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  • Base de Datos Oracle, su mejor opción para reducir costos de IT

    - by Ivan Hassig
    Por Victoria Cadavid Sr. Sales Cosultant Oracle Direct Uno de los principales desafíos en la administración de centros de datos es la reducción de costos de operación. A medida que las compañías crecen y los proveedores de tecnología ofrecen soluciones cada vez más robustas, conservar el equilibrio entre desempeño, soporte al negocio y gestión del Costo Total de Propiedad es un desafío cada vez mayor para los Gerentes de Tecnología y para los Administradores de Centros de Datos. Las estrategias más comunes para conseguir reducción en los costos de administración de Centros de Datos y en la gestión de Tecnología de una organización en general, se enfocan en la mejora del desempeño de las aplicaciones, reducción del costo de administración y adquisición de hardware, reducción de los costos de almacenamiento, aumento de la productividad en la administración de las Bases de Datos y mejora en la atención de requerimientos y prestación de servicios de mesa de ayuda, sin embargo, las estrategias de reducción de costos deben contemplar también la reducción de costos asociados a pérdida y robo de información, cumplimiento regulatorio, generación de valor y continuidad del negocio, que comúnmente se conciben como iniciativas aisladas que no siempre se adelantan con el ánimo de apoyar la reducción de costos. Una iniciativa integral de reducción de costos de TI, debe contemplar cada uno de los factores que  generan costo y pueden ser optimizados. En este artículo queremos abordar la reducción de costos de tecnología a partir de la adopción del que según los expertos es el motor de Base de Datos # del mercado.Durante años, la base de datos Oracle ha sido reconocida por su velocidad, confiabilidad, seguridad y capacidad para soportar cargas de datos tanto de aplicaciones altamente transaccionales, como de Bodegas de datos e incluso análisis de Big Data , ofreciendo alto desempeño y facilidades de administración, sin embrago, cuando pensamos en proyectos de reducción de costos de IT, además de la capacidad para soportar aplicaciones (incluso aplicaciones altamente transaccionales) con alto desempeño, pensamos en procesos de automatización, optimización de recursos, consolidación, virtualización e incluso alternativas más cómodas de licenciamiento. La Base de Datos Oracle está diseñada para proveer todas las capacidades que un área de tecnología necesita para reducir costos, adaptándose a los diferentes escenarios de negocio y a las capacidades y características de cada organización.Es así, como además del motor de Base de Datos, Oracle ofrece una serie de soluciones para optimizar la administración de la información a través de mecanismos de optimización del uso del storage, continuidad del Negocio, consolidación de infraestructura, seguridad y administración automática, que propenden por un mejor uso de los recursos de tecnología, ofrecen opciones avanzadas de configuración y direccionan la reducción de los tiempos de las tareas operativas más comunes. Una de las opciones de la base de datos que se pueden provechar para reducir costos de hardware es Oracle Real Application Clusters. Esta solución de clustering permite que varios servidores (incluso servidores de bajo costo) trabajen en conjunto para soportar Grids o Nubes Privadas de Bases de Datos, proporcionando los beneficios de la consolidación de infraestructura, los esquemas de alta disponibilidad, rápido desempeño y escalabilidad por demanda, haciendo que el aprovisionamiento, el mantenimiento de las bases de datos y la adición de nuevos nodos se lleve e cabo de una forma más rápida y con menos riesgo, además de apalancar las inversiones en servidores de menor costo. Otra de las soluciones que promueven la reducción de costos de Tecnología es Oracle In-Memory Database Cache que permite almacenar y procesar datos en la memoria de las aplicaciones, permitiendo el máximo aprovechamiento de los recursos de procesamiento de la capa media, lo que cobra mucho valor en escenarios de alta transaccionalidad. De este modo se saca el mayor provecho de los recursos de procesamiento evitando crecimiento innecesario en recursos de hardware. Otra de las formas de evitar inversiones innecesarias en hardware, aprovechando los recursos existentes, incluso en escenarios de alto crecimiento de los volúmenes de información es la compresión de los datos. Oracle Advanced Compression permite comprimir hasta 4 veces los diferentes tipos de datos, mejorando la capacidad de almacenamiento, sin comprometer el desempeño de las aplicaciones. Desde el lado del almacenamiento también se pueden conseguir reducciones importantes de los costos de IT. En este escenario, la tecnología propia de la base de Datos Oracle ofrece capacidades de Administración Automática del Almacenamiento que no solo permiten una distribución óptima de los datos en los discos físicos para garantizar el máximo desempeño, sino que facilitan el aprovisionamiento y la remoción de discos defectuosos y ofrecen balanceo y mirroring, garantizando el uso máximo de cada uno de los dispositivos y la disponibilidad de los datos. Otra de las soluciones que facilitan la administración del almacenamiento es Oracle Partitioning, una opción de la Base de Datos que permite dividir grandes tablas en estructuras más pequeñas. Esta aproximación facilita la administración del ciclo de vida de la información y permite por ejemplo, separar los datos históricos (que generalmente se convierten en información de solo lectura y no tienen un alto volumen de consulta) y enviarlos a un almacenamiento de bajo costos, conservando la data activa en dispositivos de almacenamiento más ágiles. Adicionalmente, Oracle Partitioning facilita la administración de las bases de datos que tienen un gran volumen de registros y mejora el desempeño de la base de datos gracias a la posibilidad de optimizar las consultas haciendo uso únicamente de las particiones relevantes de una tabla o índice en el proceso de búsqueda. Otros factores adicionales, que pueden generar costos innecesarios a los departamentos de Tecnología son: La pérdida, corrupción o robo de datos y la falta de disponibilidad de las aplicaciones para dar soporte al negocio. Para evitar este tipo de situaciones que pueden acarrear multas y pérdida de negocios y de dinero, Oracle ofrece soluciones que permiten proteger y auditar la base de datos, recuperar la información en caso de corrupción o ejecución de acciones que comprometan la integridad de la información y soluciones que permitan garantizar que la información de las aplicaciones tenga una disponibilidad de 7x24. Ya hablamos de los beneficios de Oracle RAC, para facilitar los procesos de Consolidación y mejorar el desempeño de las aplicaciones, sin embrago esta solución, es sumamente útil en escenarios dónde las organizaciones de quieren garantizar una alta disponibilidad de la información, ante fallo de los servidores o en eventos de desconexión planeada para realizar labores de mantenimiento. Además de Oracle RAC, existen soluciones como Oracle Data Guard y Active Data Guard que permiten replicar de forma automática las bases de datos hacia un centro de datos de contingencia, permitiendo una recuperación inmediata ante eventos que deshabiliten por completo un centro de datos. Además de lo anterior, Active Data Guard, permite aprovechar la base de datos de contingencia para realizar labores de consulta, mejorando el desempeño de las aplicaciones. Desde el punto de vista de mejora en la seguridad, Oracle cuenta con soluciones como Advanced security que permite encriptar los datos y los canales a través de los cueles se comparte la información, Total Recall, que permite visualizar los cambios realizados a la base de datos en un momento determinado del tiempo, para evitar pérdida y corrupción de datos, Database Vault que permite restringir el acceso de los usuarios privilegiados a información confidencial, Audit Vault, que permite verificar quién hizo qué y cuándo dentro de las bases de datos de una organización y Oracle Data Masking que permite enmascarar los datos para garantizar la protección de la información sensible y el cumplimiento de las políticas y normas relacionadas con protección de información confidencial, por ejemplo, mientras las aplicaciones pasan del ambiente de desarrollo al ambiente de producción. Como mencionamos en un comienzo, las iniciativas de reducción de costos de tecnología deben apalancarse en estrategias que contemplen los diferentes factores que puedan generar sobre costos, los factores de riesgo que puedan acarrear costos no previsto, el aprovechamiento de los recursos actuales, para evitar inversiones innecesarias y los factores de optimización que permitan el máximo aprovechamiento de las inversiones actuales. Como vimos, todas estas iniciativas pueden ser abordadas haciendo uso de la tecnología de Oracle a nivel de Base de Datos, lo más importante es detectar los puntos críticos a nivel de riesgo, diagnosticar las proporción en que están siendo aprovechados los recursos actuales y definir las prioridades de la organización y del área de IT, para así dar inicio a todas aquellas iniciativas que de forma gradual, van a evitar sobrecostos e inversiones innecesarias, proporcionando un mayor apoyo al negocio y un impacto significativo en la productividad de la organización. Más información http://www.oracle.com/lad/products/database/index.html?ssSourceSiteId=otnes 1Fuente: Market Share: All Software Markets, Worldwide 2011 by Colleen Graham, Joanne Correia, David Coyle, Fabrizio Biscotti, Matthew Cheung, Ruggero Contu, Yanna Dharmasthira, Tom Eid, Chad Eschinger, Bianca Granetto, Hai Hong Swinehart, Sharon Mertz, Chris Pang, Asheesh Raina, Dan Sommer, Bhavish Sood, Marianne D'Aquila, Laurie Wurster and Jie Zhang. - March 29, 2012 2Big Data: Información recopilada desde fuentes no tradicionales como blogs, redes sociales, email, sensores, fotografías, grabaciones en video, etc. que normalmente se encuentran de forma no estructurada y en un gran volumen

