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  • SQLAuthority News – Learning, Community and Book Signing at #SQLPASS 2012

    - by pinaldave
    SQLPASS event is going excellent we are having great great fun! We are having book signing events and the response is overwhelmingly positive. I am glad that all of you love our books and I totally appreciate your support. Rick and I both are feeling very motivated to write more books in future. Here is our schedule for book signing. SQL Queries 2012 Joes 2 Pros Volume1 Finally a book for the true SQL Server beginner! Whether you are brand new to databases and are thinking of getting your 70-461 certification or already a semi-pro working in the field and need some fingertip support, this is this is the book for you. Joes 2 Pros does not assume you already know anything about databases or SQL server.  This book builds on the success of the previous series and will help anyone transform themselves from a beginner “Joe” into a SQL 2012 “Pro”. Wednesday, November 7, 2012 12pm-1pm – Book Signing at Exhibit Hall Joes Pros booth#117 (FREE BOOK) Rest all the time – I will be at Exhibition Hall Joes 2 Pros Booth #117. Stop by for the goodies! This book is also available on Amazon. SQL 2012 Functions Joes 2 Pros Functions have been around for many years to make our lives easier. Because of them, thousands of lines of valuable programming can be done with one statement. When we know what functions are offered in SQL Server we can get powerful projects done very quickly. Often times, the functions you wished you had are released in the next version. Wednesday, November 7, 2012 7pm-8pm - Embarcadero Booth Book Signing (FREE BOOK) Thursday, November 8, 2012 12pm-1pm - Embarcadero Booth Book Signing (FREE BOOK) This book is also available on Amazon. If you are at SQLPASS stop by Booth #117 – I will be there and many be you can get one of my signed book! Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL PASS, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Book Review, SQLAuthority News, SQLServer, T SQL, Technology

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  • Coherence 3.7.1 Released

    - by JuergenKress
    Oracle Coherence 3.7.1 introduces REST API, exalogic infiniband integration, improved data access performance due to more efficient in-memory and disk-based storage, and query explain plan support and much more, download now! View the webcast: Unbeatable Performance for your Cloud Application Foundation. To download Coherence 3.7.1 please visit OTN. Coherence Screencasts: Coherence 3.7.1 – Extend Only Keys Coherence 3.7.1 – REST Support Coherence 3.7.1 – POF Object Identities and References Coherence 3.7.1 – POF Annotation Support Coherence 3.7.1 – Query Explain Plan For more information please visit the Oracle Coherence Knowledge Base For regular Coherence information become a member in the WebLogic Partner Community please first login at http://partner.oracle.com and then visit: http://www.oracle.com/partners/goto/wls-emea Blog Twitter LinkedIn Mix Forum Wiki Technorati Tags: Coherence,Coherence 3.7.1,Oracle,WebLogic,J2EE caching,OPN,Jürgen Kress

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  • Using XA Transactions in Coherence-based Applications

    - by jpurdy
    While the costs of XA transactions are well known (e.g. increased data contention, higher latency, significant disk I/O for logging, availability challenges, etc.), in many cases they are the most attractive option for coordinating logical transactions across multiple resources. There are a few common approaches when integrating Coherence into applications via the use of an application server's transaction manager: Use of Coherence as a read-only cache, applying transactions to the underlying database (or any system of record) instead of the cache. Use of TransactionMap interface via the included resource adapter. Use of the new ACID transaction framework, introduced in Coherence 3.6.   Each of these may have significant drawbacks for certain workloads. Using Coherence as a read-only cache is the simplest option. In this approach, the application is responsible for managing both the database and the cache (either within the business logic or via application server hooks). This approach also tends to provide limited benefit for many workloads, particularly those workloads that either have queries (given the complexity of maintaining a fully cached data set in Coherence) or are not read-heavy (where the cost of managing the cache may outweigh the benefits of reading from it). All updates are made synchronously to the database, leaving it as both a source of latency as well as a potential bottleneck. This approach also prevents addressing "hot data" problems (when certain objects are updated by many concurrent transactions) since most database servers offer no facilities for explicitly controlling concurrent updates. Finally, this option tends to be a better fit for key-based access (rather than filter-based access such as queries) since this makes it easier to aggressively invalidate cache entries without worrying about when they will be reloaded. The advantage of this approach is that it allows strong data consistency as long as optimistic concurrency control is used to ensure that database updates are applied correctly regardless of whether the cache contains stale (or even dirty) data. Another benefit of this approach is that it avoids the limitations of Coherence's write-through caching implementation. TransactionMap is generally used when Coherence acts as system of record. TransactionMap is not generally compatible with write-through caching, so it will usually be either used to manage a standalone cache or when the cache is backed by a database via write-behind caching. TransactionMap has some restrictions that may limit its utility, the most significant being: The lock-based concurrency model is relatively inefficient and may introduce significant latency and contention. As an example, in a typical configuration, a transaction that updates 20 cache entries will require roughly 40ms just for lock management (assuming all locks are granted immediately, and excluding validation and writing which will require a similar amount of time). This may be partially mitigated by denormalizing (e.g. combining a parent object and its set of child objects into a single cache entry), at the cost of increasing false contention (e.g. transactions will conflict even when updating different child objects). If the client (application server JVM) fails during the commit phase, locks will be released immediately, and the transaction may be partially committed. In practice, this is usually not as bad as it may sound since the commit phase is usually very short (all locks having been previously acquired). Note that this vulnerability does not exist when a single NamedCache is used and all updates are confined to a single partition (generally implying the use of partition affinity). The unconventional TransactionMap API is cumbersome but manageable. Only a few methods are transactional, primarily get(), put() and remove(). The ACID transactions framework (accessed via the Connection class) provides atomicity guarantees by implementing the NamedCache interface, maintaining its own cache data and transaction logs inside a set of private partitioned caches. This feature may be used as either a local transactional resource or as logging XA resource. However, a lack of database integration precludes the use of this functionality for most applications. A side effect of this is that this feature has not seen significant adoption, meaning that any use of this is subject to the usual headaches associated with being an early adopter (greater chance of bugs and greater risk of hitting an unoptimized code path). As a result, for the moment, we generally recommend against using this feature. In summary, it is possible to use Coherence in XA-oriented applications, and several customers are doing this successfully, but it is not a core usage model for the product, so care should be taken before committing to this path. For most applications, the most robust solution is normally to use Coherence as a read-only cache of the underlying data resources, even if this prevents taking advantage of certain product features.

