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  • 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 

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  • What is the best practice with KML files when adding geositemap?

    - by Floran
    Im not sure how to deal with kml files. Now important particularly in reference to the Google Venice update. My site basically is a guide of many company listings (sort of Yellow Pages). I want each company listing to have a geolocation associated with it. Which of the options I present below is the way to go? OPTION 1: all locations in a single KML file with a reference to that KML file from a geositemap.xml MYGEOSITEMAP.xml <?xml version="1.0" encoding="UTF-8"?> <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:geo="http://www.google.com/geo/schemas/sitemap/1.0"> <url><loc>http://www.mysite.com/locations.kml</loc> <geo:geo> <geo:format>kml</geo:format></geo:geo></url> </urlset> ALLLOCATIONS.kml <kml xmlns="http://www.opengis.net/kml/2.2" xmlns:atom="http://www.w3.org/2005/Atom"> <Document> <name>MyCompany</name> <atom:author> <atom:name>MyCompany</atom:name> </atom:author> <atom:link href="http://www.mysite.com/locations/3454/MyCompany" rel="related" /> <Placemark> <name>MyCompany, Kalverstraat 26 Amsterdam 1000AG</name> <description><![CDATA[<address><a href="http://www.mysite.com/locations/3454/MyCompany">MyCompany</a><br />Address: Kalverstraat 26, Amsterdam 1000AG <br />Phone: 0646598787</address><p>hello there, im MyCompany</p>]]> </description><Point><coordinates>5.420686499999965,51.6298808,0</coordinates> </Point> </Placemark> </Document> </kml> <kml xmlns="http://www.opengis.net/kml/2.2" xmlns:atom="http://www.w3.org/2005/Atom"> <Document> <name>MyCompany</name><atom:author><atom:name>MyCompany</atom:name></atom:author><atom:link href="http://www.mysite.com/locations/22/companyX" rel="related" /><Placemark><name>MyCompany, Rosestreet 45 Amsterdam 1001XF </name><description><![CDATA[<address><a href="http://www.mysite.com/locations/22/companyX">companyX</a><br />Address: Rosestreet 45, Amsterdam 1001XF <br />Phone: 0642195493</address><p>some text about companyX</p>]]></description><Point><coordinates>5.520686499889632,51.6197705,0</coordinates></Point></Placemark> </Document> </kml> OPTION 2: a separate KML file for each location and a reference to each KML file from a geositemap.xml (kml files placed in a \kmlfiles folder) MYGEOSITEMAP.xml <?xml version="1.0" encoding="UTF-8"?> <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:geo="http://www.google.com/geo/schemas/sitemap/1.0"> <url><loc>http://www.mysite.com/kmlfiles/3454_MyCompany.kml</loc> <geo:geo> <geo:format>kml</geo:format></geo:geo></url> <url><loc>http://www.mysite.com/kmlfiles/22_companyX.kml</loc> <geo:geo> <geo:format>kml</geo:format></geo:geo></url> </urlset> *3454_MyCompany.kml* <kml xmlns="http://www.opengis.net/kml/2.2" xmlns:atom="http://www.w3.org/2005/Atom"> <Document><name>MyCompany</name><atom:author><atom:name>MyCompany</atom:name></atom:author><atom:link href="http://www.mysite.com/locations/3454/MyCompany" rel="related" /><Placemark><name>MyCompany, Kalverstraat 26 Amsterdam 1000AG</name><description><![CDATA[<address><a href="http://www.mysite.com/locations/3454/MyCompany">MyCompany</a><br />Address: Kalverstraat 26, Amsterdam 1000AG <br />Phone: 0646598787</address><p>hello there, im MyCompany</p>]]></description><Point><coordinates>5.420686499999965,51.6298808,0</coordinates></Point></Placemark> </Document> </kml> *22_companyX.kml* <kml xmlns="http://www.opengis.net/kml/2.2" xmlns:atom="http://www.w3.org/2005/Atom"> <Document><name>companyX</name><atom:author><atom:name>companyX</atom:name></atom:author><atom:link href="http://www.mysite.com/locations/22/companyX" rel="related" /><Placemark><name>companyX, Rosestreet 45 Amsterdam 1001XF </name><description><![CDATA[<address><a href="http://www.mysite.com/locations/22/companyX">companyX</a><br />Address: Rosestreet 45, Amsterdam 1001XF <br />Phone: 0642195493</address><p>some text about companyX</p>]]></description><Point><coordinates>5.520686499889632,51.6197705,0</coordinates></Point></Placemark> </Document> </kml> OPTION 3?

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  • MySQL Cluster 7.2: Over 8x Higher Performance than Cluster 7.1

    - by Mat Keep
    0 0 1 893 5092 Homework 42 11 5974 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} Summary The scalability enhancements delivered by extensions to multi-threaded data nodes enables MySQL Cluster 7.2 to deliver over 8x higher performance than the previous MySQL Cluster 7.1 release on a recent benchmark What’s New in MySQL Cluster 7.2 MySQL Cluster 7.2 was released as GA (Generally Available) in February 2012, delivering many enhancements to performance on complex queries, new NoSQL Key / Value API, cross-data center replication and ease-of-use. These enhancements are summarized in the Figure below, and detailed in the MySQL Cluster New Features whitepaper Figure 1: Next Generation Web Services, Cross Data Center Replication and Ease-of-Use Once of the key enhancements delivered in MySQL Cluster 7.2 is extensions made to the multi-threading processes of the data nodes. Multi-Threaded Data Node Extensions The MySQL Cluster 7.2 data node is now functionally divided into seven thread types: 1) Local Data Manager threads (ldm). Note – these are sometimes also called LQH threads. 2) Transaction Coordinator threads (tc) 3) Asynchronous Replication threads (rep) 4) Schema Management threads (main) 5) Network receiver threads (recv) 6) Network send threads (send) 7) IO threads Each of these thread types are discussed in more detail below. MySQL Cluster 7.2 increases the maximum number of LDM threads from 4 to 16. The LDM contains the actual data, which means that when using 16 threads the data is more heavily partitioned (this is automatic in MySQL Cluster). Each LDM thread maintains its own set of data partitions, index partitions and REDO log. The number of LDM partitions per data node is not dynamically configurable, but it is possible, however, to map more than one partition onto each LDM thread, providing flexibility in modifying the number of LDM threads. The TC domain stores the state of in-flight transactions. This means that every new transaction can easily be assigned to a new TC thread. Testing has shown that in most cases 1 TC thread per 2 LDM threads is sufficient, and in many cases even 1 TC thread per 4 LDM threads is also acceptable. Testing also demonstrated that in some instances where the workload needed to sustain very high update loads it is necessary to configure 3 to 4 TC threads per 4 LDM threads. In the previous MySQL Cluster 7.1 release, only one TC thread was available. This limit has been increased to 16 TC threads in MySQL Cluster 7.2. The TC domain also manages the Adaptive Query Localization functionality introduced in MySQL Cluster 7.2 that significantly enhanced complex query performance by pushing JOIN operations down to the data nodes. Asynchronous Replication was separated into its own thread with the release of MySQL Cluster 7.1, and has not been modified in the latest 7.2 release. To scale the number of TC threads, it was necessary to separate the Schema Management domain from the TC domain. The schema management thread has little load, so is implemented with a single thread. The Network receiver domain was bound to 1 thread in MySQL Cluster 7.1. With the increase of threads in MySQL Cluster 7.2 it is also necessary to increase the number of recv threads to 8. This enables each receive thread to service one or more sockets used to communicate with other nodes the Cluster. The Network send thread is a new thread type introduced in MySQL Cluster 7.2. Previously other threads handled the sending operations themselves, which can provide for lower latency. To achieve highest throughput however, it has been necessary to create dedicated send threads, of which 8 can be configured. It is still possible to configure MySQL Cluster 7.2 to a legacy mode that does not use any of the send threads – useful for those workloads that are most sensitive to latency. The IO Thread is the final thread type and there have been no changes to this domain in MySQL Cluster 7.2. Multiple IO threads were already available, which could be configured to either one thread per open file, or to a fixed number of IO threads that handle the IO traffic. Except when using compression on disk, the IO threads typically have a very light load. Benchmarking the Scalability Enhancements The scalability enhancements discussed above have made it possible to scale CPU usage of each data node to more than 5x of that possible in MySQL Cluster 7.1. In addition, a number of bottlenecks have been removed, making it possible to scale data node performance by even more than 5x. Figure 2: MySQL Cluster 7.2 Delivers 8.4x Higher Performance than 7.1 The flexAsynch benchmark was used to compare MySQL Cluster 7.2 performance to 7.1 across an 8-node Intel Xeon x5670-based cluster of dual socket commodity servers (6 cores each). As the results demonstrate, MySQL Cluster 7.2 delivers over 8x higher performance per data nodes than MySQL Cluster 7.1. More details of this and other benchmarks will be published in a new whitepaper – coming soon, so stay tuned! In a following blog post, I’ll provide recommendations on optimum thread configurations for different types of server processor. You can also learn more from the Best Practices Guide to Optimizing Performance of MySQL Cluster Conclusion MySQL Cluster has achieved a range of impressive benchmark results, and set in context with the previous 7.1 release, is able to deliver over 8x higher performance per node. As a result, the multi-threaded data node extensions not only serve to increase performance of MySQL Cluster, they also enable users to achieve significantly improved levels of utilization from current and future generations of massively multi-core, multi-thread processor designs.

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  • MySQL Cluster 7.3 Labs Release – Foreign Keys Are In!

