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  • JPA/EclipseLink multitenancy screencast

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

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  • Replication Services in a BI environment

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

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  • Configuring thouands of related products in Magento?

    - by Anonymous -
    I'm at a stage with a Magento store I'm developing where I've added all the products (all 6000 of them) and now would like to configure related products to up my conversion rate a bit. I was wondering if there was an extension anybody knew of that functions similarly to this one, with the most current version of Magento (Community Edition, 1.6.1). If not, would anyone be able to provide some pointers for writing a script that will run through each product and add 1-5 related products. I have a fairly basic idea of taking product title text and just doing a simple text similarity query between other product titles for now, just to get some related products up there, but the Magento database isn't making a terribly large amount of sense. Thanks to anyone who can shed some light on this. :)

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  • Alien deletes .deb when converting from .rpm

    - by Andre
    I'm trying to convert .rpm to .deb using alien. sudo alien -k libtetra-1.0.0-2.i386.rpm Alien says that: libtetra-1.0.0-2.i386.deb generated But when I check the folder - there is just original .rpm and no .deb. Also - I can see that for a split second there is a .deb file in a folder. so it looks like alien create .deb and deletes it right away. I suspect that it's maybe because I run 64 bit os and package is 32? Can somebody explain why alien deletes .deb automatically? Verbose output: LANG=C rpm -qp --queryformat %{NAME} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{VERSION} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{RELEASE} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{ARCH} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{CHANGELOGTEXT} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{SUMMARY} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{DESCRIPTION} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{PREFIXES} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{POSTIN} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{POSTUN} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{PREUN} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{LICENSE} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qp --queryformat %{PREIN} libtetra-1.0.0-2.i386.rpm LANG=C rpm -qcp libtetra-1.0.0-2.i386.rpm rpm -qpi libtetra-1.0.0-2.i386.rpm LANG=C rpm -qpl libtetra-1.0.0-2.i386.rpm mkdir libtetra-1.0.0 chmod 755 libtetra-1.0.0 rpm2cpio libtetra-1.0.0-2.i386.rpm | lzma -t -q > /dev/null 2>&1 rpm2cpio libtetra-1.0.0-2.i386.rpm | (cd libtetra-1.0.0; cpio --extract --make-directories --no-absolute-filenames --preserve-modification-time) 2>&1 chmod 755 libtetra-1.0.0/./ chmod 755 libtetra-1.0.0/./usr chmod 755 libtetra-1.0.0/./usr/lib chown 0:0 libtetra-1.0.0//usr/lib/libtetra.so.1.0.0 chmod 755 libtetra-1.0.0//usr/lib/libtetra.so.1.0.0 mkdir libtetra-1.0.0/debian date -R date -R chmod 755 libtetra-1.0.0/debian/rules debian/rules binary 2>&1 libtetra_1.0.0-3_i386.deb generated find libtetra-1.0.0 -type d -exec chmod 755 {} ; rm -rf libtetra-1.0.0 Very Verbose output LANG=C rpm -qp --queryformat %{NAME} libtetra-1.0.0-2.i386.rpm libtetra LANG=C rpm -qp --queryformat %{VERSION} libtetra-1.0.0-2.i386.rpm 1.0.0 LANG=C rpm -qp --queryformat %{RELEASE} libtetra-1.0.0-2.i386.rpm 2 LANG=C rpm -qp --queryformat %{ARCH} libtetra-1.0.0-2.i386.rpm i386 LANG=C rpm -qp --queryformat %{CHANGELOGTEXT} libtetra-1.0.0-2.i386.rpm - First RPM Package LANG=C rpm -qp --queryformat %{SUMMARY} libtetra-1.0.0-2.i386.rpm Panasonic KX-MC6000 series Printer Driver for Linux. LANG=C rpm -qp --queryformat %{DESCRIPTION} libtetra-1.0.0-2.i386.rpm This software is Panasonic KX-MC6000 series Printer Driver for Linux. You can print from applications by using CUPS(Common Unix Printing System) which is the printing system for Linux. Other functions for KX-MC6000 series are not supported by this software. LANG=C rpm -qp --queryformat %{PREFIXES} libtetra-1.0.0-2.i386.rpm (none) LANG=C rpm -qp --queryformat %{POSTIN} libtetra-1.0.0-2.i386.rpm (none) LANG=C rpm -qp --queryformat %{POSTUN} libtetra-1.0.0-2.i386.rpm (none) LANG=C rpm -qp --queryformat %{PREUN} libtetra-1.0.0-2.i386.rpm (none) LANG=C rpm -qp --queryformat %{LICENSE} libtetra-1.0.0-2.i386.rpm GPL and LGPL (Version2) LANG=C rpm -qp --queryformat %{PREIN} libtetra-1.0.0-2.i386.rpm (none) LANG=C rpm -qcp libtetra-1.0.0-2.i386.rpm rpm -qpi libtetra-1.0.0-2.i386.rpm Name : libtetra Relocations: (not relocatable) Version : 1.0.0 Vendor: Panasonic Communications Co., Ltd. Release : 2 Build Date: Tue 27 Apr 2010 05:16:40 AM EDT Install Date: (not installed) Build Host: localhost.localdomain Group : System Environment/Daemons Source RPM: libtetra-1.0.0-2.src.rpm Size : 31808 License: GPL and LGPL (Version2) Signature : (none) URL : http://panasonic.net/pcc/support/fax/world.htm Summary : Panasonic KX-MC6000 series Printer Driver for Linux. Description : This software is Panasonic KX-MC6000 series Printer Driver for Linux. You can print from applications by using CUPS(Common Unix Printing System) which is the printing system for Linux. Other functions for KX-MC6000 series are not supported by this software. LANG=C rpm -qpl libtetra-1.0.0-2.i386.rpm /usr/lib/libtetra.so /usr/lib/libtetra.so.1.0.0 mkdir libtetra-1.0.0 chmod 755 libtetra-1.0.0 rpm2cpio libtetra-1.0.0-2.i386.rpm | lzma -t -q > /dev/null 2>&1 rpm2cpio libtetra-1.0.0-2.i386.rpm | (cd libtetra-1.0.0; cpio --extract --make-directories --no-absolute-filenames --preserve-modification-time) 2>&1 63 blocks chmod 755 libtetra-1.0.0/./ chmod 755 libtetra-1.0.0/./usr chmod 755 libtetra-1.0.0/./usr/lib chown 0:0 libtetra-1.0.0//usr/lib/libtetra.so.1.0.0 chmod 755 libtetra-1.0.0//usr/lib/libtetra.so.1.0.0 mkdir libtetra-1.0.0/debian date -R Mon, 07 Feb 2011 11:03:58 -0500 date -R Mon, 07 Feb 2011 11:03:58 -0500 chmod 755 libtetra-1.0.0/debian/rules debian/rules binary 2>&1 dh_testdir dh_testdir dh_testroot dh_clean -k -d dh_clean: No packages to build. dh_installdirs dh_installdocs dh_installchangelogs find . -maxdepth 1 -mindepth 1 -not -name debian -print0 | \ xargs -0 -r -i cp -a {} debian/ dh_compress dh_makeshlibs dh_installdeb dh_shlibdeps dh_gencontrol dh_md5sums dh_builddeb libtetra_1.0.0-2_i386.deb generated find libtetra-1.0.0 -type d -exec chmod 755 {} ; rm -rf libtetra-1.0.0

