:SS)' accept endTime prompt 'Enter end time (YYYY-MM-DD HH24:MI:SS)' Prompt "==== Rejected Messages ===="; REM 2012-10-24 21:00:00 REM 2012-10-24 21:59:59 select count(*), composite_dn from rejected_message where created_time >= to_timestamp('&&StartTime','YYYY-MM-DD HH24:MI:SS') and created_time <= to_timestamp('&&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_dn; Prompt " "; Prompt "==== Throughput of one-way/asynchronous messages ===="; select state, count(*) Count, composite_name composite from dlv_message where receive_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS') and receive_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_name, state order by Count; Prompt " "; Prompt "==== Throughput and latency of BPEL process instances ====" select state, count(*) Count, trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MaxTime, trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MinTime, trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) AvgTime, composite_name Composite, component_name Process, componenttype from cube_instance where creation_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype, state order by count(*) desc;  

    Read the article

< Previous Page | 109 110 111 112 113 114 115 116 117 118 119 120  | Next Page >