    - by Mat Keep
    0 0 1 1097 6254 Homework 52 14 7337 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} Summary (aka TL/DR): Support for Foreign Key constraints has been one of the most requested feature enhancements for MySQL Cluster. We are therefore extremely excited to announce that Foreign Keys are part of the first Labs Release of MySQL Cluster 7.3 – available for download, evaluation and feedback now! (Select the mysql-cluster-7.3-labs-June-2012 build) In this blog, I will attempt to discuss the design rationale, implementation, configuration and steps to get started in evaluating the first MySQL Cluster 7.3 Labs Release. Pace of Innovation It was only a couple of months ago that we announced the General Availability (GA) of MySQL Cluster 7.2, delivering 1 billion Queries per Minute, with 70x higher cross-shard JOIN performance, Memcached NoSQL key-value API and cross-data center replication.  This release has been a huge hit, with downloads and deployments quickly reaching record levels. The announcement of the first MySQL Cluster 7.3 Early Access lab release at today's MySQL Innovation Day event demonstrates the continued pace in Cluster development, and provides an opportunity for the community to evaluate and feedback on new features they want to see. What’s the Plan for MySQL Cluster 7.3? Well, Foreign Keys, as you may have gathered by now (!), and this is the focus of this first Labs Release. As with MySQL Cluster 7.2, we plan to publish a series of preview releases for 7.3 that will incrementally add new candidate features for a final GA release (subject to usual safe harbor statement below*), including: - New NoSQL APIs; - Features to automate the configuration and provisioning of multi-node clusters, on premise or in the cloud; - Performance and scalability enhancements; - Taking advantage of features in the latest MySQL 5.x Server GA. Design Rationale MySQL Cluster is designed as a “Not-Only-SQL” database. It combines attributes that enable users to blend the best of both relational and NoSQL technologies into solutions that deliver web scalability with 99.999% availability and real-time performance, including: Concurrent NoSQL and SQL access to the database; Auto-sharding with simple scale-out across commodity hardware; Multi-master replication with failover and recovery both within and across data centers; Shared-nothing architecture with no single point of failure; Online scaling and schema changes; ACID compliance and support for complex queries, across shards. Native support for Foreign Key constraints enables users to extend the benefits of MySQL Cluster into a broader range of use-cases, including: - Packaged applications in areas such as eCommerce and Web Content Management that prescribe databases with Foreign Key support. - In-house developments benefiting from Foreign Key constraints to simplify data models and eliminate the additional application logic needed to maintain data consistency and integrity between tables. Implementation The Foreign Key functionality is implemented directly within MySQL Cluster’s data nodes, allowing any client API accessing the cluster to benefit from them – whether using SQL or one of the NoSQL interfaces (Memcached, C++, Java, JPA or HTTP/REST.) The core referential actions defined in the SQL:2003 standard are implemented: CASCADE RESTRICT NO ACTION SET NULL In addition, the MySQL Cluster implementation supports the online adding and dropping of Foreign Keys, ensuring the Cluster continues to serve both read and write requests during the operation. An important difference to note with the Foreign Key implementation in InnoDB is that MySQL Cluster does not support the updating of Primary Keys from within the Data Nodes themselves - instead the UPDATE is emulated with a DELETE followed by an INSERT operation. Therefore an UPDATE operation will return an error if the parent reference is using a Primary Key, unless using CASCADE action, in which case the delete operation will result in the corresponding rows in the child table being deleted. The Engineering team plans to change this behavior in a subsequent preview release. Also note that when using InnoDB "NO ACTION" is identical to "RESTRICT". In the case of MySQL Cluster “NO ACTION” means “deferred check”, i.e. the constraint is checked before commit, allowing user-defined triggers to automatically make changes in order to satisfy the Foreign Key constraints. Configuration There is nothing special you have to do here – Foreign Key constraint checking is enabled by default. If you intend to migrate existing tables from another database or storage engine, for example from InnoDB, there are a couple of best practices to observe: 1. Analyze the structure of the Foreign Key graph and run the ALTER TABLE ENGINE=NDB in the correct sequence to ensure constraints are enforced 2. Alternatively drop the Foreign Key constraints prior to the import process and then recreate when complete. Getting Started Read this blog for a demonstration of using Foreign Keys with MySQL Cluster.  You can download MySQL Cluster 7.3 Labs Release with Foreign Keys today - (select the mysql-cluster-7.3-labs-June-2012 build) If you are new to MySQL Cluster, the Getting Started guide will walk you through installing an evaluation cluster on a singe host (these guides reflect MySQL Cluster 7.2, but apply equally well to 7.3) Post any questions to the MySQL Cluster forum where our Engineering team will attempt to assist you. Post any bugs you find to the MySQL bug tracking system (select MySQL Cluster from the Category drop-down menu) And if you have any feedback, please post them to the Comments section of this blog. Summary MySQL Cluster 7.2 is the GA, production-ready release of MySQL Cluster. This first Labs Release of MySQL Cluster 7.3 gives you the opportunity to preview and evaluate future developments in the MySQL Cluster database, and we are very excited to be able to share that with you. Let us know how you get along with MySQL Cluster 7.3, and other features that you want to see in future releases. * Safe Harbor Statement This information is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

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  • MySQL Cluster 7.3 - Join This Week's Webinar to Learn What's New

    - by Mat Keep
    The first Development Milestone and Early Access releases of MySQL Cluster 7.3 were announced just several weeks ago. To provide more detail and demonstrate the new features, Andrew Morgan and I will be hosting a live webinar this coming Thursday 25th October at 0900 Pacific Time / 16.00 UTC Even if you can't make the live webinar, it is still worth registering for the event as you will receive a notification when the replay will be available, to view on-demand at your convenience In the webinar, we will discuss the enhancements being previewed as part of MySQL Cluster 7.3, including: - Foreign Key Constraints: Yes, we've looked into the future and decided Foreign Keys are it ;-) You can read more about the implementation of Foreign Keys in MySQL Cluster 7.3 here - Node.js NoSQL API: Allowing web, mobile and cloud services to query and receive results sets from MySQL Cluster, natively in JavaScript, enables developers to seamlessly couple high performance, distributed applications with a high performance, distributed, persistence layer delivering 99.999% availability. You can study the Node.js / MySQL Cluster tutorial here - Auto-Installer: This new web-based GUI makes it simple for DevOps teams to quickly configure and provision highly optimized MySQL Cluster deployments on-premise or in the cloud You can view a YouTube tutorial on the MySQL Cluster Auto-Installer here  So we have a lot to cover in our 45 minute session. It will be time well spent if you want to know more about the future direction of MySQL Cluster and how it can help you innovate faster, with greater simplicity. Registration is open 

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  • CodePlex Daily Summary for Saturday, June 22, 2013