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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 database design and/or software to model a thesaurus?

    - by Miles O'Keefe
    I would like to design a web app that functions as a simple thesaurus : a long list of words with attributes, all of which are linked to each other. Wikipedia defines it as: In Information Science, Library Science, and Information Technology, specialized thesauri are designed for information retrieval. They are a type of controlled vocabulary, for indexing or tagging purposes. Such a thesaurus can be used as the basis of an index for online material. The Art and Architecture Thesaurus, for example, is used to index the Canadian Information retrieval thesauri are formally organized so that existing relationships between concepts are made explicit. What database software, design or model would best fit this? Are PHP and MySQL good technologies to handle it?

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

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

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  • Programmaticaly finding the Landau notation (Big O or Theta notation) of an algorithm?

    - by Julien L
    I'm used to search for the Landau (Big O, Theta...) notation of my algorithms by hand to make sure they are as optimized as they can be, but when the functions are getting really big and complex, it's taking way too much time to do it by hand. it's also prone to human errors. I spent some time on Codility (coding/algo exercises), and noticed they will give you the Landau notation for your submitted solution (both in Time and Memory usage). I was wondering how they do that... How would you do it? Is there another way besides Lexical Analysis or parsing of the code? PS: This question concerns mainly PHP and or JavaScript, but I'm opened to any language and theory.

