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• Microsoft TechEd 2010 - Day 3 @ Bangalore

  - by sathya

  Microsoft TechEd 2010 - Day 3 @ Bangalore Sorry for my delayed post on day 3 because I had to travel from Blore to Chennai So I couldnt write for the past two days. On day 3 as usual we had lot of simultaneous tracks on various sessions. This day I choose the Your Data, Our Platform Track. It had sessions on the following 5 topics :   Developing Data-tier Applications in Visual Studio 2010 - by Sanjay Nagamangalam SQL Server Query Optimization, Execution and Debugging Query Performance - by Vinod Kumar M SQL Server Utility - Its about more than 1 SQL Server - by Vinod Kumar Jagannathan Data Recovery / Consistency with CheckDB - by Vinod Kumar M Developing with SQL Server Spatial and Deep dive into Spatial Indexing - by Pinal Dave Developing Data-tier Applications in Visual Studio 2010 - by Sanjay Nagamangalam This was one of the superb sessions i have attended. He explained all the concepts in detail with a demo. The important thing in this is there is something called Data-Tier application project which is newly introduced in this VS2010 with which we can manage all our data along with our application inside our VS itself. We can create DB,Tables,Procs,Views etc. here itself and once we deploy it creates a compressed file called .dacpac which stores all the changes in Table Schema,Created procs, etc. on to that single file which reduces our (developer's) effort in preparing the deployment scripts and giving it to the DBA. It also has some policy configurations which can be managed easily by checking some rules like in outlook. For Ex : IF the SQL Server Version > 10 then deploy else dont. This rule specifies that even if we try to deploy on SQL Server DB with version less than 10 It will not do it. And if we deploy some .dacpac to SQL server production db with the option upgrade DB with this dacpac once everything completes successfully it will say success else it rollsback to the prior version. Even if it gets deployed successfully and later @ a point of time you wish to revert it back to the prior version, you can go ahead and delete the existing dacpac version so that it reverts to the older version of the db changes. And for the good questions that were asked in the session T-Shirts were given. SQL Server Query Optimization, Execution and Debugging Query Performance - by Vinod Kumar M This one too was the best session. The speaker Vinod explained everything very much clearly. This was really useful session and you dont believe, as per my knowledge, in the total 3 days in the TechEd except the Keynote, for this session seats were full (House FULL)  People were even standing out to attend this session. Such a great one it was. The speaker did a deep dive in to the Query Plan section and showed which actually causes the problem. Its all about the thing that we need to understand about the execution of SQL server Queries. We think in a way and SQL Server never executes in that way. We need to understand that first. He also told about there might be two plans generated for a single query at a point of time because of parallel processors in the system. The Key is here in every query. There is something called Estimated Row Count and Actual Row Count in the query plan. If the estimated row count by SQL server tallies with the actual row count your performance will be awesome. He said some tweaks to achieve the same. After this as usual we had lunch SQL Server Utility - Its about more than 1 SQL Server - by Vinod Kumar Jagannathan This was more of a DBA's session. Am really sorry I was totally blank and I was not interested to attend this session and walked out to attend Migrating to the cloud by Harish Ranganathan (My favorite Speaker) but unfortunately that was some other persons session. There the speaker was telling about how to configure the connection strings in such a way that we can connect to the SQL Azure platform from our VS and also showed us how to deploy the same in to Windows Azure. In between there were lot of technical problems like laptop hang, user locked and he was switching between systems, also i came in the half so i wasnt able to listen that fully. In between, Since I got an MCTS certification they gave me T-Shirt with the lines 'Iam Certified. Are you?' and they asked me to wear that. If we wear that we might get spotted and they would give us some goodies  So on the 3rd day I was wearing that T-Shirt. I got spotted by the person Tarun who was coordinating things about the certification, and he was accompanied with a cameraman and they interviewed me about the certification and I was shown live in the Teched and was seen by 60000 live viewers of the TechEd. I was really happy on that. Data Recovery / Consistency with CheckDB - by Vinod Kumar M This was one of the best sessions too in the TechEd. This guy is really amazing. In front of us he crashed a DB and showed how to recover the same in 6 different ways for different no of failures. Showed about Different types of error msgs like : 823,824,825 msdb..suspect_pages DBCC CheckDB (different parameters to it) I am really waiting for his session to get uploaded live in the Teched Website. Here is his contact info If you wish to connect to him : Twitter : @vinodk_sql Website : www.ExtremeExperts.com Blog : http://blogs.sqlxml.org/vinodkumar Developing with SQL Server Spatial and Deep dive into Spatial Indexing - by Pinal Dave Pinal Dave is a King in SQL and he is a SQL MVP and he is the owner of SQLAuthority.com He took the session on Spatial Databases from the start. Showed about the different types of Spatial : Geometric and Geographic Geometric : x and y axis its a planar surface Geographic : Spherical surface with 3600  as the maximum which is used to represent the geographic points on the earth and easy to draw maps of different kinds. He had a lot of obstacles during his session like rain coming inside the hall, mic wires got bursted due to rain, Videos off on the display screens. In spite of that he asked the audience to come in the front rows and managed to take a good session without ppts and finally we got the displays on and he was showing demos on the same what he explained orally. That was really a fun filled informative session. He gave some books for the persons who asked good questions and answered well for his questions and I got one too  (It was a book on Data Mining - Wrox Publishers) And finally after all these things there was Keynote session for close of the TechEd. and we all assembled in a big hall where Mr.Ashok Soota, a man of age around 70  co-founder of Mindtree was called to give some lecture on his successes. He was explaining about his past and what all companies he switched and for what reasons and what are all his successes and what are all his failures and the learnings of him from his past failures. and his success and failures on his partnerships with the other concern. And there were some questions for him like What is your suggestion on young entrepreneur? How did you learn from past failures? What is reiterating your success? What is your suggestion on partnerships? How to choose partnerships? etc. And they said @ 7.30 Pm there would be a party night, but unfortunately i was not able to attend that because I had to catch my train and before that i had to pack things, so I started @ 7 itself. Thats it about the TechED!!! Stay tuned for further Technology updates.

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• Chock-full of Identity Customers at Oracle OpenWorld

  - by Tanu Sood

    Oracle Openworld (OOW) 2012 kicks off this coming Sunday. Oracle OpenWorld is known to bring in Oracle customers, organizations big and small, from all over the world. And, Identity Management is no exception. If you are looking to catch up with Oracle Identity Management customers, hear first-hand about their implementation experiences and discuss industry trends, business drivers, solutions and more at OOW, here are some sessions we recommend you attend: Monday, October 1, 2012 CON9405: Trends in Identity Management 10:45 a.m. – 11:45 a.m., Moscone West 3003 Subject matter experts from Kaiser Permanente and SuperValu share the stage with Amit Jasuja, Snior Vice President, Oracle Identity Management and Security to discuss how the latest advances in Identity Management are helping customers address emerging requirements for securely enabling cloud, social and mobile environments. CON9492: Simplifying your Identity Management Implementation 3:15 p.m. – 4:15 p.m., Moscone West 3008 Implementation experts from British Telecom, Kaiser Permanente and UPMC participate in a panel to discuss best practices, key strategies and lessons learned based on their own experiences. Attendees will hear first-hand what they can do to streamline and simplify their identity management implementation framework for a quick return-on-investment and maximum efficiency. CON9444: Modernized and Complete Access Management 4:45 p.m. – 5:45 p.m., Moscone West 3008 We have come a long way from the days of web single sign-on addressing the core business requirements. Today, as technology and business evolves, organizations are seeking new capabilities like federation, token services, fine grained authorizations, web fraud prevention and strong authentication. This session will explore the emerging requirements for access management, what a complete solution is like, complemented with real-world customer case studies from ETS, Kaiser Permanente and TURKCELL and product demonstrations. Tuesday, October 2, 2012 CON9437: Mobile Access Management 10:15 a.m. – 11:15 a.m., Moscone West 3022 With more than 5 billion mobile devices on the planet and an increasing number of users using their own devices to access corporate data and applications, securely extending identity management to mobile devices has become a hot topic. This session will feature Identity Management evangelists from companies like Intuit, NetApp and Toyota to discuss how to extend your existing identity management infrastructure and policies to securely and seamlessly enable mobile user access. CON9491: Enhancing the End-User Experience with Oracle Identity Governance applications 11:45 a.m. – 12:45 p.m., Moscone West 3008 As organizations seek to encourage more and more user self service, business users are now primary end users for identity management installations.  Join experts from Visa and Oracle as they explore how Oracle Identity Governance solutions deliver complete identity administration and governance solutions with support for emerging requirements like cloud identities and mobile devices. CON9447: Enabling Access for Hundreds of Millions of Users 1:15 p.m. – 2:15 p.m., Moscone West 3008 Dealing with scale problems? Looking to address identity management requirements with million or so users in mind? Then take note of Cisco’s implementation. Join this session to hear first-hand how Cisco tackled identity management and scaled their implementation to bolster security and enforce compliance. CON9465: Next Generation Directory – Oracle Unified Directory 5:00 p.m. – 6:00 p.m., Moscone West 3008 Get the 360 degrees perspective from a solution provider, implementation services partner and the customer in this session to learn how the latest Oracle Unified Directory solutions can help you build a directory infrastructure that is optimized to support cloud, mobile and social networking and yet deliver on scale and performance. Wednesday, October 3, 2012 CON9494: Sun2Oracle: Identity Management Platform Transformation 11:45 a.m. – 12:45 p.m., Moscone West 3008 Sun customers are actively defining strategies for how they will modernize their identity deployments. Learn how customers like Avea and SuperValu are leveraging their Sun investment, evaluating areas of expansion/improvement and building momentum. CON9631: Entitlement-centric Access to SOA and Cloud Services 11:45 a.m. – 12:45 p.m., Marriott Marquis, Salon 7 How do you enforce that a junior trader can submit 10 trades/day, with a total value of $5M, if market volatility is low? How can hide sensitive patient information from clerical workers but make it visible to specialists as long as consent has been given or there is an emergency? How do you externalize such entitlements to allow dynamic changes without having to touch the application code? In this session, Uberether and HerbaLife take the stage with Oracle to demonstrate how you can enforce such entitlements on a service not just within your intranet but also right at the perimeter. CON3957 - Delivering Secure Wi-Fi on the Tube as an Olympics Legacy from London 2012 11:45 a.m. – 12:45 p.m., Moscone West 3003 In this session, Virgin Media, the U.K.’s first combined provider of broadband, TV, mobile, and home phone services, shares how it is providing free secure Wi-Fi services to the London Underground, using Oracle Virtual Directory and Oracle Entitlements Server, leveraging back-end legacy systems that were never designed to be externalized. As an Olympics 2012 legacy, the Oracle architecture will form a platform to be consumed by other Virgin Media services such as video on demand. CON9493: Identity Management and the Cloud 1:15 p.m. – 2:15 p.m., Moscone West 3008 Security is the number one barrier to cloud service adoption.  Not so for industry leading companies like SaskTel, ConAgra foods and UPMC. This session will explore how these organizations are using Oracle Identity with cloud services and how some are offering identity management as a cloud service. CON9624: Real-Time External Authorization for Middleware, Applications, and Databases 3:30 p.m. – 4:30 p.m., Moscone West 3008 As organizations seek to grant access to broader and more diverse user populations, the importance of centrally defined and applied authorization policies become critical; both to identify who has access to what and to improve the end user experience.  This session will explore how customers are using attribute and role-based access to achieve these goals. CON9625: Taking control of WebCenter Security 5:00 p.m. – 6:00 p.m., Moscone West 3008 Many organizations are extending WebCenter in a business to business scenario requiring secure identification and authorization of business partners and their users. Leveraging LADWP’s use case, this session will focus on how customers are leveraging, securing and providing access control to Oracle WebCenter portal and mobile solutions. Thursday, October 4, 2012 CON9662: Securing Oracle Applications with the Oracle Enterprise Identity Management Platform 2:15 p.m. – 3:15 p.m., Moscone West 3008 Oracle Enterprise identity Management solutions are designed to secure access and simplify compliance to Oracle Applications.  Whether you are an EBS customer looking to upgrade from Oracle Single Sign-on or a Fusion Application customer seeking to leverage the Identity instance as an enterprise security platform, this session with Qualcomm and Oracle will help you understand how to get the most out of your investment. And here’s the complete listing of all the Identity Management sessions at Oracle OpenWorld.

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• Announcing Windows Azure Mobile Services

  - by ScottGu

  I’m excited to announce a new capability we are adding to Windows Azure today: Windows Azure Mobile Services Windows Azure Mobile Services makes it incredibly easy to connect a scalable cloud backend to your client and mobile applications.  It allows you to easily store structured data in the cloud that can span both devices and users, integrate it with user authentication, as well as send out updates to clients via push notifications. Today’s release enables you to add these capabilities to any Windows 8 app in literally minutes, and provides a super productive way for you to quickly build out your app ideas.  We’ll also be adding support to enable these same scenarios for Windows Phone, iOS, and Android devices soon. Read this getting started tutorial to walkthrough how you can build (in less than 5 minutes) a simple Windows 8 “Todo List” app that is cloud enabled using Windows Azure Mobile Services.  Or watch this video of me showing how to do it step by step. Getting Started If you don’t already have a Windows Azure account, you can sign up for a no-obligation Free Trial.  Once you are signed-up, click the “preview features” section under the “account” tab of the www.windowsazure.com website and enable your account to support the “Mobile Services” preview.   Instructions on how to enable this can be found here. Once you have the mobile services preview enabled, log into the Windows Azure Portal, click the “New” button and choose the new “Mobile Services” icon to create your first mobile backend.  Once created, you’ll see a quick-start page like below with instructions on how to connect your mobile service to an existing Windows 8 client app you have already started working on, or how to create and connect a brand-new Windows 8 client app with it: Read this getting started tutorial to walkthrough how you can build (in less than 5 minutes) a simple Windows 8 “Todo List” app  that stores data in Windows Azure. Storing Data in the Cloud Storing data in the cloud with Windows Azure Mobile Services is incredibly easy.  When you create a Windows Azure Mobile Service, we automatically associate it with a SQL Database inside Windows Azure.  The Windows Azure Mobile Service backend then provides built-in support for enabling remote apps to securely store and retrieve data from it (using secure REST end-points utilizing a JSON-based ODATA format) – without you having to write or deploy any custom server code.  Built-in management support is provided within the Windows Azure portal for creating new tables, browsing data, setting indexes, and controlling access permissions. This makes it incredibly easy to connect client applications to the cloud, and enables client developers who don’t have a server-code background to be productive from the very beginning.  They can instead focus on building the client app experience, and leverage Windows Azure Mobile Services to provide the cloud backend services they require.  Below is an example of client-side Windows 8 C#/XAML code that could be used to query data from a Windows Azure Mobile Service.  Client-side C# developers can write queries like this using LINQ and strongly typed POCO objects, which are then translated into HTTP REST queries that run against a Windows Azure Mobile Service.   Developers don’t have to write or deploy any custom server-side code in order to enable client-side code below to execute and asynchronously populate their client UI: Because Mobile Services is part of Windows Azure, developers can later choose to augment or extend their initial solution and add custom server functionality and more advanced logic if they want.  This provides maximum flexibility, and enables developers to grow and extend their solutions to meet any needs. User Authentication and Push Notifications Windows Azure Mobile Services also make it incredibly easy to integrate user authentication/authorization and push notifications within your applications.  You can use these capabilities to enable authentication and fine grain access control permissions to the data you store in the cloud, as well as to trigger push notifications to users/devices when the data changes.  Windows Azure Mobile Services supports the concept of “server scripts” (small chunks of server-side script that executes in response to actions) that make it really easy to enable these scenarios. Below are some tutorials that walkthrough common authentication/authorization/push scenarios you can do with Windows Azure Mobile Services and Windows 8 apps: Enabling User Authentication Authorizing Users  Get Started with Push Notifications Push Notifications to multiple Users Manage and Monitor your Mobile Service Just like with every other service in Windows Azure, you can monitor usage and metrics of your mobile service backend using the “Dashboard” tab within the Windows Azure Portal. The dashboard tab provides a built-in monitoring view of the API calls, Bandwidth, and server CPU cycles of your Windows Azure Mobile Service.   You can also use the “Logs” tab within the portal to review error messages.  This makes it easy to monitor and track how your application is doing. Scale Up as Your Business Grows Windows Azure Mobile Services now allows every Windows Azure customer to create and run up to 10 Mobile Services in a free, shared/multi-tenant hosting environment (where your mobile backend will be one of multiple apps running on a shared set of server resources).  This provides an easy way to get started on projects at no cost beyond the database you connect your Windows Azure Mobile Service to (note: each Windows Azure free trial account also includes a 1GB SQL Database that you can use with any number of apps or Windows Azure Mobile Services). If your client application becomes popular, you can click the “Scale” tab of your Mobile Service and switch from “Shared” to “Reserved” mode.  Doing so allows you to isolate your apps so that you are the only customer within a virtual machine.  This allows you to elastically scale the amount of resources your apps use – allowing you to scale-up (or scale-down) your capacity as your traffic grows: With Windows Azure you pay for compute capacity on a per-hour basis – which allows you to scale up and down your resources to match only what you need.  This enables a super flexible model that is ideal for new mobile app scenarios, as well as startups who are just getting going.  Summary I’ve only scratched the surface of what you can do with Windows Azure Mobile Services – there are a lot more features to explore.  With Windows Azure Mobile Services you’ll be able to build mobile app experiences faster than ever, and enable even better user experiences – by connecting your client apps to the cloud. Visit the Windows Azure Mobile Services development center to learn more, and build your first Windows 8 app connected with Windows Azure today.  And read this getting started tutorial to walkthrough how you can build (in less than 5 minutes) a simple Windows 8 “Todo List” app that is cloud enabled using Windows Azure Mobile Services. Hope this helps, Scott P.S. In addition to blogging, I am also now using Twitter for quick updates and to share links. Follow me at: twitter.com/scottgu

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• SPARC T3-1 Record Results Running JD Edwards EnterpriseOne Day in the Life Benchmark with Added Batch Component