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  • New Coherence 12c White Paper: The Advantages of Coherence on Exalogic

    - by JuergenKress
    This white paper provides you with an overview of both Oracle Coherence and Oracle Exalogic Elastic Cloud, and how businesses can realize even greater benefits from these technologies when they are used in tandem. Get the white paper here. WebLogic Partner Community For regular information become a member in the WebLogic Partner Community please visit: http://www.oracle.com/partners/goto/wls-emea ( OPN account required). If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Mix Forum Wiki Technorati Tags: Coherence,OOW,Oracle OpenWorld,WebLogic,WebLogic Community,Oracle,OPN,Jürgen Kress

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  • Can't Miss Event: Oracle Coherence 12c Launch Webcast

    - by jeckels
    We're super-excited around here about the impending launch of Oracle Coherence 12c as part of the Cloud Application Foundation launch this month! We want you to join us for the Cloud Application Foundation launch event to learn more about Coherence's ability to deliver applications with a mission-critical cloud platform, enhance deployment options for high availability and simplify operations with integrated products and management. Scale your applications to meet mobile and cloud demands! Oracle Cloud Application Foundation Launch Including Oracle WebLogic Server, Oracle Coherence, Oracle Enterprise Manager and Oracle Development ToolsJuly 31st, 2013 10am Pacific Time >> Register now! (of course, it's free) This will be the first release of Coherence we're making available at the same time as an Oracle WebLogic Server release - and that's not a coincidence. One of the main focus areas of this launch is the operational simplicity that we want you to enjoy, and that includes a tight integration not only with WebLogic Server itself, but also with cloud management tools (Enterprise Manager) and developer technologies - like JDeveloper, Eclipse tools, ADF Mobile and more - to ensure you can be productive out of the box on day one. The word is, there are even some heavy-duty capabilities Coherence will be delivering around real-time data processing, elastic scalability, developer technology friendliness and even some deep integration with Oracle Database 12c, which is launching on July 10th. But, we're already giving away too much. We look forward to seeing you there!

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  • SQLAuthority News – New Book Released – SQL Server Interview Questions And Answers

    - by pinaldave
    Two days ago, on birthday of my blog – I asked simple question – Guess! What is in this box? I have received lots of interesting comments on the blog about what is in it. Many of you got it absolutely incorrect and many got it close to the right answer but no one got it 100% correct. Well, no issue at all, I am going to give away the price to whoever has the closest answer first in personal email. Here is the answer to the question about what is in the box? Here it is – the box has my new book. In fact, I should say our new book as I co-authored this book with my very good friend Vinod Kumar. We had real blast writing this book together and had lots of interesting conversation when we were writing this book. This book has one simple goal – “master the basics.” This book is not only for people who are preparing for interview. This book is for every one who wants to revisit the basics and wants to prepare themselves to the technology. One always needs to have practical knowledge to do their duty efficiently. This book talks about more than basics. There are multiple ways to present learning – either we can create simple book or make it interesting. We have decided the learning should be interactive and have opted for Interview Questions and Answer format. Here is quick interview which we have done together. Details of the books are here The core concept of this book will continue to evolve over time. I am sure many of you will come along with us on this journey and submit your suggestions to us to make this book a key reference for anybody who wants to start with SQL server. Today we want to acknowledge the fact that you will help us keep this book alive forever with the latest updates. We want to thank everyone who participates in this journey with us. You can get the books from [Amazon] | [Flipkart]. Read Vinod‘s blog post. Do not forget to wish him happy birthday as today is his birthday and also book release day – two reason to wish him congratulations. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Data Warehousing, Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL Interview Questions and Answers, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Book Review, SQLAuthority News, SQLServer, T SQL, Technology

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  • SQLAuthority Book Review – DBA Survivor: Become a Rock Star DBA