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  • Visualising data a different way with Pivot collections

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

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  • SQL SERVER – Find Weekend and Weekdays from Datetime in SQL Server 2012

    - by pinaldave
    Yesterday we had very first SQL Bangalore User Group meeting and I was asked following question right after the session. “How do we know if today is a weekend or weekday using SQL Server Functions?” Well, I assume most of us are using SQL Server 2012 so I will suggest following solution. I am using SQL Server 2012′s CHOOSE function. It is SELECT GETDATE() Today, DATENAME(dw, GETDATE()) DayofWeek, CHOOSE(DATEPART(dw, GETDATE()), 'WEEKEND','Weekday', 'Weekday','Weekday','Weekday','Weekday','WEEKEND') WorkDay GO You can use the choose function on table as well. Here is the quick example of the same. USE AdventureWorks2012 GO SELECT A.ModifiedDate, DATENAME(dw, A.ModifiedDate) DayofWeek, CHOOSE(DATEPART(dw, A.ModifiedDate), 'WEEKEND','Weekday', 'Weekday','Weekday','Weekday','Weekday','WEEKEND') WorkDay FROM [Person].[Address] A GO If you are using an earlier version of the SQL Server you can use a CASE statement instead of CHOOSE function. Please read my earlier article which discusses CHOOSE function and CASE statements. Logical Function – CHOOSE() – A Quick Introduction Reference:  Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL DateTime, SQL Function, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SQLAuthority News – Download Microsoft SQL Server 2012 RTM Now

    - by pinaldave
    SQL Server 2012 enables a cloud-ready information platform that will help organizations unlock breakthrough insights across the organization as well as quickly build solutions and extend data across on-premises and public cloud backed by capabilities for mission critical confidence: Deliver required uptime and data protection with AlwaysOn Gain breakthrough & predictable performance with ColumnStore Index Help enable security and compliance with new User-defined Roles and Default Schema for Groups Enable rapid data discovery for deeper insights across the organization with ColumnStore Index Ensure more credible, consistent data with SSIS improvements, a Master Data Services add-in for Excel, and new Data Quality Services Optimize IT and developer productivity across server and cloud with Data-tier Application Component (DAC) parity with SQL Azure and SQL Server Data Tools for a unified dev experience across database, BI, and cloud functions Download SQL Server 2012 RTM Download Microsoft SQL Server 2012 Feature Pack Download SQL Server Data Tools Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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

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

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

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

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  • Big Data Accelerator

    - by Jean-Pierre Dijcks
    For everyone who does not regularly listen to earnings calls, Oracle's Q4 call was interesting (as it mostly is). One of the announcements in the call was the Big Data Accelerator from Oracle (Seeking Alpha link here - slightly tweaked for correctness shown below):  "The big data accelerator includes some of the standard open source software, HDFS, the file system and a number of other pieces, but also some Oracle components that we think can dramatically speed up the entire map-reduce process. And will be particularly attractive to Java programmers [...]. There are some interesting applications they do, ETL is one. Log processing is another. We're going to have a lot of those features, functions and pre-built applications in our big data accelerator."  Not much else we can say right now, more on this (and Big Data in general) at Openworld!

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  • Hello With Oracle Identity Manager Architecture

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

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  • Benefits of PerformancePoint Services Using SharePoint Server 2010

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

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  • Decompilers - Myth or Fact ?

    - by Simon
    Lately I have been thinking of application security and binaries and decompilers. (FYI- Decompilers is just an anti-complier, the purpose is to get the source back from the binary) Is there such thing as "Perfect Decompiler"? or are binaries safe from reverse engineering? (For clarity sake, by "Perfect" I mean the original source files with all the variable names/macros/functions/classes/if possible comments in the respective headers and source files used to get the binary) What are some of the best practices used to prevent reverse engineering of software? Is it a major concern? Also is obfuscation/file permissions the only way to prevent unauthorized hacks on scripts? (call me a script-junky if you should)

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  • SQL SERVER – Fundamentals of Columnstore Index

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

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  • SQL SERVER – Powershell – Importing CSV File Into Database – Video

