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  • Discoverer 11g 11.1.1.2 Certified with EBS 12 on Five New Platforms

    - by Steven Chan
    Oracle Fusion Middleware 11g Release 1 includes Oracle Discoverer.  Discoverer is an ad-hoc query, reporting, analysis, and Web-publishing tool that allows end-users to work directly with Oracle E-Business Suite OLTP data.We certified Discoverer 11gR1 11.1.1.2 with the E-Business Suite Release 11i and 12 on Linux earlier this year.  Our Applications Platforms Group has just released five additional platform certifications for Discoverer 11.1.1.2 for Oracle E-Business Suite Release 12 (12.0.x and 12.1.x).Certified EBS 12 PlatformsLinux x86-64 (Oracle Enterprise Linux 4, 5) Linux x86-64 (RHEL 4, 5) Linux x86-64 (SLES 10) Oracle Solaris on SPARC (64-bit) (Solaris 9, 10) HP-UX Itanium (11.23, 11.31) HP-UX PA-RISC (64-bit) (11.23, 11.31) IBM AIX on Power Systems (64-bit) (5.3, 6.1) Microsoft Windows Server (32-bit) (2003, 2008)

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  • Hekaton – SQL Server’s in-memory database engine

    - by Christian
    Microsoft have just gone public at the PASS Summit in Seattle about a new SQL Server engine that they’re working on which is optimized for high-memory servers – an in-memory OLTP database engine which is built-in to SQL Server rather than a separate entity.  This means that you can move just the performance critical parts of your database to Hekaton. The new engine really pushes the performance boundaries by eliminating as many instructions as possible: Main memory optimized tables which are decoupled from on-disk structures; Everything is lock and latch free; More work is pushed to compile time so your T-SQL code is compiled natively into low-level code. We’re already working with a customer on an early adoption program so expect to hear from us on what we learn about implementing it!   Christian Bolton - MCA, MCM, MVP Technical Director http://coeo.com - SQL Server Consulting & Managed Services

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  • MySQL Connect Only 10 Days Away - Focus on InnoDB Sessions

    - by Bertrand Matthelié
    Time flies and MySQL Connect is only 10 days away! You can check out the full program here as well as in the September edition of the MySQL newsletter. Mat recently blogged about the MySQL Cluster sessions you’ll have the opportunity to attend, and below are those focused on InnoDB. Remember you can plan your schedule with Schedule Builder. Saturday, 1.00 pm, Room Golden Gate 3: 10 Things You Should Know About InnoDB—Calvin Sun, Oracle InnoDB is the default storage engine for Oracle’s MySQL as of MySQL Release 5.5. It provides the standard ACID-compliant transactions, row-level locking, multiversion concurrency control, and referential integrity. InnoDB also implements several innovative technologies to improve its performance and reliability. This presentation gives a brief history of InnoDB; its main features; and some recent enhancements for better performance, scalability, and availability. Saturday, 5.30 pm, Room Golden Gate 4: Demystified MySQL/InnoDB Performance Tuning—Dimitri Kravtchuk, Oracle This session covers performance tuning with MySQL and the InnoDB storage engine for MySQL and explains the main improvements made in MySQL Release 5.5 and Release 5.6. Which setting for which workload? Which value will be better for my system? How can I avoid potential bottlenecks from the beginning? Do I need a purge thread? Is it true that InnoDB doesn't need thread concurrency anymore? These and many other questions are asked by DBAs and developers. Things are changing quickly and constantly, and there is no “silver bullet.” But understanding the configuration setting’s impact is already a huge step in performance improvement. Bring your ideas and problems to share them with others—the discussion is open, just moderated by a speaker. Sunday, 10.15 am, Room Golden Gate 4: Better Availability with InnoDB Online Operations—Calvin Sun, Oracle Many top Web properties rely on Oracle’s MySQL as a critical piece of infrastructure for serving millions of users. Database availability has become increasingly important. One way to enhance availability is to give users full access to the database during data definition language (DDL) operations. The online DDL operations in recent MySQL releases offer users the flexibility to perform schema changes while having full access to the database—that is, with minimal delay of operations on a table and without rebuilding the entire table. These enhancements provide better responsiveness and availability in busy production environments. This session covers these improvements in the InnoDB storage engine for MySQL for online DDL operations such as add index, drop foreign key, and rename column. Sunday, 11.45 am, Room Golden Gate 7: Developing High-Throughput Services with NoSQL APIs to InnoDB and MySQL Cluster—Andrew Morgan and John Duncan, Oracle Ever-increasing performance demands of Web-based services have generated significant interest in providing NoSQL access methods to MySQL (MySQL Cluster and the InnoDB storage engine of MySQL), enabling users to maintain all the advantages of their existing relational databases while providing blazing-fast performance for simple queries. Get the best of both worlds: persistence; consistency; rich SQL queries; high availability; scalability; and simple, flexible APIs and schemas for agile development. This session describes the memcached connectors and examines some use cases for how MySQL and memcached fit together in application architectures. It does the same for the newest MySQL Cluster native connector, an easy-to-use, fully asynchronous connector for Node.js. Sunday, 1.15 pm, Room Golden Gate 4: InnoDB Performance Tuning—Inaam Rana, Oracle The InnoDB storage engine has always been highly efficient and includes many unique architectural elements to ensure high performance and scalability. In MySQL 5.5 and MySQL 5.6, InnoDB includes many new features that take better advantage of recent advances in operating systems and hardware platforms than previous releases did. This session describes unique InnoDB architectural elements for performance, new features, and how to tune InnoDB to achieve better performance. Sunday, 4.15 pm, Room Golden Gate 3: InnoDB Compression for OLTP—Nizameddin Ordulu, Facebook and Inaam Rana, Oracle Data compression is an important capability of the InnoDB storage engine for Oracle’s MySQL. Compressed tables reduce the size of the database on disk, resulting in fewer reads and writes and better throughput by reducing the I/O workload. Facebook pushes the limit of InnoDB compression and has made several enhancements to InnoDB, making this technology ready for online transaction processing (OLTP). In this session, you will learn the fundamentals of InnoDB compression. You will also learn the enhancements the Facebook team has made to improve InnoDB compression, such as reducing compression failures, not logging compressed page images, and allowing changes of compression level. Not registered yet? You can still save US$ 300 over the on-site fee – Register Now!

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  • NoSQL Java API for MySQL Cluster: Questions & Answers

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

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  • Oracle European Launch Event: Oracle Database In-Memory

    - by A&C Redaktion
    am 17. Juni wird Andy Mendelsohn,  Oracle Executive Vice President Database Server Technologies, als Keynote Speaker den europäischen Launch von  Oracle Database In-Memory  in Frankfurt im Radisson Blue Hotel eröffnen. Dieses Thema ist für unsere Partner und Kunden von zentraler Bedeutung. Daher ist auch die Agenda dieses Launch Events einzigartig:Neben Vorträgen von Betakunden (Postbank und Cern), dem Analysten von IDC, der auch beim HQ Launch eine Woche zuvor in Redwood Shores mit Larry Ellison auf der Bühne stehen wird, und weiteren Oracle-Experten finden auch Live Demos statt. Eine Podiumsdiskussion rundet das Programm ab. Parallel zum Event werden Presse- und Analystengespräche geführt.Mit der neuen, bahnbrechenden Oracle Database In-Memory Option profitieren Kunden von einer erheblich beschleunigten Datenbankleistung für Analytics, Data Warehousing, Reporting und Online Transaction Processing (OLTP).Das ist so revolutionär, dass wir hiermit alle unsere Partner und ihre Endkunden zu diesem herausragenden Event herzlich einladen .Hier können Sie sich und Ihre Endkunden zu dieser exklusiven Live-Veranstaltung anmelden

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  • Oracle European Launch Event: Oracle Database In-Memory

    - by A&C Redaktion
    am 17. Juni wird Andy Mendelsohn,  Oracle Executive Vice President Database Server Technologies, als Keynote Speaker den europäischen Launch von  Oracle Database In-Memory  in Frankfurt im Radisson Blue Hotel eröffnen. Dieses Thema ist für unsere Partner und Kunden von zentraler Bedeutung. Daher ist auch die Agenda dieses Launch Events einzigartig:Neben Vorträgen von Betakunden (Postbank und Cern), dem Analysten von IDC, der auch beim HQ Launch eine Woche zuvor in Redwood Shores mit Larry Ellison auf der Bühne stehen wird, und weiteren Oracle-Experten finden auch Live Demos statt. Eine Podiumsdiskussion rundet das Programm ab. Parallel zum Event werden Presse- und Analystengespräche geführt.Mit der neuen, bahnbrechenden Oracle Database In-Memory Option profitieren Kunden von einer erheblich beschleunigten Datenbankleistung für Analytics, Data Warehousing, Reporting und Online Transaction Processing (OLTP).Das ist so revolutionär, dass wir hiermit alle unsere Partner und ihre Endkunden zu diesem herausragenden Event herzlich einladen .Hier können Sie sich und Ihre Endkunden zu dieser exklusiven Live-Veranstaltung anmelden

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  • Konszolidációs stratégiák az Oracle Database Machine-hez webcast, 2010. június 16.