    - by pinaldave
    DBA Survivor: Become a Rock Star DBA – Thomas LaRock Link to Amazon Link to Flipkart First of all, I thank all my readers when I wrote that I could not get this book in any local book stores, because they offered me to send a copy of this good book. A very special mention goes to Sripada and Jayesh for they gave so much effort in finding my home address and sending me the hard copy. Before, I did not have the copy of the book, but now I have two of it already! It surprises me how my readers were able to find my home address, which I have not publicly shared. Quick Review: This is indeed a one easy-to-read and fun book. We all work day and night with technology yet we should not forget to show our love and care for our family at home. For our souls that starve for peace and guidance, this one book is the “it” book for all the technology enthusiasts. Though this book was specifically written for DBAs, the reach is not limited to DBAs only because the lessons incorporated in it actually applies to all. This is one of the most motivating technical books I have read. Detailed Review: Let us go over a few questions first: Who wants to be as famous as rockstars in the field of Database Administration? How can one learn what it takes to become a top notch software developer? If you are a beginner in your field, how will you go to next level? Your boss may be very kind or like Dilbert’s Boss, what will you do? How do you keep growing when Eco-system around you does not support you? You are almost at top but there is someone else at the TOP, what do you do and how do you avoid office politics? As a database developer what should be your basic responsibility? and many more… I was able to completely read book in one sitting and I loved it. Before I continue with my opinion, I want to echo the opinion of Kevin Kline who has written the Forward of the book. He has truly suggested that “You hold in your hands a collection of insights and wisdom on the topic of database administration gained through many years of hard-won experience, long nights of study, and direct mentorship under some of the industry’s most talented database professionals and information technology (IT) experts.” Today, IT field is getting bigger and better, while talking about terabytes of the database becomes “more” normal every single day. The gods and demigods of database professionals are taking care of these large scale databases and are carefully maintaining them. In this world, there are only a few beginnings on the first step. There are many experts in different technology fields who are asked to address the issues with databases. There is YOU and ME, who is just new to this work. So we ask ourselves WHERE to begin and HOW to begin. We adore and follow the religion of our rockstars, but oftentimes we really have no idea about their background and their struggles. Every rockstar has his success story which needs to be digested before learning his tricks and tips. This book starts with the same note and teaches the two most important lessons for anybody who wants to be a DBA Rockstar –  to focus on their single goal of learning and to excel the technology. The story starts with three simple guidelines – Get Prepared, Get Trained, Get Certified. Once a person learns the skills, and then, it would be about time that he needs to enrich or to improve those skills you have learned. I am sure that the right opportunity will come finding themselves and they will not have to go run behind it. However, the real challenge for any person is the first day or first week. A new employee, no matter how much experienced he is, sometimes has no clue about what should one do at new job. Chapter 2 and chapter 3 precisely talk about what one should do as soon as the new job begins. It is also written with keeping the fact in focus that each job can be very much different but there are few infrastructure setups and programming concepts are the same. Learning basics of database was really interesting. I like to focus on the roots of any technology. It is important to understand the structure of the database before suggesting what indexes needs to be created, the same way this book covers the most essential knowledge one must learn by most database developers. I think the title of the fourth chapter is my favorite sentence in this book. I can see that I will be saying this again and again in the future – “A Development Server Is a Production Server to a Developer“. I have worked in the software industry for almost 8 years now and I have seen so many developers sitting on their chairs and waiting for instructions from their lead about how to improve the code or what to do the next. When I talk to them, I suggest that the experiment with their server and try various techniques. I think they all should understand that for them, a development server is their production server and needs to pay proper attention to the code from the beginning. There should be NO any inappropriate code from the beginning. One has to fully focus and give their best, if they are not sure they should ask but should do something and stay active. Chapter 5 and 6 talks about two essential skills for any developer and database administration – what are the ethics of developers when they are working with production server and how to support software which is running on the production server. I have met many people who know the theory by heart but when put in front of keyboard they do not know where to start. The first thing they do opening the browser and searching online, instead of opening SQL Server Management Studio. This can very well happen to anybody who is experienced as well. Chapter 5 and 6 addresses that situation as well includes the handy scripts which can solve almost all the basic trouble shooting issues. “Where’s the Buffet?” By far, this is the best chapter in this book. If you have ever met me, you would know that I love food. I think after reading this chapter, I felt Thomas has written this just keeping me in mind. I think there will be many other people who feel the same way, too. Even my wife who read this chapter thought this was specifically written for me. I will not talk any more about this chapter as this is one must read chapter. And of course this is about real ‘FOOD‘. I am an SQL Server Trainer and Consultant and I totally agree with the point made in the chapter 8 of this book. Yes, it says here that what is necessary to train employees and people. Millions of dollars worth the labor is continuously done in the world which has faults and incorrect. Once something goes wrong, very expensive consultant comes in and fixes the problem. This whole cycle which can be stopped and improved if proper training is done. There is plenty of free trainings available as well, if one cannot afford paid training. “Connect. Learn. Share” – I think this is a great summary and bird’s eye view of this book. Networking is the key. Everything which is discussed in this book can be taken to next level if one properly uses this tips and continuously grow with it. Connecting with others, helping learn each other and building the good knowledge sharing environment should be the goal of everyone. Before I end the review I want to share a real experience. I have personally met one DBA who has worked in a single department in a company for so long that when he was put in a different department in his company due to closing that department, he could not adjust and quit the job despite the same people and company around him. Adjusting in the new environment gets much tougher as one person gets more and more experienced. This book precisely addresses the same issue along with their solutions. I just cannot stop comparing the book with my personal journey. I found so many things which are coincidently in the book is written as how we developer and DBA think. I must express special thanks to Thomas for taking time in his personal life and write this book for us. This book is indeed a book for everybody who wants to grow healthy in the tough and competitive environment. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Book Review, SQLAuthority News, SQLServer, T SQL, Technology

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  • Elastic versus Distributed in caching.

    - by Mike Reys
    Until now, I hadn't heard about Elastic Caching yet. Today I read Mike Gualtieri's Blog entry. I immediately thought about Oracle Coherence and got a little scare throughout the reading. Elastic Caching is the next step after Distributed Caching. As we've always positioned Coherence as a Distributed Cache, I thought for a brief instance that Oracle had missed a new trend/technology. But then I started reading the characteristics of an Elastic Cache. Forrester definition: Software infrastructure that provides application developers with data caching services that are distributed across two or more server nodes that consistently perform as volumes grow can be scaled without downtime provide a range of fault-tolerance levels Hey wait a minute, doesn't Coherence fullfill all these requirements? Oh yes, I think it does! The next defintion in the article is about Elastic Application Platforms. This is mainly more of the same with the addition of code execution. Now there is analytics functionality in Oracle Coherence. The analytics capability provides data-centric functions like distributed aggregation, searching and sorting. Coherence also provides continuous querying and event-handling. I think that when it comes to providing an Elastic Application Platform (as in the Forrester definition), Oracle is close, nearly there. And what's more, as Elastic Platform is the next big thing towards the big C word, Oracle Coherence makes you cloud-ready ;-) There you go! Find more info on Oracle Coherence here.

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  • Coherence Based WebLogic Server Session Management

    - by [email protected]
    Specifications Supported Configurations WebLogic Server 10.3.2( or 10.3.1 ) Coherence 3.5.2/463 If you use other verion above, then please check the following matrix:   WebLogic Server 9.2 MP1 Weblogic Server 10.3 WebLogic Smart Update Patch ID: AJQB Patch ID: 6W2W Minimum Coherence Release Level/MetaLink Patch ID 3.4.2 Patch 2-Patch ID:8429415 3.4.2 Patch6-Patch ID:11399293 Environment Variables %COHERENCE_HOME%: coherence installation directory %DOMAIN_HOME%: weblogic domain foler. Instructions We Will create to weblogic domains: domain_a, domain_b. To configure those domains with coherence-based session management . Then the changings of session variable value in one domain will propagate to another domain. Main Steps WebLogic Server create domain_a The process is ignored copy %COHERENCE_HOME%\lib\coherence.jar to %DOMAIN_HOME%\lib startup domain deploy %COHERENCE_HOME%\lib\coherence-web-spi.war as a Shared Library repeat step 1~4 at domain_b Coherence duplicate %COHERENCE_HOME%\bin\cache-server.cmd at the same folder and rename it to web-cache-server.cmd modify web-cache-server.cmd java -server -Xms512m -Xmx512m -cp %coherence_home%/lib/coherence.jar;%coherence_home%/lib/coherence-web-spi.war -Dtangosol.coherence.management.remote=true -Dtangosol.coherence.cacheconfig=WEB-INF/classes/session-cache-config.xml -Dtangosol.coherence.session.localstorage=true com.tangosol.net.DefaultCacheServer startup web-cache-server.cmd Testing develop a web app  with OEPE or JDeveloper and implment functions: changing, viewing, listing  session variables. ( or download sample codes here ) modify weblogic.xml with following content: <?xml version="1.0" encoding="UTF-8"?> <wls:weblogic-web-app xmlns:wls=http://xmlns.oracle.com/weblogic/weblogic-web-app xmlns:xsi=http://www.w3.org/2001/XMLSchema-instance xsi:schemaLocation="http://java.sun.com/xml/ns/javaee http://java.sun.com/xml/ns/javaee/web-app_2_5.xsd http://xmlns.oracle.com/weblogic/weblogic-web-app http://xmlns.oracle.com/weblogic/weblogic-web-app/1.0/weblogic-web-app.xsd"> <wls:weblogic-version>10.3.2</wls:weblogic-version> <wls:context-root>CoherenceWeb</wls:context-root> <wls:library-ref> <wls:library-name>coherence-web-spi</wls:library-name> <wls:specification-version>1.0.0.0</wls:specification-version> <wls:exact-match>true</wls:exact-match> </wls:library-ref> </wls:weblogic-web-app> deploy the web app to domain_a and domain_b change session varaible vlaue at domain_a and check whethe if changed at domain_b References Using Oracle Coherence*Web 3.4.2 with Oracle WebLogic Server 10gR3 Oracle Coherence*Web 3.4.2 with Oracle WebLogic Server 10gR3