  - by Brian

  Using Oracle's SPARC T3-1 server for the application tier and Oracle's SPARC Enterprise M3000 server for the database tier, a world record result was produced running the Oracle's JD Edwards EnterpriseOne applications Day in the Life benchmark run concurrently with a batch workload. The SPARC T3-1 server based result has 25% better performance than the IBM Power 750 POWER7 server even though the IBM result did not include running a batch component. The SPARC T3-1 server based result has 25% better space/performance than the IBM Power 750 POWER7 server as measured by the online component. The SPARC T3-1 server based result is 5x faster than the x86-based IBM x3650 M2 server system when executing the online component of the JD Edwards EnterpriseOne 9.0.1 Day in the Life benchmark. The IBM result did not include a batch component. The SPARC T3-1 server based result has 2.5x better space/performance than the x86-based IBM x3650 M2 server as measured by the online component. The combination of SPARC T3-1 and SPARC Enterprise M3000 servers delivered a Day in the Life benchmark result of 5000 online users with 0.875 seconds of average transaction response time running concurrently with 19 Universal Batch Engine (UBE) processes at 10 UBEs/minute. The solution exercises various JD Edwards EnterpriseOne applications while running Oracle WebLogic Server 11g Release 1 and Oracle Web Tier Utilities 11g HTTP server in Oracle Solaris Containers, together with the Oracle Database 11g Release 2. The SPARC T3-1 server showed that it could handle the additional workload of batch processing while maintaining the same number of online users for the JD Edwards EnterpriseOne Day in the Life benchmark. This was accomplished with minimal loss in response time. JD Edwards EnterpriseOne 9.0.1 takes advantage of the large number of compute threads available in the SPARC T3-1 server at the application tier and achieves excellent response times. The SPARC T3-1 server consolidates the application/web tier of the JD Edwards EnterpriseOne 9.0.1 application using Oracle Solaris Containers. Containers provide flexibility, easier maintenance and better CPU utilization of the server leaving processing capacity for additional growth. A number of Oracle advanced technology and features were used to obtain this result: Oracle Solaris 10, Oracle Solaris Containers, Oracle Java Hotspot Server VM, Oracle WebLogic Server 11g Release 1, Oracle Web Tier Utilities 11g, Oracle Database 11g Release 2, the SPARC T3 and SPARC64 VII+ based servers. This is the first published result running both online and batch workload concurrently on the JD Enterprise Application server. No published results are available from IBM running the online component together with a batch workload. The 9.0.1 version of the benchmark saw some minor performance improvements relative to 9.0. When comparing between 9.0.1 and 9.0 results, the reader should take this into account when the difference between results is small. Performance Landscape JD Edwards EnterpriseOne Day in the Life Benchmark Online with Batch Workload This is the first publication on the Day in the Life benchmark run concurrently with batch jobs. The batch workload was provided by Oracle's Universal Batch Engine. System RackUnits Online Users Resp Time (sec) BatchConcur(# of UBEs) BatchRate(UBEs/m) Version SPARC T3-1, 1xSPARC T3 (1.65 GHz), Solaris 10 M3000, 1xSPARC64 VII+ (2.86 GHz), Solaris 10 4 5000 0.88 19 10 9.0.1 Resp Time (sec) — Response time of online jobs reported in seconds Batch Concur (# of UBEs) — Batch concurrency presented in the number of UBEs Batch Rate (UBEs/m) — Batch transaction rate in UBEs/minute. JD Edwards EnterpriseOne Day in the Life Benchmark Online Workload Only These results are for the Day in the Life benchmark. They are run without any batch workload. System RackUnits Online Users ResponseTime (sec) Version SPARC T3-1, 1xSPARC T3 (1.65 GHz), Solaris 10 M3000, 1xSPARC64 VII (2.75 GHz), Solaris 10 4 5000 0.52 9.0.1 IBM Power 750, 1xPOWER7 (3.55 GHz), IBM i7.1 4 4000 0.61 9.0 IBM x3650M2, 2xIntel X5570 (2.93 GHz), OVM 2 1000 0.29 9.0 IBM result from http://www-03.ibm.com/systems/i/advantages/oracle/, IBM used WebSphere Configuration Summary Hardware Configuration: 1 x SPARC T3-1 server 1 x 1.65 GHz SPARC T3 128 GB memory 16 x 300 GB 10000 RPM SAS 1 x Sun Flash Accelerator F20 PCIe Card, 92 GB 1 x 10 GbE NIC 1 x SPARC Enterprise M3000 server 1 x 2.86 SPARC64 VII+ 64 GB memory 1 x 10 GbE NIC 2 x StorageTek 2540 + 2501 Software Configuration: JD Edwards EnterpriseOne 9.0.1 with Tools 8.98.3.3 Oracle Database 11g Release 2 Oracle 11g WebLogic server 11g Release 1 version 10.3.2 Oracle Web Tier Utilities 11g Oracle Solaris 10 9/10 Mercury LoadRunner 9.10 with Oracle Day in the Life kit for JD Edwards EnterpriseOne 9.0.1 Oracle’s Universal Batch Engine - Short UBEs and Long UBEs 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 other 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 workload of 15 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 UBEs, and the QPROCESS queue for short UBEs run concurrently. One of the Oracle Solaris Containers ran 4 Long UBEs, while another Container ran 15 short UBEs concurrently. The mixed size UBEs ran concurrently from the SPARC T3-1 server with the 5000 online users driven by the LoadRunner. 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 and two Oracle Fusion Middleware WebLogic Servers 11g R1 coupled with two Oracle Fusion Middleware 11g Web Tier HTTP Server instances on the SPARC T3-1 server were hosted in four separate Oracle Solaris Containers to demonstrate consolidation of multiple application and web servers. See Also SPARC T3-1 oracle.com SPARC Enterprise M3000 oracle.com Oracle Solaris oracle.com JD Edwards EnterpriseOne oracle.com Oracle Database 11g Release 2 Enterprise Edition oracle.com Disclosure Statement Copyright 2011, 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 6/27/2011.

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• Pre-rentrée Oracle Open World 2012 : à vos agendas

  - by Eric Bezille

  A maintenant moins d'un mois de l’événement majeur d'Oracle, qui se tient comme chaque année à San Francisco, fin septembre, début octobre, les spéculations vont bon train sur les annonces qui vont y être dévoilées... Et sans lever le voile, je vous engage à prendre connaissance des sujets des "Key Notes" qui seront tenues par Larry Ellison, Mark Hurd, Thomas Kurian (responsable des développements logiciels) et John Fowler (responsable des développements systèmes) afin de vous donner un avant goût. Stratégie et Roadmaps Oracle Bien entendu, au-delà des séances plénières qui vous donnerons  une vision précise de la stratégie, et pour ceux qui seront sur place, je vous engage à ne pas manquer les séances d'approfondissement qui auront lieu dans la semaine, dont voici quelques morceaux choisis : "Accelerate your Business with the Oracle Hardware Advantage" avec John Fowler, le lundi 1er Octobre, 3:15pm-4:15pm "Why Oracle Softwares Runs Best on Oracle Hardware" , avec Bradley Carlile, le responsable des Benchmarks, le lundi 1er Octobre, 12:15pm-13:15pm "Engineered Systems - from Vision to Game-changing Results", avec Robert Shimp, le lundi 1er Octobre 1:45pm-2:45pm "Database and Application Consolidation on SPARC Supercluster", avec Hugo Rivero, responsable dans les équipes d'intégration matériels et logiciels, le lundi 1er Octobre, 4:45pm-5:45pm "Oracle’s SPARC Server Strategy Update", avec Masood Heydari, responsable des développements serveurs SPARC, le mardi 2 Octobre, 10:15am - 11:15am "Oracle Solaris 11 Strategy, Engineering Insights, and Roadmap", avec Markus Flier, responsable des développements Solaris, le mercredi 3 Octobre, 10:15am - 11:15am "Oracle Virtualization Strategy and Roadmap", avec Wim Coekaerts, responsable des développement Oracle VM et Oracle Linux, le lundi 1er Octobre, 12:15pm-1:15pm "Big Data: The Big Story", avec Jean-Pierre Dijcks, responsable du développement produits Big Data, le lundi 1er Octobre, 3:15pm-4:15pm "Scaling with the Cloud: Strategies for Storage in Cloud Deployments", avec Christine Rogers,  Principal Product Manager, et Chris Wood, Senior Product Specialist, Stockage , le lundi 1er Octobre, 10:45am-11:45am Retours d'expériences et témoignages Si Oracle Open World est l'occasion de partager avec les équipes de développement d'Oracle en direct, c'est aussi l'occasion d'échanger avec des clients et experts qui ont mis en oeuvre  nos technologies pour bénéficier de leurs retours d'expériences, comme par exemple : "Oracle Optimized Solution for Siebel CRM at ACCOR", avec les témoignages d'Eric Wyttynck, directeur IT Multichannel & CRM  et Pascal Massenet, VP Loyalty & CRM systems, sur les bénéfices non seulement métiers, mais également projet et IT, le mercredi 3 Octobre, 1:15pm-2:15pm "Tips from AT&T: Oracle E-Business Suite, Oracle Database, and SPARC Enterprise", avec le retour d'expérience des experts Oracle, le mardi 2 Octobre, 11:45am-12:45pm "Creating a Maximum Availability Architecture with SPARC SuperCluster", avec le témoignage de Carte Wright, Database Engineer à CKI, le mercredi 3 Octobre, 11:45am-12:45pm "Multitenancy: Everybody Talks It, Oracle Walks It with Pillar Axiom Storage", avec le témoignage de Stephen Schleiger, Manager Systems Engineering de Navis, le lundi 1er Octobre, 1:45pm-2:45pm "Oracle Exadata for Database Consolidation: Best Practices", avec le retour d'expérience des experts Oracle ayant participé à la mise en oeuvre d'un grand client du monde bancaire, le lundi 1er Octobre, 4:45pm-5:45pm "Oracle Exadata Customer Panel: Packaged Applications with Oracle Exadata", animé par Tim Shetler, VP Product Management, mardi 2 Octobre, 1:15pm-2:15pm "Big Data: Improving Nearline Data Throughput with the StorageTek SL8500 Modular Library System", avec le témoignage du CTO de CSC, Alan Powers, le jeudi 4 Octobre, 12:45pm-1:45pm "Building an IaaS Platform with SPARC, Oracle Solaris 11, and Oracle VM Server for SPARC", avec le témoignage de Syed Qadri, Lead DBA et Michael Arnold, System Architect d'US Cellular, le mardi 2 Octobre, 10:15am-11:15am "Transform Data Center TCO with Oracle Optimized Servers: A Customer Panel", avec les témoignages notamment d'AT&T et Liberty Global, le mardi 2 Octobre, 11:45am-12:45pm "Data Warehouse and Big Data Customers’ View of the Future", avec The Nielsen Company US, Turkcell, GE Retail Finance, Allianz Managed Operations and Services SE, le lundi 1er Octobre, 4:45pm-5:45pm "Extreme Storage Scale and Efficiency: Lessons from a 100,000-Person Organization", le témoignage de l'IT interne d'Oracle sur la transformation et la migration de l'ensemble de notre infrastructure de stockage, mardi 2 Octobre, 1:15pm-2:15pm Echanges avec les groupes d'utilisateurs et les équipes de développement Oracle Si vous avez prévu d'arriver suffisamment tôt, vous pourrez également échanger dès le dimanche avec les groupes d'utilisateurs, ou tous les soirs avec les équipes de développement Oracle sur des sujets comme : "To Exalogic or Not to Exalogic: An Architectural Journey", avec Todd Sheetz - Manager of DBA and Enterprise Architecture, Veolia Environmental Services, le dimanche 30 Septembre, 2:30pm-3:30pm "Oracle Exalytics and Oracle TimesTen for Exalytics Best Practices", avec Mark Rittman, de Rittman Mead Consulting Ltd, le dimanche 30 Septembre, 10:30am-11:30am "Introduction of Oracle Exadata at Telenet: Bringing BI to Warp Speed", avec Rudy Verlinden & Eric Bartholomeus - Managers IT infrastructure à Telenet, le dimanche 30 Septembre, 1:15pm-2:00pm "The Perfect Marriage: Sun ZFS Storage Appliance with Oracle Exadata", avec Melanie Polston, directeur, Data Management, de Novation et Charles Kim, Managing Director de Viscosity, le dimanche 30 Septembre, 9:00am-10am "Oracle’s Big Data Solutions: NoSQL, Connectors, R, and Appliance Technologies", avec Jean-Pierre Dijcks et les équipes de développement Oracle, le lundi 1er Octobre, 6:15pm-7:00pm Testez et évaluez les solutions Et pour finir, vous pouvez même tester les technologies au travers du Oracle DemoGrounds, (1133 Moscone South pour la partie Systèmes Oracle, OS, et Virtualisation) et des "Hands-on-Labs", comme : "Deploying an IaaS Environment with Oracle VM", le mardi 2 Octobre, 10:15am-11:15am "Virtualize and Deploy Oracle Applications in Minutes with Oracle VM: Hands-on Lab", le mardi 2 Octobre, 11:45am-12:45pm (il est fortement conseillé d'avoir suivi le "Hands-on-Labs" précédent avant d'effectuer ce Lab. "x86 Enterprise Cloud Infrastructure with Oracle VM 3.x and Sun ZFS Storage Appliance", le mercredi 3 Octobre, 5:00pm-6:00pm "StorageTek Tape Analytics: Managing Tape Has Never Been So Simple", le mercredi 3 Octobre, 1:15pm-2:15pm "Oracle’s Pillar Axiom 600 Storage System: Power and Ease", le lundi 1er Octobre, 12:15pm-1:15pm "Enterprise Cloud Infrastructure for SPARC with Oracle Enterprise Manager Ops Center 12c", le lundi 1er Octobre, 1:45pm-2:45pm "Managing Storage in the Cloud", le mardi 2 Octobre, 5:00pm-6:00pm "Learn How to Write MapReduce on Oracle’s Big Data Platform", le lundi 1er Octobre, 12:15pm-1:15pm "Oracle Big Data Analytics and R", le mardi 2 Octobre, 1:15pm-2:15pm "Reduce Risk with Oracle Solaris Access Control to Restrain Users and Isolate Applications", le lundi 1er Octobre, 10:45am-11:45am "Managing Your Data with Built-In Oracle Solaris ZFS Data Services in Release 11", le lundi 1er Octobre, 4:45pm-5:45pm "Virtualizing Your Oracle Solaris 11 Environment", le mardi 2 Octobre, 1:15pm-2:15pm "Large-Scale Installation and Deployment of Oracle Solaris 11", le mercredi 3 Octobre, 3:30pm-4:30pm En conclusion, une semaine très riche en perspective, et qui vous permettra de balayer l'ensemble des sujets au coeur de vos préoccupations, de la stratégie à l'implémentation... Cette semaine doit se préparer, pour tailler votre agenda sur mesure, à travers les plus de 2000 sessions dont je ne vous ai fait qu'un extrait, et dont vous pouvez retrouver l'ensemble en ligne.

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

  - by Mat Keep

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

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• Optimizing AES modes on Solaris for Intel Westmere