    - by Fekete Zoltán
    Az utóbbi hetekben két tanfolyamon vettem részt, így a blogbejegyzések nem szaporodtak. Na most újult erovel. :) Regisztráció a webcast-ra. Holnap, azaz 2010. június 16-án 18h-kor (közép-európai ido) csatlakozhatunk a Consolidation Strategies for Oracle Database Machine, azaz a Konszolidációs stratégiák az Oracle Database Machine-hez címu webcasthoz, amit az Oracle BIWA és az Exadata SIG (Special Interest Group) rendez meg. Az eloadó: Dave Norris, aki az Oracle X Team tagja. Exadata SIG: http://OracleExadata.org BIWA SIG http://OracleBIWA.org The Oracle Database Machine V2 (the Exadata system) offers customers the opportunity to run combinations of consolidated workloads for both data warehousing (DW) and online transaction processing (OLTP) while maintaining superior performance. Regisztráció a webcast-ra.

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  • Oracle Database In-Memory: Launch in Frankfurt

    - by Carsten Czarski
    Diesmal gibt es etwas Altes ... und etwas Neues. Zuerst das Neue: Am 11. Juni wird Larry Ellison in Redwood Shores die neue, bahnbrechende Oracle Database In-Memory Funktionalität vorstellen. Mit dieser neuen Technologie profitieren Kunden von beschleunigter Datenbankleistung für Analytics, Data Warehousing, Reporting und Online Transaction Processing (OLTP). Nur 6 Tage später - am 17. Juni -  findet, in Frankfurt, der einzige europäische Launch-Event statt. Neben Fachvorträgen, Panelveranstaltung und Demos wird ein Vortrag von Andy Mendelsohn, Head of Database Product Development, vorgesehen. Melden Sie sich heute noch an. Und hier ist das Alte: Wer erinnert sich noch die die HTML DB ...? In den Archiven der APEX Community Seite haben wir ein Video gefunden, welches zeigt, wie man Seiten in der HTML DB für andere Entwickler sperren konnte. Das gibt es heute übrigens auch noch - es sieht nur etwas anders aus. Viel Spaß beim Ansehen.

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  • Maximize Performance and Availability with Oracle Data Integration

    - by Tanu Sood
    Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-fareast-font-family:Calibri; mso-bidi-font-family:"Times New Roman";} Alert: Oracle is hosting the 12c Launch Webcast for Oracle Data Integration and Oracle Golden Gate on Tuesday, November 12 (tomorrow) to discuss the new capabilities in detail and share customer perspectives. Hear directly from customer experts and executives from SolarWorld Industries America, British Telecom and Rittman Mead and get your questions answered live by product experts. Register for this complimentary webcast today and join in the discussion tomorrow. Author: Irem Radzik, Senior Principal Product Director, Oracle Organizations that want to use IT as a strategic point of differentiation prefer Oracle’s complete application offering to drive better business performance and optimize their IT investments. These enterprise applications are in the center of business operations and they contain critical data that needs to be accessed continuously, as well as analyzed and acted upon in a timely manner. These systems also need to operate with high-performance and availability, which means analytical functions should not degrade applications performance, and even system maintenance and upgrades should not interrupt availability. Oracle’s data integration products, Oracle Data Integrator, Oracle GoldenGate, and Oracle Enterprise Data Quality, provide the core foundation for bringing data from various business-critical systems to gain a broader, unified view. As a more advance offering to 3rd party products, Oracle’s data integration products facilitate real-time reporting for Oracle Applications without impacting application performance, and provide ability to upgrade and maintain the system without taking downtime. Oracle GoldenGate is certified for Oracle Applications, including E-Business Suite, Siebel CRM, PeopleSoft, and JD Edwards, for moving transactional data in real-time to a dedicated operational reporting environment. This solution allows the app users to offload the resource-heavy queries to the reporting instance(s), reducing CPU utilization, improving OLTP performance, and extending the lifetime of existing IT assets. In addition, having a dedicated reporting instance with up-to-the-second transactional data allows optimizing the reporting environment and even decreasing costs as GoldenGate can move only the required data from expensive mainframe environments to cost-efficient open system platforms.  With real-time data replication capabilities GoldenGate is also certified to enable application upgrades and database/hardware/OS migration without impacting business operations. GoldenGate is certified for Siebel CRM, Communications Billing and Revenue Management and JD Edwards for supporting zero downtime upgrades to the latest app version. GoldenGate synchronizes a parallel, upgraded system with the old version in real time, thus enables continuous operations during the process. Oracle GoldenGate is also certified for minimal downtime database migrations for Oracle E-Business Suite and other key applications. GoldenGate’s solution also minimizes the risk by offering a failback option after the switchover to the new environment. Furthermore, Oracle GoldenGate’s bidirectional active-active data replication is certified for Oracle ATG Web Commerce to enable geographically load balancing and high availability for ATG customers. For enabling better business insight, Oracle Data Integration products power Oracle BI Applications with high performance bulk and real-time data integration. Oracle Data Integrator (ODI) is embedded in Oracle BI Applications version 11.1.1.7.1 and helps to integrate data end-to-end across the full BI Applications architecture, supporting capabilities such as data-lineage, which helps business users identify report-to-source capabilities. ODI is integrated with Oracle GoldenGate and provides Oracle BI Applications customers the option to use real-time transactional data in analytics, and do so non-intrusively. By using Oracle GoldenGate with the latest release of Oracle BI Applications, organizations not only leverage fresh data in analytics, but also eliminate the need for an ETL batch window and minimize the impact on OLTP systems. You can learn more about Oracle Data Integration products latest 12c version in our upcoming launch webcast and access the app-specific free resources in the new Data Integration for Oracle Applications Resource Center.

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  • Adoption of Exadata - Gartner research note

    - by Javier Puerta
    Independent research note by Gartner acknowledges Oracle Exadata Database Machine has achieved significant early adoption and acceptance of its database appliance value proposition. Analyst Merv Adrian looks at some of the main issues that IT professionals have solved as they assess or deploy the Oracle Exadata solution, including: OLTP and DSS workload support workload consolidation increasing performance and scalability demands data compression improvements  Gartner reports clients using Oracle Exadata experienced the following: report significant performance improvements substantial amounts of cache memory which greatly improves processing speed Oracle Advanced Compression providing 2-4X data compression delivering significant reductions in storage requirements and driving shorter times for backup operations Tables compressed with Oracle Advanced Compression automatically recompress as data is added/updated. One client specifically reported consolidating more than 400 applications onto the Oracle Exadata platform Read the full Gartner note

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  • Join Companies in Web and Telecoms by Adopting MySQL Cluster

    - by Antoinette O'Sullivan
    Join Web and Telecom companies who have adopted MySQL Cluster to facilitate application in the following areas: Web: High volume OLTP eCommerce User profile management Session management and caching Content management On-line gaming Telecoms: Subscriber databases (HLR/HSS) Service deliver platforms VAS: VoIP, IPTV and VoD Mobile content delivery Mobile payments LTE access To come up to speed on MySQL Cluster, take the 3-day MySQL Cluster training course. Events already on the schedule include:  Location  Date  Delivery Language  Berlin, Germany  16 December 2013  German  Munich, Germany  2 December 2013  German  Budapest, Hungary  4 December 2013  Hungarian  Madrid, Spain  9 December 2013  Spanish  Jakarta Barat, Indonesia  27 January 2014  English  Singapore  20 December 2013  English  Bangkok, Thailand  28 January 2014  English  San Francisco, CA, United States  28 May 2014  English  New York, NY, United States  17 December 2013  English For more information about this course or to request an additional event, go to the MySQL Curriculum Page (http://education.oracle.com/mysql).

