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• Sun Fire X4800 M2 Delivers World Record TPC-C for x86 Systems

  - by Brian

  Oracle's Sun Fire X4800 M2 server equipped with eight 2.4 GHz Intel Xeon Processor E7-8870 chips obtained a result of 5,055,888 tpmC on the TPC-C benchmark. This result is a world record for x86 servers. Oracle demonstrated this world record database performance running Oracle Database 11g Release 2 Enterprise Edition with Partitioning. The Sun Fire X4800 M2 server delivered a new x86 TPC-C world record of 5,055,888 tpmC with a price performance of $0.89/tpmC using Oracle Database 11g Release 2. This configuration is available 06/26/12. The Sun Fire X4800 M2 server delivers 3.0x times better performance than the next 8-processor result, an IBM System p 570 equipped with POWER6 processors. The Sun Fire X4800 M2 server has 3.1x times better price/performance than the 8-processor 4.7GHz POWER6 IBM System p 570. The Sun Fire X4800 M2 server has 1.6x times better performance than the 4-processor IBM x3850 X5 system equipped with Intel Xeon processors. This is the first TPC-C result on any system using eight Intel Xeon Processor E7-8800 Series chips. The Sun Fire X4800 M2 server is the first x86 system to get over 5 million tpmC. The Oracle solution utilized Oracle Linux operating system and Oracle Database 11g Enterprise Edition Release 2 with Partitioning to produce the x86 world record TPC-C benchmark performance. Performance Landscape Select TPC-C results (sorted by tpmC, bigger is better) System p/c/t tpmC Price/tpmC Avail Database MemorySize Sun Fire X4800 M2 8/80/160 5,055,888 0.89 USD 6/26/2012 Oracle 11g R2 4 TB IBM x3850 X5 4/40/80 3,014,684 0.59 USD 7/11/2011 DB2 ESE 9.7 3 TB IBM x3850 X5 4/32/64 2,308,099 0.60 USD 5/20/2011 DB2 ESE 9.7 1.5 TB IBM System p 570 8/16/32 1,616,162 3.54 USD 11/21/2007 DB2 9.0 2 TB p/c/t - processors, cores, threads Avail - availability date Oracle and IBM TPC-C Response times System tpmC Response Time (sec) New Order 90th% Response Time (sec) New Order Average Sun Fire X4800 M2 5,055,888 0.210 0.166 IBM x3850 X5 3,014,684 0.500 0.272 Ratios - Oracle Better 1.6x 1.4x 1.3x Oracle uses average new order response time for comparison between Oracle and IBM. Graphs of Oracle's and IBM's response times for New-Order can be found in the full disclosure reports on TPC's website TPC-C Official Result Page. Configuration Summary and Results Hardware Configuration: Server Sun Fire X4800 M2 server 8 x 2.4 GHz Intel Xeon Processor E7-8870 4 TB memory 8 x 300 GB 10K RPM SAS internal disks 8 x Dual port 8 Gbs FC HBA Data Storage 10 x Sun Fire X4270 M2 servers configured as COMSTAR heads, each with 1 x 3.06 GHz Intel Xeon X5675 processor 8 GB memory 10 x 2 TB 7.2K RPM 3.5" SAS disks 2 x Sun Storage F5100 Flash Array storage (1.92 TB each) 1 x Brocade 5300 switches Redo Storage 2 x Sun Fire X4270 M2 servers configured as COMSTAR heads, each with 1 x 3.06 GHz Intel Xeon X5675 processor 8 GB memory 11 x 2 TB 7.2K RPM 3.5" SAS disks Clients 8 x Sun Fire X4170 M2 servers, each with 2 x 3.06 GHz Intel Xeon X5675 processors 48 GB memory 2 x 300 GB 10K RPM SAS disks Software Configuration: Oracle Linux (Sun Fire 4800 M2) Oracle Solaris 11 Express (COMSTAR for Sun Fire X4270 M2) Oracle Solaris 10 9/10 (Sun Fire X4170 M2) Oracle Database 11g Release 2 Enterprise Edition with Partitioning Oracle iPlanet Web Server 7.0 U5 Tuxedo CFS-R Tier 1 Results: System: Sun Fire X4800 M2 tpmC: 5,055,888 Price/tpmC: 0.89 USD Available: 6/26/2012 Database: Oracle Database 11g Cluster: no New Order Average Response: 0.166 seconds Benchmark Description TPC-C is an OLTP system benchmark. It simulates a complete environment where a population of terminal operators executes transactions against a database. The benchmark is centered around the principal activities (transactions) of an order-entry environment. These transactions include entering and delivering orders, recording payments, checking the status of orders, and monitoring the level of stock at the warehouses. Key Points and Best Practices Oracle Database 11g Release 2 Enterprise Edition with Partitioning scales easily to this high level of performance. COMSTAR (Common Multiprotocol SCSI Target) is the software framework that enables an Oracle Solaris host to serve as a SCSI Target platform. COMSTAR uses a modular approach to break the huge task of handling all the different pieces in a SCSI target subsystem into independent functional modules which are glued together by the SCSI Target Mode Framework (STMF). The modules implementing functionality at SCSI level (disk, tape, medium changer etc.) are not required to know about the underlying transport. And the modules implementing the transport protocol (FC, iSCSI, etc.) are not aware of the SCSI-level functionality of the packets they are transporting. The framework hides the details of allocation providing execution context and cleanup of SCSI commands and associated resources and simplifies the task of writing the SCSI or transport modules. Oracle iPlanet Web Server middleware is used for the client tier of the benchmark. Each web server instance supports more than a quarter-million users while satisfying the response time requirement from the TPC-C benchmark. See Also Oracle Press Release -- Sun Fire X4800 M2 TPC-C Executive Summary tpc.org Complete Sun Fire X4800 M2 TPC-C Full Disclosure Report tpc.org Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page Sun Fire X4800 M2 Server oracle.com OTN Oracle Linux oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage F5100 Flash Array oracle.com OTN Disclosure Statement TPC Benchmark C, tpmC, and TPC-C are trademarks of the Transaction Processing Performance Council (TPC). Sun Fire X4800 M2 (8/80/160) with Oracle Database 11g Release 2 Enterprise Edition with Partitioning, 5,055,888 tpmC, $0.89 USD/tpmC, available 6/26/2012. IBM x3850 X5 (4/40/80) with DB2 ESE 9.7, 3,014,684 tpmC, $0.59 USD/tpmC, available 7/11/2011. IBM x3850 X5 (4/32/64) with DB2 ESE 9.7, 2,308,099 tpmC, $0.60 USD/tpmC, available 5/20/2011. IBM System p 570 (8/16/32) with DB2 9.0, 1,616,162 tpmC, $3.54 USD/tpmC, available 11/21/2007. Source: http://www.tpc.org/tpcc, results as of 7/15/2011.

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• Big Data – Role of Cloud Computing in Big Data – Day 11 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the NewSQL. In this article we will understand the role of Cloud in Big Data Story What is Cloud? Cloud is the biggest buzzword around from last few years. Everyone knows about the Cloud and it is extremely well defined online. In this article we will discuss cloud in the context of the Big Data. Cloud computing is a method of providing a shared computing resources to the application which requires dynamic resources. These resources include applications, computing, storage, networking, development and various deployment platforms. The fundamentals of the cloud computing are that it shares pretty much share all the resources and deliver to end users as a service.  Examples of the Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud. We will discuss this later in this blog post. There are two different Cloud Deployment Models: 1) The Public Cloud and 2) The Private Cloud Public Cloud Public Cloud is the cloud infrastructure build by commercial providers (Amazon, Rackspace etc.) creates a highly scalable data center that hides the complex infrastructure from the consumer and provides various services. Private Cloud Private Cloud is the cloud infrastructure build by a single organization where they are managing highly scalable data center internally. Here is the quick comparison between Public Cloud and Private Cloud from Wikipedia:   Public Cloud Private Cloud Initial cost Typically zero Typically high Running cost Unpredictable Unpredictable Customization Impossible Possible Privacy No (Host has access to the data Yes Single sign-on Impossible Possible Scaling up Easy while within defined limits Laborious but no limits Hybrid Cloud Hybrid Cloud is the cloud infrastructure build with the composition of two or more clouds like public and private cloud. Hybrid cloud gives best of the both the world as it combines multiple cloud deployment models together. Cloud and Big Data – Common Characteristics There are many characteristics of the Cloud Architecture and Cloud Computing which are also essentially important for Big Data as well. They highly overlap and at many places it just makes sense to use the power of both the architecture and build a highly scalable framework. Here is the list of all the characteristics of cloud computing important in Big Data Scalability Elasticity Ad-hoc Resource Pooling Low Cost to Setup Infastructure Pay on Use or Pay as you Go Highly Available Leading Big Data Cloud Providers There are many players in Big Data Cloud but we will list a few of the known players in this list. Amazon Amazon is arguably the most popular Infrastructure as a Service (IaaS) provider. The history of how Amazon started in this business is very interesting. They started out with a massive infrastructure to support their own business. Gradually they figured out that their own resources are underutilized most of the time. They decided to get the maximum out of the resources they have and hence  they launched their Amazon Elastic Compute Cloud (Amazon EC2) service in 2006. Their products have evolved a lot recently and now it is one of their primary business besides their retail selling. Amazon also offers Big Data services understand Amazon Web Services. Here is the list of the included services: Amazon Elastic MapReduce – It processes very high volumes of data Amazon DynammoDB – It is fully managed NoSQL (Not Only SQL) database service Amazon Simple Storage Services (S3) – A web-scale service designed to store and accommodate any amount of data Amazon High Performance Computing – It provides low-tenancy tuned high performance computing cluster Amazon RedShift – It is petabyte scale data warehousing service Google Though Google is known for Search Engine, we all know that it is much more than that. Google Compute Engine – It offers secure, flexible computing from energy efficient data centers Google Big Query – It allows SQL-like queries to run against large datasets Google Prediction API – It is a cloud based machine learning tool Other Players Besides Amazon and Google we also have other players in the Big Data market as well. Microsoft is also attempting Big Data with the Cloud with Microsoft Azure. Additionally Rackspace and NASA together have initiated OpenStack. The goal of Openstack is to provide a massively scaled, multitenant cloud that can run on any hardware. Thing to Watch The cloud based solutions provides a great integration with the Big Data’s story as well it is very economical to implement as well. However, there are few things one should be very careful when deploying Big Data on cloud solutions. Here is a list of a few things to watch: Data Integrity Initial Cost Recurring Cost Performance Data Access Security Location Compliance Every company have different approaches to Big Data and have different rules and regulations. Based on various factors, one can implement their own custom Big Data solution on a cloud. Tomorrow In tomorrow’s blog post we will discuss about various Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• World Record Batch Rate on Oracle JD Edwards Consolidated Workload with SPARC T4-2