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  • MySQL – Scalability on Amazon RDS: Scale out to multiple RDS instances

    - by Pinal Dave
    Today, I’d like to discuss getting better MySQL scalability on Amazon RDS. The question of the day: “What can you do when a MySQL database needs to scale write-intensive workloads beyond the capabilities of the largest available machine on Amazon RDS?” Let’s take a look. In a typical EC2/RDS set-up, users connect to app servers from their mobile devices and tablets, computers, browsers, etc.  Then app servers connect to an RDS instance (web/cloud services) and in some cases they might leverage some read-only replicas.   Figure 1. A typical RDS instance is a single-instance database, with read replicas.  This is not very good at handling high write-based throughput. As your application becomes more popular you can expect an increasing number of users, more transactions, and more accumulated data.  User interactions can become more challenging as the application adds more sophisticated capabilities. The result of all this positive activity: your MySQL database will inevitably begin to experience scalability pressures. What can you do? Broadly speaking, there are four options available to improve MySQL scalability on RDS. 1. Larger RDS Instances – If you’re not already using the maximum available RDS instance, you can always scale up – to larger hardware.  Bigger CPUs, more compute power, more memory et cetera. But the largest available RDS instance is still limited.  And they get expensive. “High-Memory Quadruple Extra Large DB Instance”: 68 GB of memory 26 ECUs (8 virtual cores with 3.25 ECUs each) 64-bit platform High I/O Capacity Provisioned IOPS Optimized: 1000Mbps 2. Provisioned IOPs – You can get provisioned IOPs and higher throughput on the I/O level. However, there is a hard limit with a maximum instance size and maximum number of provisioned IOPs you can buy from Amazon and you simply cannot scale beyond these hardware specifications. 3. Leverage Read Replicas – If your application permits, you can leverage read replicas to offload some reads from the master databases. But there are a limited number of replicas you can utilize and Amazon generally requires some modifications to your existing application. And read-replicas don’t help with write-intensive applications. 4. Multiple Database Instances – Amazon offers a fourth option: “You can implement partitioning,thereby spreading your data across multiple database Instances” (Link) However, Amazon does not offer any guidance or facilities to help you with this. “Multiple database instances” is not an RDS feature.  And Amazon doesn’t explain how to implement this idea. In fact, when asked, this is the response on an Amazon forum: Q: Is there any documents that describe the partition DB across multiple RDS? I need to use DB with more 1TB but exist a limitation during the create process, but I read in the any FAQ that you need to partition database, but I don’t find any documents that describe it. A: “DB partitioning/sharding is not an official feature of Amazon RDS or MySQL, but a technique to scale out database by using multiple database instances. The appropriate way to split data depends on the characteristics of the application or data set. Therefore, there is no concrete and specific guidance.” So now what? The answer is to scale out with ScaleBase. Amazon RDS with ScaleBase: What you get – MySQL Scalability! ScaleBase is specifically designed to scale out a single MySQL RDS instance into multiple MySQL instances. Critically, this is accomplished with no changes to your application code.  Your application continues to “see” one database.   ScaleBase does all the work of managing and enforcing an optimized data distribution policy to create multiple MySQL instances. With ScaleBase, data distribution, transactions, concurrency control, and two-phase commit are all 100% transparent and 100% ACID-compliant, so applications, services and tooling continue to interact with your distributed RDS as if it were a single MySQL instance. The result: now you can cost-effectively leverage multiple MySQL RDS instance to scale out write-intensive workloads to an unlimited number of users, transactions, and data. Amazon RDS with ScaleBase: What you keep – Everything! And how does this change your Amazon environment? 1. Keep your application, unchanged – There is no change your application development life-cycle at all.  You still use your existing development tools, frameworks and libraries.  Application quality assurance and testing cycles stay the same. And, critically, you stay with an ACID-compliant MySQL environment. 2. Keep your RDS value-added services – The value-added services that you rely on are all still available. Amazon will continue to handle database maintenance and updates for you. You can still leverage High Availability via Multi A-Z.  And, if it benefits youra application throughput, you can still use read replicas. 3. Keep your RDS administration – Finally the RDS monitoring and provisioning tools you rely on still work as they did before. With your one large MySQL instance, now split into multiple instances, you can actually use less expensive, smallersmaller available RDS hardware and continue to see better database performance. Conclusion Amazon RDS is a tremendous service, but it doesn’t offer solutions to scale beyond a single MySQL instance. Larger RDS instances get more expensive.  And when you max-out on the available hardware, you’re stuck.  Amazon recommends scaling out your single instance into multiple instances for transaction-intensive apps, but offers no services or guidance to help you. This is where ScaleBase comes in to save the day. It gives you a simple and effective way to create multiple MySQL RDS instances, while removing all the complexities typically caused by “DIY” sharding andwith no changes to your applications . With ScaleBase you continue to leverage the AWS/RDS ecosystem: commodity hardware and value added services like read replicas, multi A-Z, maintenance/updates and administration with monitoring tools and provisioning. SCALEBASE ON AMAZON If you’re curious to try ScaleBase on Amazon, it can be found here – Download NOW. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Oracle Certification and virtualization Solutions.