  - by danx

  Optimizing AES modes on Solaris for Intel Westmere Review AES is a strong method of symmetric (secret-key) encryption. It is a U.S. FIPS-approved cryptographic algorithm (FIPS 197) that operates on 16-byte blocks. AES has been available since 2001 and is widely used. However, AES by itself has a weakness. AES encryption isn't usually used by itself because identical blocks of plaintext are always encrypted into identical blocks of ciphertext. This encryption can be easily attacked with "dictionaries" of common blocks of text and allows one to more-easily discern the content of the unknown cryptotext. This mode of encryption is called "Electronic Code Book" (ECB), because one in theory can keep a "code book" of all known cryptotext and plaintext results to cipher and decipher AES. In practice, a complete "code book" is not practical, even in electronic form, but large dictionaries of common plaintext blocks is still possible. Here's a diagram of encrypting input data using AES ECB mode: Block 1 Block 2 PlainTextInput PlainTextInput | | | | \/ \/ AESKey-->(AES Encryption) AESKey-->(AES Encryption) | | | | \/ \/ CipherTextOutput CipherTextOutput Block 1 Block 2 What's the solution to the same cleartext input producing the same ciphertext output? The solution is to further process the encrypted or decrypted text in such a way that the same text produces different output. This usually involves an Initialization Vector (IV) and XORing the decrypted or encrypted text. As an example, I'll illustrate CBC mode encryption: Block 1 Block 2 PlainTextInput PlainTextInput | | | | \/ \/ IV >----->(XOR) +------------->(XOR) +---> . . . . | | | | | | | | \/ | \/ | AESKey-->(AES Encryption) | AESKey-->(AES Encryption) | | | | | | | | | \/ | \/ | CipherTextOutput ------+ CipherTextOutput -------+ Block 1 Block 2 The steps for CBC encryption are: Start with a 16-byte Initialization Vector (IV), choosen randomly. XOR the IV with the first block of input plaintext Encrypt the result with AES using a user-provided key. The result is the first 16-bytes of output cryptotext. Use the cryptotext (instead of the IV) of the previous block to XOR with the next input block of plaintext Another mode besides CBC is Counter Mode (CTR). As with CBC mode, it also starts with a 16-byte IV. However, for subsequent blocks, the IV is just incremented by one. Also, the IV ix XORed with the AES encryption result (not the plain text input). Here's an illustration: Block 1 Block 2 PlainTextInput PlainTextInput | | | | \/ \/ AESKey-->(AES Encryption) AESKey-->(AES Encryption) | | | | \/ \/ IV >----->(XOR) IV + 1 >---->(XOR) IV + 2 ---> . . . . | | | | \/ \/ CipherTextOutput CipherTextOutput Block 1 Block 2 Optimization Which of these modes can be parallelized? ECB encryption/decryption can be parallelized because it does more than plain AES encryption and decryption, as mentioned above. CBC encryption can't be parallelized because it depends on the output of the previous block. However, CBC decryption can be parallelized because all the encrypted blocks are known at the beginning. CTR encryption and decryption can be parallelized because the input to each block is known--it's just the IV incremented by one for each subsequent block. So, in summary, for ECB, CBC, and CTR modes, encryption and decryption can be parallelized with the exception of CBC encryption. How do we parallelize encryption? By interleaving. Usually when reading and writing data there are pipeline "stalls" (idle processor cycles) that result from waiting for memory to be loaded or stored to or from CPU registers. Since the software is written to encrypt/decrypt the next data block where pipeline stalls usually occurs, we can avoid stalls and crypt with fewer cycles. This software processes 4 blocks at a time, which ensures virtually no waiting ("stalling") for reading or writing data in memory. Other Optimizations Besides interleaving, other optimizations performed are Loading the entire key schedule into the 128-bit %xmm registers. This is done once for per 4-block of data (since 4 blocks of data is processed, when present). The following is loaded: the entire "key schedule" (user input key preprocessed for encryption and decryption). This takes 11, 13, or 15 registers, for AES-128, AES-192, and AES-256, respectively The input data is loaded into another %xmm register The same register contains the output result after encrypting/decrypting Using SSSE 4 instructions (AESNI). Besides the aesenc, aesenclast, aesdec, aesdeclast, aeskeygenassist, and aesimc AESNI instructions, Intel has several other instructions that operate on the 128-bit %xmm registers. Some common instructions for encryption are: pxor exclusive or (very useful), movdqu load/store a %xmm register from/to memory, pshufb shuffle bytes for byte swapping, pclmulqdq carry-less multiply for GCM mode Combining AES encryption/decryption with CBC or CTR modes processing. Instead of loading input data twice (once for AES encryption/decryption, and again for modes (CTR or CBC, for example) processing, the input data is loaded once as both AES and modes operations occur at in the same function Performance Everyone likes pretty color charts, so here they are. I ran these on Solaris 11 running on a Piketon Platform system with a 4-core Intel Clarkdale processor @3.20GHz. Clarkdale which is part of the Westmere processor architecture family. The "before" case is Solaris 11, unmodified. Keep in mind that the "before" case already has been optimized with hand-coded Intel AESNI assembly. The "after" case has combined AES-NI and mode instructions, interleaved 4 blocks at-a-time. « For the first table, lower is better (milliseconds). The first table shows the performance improvement using the Solaris encrypt(1) and decrypt(1) CLI commands. I encrypted and decrypted a 1/2 GByte file on /tmp (swap tmpfs). Encryption improved by about 40% and decryption improved by about 80%. AES-128 is slighty faster than AES-256, as expected. The second table shows more detail timings for CBC, CTR, and ECB modes for the 3 AES key sizes and different data lengths. » The results shown are the percentage improvement as shown by an internal PKCS#11 microbenchmark. And keep in mind the previous baseline code already had optimized AESNI assembly! The keysize (AES-128, 192, or 256) makes little difference in relative percentage improvement (although, of course, AES-128 is faster than AES-256). Larger data sizes show better improvement than 128-byte data. Availability This software is in Solaris 11 FCS. It is available in the 64-bit libcrypto library and the "aes" Solaris kernel module. You must be running hardware that supports AESNI (for example, Intel Westmere and Sandy Bridge, microprocessor architectures). The easiest way to determine if AES-NI is available is with the isainfo(1) command. For example, $ isainfo -v 64-bit amd64 applications pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov amd_sysc cx8 tsc fpu 32-bit i386 applications pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov sep cx8 tsc fpu No special configuration or setup is needed to take advantage of this software. Solaris libraries and kernel automatically determine if it's running on AESNI-capable machines and execute the correctly-tuned software for the current microprocessor. Summary Maximum throughput of AES cipher modes can be achieved by combining AES encryption with modes processing, interleaving encryption of 4 blocks at a time, and using Intel's wide 128-bit %xmm registers and instructions. References "Block cipher modes of operation", Wikipedia Good overview of AES modes (ECB, CBC, CTR, etc.) "Advanced Encryption Standard", Wikipedia "Current Modes" describes NIST-approved block cipher modes (ECB,CBC, CFB, OFB, CCM, GCM)

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• Improved Performance on PeopleSoft Combined Benchmark using SPARC T4-4

  - by Brian

  Oracle's SPARC T4-4 server running Oracle's PeopleSoft HCM 9.1 combined online and batch benchmark achieved a world record 18,000 concurrent users experiencing subsecond response time while executing a PeopleSoft Payroll batch job of 500,000 employees in 32.4 minutes. This result was obtained with a SPARC T4-4 server running Oracle Database 11g Release 2, a SPARC T4-4 server running PeopleSoft HCM 9.1 application server and a SPARC T4-2 server running Oracle WebLogic Server in the web tier. The SPARC T4-4 server running the application tier used Oracle Solaris Zones which provide a flexible, scalable and manageable virtualization environment. The average CPU utilization on the SPARC T4-2 server in the web tier was 17%, on the SPARC T4-4 server in the application tier it was 59%, and on the SPARC T4-4 server in the database tier was 47% (online and batch) leaving significant headroom for additional processing across the three tiers. The SPARC T4-4 server used for the database tier hosted Oracle Database 11g Release 2 using Oracle Automatic Storage Management (ASM) for database files management with I/O performance equivalent to raw devices. Performance Landscape Results are presented for the PeopleSoft HRMS Self-Service and Payroll combined benchmark. The new result with 128 streams shows significant improvement in the payroll batch processing time with little impact on the self-service component response time. PeopleSoft HRMS Self-Service and Payroll Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.988 0.539 32.4 128 SPARC T4-2 (web) SPARC T4-4 (app) SPARC T4-4 (db) 18,000 0.944 0.503 43.3 64 The following results are for the PeopleSoft HRMS Self-Service benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the payroll component. PeopleSoft HRMS Self-Service 9.1 Benchmark Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-2 (web) SPARC T4-4 (app) 2x SPARC T4-2 (db) 18,000 1.048 0.742 N/A N/A The following results are for the PeopleSoft Payroll benchmark that was previous run. The results are not directly comparable with the combined results because they do not include the self-service component. PeopleSoft Payroll (N.A.) 9.1 - 500K Employees (7 Million SQL PayCalc, Unicode) Systems Users Ave Response Search (sec) Ave Response Save (sec) Batch Time (min) Streams SPARC T4-4 (db) N/A N/A N/A 30.84 96 Configuration Summary Application Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 512 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 PeopleSoft HCM 9.1 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Java Platform, Standard Edition Development Kit 6 Update 32 Database Configuration: 1 x SPARC T4-4 server with 4 x SPARC T4 processors, 3.0 GHz 256 GB memory Oracle Solaris 11 11/11 Oracle Database 11g Release 2 PeopleTools 8.52 Oracle Tuxedo, Version 10.3.0.0, 64-bit, Patch Level 031 Micro Focus Server Express (COBOL v 5.1.00) Web Tier Configuration: 1 x SPARC T4-2 server with 2 x SPARC T4 processors, 2.85 GHz 256 GB memory Oracle Solaris 11 11/11 PeopleTools 8.52 Oracle WebLogic Server 10.3.4 Java Platform, Standard Edition Development Kit 6 Update 32 Storage Configuration: 1 x Sun Server X2-4 as a COMSTAR head for data 4 x Intel Xeon X7550, 2.0 GHz 128 GB memory 1 x Sun Storage F5100 Flash Array (80 flash modules) 1 x Sun Storage F5100 Flash Array (40 flash modules) 1 x Sun Fire X4275 as a COMSTAR head for redo logs 12 x 2 TB SAS disks with Niwot Raid controller Benchmark Description This benchmark combines PeopleSoft HCM 9.1 HR Self Service online and PeopleSoft Payroll batch workloads to run on a unified database deployed on Oracle Database 11g Release 2. The PeopleSoft HRSS benchmark kit is a Oracle standard benchmark kit run by all platform vendors to measure the performance. It's an OLTP benchmark where DB SQLs are moderately complex. The results are certified by Oracle and a white paper is published. PeopleSoft HR SS defines a business transaction as a series of HTML pages that guide a user through a particular scenario. Users are defined as corporate Employees, Managers and HR administrators. The benchmark consist of 14 scenarios which emulate users performing typical HCM transactions such as viewing paycheck, promoting and hiring employees, updating employee profile and other typical HCM application transactions. All these transactions are well-defined in the PeopleSoft HR Self-Service 9.1 benchmark kit. This benchmark metric is the weighted average response search/save time for all the transactions. The PeopleSoft 9.1 Payroll (North America) benchmark demonstrates system performance for a range of processing volumes in a specific configuration. This workload represents large batch runs typical of a ERP environment during a mass update. The benchmark measures five application business process run times for a database representing large organization. They are Paysheet Creation, Payroll Calculation, Payroll Confirmation, Print Advice forms, and Create Direct Deposit File. The benchmark metric is the cumulative elapsed time taken to complete the Paysheet Creation, Payroll Calculation and Payroll Confirmation business application processes. The benchmark metrics are taken for each respective benchmark while running simultaneously on the same database back-end. Specifically, the payroll batch processes are started when the online workload reaches steady state (the maximum number of online users) and overlap with online transactions for the duration of the steady state. Key Points and Best Practices Two PeopleSoft Domain sets with 200 application servers each on a SPARC T4-4 server were hosted in 2 separate Oracle Solaris Zones to demonstrate consolidation of multiple application servers, ease of administration and performance tuning. Each Oracle Solaris Zone was bound to a separate processor set, each containing 15 cores (total 120 threads). The default set (1 core from first and third processor socket, total 16 threads) was used for network and disk interrupt handling. This was done to improve performance by reducing memory access latency by using the physical memory closest to the processors and offload I/O interrupt handling to default set threads, freeing up cpu resources for Application Servers threads and balancing application workload across 240 threads. A total of 128 PeopleSoft streams server processes where used on the database node to complete payroll batch job of 500,000 employees in 32.4 minutes. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN SPARC T4-4 Server oracle.com OTN PeopleSoft Enterprise Human Capital Managementoracle.com OTN PeopleSoft Enterprise Human Capital Management (Payroll) oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 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 8 November 2012.

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• Oracle's Global Single Schema

  - by david.butler(at)oracle.com

  Maximizing business process efficiencies in a heterogeneous environment is very difficult. The difficulty stems from the fact that the various applications across the Information Technology (IT) landscape employ different integration standards, different message passing strategies, and different workflow engines. Vendors such as Oracle and others are delivering tools to help IT organizations manage the complexities introduced by these differences. But the one remaining intractable problem impacting efficient operations is the fact that these applications have different definitions for the same business data. Business data is your business information codified for computer programs to use. A good data model will represent the way your organization does business. The computer applications your organization deploys to improve operational efficiency are built to operate on the business data organized into this schema.  If the schema does not represent how you do business, the applications on that schema cannot provide the features you need to achieve the desired efficiencies. Business processes span these applications. Data problems break these processes rendering them far less efficient than they need to be to achieve organization goals. Thus, the expected return on the investment in these applications is never realized. The success of all business processes depends on the availability of accurate master data.  Clearly, the solution to this problem is to consolidate all the master data an organization uses to run its business. Then clean it up, augment it, govern it, and connect it back to the applications that need it. Until now, this obvious solution has been difficult to achieve because no one had defined a data model sufficiently broad, deep and flexible enough to support transaction processing on all key business entities and serve as a master superset to all other operational data models deployed in heterogeneous IT environments. Today, the situation has changed. Oracle has created an operational data model (aka schema) that can support accurate and consistent master data across heterogeneous IT systems. This is foundational for providing a way to consolidate and integrate master data without having to replace investments in existing applications. This Global Single Schema (GSS) represents a revolutionary breakthrough that allows for true master data consolidation. Oracle has deep knowledge of applications dating back to the early 1990s.  It developed applications in the areas of Supply Chain Management (SCM), Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Human Capital Management (HCM), Financials and Manufacturing. In addition, Oracle applications were delivered for key industries such as Communications, Financial Services, Retail, Public Sector, High Tech Manufacturing (HTM) and more. Expertise in all these areas drove requirements for GSS. The following figure illustrates Oracle's unique position that enabled the creation of the Global Single Schema. GSS Requirements Gathering GSS defines all the key business entities and attributes including Customers, Contacts, Suppliers, Accounts, Products, Services, Materials, Employees, Installed Base, Sites, Assets, and Inventory to name just a few. In addition, Oracle delivers GSS pre-integrated with a wide variety of operational applications.  Business Process Automation EBusiness is about maximizing operational efficiency. At the highest level, these 'operations' span all that you do as an organization.  The following figure illustrates some of these high-level business processes. Enterprise Business Processes Supplies are procured. Assets are maintained. Materials are stored. Inventory is accumulated. Products and Services are engineered, produced and sold. Customers are serviced. And across this entire spectrum, Employees do the procuring, supporting, engineering, producing, selling and servicing. Not shown, but not to be overlooked, are the accounting and the financial processes associated with all this procuring, manufacturing, and selling activity. Supporting all these applications is the master data. When this data is fragmented and inconsistent, the business processes fail and inefficiencies multiply. But imagine having all the data under these operational business processes in one place. ·            The same accurate and timely customer data will be provided to all your operational applications from the call center to the point of sale. ·            The same accurate and timely supplier data will be provided to all your operational applications from supply chain planning to procurement. ·            The same accurate and timely product information will be available to all your operational applications from demand chain planning to marketing. You would have a single version of the truth about your assets, financial information, customers, suppliers, employees, products and services to support your business automation processes as they flow across your business applications. All company and partner personnel will access the same exact data entity across all your channels and across all your lines of business. Oracle's Global Single Schema enables this vision of a single version of the truth across the heterogeneous operational applications supporting the entire enterprise. Global Single Schema Oracle's Global Single Schema organizes hundreds of thousands of attributes into 165 major schema objects supporting over 180 business application modules. It is designed for international operations, and extensibility.  The schema is delivered with a full set of public Application Programming Interfaces (APIs) and an Integration Repository with modern Service Oriented Architecture interfaces to make data available as a services (DaaS) to business processes and enable operations in heterogeneous IT environments. ·         Key tables can be extended with unlimited numbers of additional attributes and attribute groups for maximum flexibility.  o    This enables model extensions that reflect business entities unique to your organization's operations. ·         The schema is multi-organization enabled so data manipulation can be controlled along organizational boundaries. ·         It uses variable byte Unicode to support over 31 languages. ·         The schema encodes flexible date and flexible address formats for easy localizations. No matter how complex your business is, Oracle's Global Single Schema can hold your business objects and support your global operations. Oracle's Global Single Schema identifies and defines the business objects an enterprise needs within the context of its business operations. The interrelationships between the business objects are also contained within the GSS data model. Their presence expresses fundamental business rules for the interaction between business entities. The following figure illustrates some of these connections.   Interconnected Business Entities Interconnecte business processes require interconnected business data. No other MDM vendor has this capability. Everyone else has either one entity they can master or separate disconnected models for various business entities. Higher level integrations are made available, but that is a weak architectural alternative to data level integration in this critically important aspect of Master Data Management.    

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• Thoughts on the Nomination Committee Campaign 2014

  - by Testas

  Congratulations to Erin, Andy and Allen on making the Nomination Committee for 2014. As Mark Broadbent (@retracement) stated in his tweet, there’s a great set of individuals for the Nom Com, and I could not agree more. I know Erin and Allen, and I know how much value they will bring to the process. I don’t know Andy as well, but I am sure he will do a great job and I hope I can meet him at PASS soon. The final candidate appointed by the PASS board is Rick Bolesta, who brings a wealth of experience to the process. I also want to take the opportunity to thank all who have voted. Not just for me, but for all the candidates during the election. Your contribution is greatly appreciated. Would I apply for the Nom Com again?  Yes I would. My first election experience has been a learning experience in itself. So I accept the result and look forward to applying next year. Moving on from this, I do want to express my opinion about the lack of international representation in the election process. One of the tweets that I saw after the result was from Adam Machanic (@AdamMachanic) who commented on the lack of international members on the Nom Com. If truth be told, I was disappointed – when the candidate list was released -- that for the second time in recent elections there was a lack of international candidates on the candidate list. It feels that only Brits and Americans partake in such elections. This is a real shame, and I can’t help thinking why this is the case. Hugo Kornelis (@Hugo_Kornelis) wrote a blog here to express his thoughts. He did raise some valid points. I don’t know why there is an absence of international candidates. I know that the team at PASS are looking to improve the situation, so I do not want to give the impression that PASS are doing nothing. For reference please see Bill Graziano’ s article here to see how PASS are addressing the situation. There is a clear direction to change the rules within PASS to give greater inclusion of international members. In addition to this, I wanted to explore a couple of potential approaches to address the situation. I am not saying that they are the right answer, but when I see challenges, I like to bring potential solutions to the table. 1.       Use the PASS mission statement to define a tactical objective that engages community leaders into the election process. If you are not familiar with the PASS mission statement, let me provide it here as laid out on the PASS website. “Empower data professionals who leverage Microsoft technologies to connect, share, and learn through networking, knowledge sharing, and peer-based learning” PASS fulfil this mission statement regularly. Whether you attend SQL Saturday, SQLRally, SQLPASS and BA conference itself. The biggest value of PASS is the ability to bring our profession together. And the 24 hour hop allows you to learn from the comfort of your own office/home. This mission should be extended to define a tactical objectives that bring greater networking and knowledge sharing between PASS Chapter leaders/Regional Mentors and PASS HQ. It should help educate the leaders about the opportunities of elections and how leaders can become involved. I know PASS engage with Chapter leaders on a regular basis to discuss community matters for the benefit of PASS members. How could this be achieved? Perhaps PASS could perform a quarterly virtual meeting that specifically looks at helping leaders become more involved with the election process 2.       Evolve the Global Growth Strategy into a Global Engagement Strategy. One of the remits of the PASS board over the last couple of years is the Global Growth strategy. This has been very successful as we have seen the massive growth of events across the world. For that, I congratulate the board for this success. Perhaps the time is now right to look at solidifying this success, through a Global Engagement Strategy that starts with the collaboration of Chapter Leaders, Regional Mentors and Evangelists in their respective Countries or Regions. The engagement strategy should look at increasing collaboration between community leaders for the benefit of their respective communities. It should also provide a channel for encouraging leaders to put themselves forward for the elections. How could this be achieved? In the UK, there has been a big growth in PASS Chapters and SQL Server Events that was approaching saturation point. The introduction of the Community Engagement Day -- channelled through the SQLBits conference -- has enabled Chapter Leaders to collaborate, connect and share with PASS, Sponsors and Microsoft. It also provides the ability for Chapter Leaders to speak directly to the PASS representatives from PASSHQ. This brings with it the ability for PASS community evangelists to communicate PASS objectives. It has also been the event where we have found out; and/or encouraged, Chapter Leaders to put themselves forward for elections. People like encouragement and validation when going for something like an election, and being able to discuss this with peers at a dedicated event provides a useful platform. PASS has the people in place already to facilitate such an event. Regional Mentors could potentially help organise such events on an annual basis, with PASSHQ providing support in providing a room/Lync access for the event to take place. It would be really good if a PASSHQ representative could attend in person as well.   3.       Restrict candidates to serve only a limited number of terms. A frequent comment I saw on social networking was that the elections can be seen by some as a popularity conference. Perhaps by limiting the number of terms that an individual can serve on either the Nom Com or the BOD, other candidates may be encouraged to be more actively involved within the PASS election process. I don’t think that the current byelaws deal with this particular suggestion. I also saw a couple of tweets that stated that more active community members did not apply for the Nom Com. I struggled to understand how the individuals of the tweets measured “more active”. It just also further solidified the subjective nature of elections. In the absence of how candidates are put forward for the elections. Then a restriction of terms enables the opportunity to be extended to others. How could this be achieved? Set a resolution that is put to a community vote as to the viability of such a solution. For example, the questions for the vote could be: Should individuals in the Nom Com and BoD be limited to a certain number of terms?  Yes/No. What is the maximum number of terms a candidate could serve?   It would be simple to execute such a vote, and the community will have an opportunity to have a say in an important aspect of the PASS organisation. And is the change is successful, then add it as a byelaw.   So there are some of my thoughts. I am not saying they are right or wrong. But I do hope that there is a concerted effort to encourage more candidates from other reaches of the Globe to become involved with future elections.   It would be good to hear your thoughts   Thanks   Chris