  - by Brian

  Oracle produced a World Record batch throughput for single system results on Oracle's JD Edwards EnterpriseOne Day-in-the-Life benchmark using Oracle's SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2. The workload includes both online and batch workload. The SPARC T4-2 server delivered a result of 8,000 online users while concurrently executing a mix of JD Edwards EnterpriseOne Long and Short batch processes at 95.5 UBEs/min (Universal Batch Engines per minute). In order to obtain this record benchmark result, the JD Edwards EnterpriseOne, Oracle WebLogic and Oracle Database 11g Release 2 servers were executed each in separate Oracle Solaris Containers which enabled optimal system resources distribution and performance together with scalable and manageable virtualization. One SPARC T4-2 server running Oracle Solaris Containers and consolidating JD Edwards EnterpriseOne, Oracle WebLogic servers and the Oracle Database 11g Release 2 utilized only 55% of the available CPU power. The Oracle DB server in a Shared Server configuration allows for optimized CPU resource utilization and significant memory savings on the SPARC T4-2 server without sacrificing performance. This configuration with SPARC T4-2 server has achieved 33% more Users/core, 47% more UBEs/min and 78% more Users/rack unit than the IBM Power 770 server. The SPARC T4-2 server with 2 processors ran the JD Edwards "Day-in-the-Life" benchmark and supported 8,000 concurrent online users while concurrently executing mixed batch workloads at 95.5 UBEs per minute. The IBM Power 770 server with twice as many processors supported only 12,000 concurrent online users while concurrently executing mixed batch workloads at only 65 UBEs per minute. This benchmark demonstrates more than 2x cost savings by consolidating the complete solution in a single SPARC T4-2 server compared to earlier published results of 10,000 users and 67 UBEs per minute on two SPARC T4-2 and SPARC T4-1. The Oracle DB server used mirrored (RAID 1) volumes for the database providing high availability for the data without impacting performance. Performance Landscape JD Edwards EnterpriseOne Day in the Life (DIL) Benchmark Consolidated Online with Batch Workload System Rack Units BatchRate(UBEs/m) Online Users Users /Units Users /Core Version SPARC T4-2 (2 x SPARC T4, 2.85 GHz) 3 95.5 8,000 2,667 500 9.0.2 IBM Power 770 (4 x POWER7, 3.3 GHz, 32 cores) 8 65 12,000 1,500 375 9.0.2 Batch Rate (UBEs/m) — Batch transaction rate in UBEs per minute Configuration Summary Hardware Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 4 x 300 GB 10K RPM SAS internal disk 2 x 300 GB internal SSD 2 x Sun Storage F5100 Flash Arrays Software Configuration: Oracle Solaris 10 Oracle Solaris Containers JD Edwards EnterpriseOne 9.0.2 JD Edwards EnterpriseOne Tools (8.98.4.2) Oracle WebLogic Server 11g (10.3.4) Oracle HTTP Server 11g Oracle Database 11g Release 2 (11.2.0.1) Benchmark Description JD Edwards EnterpriseOne is an integrated applications suite of Enterprise Resource Planning (ERP) software. Oracle offers 70 JD Edwards EnterpriseOne application modules to support a diverse set of business operations. Oracle's Day in the Life (DIL) kit is a suite of scripts that exercises most common transactions of JD Edwards EnterpriseOne applications, including business processes such as payroll, sales order, purchase order, work order, and manufacturing processes, such as ship confirmation. These are labeled by industry acronyms such as SCM, CRM, HCM, SRM and FMS. The kit's scripts execute transactions typical of a mid-sized manufacturing company. The workload consists of online transactions and the UBE – Universal Business Engine workload of 61 short and 4 long UBEs. LoadRunner runs the DIL workload, collects the user’s transactions response times and reports the key metric of Combined Weighted Average Transaction Response time. The UBE processes workload runs from the JD Enterprise Application server. Oracle's UBE processes come as three flavors: Short UBEs < 1 minute engage in Business Report and Summary Analysis, Mid UBEs > 1 minute create a large report of Account, Balance, and Full Address, Long UBEs > 2 minutes simulate Payroll, Sales Order, night only jobs. The UBE workload generates large numbers of PDF files reports and log files. The UBE Queues are categorized as the QBATCHD, a single threaded queue for large and medium UBEs, and the QPROCESS queue for short UBEs run concurrently. Oracle's UBE process performance metric is Number of Maximum Concurrent UBE processes at transaction rate, UBEs/minute. Key Points and Best Practices Two JD Edwards EnterpriseOne Application Servers, two Oracle WebLogic Servers 11g Release 1 coupled with two Oracle Web Tier HTTP server instances and one Oracle Database 11g Release 2 database on a single SPARC T4-2 server were hosted in separate Oracle Solaris Containers bound to four processor sets to demonstrate consolidation of multiple applications, web servers and the database with best resource utilizations. Interrupt fencing was configured on all Oracle Solaris Containers to channel the interrupts to processors other than the processor sets used for the JD Edwards Application server, Oracle WebLogic servers and the database server. A Oracle WebLogic vertical cluster was configured on each WebServer Container with twelve managed instances each to load balance users' requests and to provide the infrastructure that enables scaling to high number of users with ease of deployment and high availability. The database log writer was run in the real time RT class and bound to a processor set. The database redo logs were configured on the raw disk partitions. The Oracle Solaris Container running the Enterprise Application server completed 61 Short UBEs, 4 Long UBEs concurrently as the mixed size batch workload. The mixed size UBEs ran concurrently from the Enterprise Application server with the 8,000 online users driven by the LoadRunner. See Also SPARC T4-2 Server oracle.com OTN JD Edwards EnterpriseOne oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Oracle Fusion Middleware oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 09/30/2012.

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• top Tweets SOA Partner Community – May 2012

  - by JuergenKress

  Send your tweets @soacommunity #soacommunity and follow us at http://twitter.com/soacommunity SOA Community BPMN2.0 Oracle notations poster from eaiesb http://wp.me/p10C8u-pu Torsten WinterbergLook out for new Oracle #BPM edition coming up soon: The Oracle BPM Standard edtion! Great news for easy entry, small licence fees. Yes! Danilo Schmiedel Had a great chat with customer yesterday about #OracleBPM. Next step will be a 5day event combining modeling and implementation @soacommunity Frank Nimphius Still reading "Oracle Business Process Management Suite 11g Handbook". Excellent resource for a non-SOA but ADF guy like me ;-) Oracle New webcast: Maximize #Oracle #WebLogic Server ROI with Oracle #Enterprise #Manager 12c on May 2 at 10 am PT. Register http://bit.ly/JFUrR9 OTNArchBeat@OTNArchBeat BPM in Financial Services Industry | Sanjeev Sharma http://bit.ly/HCCxui JDeveloper & ADF BPEL 11.1.1.6 Certified for Prebuilt E-Business Suite 12.1.3 SOA Integrations http://dlvr.it/1V9SxR Oracle UPK & Tutor Collaborate Attendees: Visit the UPK demo pod, SIGS, and sessions: If you are attending Collaborate 2012 - Sun. http://bit.ly/J39z65 Heidi Buelow see #fmw track RT @demed: Are you going to #KSCOPE12 in San Antonio, June 24-28? http://kscope12.com/component/ seminar/seminarslist?topicsid=6 Use promo code Fusion for discount! Sabine Leitner #SIG #Middleware 15.05. Frankfurt #Oracle #DOAG Planung & Aufbau WebLogic Server #WLS http://bit.ly/HKsCWV @OracleWebLogic @soacommunity SOA Community MDS explorer by Red Samurai http://wp.me/p10C8u-pp Biemond &reg; Retrieve or set a HTTP header from Oracle BPEL: With Oracle SOA Suite 11g patch 12928372 you can finally retrie http://bit.ly/JejTHC Lucas Jellema Call for papers for UKOUG 2012 has opened: http://techandebs.ukoug.org /default.asp?p=9306 (deadline 1st of June) OTNArchBeat BPM API usage: List all BPM Processes for a user | Kavitha Srinivasan http://bit.ly/IJKVfj demed SOA, Cloud + Service Tech symposium (London, Sep 24-25) call for paper is open http://www.servicetechsymposium. com /call2012.php @techsymp #oraclesoa OracleBlogs Lessons learned configuring OER 11g Workflows http://ow.ly/1iMsKh OTNArchBeat Scripting WebLogic Admin Server Startup | Antony Reynolds http://bit.ly/IH5ciU orclateamsoa A-Team Blog #ateam: BPM API usage: List all BPM Processes for a user http://ow.ly/1iJADp Lucas Jellema Just blogged about our Live FMW Application Development show during OBUG 2012, next Tuesday 24th April in Maastricht: OracleBlogs OEG integration with OSB/OWSM - 11g http://ow.ly/1iKx7G SOA Community SOA Community Newsletter April 2012 http://wp.me/p10C8u-pl Frank DorstRT @whitehorsesnl: Whiteblog: BPM Process Spaces in Oracle Webcenter (Patch Set 5(http://bit.ly/Hxzh29) #soacommunity #bpm #oracle) David Shaffer The Advanced SOA suite training class next week in Redwood City is full! Learned a lot about accepting credit card payments. OTNArchBeat Running Built-In Test Simulator with SOA Suite Healthcare 11g in PS4 and PS5 | Shub Lahiri http://bit.ly/IgI8GN SOA Community Oracle Fusion Middleware Innovation Awards 2012, Call for Nominations #ofmaward #soa #bpm #soacommunity OTNArchBeat Updated SOA Documents now available in ITSO Reference Library http://bit.ly/I3Y6Sg Oracle Middleware Data Integrator & SOA - why 2 products better than one for integration? Webcast: Apr 24 10 AM PT http://bit.ly/IzmtKR Andrejus Baranovskis Red Samurai MDS Cleaner V2.0 http://fb.me/FxLVz82w SOA Community “@rluttikhuizen: Chapter 4 of SOA Made Simple book "Classification of Services" ready for collegial review” can #soacommunity get a preview? Xavier Verhaeghe #Gartner figures are out: #Oracle top in App Server market share (43.1%) and Relational #Database, too (48.8%) in 2011 Sabine Leitner WLS12c, Exa*, IDM, EM12c, DB @ Private, Public, Hybrid #Cloud Event 26.04. FFM #Oracle http://bit.ly/zcRuxi @OracleCloudZone @soacommunity Michel Schildmeijer@wlscommunity @MiddlewareMagic @OTNArchBeat @Oracle_Fusion Oracle WebLogic / SOA Suite 11g HACMP Cluster take-over http://lnkd.in/G78qMd Oracle Middleware Hear how ODI and SOA's unified approach are key to untangling your business. April 24 10AM PT http://bit.ly/IdcsUz #Oracle OTNArchBeat Using SAP Adapter with OSB 11g (PS3) | Shub Lahiri http://bit.ly/IswR9K SOA Community Integrating with Oracle Fusion Applications: Discovering Integration Artifacts https://blogs.oracle.com/governance /entry/integrating_with_oracle_fusion_ applications #soacommunity #oer #governance OracleBlogs Tuning B2B Server Engine Threads in SOA Suite 11g http://ow.ly/1iH5bx OracleBlogs Top Tweets SOA Partner Community April 2012 http://ow.ly/1iVHfA SOA Community Oracle SOA Suite 11g Database Growth Management http://wp.me/p10C8u-pi Sabine Leitner WLS12c,Exa*,IDM,EM12c, DB @ Private, Public, Hybrid #Cloud Event 24.04. München #Oracle http://bit.ly/zcRuxi @OracleCloudZone @soacommunity SOA Community Testing Business Rules by Mark Nelson http://redstack.wordpress.com/2012/ 04/18/testing-business-rules/ #soacommunity #soa #rules #oracle SOA CommunityTop Tweets SOA Partner Community - April 2012 http://wp.me/p10C8u-pn OTNArchBeat Webcast: Untangle Your Business with Oracle Unified SOA and Data Integration - April 24 http://bit.ly/IQexqT OTNArchBeat"Do more with SOA Integration: Best of Packt" contributors include @gschmutz, @llaszews, many others http://amzn.to/HVWwYt ServiceTechSymposium Symposium agenda page coming together - page launched today with keynotes, sessions to be added shortly. http://www.servicetechsymposium.com /agenda2012.php SOA Community Shipping Specialization plaques - congratulation #Fujitsu - request yours https://soacommunity.wordpress. com/2011/02/23/who-are-the-soa-experts-specialization-recognized-by-customers/ #soacommunity #OPN http://pic.twitter.com/YMRm2ion ServiceTechSymposium call for Presentations Submission Deadline Moved Up to May 21, 2012. Send your presentations submissions ASAP! ServiceTechSymposium Symposium Keynote by Vicente Navarro, European Space Agency, added to agenda: "SOA & Service-Orientation at the European Space Agency" SOA Community Running a large #soa project? Make sure you read - Oracle SOA Suite 11g Database Growth Management #soacommunity #opn SOA Community List all BPM Processes for a user by Yogesh l #bpm #oracle #soacommunity  For regular information on Oracle SOA Suite become a member in the SOA Partner Community for registration please visit  www.oracle.com/goto/emea/soa (OPN account required) Blog Twitter LinkedIn Mix Forum Technorati Tags: soacommunity, twitter,Oracle,SOA Community,Jürgen Kress,OPN,SOA,BPM

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• El éxito del Customer Experience

  - by Noelia Gomez

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 Desde hace más de un año Oracle está apostando por soluciones que supongan un cambio en la gestión de la relación con el cliente, mejorando su experiencia para fidelizarle mientras las empresas ahorran en costes. Por otro lado, son muchas las empresas las que se han dado cuenta de esta necesidad y de que las redes sociales permiten una conexión con el cliente que antes no se había logrado, pudiendo detectar necesidades antes desconocidas. Por todo ello, el pasado 29 de Octubre Contact Center, en colaboración con Oracle, quiso invitar la los especialistas de Customer Experience de las mayores empresas de España en una jornada ejecutiva para descubrir las novedades en este área e intercambiar opiniones con otros expertos. Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* 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:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif";} Fernando Rumbero, Iberia Applications Cluster Leader de Oracle, abrió las ponencias hablando de la “Tercera Revolución”, una presentación que nos abrió la perspectiva de la realidad en la que vivimos, clientes, usuarios y empresas. Por su parte, Victor Lopez, Sales Consulting Director de Oracle, nos condujo en Un recorrido por el mundo del cliente para lograr ofrecer una experiencia que este espera. Después, conocimos casos prácticos de la mano de Albert Valls, especialista en CX, que nos mostró los resultados de algunos de nuestros clientes y como han logrado alcanzar sus objetivos. Tras un breve descanso que dio lugar al networking, escuchamos a la ponencia más esperada de la jornada: ¿Por qué Linkedin tienen 249 millones de usuarios? Francesc Arbiol, Head of Iberia, Linkedin, fue el responsable de responder a esta pregunta, dándonos las claves para ofrecer un servicio de alta calidad y rentable con Oracle RightNow. En el momento para preguntas y respuestas, moderado por Guillermo San Roman, Applications Sales Director de Oracle, los asistentes estuvieron muy activos y fueron muchas las interacciones con los ponentes y entre los propios asistentes. En este espacio se pusieron de manifiesto las preguntas más latentes de este escenario: ¿Estamos preparados para dar respuesta y comprender al cliente de hoy? ¿Cómo dirigir y priorizar las actividades para alcanzar el mejor resultado?Infraestructuras y claves para aprender a liderar la experiencia de cliente. ¿Cómo integrar a todas las áreas de la empresa en el proceso de Customer Experience? Proactividad y multicanalidad: dos principios básicos en el Customer Experience La jornada se cerró con un coctel en el que el prevaleció el intercambio de opiniones y encuentros entre profesionales. Sin duda un evento de los que te hacen irte a casa con miles de ideas en la cabeza. ¿Estuviste en el encuentro? Cuéntanos, ¿qué te pareció? ¿No pudiste asistir? Ponte en contacto con nosotros y nos acercaremos a tu oficina.   /* 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:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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• SQL SERVER – Solution – Puzzle – SELECT * vs SELECT COUNT(*)