    - by scoter
    As stated in official MOS ( My Oracle Support ) document 249212.1 support for Oracle products on non-Oracle VM platforms follow exactly the same stance as support for VMware and, so, the only x86 virtualization software solution certified for any Oracle product is "Oracle VM". Based on the fact that: Oracle VM is totally free ( you have the option to buy Oracle-Support ) Certified is pretty different from supported ( OracleVM is certified, others could be supported ) With Oracle VM you may not require to reproduce your issue(s) on physical server Oracle VM is the only x86 software solution that allows hard-partitioning *** *** see details to these Oracle public links: http://www.oracle.com/technetwork/server-storage/vm/ovm-hardpart-168217.pdf http://www.oracle.com/us/corporate/pricing/partitioning-070609.pdf people started asking to migrate from third party virtualization software (ex. RH KVM, VMWare) to Oracle VM. Migrating RH KVM guest to Oracle VM. OracleVM has a built-in P2V utility ( Official Documentation ) but in some cases we can't use it, due to : network inaccessibility between hypervisors ( KVM and OVM ) network slowness between hypervisors (KVM and OVM) size of the guest virtual-disks Here you'll find a step-by-step guide to "manually" migrate a guest machine from KVM to OVM. 1. Verify source guest characteristics. Using KVM web console you can verify characteristics of the guest you need to migrate, such as: CPU Cores details Defined Memory ( RAM ) Name of your guest Guest operating system Disks details ( number and size ) Network details ( number of NICs and network configuration ) 2. Export your guest in OVF / OVA format.  The export from Redhat KVM ( kernel virtual machine ) will create a structured export of your guest: [root@ovmserver1 mnt]# lltotal 12drwxrwx--- 5 36 36 4096 Oct 19 2012 b8296fca-13c4-4841-a50f-773b5139fcee b8296fca-13c4-4841-a50f-773b5139fcee is the ID of the guest exported from RH-KVM [root@ovmserver1 mnt]# cd b8296fca-13c4-4841-a50f-773b5139fcee/[root@ovmserver1 b8296fca-13c4-4841-a50f-773b5139fcee]# ls -ltrtotal 12drwxr-x--- 4 36 36 4096 Oct 19  2012 masterdrwxrwx--- 2 36 36 4096 Oct 29  2012 dom_mddrwxrwx--- 4 36 36 4096 Oct 31  2012 images images contains your virtual-disks exported [root@ovmserver1 b8296fca-13c4-4841-a50f-773b5139fcee]# cd images/[root@ovmserver1 images]# ls -ltratotal 16drwxrwx--- 5 36 36 4096 Oct 19  2012 ..drwxrwx--- 2 36 36 4096 Oct 31  2012 d4ef928d-6dc6-4743-b20d-568b424728a5drwxrwx--- 2 36 36 4096 Oct 31  2012 4b241ea0-43aa-4f3b-ab7d-2fc633b491a1drwxrwx--- 4 36 36 4096 Oct 31  2012 .[root@ovmserver1 images]# cd d4ef928d-6dc6-4743-b20d-568b424728a5/[root@ovmserver1 d4ef928d-6dc6-4743-b20d-568b424728a5]# ls -ltotal 5169092-rwxr----- 1 36 36 187904819200 Oct 31  2012 4c03b1cf-67cc-4af0-ad1e-529fd665dac1-rw-rw---- 1 36 36          341 Oct 31  2012 4c03b1cf-67cc-4af0-ad1e-529fd665dac1.meta[root@ovmserver1 d4ef928d-6dc6-4743-b20d-568b424728a5]# file 4c03b1cf-67cc-4af0-ad1e-529fd665dac14c03b1cf-67cc-4af0-ad1e-529fd665dac1: LVM2 (Linux Logical Volume Manager) , UUID: sZL1Ttpy0vNqykaPahEo3hK3lGhwspv 4c03b1cf-67cc-4af0-ad1e-529fd665dac1 is the first exported disk ( physical volume ) [root@ovmserver1 d4ef928d-6dc6-4743-b20d-568b424728a5]# cd ../4b241ea0-43aa-4f3b-ab7d-2fc633b491a1/[root@ovmserver1 4b241ea0-43aa-4f3b-ab7d-2fc633b491a1]# ls -ltotal 5568076-rwxr----- 1 36 36 107374182400 Oct 31  2012 9020f2e1-7b8a-4641-8f80-749768cc237a-rw-rw---- 1 36 36          341 Oct 31  2012 9020f2e1-7b8a-4641-8f80-749768cc237a.meta[root@ovmserver1 4b241ea0-43aa-4f3b-ab7d-2fc633b491a1]# file 9020f2e1-7b8a-4641-8f80-749768cc237a9020f2e1-7b8a-4641-8f80-749768cc237a: x86 boot sector; partition 1: ID=0x83, active, starthead 1, startsector 63, 401562 sectors; partition 2: ID=0x82, starthead 0, startsector 401625, 65529135 sectors; startsector 63, 401562 sectors; partition 2: ID=0x82, starthead 0, startsector 401625, 65529135 sectors; partition 3: ID=0x83, starthead 254, startsector 65930760, 8385930 sectors; partition 4: ID=0x5, starthead 254, startsector 74316690, 135395820 sectors, code offset 0x48 9020f2e1-7b8a-4641-8f80-749768cc237a is the second exported disk, with partition 1 bootable 3. Prepare the new guest on Oracle VM. By Ovm-Manager we can prepare the guest where we will move the exported virtual-disks; under the Tab "Servers and VMs": click on  and create your guest with parameters collected before (point 1): - add NICs on different networks: - add virtual-disks; in this case we add two disks of 1.0 GB each one; we will extend the virtual disk copying the source KVM virtual-disk ( see next steps ) - verify virtual-disks created ( under Repositories tab ) 4. Verify OVM virtual-disks names. [root@ovmserver1 VirtualMachines]# grep -r hyptest_rdbms * 0004fb0000060000a906b423f44da98e/vm.cfg:OVM_simple_name = 'hyptest_rdbms' [root@ovmserver1 VirtualMachines]# cd 0004fb0000060000a906b423f44da98e [root@ovmserver1 0004fb0000060000a906b423f44da98e]# more vm.cfgvif = ['mac=00:21:f6:0f:3f:85,bridge=0004fb001089128', 'mac=00:21:f6:0f:3f:8e,bridge=0004fb00101971d'] OVM_simple_name = 'hyptest_rdbms' vnclisten = '127.0.0.1' disk = ['file:/OVS/Repositories/0004fb00000300004f17b7368139eb41/ VirtualDisks/0004fb000012000097c1bfea9834b17d.img,xvda,w', 'file:/OVS/Repositories/0004fb00000300004f17b7368139eb41/VirtualDisks/ 0004fb0000120000cde6a11c3cb1d0be.img,xvdb,w'] vncunused = '1' uuid = '0004fb00-0006-0000-a906-b423f44da98e' on_reboot = 'restart' cpu_weight = 27500 memory = 32768 cpu_cap = 0 maxvcpus = 8 OVM_high_availability = True maxmem = 32768 vnc = '1' OVM_description = '' on_poweroff = 'destroy' on_crash = 'restart' name = '0004fb0000060000a906b423f44da98e' guest_os_type = 'linux' builder = 'hvm' vcpus = 8 keymap = 'en-us' OVM_os_type = 'Oracle Linux 5' OVM_cpu_compat_group = '' OVM_domain_type = 'xen_hvm' disk2 ovm ==> /OVS/Repositories/0004fb00000300004f17b7368139eb41/VirtualDisks/ 0004fb0000120000cde6a11c3cb1d0be.img disk1 ovm ==> /OVS/Repositories/0004fb00000300004f17b7368139eb41/VirtualDisks/ 0004fb000012000097c1bfea9834b17d.img Summarizing disk1 --source ==> /mnt/b8296fca-13c4-4841-a50f-773b5139fcee/images/4b241ea0-43aa-4f3b-ab7d-2fc633b491a1/9020f2e1-7b8a-4641-8f80-749768cc237a disk1 --dest ==> /OVS/Repositories/0004fb00000300004f17b7368139eb41/VirtualDisks/ 0004fb000012000097c1bfea9834b17d.img disk2 --source ==> /mnt/b8296fca-13c4-4841-a50f-773b5139fcee/images/d4ef928d-6dc6-4743-b20d-568b424728a5/4c03b1cf-67cc-4af0-ad1e-529fd665dac1 disk2 --dest ==> /OVS/Repositories/0004fb00000300004f17b7368139eb41/VirtualDisks/ 0004fb0000120000cde6a11c3cb1d0be.img 5. Copy KVM exported virtual-disks to OVM virtual-disks. Keeping your Oracle VM guest stopped you can copy KVM exported virtual-disks to OVM virtual-disks; what I did is only to locally mount the filesystem containing the exported virtual-disk ( by an usb device ) on my OVS; the copy automatically resize OVM virtual-disks ( previously created with a size of 1GB ) . nohup cp /mnt/b8296fca-13c4-4841-a50f-773b5139fcee/images/4b241ea0-43aa-4f3b-ab7d-2fc633b491a1/9020f2e1-7b8a-4641-8f80-749768cc237a /OVS/Repositories/0004fb00000300004f17b7368139eb41/VirtualDisks/0004fb000012000097c1bfea9834b17d.img & nohup cp /mnt/b8296fca-13c4-4841-a50f-773b5139fcee/images/d4ef928d-6dc6-4743-b20d-568b424728a5/4c03b1cf-67cc-4af0-ad1e-529fd665dac1 /OVS/Repositories/0004fb00000300004f17b7368139eb41/VirtualDisks/0004fb0000120000cde6a11c3cb1d0be.img & 7. When copy completed refresh repository to aknowledge the new-disks size. 7. After "refresh repository" is completed, start guest machine by Oracle VM manager. After the first start of your guest: - verify that you can see all disks and partitions - verify that your guest is network reachable ( MAC Address of your NICs changed ) Eventually you can also evaluate to convert your guest to PVM ( Paravirtualized virtual Machine ) following official Oracle documentation. Ciao Simon COTER ps: next-time I'd like to post an article reporting how to manually migrate Virtual-Iron guests to OracleVM.  Comments and corrections are welcome. 