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  • Book Review Charlene Li's New Book: Open Leadership

    - by david.talamelli
    A few weeks ago, I was surprised when I looked in our mail box. I had received an Advance Copy of Charlene Li's new book titled "Open Leadership: How Social Technology Can Transform the Way You Lead". Charlene sent a tweet a while back asking anyone interested in receiving the book to submit their details. I sent off my details and didn't think I would hear anything back, so it was a pleasant surprise. With that I almost feel bad that it has taken me 3 weeks to read her book. It took this long mainly because it has been hard to fit in some quality reading time for myself with work, the kids, volunteering, etc..... I am happy to report I have finished her book and wanted to run through my initial thoughts with you. I first came across Charlene Li after reading her book "Groundswell" a few years ago, her latest book "Open Leadership" is a follow on from Groundswell and to me it seems like a natural progression from the question "Ok the business landscape is changing, what do we do now?" For me these two books have a different writing style to them. Groundswell from memory spoke about broad social media concepts and adoption and alerted us to some of the changes taking place in the SM landscape. Open Leadership seems to be focussed on taking those broad concepts and finding ways to implement them into your environment. That is breaking broad concepts down into individual action items that can be measured and analysed. As the business world changes Leaders must change their approach and let go of control to more control. One of the things I love reading about is seeing real life examples of how people and organisations are making these things happen. In this book Charlene has collected some great collateral and case studies from companies such as Cisco, Best Buy, The Red Cross and The State Bank of India (as a side-note, I wish now that I submitted my input for the Leaders I work with here at Oracle - there are some great examples here of people who empower their staff). As society becomes more adept at using social media it is inevitable that Leaders must become open with their employees, clients and partners. From the book some of the key points I took away are (I actually took away a lot more from this book, this is just an overview) : 1) Organisations should encourage risk taking. Without being a "hacker", how can we improve ourselves, our processes, our business, etc... The old saying you only fail by not trying applies here. If Leaders create a culture where people are afraid to stick their neck out - how will you innovate? 2) Leaders need to lead by example - if you want to promote an open and transparent business, a Leader needs to exemplify the traits they would like to see out of their employees. 3) The definition of a Leader is changing, open leadership is about being a catalyst to change that uses networks to spread a vision as opposed to traditional leadership that is viewed as a role. 4) There is a cultural and business shift taking place. Information is more wide-spread and is being disseminated faster than any other time in the past. Leaders who are open and transparent will thrive in this new business environment. 5) Leadership is not defined by a title - it is defined by a person's actions. Also anyone can be a Leader or has Leadership potential in them- it is a matter of drawing that out of people. I found this book useful and I also found myself looking at my own actions and the actions of others around me (including my management) to see how open and transparent I am in my work. For me I am glad I read this book as it validated my own thoughts of the changes we are seeing take place. This book has certainly given me some new ideas and helped me push my own boundaries of what I can do. The book has a number of action plans at the end of some of the chapters such as "Conducting you Openness Audit" that I think have helped me take thoughts and ideas and turn them into concrete action items. I have included a link to the introduction of the book here if anyone wants to have a read of it. If anyone else has read this book, it would be great to hear your thoughts/comments/review. Leave your comments below. This article was originally posted on David Talamelli's Blog - David's Journal on Tap

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  • ??????Oracle Coherence????????????????????

    - by mamoru.kobayashi
    ????????????????????????????????????????arrowhead??????????????????2010?1?4?????????????????????????Oracle Coherence???????????????? ???????????????????·??????????????????????????????????? ???????????????????????????????????????????????arrowhead????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????2009?10???Oracle Coherence????????????????????·??????????????????2010?1?4???arrowhead?????????????????????????????? ??????????????? ????????????? ??? ?????? ?????????????? ?????? ???

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  • Webcast On-Demand: Building Java EE Apps That Scale

    - by jeckels
    With some awesome work by one of our architects, Randy Stafford, we recently completed a webcast on scaling Java EE apps efficiently. Did you miss it? No problem. We have a replay available on-demand for you. Just hit the '+' sign drop-down for access.Topics include: Domain object caching Service response caching Session state caching JSR-107 HotCache and more! Further, we had several interesting questions asked by our audience, and we thought we'd share a sampling of those here for you - just in case you had the same queries yourself. Enjoy! What is the largest Coherence deployment out there? We have seen deployments with over 500 JVMs in the Coherence cluster, and deployments with over 1000 JVMs using the Coherence jar file, in one system. On the management side there is an ecosystem of monitoring tools from Oracle and third parties with dashboards graphing values from Coherence's JMX instrumentation. For lifecycle management we have seen a lot of custom scripting over the years, but we've also integrated closely with WebLogic to leverage its management ecosystem for deploying Coherence-based applications and managing process life cycles. That integration introduces a new Java EE archive type, the Grid Archive or GAR, which embeds in an EAR and can be seen by a WAR in WebLogic. That integration also doesn't require any extra WebLogic licensing if Coherence is licensed. How is Coherence different from a NoSQL Database like MongoDB? Coherence can be considered a NoSQL technology. It pre-dates the NoSQL movement, having been first released in 2001 whereas the term "NoSQL" was coined in 2009. Coherence has a key-value data model primarily but can also be used for document data models. Coherence manages data in memory currently, though disk persistence is in a future release currently in beta testing. Where the data is managed yields a few differences from the most well-known NoSQL products: access latency is faster with Coherence, though well-known NoSQL databases can manage more data. Coherence also has features that well-known NoSQL database lack, such as grid computing, eventing, and data source integration. Finally Coherence has had 15 years of maturation and hardening from usage in mission-critical systems across a variety of industries, particularly financial services. Can I use Coherence for local caching? Yes, you get additional features beyond just a java.util.Map: you get expiration capabilities, size-limitation capabilities, eventing capabilites, etc. Are there APIs available for GoldenGate HotCache? It's mostly a black box. You configure it, and it just puts objects into your caches. However you can treat it as a glass box, and use Coherence event interceptors to enhance its behavior - and there are use cases for that. Are Coherence caches updated transactionally? Coherence provides several mechanisms for concurrency control. If a project insists on full-blown JTA / XA distributed transactions, Coherence caches can participate as resources. But nobody does that because it's a performance and scalability anti-pattern. At finer granularity, Coherence guarantees strict ordering of all operations (reads and writes) against a single cache key if the operations are done using Coherence's "EntryProcessor" feature. And Coherence has a unique feature called "partition-level transactions" which guarantees atomic writes of multiple cache entries (even in different caches) without requiring JTA / XA distributed transaction semantics.