    - by pinaldave
    Laerte Junior is my very dear friend and Powershell Expert. On my request he has agreed to share Powershell knowledge with us. Laerte Junior is a SQL Server MVP and, through his technology blog and simple-talk articles, an active member of the Microsoft community in Brasil. He is a skilled Principal Database Architect, Developer, and Administrator, specializing in SQL Server and Powershell Programming with over 8 years of hands-on experience. He holds a degree in Computer Science, has been awarded a number of certifications (including MCDBA), and is an expert in SQL Server 2000 / SQL Server 2005 / SQL Server 2008 technologies. Let us read the blog post in his own words. I was reading an excellent post from my great friend Pinal about loading data from CSV files, SQL SERVER – Importing CSV File Into Database – SQL in Sixty Seconds #018 – Video,   to SQL Server and was honored to write another guest post on SQL Authority about the magic of the PowerShell. The biggest stuff in TechEd NA this year was PowerShell. Fellows, if you still don’t know about it, it is better to run. Remember that The Core Servers to SQL Server are the future and consequently the Shell. You don’t want to be out of this, right? Let’s see some PowerShell Magic now. To start our tour, first we need to download these two functions from Powershell and SQL Server Master Jedi Chad Miller.Out-DataTable and Write-DataTable. Save it in a module and add it in your profile. In my case, the module is called functions.psm1. To have some data to play, I created 10 csv files with the same content. I just put the SQL Server Errorlog into a csv file and created 10 copies of it. #Just create a CSV with data to Import. Using SQLErrorLog [reflection.assembly]::LoadWithPartialName(“Microsoft.SqlServer.Smo”) $ServerInstance=new-object (“Microsoft.SqlServer.Management.Smo.Server“) $Env:Computername $ServerInstance.ReadErrorLog() | export-csv-path“c:\SQLAuthority\ErrorLog.csv”-NoTypeInformation for($Count=1;$Count-le 10;$count++)  {       Copy-Item“c:\SQLAuthority\Errorlog.csv”“c:\SQLAuthority\ErrorLog$($count).csv” } Now in my path c:\sqlauthority, I have 10 csv files : Now it is time to create a table. In my case, the SQL Server is called R2D2 and the Database is SQLServerRepository and the table is CSV_SQLAuthority. CREATE TABLE [dbo].[CSV_SQLAuthority]( [LogDate] [datetime] NULL, [Processinfo] [varchar](20) NULL, [Text] [varchar](MAX) NULL ) Let’s play a little bit. I want to import synchronously all csv files from the path to the table: #Importing synchronously $DataImport=Import-Csv-Path ( Get-ChildItem“c:\SQLAuthority\*.csv”) $DataTable=Out-DataTable-InputObject$DataImport Write-DataTable-ServerInstanceR2D2-DatabaseSQLServerRepository-TableNameCSV_SQLAuthority-Data$DataTable Very cool, right? Let’s do it asynchronously and in background using PowerShell  Jobs: #If you want to do it to all asynchronously Start-job-Name‘ImportingAsynchronously‘ ` -InitializationScript  {IpmoFunctions-Force-DisableNameChecking} ` -ScriptBlock {    ` $DataImport=Import-Csv-Path ( Get-ChildItem“c:\SQLAuthority\*.csv”) $DataTable=Out-DataTable-InputObject$DataImport Write-DataTable   -ServerInstance“R2D2″`                   -Database“SQLServerRepository“`                   -TableName“CSV_SQLAuthority“`                   -Data$DataTable             } Oh, but if I have csv files that are large in size and I want to import each one asynchronously. In this case, this is what should be done: Get-ChildItem“c:\SQLAuthority\*.csv” | % { Start-job-Name“$($_)” ` -InitializationScript  {IpmoFunctions-Force-DisableNameChecking} ` -ScriptBlock { $DataImport=Import-Csv-Path$args[0]                $DataTable=Out-DataTable-InputObject$DataImport                Write-DataTable-ServerInstance“R2D2″`                               -Database“SQLServerRepository“`                               -TableName“CSV_SQLAuthority“`                               -Data$DataTable             } -ArgumentList$_.fullname } How cool is that? Let’s make the funny stuff now. Let’s schedule it on an SQL Server Agent Job. If you are using SQL Server 2012, you can use the PowerShell Job Step. Otherwise you need to use a CMDexec job step calling PowerShell.exe. We will use the second option. First, create a ps1 file called ImportCSV.ps1 with the script above and save it in a path. In my case, it is in c:\temp\automation. Just add the line at the end: Get-ChildItem“c:\SQLAuthority\*.csv” | % { Start-job-Name“$($_)” ` -InitializationScript  {IpmoFunctions-Force-DisableNameChecking} ` -ScriptBlock { $DataImport=Import-Csv-Path$args[0]                $DataTable=Out-DataTable-InputObject$DataImport                Write-DataTable-ServerInstance“R2D2″`                               -Database“SQLServerRepository“`                               -TableName“CSV_SQLAuthority“`                               -Data$DataTable             } -ArgumentList$_.fullname } Get-Job | Wait-Job | Out-Null Remove-Job -State Completed Why? See my post Dooh PowerShell Trick–Running Scripts That has Posh Jobs on a SQL Agent Job Remember, this trick is for  ALL scripts that will use PowerShell Jobs and any kind of schedule tool (SQL Server agent, Windows Schedule) Create a Job Called ImportCSV and a step called Step_ImportCSV and choose CMDexec. Then you just need to schedule or run it. I did a short video (with matching good background music) and you can see it at: That’s it guys. C’mon, join me in the #PowerShellLifeStyle. You will love it. If you want to check what we can do with PowerShell and SQL Server, don’t miss Laerte Junior LiveMeeting on July 18. You can have more information in : LiveMeeting VC PowerShell PASS–Troubleshooting SQL Server With PowerShell–English Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology, Video Tagged: Powershell