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  • Installation stuck on "Installation Type" screen

    - by Andrew Latham
    I am trying to install Ubuntu 11.10 with Windows 7 from a CD. I am using an HP Pavilion dm4. I've never used Ubuntu (or any Linux) before. Everything goes alright until I get to the "Installation Type" screen. Instead of giving me options, it just has a blank menu, and all the buttons are disabled. When I click "Continue", it gives me an error saying that it can't find the root or something like that. The trial version works fine, but I can't actually install it. Everything on the trial version is really slow, presumably because everything is on the CD or the Windows partition. I did some research, but the only post I could find was http://ubuntuforums.org/showthread.php?t=1870478 Where the only advice is to format the entire drive, which I'm not willing to do. Any suggestions? I'm downloading 10.04 right now and I'm going to try with that instead. EDIT: 10.04 didn't work either. I got to the partitioning screen and got the same problem. I read some more forums, loaded up 11.10 trial from the disk, opened the Terminal and typed sudo apt-get remove dmraid and then y. Then I was actually able to see something on the "Installation type" page: "Erase disk and install Ubuntu" or "Something else". Which is weird, since Windows 7 should be installed. When I click Something Else, I get: /dev/sda /dev/sdb /dev/sdb1 (ntfs) (208 MB) (69 MB used) /dev/sdb2 (ntfs) (477542 MB) (unknown used) /dev/sdb3 (ntfs) (18085 MB) (16094 MB used) /dev/sdb4 (fat32) (4265 MB) (3084 MB used) I have no idea what any of this means. Also, my device for boot loader installation changed from /dev/sda to /dev/sda ATA SAMSUNG MZMPA032 (32.0 GB)

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  • Parallelism in .NET – Part 11, Divide and Conquer via Parallel.Invoke

    - by Reed
    Many algorithms are easily written to work via recursion.  For example, most data-oriented tasks where a tree of data must be processed are much more easily handled by starting at the root, and recursively “walking” the tree.  Some algorithms work this way on flat data structures, such as arrays, as well.  This is a form of divide and conquer: an algorithm design which is based around breaking up a set of work recursively, “dividing” the total work in each recursive step, and “conquering” the work when the remaining work is small enough to be solved easily. Recursive algorithms, especially ones based on a form of divide and conquer, are often a very good candidate for parallelization. This is apparent from a common sense standpoint.  Since we’re dividing up the total work in the algorithm, we have an obvious, built-in partitioning scheme.  Once partitioned, the data can be worked upon independently, so there is good, clean isolation of data. Implementing this type of algorithm is fairly simple.  The Parallel class in .NET 4 includes a method suited for this type of operation: Parallel.Invoke.  This method works by taking any number of delegates defined as an Action, and operating them all in parallel.  The method returns when every delegate has completed: Parallel.Invoke( () => { Console.WriteLine("Action 1 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); }, () => { Console.WriteLine("Action 2 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); }, () => { Console.WriteLine("Action 3 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); } ); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Running this simple example demonstrates the ease of using this method.  For example, on my system, I get three separate thread IDs when running the above code.  By allowing any number of delegates to be executed directly, concurrently, the Parallel.Invoke method provides us an easy way to parallelize any algorithm based on divide and conquer.  We can divide our work in each step, and execute each task in parallel, recursively. For example, suppose we wanted to implement our own quicksort routine.  The quicksort algorithm can be designed based on divide and conquer.  In each iteration, we pick a pivot point, and use that to partition the total array.  We swap the elements around the pivot, then recursively sort the lists on each side of the pivot.  For example, let’s look at this simple, sequential implementation of quicksort: public static void QuickSort<T>(T[] array) where T : IComparable<T> { QuickSortInternal(array, 0, array.Length - 1); } private static void QuickSortInternal<T>(T[] array, int left, int right) where T : IComparable<T> { if (left >= right) { return; } SwapElements(array, left, (left + right) / 2); int last = left; for (int current = left + 1; current <= right; ++current) { if (array[current].CompareTo(array[left]) < 0) { ++last; SwapElements(array, last, current); } } SwapElements(array, left, last); QuickSortInternal(array, left, last - 1); QuickSortInternal(array, last + 1, right); } static void SwapElements<T>(T[] array, int i, int j) { T temp = array[i]; array[i] = array[j]; array[j] = temp; } Here, we implement the quicksort algorithm in a very common, divide and conquer approach.  Running this against the built-in Array.Sort routine shows that we get the exact same answers (although the framework’s sort routine is slightly faster).  On my system, for example, I can use framework’s sort to sort ten million random doubles in about 7.3s, and this implementation takes about 9.3s on average. Looking at this routine, though, there is a clear opportunity to parallelize.  At the end of QuickSortInternal, we recursively call into QuickSortInternal with each partition of the array after the pivot is chosen.  This can be rewritten to use Parallel.Invoke by simply changing it to: // Code above is unchanged... SwapElements(array, left, last); Parallel.Invoke( () => QuickSortInternal(array, left, last - 1), () => QuickSortInternal(array, last + 1, right) ); } This routine will now run in parallel.  When executing, we now see the CPU usage across all cores spike while it executes.  However, there is a significant problem here – by parallelizing this routine, we took it from an execution time of 9.3s to an execution time of approximately 14 seconds!  We’re using more resources as seen in the CPU usage, but the overall result is a dramatic slowdown in overall processing time. This occurs because parallelization adds overhead.  Each time we split this array, we spawn two new tasks to parallelize this algorithm!  This is far, far too many tasks for our cores to operate upon at a single time.  In effect, we’re “over-parallelizing” this routine.  This is a common problem when working with divide and conquer algorithms, and leads to an important observation: When parallelizing a recursive routine, take special care not to add more tasks than necessary to fully utilize your system. This can be done with a few different approaches, in this case.  Typically, the way to handle this is to stop parallelizing the routine at a certain point, and revert back to the serial approach.  Since the first few recursions will all still be parallelized, our “deeper” recursive tasks will be running in parallel, and can take full advantage of the machine.  This also dramatically reduces the overhead added by parallelizing, since we’re only adding overhead for the first few recursive calls.  There are two basic approaches we can take here.  The first approach would be to look at the total work size, and if it’s smaller than a specific threshold, revert to our serial implementation.  In this case, we could just check right-left, and if it’s under a threshold, call the methods directly instead of using Parallel.Invoke. The second approach is to track how “deep” in the “tree” we are currently at, and if we are below some number of levels, stop parallelizing.  This approach is a more general-purpose approach, since it works on routines which parse trees as well as routines working off of a single array, but may not work as well if a poor partitioning strategy is chosen or the tree is not balanced evenly. This can be written very easily.  If we pass a maxDepth parameter into our internal routine, we can restrict the amount of times we parallelize by changing the recursive call to: // Code above is unchanged... SwapElements(array, left, last); if (maxDepth < 1) { QuickSortInternal(array, left, last - 1, maxDepth); QuickSortInternal(array, last + 1, right, maxDepth); } else { --maxDepth; Parallel.Invoke( () => QuickSortInternal(array, left, last - 1, maxDepth), () => QuickSortInternal(array, last + 1, right, maxDepth)); } We no longer allow this to parallelize indefinitely – only to a specific depth, at which time we revert to a serial implementation.  By starting the routine with a maxDepth equal to Environment.ProcessorCount, we can restrict the total amount of parallel operations significantly, but still provide adequate work for each processing core. With this final change, my timings are much better.  On average, I get the following timings: Framework via Array.Sort: 7.3 seconds Serial Quicksort Implementation: 9.3 seconds Naive Parallel Implementation: 14 seconds Parallel Implementation Restricting Depth: 4.7 seconds Finally, we are now faster than the framework’s Array.Sort implementation.

