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  • how limit the number of open TCP streams from same IP to a local port?

    - by JMW
    Hi, i would like to limit the number of concurrent open TCP streams from the the same IP to the server's (local) port. Let's say 4 concurrent conncetions. How can this be done with ip tables? the closest thing, that i've found was: In Apache, is there a way to limit the number of new connections per second/hour/day? iptables -A INPUT -p tcp --dport 80 -i eth0 -m state --state NEW -m recent --set iptables -A INPUT -p tcp --dport 80 -i eth0 -m state --state NEW -m recent --update --seconds 86400 --hitcount 100 -j REJECT But this limitation just messures the number of new connections over the time. This might be good for controlling HTTP traffic. But this is not a good solution for me, since my TCP streams usually have a lifetime between 5 minutes and 2 hours. thanks a lot in advance for any reply :)

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  • AB failed requests - What can I do about them?

    - by matthewsteiner
    So, in the past I've never had any problems with this app. All benchmarks had 100% success rate. Yesterday I set up nginx to server static content and pass on other requests to apache. Now, if I have 1 concurrent user (-c 1) then everything is fine. But it seems the more concurrent users I have, the more failed requests I get. Not a lot, but maybe about 10 or 15 out of 350. They're "length", whatever that means. Visiting the website with a browser, I don't have any problems at all. How can I find out the cause of these failed requests?

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  • TCP and fair bandwidth sharing

    - by lxgr
    The congestion control algorithm(s) of TCP seem to distribute the available bandwidth fairly between individual TCP flows. Is there some way to enable (or more precisely, enforce) fair bandwidth sharing on a per-host instead of a per-flow basis on a router? There should not be an (easy) way for a user to gain a disproportional bandwidth share by using multiple concurrent TCP flows (the way some download managers and most P2P clients do). I'm currently running a DD-WRT router to share a residential DSL line, and currently it's possible to (inadvertently or maliciously) hog most of the bandwidth by using multiple concurrent connections, which affecty VoIP conversations badly. I've played with the QoS settings a bit, but I'm not sure how to enable fair bandwidth sharing on a per-IP basis (per-service is not an option, as most of the flows are HTTP).

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  • Can't run my servlet from tomcat server even though the classes are in package

    - by Mido
    Hi there, i am trying to get my servlet to run, i have been searching for 2 days and trying every possible solution and no luck. The servet class is in the appropriate folder (i.e under the package name). I also added the jar files needed in my servlet into lib folder. the web.xml file maps the url and defines the servlet. So i did everything in the documentation and wt people said in here and still getting this error : type Exception report message description The server encountered an internal error () that prevented it from fulfilling this request. exception javax.servlet.ServletException: Error instantiating servlet class assign1a.RPCServlet org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:108) org.apache.catalina.valves.AccessLogValve.invoke(AccessLogValve.java:558) org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:379) org.apache.coyote.http11.Http11AprProcessor.process(Http11AprProcessor.java:282) org.apache.coyote.http11.Http11AprProtocol$Http11ConnectionHandler.process(Http11AprProtocol.java:357) org.apache.tomcat.util.net.AprEndpoint$SocketProcessor.run(AprEndpoint.java:1687) java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) java.lang.Thread.run(Thread.java:619) root cause java.lang.NoClassDefFoundError: assign1a/RPCServlet (wrong name: server/RPCServlet) java.lang.ClassLoader.defineClass1(Native Method) java.lang.ClassLoader.defineClassCond(ClassLoader.java:632) java.lang.ClassLoader.defineClass(ClassLoader.java:616) java.security.SecureClassLoader.defineClass(SecureClassLoader.java:141) org.apache.catalina.loader.WebappClassLoader.findClassInternal(WebappClassLoader.java:2820) org.apache.catalina.loader.WebappClassLoader.findClass(WebappClassLoader.java:1143) org.apache.catalina.loader.WebappClassLoader.loadClass(WebappClassLoader.java:1638) org.apache.catalina.loader.WebappClassLoader.loadClass(WebappClassLoader.java:1516) org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:108) org.apache.catalina.valves.AccessLogValve.invoke(AccessLogValve.java:558) org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:379) org.apache.coyote.http11.Http11AprProcessor.process(Http11AprProcessor.java:282) org.apache.coyote.http11.Http11AprProtocol$Http11ConnectionHandler.process(Http11AprProtocol.java:357) org.apache.tomcat.util.net.AprEndpoint$SocketProcessor.run(AprEndpoint.java:1687) java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) java.lang.Thread.run(Thread.java:619) note The full stack trace of the root cause is available in the Apache Tomcat/7.0.5 logs. Also here is my servlet code : package assign1a; import java.io.IOException; import java.util.logging.Level; import java.util.logging.Logger; import javax.servlet.ServletException; import javax.servlet.http.HttpServlet; import javax.servlet.http.HttpServletRequest; import javax.servlet.http.HttpServletResponse; import lib.jsonrpc.RPCService; public class RPCServlet extends HttpServlet { /** * */ private static final long serialVersionUID = -5274024331393844879L; private static final Logger log = Logger.getLogger(RPCServlet.class.getName()); protected RPCService service = new ServiceImpl(); public void doGet(HttpServletRequest request, HttpServletResponse response) throws IOException, ServletException { response.setContentType("text/html"); response.getWriter().write("rpc service " + service.getServiceName() + " is running..."); } public void doPost(HttpServletRequest request, HttpServletResponse response) throws IOException, ServletException { try { service.dispatch(request, response); } catch (Throwable t) { log.log(Level.WARNING, t.getMessage(), t); } } } Please help me :) Thanks. EDIT: here are the contents of my web.xml file <web-app xmlns="http://java.sun.com/xml/ns/javaee" 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_3_0.xsd" version="3.0" metadata-complete="true"> <servlet> <servlet-name>jsonrpc</servlet-name> <servlet-class>assign1a.RPCServlet</servlet-class> </servlet> <servlet-mapping> <servlet-name>jsonrpc</servlet-name> <url-pattern>/rpc</url-pattern> </servlet-mapping> </web-app>