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  • Live Webcast: Private Cloud Database Consolidation with Oracle Exadata

    - by kimberly.billings
    Thursday, January 20th, 2011 at 9:00 am PT In this webcast, you'll learn how Oracle Exadata, Oracle Database 11g, and Oracle Real Application Clusters enable you to consolidate multiple applications on clustered server and storage pools to achieve extreme performance and lower your IT costs. You'll also learn how to maximize the efficiencies of private clouds, including: • Multitenancy • Rapid provisioning • Pay-for-use infrastructure Join us for this live Webcast and discover how Oracle Exadata delivers key cloud capabilities, providing elastic database services that can be quickly provisioned on demand. Register today! To learn more about how customers are consolidating on private clouds with Exadata, watch this video about how Commonwealth Bank of Australia consolidated multiple database services, including OLTP applications such as PeopleSoft Financials, onto an Exadata platform for improved performance and resilience and faster time-to-market.

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  • SSIS (missing) Pre-Build and Post-Build

    - by Raj More
    For the warehouse work under progress, we have a single solution with multiple projects in it OLTP Database Project Warehouse Database Project SSIS ETL project After the SSIS project is built, I want to move the binaries (XML, really) from the Bin folder to "C:\AutomatedTasks\ETL.Warehouse\" and "C:\AutomatedTasks\ETL" I cannot find the Post-Build events to do that for the SSIS project. Where are they? If they aren't available, how do I achieve this?

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  • What free space thresholds/limits are advisable for 640 GB and 2 TB hard disk drives with ZEVO ZFS on OS X?

    - by Graham Perrin
    Assuming that free space advice for ZEVO will not differ from advice for other modern implementations of ZFS … Question Please, what percentages or amounts of free space are advisable for hard disk drives of the following sizes? 640 GB 2 TB Thoughts A standard answer for modern implementations of ZFS might be "no more than 96 percent full". However if apply that to (say) a single-disk 640 GB dataset where some of the files most commonly used (by VirtualBox) are larger than 15 GB each, then I guess that blocks for those files will become sub optimally spread across the platters with around 26 GB free. I read that in most cases, fragmentation and defragmentation should not be a concern with ZFS. Sill, I like the mental picture of most fragments of a large .vdi in reasonably close proximity to each other. (Do features of ZFS make that wish for proximity too old-fashioned?) Side note: there might arise the question of how to optimise performance after a threshold is 'broken'. If it arises, I'll keep it separate. Background On a 640 GB StoreJet Transcend (product ID 0x2329) in the past I probably went beyond an advisable threshold. Currently the largest file is around 17 GB –  – and I doubt that any .vdi or other file on this disk will grow beyond 40 GB. (Ignore the purple masses, those are bundles of 8 MB band files.) Without HFS Plus: the thresholds of twenty, ten and five percent that I associate with Mobile Time Machine file system need not apply. I currently use ZEVO Community Edition 1.1.1 with Mountain Lion, OS X 10.8.2, but I'd like answers to be not too version-specific. References, chronological order ZFS Block Allocation (Jeff Bonwick's Blog) (2006-11-04) Space Maps (Jeff Bonwick's Blog) (2007-09-13) Doubling Exchange Performance (Bizarre ! Vous avez dit Bizarre ?) (2010-03-11) … So to solve this problem, what went in 2010/Q1 software release is multifold. The most important thing is: we increased the threshold at which we switched from 'first fit' (go fast) to 'best fit' (pack tight) from 70% full to 96% full. With TB drives, each slab is at least 5GB and 4% is still 200MB plenty of space and no need to do anything radical before that. This gave us the biggest bang. Second, instead of trying to reuse the same primary slabs until it failed an allocation we decided to stop giving the primary slab this preferential threatment as soon as the biggest allocation that could be satisfied by a slab was down to 128K (metaslab_df_alloc_threshold). At that point we were ready to switch to another slab that had more free space. We also decided to reduce the SMO bonus. Before, a slab that was 50% empty was preferred over slabs that had never been used. In order to foster more write aggregation, we reduced the threshold to 33% empty. This means that a random write workload now spread to more slabs where each one will have larger amount of free space leading to more write aggregation. Finally we also saw that slab loading was contributing to lower performance and implemented a slab prefetch mechanism to reduce down time associated with that operation. The conjunction of all these changes lead to 50% improved OLTP and 70% reduced variability from run to run … OLTP Improvements in Sun Storage 7000 2010.Q1 (Performance Profiles) (2010-03-11) Alasdair on Everything » ZFS runs really slowly when free disk usage goes above 80% (2010-07-18) where commentary includes: … OpenSolaris has changed this in onnv revision 11146 … [CFT] Improved ZFS metaslab code (faster write speed) (2010-08-22)

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  • BI Applications overview