    CodePlex Daily Summary for Saturday, June 22, 2013Popular ReleasesGac Library -- C++ Utilities for GPU Accelerated GUI and Script: Gaclib 0.5.2.0: Gaclib.zip contains the following content GacUIDemo Demo solution and projects Public Source GacUI library Document HTML document. Please start at reference_gacui.html Content Necessary CSS/JPG files for document. Improvements to the previous release Add 4 demos Controls.DataGrid.ChemicalElements This demo shows how to display data in a GuiVirtualDataGrid control using different styles for different cells. Controls.DataGrid.FileExplorer This demo shows how to use GuiVirtualDataGrid w...Three-Dimensional Maneuver Gear for Minecraft: TDMG 1.1.0.0 for 1.5.2: CodePlex???(????????) ?????????(???1/4) ??????????? ?????????? ???????????(??????????) ??????????????????????? ↑????、?????????????????????(???????) ???、??????????、?????????????????????、????????1.5?????????? Shift+W(????)??????????????????10°、?10°(?????????)???Pizarrón Virtual: Pizarron virtual codigo fuente: Código fuenteHyper-V Management Pack Extensions 2012: HyperVMPE2012: Hyper-V Management Pack Extensions 2012 Beta ReleaseOutlook 2013 Add-In: Email appointments: This new version includes the following changes: - Ability to drag emails to the calendar to create appointments. Will gather all the recipients from all the emails and create an appointment on the day you drop the emails, with the text and subject of the last selected email (if more than one selected). - Increased maximum of numbers to display appointments to 30. You will have to uninstall the previous version (add/remove programs) if you had installed it before. Before unzipping the file...Caliburn Micro: WPF, Silverlight, WP7 and WinRT/Metro made easy.: Caliburn.Micro v1.5.2: v1.5.2 - This is a service release. We've fixed a number of issues with Tasks and IoC. We've made some consistency improvements across platforms and fixed a number of minor bugs. See changes.txt for details. Packages Available on Nuget Caliburn.Micro – The full framework compiled into an assembly. Caliburn.Micro.Start - Includes Caliburn.Micro plus a starting bootstrapper, view model and view. Caliburn.Micro.Container – The Caliburn.Micro inversion of control container (IoC); source code...CODE Framework: 4.0.30618.0: See change notes in the documentation section for details on what's new. Note: If you download the class reference help file with, you have to right-click the file, pick "Properties", and then unblock the file, as many browsers flag the file as blocked during download (for security reasons) and thus hides all content.Toolbox for Dynamics CRM 2011: XrmToolBox (v1.2013.6.18): XrmToolbox improvement Use new connection controls (use of Microsoft.Xrm.Client.dll) New display capabilities for tools (size, image and colors) Added prerequisites check Added Most Used Tools feature Tools improvementNew toolSolution Transfer Tool (v1.0.0.0) developed by DamSim Updated toolView Layout Replicator (v1.2013.6.17) Double click on source view to display its layoutXml All tools list Access Checker (v1.2013.6.17) Attribute Bulk Updater (v1.2013.6.18) FetchXml Tester (v1.2013.6.1...Media Companion: Media Companion MC3.570b: New* Movie - using XBMC TMDB - now renames movies if option selected. * Movie - using Xbmc Tmdb - Actor images saved from TMDb if option selected. Fixed* Movie - Checks for poster.jpg against missing poster filter * Movie - Fixed continual scraping of vob movie file (not DVD structure) * Both - Correctly display audio channels * Both - Correctly populate audio info in nfo's if multiple audio tracks. * Both - added icons and checked for DTS ES and Dolby TrueHD audio tracks. * Both - Stream d...Document.Editor: 2013.24: What's new for Document.Editor 2013.24: Improved Video Editing support Improved Link Editing support Minor Bug Fix's, improvements and speed upsExtJS based ASP.NET Controls: FineUI v3.3.0: ??FineUI ?? ExtJS ??? ASP.NET ???。 FineUI??? ?? No JavaScript，No CSS，No UpdatePanel，No ViewState，No WebServices ???????。 ?????? IE 7.0、Firefox 3.6、Chrome 3.0、Opera 10.5、Safari 3.0+ ???? Apache License v2.0 ?：ExtJS ?? GPL v3 ?????(http://www.sencha.com/license)。 ???? ??：http://fineui.com/bbs/ ??：http://fineui.com/demo/ ??：http://fineui.com/doc/ ??：http://fineui.codeplex.com/ FineUI???? ExtJS ?????????，???? ExtJS ?。 ????? FineUI ? ExtJS ?：http://fineui.com/bbs/forum.php?mod=viewthrea...BarbaTunnel: BarbaTunnel 8.0: Check Version History for more information about this release.ExpressProfiler: ExpressProfiler v1.5: [+] added Start time, End time event columns [+] added SP:StmtStarting, SP:StmtCompleted events [*] fixed bug with Audit:Logout eventpatterns & practices: Data Access Guidance: Data Access Guidance Drop4 2013.06.17: Drop 4Kooboo CMS: Kooboo CMS 4.1.1: The stable release of Kooboo CMS 4.1.0 with fixed the following issues: https://github.com/Kooboo/CMS/issues/1 https://github.com/Kooboo/CMS/issues/11 https://github.com/Kooboo/CMS/issues/13 https://github.com/Kooboo/CMS/issues/15 https://github.com/Kooboo/CMS/issues/19 https://github.com/Kooboo/CMS/issues/20 https://github.com/Kooboo/CMS/issues/24 https://github.com/Kooboo/CMS/issues/43 https://github.com/Kooboo/CMS/issues/45 https://github.com/Kooboo/CMS/issues/46 https://github....VidCoder: 1.5.0 Beta: The betas have started up again! If you were previously on the beta track you will need to install this to get back on it. That's because you can now run both the Beta and Stable version of VidCoder side-by-side! Note that the OpenCL and Intel QuickSync changes being tested by HandBrake are not in the betas yet. They will appear when HandBrake integrates them into the main branch. Updated HandBrake core to SVN 5590. This adds a new FDK AAC encoder. The FAAC encoder has been removed and now...Wsus Package Publisher: Release v1.2.1306.16: Date/Time are displayed as Local Time. (Last Contact, Last Report and DeadLine) Wpp now remember the last used path for update publishing. (See 'Settings' Form for options) Add an option to allow users to publish an update even if the Framework has judged the certificate as invalid. (Attention : Using this option will NOT allow you to publish or revise an update if your certificate is really invalid). When publishing a new update, filter update files to ensure that there is not files wi...Employee Info Starter Kit: v6.0 - ASP.NET MVC Edition: Release Home - Getting Started - Hands on Coding Walkthrough – Technology Stack - Design & Architecture EISK v6.0 – ASP.NET MVC edition bundles most of the greatest and successful platforms, frameworks and technologies together, to enable web developers to learn and build manageable and high performance web applications with rich user experience effectively and quickly. User End SpecificationsCreating a new employee record Read existing employee records Update an existing employee reco...OLAP PivotTable Extensions: Release 0.8.1: Use the 32-bit download for... Excel 2007 Excel 2010 32-bit (even Excel 2010 32-bit on a 64-bit operating system) Excel 2013 32-bit (even Excel 2013 32-bit on a 64-bit operating system) Use the 64-bit download for... Excel 2010 64-bit Excel 2013 64-bit Just download and run the EXE. There is no need to uninstall the previous release. If you have problems getting the add-in to work, see the Troubleshooting Installation wiki page. The new features in this release are: View #VALUE! Err...DirectXTex texture processing library: June 2013: June 15, 2013 Custom filtering implementation for Resize & GenerateMipMaps(3D) - Point, Box, Linear, Cubic, and Triangle TEX_FILTER_TRIANGLE finite low-pass triangle filter TEX_FILTER_WRAP, TEX_FILTER_MIRROR texture semantics for custom filtering TEX_FILTER_BOX alias for TEX_FILTER_FANT WIC Ordered and error diffusion dithering for non-WIC conversion sRGB gamma correct custom filtering and conversion DDS_FLAGS_EXPAND_LUMINANCE - Reader conversion option for L8, L16, and A8L8 legacy ...New Projects7tin - For Business Expansion: 7tin.net one project that every Businessman can open a shop and sale everything. It's free.AntikCompta: AntikCompta is the easiest way to share comptability beetween an antiquaire and it's account manager.Community wallpaper: share your desktop wall paper with others using People Near Me (PNM) protocolDeep.NET: This project aims at providing basic tools for developers to build learning models that can extract hierarchical representations of knowledge. DynamicAccess: DynamicAccess is a library to aid connecting DLR languages such as ironpython and ironruby to non-dynamic languages like managed C++. It also fills in some gaps in the current C# support of dynamic objects, such as member access by string and deletion of members or indexes.EntInscripcion: Este es un proyecto de inscripcionesExtensible Lightweight Framework: ELF is developed in C# .NET 4.0. It offers extensions on standard components from the .NET Framework and custom components such as MVP. Most classes implement interfaces and are resolved using IoC. Currently a single feature is available, the coming month new features will be added. WCF Service Client - Exception Handling - Retry Logic - Reusable Eye tracking with Kinect for Windows: Attempts to track eyes and calculate the pupillary distance of the user using Kinect for Windows.Hanoi: Another implementation of the popular game of Towers of Hanoi for SmartPhones with Windows Mobile 2003 or newer. But it's not only the classic Towers of Hanoi, it has some value-added features like timing, time-limited gaming, column order changing, etc. It supports from 1 disk to 8. Very geeky game.HR_project: WPF ?????????? ? ???????? ? ???? ??????. ???????? ? ?????????? ????? ??????????? ? ???? ??????.HttpUtility: HttpUtility???C#?HttpRequest（HttpWebRequest）?????。 ??HttpUtility????????? 1.??HttpRequest?Get??，?????html?? 2.??HttpRequest?Post??，?????html?? 3.??HttpRequest??IL2 Stats DB: A SQL 2008 Database Scheme to store data generated by the IL2 Log Parser.maillib: Libreria mail per l'invio di posta elettronicamgl Pluginsystem: mgl is a pluginsystem. MIBETerminal: ---Mobile FIT VUT: Školní aplikace pro zacínající studenty FIT VUT. Obsahuje potrebné informace pro studium na Vysokém ucení v Brne fakulta Informacních technologií. Aplikace obsahuje: -školní prírucku -aktuální jídelní lístkem menz -kontaktní informace na zamestnance FIT VUT -mapu fakulty FIT -aktuality -plánovac úloh Aplikace se nachází na Windows Marketplace: http://www.windowsphone.com/cs-CZ/apps/c2e8036d-970a-4ab1-8ca4-b97788a0dcb5 OMANE: OMANEPDF Unlocker Software for those who want to unlock PDF Restrictions: PDF Unlocker Software has the competence to unlock PDF restrictions. With this tool user can exclusive attribute to remove PDF security completelyPortalCemil: Portal CemilResonance: Resonance is a system to train neural networks, it allows to automate the train of a neural network, distributing the calculation on multiple machines.SauravUtil: These are some small utilities created specially by saurav sarkar. 1. Wcf Tutorial using Entity Framework 5 2. Rest Coming soonSelcukEticaretDenemesi: Test için bir çalisma yürütecegimSPWTF: "SPWTF or SharePoint well thats foolish" is a project that intends to bridge various gaps in the OOTB SharePoint apps story. Enjoy!SQL Server Integration Services Reporting: The canned SSMS Integration Services reports rewritten for deployment on Reporting Services.Swyish Chess: Chess Application built using C# and WPFThree-Dimensional Maneuver Gear for Minecraft: Minecraft?????????????。Urdu Translation: Urdu Translation Project Visual Studio Templates compliant with StyleCop Rules: This project contains the templates and instructions to make your Visual Studio 2008 create new files compliant to StyleCop rules.webapps-in-action.com: Here you'll find all the Code Samples & Solutions from my Blog http://webapps-in-action.com

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  • Introducing UPK 3.6 Simulation Help (You Say It and We Do It!)

    - by kathryn.lustenberger(at)oracle.com
    We would like to thank everyone that participated in the recent documentation survey that was conducted over the last several months. Your feedback is valuable and we appreciate the time you took to provide it. Many of you commented that you would like to have "UPKs for UPK" in the documentation. In response, we are pleased to announce the availability of Simulation Help. This unique help system is a blending of the text-based Developer help and a collection of approximately 200 simulations that show authors how to create, record, refine, localize, and publish content using the Developer. You can access Simulation Help at any time using the following link: http://download.oracle.com/technology/products/upk/index.html Save this link as a favorite or bookmark in your browser for easy access anytime. We have also provided a link to a short one-question survey so you can tell us what you think of the new Simulation Help. http://www.surveymonkey.com/s/BJT7LV6 Thanks again for your valuable feedback on the product documentation!