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  • Move smaller hard drive to partition on a larger hard drive

    - by bluejeansummer
    My parents bought a new hard drive for a laptop that I've owned for several years. It's much larger than the current one, so I plan on splitting it up to dual boot it with Ubuntu. I have no problem with partitioning a drive (I always keep a LiveCD handy), but my question is this: how can I go about moving the existing partition to the new drive? This is a laptop, so I can't simply plug the new drive into another slot. Also, even if I manage to move it, will Windows still work on the new drive in a larger partition? I've had this laptop for quite a while, and I've lost the recovery discs that came with it a long time ago. I also have a lot of software without CDs to reinstall them with. This makes not reinstalling Windows a high priority. In case it helps, both drives use 2.5" PATA, and I have a 1 TB external drive available if it's needed.

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  • many partitions on a single filegroup?¿ does it make sense?

    - by river0
    Hi, I'm designing a datawarehouse solution and I'm a newbie in disk configuration issues, let me explain you. Our storage is spread over 6 storage enlosures having each of them 5 raid-1 disk arrays, and having 2 LUNS defined per each disk array, which makes a total 48 LUNS (this is following Microsoft fast track recommendations for datawarehouse architectures). I would like to partition my data, on other projects I have worked before, we always followed a 1 partition - 1 filegroup rule. On the microsoft fast track recomendations it is advised to create a filegroup and then for that filegroup a data file per each lun... but I pretend to have a week level partitioning... if I apply that rule I think that I'll get too many files and a complex layout. I'm thinking of just creating just one filegroup (with the 48 lun data files), but still create the partitions since I want to keep soem of the benefits of partitions like partition switching... Is this scenario not recommended? What would you suggest?

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  • Unable to see External HDD in Windows Explorer

    - by Jamie Keeling
    I have bought a 320GB External HDD which I want to use with my Playstation3. I know it will only work with a Fat32 file system so using some free HP software it formatted it, unbeknown to me it will only work up to 32GB. After seeing this I panicked, downloaded Partition Wizard Home Edition and deleted the partition. As I was about to create a new partition to put it back to NTFS (I'd just wanted to be able to use it in the first place at this point) I accidently knocked the cable out of my computer for the HDD and after replacing it the External HDD is no longer recognised by the My Computer option, Disk Management asks me to initialise the disc using MBR but it fails saying "Copy protected". Even the partitioning software I previously mentioned can't do anything about it, all it says is "Bad Condition" and I can't perform any operations on it. Would anybody be able to guide me in getting this sorted? I'm terrified i've wasted a perfectly good 320GB HDD.

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  • dual boot Windows 7 and Ubuntu 11.04, black screen when loading Windows

    - by Sean
    I am proficient with Windows and not so much with Linux. Here is my story: Original system came with Windows 7, got openSUSE installed on the second hard drive, and dual boot for this setup worked fine. Wanted to switch to Windows 7 and Ubuntu 11.04 dual boot so I did a Windows system recovery and it appeared to give me back a fresh Windows 7 install. I then go to install Ubuntu 11.04 and the installer informs me I have multiple operating systems already installed. I go to the advanced partitioning option and sure enough Windows 7 is on /sda while openSUSE is still on /sdb. From here I followed this guide (How to dual-boot Linux and Ubuntu with two hard drives) after I had deleted all the openSUSE partitions on /sdb through the Allocate Drive Space tab of the installer. I make the /boot, swap, /, and /home partitions and set the GRUB into the MBR of the second disk (/dev/sdb). Everything installs fine. I reboot, Windows loads automatically, install EasyBCD and add an entry for Ubuntu into the Windows Boot Manager while assigning the type as GRUB2. Reboot the system and it now shows dual booting options for both Windows and Ubuntu. Problem is: while I can use Ubuntu fine when I try to boot into Windows it just gives me a black screen and after a little while the fans start running crazy. If I restart the computer I will sometimes get the message that my system was put into hibernation mode because the temperature got too high (90C) which I presume is in accordance with the fans going crazy. I have linked the output from the Boot Info Script below, any suggestions on how to fix this issue would be greatly appreciated! UPDATED SCRIPT OUTPUT Boot Info Script output: http://paste.ubuntu.com/682152/

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  • OWB 11gR2 - Windows and Linux 64-bit clients available

    - by David Allan
    In addition to the integrated release of OWB in the 11.2.0.3 Oracle database distribution, the following 64-bit standalone clients are now available for download from Oracle Support. OWB 11.2.0.3 Standalone client for Windows 64-bit - 13365470 OWB 11.2.0.3 Standalone client for Linux X86 64-bit - 13366327 This is in addition to the previously released 32-bit client on Windows. OWB 11.2.0.3 Standalone client for Windows 32-bit - 13365457 The support document Major OWB 11.2.0.3 New Features Summary has details for OWB 11.2.0.3 which include the following. Exadata v2 and oracle Database 11gR2 support capabilities; Support for Oracle Database 11gR2 and Exadata compression types Even more partitioning: Range-Range, Composite Hash/List, System, Reference Transparent Data Encryption support Data Guard support/certification Compiled PL/SQL code generation Capabilities to support data warehouse ETL best practices; Read and write Oracle Data Pump files with external tables External table preprocessor Partition specific DML Bulk data movement code templates: Oracle, IBM DB2, Microsoft SQL Server to Oracle Integration with Fusion Middleware capabilities; Support OWB's Control Center Agent on WLS Lots of interesting capabilities in 11.2.0.3 and the availability of the 64-bit client I'm sure is welcome news for many!