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  • MYSQL – Identifying Current Version of MySQL Server Installation – Part 2

    - by Pinal Dave
    Earlier I wrote an article about Detecting Current Version of MySQL Server Installation. After the post quite a few emails I received where various users suggested that there are many more ways to figure out the version of MySQL. Here are few of the methods which I received in the email. Method 1: This method retrieves value with the help of Information Functions. SELECT VERSION(); Method 2: This method is very similar to SQL Server. SELECT @@Version Method 3: You can connect to MySQL with command prompt and type following command: STATUS; Method 4: Please refer my earlier blog post. SHOW VARIABLES LIKE "%version%"; Let me know if you know any more method and I will extend this blog post. Reference : Pinal Dave (http://blog.SQLAuthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Query, SQL Tips and Tricks, T SQL

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  • What are the pros and cons of Coffeescript?

    - by Philip
    Of course one big pro is the amount of syntactic sugar leading to shorter code in a lot of cases. On http://jashkenas.github.com/coffee-script/ there are impressive examples. On the other hand I have doubts that these examples represent code of complex real world applications. In my code for instance I never add functions to bare objects but rather to their prototypes. Moreover the prototype feature is hidden from the user, suggesting classical OOP rather than idiomatic Javascript. The array comprehension example would look in my code probably like this: cubes = $.map(list, math.cube); // which is 8 characters less using jQuery...

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  • OpenGL 3.0+ framebuffer to texture/images

    - by user827992
    I need a way to capture what is rendered on screen, i have read about glReadPixels but it looks really slow. Can you suggest a more efficient or just an alternative way for just copying what is rendered by OpenGL 3.0+ to the local RAM and in general to output this in a image or in a data stream? How i can achieve the same goal with OpenGL ES 2.0 ? EDIT: i just forgot: with this OpenGL functions how i can be sure that I'm actually reading a complete frame, meaning that there is no overlapping between 2 frames or any nasty side effect I'm actually reading the frame that comes right next to the previous one so i do not lose frames

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

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

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

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

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  • Oracle Database 12c Spatial: Vector Performance Acceleration