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  • Why does this Java code not utilize all CPU cores?

    - by ReneS
    The attached simple Java code should load all available cpu core when starting it with the right parameters. So for instance, you start it with java VMTest 8 int 0 and it will start 8 threads that do nothing else than looping and adding 2 to an integer. Something that runs in registers and not even allocates new memory. The problem we are facing now is, that we do not get a 24 core machine loaded (AMD 2 sockets with 12 cores each), when running this simple program (with 24 threads of course). Similar things happen with 2 programs each 12 threads or smaller machines. So our suspicion is that the JVM (Sun JDK 6u20 on Linux x64) does not scale well. Did anyone see similar things or has the ability to run it and report whether or not it runs well on his/her machine (= 8 cores only please)? Ideas? I tried that on Amazon EC2 with 8 cores too, but the virtual machine seems to run different from a real box, so the loading behaves totally strange. package com.test; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Future; import java.util.concurrent.TimeUnit; public class VMTest { public class IntTask implements Runnable { @Override public void run() { int i = 0; while (true) { i = i + 2; } } } public class StringTask implements Runnable { @Override public void run() { int i = 0; String s; while (true) { i++; s = "s" + Integer.valueOf(i); } } } public class ArrayTask implements Runnable { private final int size; public ArrayTask(int size) { this.size = size; } @Override public void run() { int i = 0; String[] s; while (true) { i++; s = new String[size]; } } } public void doIt(String[] args) throws InterruptedException { final String command = args[1].trim(); ExecutorService executor = Executors.newFixedThreadPool(Integer.valueOf(args[0])); for (int i = 0; i < Integer.valueOf(args[0]); i++) { Runnable runnable = null; if (command.equalsIgnoreCase("int")) { runnable = new IntTask(); } else if (command.equalsIgnoreCase("string")) { runnable = new StringTask(); } Future<?> submit = executor.submit(runnable); } executor.awaitTermination(1, TimeUnit.HOURS); } public static void main(String[] args) throws InterruptedException { if (args.length < 3) { System.err.println("Usage: VMTest threadCount taskDef size"); System.err.println("threadCount: Number 1..n"); System.err.println("taskDef: int string array"); System.err.println("size: size of memory allocation for array, "); System.exit(-1); } new VMTest().doIt(args); } }

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  • SQL SERVER – Concurrency Basics – Guest Post by Vinod Kumar