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  • What are the implications of expanding an internal subnet mask?

    - by Philip
    Our network is currently working on a 192.168.0.x subnet, all controlled through DHCP, except for the few main servers who have hard-configured IP address settings. What would I kill if I changed the DHCP-published subnet mask from 255.255.255.0 to 255.255.0.0? The reason for doing this is not because we have a huge sudden influx of machines, but because I'd like to start partitioning specific devices into specific IP ranges (to be neat and tidy). For what its worth, I don' plan on changing the allocated DHCP address range, but rather want to move some of the reserved and excluded DHCP addresses out of the address pool. e.g. printers will be 192.168.2.x I will obviously need to change the subnet mask manually on my manually configured devices.

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  • Do I need to buy Mysql cluster enterprise edition?

    - by Arman
    Hello, we have a ms-sql 2008 standard edition. The db became too huge, about 8 10^9 records.the db files are about 4.5tb each. We cannot effort us to get enterprise edition to slice the database. We need partitioning. So the idea is to use Mysql cluster with many datanodes. We already started to move data. I wondered do we need to buy a licens for mysqlcluster?are there performance difference between community edition and commercial one? Thanks Arman.

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  • xVelocity engines compared: VertiPaq vs ColumnStore #ssas #vertipaq #xvelocity #sql #tabular

    - by Marco Russo (SQLBI)
    During the last months I and Alberto worked in several projects using Analysis Services Tabular and we had to face real world issues, such as complex queries, large data volume, frequent data updates and so on. Sometime we faced the challenge of comparing Tabular performance with SQL Server. It seemed a non-sense, because even if the same core xVelocity technology is implemented in both products (SQL Server 2012 uses ColumnStore indexes, whereas Analysis Services 2012 uses VertiPaq), we initially assumed that the better optimization for the in-memory engine used by Analysis Services would have been always better than SQL Server. However, we discovered several important things: Processing time might be different and having data on SQL Server could make ColumnStore way faster for processing. Partitioning in SQL Server might be much more effective for query performance than Analysis Services. A single query can scale easily on more processor on SQL Server, whereas in Analysis Services the formula engine is single-threaded and could be a bottleneck for certain queries. In case of a large workload with many concurrent users, storage engine cache in Analysis Services could be a big advantage over SQL Server, especially for scalability As you can see, these considerations are not always obvious and you might be tempted to make other assumptions based on these information. Well, don’t do that. Before anything else, read the whitepaper VertiPaq vs ColumnStore Comparison written by Alberto Ferrari. Then, measure your workload. Finally, make some conclusion. But don’t make too many assumptions. You might be wrong, as we did at the beginning of this journey.

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  • Keep programs from using My Documents

    - by muntoo
    Is there any way to keep programs from using the My Documents folder (on Windows 7)? Everything program seems to want to get in there. I know I could manually symlink each folder a program decides to create, but I would rather not have to do that for each program. Another alternative is to put my stuff in a different location than My Documents, but then I wonder why Microsoft named it 'My Documents' and not 'Applications: put all your stuff here'... probably because filenames cannot have colons on them, but still. I'll probably end up partitioning my drive as I have been doing before, but I was wondering if there was any way to fix this painful annoyance.

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  • How to uninstall Ubuntu from an ubuntu only system

    - by Jo Blick
    I installed Ubuntu 12.4 by wiping Windows and not creating another partition for Ubuntu in the hard drive. I realize that, this was a mistake. I have tried repartitioning using various tools, So that I can run Windows alongside Ubuntu, using my copy of Windows from another PC, but it has all become too complex. I love Ubuntu, wish I could keep it on its own, but I am tired of trying because, I need Windows for work related things. In particular, I have to instal my "Wacom intuos graphics tablet" with a serial port, but this appears too technical to me to achieve that in Ubuntu. I think I now have to first remove Ubuntu, reinstall Windows and then, reinstall Ubuntu by partitioning it properly, as I was advised to begin with. I would appreciate any answers very much, but I need answers in plain English unfortunately, because I do not understand much of the abbreviations used in Ubuntu forums. I should add that my treasured Ubuntu system is on an HPMini netbook, so it all has to be done with USB's. which does complicate things. Sorri :/

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  • AABB Sweeping, algorithm to solve "stacking box" problem

    - by Ivo Wetzel
    I'm currently working on a simple AABB collision system and after some fiddling the sweeping of a single box vs. another and the calculation of the response velocity needed to push them apart works flawlessly. Now on to the new problem, imagine I'm having a stack of boxes which are falling towards a ground box which isn't moving: Each of these boxes has a vertical velocity for the "gravity" value, let's say this velocity is 5. Now, the result is that they all fall into each other: The reason is obvious, since all the boxes have a downward velocity of 5, this results in no collisions when calculating the relative velocity between the boxes during sweeping. Note: The red ground box here is static (always 0 velocity, can utilize spatial partitioning ), and all dynamic static collisions are resolved first, thus the fact that the boxes stop correctly at this ground box. So, this seems to be simply an issue with the order the boxes are sweept against each other. I imagine that sorting the boxes based on their x and y velocities and then sweeping these groups correctly against each other may resolve this issues. So, I'm looking for algorithms / examples on how to implement such a system. The code can be found here: https://github.com/BonsaiDen/aabb The two files which are of interest are [box/Dynamic.lua][3] and [box/Manager.lua][4]. The project is using Love2D in case you want to run it.

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  • Collision detection on a 2D hexagonal grid

    - by SundayMonday
    I'm making a casual grid-based 2D iPhone game using Cocos2D. The grid is a "staggered" hex-like grid consisting of uniformly sized and spaced discs. It looks something like this. I've stored the grid in a 2D array. Also I have a concept of "surrounding" grid cells. Namely the six grid cells surrounding a particular cell (except those on the boundries which can have less than six). Anyways I'm testing some collision detection and it's not working out as well as I had planned. Here's how I currently do collision detection for a moving disc that's approaching the stationary group of discs: Calculate ij-coordinates of grid cell closest to moving cell using moving cell's xy-position Get list of surrounding grid cells using ij-coordinates Examine the surrounding cells. If they're all empty then no collision If we have some non-empty surrounding cells then compare the distance between the disc centers to some minimum distance required for a collision If there's a collision then place the moving disc in grid cell ij So this works but not too well. I've considered a potentially simpler brute force approach where I just compare the moving disc to all stationary discs at each step of the game loop. This is probably feasible in terms of performance since the stationary disc count is 300 max. If not then some space-partitioning data structure could be used however that feels too complex. What are some common approaches and best practices to collision detection in a game like this?

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  • Flexible virtualization infrastructure Design with libvirt

    - by Lessfoe
    I'm going to install a CentOS6 Server with Virtualization ( libvirtd ) capabilities on a DELL Server with Hardware RAID5 of around 6T of disk space ( It has 4x2T disks in a PERC700 RAID Controller ). I'm going then to install some guests which requires few resources except one that needs 500GB of disk space, 8/16GB of RAM and good performances. I was thinking about file images for guests storage but I'm not sure about the 500GB VM what needs good performances so that an LVM device could be better. So my question is what would be the best layout concerning: RAID setup ( RAID5, RAID1 + 1 disk for OS only. ) disk partitioning ( using the entire disk/ leave free space for future use and extending it with LVM ) guests storage management ( LVM devices or file images ( considering the 500GB VM that is performance demanding ) or mixed ) Where to put guests storage? /var/lib/libvirt/images or maybe in a custom dir separated from system /home/VMs Thanks in advance for any hint.