  - by pinaldave

  Earlier I have published Puzzle Why SELECT * throws an error but SELECT COUNT(*) does not. This question have received many interesting comments. Let us go over few of the answers, which are valid. Before I start the same, let me acknowledge Rob Farley who has not only answered correctly very first but also started interesting conversation in the same thread. The usual question will be what is the right answer. I would like to point to official Microsoft Connect Items which discusses the same. RGarvao https://connect.microsoft.com/SQLServer/feedback/details/671475/select-test-where-exists-select tiberiu utan http://connect.microsoft.com/SQLServer/feedback/details/338532/count-returns-a-value-1 Rob Farley count(*) is about counting rows, not a particular column. It doesn’t even look to see what columns are available, it’ll just count the rows, which in the case of a missing FROM clause, is 1. “select *” is designed to return columns, and therefore barfs if there are none available. Even more odd is this one: select ‘blah’ where exists (select *) You might be surprised at the results… Koushik The engine performs a “Constant scan” for Count(*) where as in the case of “SELECT *” the engine is trying to perform either Index/Cluster/Table scans. amikolaj When you query ‘select * from sometable’, SQL replaces * with the current schema of that table. With out a source for the schema, SQL throws an error. so when you query ‘select count(*)’, you are counting the one row. * is just a constant to SQL here. Check out the execution plan. Like the description states – ‘Scan an internal table of constants.’ You could do ‘select COUNT(‘my name is adam and this is my answer’)’ and get the same answer. Netra Acharya SELECT * Here, * represents all columns from a table. So it always looks for a table (As we know, there should be FROM clause before specifying table name). So, it throws an error whenever this condition is not satisfied. SELECT COUNT(*) Here, COUNT is a Function. So it is not mandetory to provide a table. Check it out this: DECLARE @cnt INT SET @cnt = COUNT(*) SELECT @cnt SET @cnt = COUNT(‘x’) SELECT @cnt Naveen Select 1 / Select ‘*’ will return 1/* as expected. Select Count(1)/Count(*) will return the count of result set of select statement. Count(1)/Count(*) will have one 1/* for each row in the result set of select statement. Select 1 or Select ‘*’ result set will contain only 1 result. so count is 1. Where as “Select *” is a sysntax which expects the table or equauivalent to table (table functions, etc..). It is like compilation error for that query. Ramesh Hi Friends, Count is an aggregate function and it expects the rows (list of records) for a specified single column or whole rows for *. So, when we use ‘select *’ it definitely give and error because ‘*’ is meant to have all the fields but there is not any table and without table it can only raise an error. So, in the case of ‘Select Count(*)’, there will be an error as a record in the count function so you will get the result as ’1'. Try using : Select COUNT(‘RAMESH’) and think there is an error ‘Must specify table to select from.’ in place of ‘RAMESH’ Pinal : If i am wrong then please clarify this. Sachin Nandanwar Any aggregate function expects a constant or a column name as an expression. DO NOT be confused with * in an aggregate function.The aggregate function does not treat it as a column name or a set of column names but a constant value, as * is a key word in SQL. You can replace any value instead of * for the COUNT function.Ex Select COUNT(5) will result as 1. The error resulting from select * is obvious it expects an object where it can extract the result set. I sincerely thank you all for wonderful conversation, I personally enjoyed it and I am sure all of you have the same feeling. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: CodeProject, Pinal Dave, PostADay, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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• Auto DOP and Concurrency

  - by jean-pierre.dijcks

  After spending some time in the cloud, I figured it is time to come down to earth and start discussing some of the new Auto DOP features some more. As Database Machines (the v2 machine runs Oracle Database 11.2) are effectively selling like hotcakes, it makes some sense to talk about the new parallel features in more detail. For basic understanding make sure you have read the initial post. The focus there is on Auto DOP and queuing, which is to some extend the focus here. But now I want to discuss the concurrency a little and explain some of the relevant parameters and their impact, specifically in a situation with concurrency on the system. The goal of Auto DOP The idea behind calculating the Automatic Degree of Parallelism is to find the highest possible DOP (ideal DOP) that still scales. In other words, if we were to increase the DOP even more  above a certain DOP we would see a tailing off of the performance curve and the resource cost / performance would become less optimal. Therefore the ideal DOP is the best resource/performance point for that statement. The goal of Queuing On a normal production system we should see statements running concurrently. On a Database Machine we typically see high concurrency rates, so we need to find a way to deal with both high DOP’s and high concurrency. Queuing is intended to make sure we Don’t throttle down a DOP because other statements are running on the system Stay within the physical limits of a system’s processing power Instead of making statements go at a lower DOP we queue them to make sure they will get all the resources they want to run efficiently without trashing the system. The theory – and hopefully – practice is that by giving a statement the optimal DOP the sum of all statements runs faster with queuing than without queuing. Increasing the Number of Potential Parallel Statements To determine how many statements we will consider running in parallel a single parameter should be looked at. That parameter is called PARALLEL_MIN_TIME_THRESHOLD. The default value is set to 10 seconds. So far there is nothing new here…, but do realize that anything serial (e.g. that stays under the threshold) goes straight into processing as is not considered in the rest of this post. Now, if you have a system where you have two groups of queries, serial short running and potentially parallel long running ones, you may want to worry only about the long running ones with this parallel statement threshold. As an example, lets assume the short running stuff runs on average between 1 and 15 seconds in serial (and the business is quite happy with that). The long running stuff is in the realm of 1 – 5 minutes. It might be a good choice to set the threshold to somewhere north of 30 seconds. That way the short running queries all run serial as they do today (if it ain’t broken, don’t fix it) and allows the long running ones to be evaluated for (higher degrees of) parallelism. This makes sense because the longer running ones are (at least in theory) more interesting to unleash a parallel processing model on and the benefits of running these in parallel are much more significant (again, that is mostly the case). Setting a Maximum DOP for a Statement Now that you know how to control how many of your statements are considered to run in parallel, lets talk about the specific degree of any given statement that will be evaluated. As the initial post describes this is controlled by PARALLEL_DEGREE_LIMIT. This parameter controls the degree on the entire cluster and by default it is CPU (meaning it equals Default DOP). For the sake of an example, let’s say our Default DOP is 32. Looking at our 5 minute queries from the previous paragraph, the limit to 32 means that none of the statements that are evaluated for Auto DOP ever runs at more than DOP of 32. Concurrently Running a High DOP A basic assumption about running high DOP statements at high concurrency is that you at some point in time (and this is true on any parallel processing platform!) will run into a resource limitation. And yes, you can then buy more hardware (e.g. expand the Database Machine in Oracle’s case), but that is not the point of this post… The goal is to find a balance between the highest possible DOP for each statement and the number of statements running concurrently, but with an emphasis on running each statement at that highest efficiency DOP. The PARALLEL_SERVER_TARGET parameter is the all important concurrency slider here. Setting this parameter to a higher number means more statements get to run at their maximum parallel degree before queuing kicks in.  PARALLEL_SERVER_TARGET is set per instance (so needs to be set to the same value on all 8 nodes in a full rack Database Machine). Just as a side note, this parameter is set in processes, not in DOP, which equates to 4* Default DOP (2 processes for a DOP, default value is 2 * Default DOP, hence a default of 4 * Default DOP). Let’s say we have PARALLEL_SERVER_TARGET set to 128. With our limit set to 32 (the default) we are able to run 4 statements concurrently at the highest DOP possible on this system before we start queuing. If these 4 statements are running, any next statement will be queued. To run a system at high concurrency the PARALLEL_SERVER_TARGET should be raised from its default to be much closer (start with 60% or so) to PARALLEL_MAX_SERVERS. By using both PARALLEL_SERVER_TARGET and PARALLEL_DEGREE_LIMIT you can control easily how many statements run concurrently at good DOPs without excessive queuing. Because each workload is a little different, it makes sense to plan ahead and look at these parameters and set these based on your requirements.

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• Replication - between pools in the same system

  - by Steve Tunstall

  OK, I fully understand that's it's been a LONG time since I've blogged with any tips or tricks on the ZFSSA, and I'm way behind. Hey, I just wrote TWO BLOGS ON THE SAME DAY!!! Make sure you keep scrolling down to see the next one too, or you may have missed it. To celebrate, for the one or two of you out there who are still reading this, I got something for you. The first TWO people who make any comment below, with your real name and email so I can contact you, will get some cool Oracle SWAG that I have to give away. Don't get excited, it's not an iPad, but it pretty good stuff. Only the first two, so if you already see two below, then settle down. Now, let's talk about Replication and Migration.  I have talked before about Shadow Migration here: https://blogs.oracle.com/7000tips/entry/shadow_migrationShadow Migration lets one take a NFS or CIFS share in one pool on a system and migrate that data over to another pool in the same system. That's handy, but right now it's only for file systems like NFS and CIFS. It will not work for LUNs. LUN shadow migration is a roadmap item, however. So.... What if you have a ZFSSA cluster with multiple pools, and you have a LUN in one pool but later you decide it's best if it was in the other pool? No problem. Replication to the rescue. What's that? Replication is only for replicating data between two different systems? Who told you that? We've been able to replicate to the same system now for a few code updates back. These instructions below will also work just fine if you're setting up replication between two different systems. After replication is complete, you can easily break replication, change the new LUN into a primary LUN and then delete the source LUN. Bam. Step 1- setup a target system. In our case, the target system is ourself, but you still have to set it up like it's far away. Go to Configuration-->Services-->Remote Replication. Click the plus sign and setup the target, which is the ZFSSA you're on now. Step 2. Now you can go to the LUN you want to replicate. Take note which Pool and Project you're in. In my case, I have a LUN in Pool2 called LUNp2 that I wish to replicate to Pool1.  Step 3. In my case, I made a Project called "Luns" and it has LUNp2 inside of it. I am going to replicate the Project, which will automatically replicate all of the LUNs and/or Filesystems inside of it.  Now, you can also replicate from the Share level instead of the Project. That will only replicate the share, and not all the other shares of a project. If someone tells you that if you replicate a share, it always replicates all the other shares also in that Project, don't listen to them.Note below how I can choose not only the Target (which is myself), but I can also choose which Pool to replicate it to. So I choose Pool1.  Step 4. I did not choose a schedule or pick the "Continuous" button, which means my replication will be manual only. I can now push the Manual Replicate button on my Actions list and you will see it start. You will see both a barber pole animation and also an update in the status bar on the top of the screen that a replication event has begun. This also goes into the event log.  Step 5. The status bar will also log an event when it's done. Step 6. If you go back to Configuration-->Services-->Remote Replication, you will see your event. Step 7. Done. To see your new replica, go to the other Pool (Pool1 for me), and click the "Replica" area below the words "Filesystems | LUNs" Here, you will see any replicas that have come in from any of your sources. It's a simple matter from here to break the replication, which will change this to a "Local" LUN, and then delete the original LUN back in Pool2. Ok, that's all for now, but I promise to give out more tricks sometime in November !!! There's very exciting stuff coming down the pipe for the ZFSSA. Both new hardware and new software features that I'm just drooling over. That's all I can say, but contact your local sales SC to get a NDA roadmap talk if you want to hear more.   Happy Halloween,Steve 

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• Oracle Linux and Oracle VM pricing guide