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  • Coherence Special Interest Group: First Meeting in Toronto and Upcoming Events in New York and Calif

    - by [email protected]
    The first meeting of the Toronto Coherence Special Interest Group (TOCSIG). Date: Friday, April 23, 2010 Time: 8:30am-12:00pm Where: Oracle Mississauga Office, Customer Visitation Center, 110 Matheson Blvd. West, Suite 100, Mississauga, ON L5R3P4 Cameron Purdy, Vice President of Development (Oracle), Patrick Peralta, Senior Software Engineer (Oracle), and Noah Arliss, Software Development Manager (Oracle) will be presenting. Further information about this event can be seen here   The New York Coherence SIG is hosting its seventh meeting. Date: Thursday, Apr 15, 2010 Time: 5:30pm-5:45pm ET social and 5:45pm-8:00pm ET presentations Where: Oracle Office, Room 30076, 520 Madison Avenue, 30th Floor, Patrick Peralta, Dr. Gene Gleyzer, and Craig Blitz from Oracle, will be presenting. Further information about this event can be seen here   The Bay Area Coherence SIG is hosting its fifth meeting. Date: Thursday, Apr 29, 2009 Time: 5:30pm-5:45pm PT social and 5:45pm-8:00pm PT presentations Where: Oracle Conference Center, 350 Oracle Parkway, Room 203, Redwood Shores, CA Tom Lubinski from SL Corp., Randy Stafford from the Oracle A-team, and Taylor Gautier from Grid Dynamics will be presenting Further information about this event can be seen here   Great news, aren't they? 

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  • CPU Usage in Very Large Coherence Clusters

    - by jpurdy
    When sizing Coherence installations, one of the complicating factors is that these installations (by their very nature) tend to be application-specific, with some being large, memory-intensive caches, with others acting as I/O-intensive transaction-processing platforms, and still others performing CPU-intensive calculations across the data grid. Regardless of the primary resource requirements, Coherence sizing calculations are inherently empirical, in that there are so many permutations that a simple spreadsheet approach to sizing is rarely optimal (though it can provide a good starting estimate). So we typically recommend measuring actual resource usage (primarily CPU cycles, network bandwidth and memory) at a given load, and then extrapolating from those measurements. Of course there may be multiple types of load, and these may have varying degrees of correlation -- for example, an increased request rate may drive up the number of objects "pinned" in memory at any point, but the increase may be less than linear if those objects are naturally shared by concurrent requests. But for most reasonably-designed applications, a linear resource model will be reasonably accurate for most levels of scale. However, at extreme scale, sizing becomes a bit more complicated as certain cluster management operations -- while very infrequent -- become increasingly critical. This is because certain operations do not naturally tend to scale out. In a small cluster, sizing is primarily driven by the request rate, required cache size, or other application-driven metrics. In larger clusters (e.g. those with hundreds of cluster members), certain infrastructure tasks become intensive, in particular those related to members joining and leaving the cluster, such as introducing new cluster members to the rest of the cluster, or publishing the location of partitions during rebalancing. These tasks have a strong tendency to require all updates to be routed via a single member for the sake of cluster stability and data integrity. Fortunately that member is dynamically assigned in Coherence, so it is not a single point of failure, but it may still become a single point of bottleneck (until the cluster finishes its reconfiguration, at which point this member will have a similar load to the rest of the members). The most common cause of scaling issues in large clusters is disabling multicast (by configuring well-known addresses, aka WKA). This obviously impacts network usage, but it also has a large impact on CPU usage, primarily since the senior member must directly communicate certain messages with every other cluster member, and this communication requires significant CPU time. In particular, the need to notify the rest of the cluster about membership changes and corresponding partition reassignments adds stress to the senior member. Given that portions of the network stack may tend to be single-threaded (both in Coherence and the underlying OS), this may be even more problematic on servers with poor single-threaded performance. As a result of this, some extremely large clusters may be configured with a smaller number of partitions than ideal. This results in the size of each partition being increased. When a cache server fails, the other servers will use their fractional backups to recover the state of that server (and take over responsibility for their backed-up portion of that state). The finest granularity of this recovery is a single partition, and the single service thread can not accept new requests during this recovery. Ordinarily, recovery is practically instantaneous (it is roughly equivalent to the time required to iterate over a set of backup backing map entries and move them to the primary backing map in the same JVM). But certain factors can increase this duration drastically (to several seconds): large partitions, sufficiently slow single-threaded CPU performance, many or expensive indexes to rebuild, etc. The solution of course is to mitigate each of those factors but in many cases this may be challenging. Larger clusters also lead to the temptation to place more load on the available hardware resources, spreading CPU resources thin. As an example, while we've long been aware of how garbage collection can cause significant pauses, it usually isn't viewed as a major consumer of CPU (in terms of overall system throughput). Typically, the use of a concurrent collector allows greater responsiveness by minimizing pause times, at the cost of reducing system throughput. However, at a recent engagement, we were forced to turn off the concurrent collector and use a traditional parallel "stop the world" collector to reduce CPU usage to an acceptable level. In summary, there are some less obvious factors that may result in excessive CPU consumption in a larger cluster, so it is even more critical to test at full scale, even though allocating sufficient hardware may often be much more difficult for these large clusters.