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• J2EE Applications, SPARC T4, Solaris Containers, and Resource Pools

  - by user12620111

  I've obtained a substantial performance improvement on a SPARC T4-2 Server running a J2EE Application Server Cluster by deploying the cluster members into Oracle Solaris Containers and binding those containers to cores of the SPARC T4 Processor. This is not a surprising result, in fact, it is consistent with other results that are available on the Internet. See the "references", below, for some examples. Nonetheless, here is a summary of my configuration and results. (1.0) Before deploying a J2EE Application Server Cluster into a virtualized environment, many decisions need to be made. I'm not claiming that all of the decisions that I have a made will work well for every environment. In fact, I'm not even claiming that all of the decisions are the best possible for my environment. I'm only claiming that of the small sample of configurations that I've tested, this is the one that is working best for me. Here are some of the decisions that needed to be made: (1.1) Which virtualization option? There are several virtualization options and isolation levels that are available. Options include: Hard partitions:  Dynamic Domains on Sun SPARC Enterprise M-Series Servers Hypervisor based virtualization such as Oracle VM Server for SPARC (LDOMs) on SPARC T-Series Servers OS Virtualization using Oracle Solaris Containers Resource management tools in the Oracle Solaris OS to control the amount of resources an application receives, such as CPU cycles, physical memory, and network bandwidth. Oracle Solaris Containers provide the right level of isolation and flexibility for my environment. To borrow some words from my friends in marketing, "The SPARC T4 processor leverages the unique, no-cost virtualization capabilities of Oracle Solaris Zones"  (1.2) How to associate Oracle Solaris Containers with resources? There are several options available to associate containers with resources, including (a) resource pool association (b) dedicated-cpu resources and (c) capped-cpu resources. I chose to create resource pools and associate them with the containers because I wanted explicit control over the cores and virtual processors.  (1.3) Cluster Topology? Is it best to deploy (a) multiple application servers on one node, (b) one application server on multiple nodes, or (c) multiple application servers on multiple nodes? After a few quick tests, it appears that one application server per Oracle Solaris Container is a good solution. (1.4) Number of cluster members to deploy? I chose to deploy four big 64-bit application servers. I would like go back a test many 32-bit application servers, but that is left for another day. (2.0) Configuration tested. (2.1) I was using a SPARC T4-2 Server which has 2 CPU and 128 virtual processors. To understand the physical layout of the hardware on Solaris 10, I used the OpenSolaris psrinfo perl script available at http://hub.opensolaris.org/bin/download/Community+Group+performance/files/psrinfo.pl: test# ./psrinfo.pl -pv The physical processor has 8 cores and 64 virtual processors (0-63) The core has 8 virtual processors (0-7)   The core has 8 virtual processors (8-15)   The core has 8 virtual processors (16-23)   The core has 8 virtual processors (24-31)   The core has 8 virtual processors (32-39)   The core has 8 virtual processors (40-47)   The core has 8 virtual processors (48-55)   The core has 8 virtual processors (56-63)     SPARC-T4 (chipid 0, clock 2848 MHz) The physical processor has 8 cores and 64 virtual processors (64-127)   The core has 8 virtual processors (64-71)   The core has 8 virtual processors (72-79)   The core has 8 virtual processors (80-87)   The core has 8 virtual processors (88-95)   The core has 8 virtual processors (96-103)   The core has 8 virtual processors (104-111)   The core has 8 virtual processors (112-119)   The core has 8 virtual processors (120-127)     SPARC-T4 (chipid 1, clock 2848 MHz) (2.2) The "before" test: without processor binding. I started with a 4-member cluster deployed into 4 Oracle Solaris Containers. Each container used a unique gigabit Ethernet port for HTTP traffic. The containers shared a 10 gigabit Ethernet port for JDBC traffic. (2.3) The "after" test: with processor binding. I ran one application server in the Global Zone and another application server in each of the three non-global zones (NGZ):  (3.0) Configuration steps. The following steps need to be repeated for all three Oracle Solaris Containers. (3.1) Stop AppServers from the BUI. (3.2) Stop the NGZ. test# ssh test-z2 init 5 (3.3) Enable resource pools: test# svcadm enable pools (3.4) Create the resource pool: test# poolcfg -dc 'create pool pool-test-z2' (3.5) Create the processor set: test# poolcfg -dc 'create pset pset-test-z2' (3.6) Specify the maximum number of CPU's that may be addd to the processor set: test# poolcfg -dc 'modify pset pset-test-z2 (uint pset.max=32)' (3.7) bash syntax to add Virtual CPUs to the processor set: test# (( i = 64 )); while (( i < 96 )); do poolcfg -dc "transfer to pset pset-test-z2 (cpu $i)"; (( i = i + 1 )) ; done (3.8) Associate the resource pool with the processor set: test# poolcfg -dc 'associate pool pool-test-z2 (pset pset-test-z2)' (3.9) Tell the zone to use the resource pool that has been created: test# zonecfg -z test-z1 set pool=pool-test-z2 (3.10) Boot the Oracle Solaris Container test# zoneadm -z test-z2 boot (3.11) Save the configuration to /etc/pooladm.conf test# pooladm -s (4.0) Results. Using the resource pools improves both throughput and response time: (5.0) References: System Administration Guide: Oracle Solaris Containers-Resource Management and Oracle Solaris Zones Capitalizing on large numbers of processors with WebSphere Portal on Solaris WebSphere Application Server and T5440 (Dileep Kumar's Weblog)  http://www.brendangregg.com/zones.html Reuters Market Data System, RMDS 6 Multiple Instances (Consolidated), Performance Test Results in Solaris, Containers/Zones Environment on Sun Blade X6270 by Amjad Khan, 2009.

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• MySQL for Excel 1.1.3 has been released

  - by Javier Treviño

  The MySQL Windows Experience Team is proud to announce the release of MySQL for Excel version 1.1.3, the  latest addition to the MySQL Installer for Windows. MySQL for Excel is an application plug-in enabling data analysts to very easily access and manipulate MySQL data within Microsoft Excel. It enables you to directly work with a MySQL database from within Microsoft Excel so you can easily do tasks such as: Importing MySQL Data into Excel Exporting Excel data directly into MySQL to a new or existing table Editing MySQL data directly within Excel MySQL for Excel is installed using the MySQL Installer for Windows. The MySQL installer comes in 2 versions   Full (150 MB) which includes a complete set of MySQL products with their binaries included in the download Web (1.5 MB - a network install) which will just pull MySQL for Excel over the web and install it when run.   You can download MySQL Installer from our official Downloads page at http://dev.mysql.com/downloads/installer/. MySQL for Excel 1.1.3 introduces the following features:   Upon saving a Workbook containing Worksheets in Edit Mode, the user is asked if he wants to exit the Edit Mode on all Worksheets before their parent Workbook is saved so the Worksheets are saved unprotected, otherwise the Worksheets will remain protected and the users will be able to unprotect them later retrieving the passkeys from the application log after closing MySQL for Excel. Added background coloring to the column names header row of an Import Data operation to have the same look as the one in an Edit Data operation (i.e. gray-ish background). Connection passwords can be stored securely just like MySQL Workbench does and these secured passwords are shared with Workbench in the same way connections are. Changed the way the MySQL for Excel ribbon toggle button works, instead of just showing or hiding the add-in it actually opens and closes it. Added a connection test before any operation against the database (schema creation, data import, append, export or edition) so the operation dialog is not shown and a friendlier error message is shown.   Also this release contains the following bug fixes:   Added a check on every connection test for an expired password, if the password has been expired a dialog is now shown to the user to reset the password. Bug #17354118 - DON'T HANDLE EXPIRED PASSWORDS Added code to escape text values to be imported to an Excel worksheet that start with an equals sign so Excel does not treat those values as formulas that will fail evaluation. This is an option turned on by default that can be turned off by users if they wish to import values to be treated as Excel formulas. Bug #17354102 - ERROR IMPORTING TEXT VALUES TO EXCEL STARTING WITH AN EQUALS SIGN Added code to properly check the reason for a failing connection, if it's a failing password the user gets a dialog to retry the connection with a different password until the connection succeeds, a connection error not related to the password is thrown or the user cancels. If the failing connection is not related to a bad password an error message is shown to the users indicating the reason of the failure. Bug #16239007 - CONNECTIONS TO MYSQL SERVICES NOT RUNNING DISPLAY A WRONG PASSWORD ERROR MESSAGE Added global options dialog that can be accessed from the Schema Selection and DB Object Selection panels where the timeouts for the connection to the DB Server and for the query commands can be changed from their default values (15 seconds for the connection timeout and 30 seconds for the query timeout). MySQL Bug #68732, Bug #17191646 - QUERY TIMEOUT CANNOT BE ADJUSTED IN MYSQL FOR EXCEL Changed the Varchar(65,535) data type shown in the Export Data data type combo box to Text since the maximum row size is 65,535 bytes and any autodetected column data type with a length greater than 4,000 should be set to Text actually for the table to be created successfully. MySQL Bug #69779, Bug #17191633 - EXPORT FAILS FOR EXCEL FILES CONTAINING > 4000 CHARACTERS OF TEXT PER CELL Removed code that was replacing all spaces typed by the user in an overriden data type for a new column in an Export Data operation, also improved the data type detection code to flag as invalid data types with parenthesis but without any text inside or where the contents inside the parenthesis are not valid for the specific data type. Bug #17260260 - EXPORT DATA SET TYPE NOT WORKING WITH MEMBER VALUES CONTAINING SPACES Added support for the year data type with a length of 2 or 4 and a validation that valid values are integers between 1901-2155 (for 4-digit years) or between 0-99 (for 2-digit years). Bug #17259915 - EXPORT DATA YEAR DATA TYPE NOT RECOGNIZED IF DECLARED WITH A DISPLAY WIDTH) Fixed code for Export Data operations where users overrode the data type for columns typing Text in the data type combobox, which is a valid data type but was not recognized as such. Bug #17259490 - EXPORT DATA TEXT DATA TYPE NOT RECOGNIZED AS A VALID DATA TYPE Changed the location of the registry where the MySQL for Excel add-in is installed to HKEY_LOCAL_MACHINE instead of HKEY_CURRENT_USER so the add-in is accessible by all users and not only to the user that installed it. For this to work with Excel 2007 a hotfix may be required (see http://support.microsoft.com/kb/976477). MySQL Bug #68746, Bug #16675992 - EXCEL-ADD-IN IS ONLY INSTALLED FOR USER ACCOUNT THAT THE INSTALLATION RUNS UNDER Added support for Excel 2013 Single Document Interface, now that Excel 2013 creates 1 window per workbook also the Excel Add-In maintains an independent custom task pane in each window. MySQL Bug #68792, Bug #17272087 - MYSQL FOR EXCEL SIDEBAR DOES NOT APPEAR IN EXCEL 2013 (WITH WORKAROUND) Included the latest MySQL Utility with a code fix for the COM exception thrown when attempting to open Workbench in the Manage Connections window. Bug #17258966 - MYSQL WORKBENCH NOT OPENED BY CLICKING MANAGE CONNECTIONS HOTLABEL Fixed code for Append Data operations that was not applying a calculated automatic mapping correctly when the source and target tables had different number of columns, some columns with the same name but some of those lying on column indexes beyond the limit of the other source/target table. MySQL Bug #69220, Bug #17278349 - APPEND DOESN'T AUTOMATICALLY DETECT EXCEL COL HEADER WITH SAME NAME AS SQL FIELD Fixed some code for Edit Data operations that was escaping special characters twice (during edition in Excel and then upon sending the query to the MySQL server). MySQL Bug #68669, Bug #17271693 - A BACKSLASH IS INSERTED BEFORE AN APOSTROPHE EDITING TABLE WITH MYSQL FOR EXCEL Upgraded MySQL Utility with latest version that encapsulates dialog base classes and introduces more classes to handle Workbench connections, and removed these from the Excel project. Bug #16500331 - CAN'T DELETE CONNECTIONS CREATED WITHIN ADDIN You can access the MySQL for Excel documentation at http://dev.mysql.com/doc/refman/5.6/en/mysql-for-excel.html You can find our team’s blog at http://blogs.oracle.com/MySQLOnWindows. You can also post questions on our MySQL for Excel forum found at http://forums.mysql.com/. Enjoy and thanks for the support!

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• Cost Comparison Hard Disk Drive to Solid State Drive on Price per Gigabyte - dispelling a myth!

  - by tonyrogerson

  It is often said that Hard Disk Drive storage is significantly cheaper per GiByte than Solid State Devices – this is wholly inaccurate within the database space. People need to look at the cost of the complete solution and not just a single component part in isolation to what is really required to meet the business requirement. Buying a single Hitachi Ultrastar 600GB 3.5” SAS 15Krpm hard disk drive will cost approximately £239.60 (http://scan.co.uk, 22nd March 2012) compared to an OCZ 600GB Z-Drive R4 CM84 PCIe costing £2,316.54 (http://scan.co.uk, 22nd March 2012); I’ve not included FusionIO ioDrive because there is no public pricing available for it – something I never understand and personally when companies do this I immediately think what are they hiding, luckily in FusionIO’s case the product is proven though is expensive compared to OCZ enterprise offerings. On the face of it the single 15Krpm hard disk has a price per GB of £0.39, the SSD £3.86; this is what you will see in the press and this is what sales people will use in comparing the two technologies – do not be fooled by this bullshit people! What is the requirement? The requirement is the database will have a static size of 400GB kept static through archiving so growth and trim will balance the database size, the client requires resilience, there will be several hundred call centre staff querying the database where queries will read a small amount of data but there will be no hot spot in the data so the randomness will come across the entire 400GB of the database, estimates predict that the IOps required will be approximately 4,000IOps at peak times, because it’s a call centre system the IO latency is important and must remain below 5ms per IO. The balance between read and write is 70% read, 30% write. The requirement is now defined and we have three of the most important pieces of the puzzle – space required, estimated IOps and maximum latency per IO. Something to consider with regard SQL Server; write activity requires synchronous IO to the storage media specifically the transaction log; that means the write thread will wait until the IO is completed and hardened off until the thread can continue execution, the requirement has stated that 30% of the system activity will be write so we can expect a high amount of synchronous activity. The hardware solution needs to be defined; two possible solutions: hard disk or solid state based; the real question now is how many hard disks are required to achieve the IO throughput, the latency and resilience, ditto for the solid state. Hard Drive solution On a test on an HP DL380, P410i controller using IOMeter against a single 15Krpm 146GB SAS drive, the throughput given on a transfer size of 8KiB against a 40GiB file on a freshly formatted disk where the partition is the only partition on the disk thus the 40GiB file is on the outer edge of the drive so more sectors can be read before head movement is required: For 100% sequential IO at a queue depth of 16 with 8 worker threads 43,537 IOps at an average latency of 2.93ms (340 MiB/s), for 100% random IO at the same queue depth and worker threads 3,733 IOps at an average latency of 34.06ms (34 MiB/s). The same test was done on the same disk but the test file was 130GiB: For 100% sequential IO at a queue depth of 16 with 8 worker threads 43,537 IOps at an average latency of 2.93ms (340 MiB/s), for 100% random IO at the same queue depth and worker threads 528 IOps at an average latency of 217.49ms (4 MiB/s). From the result it is clear random performance gets worse as the disk fills up – I’m currently writing an article on short stroking which will cover this in detail. Given the work load is random in nature looking at the random performance of the single drive when only 40 GiB of the 146 GB is used gives near the IOps required but the latency is way out. Luckily I have tested 6 x 15Krpm 146GB SAS 15Krpm drives in a RAID 0 using the same test methodology, for the same test above on a 130 GiB for each drive added the performance boost is near linear, for each drive added throughput goes up by 5 MiB/sec, IOps by 700 IOps and latency reducing nearly 50% per drive added (172 ms, 94 ms, 65 ms, 47 ms, 37 ms, 30 ms). This is because the same 130GiB is spread out more as you add drives 130 / 1, 130 / 2, 130 / 3 etc. so implicit short stroking is occurring because there is less file on each drive so less head movement required. The best latency is still 30 ms but we have the IOps required now, but that’s on a 130GiB file and not the 400GiB we need. Some reality check here: a) the drive randomness is more likely to be 50/50 and not a full 100% but the above has highlighted the effect randomness has on the drive and the more a drive fills with data the worse the effect. For argument sake let us assume that for the given workload we need 8 disks to do the job, for resilience reasons we will need 16 because we need to RAID 1+0 them in order to get the throughput and the resilience, RAID 5 would degrade performance. Cost for hard drives: 16 x £239.60 = £3,833.60 For the hard drives we will need disk controllers and a separate external disk array because the likelihood is that the server itself won’t take the drives, a quick spec off DELL for a PowerVault MD1220 which gives the dual pathing with 16 disks 146GB 15Krpm 2.5” disks is priced at £7,438.00, note its probably more once we had two controller cards to sit in the server in, racking etc. Minimum cost taking the DELL quote as an example is therefore: {Cost of Hardware} / {Storage Required} £7,438.60 / 400 = £18.595 per GB £18.59 per GiB is a far cry from the £0.39 we had been told by the salesman and the myth. Yes, the storage array is composed of 16 x 146 disks in RAID 10 (therefore 8 usable) giving an effective usable storage availability of 1168GB but the actual storage requirement is only 400 and the extra disks have had to be purchased to get the  IOps up. Solid State Drive solution A single card significantly exceeds the IOps and latency required, for resilience two will be required. ( £2,316.54 * 2 ) / 400 = £11.58 per GB With the SSD solution only two PCIe sockets are required, no external disk units, no additional controllers, no redundant controllers etc. Conclusion I hope by showing you an example that the myth that hard disk drives are cheaper per GiB than Solid State has now been dispelled - £11.58 per GB for SSD compared to £18.59 for Hard Disk. I’ve not even touched on the running costs, compare the costs of running 18 hard disks, that’s a lot of heat and power compared to two PCIe cards!Just a quick note: I've left a fair amount of information out due to this being a blog! If in doubt, email me :)I'll also deal with the myth that SSD's wear out at a later date as well - that's just way over done still, yes, 5 years ago, but now - no.