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  • Extending Oracle CEP with Predictive Analytics

    - by vikram.shukla(at)oracle.com
    Introduction: OCEP is often used as a business rules engine to execute a set of business logic rules via CQL statements, and take decisions based on the outcome of those rules. There are times where configuring rules manually is sufficient because an application needs to deal with only a small and well-defined set of static rules. However, in many situations customers don't want to pre-define such rules for two reasons. First, they are dealing with events with lots of columns and manually crafting such rules for each column or a set of columns and combinations thereof is almost impossible. Second, they are content with probabilistic outcomes and do not care about 100% precision. The former is the case when a user is dealing with data with high dimensionality, the latter when an application can live with "false" positives as they can be discarded after further inspection, say by a Human Task component in a Business Process Management software. The primary goal of this blog post is to show how this can be achieved by combining OCEP with Oracle Data Mining® and leveraging the latter's rich set of algorithms and functionality to do predictive analytics in real time on streaming events. The secondary goal of this post is also to show how OCEP can be extended to invoke any arbitrary external computation in an RDBMS from within CEP. The extensible facility is known as the JDBC cartridge. The rest of the post describes the steps required to achieve this: We use the dataset available at http://blogs.oracle.com/datamining/2010/01/fraud_and_anomaly_detection_made_simple.html to showcase the capabilities. We use it to show how transaction anomalies or fraud can be detected. Building the model: Follow the self-explanatory steps described at the above URL to build the model.  It is very simple - it uses built-in Oracle Data Mining PL/SQL packages to cleanse, normalize and build the model out of the dataset.  You can also use graphical Oracle Data Miner®  to build the models. To summarize, it involves: Specifying which algorithms to use. In this case we use Support Vector Machines as we're trying to find anomalies in highly dimensional dataset.Build model on the data in the table for the algorithms specified. For this example, the table was populated in the scott/tiger schema with appropriate privileges. Configuring the Data Source: This is the first step in building CEP application using such an integration.  Our datasource looks as follows in the server config file.  It is advisable that you use the Visualizer to add it to the running server dynamically, rather than manually edit the file.    <data-source>         <name>DataMining</name>         <data-source-params>             <jndi-names>                 <element>DataMining</element>             </jndi-names>             <global-transactions-protocol>OnePhaseCommit</global-transactions-protocol>         </data-source-params>         <connection-pool-params>             <credential-mapping-enabled></credential-mapping-enabled>             <test-table-name>SQL SELECT 1 from DUAL</test-table-name>             <initial-capacity>1</initial-capacity>             <max-capacity>15</max-capacity>             <capacity-increment>1</capacity-increment>         </connection-pool-params>         <driver-params>             <use-xa-data-source-interface>true</use-xa-data-source-interface>             <driver-name>oracle.jdbc.OracleDriver</driver-name>             <url>jdbc:oracle:thin:@localhost:1522:orcl</url>             <properties>                 <element>                     <value>scott</value>                     <name>user</name>                 </element>                 <element>                     <value>{Salted-3DES}AzFE5dDbO2g=</value>                     <name>password</name>                 </element>                                 <element>                     <name>com.bea.core.datasource.serviceName</name>                     <value>oracle11.2g</value>                 </element>                 <element>                     <name>com.bea.core.datasource.serviceVersion</name>                     <value>11.2.0</value>                 </element>                 <element>                     <name>com.bea.core.datasource.serviceObjectClass</name>                     <value>java.sql.Driver</value>                 </element>             </properties>         </driver-params>     </data-source>   Designing the EPN: The EPN is very simple in this example. We briefly describe each of the components. The adapter ("DataMiningAdapter") reads data from a .csv file and sends it to the CQL processor downstream. The event payload here is same as that of the table in the database (refer to the attached project or do a "desc table-name" from a SQL*PLUS prompt). While this is for convenience in this example, it need not be the case. One can still omit fields in the streaming events, and need not match all columns in the table on which the model was built. Better yet, it does not even need to have the same name as columns in the table, as long as you alias them in the USING clause of the mining function. (Caveat: they still need to draw values from a similar universe or domain, otherwise it constitutes incorrect usage of the model). There are two things in the CQL processor ("DataMiningProc") that make scoring possible on streaming events. 1.      User defined cartridge function Please refer to the OCEP CQL reference manual to find more details about how to define such functions. We include the function below in its entirety for illustration. <?xml version="1.0" encoding="UTF-8"?> <jdbcctxconfig:config     xmlns:jdbcctxconfig="http://www.bea.com/ns/wlevs/config/application"     xmlns:jc="http://www.oracle.com/ns/ocep/config/jdbc">        <jc:jdbc-ctx>         <name>Oracle11gR2</name>         <data-source>DataMining</data-source>               <function name="prediction2">                                 <param name="CQLMONTH" type="char"/>                      <param name="WEEKOFMONTH" type="int"/>                      <param name="DAYOFWEEK" type="char" />                      <param name="MAKE" type="char" />                      <param name="ACCIDENTAREA"   type="char" />                      <param name="DAYOFWEEKCLAIMED"  type="char" />                      <param name="MONTHCLAIMED" type="char" />                      <param name="WEEKOFMONTHCLAIMED" type="int" />                      <param name="SEX" type="char" />                      <param name="MARITALSTATUS"   type="char" />                      <param name="AGE" type="int" />                      <param name="FAULT" type="char" />                      <param name="POLICYTYPE"   type="char" />                      <param name="VEHICLECATEGORY"  type="char" />                      <param name="VEHICLEPRICE" type="char" />                      <param name="FRAUDFOUND" type="int" />                      <param name="POLICYNUMBER" type="int" />                      <param name="REPNUMBER" type="int" />                      <param name="DEDUCTIBLE"   type="int" />                      <param name="DRIVERRATING"  type="int" />                      <param name="DAYSPOLICYACCIDENT"   type="char" />                      <param name="DAYSPOLICYCLAIM" type="char" />                      <param name="PASTNUMOFCLAIMS" type="char" />                      <param name="AGEOFVEHICLES" type="char" />                      <param name="AGEOFPOLICYHOLDER" type="char" />                      <param name="POLICEREPORTFILED" type="char" />                      <param name="WITNESSPRESNT" type="char" />                      <param name="AGENTTYPE" type="char" />                      <param name="NUMOFSUPP" type="char" />                      <param name="ADDRCHGCLAIM"   type="char" />                      <param name="NUMOFCARS" type="char" />                      <param name="CQLYEAR" type="int" />                      <param name="BASEPOLICY" type="char" />                                     <return-component-type>char</return-component-type>                                                      <sql><![CDATA[             SELECT to_char(PREDICTION_PROBABILITY(CLAIMSMODEL, '0' USING *))               AS probability             FROM (SELECT  :CQLMONTH AS MONTH,                                            :WEEKOFMONTH AS WEEKOFMONTH,                          :DAYOFWEEK AS DAYOFWEEK,                           :MAKE AS MAKE,                           :ACCIDENTAREA AS ACCIDENTAREA,                           :DAYOFWEEKCLAIMED AS DAYOFWEEKCLAIMED,                           :MONTHCLAIMED AS MONTHCLAIMED,                           :WEEKOFMONTHCLAIMED,                             :SEX AS SEX,                           :MARITALSTATUS AS MARITALSTATUS,                            :AGE AS AGE,                           :FAULT AS FAULT,                           :POLICYTYPE AS POLICYTYPE,                            :VEHICLECATEGORY AS VEHICLECATEGORY,                           :VEHICLEPRICE AS VEHICLEPRICE,                           :FRAUDFOUND AS FRAUDFOUND,                           :POLICYNUMBER AS POLICYNUMBER,                           :REPNUMBER AS REPNUMBER,                           :DEDUCTIBLE AS DEDUCTIBLE,                            :DRIVERRATING AS DRIVERRATING,                           :DAYSPOLICYACCIDENT AS DAYSPOLICYACCIDENT,                            :DAYSPOLICYCLAIM AS DAYSPOLICYCLAIM,                           :PASTNUMOFCLAIMS AS PASTNUMOFCLAIMS,                           :AGEOFVEHICLES AS AGEOFVEHICLES,                           :AGEOFPOLICYHOLDER AS AGEOFPOLICYHOLDER,                           :POLICEREPORTFILED AS POLICEREPORTFILED,                           :WITNESSPRESNT AS WITNESSPRESENT,                           :AGENTTYPE AS AGENTTYPE,                           :NUMOFSUPP AS NUMOFSUPP,                           :ADDRCHGCLAIM AS ADDRCHGCLAIM,                            :NUMOFCARS AS NUMOFCARS,                           :CQLYEAR AS YEAR,                           :BASEPOLICY AS BASEPOLICY                 FROM dual)                 ]]>         </sql>        </function>     </jc:jdbc-ctx> </jdbcctxconfig:config> 2.      Invoking the function for each event. Once this function is defined, you can invoke it from CQL as follows: <?xml version="1.0" encoding="UTF-8"?> <wlevs:config xmlns:wlevs="http://www.bea.com/ns/wlevs/config/application">   <processor>     <name>DataMiningProc</name>     <rules>        <query id="q1"><![CDATA[                     ISTREAM(SELECT S.CQLMONTH,                                   S.WEEKOFMONTH,                                   S.DAYOFWEEK, S.MAKE,                                   :                                         S.BASEPOLICY,                                    C.F AS probability                                                 FROM                                 StreamDataChannel [NOW] AS S,                                 TABLE(prediction2@Oracle11gR2(S.CQLMONTH,                                      S.WEEKOFMONTH,                                      S.DAYOFWEEK,                                       S.MAKE, ...,                                      S.BASEPOLICY) AS F of char) AS C)                       ]]></query>                 </rules>               </processor>           </wlevs:config>   Finally, the last stage in the EPN prints out the probability of the event being an anomaly. One can also define a threshold in CQL to filter out events that are normal, i.e., below a certain mark as defined by the analyst or designer. Sample Runs: Now let's see how this behaves when events are streamed through CEP. We use only two events for brevity, one normal and other one not. This is one of the "normal" looking events and the probability of it being anomalous is less than 60%. Event is: eventType=DataMiningOutEvent object=q1  time=2904821976256 S.CQLMONTH=Dec, S.WEEKOFMONTH=5, S.DAYOFWEEK=Wednesday, S.MAKE=Honda, S.ACCIDENTAREA=Urban, S.DAYOFWEEKCLAIMED=Tuesday, S.MONTHCLAIMED=Jan, S.WEEKOFMONTHCLAIMED=1, S.SEX=Female, S.MARITALSTATUS=Single, S.AGE=21, S.FAULT=Policy Holder, S.POLICYTYPE=Sport - Liability, S.VEHICLECATEGORY=Sport, S.VEHICLEPRICE=more than 69000, S.FRAUDFOUND=0, S.POLICYNUMBER=1, S.REPNUMBER=12, S.DEDUCTIBLE=300, S.DRIVERRATING=1, S.DAYSPOLICYACCIDENT=more than 30, S.DAYSPOLICYCLAIM=more than 30, S.PASTNUMOFCLAIMS=none, S.AGEOFVEHICLES=3 years, S.AGEOFPOLICYHOLDER=26 to 30, S.POLICEREPORTFILED=No, S.WITNESSPRESENT=No, S.AGENTTYPE=External, S.NUMOFSUPP=none, S.ADDRCHGCLAIM=1 year, S.NUMOFCARS=3 to 4, S.CQLYEAR=1994, S.BASEPOLICY=Liability, probability=.58931702982118561 isTotalOrderGuarantee=true\nAnamoly probability: .58931702982118561 However, the following event is scored as an anomaly with a very high probability of  89%. So there is likely to be something wrong with it. A close look reveals that the value of "deductible" field (10000) is not "normal". What exactly constitutes normal here?. If you run the query on the database to find ALL distinct values for the "deductible" field, it returns the following set: {300, 400, 500, 700} Event is: eventType=DataMiningOutEvent object=q1  time=2598483773496 S.CQLMONTH=Dec, S.WEEKOFMONTH=5, S.DAYOFWEEK=Wednesday, S.MAKE=Honda, S.ACCIDENTAREA=Urban, S.DAYOFWEEKCLAIMED=Tuesday, S.MONTHCLAIMED=Jan, S.WEEKOFMONTHCLAIMED=1, S.SEX=Female, S.MARITALSTATUS=Single, S.AGE=21, S.FAULT=Policy Holder, S.POLICYTYPE=Sport - Liability, S.VEHICLECATEGORY=Sport, S.VEHICLEPRICE=more than 69000, S.FRAUDFOUND=0, S.POLICYNUMBER=1, S.REPNUMBER=12, S.DEDUCTIBLE=10000, S.DRIVERRATING=1, S.DAYSPOLICYACCIDENT=more than 30, S.DAYSPOLICYCLAIM=more than 30, S.PASTNUMOFCLAIMS=none, S.AGEOFVEHICLES=3 years, S.AGEOFPOLICYHOLDER=26 to 30, S.POLICEREPORTFILED=No, S.WITNESSPRESENT=No, S.AGENTTYPE=External, S.NUMOFSUPP=none, S.ADDRCHGCLAIM=1 year, S.NUMOFCARS=3 to 4, S.CQLYEAR=1994, S.BASEPOLICY=Liability, probability=.89171554529576691 isTotalOrderGuarantee=true\nAnamoly probability: .89171554529576691 Conclusion: By way of this example, we show: real-time scoring of events as they flow through CEP leveraging Oracle Data Mining.how CEP applications can invoke complex arbitrary external computations (function shipping) in an RDBMS.