    - by sv744
    Welcome to Oracle BI applications blog! This blog will talk about various features, general roadmap, description of functionality and implementation steps related to Oracle BI applications. In the first post we start with an overview of the BI apps and will delve deeper into some of the topics below in the upcoming weeks and months. If there are other topics you would like us to talk about, pl feel free to provide feedback on that. The Oracle BI applications are a set of pre-built applications that enable pervasive BI by providing role-based insight for each functional area, including sales, service, marketing, contact center, finance, supplier/supply chain, HR/workforce, and executive management. For example, Sales Analytics includes role-based applications for sales executives, sales management, as well as front-line sales reps, each of whom have different needs. The applications integrate and transform data from a range of enterprise sources—including Siebel, Oracle, PeopleSoft, SAP, and others—into actionable intelligence for each business function and user role. This blog  starts with the key benefits and characteristics of Oracle BI applications. In a series of subsequent blogs, each of these points will be explained in detail. Why BI apps? Demonstrate the value of BI to a business user, show reports / dashboards / model that can answer their business questions as part of the sales cycle. Demonstrate technical feasibility of BI project and significantly lower risk and improve success Build Vs Buy benefit Don’t have to start with a blank sheet of paper. Help consolidate disparate systems Data integration in M&A situations Insulate BI consumers from changes in the OLTP Present OLTP data and highlight issues of poor data / missing data – and improve data quality and accuracy Prebuilt Integrations BI apps support prebuilt integrations against leading ERP sources: Fusion Applications, E- Business Suite, Peoplesoft, JD Edwards, Siebel, SAP Co-developed with inputs from functional experts in BI and Applications teams. Out of the box dimensional model to source model mappings Multi source and Multi Instance support Rich Data Model    BI apps have a very rich dimensionsal data model built over 10 years that incorporates best practises from BI modeling perspective as well as reflect the source system complexities  Thanks for reading a long post, and be on the lookout for future posts.  We will look forward to your valuable feedback on these topics as well as suggestions on what other topics would you like us to cover. I Conformed dimensional model across all business subject areas allows cross functional reporting, e.g. customer / supplier 360 Over 360 fact tables across 7 product areas CRM – 145, SCM – 47, Financials – 28, Procurement – 20, HCM – 27, Projects – 18, Campus Solutions – 21, PLM - 56 Supported by 300 physical dimensions Support for extensive calendars; Gregorian, enterprise and ledger based Conformed data model and metrics for real time vs warehouse based reporting  Multi-tenant enabled Extensive BI related transformations BI apps ETL and data integration support various transformations required for dimensional models and reporting requirements. All these have been distilled into common patterns and abstracted logic which can be readily reused across different modules Slowly Changing Dimension support Hierarchy flattening support Row / Column Hybrid Hierarchy Flattening As Is vs. As Was hierarchy support Currency Conversion :-  Support for 3 corporate, CRM, ledger and transaction currencies UOM conversion Internationalization / Localization Dynamic Data translations Code standardization (Domains) Historical Snapshots Cycle and process lifecycle computations Balance Facts Equalization of GL accounting chartfields/segments Standardized values for categorizing GL accounts Reconciliation between GL and subledgers to track accounted/transferred/posted transactions to GL Materialization of data only available through costly and complex APIs e.g. Fusion Payroll, EBS / Fusion Accruals Complex event Interpretation of source data – E.g. o    What constitutes a transfer o    Deriving supervisors via position hierarchy o    Deriving primary assignment in PSFT o    Categorizing and transposition to measures of Payroll Balances to specific metrics to support side by side comparison of measures of for example Fixed Salary, Variable Salary, Tax, Bonus, Overtime Payments. o    Counting of Events – E.g. converting events to fact counters so that for example the number of hires can easily be added up and compared alongside the total transfers and terminations. Multi pass processing of multiple sources e.g. headcount, salary, promotion, performance to allow side to side comparison. Adding value to data to aid analysis through banding, additional domain classifications and groupings to allow higher level analytical reporting and data discovery Calculation of complex measures examples: o    COGs, DSO, DPO, Inventory turns  etc o    Transfers within a Hierarchy or out of / into a hierarchy relative to view point in hierarchy. Configurability and Extensibility support  BI apps offer support for extensibility for various entities as automated extensibility or part of extension methodology Key Flex fields and Descriptive Flex support  Extensible attribute support (JDE)  Conformed Domains ETL Architecture BI apps offer a modular adapter architecture which allows support of multiple product lines into a single conformed model Multi Source Multi Technology Orchestration – creates load plan taking into account task dependencies and customers deployment to generate a plan based on a customers of multiple complex etl tasks Plan optimization allowing parallel ETL tasks Oracle: Bit map indexes and partition management High availability support    Follow the sun support. TCO BI apps support several utilities / capabilities that help with overall total cost of ownership and ensure a rapid implementation Improved cost of ownership – lower cost to deploy On-going support for new versions of the source application Task based setups flows Data Lineage Functional setup performed in Web UI by Functional person Configuration Test to Production support Security BI apps support both data and object security enabling implementations to quickly configure the application as per the reporting security needs Fine grain object security at report / dashboard and presentation catalog level Data Security integration with source systems  Extensible to support external data security rules Extensive Set of KPIs Over 7000 base and derived metrics across all modules Time series calculations (YoY, % growth etc) Common Currency and UOM reporting Cross subject area KPIs (analyzing HR vs GL data, drill from GL to AP/AR, etc) Prebuilt reports and dashboards 3000+ prebuilt reports supporting a large number of industries Hundreds of role based dashboards Dynamic currency conversion at dashboard level Highly tuned Performance The BI apps have been tuned over the years for both a very performant ETL and dashboard performance. The applications use best practises and advanced database features to enable the best possible performance. Optimized data model for BI and analytic queries Prebuilt aggregates& the ability for customers to create their own aggregates easily on warehouse facts allows for scalable end user performance Incremental extracts and loads Incremental Aggregate build Automatic table index and statistics management Parallel ETL loads Source system deletes handling Low latency extract with Golden Gate Micro ETL support Bitmap Indexes Partitioning support Modularized deployment, start small and add other subject areas seamlessly Source Specfic Staging and Real Time Schema Support for source specific operational reporting schema for EBS, PSFT, Siebel and JDE Application Integrations The BI apps also allow for integration with source systems as well as other applications that provide value add through BI and enable BI consumption during operational decision making Embedded dashboards for Fusion, EBS and Siebel applications Action Link support Marketing Segmentation Sales Predictor Dashboard Territory Management External Integrations The BI apps data integration choices include support for loading extenral data External data enrichment choices : UNSPSC, Item class etc. Extensible Spend Classification Broad Deployment Choices Exalytics support Databases :  Oracle, Exadata, Teradata, DB2, MSSQL ETL tool of choice : ODI (coming), Informatica Extensible and Customizable Extensible architecture and Methodology to add custom and external content Upgradable across releases

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  • MySQL is running VERY slow on CentOS 6x (not 5x)