    - by pinaldave
    This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions from SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. Learning is always fun when it comes to SQL Server and learning the basics again can be more fun. I did write about Transaction Logs and recovery over my blogs and the concept of simplifying the basics is a challenge. In the real world we always see checks and queues for a process – say railway reservation, banks, customer supports etc there is a process of line and queue to facilitate everyone. Shorter the queue higher is the efficiency of system (a.k.a higher is the concurrency). Every database does implement this using checks like locking, blocking mechanisms and they implement the standards in a way to facilitate higher concurrency. In this post, let us talk about the topic of Concurrency and what are the various aspects that one needs to know about concurrency inside SQL Server. Let us learn the concepts as one-liners: Concurrency can be defined as the ability of multiple processes to access or change shared data at the same time. The greater the number of concurrent user processes that can be active without interfering with each other, the greater the concurrency of the database system. Concurrency is reduced when a process that is changing data prevents other processes from reading that data or when a process that is reading data prevents other processes from changing that data. Concurrency is also affected when multiple processes are attempting to change the same data simultaneously. Two approaches to managing concurrent data access: Optimistic Concurrency Model Pessimistic Concurrency Model Concurrency Models Pessimistic Concurrency Default behavior: acquire locks to block access to data that another process is using. Assumes that enough data modification operations are in the system that any given read operation is likely affected by a data modification made by another user (assumes conflicts will occur). Avoids conflicts by acquiring a lock on data being read so no other processes can modify that data. Also acquires locks on data being modified so no other processes can access the data for either reading or modifying. Readers block writer, writers block readers and writers. Optimistic Concurrency Assumes that there are sufficiently few conflicting data modification operations in the system that any single transaction is unlikely to modify data that another transaction is modifying. Default behavior of optimistic concurrency is to use row versioning to allow data readers to see the state of the data before the modification occurs. Older versions of the data are saved so a process reading data can see the data as it was when the process started reading and not affected by any changes being made to that data. Processes modifying the data is unaffected by processes reading the data because the reader is accessing a saved version of the data rows. Readers do not block writers and writers do not block readers, but, writers can and will block writers. Transaction Processing A transaction is the basic unit of work in SQL Server. Transaction consists of SQL commands that read and update the database but the update is not considered final until a COMMIT command is issued (at least for an explicit transaction: marked with a BEGIN TRAN and the end is marked by a COMMIT TRAN or ROLLBACK TRAN). Transactions must exhibit all the ACID properties of a transaction. ACID Properties Transaction processing must guarantee the consistency and recoverability of SQL Server databases. Ensures all transactions are performed as a single unit of work regardless of hardware or system failure. A – Atomicity C – Consistency I – Isolation D- Durability Atomicity: Each transaction is treated as all or nothing – it either commits or aborts. Consistency: ensures that a transaction won’t allow the system to arrive at an incorrect logical state – the data must always be logically correct.  Consistency is honored even in the event of a system failure. Isolation: separates concurrent transactions from the updates of other incomplete transactions. SQL Server accomplishes isolation among transactions by locking data or creating row versions. Durability: After a transaction commits, the durability property ensures that the effects of the transaction persist even if a system failure occurs. If a system failure occurs while a transaction is in progress, the transaction is completely undone, leaving no partial effects on data. Transaction Dependencies In addition to supporting all four ACID properties, a transaction might exhibit few other behaviors (known as dependency problems or consistency problems). Lost Updates: Occur when two processes read the same data and both manipulate the data, changing its value and then both try to update the original data to the new value. The second process might overwrite the first update completely. Dirty Reads: Occurs when a process reads uncommitted data. If one process has changed data but not yet committed the change, another process reading the data will read it in an inconsistent state. Non-repeatable Reads: A read is non-repeatable if a process might get different values when reading the same data in two reads within the same transaction. This can happen when another process changes the data in between the reads that the first process is doing. Phantoms: Occurs when membership in a set changes. It occurs if two SELECT operations using the same predicate in the same transaction return a different number of rows. Isolation Levels SQL Server supports 5 isolation levels that control the behavior of read operations. Read Uncommitted All behaviors except for lost updates are possible. Implemented by allowing the read operations to not take any locks, and because of this, it won’t be blocked by conflicting locks acquired by other processes. The process can read data that another process has modified but not yet committed. When using the read uncommitted isolation level and scanning an entire table, SQL Server can decide to do an allocation order scan (in page-number order) instead of a logical order scan (following page pointers). If another process doing concurrent operations changes data and move rows to a new location in the table, the allocation order scan can end up reading the same row twice. Also can happen if you have read a row before it is updated and then an update moves the row to a higher page number than your scan encounters later. Performing an allocation order scan under Read Uncommitted can cause you to miss a row completely – can happen when a row on a high page number that hasn’t been read yet is updated and moved to a lower page number that has already been read. Read Committed Two varieties of read committed isolation: optimistic and pessimistic (default). Ensures that a read never reads data that another application hasn’t committed. If another transaction is updating data and has exclusive locks on data, your transaction will have to wait for the locks to be released. Your transaction must put share locks on data that are visited, which means that data might be unavailable for others to use. A share lock doesn’t prevent others from reading but prevents them from updating. Read committed (snapshot) ensures that an operation never reads uncommitted data, but not by forcing other processes to wait. SQL Server generates a version of the changed row with its previous committed values. Data being changed is still locked but other processes can see the previous versions of the data as it was before the update operation began. Repeatable Read This is a Pessimistic isolation level. Ensures that if a transaction revisits data or a query is reissued the data doesn’t change. That is, issuing the same query twice within a transaction cannot pickup any changes to data values made by another user’s transaction because no changes can be made by other transactions. However, this does allow phantom rows to appear. Preventing non-repeatable read is a desirable safeguard but cost is that all shared locks in a transaction must be held until the completion of the transaction. Snapshot Snapshot Isolation (SI) is an optimistic isolation level. Allows for processes to read older versions of committed data if the current version is locked. Difference between snapshot and read committed has to do with how old the older versions have to be. It’s possible to have two transactions executing simultaneously that give us a result that is not possible in any serial execution. Serializable This is the strongest of the pessimistic isolation level. Adds to repeatable read isolation level by ensuring that if a query is reissued rows were not added in the interim, i.e, phantoms do not appear. Preventing phantoms is another desirable safeguard, but cost of this extra safeguard is similar to that of repeatable read – all shared locks in a transaction must be held until the transaction completes. In addition serializable isolation level requires that you lock data that has been read but also data that doesn’t exist. Ex: if a SELECT returned no rows, you want it to return no. rows when the query is reissued. This is implemented in SQL Server by a special kind of lock called the key-range lock. Key-range locks require that there be an index on the column that defines the range of values. If there is no index on the column, serializable isolation requires a table lock. Gets its name from the fact that running multiple serializable transactions at the same time is equivalent of running them one at a time. Now that we understand the basics of what concurrency is, the subsequent blog posts will try to bring out the basics around locking, blocking, deadlocks because they are the fundamental blocks that make concurrency possible. Now if you are with me – let us continue learning for SQL Server Locking Basics. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Concurrency