  - by wcoekaer

  A few days ago someone showed me a pricing guide from a Linux vendor and I was a bit surprised at the complexity of it. Especially when you look at larger servers (4 or 8 sockets) and when adding virtual machine use into the mix. I think we have a very compelling and simple pricing model for both Oracle Linux and Oracle VM. Let me see if I can explain it in 1 page, not 10 pages. This pricing information is publicly available on the Oracle store, I am using the current public list prices. Also keep in mind that this is for customers using non-oracle x86 servers. When a customer purchases an Oracle x86 server, the annual systems support includes full use (all you can eat) of Oracle Linux, Oracle VM and Oracle Solaris (no matter how many VMs you run on that server, in case you deploy guests on a hypervisor). This support level is the equivalent of premier support in the list below. Let's start with Oracle VM (x86) : Oracle VM support subscriptions are per physical server on which you deploy the Oracle VM Server product. (1) Oracle VM Premier Limited - 1- or 2 socket server : $599 per server per year (2) Oracle VM Premier - more than 2 socket server (4, or 8 or whatever more) : $1199 per server per year The above includes the use of Oracle VM Manager and Oracle Enterprise Manager Cloud Control's Virtualization management pack (including self service cloud portal, etc..) 24x7 support, access to bugfixes, updates and new releases. It also includes all options, live migrate, dynamic resource scheduling, high availability, dynamic power management, etc If you want to play with the product, or even use the product without access to support services, the product is freely downloadable from edelivery. Next, Oracle Linux : Oracle Linux support subscriptions are per physical server. If you plan to run Oracle Linux as a guest on Oracle VM, VMWare or Hyper-v, you only have to pay for a single subscription per system, we do not charge per guest or per number of guests. In other words, you can run any number of Oracle Linux guests per physical server and count it as just a single subscription. (1) Oracle Linux Network Support - any number of sockets per server : $119 per server per year Network support does not offer support services. It provides access to the Unbreakable Linux Network and also offers full indemnification for Oracle Linux. (2) Oracle Linux Basic Limited Support - 1- or 2 socket servers : $499 per server per year This subscription provides 24x7 support services, access to the Unbreakable Linux Network and the Oracle Support portal, indemnification, use of Oracle Clusterware for Linux HA and use of Oracle Enterprise Manager Cloud control for Linux OS management. It includes ocfs2 as a clustered filesystem. (3) Oracle Linux Basic Support - more than 2 socket server (4, or 8 or more) : $1199 per server per year This subscription provides 24x7 support services, access to the Unbreakable Linux Network and the Oracle Support portal, indemnification, use of Oracle Clusterware for Linux HA and use of Oracle Enterprise Manager Cloud control for Linux OS management. It includes ocfs2 as a clustered filesystem (4) Oracle Linux Premier Limited Support - 1- or 2 socket servers : $1399 per server per year This subscription provides 24x7 support services, access to the Unbreakable Linux Network and the Oracle Support portal, indemnification, use of Oracle Clusterware for Linux HA and use of Oracle Enterprise Manager Cloud control for Linux OS management, XFS filesystem support. It also offers Oracle Lifetime support, backporting of patches for critical customers in previous versions of package and ksplice zero-downtime updates. (5) Oracle Linux Premier Support - more than 2 socket servers : $2299 per server per year This subscription provides 24x7 support services, access to the Unbreakable Linux Network and the Oracle Support portal, indemnification, use of Oracle Clusterware for Linux HA and use of Oracle Enterprise Manager Cloud control for Linux OS management, XFS filesystem support. It also offers Oracle Lifetime support, backporting of patches for critical customers in previous versions of package and ksplice zero-downtime updates. (6) Freely available Oracle Linux - any number of sockets You can freely download Oracle Linux, install it on any number of servers and use it for any reason, without support, without right to use of these extra features like Oracle Clusterware or ksplice, without indemnification. However, you do have full access to all errata as well. Need support? then use options (1)..(5) So that's it. Count number of 2 socket boxes, more than 2 socket boxes, decide on basic or premier support level and you are done. You don't have to worry about different levels based on how many virtual instance you deploy or want to deploy. A very simple menu of choices. We offer, inclusive, Linux OS clusterware, Linux OS Management, provisioning and monitoring, cluster filesystem (ocfs), high performance filesystem (xfs), dtrace, ksplice, ofed (infiniband stack for high performance networking). No separate add-on menus. NOTE : socket/cpu can have any number of cores. So whether you have a 4,6,8,10 or 12 core CPU doesn't matter, we count the number of physical CPUs.

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• FairScheduling Conventions in Hadoop

  - by dan.mcclary

  While scheduling and resource allocation control has been present in Hadoop since 0.20, a lot of people haven't discovered or utilized it in their initial investigations of the Hadoop ecosystem. We could chalk this up to many things: Organizations are still determining what their dataflow and analysis workloads will comprise Small deployments under tests aren't likely to show the signs of strains that would send someone looking for resource allocation options The default scheduling options -- the FairScheduler and the CapacityScheduler -- are not placed in the most prominent position within the Hadoop documentation. However, for production deployments, it's wise to start with at least the foundations of scheduling in place so that you can tune the cluster as workloads emerge. To do that, we have to ask ourselves something about what the off-the-rack scheduling options are. We have some choices: The FairScheduler, which will work to ensure resource allocations are enforced on a per-job basis. The CapacityScheduler, which will ensure resource allocations are enforced on a per-queue basis. Writing your own implementation of the abstract class org.apache.hadoop.mapred.job.TaskScheduler is an option, but usually overkill. If you're going to have several concurrent users and leverage the more interactive aspects of the Hadoop environment (e.g. Pig and Hive scripting), the FairScheduler is definitely the way to go. In particular, we can do user-specific pools so that default users get their fair share, and specific users are given the resources their workloads require. To enable fair scheduling, we're going to need to do a couple of things. First, we need to tell the JobTracker that we want to use scheduling and where we're going to be defining our allocations. We do this by adding the following to the mapred-site.xml file in HADOOP_HOME/conf: <property> <name>mapred.jobtracker.taskScheduler</name> <value>org.apache.hadoop.mapred.FairScheduler</value> </property> <property> <name>mapred.fairscheduler.allocation.file</name> <value>/path/to/allocations.xml</value> </property> <property> <name>mapred.fairscheduler.poolnameproperty</name> <value>pool.name</value> </property> <property> <name>pool.name</name> <value>${user.name}</name> </property> What we've done here is simply tell the JobTracker that we'd like to task scheduling to use the FairScheduler class rather than a single FIFO queue. Moreover, we're going to be defining our resource pools and allocations in a file called allocations.xml For reference, the allocation file is read every 15s or so, which allows for tuning allocations without having to take down the JobTracker. Our allocation file is now going to look a little like this <?xml version="1.0"?> <allocations> <pool name="dan"> <minMaps>5</minMaps> <minReduces>5</minReduces> <maxMaps>25</maxMaps> <maxReduces>25</maxReduces> <minSharePreemptionTimeout>300</minSharePreemptionTimeout> </pool> <mapreduce.job.user.name="dan"> <maxRunningJobs>6</maxRunningJobs> </user> <userMaxJobsDefault>3</userMaxJobsDefault> <fairSharePreemptionTimeout>600</fairSharePreemptionTimeout> </allocations> In this case, I've explicitly set my username to have upper and lower bounds on the maps and reduces, and allotted myself double the number of running jobs. Now, if I run hive or pig jobs from either the console or via the Hue web interface, I'll be treated "fairly" by the JobTracker. There's a lot more tweaking that can be done to the allocations file, so it's best to dig down into the description and start trying out allocations that might fit your workload.

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• SPARC M7 Chip - 32 cores - Mind Blowing performance

  - by Angelo-Oracle

  The M7 Chip Oracle just announced its Next Generation Processor at the HotChips HC26 conference. As the Tech Lead in our Systems Division's Partner group, I had a front row seat to the extraordinary price performance advantage of Oracle current T5 and M6 based systems. Partner after partner tested  these systems and were impressed with it performance. Just read some of the quotes to see what our partner has been saying about our hardware. We just announced our next generation processor, the M7. This has 32 cores (up from 16-cores in T5 and 12-cores in M6). With 20 nm technology  this is our most advanced processor. The processor has more cores than anything else in the industry today. After the Sun acquisition Oracle has released 5 processors in 4 years and this is the 6th.  The S4 core  The M7 is built using the foundation of the S4 core. This is the next generation core technology. Like its predecessor, the S4 has 8 dynamic threads. It increases the frequency while maintaining the Pipeline depth. Each core has its own fine grain power estimator that keeps the core within its power envelop in 250 nano-sec granularity. Each core also includes Software in Silicon features for Application Acceleration Support. Each core includes features to improve Application Data Integrity, with almost no performance loss. The core also allows using part of the Virtual Address to store meta-data.  User-Level Synchronization Instructions are also part of the S4 core. Each core has 16 KB Instruction and 16 KB Data L1 cache. The Core Clusters  The cores on the M7 chip are organized in sets of 4-core clusters. The core clusters share  L2 cache.  All four cores in the complex share 256 KB of 4 way set associative L2 Instruction Cache, with over 1/2 TB/s of throughput. Two cores share 256 KB of 8 way set associative L2 Data Cache, with over 1/2 TB/s of throughput. With this innovative Core Cluster architecture, the M7 doubles core execution bandwidth. to maximize per-thread performance.  The Chip  Each  M7 chip has 8 sets of these core-clusters. The chip has 64 MB on-chip L3 cache. This L3 caches is shared among all the cores and is partitioned into 8 x 8 MB chunks. Each chunk is  8-way set associative cache. The aggregate bandwidth for the L3 cache on the chip is over 1.6TB/s. Each chip has 4 DDR4 memory controllers and can support upto 16 DDR4 DIMMs, allowing for 2 TB of RAM/chip. The chip also includes 4 internal links of PCIe Gen3 I/O controllers.  Each chip has 7 coherence links, allowing for 8 of these chips to be connected together gluelessly. Also 32 of these chips can be connected in an SMP configuration. A potential system with 32 chips will have 1024 cores and 8192 threads and 64 TB of RAM.  Software in Silicon The M7 chip has many built in Application Accelerators in Silicon. These features will be exposed to our Software partners using the SPARC Accelerator Program.  The M7  has built-in logic to decompress data at the speed of memory access. This means that applications can directly work on compressed data in memory increasing the data access rates. The VA Masking feature allows the use of part of the virtual address to store meta-data.  Realtime Application Data Integrity The Realtime Application Data Integrity feature helps applications safeguard against invalid, stale memory reference and buffer overflows. The first 4-bits if the Pointer can be used to store a version number and this version number is also maintained in the memory & cache lines. When a pointer accesses memory the hardware checks to make sure the two versions match. A SEGV signal is raised when there is a mismatch. This feature can be used by the Database, applications and the OS.  M7 Database In-Memory Query Accelerator The M7 chip also includes a In-Silicon Query Engines.  These accelerate tasks that work on In-Memory Columnar Vectors. Oracle In-Memory options stores data in Column Format. The M7 Query Engine can speed up In-Memory Format Conversion, Value and Range Comparisons and Set Membership lookups. This engine can work on Compressed data - this means not only are we accelerating the query performance but also increasing the memory bandwidth for queries.  SPARC Accelerated Program  At the Hotchips conference we also introduced the SPARC Accelerated Program to provide our partners and third part developers access to all the goodness of the M7's SPARC Application Acceleration features. Please get in touch with us if you are interested in knowing more about this program. 

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• Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned how to become a Data Scientist for Big Data. In this article we will go over various learning resources related to Big Data. In this series we have covered many of the most essential details about Big Data. At the beginning of this series, I have encouraged readers to send me questions. One of the most popular questions is - “I want to learn more about Big Data. Where can I learn it?” This is indeed a great question as there are plenty of resources out to learn about Big Data and it is indeed difficult to select on one resource to learn Big Data. Hence I decided to write here a few of the very important resources which are related to Big Data. Learn from Pluralsight Pluralsight is a global leader in high-quality online training for hardcore developers.  It has fantastic Big Data Courses and I started to learn about Big Data with the help of Pluralsight. Here are few of the courses which are directly related to Big Data. Big Data: The Big Picture Big Data Analytics with Tableau NoSQL: The Big Picture Understanding NoSQL Data Analysis Fundamentals with Tableau I encourage all of you start with this video course as they are fantastic fundamentals to learn Big Data. Learn from Apache Resources at Apache are single point the most authentic learning resources. If you want to learn fundamentals and go deep about every aspect of the Big Data, I believe you must understand various concepts in Apache’s library. I am pretty impressed with the documentation and I am personally referencing it every single day when I work with Big Data. I strongly encourage all of you to bookmark following all the links for authentic big data learning. Haddop - The Apache Hadoop® project develops open-source software for reliable, scalable, distributed computing. Ambari: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which include support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heat maps and ability to view MapReduce, Pig and Hive applications visually along with features to diagnose their performance characteristics in a user-friendly manner. Avro: A data serialization system. Cassandra: A scalable multi-master database with no single points of failure. Chukwa: A data collection system for managing large distributed systems. HBase: A scalable, distributed database that supports structured data storage for large tables. Hive: A data warehouse infrastructure that provides data summarization and ad hoc querying. Mahout: A Scalable machine learning and data mining library. Pig: A high-level data-flow language and execution framework for parallel computation. ZooKeeper: A high-performance coordination service for distributed applications. Learn from Vendors One of the biggest issues with about learning Big Data is setting up the environment. Every Big Data vendor has different environment request and there are lots of things require to set up Big Data framework. Many of the users do not start with Big Data as they are afraid about the resources required to set up framework as well as a time commitment. Here Hortonworks have created fantastic learning environment. They have created Sandbox with everything one person needs to learn Big Data and also have provided excellent tutoring along with it. Sandbox comes with a dozen hands-on tutorial that will guide you through the basics of Hadoop as well it contains the Hortonworks Data Platform. I think Hortonworks did a fantastic job building this Sandbox and Tutorial. Though there are plenty of different Big Data Vendors I have decided to list only Hortonworks due to their unique setup. Please leave a comment if there are any other such platform to learn Big Data. I will include them over here as well. Learn from Books There are indeed few good books out there which one can refer to learn Big Data. Here are few good books which I have read. I will update the list as I will learn more. Ethics of Big Data Balancing Risk and Innovation Big Data for Dummies Head First Data Analysis: A Learner’s Guide to Big Numbers, Statistics, and Good Decisions If you search on Amazon there are millions of the books but I think above three books are a great set of books and it will give you great ideas about Big Data. Once you go through above books, you will have a clear idea about what is the next step you should follow in this series. You will be capable enough to make the right decision for yourself. Tomorrow In tomorrow’s blog post we will wrap up this series of Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• New Replication, Optimizer and High Availability features in MySQL 5.6.5!