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  • SQLAuthority News – Scaling Up Your Data Warehouse with SQL Server 2008 R2

    - by pinaldave
    Data Warehouses are suppose to be containing huge amount of the data from the beginning. However, there are cases when too big is not enough. Every Data Warehouse Admin will agree that they have faced situation where they will need to scale up their data warehouse. Microsoft has released white paper discussing the same. Here is the abstract from the Microsoft Official site: SQL Server 2008 introduced many new functional and performance improvements for data warehousing, and SQL Server 2008 R2 includes all these and more. This paper discusses how to use SQL Server 2008 R2 to get great performance as your data warehouse scales up. We present lessons learned during extensive internal data warehouse testing on a 64-core HP Integrity Superdome during the development of the SQL Server 2008 release, and via production experience with large-scale SQL Server customers. Our testing indicates that many customers can expect their performance to nearly double on the same hardware they are currently using, merely by upgrading to SQL Server 2008 R2 from SQL Server 2005 or earlier, and compressing their fact tables. We cover techniques to improve manageability and performance at high-scale, encompassing data loading (extract, transform, load), query processing, partitioning, index maintenance, indexed view (aggregate) management, and backup and restore. Scaling Up Your Data Warehouse with SQL Server 2008 R2 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, T SQL, Technology

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  • Convert ddply {plyr} to Oracle R Enterprise, or use with Embedded R Execution

    - by Mark Hornick
    The plyr package contains a set of tools for partitioning a problem into smaller sub-problems that can be more easily processed. One function within {plyr} is ddply, which allows you to specify subsets of a data.frame and then apply a function to each subset. The result is gathered into a single data.frame. Such a capability is very convenient. The function ddply also has a parallel option that if TRUE, will apply the function in parallel, using the backend provided by foreach. This type of functionality is available through Oracle R Enterprise using the ore.groupApply function. In this blog post, we show a few examples from Sean Anderson's "A quick introduction to plyr" to illustrate the correpsonding functionality using ore.groupApply. To get started, we'll create a demo data set and load the plyr package. set.seed(1) d <- data.frame(year = rep(2000:2014, each = 3),         count = round(runif(45, 0, 20))) dim(d) library(plyr) This first example takes the data frame, partitions it by year, and calculates the coefficient of variation of the count, returning a data frame. # Example 1 res <- ddply(d, "year", function(x) {   mean.count <- mean(x$count)   sd.count <- sd(x$count)   cv <- sd.count/mean.count   data.frame(cv.count = cv)   }) To illustrate the equivalent functionality in Oracle R Enterprise, using embedded R execution, we use the ore.groupApply function on the same data, but pushed to the database, creating an ore.frame. The function ore.push creates a temporary table in the database, returning a proxy object, the ore.frame. D <- ore.push(d) res <- ore.groupApply (D, D$year, function(x) {   mean.count <- mean(x$count)   sd.count <- sd(x$count)   cv <- sd.count/mean.count   data.frame(year=x$year[1], cv.count = cv)   }, FUN.VALUE=data.frame(year=1, cv.count=1)) You'll notice the similarities in the first three arguments. With ore.groupApply, we augment the function to return the specific data.frame we want. We also specify the argument FUN.VALUE, which describes the resulting data.frame. From our previous blog posts, you may recall that by default, ore.groupApply returns an ore.list containing the results of each function invocation. To get a data.frame, we specify the structure of the result. The results in both cases are the same, however the ore.groupApply result is an ore.frame. In this case the data stays in the database until it's actually required. This can result in significant memory and time savings whe data is large. R> class(res) [1] "ore.frame" attr(,"package") [1] "OREbase" R> head(res)    year cv.count 1 2000 0.3984848 2 2001 0.6062178 3 2002 0.2309401 4 2003 0.5773503 5 2004 0.3069680 6 2005 0.3431743 To make the ore.groupApply execute in parallel, you can specify the argument parallel with either TRUE, to use default database parallelism, or to a specific number, which serves as a hint to the database as to how many parallel R engines should be used. The next ddply example uses the summarise function, which creates a new data.frame. In ore.groupApply, the year column is passed in with the data. Since no automatic creation of columns takes place, we explicitly set the year column in the data.frame result to the value of the first row, since all rows received by the function have the same year. # Example 2 ddply(d, "year", summarise, mean.count = mean(count)) res <- ore.groupApply (D, D$year, function(x) {   mean.count <- mean(x$count)   data.frame(year=x$year[1], mean.count = mean.count)   }, FUN.VALUE=data.frame(year=1, mean.count=1)) R> head(res)    year mean.count 1 2000 7.666667 2 2001 13.333333 3 2002 15.000000 4 2003 3.000000 5 2004 12.333333 6 2005 14.666667 Example 3 uses the transform function with ddply, which modifies the existing data.frame. With ore.groupApply, we again construct the data.frame explicilty, which is returned as an ore.frame. # Example 3 ddply(d, "year", transform, total.count = sum(count)) res <- ore.groupApply (D, D$year, function(x) {   total.count <- sum(x$count)   data.frame(year=x$year[1], count=x$count, total.count = total.count)   }, FUN.VALUE=data.frame(year=1, count=1, total.count=1)) > head(res)    year count total.count 1 2000 5 23 2 2000 7 23 3 2000 11 23 4 2001 18 40 5 2001 4 40 6 2001 18 40 In Example 4, the mutate function with ddply enables you to define new columns that build on columns just defined. Since the construction of the data.frame using ore.groupApply is explicit, you always have complete control over when and how to use columns. # Example 4 ddply(d, "year", mutate, mu = mean(count), sigma = sd(count),       cv = sigma/mu) res <- ore.groupApply (D, D$year, function(x) {   mu <- mean(x$count)   sigma <- sd(x$count)   cv <- sigma/mu   data.frame(year=x$year[1], count=x$count, mu=mu, sigma=sigma, cv=cv)   }, FUN.VALUE=data.frame(year=1, count=1, mu=1,sigma=1,cv=1)) R> head(res)    year count mu sigma cv 1 2000 5 7.666667 3.055050 0.3984848 2 2000 7 7.666667 3.055050 0.3984848 3 2000 11 7.666667 3.055050 0.3984848 4 2001 18 13.333333 8.082904 0.6062178 5 2001 4 13.333333 8.082904 0.6062178 6 2001 18 13.333333 8.082904 0.6062178 In Example 5, ddply is used to partition data on multiple columns before constructing the result. Realizing this with ore.groupApply involves creating an index column out of the concatenation of the columns used for partitioning. This example also allows us to illustrate using the ORE transparency layer to subset the data. # Example 5 baseball.dat <- subset(baseball, year > 2000) # data from the plyr package x <- ddply(baseball.dat, c("year", "team"), summarize,            homeruns = sum(hr)) We first push the data set to the database to get an ore.frame. We then add the composite column and perform the subset, using the transparency layer. Since the results from database execution are unordered, we will explicitly sort these results and view the first 6 rows. BB.DAT <- ore.push(baseball) BB.DAT$index <- with(BB.DAT, paste(year, team, sep="+")) BB.DAT2 <- subset(BB.DAT, year > 2000) X <- ore.groupApply (BB.DAT2, BB.DAT2$index, function(x) {   data.frame(year=x$year[1], team=x$team[1], homeruns=sum(x$hr))   }, FUN.VALUE=data.frame(year=1, team="A", homeruns=1), parallel=FALSE) res <- ore.sort(X, by=c("year","team")) R> head(res)    year team homeruns 1 2001 ANA 4 2 2001 ARI 155 3 2001 ATL 63 4 2001 BAL 58 5 2001 BOS 77 6 2001 CHA 63 Our next example is derived from the ggplot function documentation. This illustrates the use of ddply within using the ggplot2 package. We first create a data.frame with demo data and use ddply to create some statistics for each group (gp). We then use ggplot to produce the graph. We can take this same code, push the data.frame df to the database and invoke this on the database server. The graph will be returned to the client window, as depicted below. # Example 6 with ggplot2 library(ggplot2) df <- data.frame(gp = factor(rep(letters[1:3], each = 10)),                  y = rnorm(30)) # Compute sample mean and standard deviation in each group library(plyr) ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y)) # Set up a skeleton ggplot object and add layers: ggplot() +   geom_point(data = df, aes(x = gp, y = y)) +   geom_point(data = ds, aes(x = gp, y = mean),              colour = 'red', size = 3) +   geom_errorbar(data = ds, aes(x = gp, y = mean,                                ymin = mean - sd, ymax = mean + sd),              colour = 'red', width = 0.4) DF <- ore.push(df) ore.tableApply(DF, function(df) {   library(ggplot2)   library(plyr)   ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))   ggplot() +     geom_point(data = df, aes(x = gp, y = y)) +     geom_point(data = ds, aes(x = gp, y = mean),                colour = 'red', size = 3) +     geom_errorbar(data = ds, aes(x = gp, y = mean,                                  ymin = mean - sd, ymax = mean + sd),                   colour = 'red', width = 0.4) }) But let's take this one step further. Suppose we wanted to produce multiple graphs, partitioned on some index column. We replicate the data three times and add some noise to the y values, just to make the graphs a little different. We also create an index column to form our three partitions. Note that we've also specified that this should be executed in parallel, allowing Oracle Database to control and manage the server-side R engines. The result of ore.groupApply is an ore.list that contains the three graphs. Each graph can be viewed by printing the list element. df2 <- rbind(df,df,df) df2$y <- df2$y + rnorm(nrow(df2)) df2$index <- c(rep(1,300), rep(2,300), rep(3,300)) DF2 <- ore.push(df2) res <- ore.groupApply(DF2, DF2$index, function(df) {   df <- df[,1:2]   library(ggplot2)   library(plyr)   ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))   ggplot() +     geom_point(data = df, aes(x = gp, y = y)) +     geom_point(data = ds, aes(x = gp, y = mean),                colour = 'red', size = 3) +     geom_errorbar(data = ds, aes(x = gp, y = mean,                                  ymin = mean - sd, ymax = mean + sd),                   colour = 'red', width = 0.4)   }, parallel=TRUE) res[[1]] res[[2]] res[[3]] To recap, we've illustrated how various uses of ddply from the plyr package can be realized in ore.groupApply, which affords the user explicit control over the contents of the data.frame result in a straightforward manner. We've also highlighted how ddply can be used within an ore.groupApply call.