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  • NoSQL Java API for MySQL Cluster: Questions & Answers

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

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  • Redehost Transforms Cloud & Hosting Services with MySQL Enterprise Edition

    - by Mat Keep
    RedeHost are one of Brazil's largest cloud computing and web hosting providers, with more than 60,000 customers and 52,000 web sites running on its infrastructure. As the company grew, Redehost needed to automate operations, such as system monitoring, making the operations team more proactive in solving problems. Redehost also sought to improve server uptime, robustness, and availability, especially during backup windows, when performance would often dip. To address the needs of the business, Redehost migrated from the community edition of MySQL to MySQL Enterprise Edition, which has delivered a host of benefits: - Pro-active database management and monitoring using MySQL Enterprise Monitor, enabling Redehost to fulfil customer SLAs. Using the Query Analyzer, Redehost were able to more rapidly identify slow queries, improving customer support - Quadrupled backup speed with MySQL Enterprise Backup, leading to faster data recovery and improved system availability - Reduced DBA overhead by 50% due to the improved support capabilities offered by MySQL Enterprise Edition. - Enabled infrastructure consolidation, avoiding unnecessary energy costs and premature hardware acquisition You can learn more from the full Redehost Case Study Also, take a look at the recently updated MySQL in the Cloud whitepaper for the latest developments that are making it even simpler and more efficient to develop and deploy new services with MySQL in the cloud

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  • ADF Mobile Client Developer Preview announced!

    - by [email protected]
    Today at the RIM WES conference, Ted Farrell, Chief Architect and SVP, announed the general availability of the ADF Mobile Client Developer Preview.  This is an extension to JDeveloper that allows developers to rapidly develop mobile applications that reside on the mobile device and access a local database and can be used while completely disconnected from the network with a data synchronization technology to get the data back to the server.  You can quickly develop applications declaratively that run on multiple platforms without having to do native coding.  Go download JDeveloper at http://www.oracle.com/technology/software/products/jdev/index.html You can get more info about ADF Mobile Client here at:  http://www.oracle.com/technology/tech/wireless/adf_mobile.html   Check back here for coding examples and how-to's that will be posted regularly.

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  • MySQL and Hadoop Integration - Unlocking New Insight

    - by Mat Keep
    “Big Data” offers the potential for organizations to revolutionize their operations. With the volume of business data doubling every 1.2 years, analysts and business users are discovering very real benefits when integrating and analyzing data from multiple sources, enabling deeper insight into their customers, partners, and business processes. As the world’s most popular open source database, and the most deployed database in the web and cloud, MySQL is a key component of many big data platforms, with Hadoop vendors estimating 80% of deployments are integrated with MySQL. The new Guide to MySQL and Hadoop presents the tools enabling integration between the two data platforms, supporting the data lifecycle from acquisition and organisation to analysis and visualisation / decision, as shown in the figure below The Guide details each of these stages and the technologies supporting them: Acquire: Through new NoSQL APIs, MySQL is able to ingest high volume, high velocity data, without sacrificing ACID guarantees, thereby ensuring data quality. Real-time analytics can also be run against newly acquired data, enabling immediate business insight, before data is loaded into Hadoop. In addition, sensitive data can be pre-processed, for example healthcare or financial services records can be anonymized, before transfer to Hadoop. Organize: Data is transferred from MySQL tables to Hadoop using Apache Sqoop. With the MySQL Binlog (Binary Log) API, users can also invoke real-time change data capture processes to stream updates to HDFS. Analyze: Multi-structured data ingested from multiple sources is consolidated and processed within the Hadoop platform. Decide: The results of the analysis are loaded back to MySQL via Apache Sqoop where they inform real-time operational processes or provide source data for BI analytics tools. So how are companies taking advantage of this today? As an example, on-line retailers can use big data from their web properties to better understand site visitors’ activities, such as paths through the site, pages viewed, and comments posted. This knowledge can be combined with user profiles and purchasing history to gain a better understanding of customers, and the delivery of highly targeted offers. Of course, it is not just in the web that big data can make a difference. Every business activity can benefit, with other common use cases including: - Sentiment analysis; - Marketing campaign analysis; - Customer churn modeling; - Fraud detection; - Research and Development; - Risk Modeling; - And more. As the guide discusses, Big Data is promising a significant transformation of the way organizations leverage data to run their businesses. MySQL can be seamlessly integrated within a Big Data lifecycle, enabling the unification of multi-structured data into common data platforms, taking advantage of all new data sources and yielding more insight than was ever previously imaginable. Download the guide to MySQL and Hadoop integration to learn more. I'd also be interested in hearing about how you are integrating MySQL with Hadoop today, and your requirements for the future, so please use the comments on this blog to share your insights.

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  • New Options for MySQL High Availability

    - by Mat Keep
    Data is the currency of today’s web, mobile, social, enterprise and cloud applications. Ensuring data is always available is a top priority for any organization – minutes of downtime will result in significant loss of revenue and reputation. There is not a “one size fits all” approach to delivering High Availability (HA). Unique application attributes, business requirements, operational capabilities and legacy infrastructure can all influence HA technology selection. And then technology is only one element in delivering HA – “People and Processes” are just as critical as the technology itself. For this reason, MySQL Enterprise Edition is available supporting a range of HA solutions, fully certified and supported by Oracle. MySQL Enterprise HA is not some expensive add-on, but included within the core Enterprise Edition offering, along with the management tools, consulting and 24x7 support needed to deliver true HA. At the recent MySQL Connect conference, we announced new HA options for MySQL users running on both Linux and Solaris: - DRBD for MySQL - Oracle Solaris Clustering for MySQL DRBD (Distributed Replicated Block Device) is an open source Linux kernel module which leverages synchronous replication to deliver high availability database applications across local storage. DRBD synchronizes database changes by mirroring data from an active node to a standby node and supports automatic failover and recovery. Linux, DRBD, Corosync and Pacemaker, provide an integrated stack of mature and proven open source technologies. DRBD Stack: Providing Synchronous Replication for the MySQL Database with InnoDB Download the DRBD for MySQL whitepaper to learn more, including step-by-step instructions to install, configure and provision DRBD with MySQL Oracle Solaris Cluster provides high availability and load balancing to mission-critical applications and services in physical or virtualized environments. With Oracle Solaris Cluster, organizations have a scalable and flexible solution that is suited equally to small clusters in local datacenters or larger multi-site, multi-cluster deployments that are part of enterprise disaster recovery implementations. The Oracle Solaris Cluster MySQL agent integrates seamlessly with MySQL offering a selection of configuration options in the various Oracle Solaris Cluster topologies. Putting it All Together When you add MySQL Replication and MySQL Cluster into the HA mix, along with 3rd party solutions, users have extensive choice (and decisions to make) to deliver HA services built on MySQL To make the decision process simpler, we have also published a new MySQL HA Solutions Guide. Exploring beyond just the technology, the guide presents a methodology to select the best HA solution for your new web, cloud and mobile services, while also discussing the importance of people and process in ensuring service continuity. This is subject recently presented at Oracle Open World, and the slides are available here. Whatever your uptime requirements, you can be sure MySQL has an HA solution for your needs Please don't hesitate to let us know of your HA requirements in the comments section of this blog. You can also contact MySQL consulting to learn more about their HA Jumpstart offering which will help you scope out your scaling and HA requirements.