    - by user1032531
    I have two servers: a VPS and a laptop. I recently re-built both of them, and MySQL is running about 20 times slower on the laptop. Both servers used to run CentOS 5.8 and I think MySQL 5.1, and the laptop used to do great so I do not think it is the hardware. For the VPS, my provider installed CentOS 6.4, and then I installed MySQL 5.1.69 using yum with the CentOS repo. For the laptop, I installed CentOS 6.4 basic server and then installed MySQL 5.1.69 using yum with the CentOS repo. my.cnf for both servers are identical, and I have shown below. For both servers, I've also included below the output from SHOW VARIABLES; as well as output from sysbench, file system information, and cpu information. I have tried adding skip-name-resolve, but it didn't help. The matrix below shows the SHOW VARIABLES output from both servers which is different. Again, MySQL was installed the same way, so I do not know why it is different, but it is and I think this might be why the laptop is executing MySQL so slowly. Why is the laptop running MySQL slowly, and how do I fix it? Differences between SHOW VARIABLES on both servers +---------------------------+-----------------------+-------------------------+ | Variable | Value-VPS | Value-Laptop | +---------------------------+-----------------------+-------------------------+ | hostname | vps.site1.com | laptop.site2.com | | max_binlog_cache_size | 4294963200 | 18446744073709500000 | | max_seeks_for_key | 4294967295 | 18446744073709500000 | | max_write_lock_count | 4294967295 | 18446744073709500000 | | myisam_max_sort_file_size | 2146435072 | 9223372036853720000 | | myisam_mmap_size | 4294967295 | 18446744073709500000 | | plugin_dir | /usr/lib/mysql/plugin | /usr/lib64/mysql/plugin | | pseudo_thread_id | 7568 | 2 | | system_time_zone | EST | PDT | | thread_stack | 196608 | 262144 | | timestamp | 1372252112 | 1372252046 | | version_compile_machine | i386 | x86_64 | +---------------------------+-----------------------+-------------------------+ my.cnf for both servers [root@server1 ~]# cat /etc/my.cnf [mysqld] datadir=/var/lib/mysql socket=/var/lib/mysql/mysql.sock user=mysql # Disabling symbolic-links is recommended to prevent assorted security risks symbolic-links=0 [mysqld_safe] log-error=/var/log/mysqld.log pid-file=/var/run/mysqld/mysqld.pid innodb_strict_mode=on sql_mode=TRADITIONAL # sql_mode=STRICT_TRANS_TABLES,NO_ZERO_DATE,NO_ZERO_IN_DATE character-set-server=utf8 collation-server=utf8_general_ci log=/var/log/mysqld_all.log [root@server1 ~]# VPS SHOW VARIABLES Info Same as Laptop shown below but changes per above matrix (removed to allow me to be under the 30000 characters as required by ServerFault) Laptop SHOW VARIABLES Info auto_increment_increment 1 auto_increment_offset 1 autocommit ON automatic_sp_privileges ON back_log 50 basedir /usr/ big_tables OFF binlog_cache_size 32768 binlog_direct_non_transactional_updates OFF binlog_format STATEMENT bulk_insert_buffer_size 8388608 character_set_client utf8 character_set_connection utf8 character_set_database latin1 character_set_filesystem binary character_set_results utf8 character_set_server latin1 character_set_system utf8 character_sets_dir /usr/share/mysql/charsets/ collation_connection utf8_general_ci collation_database latin1_swedish_ci collation_server latin1_swedish_ci completion_type 0 concurrent_insert 1 connect_timeout 10 datadir /var/lib/mysql/ date_format %Y-%m-%d datetime_format %Y-%m-%d %H:%i:%s default_week_format 0 delay_key_write ON delayed_insert_limit 100 delayed_insert_timeout 300 delayed_queue_size 1000 div_precision_increment 4 engine_condition_pushdown ON error_count 0 event_scheduler OFF expire_logs_days 0 flush OFF flush_time 0 foreign_key_checks ON ft_boolean_syntax + -><()~*:""&| ft_max_word_len 84 ft_min_word_len 4 ft_query_expansion_limit 20 ft_stopword_file (built-in) general_log OFF general_log_file /var/run/mysqld/mysqld.log group_concat_max_len 1024 have_community_features YES have_compress YES have_crypt YES have_csv YES have_dynamic_loading YES have_geometry YES have_innodb YES have_ndbcluster NO have_openssl DISABLED have_partitioning YES have_query_cache YES have_rtree_keys YES have_ssl DISABLED have_symlink DISABLED hostname server1.site2.com identity 0 ignore_builtin_innodb OFF init_connect init_file init_slave innodb_adaptive_hash_index ON innodb_additional_mem_pool_size 1048576 innodb_autoextend_increment 8 innodb_autoinc_lock_mode 1 innodb_buffer_pool_size 8388608 innodb_checksums ON innodb_commit_concurrency 0 innodb_concurrency_tickets 500 innodb_data_file_path ibdata1:10M:autoextend innodb_data_home_dir innodb_doublewrite ON innodb_fast_shutdown 1 innodb_file_io_threads 4 innodb_file_per_table OFF innodb_flush_log_at_trx_commit 1 innodb_flush_method innodb_force_recovery 0 innodb_lock_wait_timeout 50 innodb_locks_unsafe_for_binlog OFF innodb_log_buffer_size 1048576 innodb_log_file_size 5242880 innodb_log_files_in_group 2 innodb_log_group_home_dir ./ innodb_max_dirty_pages_pct 90 innodb_max_purge_lag 0 innodb_mirrored_log_groups 1 innodb_open_files 300 innodb_rollback_on_timeout OFF innodb_stats_method nulls_equal innodb_stats_on_metadata ON innodb_support_xa ON innodb_sync_spin_loops 20 innodb_table_locks ON innodb_thread_concurrency 8 innodb_thread_sleep_delay 10000 innodb_use_legacy_cardinality_algorithm ON insert_id 0 interactive_timeout 28800 join_buffer_size 131072 keep_files_on_create OFF key_buffer_size 8384512 key_cache_age_threshold 300 key_cache_block_size 1024 key_cache_division_limit 100 language /usr/share/mysql/english/ large_files_support ON large_page_size 0 large_pages OFF last_insert_id 0 lc_time_names en_US license GPL local_infile ON locked_in_memory OFF log OFF log_bin OFF log_bin_trust_function_creators OFF log_bin_trust_routine_creators OFF log_error /var/log/mysqld.log log_output FILE log_queries_not_using_indexes OFF log_slave_updates OFF log_slow_queries OFF log_warnings 1 long_query_time 10.000000 low_priority_updates OFF lower_case_file_system OFF lower_case_table_names 0 max_allowed_packet 1048576 max_binlog_cache_size 18446744073709547520 max_binlog_size 1073741824 max_connect_errors 10 max_connections 151 max_delayed_threads 20 max_error_count 64 max_heap_table_size 16777216 max_insert_delayed_threads 20 max_join_size 18446744073709551615 max_length_for_sort_data 1024 max_long_data_size 1048576 max_prepared_stmt_count 16382 max_relay_log_size 0 max_seeks_for_key 18446744073709551615 max_sort_length 1024 max_sp_recursion_depth 0 max_tmp_tables 32 max_user_connections 0 max_write_lock_count 18446744073709551615 min_examined_row_limit 0 multi_range_count 256 myisam_data_pointer_size 6 myisam_max_sort_file_size 9223372036853727232 myisam_mmap_size 18446744073709551615 myisam_recover_options OFF myisam_repair_threads 1 myisam_sort_buffer_size 8388608 myisam_stats_method nulls_unequal myisam_use_mmap OFF net_buffer_length 16384 net_read_timeout 30 net_retry_count 10 net_write_timeout 60 new OFF old OFF old_alter_table OFF old_passwords OFF open_files_limit 1024 optimizer_prune_level 1 optimizer_search_depth 62 optimizer_switch index_merge=on,index_merge_union=on,index_merge_sort_union=on,index_merge_intersection=on pid_file /var/run/mysqld/mysqld.pid plugin_dir /usr/lib64/mysql/plugin port 3306 preload_buffer_size 32768 profiling OFF profiling_history_size 15 protocol_version 10 pseudo_thread_id 3 query_alloc_block_size 8192 query_cache_limit 1048576 query_cache_min_res_unit 4096 query_cache_size 0 query_cache_type ON query_cache_wlock_invalidate OFF query_prealloc_size 8192 rand_seed1 rand_seed2 range_alloc_block_size 4096 read_buffer_size 131072 read_only OFF read_rnd_buffer_size 262144 relay_log relay_log_index relay_log_info_file relay-log.info relay_log_purge ON relay_log_space_limit 0 report_host report_password report_port 3306 report_user rpl_recovery_rank 0 secure_auth OFF secure_file_priv server_id 0 skip_external_locking ON skip_name_resolve OFF skip_networking OFF skip_show_database OFF slave_compressed_protocol OFF slave_exec_mode STRICT slave_load_tmpdir /tmp slave_max_allowed_packet 1073741824 slave_net_timeout 3600 slave_skip_errors OFF slave_transaction_retries 10 slow_launch_time 2 slow_query_log OFF slow_query_log_file /var/run/mysqld/mysqld-slow.log socket /var/lib/mysql/mysql.sock sort_buffer_size 2097144 sql_auto_is_null ON sql_big_selects ON sql_big_tables OFF sql_buffer_result OFF sql_log_bin ON sql_log_off OFF sql_log_update ON sql_low_priority_updates OFF sql_max_join_size 18446744073709551615 sql_mode sql_notes ON sql_quote_show_create ON sql_safe_updates OFF sql_select_limit 18446744073709551615 sql_slave_skip_counter sql_warnings OFF ssl_ca ssl_capath ssl_cert ssl_cipher ssl_key storage_engine MyISAM sync_binlog 0 sync_frm ON system_time_zone PDT table_definition_cache 256 table_lock_wait_timeout 50 table_open_cache 64 table_type MyISAM thread_cache_size 0 thread_handling one-thread-per-connection thread_stack 262144 time_format %H:%i:%s time_zone SYSTEM timed_mutexes OFF timestamp 1372254399 tmp_table_size 16777216 tmpdir /tmp transaction_alloc_block_size 8192 transaction_prealloc_size 4096 tx_isolation REPEATABLE-READ unique_checks ON updatable_views_with_limit YES version 5.1.69 version_comment Source distribution version_compile_machine x86_64 version_compile_os redhat-linux-gnu wait_timeout 28800 warning_count 0 VPS Sysbench Info Deleted to stay under 30000 characters. Laptop Sysbench Info [root@server1 ~]# cat sysbench.txt sysbench 0.4.12: multi-threaded system evaluation benchmark Running the test with following options: Number of threads: 8 Doing OLTP test. Running mixed OLTP test Doing read-only test Using Special distribution (12 iterations, 1 pct of values are returned in 75 pct cases) Using "BEGIN" for starting transactions Using auto_inc on the id column Threads started! Time limit exceeded, exiting... (last message repeated 7 times) Done. OLTP test statistics: queries performed: read: 634718 write: 0 other: 90674 total: 725392 transactions: 45337 (755.56 per sec.) deadlocks: 0 (0.00 per sec.) read/write requests: 634718 (10577.78 per sec.) other operations: 90674 (1511.11 per sec.) Test execution summary: total time: 60.0048s total number of events: 45337 total time taken by event execution: 479.4912 per-request statistics: min: 2.04ms avg: 10.58ms max: 85.56ms approx. 