  - by Rob Young

  As the Product Manager for the MySQL database it is always great to announce when the MySQL Engineering team delivers another great product release.  As a field DBA and developer it is even better when that release contains improvements and innovation that I know will help those currently using MySQL for apps that range from modest intranet sites to the most highly trafficked web sites on the web.  That said, it is my pleasure to take my hat off to MySQL Engineering for today's release of the MySQL 5.6.5 Development Milestone Release ("DMR"). The new highlighted features in MySQL 5.6.5 are discussed here: New Self-Healing Replication ClustersThe 5.6.5 DMR improves MySQL Replication by adding Global Transaction Ids and automated utilities for self-healing Replication clusters.  Prior to 5.6.5 this has been somewhat of a pain point for MySQL users with most developing custom solutions or looking to costly, complex third-party solutions for these capabilities.  With 5.6.5 these shackles are all but removed by a solution that is included with the GPL version of the database and supporting GPL tools.  You can learn all about the details of the great, problem solving Replication features in MySQL 5.6 in Mat Keep's Developer Zone article.  New Replication Administration and Failover UtilitiesAs mentioned above, the new Replication features, Global Transaction Ids specifically, are now supported by a set of automated GPL utilities that leverage the new GTIDs to provide administration and manual or auto failover to the most up to date slave (that is the default, but user configurable if needed) in the event of a master failure. The new utilities, along with links to Engineering related blogs, are discussed in detail in the DevZone Article noted above. Better Query Optimization and ThroughputThe MySQL Optimizer team continues to amaze with the latest round of improvements in 5.6.5. Along with much refactoring of the legacy code base, the Optimizer team has improved complex query optimization and throughput by adding these functional improvements: Subquery Optimizations - Subqueries are now included in the Optimizer path for runtime optimization.  Better throughput of nested queries enables application developers to simplify and consolidate multiple queries and result sets into a single unit or work. Optimizer now uses CURRENT_TIMESTAMP as default for DATETIME columns - For simplification, this eliminates the need for application developers to assign this value when a column of this type is blank by default. Optimizations for Range based queries - Optimizer now uses ready statistics vs Index based scans for queries with multiple range values. Optimizations for queries using filesort and ORDER BY.  Optimization criteria/decision on execution method is done now at optimization vs parsing stage. Print EXPLAIN in JSON format for hierarchical readability and Enterprise tool consumption. You can learn the details about these new features as well all of the Optimizer based improvements in MySQL 5.6 by following the Optimizer team blog. You can download and try the MySQL 5.6.5 DMR here. (look under "Development Releases")  Please let us know what you think!  The new HA utilities for Replication Administration and Failover are available as part of the MySQL Workbench Community Edition, which you can download here .Also New in MySQL LabsAs has become our tradition when announcing DMRs we also like to provide "Early Access" development features to the MySQL Community via the MySQL Labs.  Today is no exception as we are also releasing the following to Labs for you to download, try and let us know your thoughts on where we need to improve:InnoDB Online OperationsMySQL 5.6 now provides Online ADD Index, FK Drop and Online Column RENAME.  These operations are non-blocking and will continue to evolve in future DMRs.  You can learn the grainy details by following John Russell's blog.InnoDB data access via Memcached API ("NotOnlySQL") - Improved refresh of an earlier feature releaseSimilar to Cluster 7.2, MySQL 5.6 provides direct NotOnlySQL access to InnoDB data via the familiar Memcached API. This provides the ultimate in flexibility for developers who need fast, simple key/value access and complex query support commingled within their applications.Improved Transactional Performance, ScaleThe InnoDB Engineering team has once again under promised and over delivered in the area of improved performance and scale.  These improvements are also included in the aggregated Spring 2012 labs release:InnoDB CPU cache performance improvements for modern, multi-core/CPU systems show great promise with internal tests showing:    2x throughput improvement for read only activity 6x throughput improvement for SELECT range Read/Write benchmarks are in progress More details on the above are available here. You can download all of the above in an aggregated "InnoDB 2012 Spring Labs Release" binary from the MySQL Labs. You can also learn more about these improvements and about related fixes to mysys mutex and hash sort by checking out the InnoDB team blog.MySQL 5.6.5 is another installment in what we believe will be the best release of the MySQL database ever.  It also serves as a shining example of how the MySQL Engineering team at Oracle leads in MySQL innovation.You can get the overall Oracle message on the MySQL 5.6.5 DMR and Early Access labs features here. As always, thanks for your continued support of MySQL, the #1 open source database on the planet!

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• PASS Summit 2011 &ndash; Part III

  - by Tara Kizer

  Well we’re about a month past PASS Summit 2011, and yet I haven’t finished blogging my notes! Between work and home life, I haven’t been able to come up for air in a bit.  Now on to my notes… On Thursday of the PASS Summit 2011, I attended Klaus Aschenbrenner’s (blog|twitter) “Advanced SQL Server 2008 Troubleshooting”, Joe Webb’s (blog|twitter) “SQL Server Locking & Blocking Made Simple”, Kalen Delaney’s (blog|twitter) “What Happened? Exploring the Plan Cache”, and Paul Randal’s (blog|twitter) “More DBA Mythbusters”.  I think my head grew two times in size from the Thursday sessions.  Just WOW! I took a ton of notes in Klaus' session.  He took a deep dive into how to troubleshoot performance problems.  Here is how he goes about solving a performance problem: Start by checking the wait stats DMV System health Memory issues I/O issues I normally start with blocking and then hit the wait stats.  Here’s the wait stat query (Paul Randal’s) that I use when working on a performance problem.  He highlighted a few waits to be aware of such as WRITELOG (indicates IO subsystem problem), SOS_SCHEDULER_YIELD (indicates CPU problem), and PAGEIOLATCH_XX (indicates an IO subsystem problem or a buffer pool problem).  Regarding memory issues, Klaus recommended that as a bare minimum, one should set the “max server memory (MB)” in sp_configure to 2GB or 10% reserved for the OS (whichever comes first).  This is just a starting point though! Regarding I/O issues, Klaus talked about disk partition alignment, which can improve SQL I/O performance by up to 100%.  You should use 64kb for NTFS cluster, and it’s automatic in Windows 2008 R2. Joe’s locking and blocking presentation was a good session to really clear up the fog in my mind about locking.  One takeaway that I had no idea could be done was that you can set a timeout in T-SQL code view LOCK_TIMEOUT.  If you do this via the application, you should trap error 1222. Kalen’s session went into execution plans.  The minimum size of a plan is 24k.  This adds up fast especially if you have a lot of plans that don’t get reused much.  You can use sys.dm_exec_cached_plans to check how often a plan is being reused by checking the usecounts column.  She said that we can use DBCC FLUSHPROCINDB to clear out the stored procedure cache for a specific database.  I didn’t know we had this available, so this was great to hear.  This will be less intrusive when an emergency comes up where I’ve needed to run DBCC FREEPROCCACHE. Kalen said one should enable “optimize for ad hoc workloads” if you have an adhoc loc.  This stores only a 300-byte stub of the first plan, and if it gets run again, it’ll store the whole thing.  This helps with plan cache bloat.  I have a lot of systems that use prepared statements, and Kalen says we simulate those calls by using sp_executesql.  Cool! Paul did a series of posts last year to debunk various myths and misconceptions around SQL Server.  He continues to debunk things via “DBA Mythbusters”.  You can get a PDF of a bunch of these here.  One of the myths he went over is the number of tempdb data files that you should have.  Back in 2000, the recommendation was to have as many tempdb data files as there are CPU cores on your server.  This no longer holds true due to the numerous cores we have on our servers.  Paul says you should start out with 1/4 to 1/2 the number of cores and work your way up from there.  BUT!  Paul likes what Bob Ward (twitter) says on this topic: 8 or less cores –> set number of files equal to the number of cores Greater than 8 cores –> start with 8 files and increase in blocks of 4 One common myth out there is to set your MAXDOP to 1 for an OLTP workload with high CXPACKET waits.  Instead of that, dig deeper first.  Look for missing indexes, out-of-date statistics, increase the “cost threshold for parallelism” setting, and perhaps set MAXDOP at the query level.  Paul stressed that you should not plan a backup strategy but instead plan a restore strategy.  What are your recoverability requirements?  Once you know that, now plan out your backups. As Paul always does, he talked about DBCC CHECKDB.  He said how fabulous it is.  I didn’t want to interrupt the presentation, so after his session had ended, I asked Paul about the need to run DBCC CHECKDB on your mirror systems.  You could have data corruption occur at the mirror and not at the principal server.  If you aren’t checking for data corruption on your mirror systems, you could be failing over to a corrupt database in the case of a disaster or even a planned failover.  You can’t run DBCC CHECKDB against the mirrored database, but you can run it against a snapshot off the mirrored database.

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• Interview with Ronald Bradford about MySQL Connect

  - by Keith Larson

  Ronald Bradford,  an Oracle ACE Director has been busy working with  database consulting, book writing (EffectiveMySQL) while traveling and speaking around the world in support of MySQL. I was able to take some of his time to get an interview on this thoughts about theMySQL Connect conference. Keith Larson: What where your thoughts when you heard that Oracle was going to provide the community the MySQL Conference ?Ronald Bradford: Oracle has already been providing various different local community events including OTN Tech Days and  MySQL community days. These are great for local regions both in the US and abroad.  In previous years there has been an increase of content at Oracle Open World, however that benefits the Oracle community far more then the MySQL community.  It is good to see that Oracle is realizing the benefit in providing a large scale dedicated event for the MySQL community that includes speakers from the MySQL development teams, invested companies in the ecosystem and other community evangelists.I fully expect a successful event and look forward to hopefully seeing MySQL Connect at the upcoming Brazil and Japan OOW conferences and perhaps an event on the East Coast.Keith Larson: Since you are part of the content committee, what did you think of the submissions that were received during call for papers?Ronald Bradford: There was a large number of quality submissions to the number of available presentation sessions. As with the previous years as a committee member for the annual MySQL conference, there is always a large variety of common cornerstone MySQL features as well as new products and upcoming companies sharing their MySQL experiences. All of the usual major players in the ecosystem will in presenting at MySQL Connect including Facebook, Twitter, Yahoo, Continuent, Percona, Tokutek, Sphinx and Amazon to name a few.  This is ensuring the event will have a large number of quality speakers and a difficult time in choosing what to attend. Keith Larson: What sessions do you look forwarding to attending? Ronald Bradford: As with most quality conferences you can only be in one place at one time, so with multiple tracks per session it is always difficult to decide. The continued work and success with MySQL Cluster, and with a number of sessions I am sure will be popular. The features that interest me the most are around the optimizer, where there are several sessions on new features, and on the importance of backups. There are three presentations in this area to choose from.Keith Larson: Are you going to cover any of the content in your books at your MySQL Connect sessions?Ronald Bradford: I will be giving two presentations at MySQL Connect. The first will include the techniques available for creating better indexes where I will be touching on some aspects of the first Effective MySQL book on Optimizing SQL Statements.  In my second presentation from experiences of managing 500+ AWS MySQL instances, I will be touching on areas including SQL tuning, backup and recovery and scale out with replication.   These are the key topics of the initial books in the Effective MySQL series that focus on performance, scalability and business continuity.  The books however cover a far greater amount of detail then can be presented in a 1 hour session. Keith Larson: What features of MySQL 5.6 do you look forward to the most ?Ronald Bradford: I am very impressed with the optimizer trace feature. The ability to see exposed information is invaluable not just for MySQL 5.6, but to also apply information discerned for optimizing SQL statements in earlier versions of MySQL.  Not everybody understands that it is easy to deploy a MySQL 5.6 slave into an existing topology running an older version if MySQL for evaluation of many new features.  You can use the new mysqlbinlog streaming feature for duplicating master binary logs on an older version with a MySQL 5.6 slave.  The improvements in instrumentation in the Performance Schema are exciting.   However, as with my upcoming Replication Techniques in Depth title, that will be available for sale at MySQL Connect, there are numerous replication features, some long overdue with provide significant management benefits. Crash Save Slaves, Global transaction Identifiers (GTID)  and checksums just to mention a few.Keith Larson: You have been to numerous conferences, what would you recommend for people at the conference? Ronald Bradford: Make the time to meet and introduce yourself to the speakers that cover the topics that most interest you. The MySQL ecosystem has a very strong community.  The relationships you build with presenters, developers and architects in MySQL can be invaluable, however they are created over time. Get to know these people, interact with them over time.  This is the opportunity to learn more then just the content from a 1 hour session. Keith Larson: Any additional tips to handling the long hours ? Ronald Bradford: Conferences can be hard, especially with all the post event drinking.  This is a two day event and I am sure will include additional events on Friday and Saturday night so come well prepared, and leave work behind. Take the time to learn something new.   You can always catchup on sleep later. Keith Larson: Thank you so much for taking some time to do this I look forward to seeing you at the MySQL Connect conference.  Please stay tuned here for more updates on MySQL. 