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  • Fog shader camera problem

    - by MaT
    I have some difficulties with my vertex-fragment fog shader in Unity. I have a good visual result but the problem is that the gradient is based on the camera's position, it moves as the camera moves. I don't know how to fix it. Here is the shader code. struct v2f { float4 pos : SV_POSITION; float4 grabUV : TEXCOORD0; float2 uv_depth : TEXCOORD1; float4 interpolatedRay : TEXCOORD2; float4 screenPos : TEXCOORD3; }; v2f vert(appdata_base v) { v2f o; o.pos = mul(UNITY_MATRIX_MVP, v.vertex); o.uv_depth = v.texcoord.xy; o.grabUV = ComputeGrabScreenPos(o.pos); half index = v.vertex.z; o.screenPos = ComputeScreenPos(o.pos); o.interpolatedRay = mul(UNITY_MATRIX_MV, v.vertex); return o; } sampler2D _GrabTexture; float4 frag(v2f IN) : COLOR { float3 uv = UNITY_PROJ_COORD(IN.grabUV); float dpth = UNITY_SAMPLE_DEPTH(tex2Dproj(_CameraDepthTexture, uv)); dpth = LinearEyeDepth(dpth); float4 wsPos = (IN.screenPos + dpth * IN.interpolatedRay); // Here is the problem but how to fix it float fogVert = max(0.0, (wsPos.y - _Depth) * (_DepthScale * 0.1f)); fogVert *= fogVert; fogVert = (exp (-fogVert)); return fogVert; } Thanks a lot !

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  • Benchmarking MySQL Replication with Multi-Threaded Slaves

    - by Mat Keep
    0 0 1 1145 6530 Homework 54 15 7660 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} The objective of this benchmark is to measure the performance improvement achieved when enabling the Multi-Threaded Slave enhancement delivered as a part MySQL 5.6. As the results demonstrate, Multi-Threaded Slaves delivers 5x higher replication performance based on a configuration with 10 databases/schemas. For real-world deployments, higher replication performance directly translates to: · Improved consistency of reads from slaves (i.e. reduced risk of reading "stale" data) · Reduced risk of data loss should the master fail before replicating all events in its binary log (binlog) The multi-threaded slave splits processing between worker threads based on schema, allowing updates to be applied in parallel, rather than sequentially. This delivers benefits to those workloads that isolate application data using databases - e.g. multi-tenant systems deployed in cloud environments. Multi-Threaded Slaves are just one of many enhancements to replication previewed as part of the MySQL 5.6 Development Release, which include: · Global Transaction Identifiers coupled with MySQL utilities for automatic failover / switchover and slave promotion · Crash Safe Slaves and Binlog · Optimized Row Based Replication · Replication Event Checksums · Time Delayed Replication These and many more are discussed in the “MySQL 5.6 Replication: Enabling the Next Generation of Web & Cloud Services” Developer Zone article  Back to the benchmark - details are as follows. Environment The test environment consisted of two Linux servers: · one running the replication master · one running the replication slave. Only the slave was involved in the actual measurements, and was based on the following configuration: - Hardware: Oracle Sun Fire X4170 M2 Server - CPU: 2 sockets, 6 cores with hyper-threading, 2930 MHz. - OS: 64-bit Oracle Enterprise Linux 6.1 - Memory: 48 GB Test Procedure Initial Setup: Two MySQL servers were started on two different hosts, configured as replication master and slave. 10 sysbench schemas were created, each with a single table: CREATE TABLE `sbtest` (    `id` int(10) unsigned NOT NULL AUTO_INCREMENT,    `k` int(10) unsigned NOT NULL DEFAULT '0',    `c` char(120) NOT NULL DEFAULT '',    `pad` char(60) NOT NULL DEFAULT '',    PRIMARY KEY (`id`),    KEY `k` (`k`) ) ENGINE=InnoDB DEFAULT CHARSET=latin1 10,000 rows were inserted in each of the 10 tables, for a total of 100,000 rows. When the inserts had replicated to the slave, the slave threads were stopped. The slave data directory was copied to a backup location and the slave threads position in the master binlog noted. 10 sysbench clients, each configured with 10 threads, were spawned at the same time to generate a random schema load against each of the 10 schemas on the master. Each sysbench client executed 10,000 "update key" statements: UPDATE sbtest set k=k+1 WHERE id = <random row> In total, this generated 100,000 update statements to later replicate during the test itself. Test Methodology: The number of slave workers to test with was configured using: SET GLOBAL slave_parallel_workers=<workers> Then the slave IO thread was started and the test waited for all the update queries to be copied over to the relay log on the slave. The benchmark clock was started and then the slave SQL thread was started. The test waited for the slave SQL thread to finish executing the 100k update queries, doing "select master_pos_wait()". When master_pos_wait() returned, the benchmark clock was stopped and the duration calculated. The calculated duration from the benchmark clock should be close to the time it took for the SQL thread to execute the 100,000 update queries. The 100k queries divided by this duration gave the benchmark metric, reported as Queries Per Second (QPS). Test Reset: The test-reset cycle was implemented as follows: · the slave was stopped · the slave data directory replaced with the previous backup · the slave restarted with the slave threads replication pointer repositioned to the point before the update queries in the binlog. The test could then be repeated with identical set of queries but a different number of slave worker threads, enabling a fair comparison. The Test-Reset cycle was repeated 3 times for 0-24 number of workers and the QPS metric calculated and averaged for each worker count. MySQL Configuration The relevant configuration settings used for MySQL are as follows: binlog-format=STATEMENT relay-log-info-repository=TABLE master-info-repository=TABLE As described in the test procedure, the slave_parallel_workers setting was modified as part of the test logic. The consequence of changing this setting is: 0 worker threads:    - current (i.e. single threaded) sequential mode    - 1 x IO thread and 1 x SQL thread    - SQL thread both reads and executes the events 1 worker thread:    - sequential mode    - 1 x IO thread, 1 x Coordinator SQL thread and 1 x Worker thread    - coordinator reads the event and hands it to the worker who executes 2+ worker threads:    - parallel execution    - 1 x IO thread, 1 x Coordinator SQL thread and 2+ Worker threads    - coordinator reads events and hands them to the workers who execute them Results Figure 1 below shows that Multi-Threaded Slaves deliver ~5x higher replication performance when configured with 10 worker threads, with the load evenly distributed across our 10 x schemas. This result is compared to the current replication implementation which is based on a single SQL thread only (i.e. zero worker threads). Figure 1: 5x Higher Performance with Multi-Threaded Slaves The following figure shows more detailed results, with QPS sampled and reported as the worker threads are incremented. The raw numbers behind this graph are reported in the Appendix section of this post. Figure 2: Detailed Results As the results above show, the configuration does not scale noticably from 5 to 9 worker threads. When configured with 10 worker threads however, scalability increases significantly. The conclusion therefore is that it is desirable to configure the same number of worker threads as schemas. Other conclusions from the results: · Running with 1 worker compared to zero workers just introduces overhead without the benefit of parallel execution. · As expected, having more workers than schemas adds no visible benefit. Aside from what is shown in the results above, testing also demonstrated that the following settings had a very positive effect on slave performance: relay-log-info-repository=TABLE master-info-repository=TABLE For 5+ workers, it was up to 2.3 times as fast to run with TABLE compared to FILE. Conclusion As the results demonstrate, Multi-Threaded Slaves deliver significant performance increases to MySQL replication when handling multiple schemas. This, and the other replication enhancements introduced in MySQL 5.6 are fully available for you to download and evaluate now from the MySQL Developer site (select Development Release tab). You can learn more about MySQL 5.6 from the documentation  Please don’t hesitate to comment on this or other replication blogs with feedback and questions. Appendix – Detailed Results

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  • How to make unit selection circles merge?

    - by MaT
    I would like to know how to make this effect of merged circle selection. Here are images to illustrate: Basically I'm looking for this effect: How the merge effect of the circles can be achieved ? I didn't found any explanation concerning this effect. I know that to project those texture I can develop a decal system but I don't know how to create the merging effect. If possible, I'm looking for purely shaders solution.