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• Oracle Flashback Technologies - Overview

  - by Sridhar_R-Oracle

  Oracle Flashback Technologies - IntroductionIn his May 29th 2014 blog, my colleague Joe Meeks introduced Oracle Maximum Availability Architecture (MAA) and discussed both planned and unplanned outages. Let’s take a closer look at unplanned outages. These can be caused by physical failures (e.g., server, storage, network, file deletion, physical corruption, site failures) or by logical failures – cases where all components and files are physically available, but data is incorrect or corrupt. These logical failures are usually caused by human errors or application logic errors. This blog series focuses on these logical errors – what causes them and how to address and recover from them using Oracle Database Flashback. In this introductory blog post, I’ll provide an overview of the Oracle Database Flashback technologies and will discuss the features in detail in future blog posts. Let’s get started. We are all human beings (unless a machine is reading this), and making mistakes is a part of what we do…often what we do best!  We “fat finger”, we spill drinks on keyboards, unplug the wrong cables, etc.  In addition, many of us, in our lives as DBAs or developers, must have observed, caused, or corrected one or more of the following unpleasant events: Accidentally updated a table with wrong values !! Performed a batch update that went wrong - due to logical errors in the code !! Dropped a table !! How do DBAs typically recover from these types of errors? First, data needs to be restored and recovered to the point-in-time when the error occurred (incomplete or point-in-time recovery).  Moreover, depending on the type of fault, it’s possible that some services – or even the entire database – would have to be taken down during the recovery process.Apart from error conditions, there are other questions that need to be addressed as part of the investigation. For example, what did the data look like in the morning, prior to the error? What were the various changes to the row(s) between two timestamps? Who performed the transaction and how can it be reversed?  Oracle Database includes built-in Flashback technologies, with features that address these challenges and questions, and enable you to perform faster, easier, and convenient recovery from logical corruptions. HistoryFlashback Query, the first Flashback Technology, was introduced in Oracle 9i. It provides a simple, powerful and completely non-disruptive mechanism for data verification and recovery from logical errors, and enables users to view the state of data at a previous point in time.Flashback Technologies were further enhanced in Oracle 10g, to provide fast, easy recovery at the database, table, row, and even at a transaction level.Oracle Database 11g introduced an innovative method to manage and query long-term historical data with Flashback Data Archive. The 11g release also introduced Flashback Transaction, which provides an easy, one-step operation to back out a transaction. Oracle Database versions 11.2.0.2 and beyond further enhanced the performance of these features. Note that all the features listed here work without requiring any kind of restore operation.In addition, Flashback features are fully supported with the new multi-tenant capabilities introduced with Oracle Database 12c, Flashback Features Oracle Flashback Database enables point-in-time-recovery of the entire database without requiring a traditional restore and recovery operation. It rewinds the entire database to a specified point in time in the past by undoing all the changes that were made since that time.Oracle Flashback Table enables an entire table or a set of tables to be recovered to a point in time in the past.Oracle Flashback Drop enables accidentally dropped tables and all dependent objects to be restored.Oracle Flashback Query enables data to be viewed at a point-in-time in the past. This feature can be used to view and reconstruct data that was lost due to unintentional change(s) or deletion(s). This feature can also be used to build self-service error correction into applications, empowering end-users to undo and correct their errors.Oracle Flashback Version Query offers the ability to query the historical changes to data between two points in time or system change numbers (SCN) Oracle Flashback Transaction Query enables changes to be examined at the transaction level. This capability can be used to diagnose problems, perform analysis, audit transactions, and even revert the transaction by undoing SQLOracle Flashback Transaction is a procedure used to back-out a transaction and its dependent transactions.Flashback technologies eliminate the need for a traditional restore and recovery process to fix logical corruptions or make enquiries. Using these technologies, you can recover from the error in the same amount of time it took to generate the error. All the Flashback features can be accessed either via SQL command line (or) via Enterprise Manager.  Most of the Flashback technologies depend on the available UNDO to retrieve older data. The following table describes the various Flashback technologies: their purpose, dependencies and situations where each individual technology can be used.   Example Syntax Error investigation related:The purpose is to investigate what went wrong and what the values were at certain points in timeFlashback Queries  ( select .. as of SCN | Timestamp )   - Helps to see the value of a row/set of rows at a point in timeFlashback Version Queries  ( select .. versions between SCN | Timestamp and SCN | Timestamp)  - Helps determine how the value evolved between certain SCNs or between timestamps Flashback Transaction Queries (select .. XID=)   - Helps to understand how the transaction caused the changes.Error correction related:The purpose is to fix the error and correct the problems,Flashback Table  (flashback table .. to SCN | Timestamp)  - To rewind the table to a particular timestamp or SCN to reverse unwanted updates Flashback Drop (flashback table ..  to before drop )  - To undrop or undelete a table Flashback Database (flashback database to SCN  | Restore Point )  - This is the rewind button for Oracle databases. You can revert the entire database to a particular point in time. It is a fast way to perform a PITR (point-in-time recovery). Flashback Transaction (DBMS_FLASHBACK.TRANSACTION_BACKOUT(XID..))  - To reverse a transaction and its related transactions Advanced use cases Flashback technology is integrated into Oracle Recovery Manager (RMAN) and Oracle Data Guard. So, apart from the basic use cases mentioned above, the following use cases are addressed using Oracle Flashback. Block Media recovery by RMAN - to perform block level recovery Snapshot Standby - where the standby is temporarily converted to a read/write environment for testing, backup, or migration purposes Re-instate old primary in a Data Guard environment – this avoids the need to restore an old backup and perform a recovery to make it a new standby. Guaranteed Restore Points - to bring back the entire database to an older point-in-time in a guaranteed way. and so on..I hope this introductory overview helps you understand how Flashback features can be used to investigate and recover from logical errors.  As mentioned earlier, I will take a deeper-dive into to some of the critical Flashback features in my upcoming blogs and address common use cases.

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• Elegance, thy Name is jQuery

  - by SGWellens

  So, I'm browsing though some questions over on the Stack Overflow website and I found a good jQuery question just a few minutes old. Here is a link to it. It was a tough question; I knew that by answering it, I could learn new stuff and reinforce what I already knew: Reading is good, doing is better. Maybe I could help someone in the process too. I cut and pasted the HTML from the question into my Visual Studio IDE and went back to Stack Overflow to reread the question. Dang, someone had already answered it! And it was a great answer. I never even had a chance to start analyzing the issue. Now I know what a one-legged man feels like in an ass-kicking contest. Nevertheless, since the question and answer were so interesting, I decided to dissect them and learn as much as possible. The HTML consisted of some divs separated by h3 headings.  Note the elements are laid out sequentially with no programmatic grouping: <h3 class="heading">Heading 1</h3> <div>Content</div> <div>More content</div> <div>Even more content</div><h3 class="heading">Heading 2</h3> <div>some content</div> <div>some more content</div><h3 class="heading">Heading 3</h3> <div>other content</div></form></body>  The requirement was to wrap a div around each h3 heading and the subsequent divs grouping them into sections. Why? I don't know, I suppose if you screen-scrapped some HTML from another site, you might want to reformat it before displaying it on your own. Anyways… Here is the marvelously, succinct posted answer: $('.heading').each(function(){ $(this).nextUntil('.heading').andSelf().wrapAll('<div class="section">');}); I was familiar with all the parts except for nextUntil and andSelf. But, I'll analyze the whole answer for completeness. I'll do this by rewriting the posted answer in a different style and adding a boat-load of comments: function Test(){ // $Sections is a jQuery object and it will contain three elements var $Sections = $('.heading'); // use each to iterate over each of the three elements $Sections.each(function () { // $this is a jquery object containing the current element // being iterated var $this = $(this); // nextUntil gets the following sibling elements until it reaches // an element with the CSS class 'heading' // andSelf adds in the source element (this) to the collection $this = $this.nextUntil('.heading').andSelf(); // wrap the elements with a div $this.wrapAll('<div class="section" >'); });}  The code here doesn't look nearly as concise and elegant as the original answer. However, unless you and your staff are jQuery masters, during development it really helps to work through algorithms step by step. You can step through this code in the debugger and examine the jQuery objects to make sure one step is working before proceeding on to the next. It's much easier to debug and troubleshoot when each logical coding step is a separate line of code. Note: You may think the original code runs much faster than this version. However, the time difference is trivial: Not enough to worry about: Less than 1 millisecond (tested in IE and FF). Note: You may want to jam everything into one line because it results in less traffic being sent to the client. That is true. However, most Internet servers now compress HTML and JavaScript by stripping out comments and white space (go to Bing or Google and view the source). This feature should be enabled on your server: Let the server compress your code, you don't need to do it. Free Career Advice: Creating maintainable code is Job One—Maximum Priority—The Prime Directive. If you find yourself suddenly transferred to customer support, it may be that the code you are writing is not as readable as it could be and not as readable as it should be. Moving on… I created a CSS class to enhance the results: .section{ background-color: yellow; border: 2px solid black; margin: 5px;} Here is the rendered output before:   …and after the jQuery code runs.   Pretty Cool! But, while playing with this code, the logic of nextUntil began to bother me: What happens in the last section? What stops elements from being collected since there are no more elements with the .heading class? The answer is nothing.  In this case it stopped collecting elements because it was at the end of the page.  But what if there were additional HTML elements? I added an anchor tag and another div to the HTML: <h3 class="heading">Heading 1</h3> <div>Content</div> <div>More content</div> <div>Even more content</div><h3 class="heading">Heading 2</h3> <div>some content</div> <div>some more content</div><h3 class="heading">Heading 3</h3> <div>other content</div><a>this is a link</a><div>unrelated div</div> </form></body> The code as-is will include both the anchor and the unrelated div. This isn't what we want.   My first attempt to correct this used the filter parameter of the nextUntil function: nextUntil('.heading', 'div')  This will only collect div elements. But it merely skipped the anchor tag and it still collected the unrelated div:   The problem is we need a way to tell the nextUntil function when to stop. CSS selectors to the rescue! nextUntil('.heading, a')  This tells nextUntil to stop collecting elements when it gets to an element with a .heading class OR when it gets to an anchor tag. In this case it solved the problem. FYI: The comma operator in a CSS selector allows multiple criteria.   Bingo! One final note, we could have broken the code down even more: We could have replaced the andSelf function here: $this = $this.nextUntil('.heading, a').andSelf(); With this: // get all the following siblings and then add the current item$this = $this.nextUntil('.heading, a');$this.add(this);  But in this case, the andSelf function reads real nice. In my opinion. Here's a link to a jsFiddle if you want to play with it. I hope someone finds this useful Steve Wellens CodeProject

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• Network communications mechanisms for SQL Server

  - by Akshay Deep Lamba

  Problem I am trying to understand how SQL Server communicates on the network, because I'm having to tell my networking team what ports to open up on the firewall for an edge web server to communicate back to the SQL Server on the inside. What do I need to know? Solution In order to understand what needs to be opened where, let's first talk briefly about the two main protocols that are in common use today: TCP - Transmission Control Protocol UDP - User Datagram Protocol Both are part of the TCP/IP suite of protocols. We'll start with TCP. TCP TCP is the main protocol by which clients communicate with SQL Server. Actually, it is more correct to say that clients and SQL Server use Tabular Data Stream (TDS), but TDS actually sits on top of TCP and when we're talking about Windows and firewalls and other networking devices, that's the protocol that rules and controls are built around. So we'll just speak in terms of TCP. TCP is a connection-oriented protocol. What that means is that the two systems negotiate the connection and both agree to it. Think of it like a phone call. While one person initiates the phone call, the other person has to agree to take it and both people can end the phone call at any time. TCP is the same way. Both systems have to agree to the communications, but either side can end it at any time. In addition, there is functionality built into TCP to ensure that all communications can be disassembled and reassembled as necessary so it can pass over various network devices and be put together again properly in the right order. It also has mechanisms to handle and retransmit lost communications. Because of this functionality, TCP is the protocol used by many different network applications. The way the applications all can share is through the use of ports. When a service, like SQL Server, comes up on a system, it must listen on a port. For a default SQL Server instance, the default port is 1433. Clients connect to the port via the TCP protocol, the connection is negotiated and agreed to, and then the two sides can transfer information as needed until either side decides to end the communication. In actuality, both sides will have a port to use for the communications, but since the client's port is typically determined semi-randomly, when we're talking about firewalls and the like, typically we're interested in the port the server or service is using. UDP UDP, unlike TCP, is not connection oriented. A "client" can send a UDP communications to anyone it wants. There's nothing in place to negotiate a communications connection, there's nothing in the protocol itself to coordinate order of communications or anything like that. If that's needed, it's got to be handled by the application or by a protocol built on top of UDP being used by the application. If you think of TCP as a phone call, think of UDP as a postcard. I can put a postcard in the mail to anyone I want, and so long as it is addressed properly and has a stamp on it, the postal service will pick it up. Now, what happens it afterwards is not guaranteed. There's no mechanism for retransmission of lost communications. It's great for short communications that doesn't necessarily need an acknowledgement. Because multiple network applications could be communicating via UDP, it uses ports, just like TCP. The SQL Browser or the SQL Server Listener Service uses UDP. Network Communications - Talking to SQL Server When an instance of SQL Server is set up, what TCP port it listens on depends. A default instance will be set up to listen on port 1433. A named instance will be set to a random port chosen during installation. In addition, a named instance will be configured to allow it to change that port dynamically. What this means is that when a named instance starts up, if it finds something already using the port it normally uses, it'll pick a new port. If you have a named instance, and you have connections coming across a firewall, you're going to want to use SQL Server Configuration Manager to set a static port. This will allow the networking and security folks to configure their devices for maximum protection. While you can change the network port for a default instance of SQL Server, most people don't. Network Communications - Finding a SQL Server When just the name is specified for a client to connect to SQL Server, for instance, MySQLServer, this is an attempt to connect to the default instance. In this case the client will automatically attempt to communicate to port 1433 on MySQLServer. If you've switched the port for the default instance, you'll need to tell the client the proper port, usually by specifying the following syntax in the connection string: <server>,<port>. For instance, if you moved SQL Server to listen on 14330, you'd use MySQLServer,14330 instead of just MySQLServer. However, because a named instance sets up its port dynamically by default, the client never knows at the outset what the port is it should talk to. That's what the SQL Browser or the SQL Server Listener Service (SQL Server 2000) is for. In this case, the client sends a communication via the UDP protocol to port 1434. It asks, "Where is the named instance?" So if I was running a named instance called SQL2008R2, it would be asking the SQL Browser, "Hey, how do I talk to MySQLServer\SQL2008R2?" The SQL Browser would then send back a communications from UDP port 1434 back to the client telling the client how to talk to the named instance. Of course, you can skip all of this of you set that named instance's port statically. Then you can use the <server>,<port> mechanism to connect and the client won't try to talk to the SQL Browser service. It'll simply try to make the connection. So, for instance, is the SQL2008R2 instance was listening on port 20080, specifying MySQLServer,20080 would attempt a connection to the named instance. Network Communications - Named Pipes Named pipes is an older network library communications mechanism and it's generally not used any longer. It shouldn't be used across a firewall. However, if for some reason you need to connect to SQL Server with it, this protocol also sits on top of TCP. Named Pipes is actually used by the operating system and it has its own mechanism within the protocol to determine where to route communications. As far as network communications is concerned, it listens on TCP port 445. This is true whether we're talking about a default or named instance of SQL Server. The Summary Table To put all this together, here is what you need to know: Type of Communication Protocol Used Default Port Finding a SQL Server or SQL Server Named Instance UDP 1434 Communicating with a default instance of SQL Server TCP 1433 Communicating with a named instance of SQL Server TCP * Determined dynamically at start up Communicating with SQL Server via Named Pipes TCP 445

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• HTG Explains: Should You Build Your Own PC?