95 percentile: 19.70ms Threads fairness: events (avg/stddev): 5667.1250/42.18 execution time (avg/stddev): 59.9364/0.00 [root@server1 ~]# VPS File Info [root@vps ~]# df -T Filesystem Type 1K-blocks Used Available Use% Mounted on /dev/simfs simfs 20971520 16187440 4784080 78% / none tmpfs 6224432 4 6224428 1% /dev none tmpfs 6224432 0 6224432 0% /dev/shm [root@vps ~]# Laptop File Info [root@server1 ~]# df -T Filesystem Type 1K-blocks Used Available Use% Mounted on /dev/mapper/vg_server1-lv_root ext4 72383800 4243964 64462860 7% / tmpfs tmpfs 956352 0 956352 0% /dev/shm /dev/sdb1 ext4 495844 60948 409296 13% /boot [root@server1 ~]# VPS CPU Info Removed to stay under the 30000 character limit required by ServerFault Laptop CPU Info [root@server1 ~]# cat /proc/cpuinfo processor : 0 vendor_id : GenuineIntel cpu family : 6 model : 15 model name : Intel(R) Core(TM)2 Duo CPU T7100 @ 1.80GHz stepping : 13 cpu MHz : 800.000 cache size : 2048 KB physical id : 0 siblings : 2 core id : 0 cpu cores : 2 apicid : 0 initial apicid : 0 fpu : yes fpu_exception : yes cpuid level : 10 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx lm constant_tsc arch_perfmon pebs bts rep_good aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm lahf_lm ida dts tpr_shadow vnmi flexpriority bogomips : 3591.39 clflush size : 64 cache_alignment : 64 address sizes : 36 bits physical, 48 bits virtual power management: processor : 1 vendor_id : GenuineIntel cpu family : 6 model : 15 model name : Intel(R) Core(TM)2 Duo CPU T7100 @ 1.80GHz stepping : 13 cpu MHz : 800.000 cache size : 2048 KB physical id : 0 siblings : 2 core id : 1 cpu cores : 2 apicid : 1 initial apicid : 1 fpu : yes fpu_exception : yes cpuid level : 10 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx lm constant_tsc arch_perfmon pebs bts rep_good aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm lahf_lm ida dts tpr_shadow vnmi flexpriority bogomips : 3591.39 clflush size : 64 cache_alignment : 64 address sizes : 36 bits physical, 48 bits virtual power management: [root@server1 ~]# EDIT New Info requested by shakalandy [root@localhost ~]# cat /proc/meminfo MemTotal: 2044804 kB MemFree: 761464 kB Buffers: 68868 kB Cached: 369708 kB SwapCached: 0 kB Active: 881080 kB Inactive: 246016 kB Active(anon): 688312 kB Inactive(anon): 4416 kB Active(file): 192768 kB Inactive(file): 241600 kB Unevictable: 0 kB Mlocked: 0 kB SwapTotal: 4095992 kB SwapFree: 4095992 kB Dirty: 0 kB Writeback: 0 kB AnonPages: 688428 kB Mapped: 65156 kB Shmem: 4216 kB Slab: 92428 kB SReclaimable: 31260 kB SUnreclaim: 61168 kB KernelStack: 2392 kB PageTables: 28356 kB NFS_Unstable: 0 kB Bounce: 0 kB WritebackTmp: 0 kB CommitLimit: 5118392 kB Committed_AS: 1530212 kB VmallocTotal: 34359738367 kB VmallocUsed: 343604 kB VmallocChunk: 34359372920 kB HardwareCorrupted: 0 kB AnonHugePages: 520192 kB HugePages_Total: 0 HugePages_Free: 0 HugePages_Rsvd: 0 HugePages_Surp: 0 Hugepagesize: 2048 kB DirectMap4k: 8556 kB DirectMap2M: 2078720 kB [root@localhost ~]# ps aux | grep mysql root 2227 0.0 0.0 108332 1504 ? S 07:36 0:00 /bin/sh /usr/bin/mysqld_safe --datadir=/var/lib/mysql --pid-file=/var/lib/mysql/localhost.badobe.com.pid mysql 2319 0.1 24.5 1470068 501360 ? Sl 07:36 0:57 /usr/sbin/mysqld --basedir=/usr --datadir=/var/lib/mysql --plugin-dir=/usr/lib64/mysql/plugin --user=mysql --log-error=/var/lib/mysql/localhost.badobe.com.err --pid-file=/var/lib/mysql/localhost.badobe.com.pid root 3579 0.0 0.1 201840 3028 pts/0 S+ 07:40 0:00 mysql -u root -p root 13887 0.0 0.1 201840 3036 pts/3 S+ 18:08 0:00 mysql -uroot -px xxxxxxxxxx root 14449 0.0 0.0 103248 840 pts/2 S+ 18:16 0:00 grep mysql [root@localhost ~]# ps aux | grep mysql root 2227 0.0 0.0 108332 1504 ? S 07:36 0:00 /bin/sh /usr/bin/mysqld_safe --datadir=/var/lib/mysql --pid-file=/var/lib/mysql/localhost.badobe.com.pid mysql 2319 0.1 24.5 1470068 501356 ? Sl 07:36 0:57 /usr/sbin/mysqld --basedir=/usr --datadir=/var/lib/mysql --plugin-dir=/usr/lib64/mysql/plugin --user=mysql --log-error=/var/lib/mysql/localhost.badobe.com.err --pid-file=/var/lib/mysql/localhost.badobe.com.pid root 3579 0.0 0.1 201840 3028 pts/0 S+ 07:40 0:00 mysql -u root -p root 13887 0.0 0.1 201840 3048 pts/3 S+ 18:08 0:00 mysql -uroot -px xxxxxxxxxx root 14470 0.0 0.0 103248 840 pts/2 S+ 18:16 0:00 grep mysql [root@localhost ~]# vmstat 1 procs -----------memory---------- ---swap-- -----io---- --system-- -----cpu----- r b swpd free buff cache si so bi bo in cs us sy id wa st 0 0 0 742172 76376 371064 0 0 6 6 78 202 2 1 97 1 0 0 0 0 742164 76380 371060 0 0 0 16 191 467 2 1 93 5 0 0 0 0 742164 76380 371064 0 0 0 0 148 388 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 159 418 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 145 380 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 166 429 2 1 97 0 0 1 0 0 742164 76380 371064 0 0 0 0 148 373 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 149 382 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 168 408 2 0 97 0 0 0 0 0 742164 76380 371064 0 0 0 0 165 394 2 1 98 0 0 0 0 0 742164 76380 371064 0 0 0 0 159 354 2 1 98 0 0 0 0 0 742164 76388 371060 0 0 0 16 180 447 2 0 91 6 0 0 0 0 742164 76388 371064 0 0 0 0 143 344 2 1 98 0 0 0 1 0 742784 76416 370044 0 0 28 580 360 678 3 1 74 23 0 1 0 0 744768 76496 367772 0 0 40 1036 437 865 3 1 53 43 0 0 1 0 747248 76596 365412 0 0 48 1224 561 923 3 2 53 43 0 0 1 0 749232 76696 363092 0 0 32 1132 512 883 3 2 52 44 0 0 1 0 751340 76772 361020 0 0 32 1008 472 872 2 1 52 45 0 0 1 0 753448 76840 358540 0 0 36 1088 512 860 2 1 51 46 0 0 1 0 755060 76936 357636 0 0 28 1012 481 922 2 2 52 45 0 0 1 0 755060 77064 357988 0 0 12 896 444 902 2 1 53 45 0 0 1 0 754688 77148 358448 0 0 16 1096 506 1007 1 1 56 42 0 0 2 0 754192 77268 358932 0 0 12 1060 481 957 1 2 53 44 0 0 1 0 753696 77380 359392 0 0 12 1052 512 1025 2 1 55 42 0 0 1 0 751028 77480 359828 0 0 8 984 423 909 2 2 52 45 0 0 1 0 750524 77620 360200 0 0 8 788 367 869 1 2 54 44 0 0 1 0 749904 77700 360664 0 0 8 928 439 924 2 2 55 43 0 0 1 0 749408 77796 361084 0 0 12 976 468 967 1 1 56 43 0 0 1 0 748788 77896 361464 0 0 12 992 453 944 1 2 54 43 0 1 1 0 748416 77992 361996 0 0 12 784 392 868 2 1 52 46 0 0 1 0 747920 78092 362336 0 0 4 896 382 874 1 1 52 46 0 0 1 0 745252 78172 362780 0 0 12 1040 444 923 1 1 56 42 0 0 1 0 744764 78288 363220 0 0 8 1024 448 934 2 1 55 43 0 0 1 0 744144 78408 363668 0 0 8 1000 461 982 2 1 53 44 0 0 1 0 743648 78488 364148 0 0 8 872 443 888 2 1 54 43 0 0 1 0 743152 78548 364468 0 0 16 1020 511 995 2 1 55 43 0 0 1 0 742656 78632 365024 0 0 12 928 431 913 1 2 53 44 0 0 1 0 742160 78728 365468 0 0 12 996 470 955 2 2 54 44 0 1 1 0 739492 78840 365896 0 0 8 988 447 939 1 2 52 46 0 0 1 0 738872 78996 366352 0 0 12 972 442 928 1 1 55 44 0 1 1 0 738244 79148 366812 0 0 8 948 549 1126 2 2 54 43 0 0 1 0 737624 79312 367188 0 0 12 996 456 953 2 2 54 43 0 0 1 0 736880 79456 367660 0 0 12 960 444 918 1 1 53 46 0 0 1 0 736260 79584 368124 0 0 8 884 414 921 1 1 54 44 0 0 1 0 735648 79716 368488 0 0 12 976 450 955 2 1 56 41 0 0 1 0 733104 79840 368988 0 0 12 932 453 918 1 2 55 43 0 0 1 0 732608 79996 369356 0 0 16 916 444 889 1 2 54 43 0 1 1 0 731476 80128 369800 0 0 16 852 514 978 2 2 54 43 0 0 1 0 731244 80252 370200 0 0 8 904 398 870 2 1 55 43 0 1 1 0 730624 80384 370612 0 0 12 1032 447 977 1 2 57 41 0 0 1 0 730004 80524 371096 0 0 12 984 469 941 2 2 52 45 0 0 1 0 729508 80636 371544 0 0 12 928 438 922 2 1 52 46 0 0 1 0 728888 80756 371948 0 0 16 972 439 943 2 1 55 43 0 0 1 0 726468 80900 372272 0 0 8 960 545 1024 2 1 54 43 0 1 1 0 726344 81024 372272 0 0 8 464 490 1057 1 2 53 44 0 0 1 0 726096 81148 372276 0 0 4 328 441 1063 2 1 53 45 0 1 1 0 726096 81256 372292 0 0 0 296 387 975 1 1 53 45 0 0 1 0 725848 81380 372284 0 0 4 332 425 1034 2 1 54 44 0 1 1 0 725848 81496 372300 0 0 4 308 386 992 2 1 54 43 0 0 1 0 725600 81616 372296 0 0 4 328 404 1060 1 1 54 44 0 procs -----------memory---------- ---swap-- -----io---- --system-- -----cpu----- r b swpd free buff cache si so bi bo in cs us sy id wa st 0 1 0 725600 81732 372296 0 0 4 328 439 1011 1 1 53 44 0 0 1 0 725476 81848 372308 0 0 0 316 441 1023 2 2 52 46 0 1 1 0 725352 81972 372300 0 0 4 344 451 1021 1 1 55 43 0 2 1 0 725228 82088 372320 0 0 0 328 427 1058 1 1 54 44 0 1 1 0 724980 82220 372300 0 0 4 336 419 999 2 1 54 44 0 1 1 0 724980 82328 372320 0 0 4 320 430 1019 1 1 54 44 0 1 1 0 724732 82436 372328 0 0 0 388 363 942 2 1 54 44 0 1 1 0 724608 82560 372312 0 0 4 308 419 993 1 2 54 44 0 1 0 0 724360 82684 372320 0 0 0 304 421 1028 2 1 55 42 0 1 0 0 724360 82684 372388 0 0 0 0 158 416 2 1 98 0 0 1 1 0 724236 82720 372360 0 0 0 6464 243 855 3 2 84 12 0 1 0 0 724112 82748 372360 0 0 0 5356 266 895 3 1 84 12 0 2 1 0 724112 82764 372380 0 0 0 3052 221 511 2 2 93 4 0 1 0 0 724112 82796 372372 0 0 0 4548 325 1067 2 2 81 16 0 1 0 0 724112 82816 372368 0 0 0 3240 259 829 3 1 90 6 0 1 0 0 724112 82836 372380 0 0 0 3260 309 822 3 2 88 8 0 1 1 0 724112 82876 372364 0 0 0 4680 326 978 3 1 77 19 0 1 0 0 724112 82884 372380 0 0 0 512 207 508 2 1 95 2 0 1 0 0 724112 82884 372388 0 0 0 0 138 361 2 1 98 0 0 1 0 0 724112 82884 372388 0 0 0 0 158 397 2 1 98 0 0 1 0 0 724112 82884 372388 0 0 0 0 146 395 2 1 98 0 0 2 0 0 724112 82884 372388 0 0 0 0 160 395 2 1 98 0 0 1 0 0 724112 82884 372388 0 0 0 0 163 382 1 1 98 0 0 1 0 0 724112 82884 372388 0 0 0 0 176 422 2 1 98 0 0 1 0 0 724112 82884 372388 0 0 0 0 134 351 2 1 98 0 0 0 0 0 724112 82884 372388 0 0 0 0 190 429 2 1 97 0 0 0 0 0 724104 82884 372392 0 0 0 0 139 358 2 1 98 0 0 0 0 0 724848 82884 372392 0 0 0 4 211 432 2 1 97 0 0 1 0 0 724980 82884 372392 0 0 0 0 166 370 2 1 98 0 0 0 0 0 724980 82884 372392 0 0 0 0 164 397 2 1 98 0 0 ^C [root@localhost ~]# Database size mysql> SELECT table_schema "Data Base Name", sum( data_length + index_length ) / 1024 / 1024 "Data Base Size in MB", sum( data_free )/ 1024 / 1024 "Free Space in MB" FROM information_schema.TABLES GROUP BY table_schema; +--------------------+----------------------+------------------+ | Data Base Name | Data Base Size in MB | Free Space in MB | +--------------------+----------------------+------------------+ | bidjunction | 4.68750000 | 0.00000000 | | information_schema | 0.00976563 | 0.00000000 | | mysql | 0.63899899 | 0.00105286 | +--------------------+----------------------+------------------+ 3 rows in set (0.01 sec) mysql> Before Query mysql> SHOW SESSION STATUS like '%Tmp%'; +-------------------------+-------+ | Variable_name | Value | +-------------------------+-------+ | Created_tmp_disk_tables | 0 | | Created_tmp_files | 6 | | Created_tmp_tables | 0 | +-------------------------+-------+ 3 rows in set (0.00 sec) mysql> After Query mysql> SHOW SESSION STATUS like '%Tmp%'; +-------------------------+-------+ | Variable_name | Value | +-------------------------+-------+ | Created_tmp_disk_tables | 0 | | Created_tmp_files | 6 | | Created_tmp_tables | 2 | +-------------------------+-------+ 3 rows in set (0.00 sec) mysql>