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  • 15 Oracle Winners at Progressive Manufacturing 100 Awards Event

    - by [email protected]
    Oracle is pleased to congratulate its 15 winners for the PM100 awards program at the Breakers Hotel in Palm Beach Florida, May 3-5, 2010.  The Progressive Manufacturing Summit is where today's top manufacturing executives  come together and share their strategies, experiences and best practices on becoming more competitive in today's global market. The format is extremely interactive, providing the rarest of opportunities to participate in a high level conversation with leaders in supply chain and manufacturing. Attendees walk away with new insights and strategies on growing and moving their business forward, new contacts and a tangible action plan to address a tough. For more information. Event: http://www.managingautomation.com/summit/index.aspx Winners: http://www.managingautomation.com/awards/winners.aspx  

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  • A Hot Topic - Profitability and Cost Management

    - by john.orourke(at)oracle.com
    Maybe it's due to the recent recession, or current economic recovery but a hot topic and area of focus for many organizations these days is profitability and cost management.  For most organizations, aggressive cost-cutting and cost management were critical to remaining profitable while top line revenue was flat or shrinking.  However, now we are seeing many organizations taking a more "surgical" approach to profitability and cost management, by accurately allocating revenue and costs to individual product lines, services, customer segments, locations, channels and other lines of business to understand which ones are truly profitable and which ones are not.  Based on these insights, managers can make more informed decisions about which products or services to invest in or retire, how to price their products or services for different customer segments, and where to focus their marketing and customer service resources. The most common industries where this product, service and customer-focused costing and profitability analysis is being adopted include financial services, consumer packaged goods, retail and manufacturing.  However we are seeing adoption of profitability and cost management applications in other industries and use cases.  Here are a few examples: Telecommunications Industry:  Network Costing and Management to identify the most cost effective and/or profitable network areas, to optimize existing resources, infrastructure and network capacity.  Regulatory Cost Accounting to perform more accurate allocations of revenue and costs across services and customer segments, improve ability to set billing rates for future periods, for various products and customer segments and more easily develop analysis needed for rate case proposals. Healthcare Insurance:  Visually, justifiable Medical Loss Ratio results, better knowledge of the cost to service healthcare plans and members, accurate understanding of member segment and plan profitability, improved marketing programs through better member segmentation. Public Sector:  Statutory / Regulatory Compliance:  A variety of statutory and regulatory documents state explicitly or implicitly that the use of government resources must be properly tracked and tied to performance goals.  Managerial costing methods implemented through Cost Management applications provide unparalleled visibility into costs and shared services usage throughout a Public Sector agency. Funding Support:  Regulations require public sector funding requests to be evaluated based upon the ability to achieve performance goals against the associated cost.   Improved visibility and understanding of costs of different programs/services means that organizations can demonstrably monitor performance and the associated resource costs improve the chances of having their funding requests granted. Profitability and Cost Management is one of the fastest-growing solution areas in Oracle's Enterprise Performance Management product line and we are seeing a growing number of customer successes across geographies and industries.  Listed below are just a few examples.  Here's a link to the replay from a recent webcast on this topic which featured Schroders Plc, a UK-based Financial Services company: http://www.oracle.com/go/?&Src=7011668&Act=168&pcode=WWMK10037859MPP043 Here's a link to a case study on Shenhua Guohua Power in China: http://www.oracle.com/us/corporate/customers/shenhua-snapshot-159574.pdf Here's a link to information on Oracle's web site about our profitability and cost management solutions: http://www.oracle.com/us/solutions/ent-performance-bi/performance-management/profitability-cost-mgmt/index.html

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  • How do I disable all lid close processes?

    - by Mat
    I want to be able to close my laptop without Ubuntu registering it. I've been looking everywhere and I've found plenty of people with the same problem but no real solutions. Obviously I have set the lid close setting to 'do nothing' for both AC and battery, but when I close the lid it still blanks the screen, disconnects from external monitors, and brings up the lock screen when I reopen it. Some people have suggested disabling the lock screen, but this doesn't stop the screen blanking and external displays disconnecting, and I don't want to disable the lock anyway, as I still want it when I tell Ubuntu to lock or sleep or whatever else. Others have suggested it's something to do with ACPI support, but I have tried changing some ACPI scripts, and even removed them completely (e.g. /etc/acpi/lid.sh and /etc/acpi/events/lidbtn) and it makes no difference. There must be a bit of code somewhere that can just be removed or commented out or altered to prevent any lid close actions - does anyone know where? I know this has been asked before, but I'm getting really frustrated with this problem. I'm disappointed to say that I'm actually using Windows 7 more often just because it's quite happy to completely ignore the closed lid. So I just wanted to check, are we any closer to a real solution for this problem?

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  • LIBGDX "parsing error emitter" with 2 or more emitters [on hold]

    - by flow969
    I have a problem with the use of particle effect of LIBGDX with 2 or more emitters. After using ParticleEditor to create my .p file, I use it in my code BUT...when I use only 1 emitter it's fine but with more than 1, not fine ! :( Here is my error code in java console : Exception in thread "LWJGL Application" java.lang.RuntimeException: Error parsing emitter: - Delay - at com.badlogic.gdx.graphics.g2d.ParticleEmitter.load(ParticleEmitter.java:910) at com.badlogic.gdx.graphics.g2d.ParticleEmitter.<init>(ParticleEmitter.java:95) at com.badlogic.gdx.graphics.g2d.ParticleEffect.loadEmitters(ParticleEffect.java:154) at com.badlogic.gdx.graphics.g2d.ParticleEffect.load(ParticleEffect.java:138) at com.fasgame.fishtrip.android.screens.GameScreen.show(GameScreen.java:313) at com.badlogic.gdx.Game.setScreen(Game.java:61) at com.fasgame.fishtrip.android.screens.MainMenuScreen.render(MainMenuScreen.java:71) at com.badlogic.gdx.Game.render(Game.java:46) at com.badlogic.gdx.backends.lwjgl.LwjglApplication.mainLoop(LwjglApplication.java:206) at com.badlogic.gdx.backends.lwjgl.LwjglApplication$1.run(LwjglApplication.java:114) Caused by: java.lang.NumberFormatException: For input string: "- Count -" at sun.misc.FloatingDecimal.readJavaFormatString(Unknown Source) at sun.misc.FloatingDecimal.parseFloat(Unknown Source) at java.lang.Float.parseFloat(Unknown Source) at com.badlogic.gdx.graphics.g2d.ParticleEmitter.readFloat(ParticleEmitter.java:929) at com.badlogic.gdx.graphics.g2d.ParticleEmitter$RangedNumericValue.load(ParticleEmitter.java:1062) at com.badlogic.gdx.graphics.g2d.ParticleEmitter.load(ParticleEmitter.java:866) ... 9 more And here is my particle effect .p file : Blanc - Delay - active: false - Duration - lowMin: 3000.0 lowMax: 3000.0 - Count - min: 0 max: 200 - Emission - lowMin: 0.0 lowMax: 0.0 highMin: 250.0 highMax: 250.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Life - lowMin: 500.0 lowMax: 500.0 highMin: 500.0 highMax: 500.0 relative: false scalingCount: 3 scaling0: 1.0 scaling1: 0.47058824 scaling2: 0.0 timelineCount: 3 timeline0: 0.0 timeline1: 0.51369864 timeline2: 1.0 - Life Offset - active: false - X Offset - active: false - Y Offset - active: false - Spawn Shape - shape: point - Spawn Width - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Spawn Height - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Scale - lowMin: 0.0 lowMax: 0.0 highMin: 70.0 highMax: 70.0 relative: true scalingCount: 2 scaling0: 1.0 scaling1: 0.0 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Velocity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 30.0 highMax: 300.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Angle - active: true lowMin: 220.0 lowMax: 320.0 highMin: 220.0 highMax: 320.0 relative: false scalingCount: 2 scaling0: 0.0 scaling1: 0.98039216 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Rotation - active: false - Wind - active: false - Gravity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Tint - colorsCount: 3 colors0: 0.50980395 colors1: 0.7647059 colors2: 0.7921569 timelineCount: 1 timeline0: 0.0 - Transparency - lowMin: 0.0 lowMax: 0.0 highMin: 1.0 highMax: 1.0 relative: false scalingCount: 4 scaling0: 1.0 scaling1: 1.0 scaling2: 1.0 scaling3: 1.0 timelineCount: 4 timeline0: 0.0 timeline1: 0.36301368 timeline2: 0.6164383 timeline3: 1.0 - Options - attached: false continuous: true aligned: false additive: true behind: false premultipliedAlpha: false pre_particle.png Bleu - Delay - active: false - Duration - lowMin: 3000.0 lowMax: 3000.0 - Count - min: 0 max: 200 - Emission - lowMin: 0.0 lowMax: 0.0 highMin: 250.0 highMax: 250.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Life - lowMin: 500.0 lowMax: 500.0 highMin: 500.0 highMax: 500.0 relative: false scalingCount: 3 scaling0: 1.0 scaling1: 0.47058824 scaling2: 0.0 timelineCount: 3 timeline0: 0.0 timeline1: 0.51369864 timeline2: 1.0 - Life Offset - active: false - X Offset - active: false - Y Offset - active: false - Spawn Shape - shape: point - Spawn Width - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Spawn Height - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Scale - lowMin: 0.0 lowMax: 0.0 highMin: 70.0 highMax: 70.0 relative: true scalingCount: 2 scaling0: 1.0 scaling1: 0.0 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Velocity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 30.0 highMax: 300.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Angle - active: true lowMin: 220.0 lowMax: 320.0 highMin: 220.0 highMax: 320.0 relative: false scalingCount: 2 scaling0: 0.0 scaling1: 0.98039216 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Rotation - active: false - Wind - active: false - Gravity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Tint - colorsCount: 3 colors0: 0.0 colors1: 0.7254902 colors2: 0.7921569 timelineCount: 1 timeline0: 0.0 - Transparency - lowMin: 0.0 lowMax: 0.0 highMin: 1.0 highMax: 1.0 relative: false scalingCount: 6 scaling0: 0.0 scaling1: 1.0 scaling2: 1.0 scaling3: 1.0 scaling4: 1.0 scaling5: 0.0 timelineCount: 6 timeline0: 0.0 timeline1: 0.047945205 timeline2: 0.34246576 timeline3: 0.6712329 timeline4: 0.94520545 timeline5: 1.0 - Options - attached: false continuous: true aligned: false additive: true behind: false premultipliedAlpha: false pre_particle.png BleuFonce - Delay - active: false - Duration - lowMin: 3000.0 lowMax: 3000.0 - Count - min: 0 max: 200 - Emission - lowMin: 0.0 lowMax: 0.0 highMin: 250.0 highMax: 250.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Life - lowMin: 500.0 lowMax: 500.0 highMin: 500.0 highMax: 500.0 relative: false scalingCount: 3 scaling0: 1.0 scaling1: 0.47058824 scaling2: 0.0 timelineCount: 3 timeline0: 0.0 timeline1: 0.51369864 timeline2: 1.0 - Life Offset - active: false - X Offset - active: false - Y Offset - active: false - Spawn Shape - shape: point - Spawn Width - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Spawn Height - lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Scale - lowMin: 0.0 lowMax: 0.0 highMin: 70.0 highMax: 70.0 relative: true scalingCount: 2 scaling0: 1.0 scaling1: 0.0 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Velocity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 30.0 highMax: 300.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Angle - active: true lowMin: 220.0 lowMax: 320.0 highMin: 220.0 highMax: 320.0 relative: false scalingCount: 2 scaling0: 0.0 scaling1: 0.98039216 timelineCount: 2 timeline0: 0.0 timeline1: 1.0 - Rotation - active: false - Wind - active: false - Gravity - active: true lowMin: 0.0 lowMax: 0.0 highMin: 0.0 highMax: 0.0 relative: false scalingCount: 1 scaling0: 1.0 timelineCount: 1 timeline0: 0.0 - Tint - colorsCount: 3 colors0: 0.0 colors1: 0.7294118 colors2: 1.0 timelineCount: 1 timeline0: 0.0 - Transparency - lowMin: 0.0 lowMax: 0.0 highMin: 1.0 highMax: 1.0 relative: false scalingCount: 4 scaling0: 1.0 scaling1: 0.0 scaling2: 0.0 scaling3: 1.0 timelineCount: 4 timeline0: 0.0 timeline1: 0.001 timeline2: 0.5753425 timeline3: 0.79452056 - Options - attached: false continuous: true aligned: false additive: true behind: false premultipliedAlpha: false pre_particle.png For the "- Image Path -" missing it's normal if I let them in it doesn't work even with only 1 emitter PS : I've already updated my lib to the last release