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• Simple Project Templates

  - by Geertjan

  The NetBeans sources include a module named "simple.project.templates": In the module sources, Tim Boudreau turns out to be the author of the code, so I asked him what it was all about, and if he could provide some usage code. His response, from approximately this time last year because it's been sitting in my inbox for a while, is below. Sure - though I think the javadoc in it is fairly complete.  I wrote it because I needed to create a bunch of project templates for Javacard, and all of the ways that is usually done were grotesque and complicated.  I figured we already have the ability to create files from templates, and we already have the ability to do substitutions in templates, so why not have a single file that defines the project as a list of file templates to create (with substitutions in the names) and some definitions of what should be in project properties. You can also add files to the project programmatically if you want.Basically, a template for an entire project is a .properties file.  Any line which doesn't have the prefix 'pp.' or 'pvp.' is treated as the definition of one file which should be created in the new project.  Any such line where the key ends in * means that file should be opened once the new project is created.  So, for example, in the nodejs module, the definition looks like: {{projectName}}.js*=Templates/javascript/HelloWorld.js .npmignore=node_hidden_templates/npmignore So, the first line means:  - Create a file with the same name as the project, using the HelloWorld template    - I.e. the left side of the line is the relative path of the file to create, and the right side is the path in the system filesystem for the template to use       - If the template is not one you normally want users to see, just register it in the system filesystem somewhere other than Templates/ (but remember to set the attribute that marks it as a template)  - Include that file in the set of files which should be opened in the editor once the new project is created. To actually create a project, first you just create a new ProjectCreator: ProjectCreator gen = new ProjectCreator( parentFolderOfNewProject ); Now, if you want to programmatically generate any files, in addition to those defined in the template, you can: gen.add (new FileCreator("nbproject", "project.xml", false) {     public DataObject create (FileObject project, Map<String,String> substitutions) throws IOException {          ...     } }); Then pass the FileObject for the project template (the properties file) to the ProjectCreator's createProject method (hmm, maybe it should be the string path to the project template instead, to save the caller trouble looking up the FileObject for the template).  That method looks like this: public final GeneratedProject createProject(final ProgressHandle handle, final String name, final FileObject template, final Map<String, String> substitutions) throws IOException { The name parameter should be the directory name for the new project;  the map is the strings you gathered in the wizard which should be used for substitutions.  createProject should be called on a background thread (i.e. use a ProgressInstantiatingIterator for the wizard iterator and just pass in the ProgressHandle you are given). The return value is a GeneratedProject object, which is just a holder for the created project directory and the set of DataObjects which should be opened when the wizard finishes. I'd love to see simple.project.templates moved out of the javacard cluster, as it is really useful and much simpler than any of the stuff currently done for generating projects.  It would also be possible to do much richer tools for creating projects in apisupport - i.e. choose (or create in the wizard) the templates you want to use, generate a skeleton wizard with a UI for all the properties you'd like to substitute, etc. Here is a partial project template from Javacard - for example usage, see org.netbeans.modules.javacard.wizard.ProjectWizardIterator in javacard.project (or the much simpler one in contrib/nodejs). #This properties file describes what to create when a project template is#instantiated.  The keys are paths on disk relative to the project root. #The values are paths to the templates to use for those files in the system#filesystem.  Any string inside {{ and }}'s will be substituted using properties#gathered in the template wizard.#Special key prefixes are #  pp. - indicates an entry for nbproject/project.properties#  pvp. - indicates an entry for nbproject/private/private.properties #File templates, in format [path-in-project=path-to-template]META-INF/javacard.xml=org-netbeans-modules-javacard/templates/javacard.xmlMETA-INF/MANIFEST.MF=org-netbeans-modules-javacard/templates/EAP_MANIFEST.MF APPLET-INF/applet.xml=org-netbeans-modules-javacard/templates/applet.xmlscripts/{{classnamelowercase}}.scr=org-netbeans-modules-javacard/templates/test.scrsrc/{{packagepath}}/{{classname}}.java*=Templates/javacard/ExtendedApplet.java nbproject/deployment.xml=org-netbeans-modules-javacard/templates/deployment.xml#project.properties contentspp.display.name={{projectname}}pp.platform.active={{activeplatform}} pp.active.device={{activedevice}}pp.includes=**pp.excludes= I will be using the above info in an upcoming blog entry and provide step by step instructions showing how to use them. However, anyone else out there should have enough info from the above to get started yourself!

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• Availability Best Practices on Oracle VM Server for SPARC

  - by jsavit

  This is the first of a series of blog posts on configuring Oracle VM Server for SPARC (also called Logical Domains) for availability. This series will show how to how to plan for availability, improve serviceability, avoid single points of failure, and provide resiliency against hardware and software failures. Availability is a broad topic that has filled entire books, so these posts will focus on aspects specifically related to Oracle VM Server for SPARC. The goal is to improve Reliability, Availability and Serviceability (RAS): An article defining RAS can be found here. Oracle VM Server for SPARC Principles for Availability Let's state some guiding principles for availability that apply to Oracle VM Server for SPARC: Avoid Single Points Of Failure (SPOFs). Systems should be configured so a component failure does not result in a loss of application service. The general method to avoid SPOFs is to provide redundancy so service can continue without interruption if a component fails. For a critical application there may be multiple levels of redundancy so multiple failures can be tolerated. Oracle VM Server for SPARC makes it possible to configure systems that avoid SPOFs. Configure for availability at a level of resource and effort consistent with business needs. Effort and resource should be consistent with business requirements. Production has different availability requirements than test/development, so it's worth expending resources to provide higher availability. Even within the category of production there may be different levels of criticality, outage tolerances, recovery and repair time requirements. Keep in mind that a simple design may be more understandable and effective than a complex design that attempts to "do everything". Design for availability at the appropriate tier or level of the platform stack. Availability can be provided in the application, in the database, or in the virtualization, hardware and network layers they depend on - or using a combination of all of them. It may not be necessary to engineer resilient virtualization for stateless web applications applications where availability is provided by a network load balancer, or for enterprise applications like Oracle Real Application Clusters (RAC) and WebLogic that provide their own resiliency. It's (often) the same architecture whether virtual or not: For example, providing resiliency against a lost device path or failing disk media is done for the same reasons and may use the same design whether in a domain or not. It's (often) the same technique whether using domains or not: Many configuration steps are the same. For example, configuring IPMP or creating a redundant ZFS pool is pretty much the same within the guest whether you're in a guest domain or not. There are configuration steps and choices for provisioning the guest with the virtual network and disk devices, which we will discuss. Sometimes it is different using domains: There are new resources to configure. Most notable is the use of alternate service domains, which provides resiliency in case of a domain failure, and also permits improved serviceability via "rolling upgrades". This is an important differentiator between Oracle VM Server for SPARC and traditional virtual machine environments where all virtual I/O is provided by a monolithic infrastructure that itself is a SPOF. Alternate service domains are widely used to provide resiliency in production logical domains environments. Some things are done via logical domains commands, and some are done in the guest: For example, with Oracle VM Server for SPARC we provide multiple network connections to the guest, and then configure network resiliency in the guest via IP Multi Pathing (IPMP) - essentially the same as for non-virtual systems. On the other hand, we configure virtual disk availability in the virtualization layer, and the guest sees an already-resilient disk without being aware of the details. These blogs will discuss configuration details like this. Live migration is not "high availability" in the sense of "continuous availability": If the server is down, then you don't live migrate from it! (A cluster or VM restart elsewhere would be used). However, live migration can be part of the RAS (Reliability, Availability, Serviceability) picture by improving Serviceability - you can move running domains off of a box before planned service or maintenance. The blog Best Practices - Live Migration on Oracle VM Server for SPARC discusses this. Topics Here are some of the topics that will be covered: Network availability using IP Multipathing and aggregates Disk path availability using virtual disks defined with multipath groups ("mpgroup") Disk media resiliency configuring for redundant disks that can tolerate media loss Multiple service domains - this is probably the most significant item and the one most specific to Oracle VM Server for SPARC. It is very widely deployed in production environments as the means to provide network and disk availability, but it can be confusing. Subsequent articles will describe why and how to configure multiple service domains. Note, for the sake of precision: an I/O domain is any domain that has a physical I/O resource (such as a PCIe bus root complex). A service domain is a domain providing virtual device services to other domains; it is almost always an I/O domain too (so it can have something to serve). Resources Here are some important links; we'll be drawing on their content in the next several articles: Oracle VM Server for SPARC Documentation Maximizing Application Reliability and Availability with SPARC T5 Servers whitepaper by Gary Combs Maximizing Application Reliability and Availability with the SPARC M5-32 Server whitepaper by Gary Combs Summary Oracle VM Server for SPARC offers features that can be used to provide highly-available environments. This and the following blog entries will describe how to plan and deploy them.

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• SQL Table stored as a Heap - the dangers within

  - by MikeD

  Nearly all of the time I create a table, I include a primary key, and often that PK is implemented as a clustered index. Those two don't always have to go together, but in my world they almost always do. On a recent project, I was working on a data warehouse and a set of SSIS packages to import data from an OLTP database into my data warehouse. The data I was importing from the business database into the warehouse was mostly new rows, sometimes updates to existing rows, and sometimes deletes. I decided to use the MERGE statement to implement the insert, update or delete in the data warehouse, I found it quite performant to have a stored procedure that extracted all the new, updated, and deleted rows from the source database and dump it into a working table in my data warehouse, then run a stored proc in the warehouse that was the MERGE statement that took the rows from the working table and updated the real fact table. Use Warehouse CREATE TABLE Integration.MergePolicy (PolicyId int, PolicyTypeKey int, Premium money, Deductible money, EffectiveDate date, Operation varchar(5)) CREATE TABLE fact.Policy (PolicyKey int identity primary key, PolicyId int, PolicyTypeKey int, Premium money, Deductible money, EffectiveDate date) CREATE PROC Integration.MergePolicy as begin begin tran Merge fact.Policy as tgtUsing Integration.MergePolicy as SrcOn (tgt.PolicyId = Src.PolicyId) When not matched by Target then Insert (PolicyId, PolicyTypeKey, Premium, Deductible, EffectiveDate)values (src.PolicyId, src.PolicyTypeKey, src.Premium, src.Deductible, src.EffectiveDate) When matched and src.Operation = 'U' then Update set PolicyTypeKey = src.PolicyTypeKey,Premium = src.Premium,Deductible = src.Deductible,EffectiveDate = src.EffectiveDate When matched and src.Operation = 'D' then Delete ;delete from Integration.WorkPolicy commit end Notice that my worktable (Integration.MergePolicy) doesn't have any primary key or clustered index. I didn't think this would be a problem, since it was relatively small table and was empty after each time I ran the stored proc. For one of the work tables, during the initial loads of the warehouse, it was getting about 1.5 million rows inserted, processed, then deleted. Also, because of a bug in the extraction process, the same 1.5 million rows (plus a few hundred more each time) was getting inserted, processed, and deleted. This was being sone on a fairly hefty server that was otherwise unused, and no one was paying any attention to the time it was taking. This week I received a backup of this database and loaded it on my laptop to troubleshoot the problem, and of course it took a good ten minutes or more to run the process. However, what seemed strange to me was that after I fixed the problem and happened to run the merge sproc when the work table was completely empty, it still took almost ten minutes to complete. I immediately looked back at the MERGE statement to see if I had some sort of outer join that meant it would be scanning the target table (which had about 2 million rows in it), then turned on the execution plan output to see what was happening under the hood. Running the stored procedure again took a long time, and the plan output didn't show me much - 55% on the MERGE statement, and 45% on the DELETE statement, and table scans on the work table in both places. I was surprised at the relative cost of the DELETE statement, because there were really 0 rows to delete, but I was expecting to see the table scans. (I was beginning now to suspect that my problem was because the work table was being stored as a heap.) Then I turned on STATS_IO and ran the sproc again. The output was quite interesting.Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.Table 'Policy'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.Table 'MergePolicy'. Scan count 1, logical reads 433276, physical reads 60, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. I've reproduced the above from memory, the details aren't exact, but the essential bit was the very high number of logical reads on the table stored as a heap. Even just doing a SELECT Count(*) from Integration.MergePolicy incurred that sort of output, even though the result was always 0. I suppose I should research more on the allocation and deallocation of pages to tables stored as a heap, but I haven't, and my original assumption that a table stored as a heap with no rows would only need to read one page to answer any query was definitely proven wrong. It's likely that some sort of physical defragmentation of the table may have cleaned that up, but it seemed that the easiest answer was to put a clustered index on the table. After doing so, the execution plan showed a cluster index scan, and the IO stats showed only a single page read. (I aborted my first attempt at adding a clustered index on the table because it was taking too long - instead I ran TRUNCATE TABLE Integration.MergePolicy first and added the clustered index, both of which took very little time). I suspect I may not have noticed this if I had used TRUNCATE TABLE Integration.MergePolicy instead of DELETE FROM Integration.MergePolicy, since I'm guessing that the truncate operation does some rather quick releasing of pages allocated to the heap table. In the future, I will likely be much more careful to have a clustered index on every table I use, even the working tables. Mike  