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  • The C++ Standard Template Library as a BDB Database (part 1)

    - by Gregory Burd
    If you've used C++ you undoubtedly have used the Standard Template Libraries. Designed for in-memory management of data and collections of data this is a core aspect of all C++ programs. Berkeley DB is a database library with a variety of APIs designed to ease development, one of those APIs extends and makes use of the STL for persistent, transactional data storage. dbstl is an STL standard compatible API for Berkeley DB. You can make use of Berkeley DB via this API as if you are using C++ STL classes, and still make full use of Berkeley DB features. Being an STL library backed by a database, there are some important and useful features that dbstl can provide, while the C++ STL library can't. The following are a few typical use cases to use the dbstl extensions to the C++ STL for data storage. When data exceeds available physical memory.Berkeley DB dbstl can vastly improve performance when managing a dataset which is larger than available memory. Performance suffers when the data can't reside in memory because the OS is forced to use virtual memory and swap pages of memory to disk. Switching to BDB's dbstl improves performance while allowing you to keep using STL containers. When you need concurrent access to C++ STL containers.Few existing C++ STL implementations support concurrent access (create/read/update/delete) within a container, at best you'll find support for accessing different containers of the same type concurrently. With the Berkeley DB dbstl implementation you can concurrently access your data from multiple threads or processes with confidence in the outcome. When your objects are your database.You want to have object persistence in your application, and store objects in a database, and use the objects across different runs of your application without having to translate them to/from SQL. The dbstl is capable of storing complicated objects, even those not located on a continous chunk of memory space, directly to disk without any unnecessary overhead. These are a few reasons why you should consider using Berkeley DB's C++ STL support for your embedded database application. In the next few blog posts I'll show you a few examples of this approach, it's easy to use and easy to learn.