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  • Implementing a Custom Coherence PartitionAssignmentStrategy

    - by jpurdy
    A recent A-Team engagement required the development of a custom PartitionAssignmentStrategy (PAS). By way of background, a PAS is an implementation of a Java interface that controls how a Coherence partitioned cache service assigns partitions (primary and backup copies) across the available set of storage-enabled members. While seemingly straightforward, this is actually a very difficult problem to solve. Traditionally, Coherence used a distributed algorithm spread across the cache servers (and as of Coherence 3.7, this is still the default implementation). With the introduction of the PAS interface, the model of operation was changed so that the logic would run solely in the cache service senior member. Obviously, this makes the development of a custom PAS vastly less complex, and in practice does not introduce a significant single point of failure/bottleneck. Note that Coherence ships with a default PAS implementation but it is not used by default. Further, custom PAS implementations are uncommon (this engagement was the first custom implementation that we know of). The particular implementation mentioned above also faced challenges related to managing multiple backup copies but that won't be discussed here. There were a few challenges that arose during design and implementation: Naive algorithms had an unreasonable upper bound of computational cost. There was significant complexity associated with configurations where the member count varied significantly between physical machines. Most of the complexity of a PAS is related to rebalancing, not initial assignment (which is usually fairly simple). A custom PAS may need to solve several problems simultaneously, such as: Ensuring that each member has a similar number of primary and backup partitions (e.g. each member has the same number of primary and backup partitions) Ensuring that each member carries similar responsibility (e.g. the most heavily loaded member has no more than one partition more than the least loaded). Ensuring that each partition is on the same member as a corresponding local resource (e.g. for applications that use partitioning across message queues, to ensure that each partition is collocated with its corresponding message queue). Ensuring that a given member holds no more than a given number of partitions (e.g. no member has more than 10 partitions) Ensuring that backups are placed far enough away from the primaries (e.g. on a different physical machine or a different blade enclosure) Achieving the above goals while ensuring that partition movement is minimized. These objectives can be even more complicated when the topology of the cluster is irregular. For example, if multiple cluster members may exist on each physical machine, then clearly the possibility exists that at certain points (e.g. following a member failure), the number of members on each machine may vary, in certain cases significantly so. Consider the case where there are three physical machines, with 3, 3 and 9 members each (respectively). This introduces complexity since the backups for the 9 members on the the largest machine must be spread across the other 6 members (to ensure placement on different physical machines), preventing an even distribution. For any given problem like this, there are usually reasonable compromises available, but the key point is that objectives may conflict under extreme (but not at all unlikely) circumstances. The most obvious general purpose partition assignment algorithm (possibly the only general purpose one) is to define a scoring function for a given mapping of partitions to members, and then apply that function to each possible permutation, selecting the most optimal permutation. This would result in N! (factorial) evaluations of the scoring function. This is clearly impractical for all but the smallest values of N (e.g. a partition count in the single digits). It's difficult to prove that more efficient general purpose algorithms don't exist, but the key take away from this is that algorithms will tend to either have exorbitant worst case performance or may fail to find optimal solutions (or both) -- it is very important to be able to show that worst case performance is acceptable. This quickly leads to the conclusion that the problem must be further constrained, perhaps by limiting functionality or by using domain-specific optimizations. Unfortunately, it can be very difficult to design these more focused algorithms. In the specific case mentioned, we constrained the solution space to very small clusters (in terms of machine count) with small partition counts and supported exactly two backup copies, and accepted the fact that partition movement could potentially be significant (preferring to solve that issue through brute force). We then used the out-of-the-box PAS implementation as a fallback, delegating to it for configurations that were not supported by our algorithm. Our experience was that the PAS interface is quite usable, but there are intrinsic challenges to designing PAS implementations that should be very carefully evaluated before committing to that approach.

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  • Coherence on YouTube

    - by jeckels
    As we're busy preparing the next version of Coherence for you to enjoy, don't forget you can always take a peek at our YouTube channel for customer testimonials, how-to tutorials and a plethora of content around the #1 in-memory solution across conventional and cloud environments. Spoiler alert: we have a bunch more coming very soon. Stay tuned... Also, don't forget to join us at Oracle Open World in September for in-depth sessions on Coherence and other Fusion Middleware products. We look forward to seeing you there! 

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  • Video: Coherence Community on Java.net - 4 Projects available under CDDL-1.0

    - by OTN ArchBeat
    If you work with Oracle Coherence and you're not familiar with the Coherence Community on Java.net you're missing out. The Coherence Community was launched on Java.net in June 2013, operating under the Open Source Initiative's Common Development and Distribution License (CDDL-1.0). Four projects are currently available for your participation: Coherence Hibernate Integration Coherence Spring Integration Oracle Tools The Coherence Incubator You'll learn a lot more about the Coherence Community in the video above, which features my conversation with Oracle Coherence Senior Principal Solutions Architect Brian Oliver and Oracle Coherence Consulting Solutions Architect Randy Stafford, two of the people behind the creation and management of the Community and it's projects.

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  • Dealing with Fine-Grained Cache Entries in Coherence

    - by jpurdy
    On occasion we have seen significant memory overhead when using very small cache entries. Consider the case where there is a small key (say a synthetic key stored in a long) and a small value (perhaps a number or short string). With most backing maps, each cache entry will require an instance of Map.Entry, and in the case of a LocalCache backing map (used for expiry and eviction), there is additional metadata stored (such as last access time). Given the size of this data (usually a few dozen bytes) and the granularity of Java memory allocation (often a minimum of 32 bytes per object, depending on the specific JVM implementation), it is easily possible to end up with the case where the cache entry appears to be a couple dozen bytes but ends up occupying several hundred bytes of actual heap, resulting in anywhere from a 5x to 10x increase in stated memory requirements. In most cases, this increase applies to only a few small NamedCaches, and is inconsequential -- but in some cases it might apply to one or more very large NamedCaches, in which case it may dominate memory sizing calculations. Ultimately, the requirement is to avoid the per-entry overhead, which can be done either at the application level by grouping multiple logical entries into single cache entries, or at the backing map level, again by combining multiple entries into a smaller number of larger heap objects. At the application level, it may be possible to combine objects based on parent-child or sibling relationships (basically the same requirements that would apply to using partition affinity). If there is no natural relationship, it may still be possible to combine objects, effectively using a Coherence NamedCache as a "map of maps". This forces the application to first find a collection of objects (by performing a partial hash) and then to look within that collection for the desired object. This is most naturally implemented as a collection of entry processors to avoid pulling unnecessary data back to the client (and also to encapsulate that logic within a service layer). At the backing map level, the NIO storage option keeps keys on heap, and so has limited benefit for this situation. The Elastic Data features of Coherence naturally combine entries into larger heap objects, with the caveat that only data -- and not indexes -- can be stored in Elastic Data.