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• Big Data – Buzz Words: What is HDFS – Day 8 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned what is MapReduce. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – HDFS. What is HDFS ? HDFS stands for Hadoop Distributed File System and it is a primary storage system used by Hadoop. It provides high performance access to data across Hadoop clusters. It is usually deployed on low-cost commodity hardware. In commodity hardware deployment server failures are very common. Due to the same reason HDFS is built to have high fault tolerance. The data transfer rate between compute nodes in HDFS is very high, which leads to reduced risk of failure. HDFS creates smaller pieces of the big data and distributes it on different nodes. It also copies each smaller piece to multiple times on different nodes. Hence when any node with the data crashes the system is automatically able to use the data from a different node and continue the process. This is the key feature of the HDFS system. Architecture of HDFS The architecture of the HDFS is master/slave architecture. An HDFS cluster always consists of single NameNode. This single NameNode is a master server and it manages the file system as well regulates access to various files. In additional to NameNode there are multiple DataNodes. There is always one DataNode for each data server. In HDFS a big file is split into one or more blocks and those blocks are stored in a set of DataNodes. The primary task of the NameNode is to open, close or rename files and directory and regulate access to the file system, whereas the primary task of the DataNode is read and write to the file systems. DataNode is also responsible for the creation, deletion or replication of the data based on the instruction from NameNode. In reality, NameNode and DataNode are software designed to run on commodity machine build in Java language. Visual Representation of HDFS Architecture Let us understand how HDFS works with the help of the diagram. Client APP or HDFS Client connects to NameSpace as well as DataNode. Client App access to the DataNode is regulated by NameSpace Node. NameSpace Node allows Client App to connect to the DataNode based by allowing the connection to the DataNode directly. A big data file is divided into multiple data blocks (let us assume that those data chunks are A,B,C and D. Client App will later on write data blocks directly to the DataNode. Client App does not have to directly write to all the node. It just has to write to any one of the node and NameNode will decide on which other DataNode it will have to replicate the data. In our example Client App directly writes to DataNode 1 and detained 3. However, data chunks are automatically replicated to other nodes. All the information like in which DataNode which data block is placed is written back to NameNode. High Availability During Disaster Now as multiple DataNode have same data blocks in the case of any DataNode which faces the disaster, the entire process will continue as other DataNode will assume the role to serve the specific data block which was on the failed node. This system provides very high tolerance to disaster and provides high availability. If you notice there is only single NameNode in our architecture. If that node fails our entire Hadoop Application will stop performing as it is a single node where we store all the metadata. As this node is very critical, it is usually replicated on another clustered as well as on another data rack. Though, that replicated node is not operational in architecture, it has all the necessary data to perform the task of the NameNode in the case of the NameNode fails. The entire Hadoop architecture is built to function smoothly even there are node failures or hardware malfunction. It is built on the simple concept that data is so big it is impossible to have come up with a single piece of the hardware which can manage it properly. We need lots of commodity (cheap) hardware to manage our big data and hardware failure is part of the commodity servers. To reduce the impact of hardware failure Hadoop architecture is built to overcome the limitation of the non-functioning hardware. Tomorrow In tomorrow’s blog post we will discuss the importance of the relational database in Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Computer Networks UNISA - Chap 14 &ndash; Insuring Integrity &amp; Availability

  - by MarkPearl

  After reading this section you should be able to Identify the characteristics of a network that keep data safe from loss or damage Protect an enterprise-wide network from viruses Explain network and system level fault tolerance techniques Discuss issues related to network backup and recovery strategies Describe the components of a useful disaster recovery plan and the options for disaster contingencies What are integrity and availability? Integrity – the soundness of a networks programs, data, services, devices, and connections Availability – How consistently and reliably a file or system can be accessed by authorized personnel A number of phenomena can compromise both integrity and availability including… security breaches natural disasters malicious intruders power flaws human error users etc Although you cannot predict every type of vulnerability, you can take measures to guard against the most damaging events. The following are some guidelines… Allow only network administrators to create or modify NOS and application system users. Monitor the network for unauthorized access or changes Record authorized system changes in a change management system’ Install redundant components Perform regular health checks on the network Check system performance, error logs, and the system log book regularly Keep backups Implement and enforce security and disaster recovery policies These are just some of the basics… Malware Malware refers to any program or piece of code designed to intrude upon or harm a system or its resources. Types of Malware… Boot sector viruses Macro viruses File infector viruses Worms Trojan Horse Network Viruses Bots Malware characteristics Some common characteristics of Malware include… Encryption Stealth Polymorphism Time dependence Malware Protection There are various tools available to protect you from malware called anti-malware software. These monitor your system for indications that a program is performing potential malware operations. A number of techniques are used to detect malware including… Signature Scanning Integrity Checking Monitoring unexpected file changes or virus like behaviours It is important to decide where anti-malware tools will be installed and find a balance between performance and protection. There are several general purpose malware policies that can be implemented to protect your network including… Every compute in an organization should be equipped with malware detection and cleaning software that regularly runs Users should not be allowed to alter or disable the anti-malware software Users should know what to do in case the anti-malware program detects a malware virus Users should be prohibited from installing any unauthorized software on their systems System wide alerts should be issued to network users notifying them if a serious malware virus has been detected. Fault Tolerance Besides guarding against malware, another key factor in maintaining the availability and integrity of data is fault tolerance. Fault tolerance is the ability for a system to continue performing despite an unexpected hardware or software malfunction. Fault tolerance can be realized in varying degrees, the optimal level of fault tolerance for a system depends on how critical its services and files are to productivity. Generally the more fault tolerant the system, the more expensive it is. The following describe some of the areas that need to be considered for fault tolerance. Environment (Temperature and humidity) Power Topology and Connectivity Servers Storage Power Typical power flaws include Surges – a brief increase in voltage due to lightening strikes, solar flares or some idiot at City Power Noise – Fluctuation in voltage levels caused by other devices on the network or electromagnetic interference Brownout – A sag in voltage for just a moment Blackout – A complete power loss The are various alternate power sources to consider including UPS’s and Generators. UPS’s are found in two categories… Standby UPS – provides continuous power when mains goes down (brief period of switching over) Online UPS – is online all the time and the device receives power from the UPS all the time (the UPS is charged continuously) Servers There are various techniques for fault tolerance with servers. Server mirroring is an option where one device or component duplicates the activities of another. It is generally an expensive process. Clustering is a fault tolerance technique that links multiple servers together to appear as a single server. They share processing and storage responsibilities and if one unit in the cluster goes down, another unit can be brought in to replace it. Storage There are various techniques available including the following… RAID Arrays NAS (Storage (Network Attached Storage) SANs (Storage Area Networks) Data Backup A backup is a copy of data or program files created for archiving or safekeeping. Many different options for backups exist with various media including… These vary in cost and speed. Optical Media Tape Backup External Disk Drives Network Backups Backup Strategy After selecting the appropriate tool for performing your servers backup, devise a backup strategy to guide you through performing reliable backups that provide maximum data protection. Questions that should be answered include… What data must be backed up At what time of day or night will the backups occur How will you verify the accuracy of the backups Where and for how long will backup media be stored Who will take responsibility for ensuring that backups occurred How long will you save backups Where will backup and recovery documentation be stored Different backup methods provide varying levels of certainty and corresponding labour cost. There are also different ways to determine which files should be backed up including… Full backup – all data on all servers is copied to storage media Incremental backup – Only data that has changed since the last full or incremental backup is copied to a storage medium Differential backup – Only data that has changed since the last backup is coped to a storage medium Disaster Recovery Disaster recovery is the process of restoring your critical functionality and data after an enterprise wide outage has occurred. A disaster recovery plan is for extreme scenarios (i.e. fire, line fault, etc). A cold site is a place were the computers, devices, and connectivity necessary to rebuild a network exist but they are not appropriately configured. A warm site is a place where the computers, devices, and connectivity necessary to rebuild a network exists with some appropriately configured devices. A hot site is a place where the computers, devices, and connectivity necessary to rebuild a network exists and all are appropriately configured.

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• Oracle NoSQL Database Exceeds 1 Million Mixed YCSB Ops/Sec

  - by Charles Lamb

  We ran a set of YCSB performance tests on Oracle NoSQL Database using SSD cards and Intel Xeon E5-2690 CPUs with the goal of achieving 1M mixed ops/sec on a 95% read / 5% update workload. We used the standard YCSB parameters: 13 byte keys and 1KB data size (1,102 bytes after serialization). The maximum database size was 2 billion records, or approximately 2 TB of data. We sized the shards to ensure that this was not an "in-memory" test (i.e. the data portion of the B-Trees did not fit into memory). All updates were durable and used the "simple majority" replica ack policy, effectively 'committing to the network'. All read operations used the Consistency.NONE_REQUIRED parameter allowing reads to be performed on any replica. In the past we have achieved 100K ops/sec using SSD cards on a single shard cluster (replication factor 3) so for this test we used 10 shards on 15 Storage Nodes with each SN carrying 2 Rep Nodes and each RN assigned to its own SSD card. After correcting a scaling problem in YCSB, we blew past the 1M ops/sec mark with 8 shards and proceeded to hit 1.2M ops/sec with 10 shards.  Hardware Configuration We used 15 servers, each configured with two 335 GB SSD cards. We did not have homogeneous CPUs across all 15 servers available to us so 12 of the 15 were Xeon E5-2690, 2.9 GHz, 2 sockets, 32 threads, 193 GB RAM, and the other 3 were Xeon E5-2680, 2.7 GHz, 2 sockets, 32 threads, 193 GB RAM.  There might have been some upside in having all 15 machines configured with the faster CPU, but since CPU was not the limiting factor we don't believe the improvement would be significant. The client machines were Xeon X5670, 2.93 GHz, 2 sockets, 24 threads, 96 GB RAM. Although the clients had 96 GB of RAM, neither the NoSQL Database or YCSB clients require anywhere near that amount of memory and the test could have just easily been run with much less. Networking was all 10GigE. YCSB Scaling Problem We made three modifications to the YCSB benchmark. The first was to allow the test to accommodate more than 2 billion records (effectively int's vs long's). To keep the key size constant, we changed the code to use base 32 for the user ids. The second change involved to the way we run the YCSB client in order to make the test itself horizontally scalable.The basic problem has to do with the way the YCSB test creates its Zipfian distribution of keys which is intended to model "real" loads by generating clusters of key collisions. Unfortunately, the percentage of collisions on the most contentious keys remains the same even as the number of keys in the database increases. As we scale up the load, the number of collisions on those keys increases as well, eventually exceeding the capacity of the single server used for a given key.This is not a workload that is realistic or amenable to horizontal scaling. YCSB does provide alternate key distribution algorithms so this is not a shortcoming of YCSB in general. We decided that a better model would be for the key collisions to be limited to a given YCSB client process. That way, as additional YCSB client processes (i.e. additional load) are added, they each maintain the same number of collisions they encounter themselves, but do not increase the number of collisions on a single key in the entire store. We added client processes proportionally to the number of records in the database (and therefore the number of shards). This change to the use of YCSB better models a use case where new groups of users are likely to access either just their own entries, or entries within their own subgroups, rather than all users showing the same interest in a single global collection of keys. If an application finds every user having the same likelihood of wanting to modify a single global key, that application has no real hope of getting horizontal scaling. Finally, we used read/modify/write (also known as "Compare And Set") style updates during the mixed phase. This uses versioned operations to make sure that no updates are lost. This mode of operation provides better application behavior than the way we have typically run YCSB in the past, and is only practical at scale because we eliminated the shared key collision hotspots.It is also a more realistic testing scenario. To reiterate, all updates used a simple majority replica ack policy making them durable. Scalability Results In the table below, the "KVS Size" column is the number of records with the number of shards and the replication factor. Hence, the first row indicates 400m total records in the NoSQL Database (KV Store), 2 shards, and a replication factor of 3. The "Clients" column indicates the number of YCSB client processes. "Threads" is the number of threads per process with the total number of threads. Hence, 90 threads per YCSB process for a total of 360 threads. The client processes were distributed across 10 client machines. Shards KVS Size Clients Mixed (records) Threads OverallThroughput(ops/sec) Read Latencyav/95%/99%(ms) Write Latencyav/95%/99%(ms) 2 400m(2x3) 4 90(360) 302,152 0.76/1/3 3.08/8/35 4 800m(4x3) 8 90(720) 558,569 0.79/1/4 3.82/16/45 8 1600m(8x3) 16 90(1440) 1,028,868 0.85/2/5 4.29/21/51 10 2000m(10x3) 20 90(1800) 1,244,550 0.88/2/6 4.47/23/53

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• Understanding the 'High Performance' meaning in Extreme Transaction Processing