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  • Resize NTFS System Partition on Perc 6/i RAID

    - by Cipher42
    I've inherited a Dell server that is running out of space on C:. I'd like to quickly and painlessly resize the C drive with partitioning software. However, the RAID card is causing me some troubles. I've resized plenty of desktops in my time, but never a server with hardware RAID. Can anyone recommend software that is GUARANTEED to work with the Dell PERC 6/i? Hopefully someone has resized the system partition with this RAID card before! :) Of course, proper backups are available but I'd be more comfortable with a tried and true solution to save the headache of the restore.... Thanks in advance!

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  • Shrink Partition on Production Server

    - by Campo
    SO our production server was only setup with one large partition. I have setup a standby server and properly partitioned it. Now the boss wants the production environment's partition shrunk. It is an HP DL380 G5 We have 4 hot swap drives in a raid 5. How best should I go about doing this. Seems like a bad idea to me. Should I use windows or HP to do the partitioning? What should I be aware of in a production environment? The idea is to put the site (Inetpub) on a separate partition instead of the C: drive. How much downtime should I expect? Is this a terrible idea? Anything else I have missed?

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  • Ubuntu installation does not recognize previous partitions

    - by Hawkcannon
    I have been attempting to install Ubuntu (10.04, Lucid Lynx) on my computer. I wasn't ready to take the pure-Linux plunge yet, so I reserved a partition on which I would install Ubuntu. I ran the installer and answered the 'minor' questions (keyboard layout, time zone, etc.), but had trouble when I reached the partitioning. I have several partitions, but Ubuntu only saw one of them, which was not the ext3 partition that I had set up. I tried deleting the partition in hope that the installer would find and utilize the empty space, but it only saw the original partition. I do not have an external hard drive to use, and I cannot clear any existing partitions. Am I running the installer incorrectly, or is there a more serious problem?

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  • Ubuntu installation does not recognize previous partitions

    - by Hawkcannon
    I have been attempting to install Ubuntu (10.04, Lucid Lynx) on my computer. I wasn't ready to take the pure-Linux plunge yet, so I reserved a partition on which I would install Ubuntu. I ran the installer and answered the 'minor' questions (keyboard layout, time zone, etc.), but had trouble when I reached the partitioning. I have several partitions, but Ubuntu only saw one of them, which was not the ext3 partition that I had set up. I tried deleting the partition in hope that the installer would find and utilize the empty space, but it only saw the original partition. I do not have an external hard drive to use, and I cannot clear any existing partitions. Am I running the installer incorrectly, or is there a more serious problem?

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  • White Paper on Analysis Services Tabular Large-scale Solution #ssas #tabular

    - by Marco Russo (SQLBI)
    Since the first beta of Analysis Services 2012, I worked with many companies designing and implementing solutions based on Analysis Services Tabular. I am glad that Microsoft published a white paper about a case-study using one of these scenarios: An Analysis Services Case Study: Using Tabular Models in a Large-scale Commercial Solution. Alberto Ferrari is the author of the white paper and many people contributed to it. The final result is a very technical document based on a case study, which provides a level of detail that I don’t see often in other case studies (which are usually more marketing-oriented). This white paper has the following structure: Requirements (data model, capacity planning, client tool) Options considered (SQL Server Columnstore Indexes, SSAS Multidimensional, SSAS Tabular) Data Model optimizations (memory compression, query performance, scalability) Partitioning and Processing strategy for near real-time latency Hardware selection (NUMA analysis, Azure VM tests) Scalability tests (estimation of maximum users per node) If you are in charge of evaluating Tabular as analytical engine, or if you have to design your solution based on Tabular, this white paper is a must read. But if you just want to increase your knowledge of Analysis Services, you will find a lot of useful technical information. That said, my favorite quote of the document is the following one, funny but true: […] After several trials, the clear winner was a video gaming machine that one guy on the team used at home. That computer outperformed any available server, running twice as fast as the server-class machines we had in house. At that point, it was clear that the criteria for choosing the server would have to be expanded a bit, simply because it would have been impossible to convince the boss to build a cluster of gaming machines and trust it to serve our customers.  But, honestly, if a business has the flexibility to buy gaming machines (assuming the machines can handle capacity) – do this. Owen Graupman, inContact I want to write a longer discussion about how companies are adopting Tabular in scenarios where it is the hidden engine of a more complex solution (and not the classical “BI system”), because it is more frequent than you might expect (and has several advantages over many alternative approaches).

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