    - by Okcan Yasin Saygili-Oracle
    Most business information has a location component, such as customer addresses, sales territories and physical assets. Businesses can take advantage of their geographic information by incorporating location analysis and intelligence into their information systems. This allows organizations to make better decisions, respond to customers more effectively, and reduce operational costs – increasing ROI and creating competitive advantage. Oracle Database, the industry’s most advanced database,  includes native location capabilities, fully integrated in the kernel, for fast, scalable, reliable and secure spatial and massive graph applications. It is a foundation for deploying enterprise-wide spatial information systems and locationenabled business applications. Developers can extend existing Oracle-based tools and applications, since they can easily incorporate location information directly in their applications, workflows, and services. Spatial Features The geospatial data features of Oracle Spatial and Graph option support complex geographic information systems (GIS) applications, enterprise applications and location services applications. Oracle Spatial and Graph option extends the spatial query and analysis features included in every edition of Oracle Database with the Oracle Locator feature, and provides a robust foundation for applications that require advanced spatial analysis and processing in the Oracle Database. It supports all major spatial data types and models, addressing challenging business-critical requirements from various industries, including transportation, utilities, energy, public sector, defense and commercial location intelligence. Network Data Model Graph Features The Network Data Model graph explicitly stores and maintains a persistent data model withnetwork connectivity and provides network analysis capability such as shortest path, nearest neighbors, within cost and reachability. It loads partitioned networks into memory on demand, overcomingthe limitations of in-memory analysis. Partitioning massive networks into manageable sub-networkssimplifies the network analysis. RDF Semantic Graph Features RDF Semantic Graph has native support for World Wide Web Consortium standards. It has open, scalable, and secure features for storing RDF/OWL ontologies anddata; native inference with OWL 2, SKOS and user-defined rules; and querying RDF/OWL data withSPARQL 1.1, Java APIs, and SPARQLgraph patterns in SQL. Video: Oracle Spatial and Graph Overview Oracle spatial is embeded on oracle database product. So ,we can use oracle installer (OUI).The Oracle Universal Installer (OUI) is used to install Oracle Database software. OUI is a graphical user interface utility that enables you to view the Oracle software that is installed on your machine, install new Oracle Database software, and delete Oracle software that you no longer need to use. Online Help is available to guide you through the installation process. One of the installation options is to create a database. If you select database creation, OUI automatically starts Oracle Database Configuration Assistant (DBCA) to guide you through the process of creating and configuring a database. If you do not create a database during installation, you must invoke DBCA after you have installed the software to create a database. You can also use DBCA to create additional databases. For installing Oracle Database 12c you may check the Installing Oracle Database Software and Creating a Database tutorial under the Oracle Database 12c 2-Day DBA Series.You can always check if spatial is available in your database using  "select comp_id, version, status, comp_name from dba_registry where comp_id='SDO';"   One of the most notable improvements with Oracle Spatial and Graph 12c can be seen in performance increases in vector data operations. Enabling the Spatial Vector Acceleration feature (available with the Spatial option) dramatically improves the performance of commonly used vector data operations, such as sdo_distance, sdo_aggr_union, and sdo_inside. With 12c, these operations also run more efficiently in parallel than in prior versions through the use of metadata caching. For organizations that have been facing processing limitations, these enhancements enable developers to make a small set of configuration changes and quickly realize significant performance improvements. Results include improved index performance, enhanced geometry engine performance, optimized secondary filter optimizations for Spatial operators, and improved CPU and memory utilization for many advanced vector functions. Vector performance acceleration is especially beneficial when using Oracle Exadata Database Machine and other large-scale systems. Oracle Spatial and Graph vector performance acceleration builds on general improvements available to all SDO_GEOMETRY operations in these areas: Caching of index metadata, Concurrent update mechanisms, and Optimized spatial predicate selectivity and cost functions. These optimizations enable more efficient use of: CPU, Memory, and Partitioning Resulting in substantial query performance improvements.UsageTo accelerate the performance of spatial operators, it is recommended that you set the SPATIAL_VECTOR_ACCELERATION database system parameter to the value TRUE. (This parameter is authorized for use only by licensed Oracle Spatial users, and its default value is FALSE.) You can set this parameter for the whole system or for a single session. To set the value for the whole system, do either of the following:Enter the following statement from a suitably privileged account:   ALTER SYSTEM SET SPATIAL_VECTOR_ACCELERATION = TRUE;Add the following to the database initialization file (xxxinit.ora):   SPATIAL_VECTOR_ACCELERATION = TRUE;To set the value for the current session, enter the following statement from a suitably privileged account:   ALTER SESSION SET SPATIAL_VECTOR_ACCELERATION = TRUE; Checkout the complete list of new features on Oracle.com @ http://www.oracle.com/technetwork/database/options/spatialandgraph/overview/index.html Spatial and Graph Data Sheet (PDF) Spatial and Graph White Paper (PDF)

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