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  • Xeon X5550 vs Six-core Opteron for database server

    - by gregmac
    I'm specing out a database server, and the price works out to be reasonably close between an Intel X5550 Quad-core and an AMD 2425HE (2.1Ghz) Six-core Opteron. I've been looking for some comparisons between the two, but the only thing useful I've found is an AnandTech Review of the 2435 which compares it to Intel Xeons, but concludes they both have their place. My load is MS SQL Server 2008, with an OLTP database that has about an equal amount of reading/writing (and it's a reasonably heavy load). So my question is, what is going to work better in this situation, assuming the drives are the same: Xeon X5550, with 16GB 1333Mhz RAM (Dell R510) or an Opteron 2425HE (2.1Ghz) or 2439SE (2.8Ghz) with 16GB of 800Mhz RAM? (Dell 2970) Note: the 2439 adds $500, but the overall pricing works out that it's not that much more than the R510. Using the 2425HE, the Dell 2970 server is slightly less than the similarly-equipped R510). If it adds a decent amount of performance, it's worthwhile to go faster. (single CPU, in both cases).

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  • SQL Server: One 12-drive RAID-10 array or 2 arrays of 8-drives and 4-drives

    - by ben
    Setting up a box for SQL Server 2008, which would give the best performance (heavy OLTP)? The more drives in a RAID-10 array the better performance, but will losing 4 drives to dedicate them to the transaction logs give us more performance. 12-drives in RAID-10 plus one hot spare. OR 8-drives in RAID-10 for database and 4-drives RAID-10 for transaction logs plus 2 hot spares (one for each array). We have 14-drive slots to work with and it's an older PowerVault that doesn't support global hot spares.

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  • Best SQL Server Configuration with this hardware.