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  • Application Lifecycle Management Tools

    - by John K. Hines
    Leading a team comprised of three former teams means that we have three of everything.  Three places to gather requirements, three (actually eight or nine) places for customers to submit support requests, three places to plan and track work. We’ve been looking into tools that combine these features into a single product.  Not just Agile planning tools, but those that allow us to look in a single place for requirements, work items, and reports. One of the interesting choices is Software Planner by Automated QA (the makers of Test Complete).  It's a lovely tool with real end-to-end process support.  We’re probably not going to use it for one reason – cost.  I’m sure our company could get a discount, but it’s on a concurrent user license that isn’t cheap for a large number of users.  Some initial guesswork had us paying over $6,000 for 3 concurrent users just to get started with the Enterprise version.  Still, it’s intuitive, has great Agile capabilities, and has a reputation for excellent customer support. At the moment we’re digging deeper into Rational Team Concert by IBM.  Reading the docs on this product makes me want to submit my resume to Big Blue.  Not only does RTC integrate everything we need, but it’s free for up to 10 developers.  It has beautiful support for all phases of Scrum.  We’re going to bring the sales representative in for a demo. This marks one of the few times that we’re trying to resist the temptation to write our own tool.  And I think this is the first time that something so complex may actually be capably provided by an external source.   Hooray for less work! Technorati tags: Scrum Scrum Tools

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  • UML Diagrams of Multi-Threaded Applications

    - by PersonalNexus
    For single-threaded applications I like to use class diagrams to get an overview of the architecture of that application. This type of diagram, however, hasn’t been very helpful when trying to understand heavily multi-threaded/concurrent applications, for instance because different instances of a class "live" on different threads (meaning accessing an instance is save only from the one thread it lives on). Consequently, associations between classes don’t necessarily mean that I can call methods on those objects, but instead I have to make that call on the target object's thread. Most literature I have dug up on the topic such as Designing Concurrent, Distributed, and Real-Time Applications with UML by Hassan Gomaa had some nice ideas, such as drawing thread boundaries into object diagrams, but overall seemed a bit too academic and wordy to be really useful. I don’t want to use these diagrams as a high-level view of the problem domain, but rather as a detailed description of my classes/objects, their interactions and the limitations due to thread-boundaries I mentioned above. I would therefore like to know: What types of diagrams have you found to be most helpful in understanding multi-threaded applications? Are there any extensions to classic UML that take into account the peculiarities of multi-threaded applications, e.g. through annotations illustrating that some objects might live in a certain thread while others have no thread-affinity; some fields of an object may be read from any thread, but written to only from one; some methods are synchronous and return a result while others are asynchronous that get requests queued up and return results for instance via a callback on a different thread.

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  • Exalogic Elastic Cloud Software (EECS) version 2.0.1 available

    - by JuergenKress
    We are pleased to announce that as of today (May 14, 2012) the Exalogic Elastic Cloud Software (EECS) version 2.0.1 has been made Generally Available. This release is the culmination of over two and a half years of engineering effort from an extended team spanning 18 product development organizations on three continents, and is the most powerful, sophisticated and comprehensive Exalogic Elastic Cloud Software release to date. With this new EECS release, Exalogic customers now have an ideal platform for not only high-performance and mission critical applications, but for standardization and consolidation of virtually all Oracle Fusion Middleware, Fusion Applications, Application Unlimited and Oracle GBU Applications. With the release of EECS 2.0.1, Exalogic is now capable of hosting multiple concurrent tenants, business applications and middleware deployments with fine-grained resource management, enterprise-grade security, unmatched manageability and extreme performance in a fully virtualized environment. The Exalogic Elastic Cloud Software 2.0.1 release brings important new technologies to the Exalogic platform: Exalogic is now capable of hosting multiple concurrent tenants, business applications and middleware deployments with fine-grained resource management, enterprise-grade security, unmatched manageabi! lity and extreme performance in a fully virtualized environment. Support for extremely high-performance x86 server virtualization via a highly optimized version of Oracle VM 3.x. A rich, fully integrated Infrastructure-as-a-Service management system called Exalogic Control which provides graphical, command line and Java interfaces that allows Cloud Users, or external systems, to create and manage users, virtual servers, virtual storage and virtual network resources. Webcast Series: Rethink Your Business Application Deployment Strategy Redefining the CRM and E-Commerce Experience with Oracle Exalogic, 7-Jun@10am PT & On-Demand: ‘The Road to a Cloud-Enabled, Infinitely Elastic Application Infrastructure’ (featuring Gartner Analysts). 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: ExaLogic Elastic Cloud,ExaLogic,WebLogic,WebLogic Community,Oracle,OPN,Jürgen Kress,ExaLogic 2.0.1