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  • Impact of Server Failure on Coherence Request Processing

    - by jpurdy
    Requests against a given cache server may be temporarily blocked for several seconds following the failure of other cluster members. This may cause issues for applications that can not tolerate multi-second response times even during failover processing (ignoring for the moment that in practice there are a variety of issues that make such absolute guarantees challenging even when there are no server failures). In general, Coherence is designed around the principle that failures in one member should not affect the rest of the cluster if at all possible. However, it's obvious that if that failed member was managing a piece of state that another member depends on, the second member will need to wait until a new member assumes responsibility for managing that state. This transfer of responsibility is (as of Coherence 3.7) performed by the primary service thread for each cache service. The finest possible granularity for transferring responsibility is a single partition. So the question becomes how to minimize the time spent processing each partition. Here are some optimizations that may reduce this period: Reduce the size of each partition (by increasing the partition count) Increase the number of JVMs across the cluster (increasing the total number of primary service threads) Increase the number of CPUs across the cluster (making sure that each JVM has a CPU core when needed) Re-evaluate the set of configured indexes (as these will need to be rebuilt when a partition moves) Make sure that the backing map is as fast as possible (in most cases this means running on-heap) Make sure that the cluster is running on hardware with fast CPU cores (since the partition processing is single-threaded) As always, proper testing is required to make sure that configuration changes have the desired effect (and also to quantify that effect).

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  • Using Queries with Coherence Write-Behind Caches

    - by jpurdy
    Applications that use write-behind caching and wish to query the logical entity set have the option of querying the NamedCache itself or querying the database. In the former case, no particular restrictions exist beyond the limitations intrinsic to the Coherence query engine itself. In the latter case, queries may see partially committed transactions (e.g. with a parent-child relationship, the version of the parent may be different than the version of the child objects) and/or significant version skew (the query may see the current version of one object and a far older version of another object). This is consistent with "read committed" semantics, but the read skew may be far greater than would ever occur in a non-cached environment. As is usually the case, the application developer may choose to accept these limitations (with the hope that they are sufficiently infrequent), or they may choose to validate the reads (perhaps via a version flag on the objects). This also applies to situations where a third party application (such as a reporting tool) is querying the database. In many cases, the database may only be in a consistent state after the Coherence cluster has been halted.

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  • Partner Webcast – Oracle Coherence Applications on WebLogic 12c Grid - 21st Nov 2013

    - by Thanos Terentes Printzios
    Oracle Coherence is the industry leading in-memory data grid solution that enables organizations to predictably scale mission-critical applications by providing fast access to frequently used data. As data volumes and customer expectations increase, driven by the “internet of things”, social, mobile, cloud and always-connected devices, so does the need to handle more data in real-time, offload over-burdened shared data services and provide availability guarantees. The latest release of Oracle Coherence 12c comes with great improvements in ease of use, integration and RASP (Reliability, Availability, Scalability, and Performance) areas. In addition it features an innovating approach to build and deploy Coherence Application as an integral part of typical JEE Enterprise Application. Coherence GAR archives and Coherence Managed Servers are now first-class citizens of all JEE applications and Oracle WebLogic domains respectively. That enables even easier development, deployment and management of complex multi-tier enterprise applications powered by data grid rich features. Oracle Coherence 12c makes your solution ready for the future of big data and always-on-line world. This webcast is focused on demonstrating How to create a Coherence Application using Oracle Enterprise Pack for Eclipse 12.1.2.1.1 (Kepler release). How to package the application in form of GAR archive inside the EAR deployable application. How to deploy the application to multi-tier WebLogic clusters. How to define and configure the WebLogic domain for the tiered clusters hosting both data grid and client JEE applications.  Finally we will expose the data in grid to external systems using REST services and create a simple web interface to the underlying data using Oracle ADF Faces components. Join us on this technology webcast, to find out more about how Oracle Cloud Application Frameworks brings together the key industry leading technologies of Oracle Coherence and Weblogic 12c, delivering next-generation applications. Agenda: Introduction to Oracle Coherence What's new in 12c release POF annotations Live Events Elastic Data (Flash storage support) Managed Coherence Servers for Oracle WebLogic Coherence Applications (Grid Archive) Live Demonstration Creating and configuring Coherence Servers forming the data tier cluster Creating a simple Coherence Grid Application in Eclipse Adding REST support and creating simple ADF Faces client application Deploying the grid and client applications to separate tiers in WebLogic topology HA capabilities of the data tier Summary - Q&A Delivery Format This FREE online LIVE eSeminar will be delivered over the Web. Registrations received less than 24hours prior to start time may not receive confirmation to attend. Duration: 1 hour REGISTER NOW For any questions please contact us at partner.imc-AT-beehiveonline.oracle-DOT-com Stay Connected Oracle Newsletters

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  • Coherence Data Guarantees for Data Reads - Basic Terminology

    - by jpurdy
    When integrating Coherence into applications, each application has its own set of requirements with respect to data integrity guarantees. Developers often describe these requirements using expressions like "avoiding dirty reads" or "making sure that updates are transactional", but we often find that even in a small group of people, there may be a wide range of opinions as to what these terms mean. This may simply be due to a lack of familiarity, but given that Coherence sits at an intersection of several (mostly) unrelated fields, it may be a matter of conflicting vocabularies (e.g. "consistency" is similar but different in transaction processing versus multi-threaded programming). Since almost all data read consistency issues are related to the concept of concurrency, it is helpful to start with a definition of that, or rather what it means for two operations to be concurrent. Rather than implying that they occur "at the same time", concurrency is a slightly weaker statement -- it simply means that it can't be proven that one event precedes (or follows) the other. As an example, in a Coherence application, if two client members mutate two different cache entries sitting on two different cache servers at roughly the same time, it is likely that one update will precede the other by a significant amount of time (say 0.1ms). However, since there is no guarantee that all four members have their clocks perfectly synchronized, and there is no way to precisely measure the time it takes to send a given message between any two members (that have differing clocks), we consider these to be concurrent operations since we can not (easily) prove otherwise. So this leads to a question that we hear quite frequently: "Are the contents of the near cache always synchronized with the underlying distributed cache?". It's easy to see that if an update on a cache server results in a message being sent to each near cache, and then that near cache being updated that there is a window where the contents are different. However, this is irrelevant, since even if the application reads directly from the distributed cache, another thread update the cache before the read is returned to the application. Even if no other member modifies a cache entry prior to the local near cache entry being updated (and subsequently read), the purpose of reading a cache entry is to do something with the result, usually either displaying for consumption by a human, or by updating the entry based on the current state of the entry. In the former case, it's clear that if the data is updated faster than a human can perceive, then there is no problem (and in many cases this can be relaxed even further). For the latter case, the application must assume that the value might potentially be updated before it has a chance to update it. This almost aways the case with read-only caches, and the solution is the traditional optimistic transaction pattern, which requires the application to explicitly state what assumptions it made about the old value of the cache entry. If the application doesn't want to bother stating those assumptions, it is free to lock the cache entry prior to reading it, ensuring that no other threads will mutate the entry, a pessimistic approach. The optimistic approach relies on what is sometimes called a "fuzzy read". In other words, the application assumes that the read should be correct, but it also acknowledges that it might not be. (I use the qualifier "sometimes" because in some writings, "fuzzy read" indicates the situation where the application actually sees an original value and then later sees an updated value within the same transaction -- however, both definitions are roughly equivalent from an application design perspective). If the read is not correct it is called a "stale read". Going back to the definition of concurrency, it may seem difficult to precisely define a stale read, but the practical way of detecting a stale read is that is will cause the encompassing transaction to roll back if it tries to update that value. The pessimistic approach relies on a "coherent read", a guarantee that the value returned is not only the same as the primary copy of that value, but also that it will remain that way. In most cases this can be used interchangeably with "repeatable read" (though that term has additional implications when used in the context of a database system). In none of cases above is it possible for the application to perform a "dirty read". A dirty read occurs when the application reads a piece of data that was never committed. In practice the only way this can occur is with multi-phase updates such as transactions, where a value may be temporarily update but then withdrawn when a transaction is rolled back. If another thread sees that value prior to the rollback, it is a dirty read. If an application uses optimistic transactions, dirty reads will merely result in a lack of forward progress (this is actually one of the main risks of dirty reads -- they can be chained and potentially cause cascading rollbacks). The concepts of dirty reads, fuzzy reads, stale reads and coherent reads are able to describe the vast majority of requirements that we see in the field. However, the important thing is to define the terms used to define requirements. A quick web search for each of the terms in this article will show multiple meanings, so I've selected what are generally the most common variations, but it never hurts to state each definition explicitly if they are critical to the success of a project (many applications have sufficiently loose requirements that precise terminology can be avoided).