  - by Chris Hoffman

  There was a time when every geek seemed to build their own PC. While the masses bought eMachines and Compaqs, geeks built their own more powerful and reliable desktop machines for cheaper. But does this still make sense? Building your own PC still offers as much flexibility in component choice as it ever did, but prebuilt computers are available at extremely competitive prices. Building your own PC will no longer save you money in most cases. The Rise of Laptops It’s impossible to look at the decline of geeks building their own PCs without considering the rise of laptops. There was a time when everyone seemed to use desktops — laptops were more expensive and significantly slower in day-to-day tasks. With the diminishing importance of computing power — nearly every modern computer has more than enough power to surf the web and use typical programs like Microsoft Office without any trouble — and the rise of laptop availability at nearly every price point, most people are buying laptops instead of desktops. And, if you’re buying a laptop, you can’t really build your own. You can’t just buy a laptop case and start plugging components into it — even if you could, you would end up with an extremely bulky device. Ultimately, to consider building your own desktop PC, you have to actually want a desktop PC. Most people are better served by laptops. Benefits to PC Building The two main reasons to build your own PC have been component choice and saving money. Building your own PC allows you to choose all the specific components you want rather than have them chosen for you. You get to choose everything, including the PC’s case and cooling system. Want a huge case with room for a fancy water-cooling system? You probably want to build your own PC. In the past, this often allowed you to save money — you could get better deals by buying the components yourself and combining them, avoiding the PC manufacturer markup. You’d often even end up with better components — you could pick up a more powerful CPU that was easier to overclock and choose more reliable components so you wouldn’t have to put up with an unstable eMachine that crashed every day. PCs you build yourself are also likely more upgradable — a prebuilt PC may have a sealed case and be constructed in such a way to discourage you from tampering with the insides, while swapping components in and out is generally easier with a computer you’ve built on your own. If you want to upgrade your CPU or replace your graphics card, it’s a definite benefit. Downsides to Building Your Own PC It’s important to remember there are downsides to building your own PC, too. For one thing, it’s just more work — sure, if you know what you’re doing, building your own PC isn’t that hard. Even for a geek, researching the best components, price-matching, waiting for them all to arrive, and building the PC just takes longer. Warranty is a more pernicious problem. If you buy a prebuilt PC and it starts malfunctioning, you can contact the computer’s manufacturer and have them deal with it. You don’t need to worry about what’s wrong. If you build your own PC and it starts malfunctioning, you have to diagnose the problem yourself. What’s malfunctioning, the motherboard, CPU, RAM, graphics card, or power supply? Each component has a separate warranty through its manufacturer, so you’ll have to determine which component is malfunctioning before you can send it off for replacement. Should You Still Build Your Own PC? Let’s say you do want a desktop and are willing to consider building your own PC. First, bear in mind that PC manufacturers are buying in bulk and getting a better deal on each component. They also have to pay much less for a Windows license than the $120 or so it would cost you to to buy your own Windows license. This is all going to wipe out the cost savings you’ll see — with everything all told, you’ll probably spend more money building your own average desktop PC than you would picking one up from Amazon or the local electronics store. If you’re an average PC user that uses your desktop for the typical things, there’s no money to be saved from building your own PC. But maybe you’re looking for something higher end. Perhaps you want a high-end gaming PC with the fastest graphics card and CPU available. Perhaps you want to pick out each individual component and choose the exact components for your gaming rig. In this case, building your own PC may be a good option. As you start to look at more expensive, high-end PCs, you may start to see a price gap — but you may not. Let’s say you wanted to blow thousands of dollars on a gaming PC. If you’re looking at spending this kind of money, it would be worth comparing the cost of individual components versus a prebuilt gaming system. Still, the actual prices may surprise you. For example, if you wanted to upgrade Dell’s $2293 Alienware Aurora to include a second NVIDIA GeForce GTX 780 graphics card, you’d pay an additional $600 on Alienware’s website. The same graphics card costs $650 on Amazon or Newegg, so you’d be spending more money building the system yourself. Why? Dell’s Alienware gets bulk discounts you can’t get — and this is Alienware, which was once regarded as selling ridiculously overpriced gaming PCs to people who wouldn’t build their own. Building your own PC still allows you to get the most freedom when choosing and combining components, but this is only valuable to a small niche of gamers and professional users — most people, even average gamers, would be fine going with a prebuilt system. If you’re an average person or even an average gamer, you’ll likely find that it’s cheaper to purchase a prebuilt PC rather than assemble your own. Even at the very high end, components may be more expensive separately than they are in a prebuilt PC. Enthusiasts who want to choose all the individual components for their dream gaming PC and want maximum flexibility may want to build their own PCs. Even then, building your own PC these days is more about flexibility and component choice than it is about saving money. In summary, you probably shouldn’t build your own PC. If you’re an enthusiast, you may want to — but only a small minority of people would actually benefit from building their own systems. Feel free to compare prices, but you may be surprised which is cheaper. Image Credit: Richard Jones on Flickr, elPadawan on Flickr, Richard Jones on Flickr     

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• Dual Monitor (Monitor and TV)

  - by umpirsky

  I connected TV to my computer, and trying to set dual display. Whatever resolution I choose for my second display (TV) I get message like this: The selected configuration for displays could not be applied required virtual size does not fit available size: requested=(2704, 1050), minimum=(320, 200), maximum=(1680, 1680) How can I fix this? Also, while I was experimenting system went to deadlock, I restarted and after boot monitor just turns off once system is up. I boot in recovery mode and after several retries fixed it somehow, I don't know how, probably by changing display config from display manager. now I found xorg.conf.new file in my home dir: Section "ServerLayout" Identifier "X.org Configured" Screen 0 "Screen0" 0 0 Screen 1 "Screen1" RightOf "Screen0" Screen 2 "Screen2" RightOf "Screen1" InputDevice "Mouse0" "CorePointer" InputDevice "Keyboard0" "CoreKeyboard" EndSection Section "Files" ModulePath "/usr/lib/xorg/modules" FontPath "/usr/share/fonts/X11/misc" FontPath "/usr/share/fonts/X11/cyrillic" FontPath "/usr/share/fonts/X11/100dpi/:unscaled" FontPath "/usr/share/fonts/X11/75dpi/:unscaled" FontPath "/usr/share/fonts/X11/Type1" FontPath "/usr/share/fonts/X11/100dpi" FontPath "/usr/share/fonts/X11/75dpi" FontPath "/var/lib/defoma/x-ttcidfont-conf.d/dirs/TrueType" FontPath "built-ins" EndSection Section "Module" Load "extmod" Load "dbe" Load "glx" Load "dri" Load "dri2" Load "record" EndSection Section "InputDevice" Identifier "Keyboard0" Driver "kbd" EndSection Section "InputDevice" Identifier "Mouse0" Driver "mouse" Option "Protocol" "auto" Option "Device" "/dev/input/mice" Option "ZAxisMapping" "4 5 6 7" EndSection Section "Monitor" Identifier "Monitor0" VendorName "Monitor Vendor" ModelName "Monitor Model" EndSection Section "Monitor" Identifier "Monitor1" VendorName "Monitor Vendor" ModelName "Monitor Model" EndSection Section "Monitor" Identifier "Monitor2" VendorName "Monitor Vendor" ModelName "Monitor Model" EndSection Section "Device" ### Available Driver options are:- ### Values: <i>: integer, <f>: float, <bool>: "True"/"False", ### <string>: "String", <freq>: "<f> Hz/kHz/MHz", ### <percent>: "<f>%" ### [arg]: arg optional #Option "NoAccel" # [<bool>] #Option "SWcursor" # [<bool>] #Option "Dac6Bit" # [<bool>] #Option "Dac8Bit" # [<bool>] #Option "BusType" # [<str>] #Option "CPPIOMode" # [<bool>] #Option "CPusecTimeout" # <i> #Option "AGPMode" # <i> #Option "AGPFastWrite" # [<bool>] #Option "AGPSize" # <i> #Option "GARTSize" # <i> #Option "RingSize" # <i> #Option "BufferSize" # <i> #Option "EnableDepthMoves" # [<bool>] #Option "EnablePageFlip" # [<bool>] #Option "NoBackBuffer" # [<bool>] #Option "DMAForXv" # [<bool>] #Option "FBTexPercent" # <i> #Option "DepthBits" # <i> #Option "PCIAPERSize" # <i> #Option "AccelDFS" # [<bool>] #Option "IgnoreEDID" # [<bool>] #Option "CustomEDID" # [<str>] #Option "DisplayPriority" # [<str>] #Option "PanelSize" # [<str>] #Option "ForceMinDotClock" # <freq> #Option "ColorTiling" # [<bool>] #Option "VideoKey" # <i> #Option "RageTheatreCrystal" # <i> #Option "RageTheatreTunerPort" # <i> #Option "RageTheatreCompositePort" # <i> #Option "RageTheatreSVideoPort" # <i> #Option "TunerType" # <i> #Option "RageTheatreMicrocPath" # <str> #Option "RageTheatreMicrocType" # <str> #Option "ScalerWidth" # <i> #Option "RenderAccel" # [<bool>] #Option "SubPixelOrder" # [<str>] #Option "ClockGating" # [<bool>] #Option "VGAAccess" # [<bool>] #Option "ReverseDDC" # [<bool>] #Option "LVDSProbePLL" # [<bool>] #Option "AccelMethod" # <str> #Option "DRI" # [<bool>] #Option "ConnectorTable" # <str> #Option "DefaultConnectorTable" # [<bool>] #Option "DefaultTMDSPLL" # [<bool>] #Option "TVDACLoadDetect" # [<bool>] #Option "ForceTVOut" # [<bool>] #Option "TVStandard" # <str> #Option "IgnoreLidStatus" # [<bool>] #Option "DefaultTVDACAdj" # [<bool>] #Option "Int10" # [<bool>] #Option "EXAVSync" # [<bool>] #Option "ATOMTVOut" # [<bool>] #Option "R4xxATOM" # [<bool>] #Option "ForceLowPowerMode" # [<bool>] #Option "DynamicPM" # [<bool>] #Option "NewPLL" # [<bool>] #Option "ZaphodHeads" # <str> Identifier "Card0" Driver "radeon" BusID "PCI:2:0:0" EndSection Section "Device" ### Available Driver options are:- ### Values: <i>: integer, <f>: float, <bool>: "True"/"False", ### <string>: "String", <freq>: "<f> Hz/kHz/MHz", ### <percent>: "<f>%" ### [arg]: arg optional #Option "ShadowFB" # [<bool>] #Option "Rotate" # <str> #Option "fbdev" # <str> #Option "debug" # [<bool>] Identifier "Card1" Driver "fbdev" BusID "PCI:2:0:0" EndSection Section "Device" ### Available Driver options are:- ### Values: <i>: integer, <f>: float, <bool>: "True"/"False", ### <string>: "String", <freq>: "<f> Hz/kHz/MHz", ### <percent>: "<f>%" ### [arg]: arg optional #Option "ShadowFB" # [<bool>] #Option "DefaultRefresh" # [<bool>] #Option "ModeSetClearScreen" # [<bool>] Identifier "Card2" Driver "vesa" BusID "PCI:2:0:0" EndSection Section "Screen" Identifier "Screen0" Device "Card0" Monitor "Monitor0" SubSection "Display" Viewport 0 0 Depth 1 EndSubSection SubSection "Display" Viewport 0 0 Depth 4 EndSubSection SubSection "Display" Viewport 0 0 Depth 8 EndSubSection SubSection "Display" Viewport 0 0 Depth 15 EndSubSection SubSection "Display" Viewport 0 0 Depth 16 EndSubSection SubSection "Display" Viewport 0 0 Depth 24 EndSubSection EndSection Section "Screen" Identifier "Screen1" Device "Card1" Monitor "Monitor1" SubSection "Display" Viewport 0 0 Depth 1 EndSubSection SubSection "Display" Viewport 0 0 Depth 4 EndSubSection SubSection "Display" Viewport 0 0 Depth 8 EndSubSection SubSection "Display" Viewport 0 0 Depth 15 EndSubSection SubSection "Display" Viewport 0 0 Depth 16 EndSubSection SubSection "Display" Viewport 0 0 Depth 24 EndSubSection EndSection Section "Screen" Identifier "Screen2" Device "Card2" Monitor "Monitor2" SubSection "Display" Viewport 0 0 Depth 1 EndSubSection SubSection "Display" Viewport 0 0 Depth 4 EndSubSection SubSection "Display" Viewport 0 0 Depth 8 EndSubSection SubSection "Display" Viewport 0 0 Depth 15 EndSubSection SubSection "Display" Viewport 0 0 Depth 16 EndSubSection SubSection "Display" Viewport 0 0 Depth 24 EndSubSection EndSection Can I delete it? Second display (TV) only works when I check Mirror displays option.

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• Developing a Cost Model for Cloud Applications

  - by BuckWoody

  Note - please pay attention to the date of this post. As much as I attempt to make the information below accurate, the nature of distributed computing means that components, units and pricing will change over time. The definitive costs for Microsoft Windows Azure and SQL Azure are located here, and are more accurate than anything you will see in this post: http://www.microsoft.com/windowsazure/offers/  When writing software that is run on a Platform-as-a-Service (PaaS) offering like Windows Azure / SQL Azure, one of the questions you must answer is how much the system will cost. I will not discuss the comparisons between on-premise costs (which are nigh impossible to calculate accurately) versus cloud costs, but instead focus on creating a general model for estimating costs for a given application. You should be aware that there are (at this writing) two billing mechanisms for Windows and SQL Azure: “Pay-as-you-go” or consumption, and “Subscription” or commitment. Conceptually, you can consider the former a pay-as-you-go cell phone plan, where you pay by the unit used (at a slightly higher rate) and the latter as a standard cell phone plan where you commit to a contract and thus pay lower rates. In this post I’ll stick with the pay-as-you-go mechanism for simplicity, which should be the maximum cost you would pay. From there you may be able to get a lower cost if you use the other mechanism. In any case, the model you create should hold. Developing a good cost model is essential. As a developer or architect, you’ll most certainly be asked how much something will cost, and you need to have a reliable way to estimate that. Businesses and Organizations have been used to paying for servers, software licenses, and other infrastructure as an up-front cost, and power, people to the systems and so on as an ongoing (and sometimes not factored) cost. When presented with a new paradigm like distributed computing, they may not understand the true cost/value proposition, and that’s where the architect and developer can guide the conversation to make a choice based on features of the application versus the true costs. The two big buckets of use-types for these applications are customer-based and steady-state. In the customer-based use type, each successful use of the program results in a sale or income for your organization. Perhaps you’ve written an application that provides the spot-price of foo, and your customer pays for the use of that application. In that case, once you’ve estimated your cost for a successful traversal of the application, you can build that into the price you charge the user. It’s a standard restaurant model, where the price of the meal is determined by the cost of making it, plus any profit you can make. In the second use-type, the application will be used by a more-or-less constant number of processes or users and no direct revenue is attached to the system. A typical example is a customer-tracking system used by the employees within your company. In this case, the cost model is often created “in reverse” - meaning that you pilot the application, monitor the use (and costs) and that cost is held steady. This is where the comparison with an on-premise system becomes necessary, even though it is more difficult to estimate those on-premise true costs. For instance, do you know exactly how much cost the air conditioning is because you have a team of system administrators? This may sound trivial, but that, along with the insurance for the building, the wiring, and every other part of the system is in fact a cost to the business. There are three primary methods that I’ve been successful with in estimating the cost. None are perfect, all are demand-driven. The general process is to lay out a matrix of: components units cost per unit and then multiply that times the usage of the system, based on which components you use in the program. That sounds a bit simplistic, but using those metrics in a calculation becomes more detailed. In all of the methods that follow, you need to know your application. The components for a PaaS include computing instances, storage, transactions, bandwidth and in the case of SQL Azure, database size. In most cases, architects start with the first model and progress through the other methods to gain accuracy. Simple Estimation The simplest way to calculate costs is to architect the application (even UML or on-paper, no coding involved) and then estimate which of the components you’ll use, and how much of each will be used. Microsoft provides two tools to do this - one is a simple slider-application located here: http://www.microsoft.com/windowsazure/pricing-calculator/  The other is a tool you download to create an “Return on Investment” (ROI) spreadsheet, which has the advantage of leading you through various questions to estimate what you plan to use, located here: https://roianalyst.alinean.com/msft/AutoLogin.do?d=176318219048082115  You can also just create a spreadsheet yourself with a structure like this: Program Element Azure Component Unit of Measure Cost Per Unit Estimated Use of Component Total Cost Per Component Cumulative Cost               Of course, the consideration with this model is that it is difficult to predict a system that is not running or hasn’t even been developed. Which brings us to the next model type. Measure and Project A more accurate model is to actually write the code for the application, using the Software Development Kit (SDK) which can run entirely disconnected from Azure. The code should be instrumented to estimate the use of the application components, logging to a local file on the development system. A series of unit and integration tests should be run, which will create load on the test system. You can use standard development concepts to track this usage, and even use Windows Performance Monitor counters. The best place to start with this method is to use the Windows Azure Diagnostics subsystem in your code, which you can read more about here: http://blogs.msdn.com/b/sumitm/archive/2009/11/18/introducing-windows-azure-diagnostics.aspx This set of API’s greatly simplifies tracking the application, and in fact you can use this information for more than just a cost model. After you have the tracking logs, you can plug the numbers into ay of the tools above, which should give a representative cost or in some cases a unit cost. The consideration with this model is that the SDK fabric is not a one-to-one comparison with performance on the actual Windows Azure fabric. Those differences are usually smaller, but they do need to be considered. Also, you may not be able to accurately predict the load on the system, which might lead to an architectural change, which changes the model. This leads us to the next, most accurate method for a cost model. Sample and Estimate Using standard statistical and other predictive math, once the application is deployed you will get a bill each month from Microsoft for your Azure usage. The bill is quite detailed, and you can export the data from it to do analysis, and using methods like regression and so on project out into the future what the costs will be. I normally advise that the architect also extrapolate a unit cost from those metrics as well. This is the information that should be reported back to the executives that pay the bills: the past cost, future projected costs, and unit cost “per click” or “per transaction”, as your case warrants. The challenge here is in the model itself - statistical methods are not foolproof, and the larger the sample (in this case I recommend the entire population, not a smaller sample) is key. References and Tools Articles: http://blogs.msdn.com/b/patrick_butler_monterde/archive/2010/02/10/windows-azure-billing-overview.aspx http://technet.microsoft.com/en-us/magazine/gg213848.aspx http://blog.codingoutloud.com/2011/06/05/azure-faq-how-much-will-it-cost-me-to-run-my-application-on-windows-azure/ http://blogs.msdn.com/b/johnalioto/archive/2010/08/25/10054193.aspx http://geekswithblogs.net/iupdateable/archive/2010/02/08/qampa-how-can-i-calculate-the-tco-and-roi-when.aspx   Other Tools: http://cloud-assessment.com/ http://communities.quest.com/community/cloud_tools

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• From J2EE to Java EE: what has changed?