  - by kyap

  Despite my previous blogs entries on SOA/BPM and Identity Management, the domain where I'm the most passionated is definitely the Extreme Transaction Processing, commonly called XTP.I came across XTP back to 2007 while I was still FMW Product Manager in EMEA. At that time Oracle acquired a company called Tangosol, which owned an unique product called Coherence that we renamed to Oracle Coherence. Beside this innovative renaming of the product, to be honest, I didn't know much about it, except being a "distributed in-memory cache for Extreme Transaction Processing"... not very helpful still.In general when people doesn't fully understand a technology or a concept, they tend to find some shortcuts, either correct or not, to justify their lack-of understanding... and of course I was part of this category of individuals. And the shortcut was "Oracle Coherence Cache helps to improve Performance". Excellent marketing slogan... but not very meaningful still. By chance I was able to get away quickly from that group in July 2007* at Thames Valley Park (UK), after I attended one of the most interesting workshops, in my 10 years career in Oracle, delivered by Brian Oliver. The biggest mistake I made was to assume that performance improvement with Coherence was related to the response time. Which can be considered as legitimus at that time, because after-all caches help to reduce latency on cached data access, hence reduce the response-time. But like all caches, you need to define caching and expiration policies, thinking about the cache-missed strategy, and most of the time you have to re-write partially your application in order to work with the cache. At a result, the expected benefit vanishes... so, not very useful then?The key mistake I made was my perception or obsession on how performance improvement should be driven, but I strongly believe this is still a common problem to most of the developers. In fact we all know the that the performance of a system is generally presented by the Capacity (or Throughput), with the 2 important dimensions Speed (response-time) and Volume (load) :Capacity (TPS) = Volume (T) / Speed (S)To increase the Capacity, we can either reduce the Speed(in terms of response-time), or to increase the Volume. However we tend to only focus on reducing the Speed dimension, perhaps it is more concrete and tangible to measure, and nicer to present to our management because there's a direct impact onto the end-users experience. On the other hand, we assume the Volume can be addressed by the underlying hardware or software stack, so if we need more capacity (scale out), we just add more hardware or software. Unfortunately, the reality proves that IT is never as ideal as we assume...The challenge with Speed improvement approach is that it is generally difficult and costly to make things already fast... faster. And by adding Coherence will not necessarily help either. Even though we manage to do so, the Capacity can not increase forever because... the Speed can be influenced by the Volume. For all system, we always have a performance illustration as follow: In all traditional system, the increase of Volume (Transaction) will also increase the Speed (Response-Time) as some point. The reason is simple: most of the time the Application logics were not designed to scale. As an example, if you have a while-loop in your application, it is natural to conceive that parsing 200 entries will require double execution-time compared to 100 entries. If you need to "Speed-up" the execution, you can only upgrade your hardware (scale-up) with faster CPU and/or network to reduce network latency. It is technically limited and economically inefficient. And this is exactly where XTP and Coherence kick in. The primary objective of XTP is about designing applications which can scale-out for increasing the Volume, by applying coding techniques to keep the execution-time as constant as possible, independently of the number of runtime data being manipulated. It is actually not just about having an application running as fast as possible, but about having a much more predictable system, with constant response-time and linearly scale, so we can easily increase throughput by adding more hardwares in parallel. It is in general combined with the Low Latency Programming model, where we tried to optimize the network usage as much as possible, either from the programmatic angle (less network-hoops to complete a task), and/or from a hardware angle (faster network equipments). In this picture, Oracle Coherence can be considered as software-level XTP enabler, via the Distributed-Cache because it can guarantee: - Constant Data Objects access time, independently from the number of Objects and the Coherence Cluster size - Data Objects Distribution by Affinity for in-memory data grouping - In-place Data Processing for parallel executionTo summarize, Oracle Coherence is indeed useful to improve your application performance, just not in the way we commonly think. It's not about the Speed itself, but about the overall Capacity with Extreme Load while keeping consistant Speed. In the future I will keep adding new blog entries around this topic, with some sample codes experiences sharing that I capture in the last few years. In the meanwhile if you want to know more how Oracle Coherence, I strongly suggest you to start with checking how our worldwide customers are using Oracle Coherence first, then you can start playing with the product through our tutorial.Have Fun !

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• What do the participants say about the Open Day in South Africa?

  - by Maria Sandu

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 On the 26th of September, a group of students who were specifically selected to attend an Open day at Oracle South Africa, joined us at our offices in Woodmead, Johannesburg. The Conference room was filled with inquisitive minds. What we had in store for them was a detailed presentation about Oracle which was delivered by Zuko - Cluster Leader: Tech GB South Africa. The student’s many questions were all answered especially when we started addressing the opportunities we have and detailed information on our Graduate Programme. Our employees then came to talk about their experience. This allowed all the students to have an integrated learning experience. By inviting the students to walk around our Oracle Offices allowed them to see, talk, experience a bit of the culture and ask more questions. Here is some of the feedback from the attendees: Maxwell Moloi: “The open day truly served its purpose and exceeded expectations in the sense that I got to find out more about Oracle and all the different opportunities it has to offer. The fact that Oracle supplies a full solution to a customer and not just part of it and how the company manages to setup professional development for their employees is what entices me to want to join the rapidly growing team of Oracle.” Nqobile Mabaso: “I found the open day to be quite informative and enlightening because coming from a marketing background I could apply the knowledge I got from varsity to the Company I was able to point out what they do as part of their corporate social responsibility (Oracle recently partnered with the department of education to build a school), how Oracle emphasizes on relationship building because they know they sell to people and not companies and how they offer the full stack of solutions which gives them a competitive advantage over their competitors.” Nondumiso Mvelase: “The Open Day was a wonderful experience for me especially because I have never been part of an Open Day before, so it was absolutely amazing for me. It gave me a good idea of how it is to be part of Oracle. We were served with lovely breakfast and lunch which I enjoyed. I wish the Open Day went on for a whole week. Seeing and hearing from 2013 Graduates, telling us about their experience within Oracle was very inspiring to me. They were encouraging us to work hard if we ever got the opportunity they had. After hearing this from them I will definitely not take it for granted.” Itumeleng Moraka: “Before I walked into the Oracle offices all that was in my mind was databases and cloud storage. I was then surrounded by passionate, enthusiastic and welcoming employees. I came across a positive energy within the multinational company. I realized that Oracle is not a company that operates in survival mode. This may sound idealistic, but they operate in a non-traditional way investing more into innovation, they stay focused on what matters most about where technology is going and at the same time they are not losing sight of how their products make a difference in the world.” For more information on how to be part of the Oracle Graduate Programme please follow us on Facebook! https://www.facebook.com/CampusAtOracle /* 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-family:"Calibri","sans-serif"; mso-ascii- mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi- mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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• Lesi, from Graduate Trainee to Territory Manager

  - by Maria Sandu

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 It’s the final year, University is now coming to an end. A new chapter now awaits my arrival. This part of my life is called “Looking for a Job”. With no form of experience whatsoever, getting a job at a well renowned IT company is something that every IT student dreams about. CV: v, Application form: v, interviews: v. Acceptance Call, “Lesi I’m pleased to inform you that you have been accepted to be part of the Oracle Graduate Program for 2012”. Life would never again be the same. Being Part of the Graduate Program Going into the Graduate program, I felt like a baby seeing candy for the first time. The Program gave me the platform to not only break in to the workplace but also to help launch my career. Over the next 3 months, I went through various trainings / workshops / events / coaching / mentorship sessions. Like a construction worker building a solid foundation for a beautifully designed architecture, a clear path to build my career was set. With training out the way, it was now time to start working closely with my team. For the rest of the year, it was all about selling. Sales, Pipeline, Forecasting and numbers soon became the common words in my career. As the saying goes, “once a sales man, always a sales man”. There was no turning back now, a career in sales was the new hustle in my life. I worked closely with my mentor & coach (Ibrahim) who was heading up Zambia and Malawi. This was to be one of my best moments in the program as I started engaging with customers and getting some hands on experience in the field. By the end of the program all the experience, hard work, training and resources came in handy as I was now ready and fully groomed to be a sales rep. Life after the Graduate Program I’m proud to say that now I’m a Territory Manager, heading up Malawi, selling Technology, Middleware & Applications across all industries. I’m part of the Transition Cluster Team, a powerful team headed by the seasoned Senior Director. As a Territory Manager my role is to push for coverage, to penetrate the market by selling Oracle from end- to- end to all accounts in Malawi. I now spend my days living out of a suitcase, moving from hotel to hotel, chasing after business in all areas of Malawi. It’s the life of a Sales Man and I’m enjoying every minute of it. I’m truly fortunate and grateful to have been part of such a wonderful graduate program. I owe my Sales career to the graduate program, and I truly hope that the program will continue to develop and to groom new talent amongst the youth of this world. If you're interested in joining the Graduate Program in South Africa keep an eye on our CampusatOracle Facebook Page page to get the latest updates! /* 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:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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• GI ????

  - by Allen Gao

  Normal 0 7.8 ? 0 2 false false false MicrosoftInternetExplorer4 classid="clsid:38481807-CA0E-42D2-BF39-B33AF135CC4D" id=ieooui st1\:*{behavior:url(#ieooui) } /* Style Definitions */ table.MsoNormalTable {mso-style-name:????; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} ??????????11gR2 GI ?????????,??????GI????????????????????? ????????GI???????3???,ohasd??,??????,??????? ??,ohasd??? 1. /etc/inittab?????? h1:35:respawn:/etc/init.d/init.ohasd run >/dev/null 2>&1 </dev/null ???,??????? root 4865 1 0 Dec02 ? 00:01:01 /bin/sh /etc/init.d/init.ohasd run ??????????????,???? +init.ohasd ????????? + os????????? + ??S* ohasd????, ??S96ohasd + GI????????(crsctl enable crs) ??,ohasd.bin ??????,????OLR????,??,??ohasd.bin??????,?????OLR??????????????OLR???$GRID_HOME/cdata/${HOSTNAME}.olr 2. ohasd.bin????????agents(orarootagent, oraagent, cssdagnet ? cssdmonitor) ???????????????,?????agent??????,??????????$GRID_HOME/bin ???????????,??,?????????,??corruption. ???,??????? 1. Mdnsd ??????(Multicast)???????????????????,??????????????????????????? 2. Gpnpd ????,??????????bootstrap ??,??????????????gpnp profile???,?????mdnsd??????,???????????,?????????????,??gpnp profile (<gi_home>/gpnp/profiles/peer/profile.xml)?????????? 3. Gipcd ????,????????????????(cluster interconnect)?????,???????gpnpd???,??,??????????,?????gpnpd ??????? 4. Ocssd.bin ?????????????gpnp profile?????????(Voting Disk),????gpnpd ??????????,?????????????,??ocssd.bin ??????,?????????? + gpnp profile ?????????? + gpnpd ??????? + ??????asm disk ??????????? + ??????????? 5. ??????????:ora.ctssd, ora.asm, ora.cluster_interconnect.haip, ora.crf, ora.crsd ?? ??:????????????????ocssd.bin, gpnpd.bin ? gipcd.bin ????,??gpnpd.bin????,ocssd.bin ? gipcd.bin ?????????,?gpnpd.bin????????,ocssd.bin ? gipcd.bin ????????gpnp profile?????????? ??,????????????,?????crsd????????? 1. Crsd?????????????OCR,????OCR????ASM?,???? ASM??????,??OCR???ASM??????????OCR???????,???????????????? 2. Crsd ?????agents(orarootagent, oraagent_<rdbms_owner>, oraagent_<gi_owner> )???agent????,??????????$GRID_HOME/bin ???????????,??,?????????,??corruption. 3. ????????  ora.net1.network : ????,?????????????,scanvip, vip, listener?????????????,??????????,vip, scanvip ?listener ??offline,?????????????? ora.<scan_name>.vip:scan???vip??,?????3?? ora.<node_name>.vip : ?????vip ?? ora.<listener_name>.lsnr: ???????????????,?11gR2??,listener.ora???????,????????? ora.LISTENER_SCAN<n>.lsnr: scan ????? ora.<????>.dg: ASM ????????????????mount???,dismount???? ora.<????>.db: ???????11gR2????????????,??????????rac ????????,??????????,???????“USR_ORA_INST_NAME@SERVERNAME(<node name> )”???????,??????????ASM???,???????????????????,??dependency?????????,??????????????????,???dependancy???????,??????(crsctl modify res ……)? ora.<???>.svc:?????????11gR2 ??,?????????,???10gR2??,???????????,srv ?cs ????? ora.cvu :?????11.2.0.2???,???????cluvfy??,???????????????? ora.ons : ONS??,????????,????? ??,?????GI??????????????????? $GRID_HOME/log/<node_name>/ocssd <== ocssd.bin ?? $GRID_HOME/log/<node_name>/gpnpd <== gpnpd.bin ?? $GRID_HOME/log/<node_name>/gipcd <== gipcd.bin ?? $GRID_HOME/log/<node_name>/agent/crsd <== crsd.bin ?? $GRID_HOME/log/<node_name>/agent/ohasd <== ohasd.bin ?? $GRID_HOME/log/<node_name>/mdnsd <== mdnsd.bin ?? $GRID_HOME/log/<node_name>/client <== ????GI ??(ocrdump, crsctl, ocrcheck, gpnptool??)??????????? $GRID_HOME/log/<node_name>/ctssd <== ctssd.bin ?? $GRID_HOME/log/<node_name>/crsd <== crsd.bin ?? $GRID_HOME/log/<node_name>/cvu <== cluvfy ????????? $GRID_HOME/bin/diagcollection.sh <== ????????????????? ??,????????(/var/tmp/.oracle ? /tmp/.oracle),??????????????????ipc???,??,?????????????????????,???GI?????????????????????,??????????GI??????????????

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