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  • Using WKA in Large Coherence Clusters (Disabling Multicast)

    - by jpurdy
    Disabling hardware multicast (by configuring well-known addresses aka WKA) will place significant stress on the network. For messages that must be sent to multiple servers, rather than having a server send a single packet to the switch and having the switch broadcast that packet to the rest of the cluster, the server must send a packet to each of the other servers. While hardware varies significantly, consider that a server with a single gigabit connection can send at most ~70,000 packets per second. To continue with some concrete numbers, in a cluster with 500 members, that means that each server can send at most 140 cluster-wide messages per second. And if there are 10 cluster members on each physical machine, that number shrinks to 14 cluster-wide messages per second (or with only mild hyperbole, roughly zero). It is also important to keep in mind that network I/O is not only expensive in terms of the network itself, but also the consumption of CPU required to send (or receive) a message (due to things like copying the packet bytes, processing a interrupt, etc). Fortunately, Coherence is designed to rely primarily on point-to-point messages, but there are some features that are inherently one-to-many: Announcing the arrival or departure of a member Updating partition assignment maps across the cluster Creating or destroying a NamedCache Invalidating a cache entry from a large number of client-side near caches Distributing a filter-based request across the full set of cache servers (e.g. queries, aggregators and entry processors) Invoking clear() on a NamedCache The first few of these are operations that are primarily routed through a single senior member, and also occur infrequently, so they usually are not a primary consideration. There are cases, however, where the load from introducing new members can be substantial (to the point of destabilizing the cluster). Consider the case where cluster in the first paragraph grows from 500 members to 1000 members (holding the number of physical machines constant). During this period, there will be 500 new member introductions, each of which may consist of several cluster-wide operations (for the cluster membership itself as well as the partitioned cache services, replicated cache services, invocation services, management services, etc). Note that all of these introductions will route through that one senior member, which is sharing its network bandwidth with several other members (which will be communicating to a lesser degree with other members throughout this process). While each service may have a distinct senior member, there's a good chance during initial startup that a single member will be the senior for all services (if those services start on the senior before the second member joins the cluster). It's obvious that this could cause CPU and/or network starvation. In the current release of Coherence (3.7.1.3 as of this writing), the pure unicast code path also has less sophisticated flow-control for cluster-wide messages (compared to the multicast-enabled code path), which may also result in significant heap consumption on the senior member's JVM (from the message backlog). This is almost never a problem in practice, but with sufficient CPU or network starvation, it could become critical. For the non-operational concerns (near caches, queries, etc), the application itself will determine how much load is placed on the cluster. Applications intended for deployment in a pure unicast environment should be careful to avoid excessive dependence on these features. Even in an environment with multicast support, these operations may scale poorly since even with a constant request rate, the underlying workload will increase at roughly the same rate as the underlying resources are added. Unless there is an infrastructural requirement to the contrary, multicast should be enabled. If it can't be enabled, care should be taken to ensure the added overhead doesn't lead to performance or stability issues. This is particularly crucial in large clusters.

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  • New Coherence 3.6 Oracle University Course

    - by cristobal.soto(at)oracle.com
    The new "Oracle Coherence 3.6: Share and Manage Data in Clusters" course is now available through Oracle University. This new course was completed by the Curriculum Development team and the First Global Teach delivered by OU was a huge success, receiving very positive reviews from attendees. See the Course Page on education.oracle.com for course details and to view scheduled training. To request a course you can register your demand for the course (i.e need for future events) via the Course Page: Click the "View Schedule" link on the page for either the Instructor-Led Training (ILT) or the Live Virtual Class (LVC) Then click the "register a request" link in the middle of the page towards the bottom. You can register the demand with details on the preference such as event date, region, location, etc. After which, respective schedulers in the region will be notified. The regional schedulers will then take the request forward.

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  • Coherence Query Performance in Large Clusters

    - by jpurdy
    Large clusters (measured in terms of the number of storage-enabled members participating in the largest cache services) may introduce challenges when issuing queries. There is no particular cluster size threshold for this, rather a gradually increasing tendency for issues to arise. The most obvious challenges are that a client's perceived query latency will be determined by the slowest responder (more likely to be a factor in larger clusters) as well as the fact that adding additional cache servers will not increase query throughput if the query processing is not compute-bound (which would generally be the case for most indexed queries). If the data set can take advantage of the partition affinity features of Coherence, then the application can use a PartitionedFilter to target a query to a single server (using partition affinity to ensure that all data is in a single partition). If this can not be done, then avoiding an excessive number of cache server JVMs will help, as will ensuring that each cache server has sufficient CPU resources available and is also properly configured to minimize GC pauses (the most common cause of a slow-responding cache server).

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