  - by Bruno.Borges

  See original @Java_EE tweet on 29 May 2014 Yeap, it has been 8 years since the term J2EE was replaced, and still some people refer to it (mostly recruiters, luckily!). But then comes the question: what has changed besides the name? Our community friend Abhishek Gupta worked on this question and provided an excellent response titled "What's in a name? Java EE? J2EE?". But let me give you a few highlights here so you don't lose yourself with YATO (yet another tab opened): J2EE used to be an infrastructure and resources provider only, requiring developers to depend on external 3rd-party frameworks to then implement application requirements or improve productivity J2EE used to require hundreds of XML lines of codes to define just a dozen of resources like EJBs, MDBs, Servlets, and so on J2EE used to support only EAR (Enterprise Archives) with a bunch of other archives like JARs and WARs just to run a simple Web application And so on, and so on! It was a great technology but still required a lot of work to get something up and running. Remember xDoclet? Remember Struts? The old days of pure Hibernate code? Or when Ajax became a trending topic and we were all implementing it with DWR Servlet? Still, we J2EE developers survived, and learned, and helped evolve the platform to a whole new level of DX (Developer Experience). A new DX for J2EE suggested a new name. One that referred to the platform as the Enterprise Edition of Java, because "Java is why we're here" quoting Bill Shannon. The release of Java EE 5 included so many features that clearly showed developers the platform was going after all those DX gaps. Radical simplification of the persistence model with the introduction of JPA Support of Annotations following the launch of Java SE 5.0 Updated XML APIs with the introduction of StAX Drastic simplification of the EJB component model (with annotations!) Convention over Configuration and Dependency Injection A few bullets you may say but that represented a whole new DX and a vision for upcoming versions. Clearly, the release of Java EE 5 helped drive the future of the platform by reducing the number of XMLs, Java Interfaces, simplified configurations, provided convention-over-configuration, etc! We then saw the release of Java EE 6 with even more great features like Managed Beans, CDI, Bean Validation, improved JSP and Servlets APIs, JASPIC, the posisbility to deploy plain WARs and so many other improvements it is difficult to list in one sentence. And we've gotta give Spring Framework some credit here: thanks to Rod Johnson and team, concepts like Dependency Injection fit perfectly into the Java EE Platform. Clearly, Spring used to be one of the most inspiring frameworks for the Java EE platform, and it is great to see things like Pivotal and Spring supporting JSR 352 Batch API standard! Cooperation to keep improving DX at maximum in the server-side Java landscape.  The master piece result of these previous releases is seen and called today as Java EE 7, which by providing a newly and improved JavaServer Faces release, with new features for Web Development like WebSockets API, improved JAX-RS, and JSON-P, but also including Batch API and so many other great improvements, has increased developer productivity and brought innovation to server-side Java developers. Java EE is not just a new name (which was introduced back in May 2006!) but a new Developer Experience for server-side Java developers. To show you why we are here and where we are going (see the Java EE 8 update), we wanted to share with you a draft of the new Java EE logos that the evangelist team created, to help you spread the word about Java EE. You can get access to these images at the Java EE Platform Facebook Album, or the Google+ Java EE Platform Album whichever is better for you, but don't forget to like and/or +1 those social network profiles :-) A message to all job recruiters: stop using J2EE and start using Java EE if you want to find great Java EE 5, Java EE 6, or Java EE 7 developers To not only save you recruiter valuable characters when tweeting that job opportunity but to also match the correct term, we invite you to replace long terms like "Java/J2EE" or even worse "#Java #J2EE #JEE" or all these awkward combinations with the only acceptable hashtag: #JavaEE. And to prove that Java EE is catching among developers and even recruiters, and that J2EE is past, let me highlight here how are the jobs trends! The image below is from Indeed.com trends page, for the following keywords: J2EE, Java/J2EE, Java/JEE, JEE. As you can see, J2EE is indeed going away, while JEE saw some increase. Perhaps because some people are just lazy to type "Java" but at the same time they are aware that J2EE (the '2') is past. We shall forgive that for a while :-) Another proof that J2EE is going away is by looking at its trending statistics at Google. People have been showing less and less interest in the term J2EE. See the chart below:  Recruiter, if you still need proof that J2EE is past, that Java EE is trending, and that other job recruiters are seeking for Java EE developers, and that the developer community is aware of the new term, perhaps these other charts can show you what term you should be using. See for example the Job Trends for Java EE at Indeed.com and notice where it started... 2006! 8 years ago :-) Last but not least, the Google Trends for Java EE term (including the still wrong but forgivable JavaEE term) shows us that the new term is catching up very well. J2EE is past. Oh, and don't worry about the curves going down. We developers like to be hipsters sometimes and today only AngularJS, NodeJS, BigData are going up. Java EE and other traditional server-side technologies such as Spring, or even from other platforms such as Ruby on Rails, PHP, Grails, are pretty much consolidated and the curves... well, they are consolidated too. So If you are a Java EE developer, drop that J2EE from your résumé, and let recruiters also know that this term is past. Embrace Java EE, and enjoy a new developer experience for server-side Java developers. Java EE on TwitterJava EE on Google+Java EE on Facebook

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• Elegance, thy Name is jQuery

  - by SGWellens

  So, I'm browsing though some questions over on the Stack Overflow website and I found a good jQuery question just a few minutes old. Here is a link to it. It was a tough question; I knew that by answering it, I could learn new stuff and reinforce what I already knew: Reading is good, doing is better. Maybe I could help someone in the process too. I cut and pasted the HTML from the question into my Visual Studio IDE and went back to Stack Overflow to reread the question. Dang, someone had already answered it! And it was a great answer. I never even had a chance to start analyzing the issue. Now I know what a one-legged man feels like in an ass-kicking contest. Nevertheless, since the question and answer were so interesting, I decided to dissect them and learn as much as possible. The HTML consisted of some divs separated by h3 headings.  Note the elements are laid out sequentially with no programmatic grouping: <h3 class="heading">Heading 1</h3> <div>Content</div> <div>More content</div> <div>Even more content</div><h3 class="heading">Heading 2</h3> <div>some content</div> <div>some more content</div><h3 class="heading">Heading 3</h3> <div>other content</div></form></body>  The requirement was to wrap a div around each h3 heading and the subsequent divs grouping them into sections. Why? I don't know, I suppose if you screen-scrapped some HTML from another site, you might want to reformat it before displaying it on your own. Anyways… Here is the marvelously, succinct posted answer: $('.heading').each(function(){ $(this).nextUntil('.heading').andSelf().wrapAll('<div class="section">');}); I was familiar with all the parts except for nextUntil and andSelf. But, I'll analyze the whole answer for completeness. I'll do this by rewriting the posted answer in a different style and adding a boat-load of comments: function Test(){ // $Sections is a jQuery object and it will contain three elements var $Sections = $('.heading'); // use each to iterate over each of the three elements $Sections.each(function () { // $this is a jquery object containing the current element // being iterated var $this = $(this); // nextUntil gets the following sibling elements until it reaches // an element with the CSS class 'heading' // andSelf adds in the source element (this) to the collection $this = $this.nextUntil('.heading').andSelf(); // wrap the elements with a div $this.wrapAll('<div class="section" >'); });}  The code here doesn't look nearly as concise and elegant as the original answer. However, unless you and your staff are jQuery masters, during development it really helps to work through algorithms step by step. You can step through this code in the debugger and examine the jQuery objects to make sure one step is working before proceeding on to the next. It's much easier to debug and troubleshoot when each logical coding step is a separate line. Note: You may think the original code runs much faster than this version. However, the time difference is trivial: Not enough to worry about: Less than 1 millisecond (tested in IE and FF). Note: You may want to jam everything into one line because it results in less traffic being sent to the client. That is true. However, most Internet servers now compress HTML and JavaScript by stripping out comments and white space (go to Bing or Google and view the source). This feature should be enabled on your server: Let the server compress your code, you don't need to do it. Free Career Advice: Creating maintainable code is Job One—Maximum Priority—The Prime Directive. If you find yourself suddenly transferred to customer support, it may be that the code you are writing is not as readable as it could be and not as readable as it should be. Moving on… I created a CSS class to see the results: .section{ background-color: yellow; border: 2px solid black; margin: 5px;} Here is the rendered output before:   …and after the jQuery code runs.   Pretty Cool! But, while playing with this code, the logic of nextUntil began to bother me: What happens in the last section? What stops elements from being collected since there are no more elements with the .heading class? The answer is nothing.  In this case it stopped because it was at the end of the page.  But what if there were additional HTML elements? I added an anchor tag and another div to the HTML: <h3 class="heading">Heading 1</h3> <div>Content</div> <div>More content</div> <div>Even more content</div><h3 class="heading">Heading 2</h3> <div>some content</div> <div>some more content</div><h3 class="heading">Heading 3</h3> <div>other content</div><a>this is a link</a><div>unrelated div</div> </form></body> The code as-is will include both the anchor and the unrelated div. This isn't what we want.   My first attempt to correct this used the filter parameter of the nextUntil function: nextUntil('.heading', 'div')  This will only collect div elements. But it merely skipped the anchor tag and it still collected the unrelated div:   The problem is we need a way to tell the nextUntil function when to stop. CSS selectors to the rescue: nextUntil('.heading, a')  This tells nextUntil to stop collecting sibling elements when it gets to an element with a .heading class OR when it gets to an anchor tag. In this case it solved the problem. FYI: The comma operator in a CSS selector allows multiple criteria.   Bingo! One final note, we could have broken the code down even more: We could have replaced the andSelf function here: $this = $this.nextUntil('.heading, a').andSelf(); With this: // get all the following siblings and then add the current item$this = $this.nextUntil('.heading, a');$this.add(this);  But in this case, the andSelf function reads real nice. In my opinion. Here's a link to a jsFiddle if you want to play with it. I hope someone finds this useful Steve Wellens CodeProject

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• External File Upload Optimizations for Windows Azure

  - by rgillen

  [Cross posted from here: http://rob.gillenfamily.net/post/External-File-Upload-Optimizations-for-Windows-Azure.aspx] I’m wrapping up a bit of the work we’ve been doing on data movement optimizations for cloud computing and the latest set of data yielded some interesting points I thought I’d share. The work done here is not really rocket science but may, in some ways, be slightly counter-intuitive and therefore seemed worthy of posting. Summary: for those who don’t like to read detailed posts or don’t have time, the synopsis is that if you are uploading data to Azure, block your data (even down to 1MB) and upload in parallel. Set your block size based on your source file size, but if you must choose a fixed value, use 1MB. Following the above will result in significant performance gains… upwards of 10x-24x and a reduction in overall file transfer time of upwards of 90% (eg, uploading a 1GB file averaged 46.37 minutes prior to optimizations and averaged 1.86 minutes afterwards). Detail: For those of you who want more detail, or think that the claims at the end of the preceding paragraph are over-reaching, what follows is information and code supporting these claims. As the title would indicate, these tests were run from our research facility pointing to the Azure cloud (specifically US North Central as it is physically closest to us) and do not represent intra-cloud results… we have performed intra-cloud tests and the overall results are similar in notion but the data rates are significantly different as well as the tipping points for the various block sizes… this will be detailed separately). We started by building a very simple console application that would loop through a directory and upload each file to Azure storage. This application used the shipping storage client library from the 1.1 version of the azure tools. The only real variation from the client library is that we added code to collect and record the duration (in ms) and size (in bytes) for each file transferred. The code is available here. We then created a directory that had a collection of files for the following sizes: 2KB, 32KB, 64KB, 128KB, 512KB, 1MB, 5MB, 10MB, 25MB, 50MB, 100MB, 250MB, 500MB, 750MB, and 1GB (50 files for each size listed). These files contained randomly-generated binary data and do not benefit from compression (a separate discussion topic). Our file generation tool is available here. The baseline was established by running the application described above against the directory containing all of the data files. This application uploads the files in a random order so as to avoid transferring all of the files of a given size sequentially and thereby spreading the affects of periodic Internet delays across the collection of results.  We then ran some scripts to split the resulting data and generate some reports. The raw data collected for our non-optimized tests is available via the links in the Related Resources section at the bottom of this post. For each file size, we calculated the average upload time (and standard deviation) and the average transfer rate (and standard deviation). As you likely are aware, transferring data across the Internet is susceptible to many transient delays which can cause anomalies in the resulting data. It is for this reason that we randomized the order of source file processing as well as executed the tests 50x for each file size. We expect that these steps will yield a sufficiently balanced set of results. Once the baseline was collected and analyzed, we updated the test harness application with some methods to split the source file into user-defined block sizes and then to upload those blocks in parallel (using the PutBlock() method of Azure storage). The parallelization was handled by simply relying on the Parallel Extensions to .NET to provide a Parallel.For loop (see linked source for specific implementation details in Program.cs, line 173 and following… less than 100 lines total). Once all of the blocks were uploaded, we called PutBlockList() to assemble/commit the file in Azure storage. For each block transferred, the MD5 was calculated and sent ensuring that the bits that arrived matched was was intended. The timer for the blocked/parallelized transfer method wraps the entire process (source file splitting, block transfer, MD5 validation, file committal). A diagram of the process is as follows: We then tested the affects of blocking & parallelizing the transfers by running the updated application against the same source set and did a parameter sweep on the block size including 256KB, 512KB, 1MB, 2MB, and 4MB (our assumption was that anything lower than 256KB wasn’t worth the trouble and 4MB is the maximum size of a block supported by Azure). The raw data for the parallel tests is available via the links in the Related Resources section at the bottom of this post. This data was processed and then compared against the single-threaded / non-optimized transfer numbers and the results were encouraging. The Excel version of the results is available here. Two semi-obvious points need to be made prior to reviewing the data. The first is that if the block size is larger than the source file size you will end up with a “negative optimization” due to the overhead of attempting to block and parallelize. The second is that as the files get smaller, the clock-time cost of blocking and parallelizing (overhead) is more apparent and can tend towards negative optimizations. For this reason (and is supported in the raw data provided in the linked worksheet) the charts and dialog below ignore source file sizes less than 1MB. (click chart for full size image) The chart above illustrates some interesting points about the results: When the block size is smaller than the source file, performance increases but as the block size approaches and then passes the source file size, you see decreasing benefit to the point of negative gains (see the values for the 1MB file size) For some of the moderately-sized source files, small blocks (256KB) are best As the size of the source file gets larger (see values for 50MB and up), the smallest block size is not the most efficient (presumably due, at least in part, to the increased number of blocks, increased number of individual transfer requests, and reassembly/committal costs). Once you pass the 250MB source file size, the difference in rate for 1MB to 4MB blocks is more-or-less constant The 1MB block size gives the best average improvement (~16x) but the optimal approach would be to vary the block size based on the size of the source file.    (click chart for full size image) The above is another view of the same data as the prior chart just with the axis changed (x-axis represents file size and plotted data shows improvement by block size). It again highlights the fact that the 1MB block size is probably the best overall size but highlights the benefits of some of the other block sizes at different source file sizes. This last chart shows the change in total duration of the file uploads based on different block sizes for the source file sizes. Nothing really new here other than this view of the data highlights the negative affects of poorly choosing a block size for smaller files.   Summary What we have found so far is that blocking your file uploads and uploading them in parallel results in significant performance improvements. Further, utilizing extension methods and the Task Parallel Library (.NET 4.0) make short work of altering the shipping client library to provide this functionality while minimizing the amount of change to existing applications that might be using the client library for other interactions.   Related Resources Source code for upload test application Source code for random file generator ODatas feed of raw data from non-optimized transfer tests Experiment Metadata Experiment Datasets 2KB Uploads 32KB Uploads 64KB Uploads 128KB Uploads 256KB Uploads 512KB Uploads 1MB Uploads 5MB Uploads 10MB Uploads 25MB Uploads 50MB Uploads 100MB Uploads 250MB Uploads 500MB Uploads 750MB Uploads 1GB Uploads Raw Data OData feeds of raw data from blocked/parallelized transfer tests Experiment Metadata Experiment Datasets Raw Data 256KB Blocks 512KB Blocks 1MB Blocks 2MB Blocks 4MB Blocks Excel worksheet showing summarizations and comparisons

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• I see no LOBs!