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  • Gnome shell not starting at login, but can start from terminal (Ubuntu 12.04)

    - by Mat Leonard
    I upgraded to Ubuntu 12.04 recently and for some reason it broke Gnome 3. The shell doesn't start up at login. My .xsession-errors looks like this right after I log in: gnome-session[1689]: WARNING: Session 'gnome' runnable check failed: Timed out (gnome-settings-daemon:1744): color-plugin-WARNING **: failed to get edid: unable to get EDID for output (gnome-settings-daemon:1744): color-plugin-WARNING **: unable to get EDID for xrandr-default: unable to get EDID for output (gnome-settings-daemon:1744): color-plugin-WARNING **: failed to reset xrandr-default gamma tables: gamma size is zero ** Message: applet now removed from the notification area ** Message: using fallback from indicator to GtkStatusIcon ** Message: moving back from GtkStatusIcon to indicator Then I can run gnome-shell --replace, the shell starts up and everything works. This is what I get immediately after: Window manager warning: Log level 16: Unable to register authentication agent: GDBus.Error:org.freedesktop.PolicyKit1.Error.Failed: An authentication agent already exists for the given subject Window manager warning: Log level 16: Error registering polkit authentication agent: GDBus.Error:org.freedesktop.PolicyKit1.Error.Failed: An authentication agent already exists for the given subject (polkit-error-quark 0) (gnome-shell:2442): folks-WARNING **: Failed to find primary PersonaStore with type ID 'eds' and ID 'system'. Individuals will not be linked properly and creating new links between Personas will not work. The configured primary PersonaStore's backend may not be installed. If you are unsure, check with your distribution Also, if I run /usr/lib/nux/unity_support_test -p, everything comes back as Yes and this checks out: OpenGL vendor string: NVIDIA Corporation OpenGL renderer string: GeForce 8300 GS/PCIe/SSE2 OpenGL version string: 3.3.0 NVIDIA 295.40 It isn't a huge problem since I can get gnome shell to work, but it is a little annoying. So, I'd like to fix this. Thanks for your help.

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  • The Other Side of XBRL

    - by john.orourke(at)oracle.com
    With the United States SEC's mandate for XBRL filings entering its third year, and impacting over 7000 additional companies in 2011, there's a lot of buzz in the industry about how companies should address the new reporting requirements.  Should they outsource the XBRL tagging process to a third party publisher, handle the process in-house with a bolt-on XBRL tool, or should they integrate XBRL tagging with the financial close and reporting process?  Oracle is recommending the latter approach, in fact  here's a link to a recent webcast that I did with CFO.com on this topic: http://www.cfo.com/webcasts/index.cfm/l_eventarchive/14548560 But production of XBRL-based filings is only half of the story. The other half is consumption of XBRL by regulators, academics, financial analysts and investors.  As I mentioned in my December article on the XBRL US conference, the feedback from these groups is that they are not really leveraging XBRL for analysis of companies due to a lack of tools and historic XBRL-based data on public companies.   The good news here is that the historic data problem is getting better as large, accelerated filers enter their third year of XBRL filings.  And the situation is getting better on the reporting and analysis tools side of the equation as well - and Oracle is leading the way. In early January, Oracle released the Oracle XBRL Extension for Oracle Database 11g.  This is a "no cost option" on top of the latest Oracle Database 11.2.0.2.0 release. With this added functionality organizations will have the ability to create one or more back-end XBRL repositories based on Oracle Database, which provide XBRL storage and query-ability with a set of XBRL-specific services.  The XBRL Extension to Oracle XML DB integrates easily with Oracle Business Intelligence Suite Enterprise Edition (OBIEE) for analytics and with interactive development environments (IDEs) and design tools for creating and editing XBRL taxonomies. The Oracle XBRL Extension to Oracle Database 11g should be attractive to regulators, stock exchanges, universities and other organizations that need to collect, analyze and disseminate XBRL-based filings.  It should also be attractive to organizations that produce XBRL filings, and need a way to store and compare their own XBRL-based financial filings to those of their peers and competitors. If you would like more information, here's a link to a web page on the Oracle Technology Network with the details about Oracle XBRL Extension for Oracle Database 11g, including data sheet, white paper, presentation, demos and other information: http://www.oracle.com/technetwork/database/features/xmldb/index-087631.html

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  • How to make room reflection using Cubemap

    - by MaT
    I am trying to use a cube map of the inside of a room to create some reflections on walls, ceiling and floor. But when I use the cube map, the reflected image is not correct. The point of view seems to be false. To be correct I use a different cube map for each walls, floor or ceiling. The cube map is calculated from the center of the plane looking at the room. Are there specialized techniques to achieve such effect ? Thanks a lot !

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  • Configuring MySQL Cluster Data Nodes

    - by Mat Keep
    0 0 1 692 3948 Homework 32 9 4631 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} In my previous blog post, I discussed the enhanced performance and scalability delivered by extensions to the multi-threaded data nodes in MySQL Cluster 7.2. In this post, I’ll share best practices on the configuration of data nodes to achieve optimum performance on the latest generations of multi-core, multi-thread CPU designs. Configuring the Data Nodes The configuration of data node threads can be managed in two ways via the config.ini file: - Simply set MaxNoOfExecutionThreads to the appropriate number of threads to be run in the data node, based on the number of threads presented by the processors used in the host or VM. - Use the new ThreadConfig variable that enables users to configure both the number of each thread type to use and also which CPUs to bind them too. The flexible configuration afforded by the multi-threaded data node enhancements means that it is possible to optimise data nodes to use anything from a single CPU/thread up to a 48 CPU/thread server. Co-locating the MySQL Server with a single data node can fully utilize servers with 64 – 80 CPU/threads. It is also possible to co-locate multiple data nodes per server, but this is now only required for very large servers with 4+ CPU sockets dense multi-core processors. 24 Threads and Beyond! An example of how to make best use of a 24 CPU/thread server box is to configure the following: - 8 ldm threads - 4 tc threads - 3 recv threads - 3 send threads - 1 rep thread for asynchronous replication. Each of those threads should be bound to a CPU. It is possible to bind the main thread (schema management domain) and the IO threads to the same CPU in most installations. In the configuration above, we have bound threads to 20 different CPUs. We should also protect these 20 CPUs from interrupts by using the IRQBALANCE_BANNED_CPUS configuration variable in /etc/sysconfig/irqbalance and setting it to 0x0FFFFF. The reason for doing this is that MySQL Cluster generates a lot of interrupt and OS kernel processing, and so it is recommended to separate activity across CPUs to ensure conflicts with the MySQL Cluster threads are eliminated. When booting a Linux kernel it is also possible to provide an option isolcpus=0-19 in grub.conf. The result is that the Linux scheduler won't use these CPUs for any task. Only by using CPU affinity syscalls can a process be made to run on those CPUs. By using this approach, together with binding MySQL Cluster threads to specific CPUs and banning CPUs IRQ processing on these tasks, a very stable performance environment is created for a MySQL Cluster data node. On a 32 CPU/Thread server: - Increase the number of ldm threads to 12 - Increase tc threads to 6 - Provide 2 more CPUs for the OS and interrupts. - The number of send and receive threads should, in most cases, still be sufficient. On a 40 CPU/Thread server, increase ldm threads to 16, tc threads to 8 and increment send and receive threads to 4. On a 48 CPU/Thread server it is possible to optimize further by using: - 12 tc threads - 2 more CPUs for the OS and interrupts - Avoid using IO threads and main thread on same CPU - Add 1 more receive thread. Summary As both this and the previous post seek to demonstrate, the multi-threaded data node extensions not only serve to increase performance of MySQL Cluster, they also enable users to achieve significantly improved levels of utilization from current and future generations of massively multi-core, multi-thread processor designs. A big thanks to Mikael Ronstrom, Senior MySQL Architect at Oracle, for his work in developing these enhancements and best practices. You can download MySQL Cluster 7.2 today and try out all of these enhancements. The Getting Started guides are an invaluable aid to quickly building a Proof of Concept Don’t forget to check out the MySQL Cluster 7.2 New Features whitepaper to discover everything that is new in the latest GA release

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