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  • Install Base Transaction Error Troubleshooting

    - by LuciaC
    Oracle Installed Base is an item instance life cycle tracking application that facilitates enterprise-wide life cycle item management and tracking capability.In a typical process flow a sales order is created and shipped, this updates Inventory and creates a new item instance in Install Base (IB).  The Inventory update results in a record being placed in the SFM Event Queue.  If the record is successfully processed the IB tables are updated, if there is an error the record is placed in the csi_txn_errors table and the error needs to be resolved so that the IB instance can be created.It's extremely important to be proactive and monitor IB Transaction Errors regularly.  Errors cascade and can build up exponentially if not resolved. Due to this cascade effect, error records need to be considered as a whole and not individually; the root cause of any error needs to be resolved first and this may result in the subsequent errors resolving themselves. Install Base Transaction Error Diagnostic Program In the past the IBtxnerr.sql script was used to diagnose transaction errors, this is now replaced by an enhanced concurrent program version of the script. See the following note for details of how to download, install and run the concurrent program as well as details of how to interpret the results: Doc ID 1501025.1 - Install Base Transaction Error Diagnostic Program  The program provides comprehensive information about the errors found as well as links to known knowledge articles which can help to resolve the specific error. Troubleshooting Watch the replay of the 'EBS CRM: 11i and R12 Transaction Error Troubleshooting - an Overview' webcast or download the presentation PDF (go to Doc ID 1455786.1 and click on 'Archived 2011' tab).  The webcast and PDF include more information, including SQL statements that you can use to identify errors and their sources as well as recommended setup and troubleshooting tips. Refer to these notes for comprehensive information: Doc ID 1275326.1: E-Business Oracle Install Base Product Information Center Doc ID 1289858.1: Install Base Transaction Errors Master Repository Doc ID: 577978.1: Troubleshooting Install Base Errors in the Transaction Errors Processing Form  Don't forget your Install Base Community where you can ask questions to help you resolve your IB transaction errors.

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  • Optimized Image Loading in a UIScrollView