  - by Paul White

  Is it possible to see LOB (large object) logical reads from STATISTICS IO output on a table with no LOB columns? I was asked this question today by someone who had spent a good fraction of their afternoon trying to work out why this was occurring – even going so far as to re-run DBCC CHECKDB to see if any corruption had taken place.  The table in question wasn’t particularly pretty – it had grown somewhat organically over time, with new columns being added every so often as the need arose.  Nevertheless, it remained a simple structure with no LOB columns – no TEXT or IMAGE, no XML, no MAX types – nothing aside from ordinary INT, MONEY, VARCHAR, and DATETIME types.  To add to the air of mystery, not every query that ran against the table would report LOB logical reads – just sometimes – but when it did, the query often took much longer to execute. Ok, enough of the pre-amble.  I can’t reproduce the exact structure here, but the following script creates a table that will serve to demonstrate the effect: IF OBJECT_ID(N'dbo.Test', N'U') IS NOT NULL DROP TABLE dbo.Test GO CREATE TABLE dbo.Test ( row_id NUMERIC IDENTITY NOT NULL,   col01 NVARCHAR(450) NOT NULL, col02 NVARCHAR(450) NOT NULL, col03 NVARCHAR(450) NOT NULL, col04 NVARCHAR(450) NOT NULL, col05 NVARCHAR(450) NOT NULL, col06 NVARCHAR(450) NOT NULL, col07 NVARCHAR(450) NOT NULL, col08 NVARCHAR(450) NOT NULL, col09 NVARCHAR(450) NOT NULL, col10 NVARCHAR(450) NOT NULL, CONSTRAINT [PK dbo.Test row_id] PRIMARY KEY CLUSTERED (row_id) ) ; The next script loads the ten variable-length character columns with one-character strings in the first row, two-character strings in the second row, and so on down to the 450th row: WITH Numbers AS ( -- Generates numbers 1 - 450 inclusive SELECT TOP (450) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) INSERT dbo.Test WITH (TABLOCKX) SELECT REPLICATE(N'A', N.n), REPLICATE(N'B', N.n), REPLICATE(N'C', N.n), REPLICATE(N'D', N.n), REPLICATE(N'E', N.n), REPLICATE(N'F', N.n), REPLICATE(N'G', N.n), REPLICATE(N'H', N.n), REPLICATE(N'I', N.n), REPLICATE(N'J', N.n) FROM Numbers AS N ORDER BY N.n ASC ; Once those two scripts have run, the table contains 450 rows and 10 columns of data like this: Most of the time, when we query data from this table, we don’t see any LOB logical reads, for example: -- Find the maximum length of the data in -- column 5 for a range of rows SELECT result = MAX(DATALENGTH(T.col05)) FROM dbo.Test AS T WHERE row_id BETWEEN 50 AND 100 ; But with a different query… -- Read all the data in column 1 SELECT result = MAX(DATALENGTH(T.col01)) FROM dbo.Test AS T ; …suddenly we have 49 LOB logical reads, as well as the ‘normal’ logical reads we would expect. The Explanation If we had tried to create this table in SQL Server 2000, we would have received a warning message to say that future INSERT or UPDATE operations on the table might fail if the resulting row exceeded the in-row storage limit of 8060 bytes.  If we needed to store more data than would fit in an 8060 byte row (including internal overhead) we had to use a LOB column – TEXT, NTEXT, or IMAGE.  These special data types store the large data values in a separate structure, with just a small pointer left in the original row. Row Overflow SQL Server 2005 introduced a feature called row overflow, which allows one or more variable-length columns in a row to move to off-row storage if the data in a particular row would otherwise exceed 8060 bytes.  You no longer receive a warning when creating (or altering) a table that might need more than 8060 bytes of in-row storage; if SQL Server finds that it can no longer fit a variable-length column in a particular row, it will silently move one or more of these columns off the row into a separate allocation unit. Only variable-length columns can be moved in this way (for example the (N)VARCHAR, VARBINARY, and SQL_VARIANT types).  Fixed-length columns (like INTEGER and DATETIME for example) never move into ‘row overflow’ storage.  The decision to move a column off-row is done on a row-by-row basis – so data in a particular column might be stored in-row for some table records, and off-row for others. In general, if SQL Server finds that it needs to move a column into row-overflow storage, it moves the largest variable-length column record for that row.  Note that in the case of an UPDATE statement that results in the 8060 byte limit being exceeded, it might not be the column that grew that is moved! Sneaky LOBs Anyway, that’s all very interesting but I don’t want to get too carried away with the intricacies of row-overflow storage internals.  The point is that it is now possible to define a table with non-LOB columns that will silently exceed the old row-size limit and result in ordinary variable-length columns being moved to off-row storage.  Adding new columns to a table, expanding an existing column definition, or simply storing more data in a column than you used to – all these things can result in one or more variable-length columns being moved off the row. Note that row-overflow storage is logically quite different from old-style LOB and new-style MAX data type storage – individual variable-length columns are still limited to 8000 bytes each – you can just have more of them now.  Having said that, the physical mechanisms involved are very similar to full LOB storage – a column moved to row-overflow leaves a 24-byte pointer record in the row, and the ‘separate storage’ I have been talking about is structured very similarly to both old-style LOBs and new-style MAX types.  The disadvantages are also the same: when SQL Server needs a row-overflow column value it needs to follow the in-row pointer a navigate another chain of pages, just like retrieving a traditional LOB. And Finally… In the example script presented above, the rows with row_id values from 402 to 450 inclusive all exceed the total in-row storage limit of 8060 bytes.  A SELECT that references a column in one of those rows that has moved to off-row storage will incur one or more lob logical reads as the storage engine locates the data.  The results on your system might vary slightly depending on your settings, of course; but in my tests only column 1 in rows 402-450 moved off-row.  You might like to play around with the script – updating columns, changing data type lengths, and so on – to see the effect on lob logical reads and which columns get moved when.  You might even see row-overflow columns moving back in-row if they are updated to be smaller (hint: reduce the size of a column entry by at least 1000 bytes if you hope to see this). Be aware that SQL Server will not warn you when it moves ‘ordinary’ variable-length columns into overflow storage, and it can have dramatic effects on performance.  It makes more sense than ever to choose column data types sensibly.  If you make every column a VARCHAR(8000) or NVARCHAR(4000), and someone stores data that results in a row needing more than 8060 bytes, SQL Server might turn some of your column data into pseudo-LOBs – all without saying a word. Finally, some people make a distinction between ordinary LOBs (those that can hold up to 2GB of data) and the LOB-like structures created by row-overflow (where columns are still limited to 8000 bytes) by referring to row-overflow LOBs as SLOBs.  I find that quite appealing, but the ‘S’ stands for ‘small’, which makes expanding the whole acronym a little daft-sounding…small large objects anyone? © Paul White 2011 email: [email protected] twitter: @SQL_Kiwi

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• NUMA-aware placement of communication variables

  - by Dave

  For classic NUMA-aware programming I'm typically most concerned about simple cold, capacity and compulsory misses and whether we can satisfy the miss by locally connected memory or whether we have to pull the line from its home node over the coherent interconnect -- we'd like to minimize channel contention and conserve interconnect bandwidth. That is, for this style of programming we're quite aware of where memory is homed relative to the threads that will be accessing it. Ideally, a page is collocated on the node with the thread that's expected to most frequently access the page, as simple misses on the page can be satisfied without resorting to transferring the line over the interconnect. The default "first touch" NUMA page placement policy tends to work reasonable well in this regard. When a virtual page is first accessed, the operating system will attempt to provision and map that virtual page to a physical page allocated from the node where the accessing thread is running. It's worth noting that the node-level memory interleaving granularity is usually a multiple of the page size, so we can say that a given page P resides on some node N. That is, the memory underlying a page resides on just one node. But when thinking about accesses to heavily-written communication variables we normally consider what caches the lines underlying such variables might be resident in, and in what states. We want to minimize coherence misses and cache probe activity and interconnect traffic in general. I don't usually give much thought to the location of the home NUMA node underlying such highly shared variables. On a SPARC T5440, for instance, which consists of 4 T2+ processors connected by a central coherence hub, the home node and placement of heavily accessed communication variables has very little impact on performance. The variables are frequently accessed so likely in M-state in some cache, and the location of the home node is of little consequence because a requester can use cache-to-cache transfers to get the line. Or at least that's what I thought. Recently, though, I was exploring a simple shared memory point-to-point communication model where a client writes a request into a request mailbox and then busy-waits on a response variable. It's a simple example of delegation based on message passing. The server polls the request mailbox, and having fetched a new request value, performs some operation and then writes a reply value into the response variable. As noted above, on a T5440 performance is insensitive to the placement of the communication variables -- the request and response mailbox words. But on a Sun/Oracle X4800 I noticed that was not the case and that NUMA placement of the communication variables was actually quite important. For background an X4800 system consists of 8 Intel X7560 Xeons . Each package (socket) has 8 cores with 2 contexts per core, so the system is 8x8x2. Each package is also a NUMA node and has locally attached memory. Every package has 3 point-to-point QPI links for cache coherence, and the system is configured with a twisted ladder "mobius" topology. The cache coherence fabric is glueless -- there's not central arbiter or coherence hub. The maximum distance between any two nodes is just 2 hops over the QPI links. For any given node, 3 other nodes are 1 hop distant and the remaining 4 nodes are 2 hops distant. Using a single request (client) thread and a single response (server) thread, a benchmark harness explored all permutations of NUMA placement for the two threads and the two communication variables, measuring the average round-trip-time and throughput rate between the client and server. In this benchmark the server simply acts as a simple transponder, writing the request value plus 1 back into the reply field, so there's no particular computation phase and we're only measuring communication overheads. In addition to varying the placement of communication variables over pairs of nodes, we also explored variations where both variables were placed on one page (and thus on one node) -- either on the same cache line or different cache lines -- while varying the node where the variables reside along with the placement of the threads. The key observation was that if the client and server threads were on different nodes, then the best placement of variables was to have the request variable (written by the client and read by the server) reside on the same node as the client thread, and to place the response variable (written by the server and read by the client) on the same node as the server. That is, if you have a variable that's to be written by one thread and read by another, it should be homed with the writer thread. For our simple client-server model that means using split request and response communication variables with unidirectional message flow on a given page. This can yield up to twice the throughput of less favorable placement strategies. Our X4800 uses the QPI 1.0 protocol with source-based snooping. Briefly, when node A needs to probe a cache line it fires off snoop requests to all the nodes in the system. Those recipients then forward their response not to the original requester, but to the home node H of the cache line. H waits for and collects the responses, adjudicates and resolves conflicts and ensures memory-model ordering, and then sends a definitive reply back to the original requester A. If some node B needed to transfer the line to A, it will do so by cache-to-cache transfer and let H know about the disposition of the cache line. A needs to wait for the authoritative response from H. So if a thread on node A wants to write a value to be read by a thread on node B, the latency is dependent on the distances between A, B, and H. We observe the best performance when the written-to variable is co-homed with the writer A. That is, we want H and A to be the same node, as the writer doesn't need the home to respond over the QPI link, as the writer and the home reside on the very same node. With architecturally informed placement of communication variables we eliminate at least one QPI hop from the critical path. Newer Intel processors use the QPI 1.1 coherence protocol with home-based snooping. As noted above, under source-snooping a requester broadcasts snoop requests to all nodes. Those nodes send their response to the home node of the location, which provides memory ordering, reconciles conflicts, etc., and then posts a definitive reply to the requester. In home-based snooping the snoop probe goes directly to the home node and are not broadcast. The home node can consult snoop filters -- if present -- and send out requests to retrieve the line if necessary. The 3rd party owner of the line, if any, can respond either to the home or the original requester (or even to both) according to the protocol policies. There are myriad variations that have been implemented, and unfortunately vendor terminology doesn't always agree between vendors or with the academic taxonomy papers. The key is that home-snooping enables the use of a snoop filter to reduce interconnect traffic. And while home-snooping might have a longer critical path (latency) than source-based snooping, it also may require fewer messages and less overall bandwidth. It'll be interesting to reprise these experiments on a platform with home-based snooping. While collecting data I also noticed that there are placement concerns even in the seemingly trivial case when both threads and both variables reside on a single node. Internally, the cores on each X7560 package are connected by an internal ring. (Actually there are multiple contra-rotating rings). And the last-level on-chip cache (LLC) is partitioned in banks or slices, which with each slice being associated with a core on the ring topology. A hardware hash function associates each physical address with a specific home bank. Thus we face distance and topology concerns even for intra-package communications, although the latencies are not nearly the magnitude we see inter-package. I've not seen such communication distance artifacts on the T2+, where the cache banks are connected to the cores via a high-speed crossbar instead of a ring -- communication latencies seem more regular.

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• DTracing TCP congestion control

  - by user12820842

  In a previous post, I showed how we can use DTrace to probe TCP receive and send window events. TCP receive and send windows are in effect both about flow-controlling how much data can be received - the receive window reflects how much data the local TCP is prepared to receive, while the send window simply reflects the size of the receive window of the peer TCP. Both then represent flow control as imposed by the receiver. However, consider that without the sender imposing flow control, and a slow link to a peer, TCP will simply fill up it's window with sent segments. Dealing with multiple TCP implementations filling their peer TCP's receive windows in this manner, busy intermediate routers may drop some of these segments, leading to timeout and retransmission, which may again lead to drops. This is termed congestion, and TCP has multiple congestion control strategies. We can see that in this example, we need to have some way of adjusting how much data we send depending on how quickly we receive acknowledgement - if we get ACKs quickly, we can safely send more segments, but if acknowledgements come slowly, we should proceed with more caution. More generally, we need to implement flow control on the send side also. Slow Start and Congestion Avoidance From RFC2581, let's examine the relevant variables: "The congestion window (cwnd) is a sender-side limit on the amount of data the sender can transmit into the network before receiving an acknowledgment (ACK). Another state variable, the slow start threshold (ssthresh), is used to determine whether the slow start or congestion avoidance algorithm is used to control data transmission" Slow start is used to probe the network's ability to handle transmission bursts both when a connection is first created and when retransmission timers fire. The latter case is important, as the fact that we have effectively lost TCP data acts as a motivator for re-probing how much data the network can handle from the sending TCP. The congestion window (cwnd) is initialized to a relatively small value, generally a low multiple of the sending maximum segment size. When slow start kicks in, we will only send that number of bytes before waiting for acknowledgement. When acknowledgements are received, the congestion window is increased in size until cwnd reaches the slow start threshold ssthresh value. For most congestion control algorithms the window increases exponentially under slow start, assuming we receive acknowledgements. We send 1 segment, receive an ACK, increase the cwnd by 1 MSS to 2*MSS, send 2 segments, receive 2 ACKs, increase the cwnd by 2*MSS to 4*MSS, send 4 segments etc. When the congestion window exceeds the slow start threshold, congestion avoidance is used instead of slow start. During congestion avoidance, the congestion window is generally updated by one MSS for each round-trip-time as opposed to each ACK, and so cwnd growth is linear instead of exponential (we may receive multiple ACKs within a single RTT). This continues until congestion is detected. If a retransmit timer fires, congestion is assumed and the ssthresh value is reset. It is reset to a fraction of the number of bytes outstanding (unacknowledged) in the network. At the same time the congestion window is reset to a single max segment size. Thus, we initiate slow start until we start receiving acknowledgements again, at which point we can eventually flip over to congestion avoidance when cwnd ssthresh. Congestion control algorithms differ most in how they handle the other indication of congestion - duplicate ACKs. A duplicate ACK is a strong indication that data has been lost, since they often come from a receiver explicitly asking for a retransmission. In some cases, a duplicate ACK may be generated at the receiver as a result of packets arriving out-of-order, so it is sensible to wait for multiple duplicate ACKs before assuming packet loss rather than out-of-order delivery. This is termed fast retransmit (i.e. retransmit without waiting for the retransmission timer to expire). Note that on Oracle Solaris 11, the congestion control method used can be customized. See here for more details. In general, 3 or more duplicate ACKs indicate packet loss and should trigger fast retransmit . It's best not to revert to slow start in this case, as the fact that the receiver knew it was missing data suggests it has received data with a higher sequence number, so we know traffic is still flowing. Falling back to slow start would be excessive therefore, so fast recovery is used instead. Observing slow start and congestion avoidance The following script counts TCP segments sent when under slow start (cwnd ssthresh). #!/usr/sbin/dtrace -s #pragma D option quiet tcp:::connect-request / start[args[1]-cs_cid] == 0/ { start[args[1]-cs_cid] = 1; } tcp:::send / start[args[1]-cs_cid] == 1 && args[3]-tcps_cwnd tcps_cwnd_ssthresh / { @c["Slow start", args[2]-ip_daddr, args[4]-tcp_dport] = count(); } tcp:::send / start[args[1]-cs_cid] == 1 && args[3]-tcps_cwnd args[3]-tcps_cwnd_ssthresh / { @c["Congestion avoidance", args[2]-ip_daddr, args[4]-tcp_dport] = count(); } As we can see the script only works on connections initiated since it is started (using the start[] associative array with the connection ID as index to set whether it's a new connection (start[cid] = 1). From there we simply differentiate send events where cwnd ssthresh (congestion avoidance). Here's the output taken when I accessed a YouTube video (where rport is 80) and from an FTP session where I put a large file onto a remote system. # dtrace -s tcp_slow_start.d ^C ALGORITHM RADDR RPORT #SEG Slow start 10.153.125.222 20 6 Slow start 138.3.237.7 80 14 Slow start 10.153.125.222 21 18 Congestion avoidance 10.153.125.222 20 1164 We see that in the case of the YouTube video, slow start was exclusively used. Most of the segments we sent in that case were likely ACKs. Compare this case - where 14 segments were sent using slow start - to the FTP case, where only 6 segments were sent before we switched to congestion avoidance for 1164 segments. In the case of the FTP session, the FTP data on port 20 was predominantly sent with congestion avoidance in operation, while the FTP session relied exclusively on slow start. For the default congestion control algorithm - "newreno" - on Solaris 11, slow start will increase the cwnd by 1 MSS for every acknowledgement received, and by 1 MSS for each RTT in congestion avoidance mode. Different pluggable congestion control algorithms operate slightly differently. For example "highspeed" will update the slow start cwnd by the number of bytes ACKed rather than the MSS. And to finish, here's a neat oneliner to visually display the distribution of congestion window values for all TCP connections to a given remote port using a quantization. In this example, only port 80 is in use and we see the majority of cwnd values for that port are in the 4096-8191 range. # dtrace -n 'tcp:::send { @q[args[4]-tcp_dport] = quantize(args[3]-tcps_cwnd); }' dtrace: description 'tcp:::send ' matched 10 probes ^C 80 value ------------- Distribution ------------- count -1 | 0 0 |@@@@@@ 5 1 | 0 2 | 0 4 | 0 8 | 0 16 | 0 32 | 0 64 | 0 128 | 0 256 | 0 512 | 0 1024 | 0 2048 |@@@@@@@@@ 8 4096 |@@@@@@@@@@@@@@@@@@@@@@@@@@ 23 8192 | 0

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