    - by DavidStein
    I just received my new SQL Server from Dell. The server will be serve approximately 15 OLTP databases which average 10GB in size. Here are the basic specs: Dell PowerEdge R510 with up to 12 Hot Swap HDDs,LED Intel Xeon E5649 2.53GHz, 12M Cache, 5.86 GT/s QPI, 6 core (Quantity of 2) 48GB Memory (6x8GB), 1333MHz Dual Ranked RDIMMs for 2 Processors, Optimized PERC H700 Integrated RAID Controller, 1GB NV Cache 300GB 15K RPM SA SCSI 6Gbps 3.5in Hotplug Hard Drive (Quantity of 4) 600GB 15K RPM SA SCSI 6Gbps 3.5in Hotplug Hard Drive (Quantity of 6) My first thought was to use 3 arrays. OS - Raid 1 - (2)300GB T-Log - Raid 1 (2)300GB DB - Raid 5 (5) 600GB Backup - (1) 600GB - non-raided. However, I could also do the following after purchasing one more drive for backup. OS and T-Log - Raid 10 - (4)300GB DB - Raid 10 (6)600GB The hard drive space is not an issue as the databases are not that large. I'm just trying to optimize the speed of the applications using these databases. So, what would you guys recommend?

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  • Building a Data Warehouse

    - by Paul
    I've seen tutorials articles and posts on how to build datawarehouses with star and snowflakes schemas, denormalization of OLTP databases fact and dimension tables and so on. Also seen comments like: Star schemas are for datamarts, at best. There is absolutely no way a true enterprise data warehouse could be represented in a star schema, or snowflake either. I want to create a database that will server for reporting services and maybe (if that isn't enough) install analisys services and extract reports and data from cubes. My question was : Is it really necesarry to redesign my current database and follow the star/snowflake schemas with fact and dimension tables ? Thank you

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  • is there a GOTCHA - DBCC CHECKDB ('DBNAME', NOINDEX)?

    - by Deb Anderson
    I am turning on DBCC CHECKDB in our OLTP environment (SQL 2005,2008). System overhead is a very visible thing on our serversso I want them to be as efficient as it makes sense for them to be. HENCE - I want to turn on the NOINDEX option, an option I've never used before. My thoughts are these: if there is a problem with an index that is detected outside the integrity check, that I can just rebuild the index. Also the duration of the integrity checks will be drastically reduced, and the nastier corruption will be detected. What is the flaw in my plan? Thanks, Deb

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  • Partner Webcast – More out of Database Appliance with DB Options - 13 September 2012

    - by Thanos
    The Oracle Database Appliance is a new way to take advantage of the world's most popular database—Oracle Database 11g —in a single, easy-to-deploy and manage system. It's a complete package of software, server, storage, and networking that's engineered for simplicity; saving time and money by simplifying deployment, maintenance, and support of database workloads. But that is not all, with the support for all Oracle Database Options, Oracle Database Appliance can be the ideal solution for many use cases. Feature Benefit Simplifies deployment, maintenance, and support of high-availability database workloads Saves significant time and effort throughout the database administration lifecycle An engineered system of software, server, storage, and networking High availability for a wide range of custom and packaged OLTP and data warehousing application databases Simple one-button Installation, full-stack integrated patching and diagnostics Reduces planned and unplanned downtime by automatically monitoring and logging service requests with Oracle Support Built using the world’s #1 database Protects databases from server and storage failures with Oracle Real Application Clusters and Automatic Storage Management Unique Pay-As-You-Grow software licensing Reduces cost with flexibility to adjust your software spend as your business grows without the need for any hardware upgrades Discover the Oracle Database Appliance Value Proposition and learn how to position and combine it with database options to capture new business and easily roll out solutions safely and with maximum cost efficiency. This webcast is repeated once again for your benefit. Agenda: Oracle Database& Engineered Systems Innovation. What’s the Oracle Database Appliance ? Oracle Database Appliance Value Proposition. Oracle Database Appliance with Database Options Oracle Database Appliance Partners Business Delivery FormatThis FREE online LIVE eSeminar will be delivered over the Web. Registrations received less than 24hours prior to start time may not receive confirmation to attend. Duration: 1 hour Register Now! Oracle Database Appliance is available for purchase at the Oracle Store under Engineered Systems. For any questions please contact us at partner.imc-AT-beehiveonline.oracle-DOT-com Visit regularly our ISV Migration Center blog Or Follow us @oracleimc to learn more on Oracle Technologies as well as upcoming partner webcasts and events.

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  • SQLAuthority News – Happy Deepavali and Happy News Year

    - by pinaldave
    Diwali or Deepavali is popularly known as the festival of lights. It literally means “array of light” or “row of lamps“. Today we build a small clay maps and fill it with oil and light it up. The significance of lighting the lamp is the triumph of good over evil. I work every single day in a year but today I am spending my time with family and little one. I make sure that my daughter is aware of our culture and she learns to celebrate the festival with the same passion and values which I have. Every year on this day, I do not write a long blog post but rather write a small post with various SQL Tips and Tricks. After reading them you should quickly get back to your friends and family – it is the most important festival day. Here are a few tips and tricks: Take regular full backup of your database Avoid cursors if they can be replaced by set based process Keep your index maintenance script handy and execute them at intervals Consider Solid State Drive (SDD) for crucial database and tempdb placement Update statistics for OLTP transactions at intervals I guess that’s it for today. If you still have more time to learn. Here are few things you should consider. Get FREE Books by Sign up for tomorrow’s webcast by Rick Morelan Watch SQL in Sixty Seconds Series – FREE SQL Learning Read my earlier 2300+ articles Well, I am sure that will keep you busy for the rest of the day! Happy Diwali to All of You! Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority News, T SQL, Technology

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  • Flash Technology Can Revolutionize your IT Infrastructure

    - by kimberly.billings
    A recent article in the Data Center Journal written by Mark Teter outlines how flash is becoming a disruptive technology in the data center and how it will soon replace HDDs in the storage hierarchy. As Teter explains, the drivers behind this trend are lower cost/performance and power savings; flash is over 100x faster for reads than the fastest HDD, and while it is expensive, it can produce dramatic reductions in the cost of performance as measured in Input/Outputs per second (IOPS). What's more, flash consumes 1/5th the power of HDD, so it's faster AND greener. Teter writes, "when appropriately used, flash turns the current economics of IT performance on its head. That's disruptive." Exadata Smart Flash Cache in the Sun Oracle Database Machine makes intelligent use of flash storage to deliver extreme performance for OLTP and mixed workloads. It intelligently caches data from the Oracle Database replacing slow mechanical I/O operations to disk with very rapid flash memory operations. Exadata Smart Flash Cache is the fundamental technology of the Sun Oracle Database Machine that enables the processing of up to 1 million random I/O operations per second (IOPS), and the scanning of data within Exadata storage at up to 50 GB/second. Are you incorporating flash into your storage strategy? Let us know! Read more: "Flash technology can revolutionize your IT infrastructure", The Data Center Journal, March 30, 2010. Exadata Smart Flash Cache and the Sun Oracle Database Machine white paper var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-13185312-1"); pageTracker._trackPageview(); } catch(err) {}

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  • SQL SERVER – Microsoft SQL Server 2014 CTP1 Product Guide

    - by Pinal Dave
    Today in User Group meeting there were lots of questions related to SQL Server 2014. There are plenty of people still using SQL Server 2005 but everybody is curious about what is coming in SQL Server 2014.  Microsoft has officially released SQL Server 2014 CTP1 Product Guide. You can easily download the product guide and explore various learning around SQL Server 2014 as well explore the new concepts introduced in this latest version. This SQL Server 2014 CTP1 Product Guide contains few interesting White Papers, a Datasheet and Presentation Deck. Here is the list of the white papers: Mission-Critical Performance and Scale with SQL Server and Windows Server Faster Insights from Any Data Platform for Hybrid Cloud SQL Server In-Memory OLTP Internals Overview for CTP1 SQL Server 2014 CTP1 Frequently Asked Questions for TechEd 2013 North America Here is the list of slide decks: SQL Server 2014 Level 100 Deck SQL Server 2014 Mission Critical Performance LEvel 300 Deck SQL Server 2014 Faster Insights from Any Data Level Level 300 Deck SQL Server 2014 Platform for Hybrid Cloud Level 100 Deck I have earlier downloaded the Product Guide and I have yet not completed reading everything SQL Server 2014 has to offer. If you want to read what are the features which I am going to use in SQL Server 2014, you can read over here. Download Microsoft SQL Server 2014 CTP1 Product Guide 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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