    - by Michael Gaylord
    I have a UIScrollView that has a set of images loaded side-by-side inside it. You can see an example of my app here: http://www.42restaurants.com. My problem comes in with memory usage. I want to lazy load the images as they are about to appear on the screen and unload images that aren't on screen. As you can see in the code I work out at a minimum which image I need to load and then assign the loading portion to an NSOperation and place it on an NSOperationQueue. Everything works great apart from a jerky scrolling experience. I don't know if anyone has any ideas as to how I can make this even more optimized, so that the loading time of each image is minimized or so that the scrolling is less jerky. - (void)scrollViewDidScroll:(UIScrollView *)scrollView{ [self manageThumbs]; } - (void) manageThumbs{ int centerIndex = [self centerThumbIndex]; if(lastCenterIndex == centerIndex){ return; } if(centerIndex >= totalThumbs){ return; } NSRange unloadRange; NSRange loadRange; int totalChange = lastCenterIndex - centerIndex; if(totalChange > 0){ //scrolling backwards loadRange.length = fabsf(totalChange); loadRange.location = centerIndex - 5; unloadRange.length = fabsf(totalChange); unloadRange.location = centerIndex + 6; }else if(totalChange < 0){ //scrolling forwards unloadRange.length = fabsf(totalChange); unloadRange.location = centerIndex - 6; loadRange.length = fabsf(totalChange); loadRange.location = centerIndex + 5; } [self unloadImages:unloadRange]; [self loadImages:loadRange]; lastCenterIndex = centerIndex; return; } - (void) unloadImages:(NSRange)range{ UIScrollView *scrollView = (UIScrollView *)[[self.view subviews] objectAtIndex:0]; for(int i = 0; i < range.length && range.location + i < [scrollView.subviews count]; i++){ UIView *subview = [scrollView.subviews objectAtIndex:(range.location + i)]; if(subview != nil && [subview isKindOfClass:[ThumbnailView class]]){ ThumbnailView *thumbView = (ThumbnailView *)subview; if(thumbView.loaded){ UnloadImageOperation *unloadOperation = [[UnloadImageOperation alloc] initWithOperableImage:thumbView]; [queue addOperation:unloadOperation]; [unloadOperation release]; } } } } - (void) loadImages:(NSRange)range{ UIScrollView *scrollView = (UIScrollView *)[[self.view subviews] objectAtIndex:0]; for(int i = 0; i < range.length && range.location + i < [scrollView.subviews count]; i++){ UIView *subview = [scrollView.subviews objectAtIndex:(range.location + i)]; if(subview != nil && [subview isKindOfClass:[ThumbnailView class]]){ ThumbnailView *thumbView = (ThumbnailView *)subview; if(!thumbView.loaded){ LoadImageOperation *loadOperation = [[LoadImageOperation alloc] initWithOperableImage:thumbView]; [queue addOperation:loadOperation]; [loadOperation release]; } } } } EDIT: Thanks for the really great responses. Here is my NSOperation code and ThumbnailView code. I tried a couple of things over the weekend but I only managed to improve performance by suspending the operation queue during scrolling and resuming it when scrolling is finished. Here are my code snippets: //In the init method queue = [[NSOperationQueue alloc] init]; [queue setMaxConcurrentOperationCount:4]; //In the thumbnail view the loadImage and unloadImage methods - (void) loadImage{ if(!loaded){ NSString *filename = [NSString stringWithFormat:@"%03d-cover-front", recipe.identifier, recipe.identifier]; NSString *directory = [NSString stringWithFormat:@"RestaurantContent/%03d", recipe.identifier]; NSString *path = [[NSBundle mainBundle] pathForResource:filename ofType:@"png" inDirectory:directory]; UIImage *image = [UIImage imageWithContentsOfFile:path]; imageView = [[ImageView alloc] initWithImage:image andFrame:CGRectMake(0.0f, 0.0f, 176.0f, 262.0f)]; [self addSubview:imageView]; [self sendSubviewToBack:imageView]; [imageView release]; loaded = YES; } } - (void) unloadImage{ if(loaded){ [imageView removeFromSuperview]; imageView = nil; loaded = NO; } } Then my load and unload operations: - (id) initWithOperableImage:(id<OperableImage>) anOperableImage{ self = [super init]; if (self != nil) { self.image = anOperableImage; } return self; } //This is the main method in the load image operation - (void)main { [image loadImage]; } //This is the main method in the unload image operation - (void)main { [image unloadImage]; }

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  • Windows Azure Evolution &ndash; Welcome to VS2012

    - by Shaun
    When the Microsoft released the first preview version of Windows 8 and Visual Studio, many people in the community were asking if the windows azure tool is available to it. The answer was “NO”. Microsoft promised that the windows azure tool will only support the Visual Studio 2010 but when the 2012 was final released, windows azure tool should be work. But now alone with the new windows azure platform was published we got the latest Windows Azure SDK 1.7, which is compatible to the Visual Studio 2012 RC.   You can retrieve the latest version of the Windows Azure SDK through Web Platform Installer, which I think it’s the easiest and simplest way to download and install, since besides the SDK itself it also needs some other components. To download the latest windows azure SDK from Web Platform Installer, just go to the windows azure website and clicked the Develop, .NET and click the blue “install” button. Then you need to select which version of Visual Studio you want to use, Visual Studio 2010 or Visual Studio 2012 RC. After selected the current version you will download an EXE file. This file will lead you to install the Web Platform Installer 4.0 (if you haven’t installed) and the latest windows azure SDK. You can see the version name is June 2012, 1.7. Finally the WebPI will detect the dependent components you need to download and begin to install. But if you want to challenge yourself you can download the components and install them manually. The standalone installations are listed in this page with the instruction on how to install them with necessary pre-requirements.   Once you finished the installation you can open the Visual Studio 2012 RC and as usual, it need to be run as administrator. If you clicked the New Project link from the start page, navigated to Cloud category you will find that there no project template available. Is there anything wrong? So, if you changed the target framework from the default .NET 4.5 to .NET 4 you will see the azure project template. This is because, currently the windows azure instance does not support .NET 4.5. After clicked OK you will see the role creation window, which is similar as what you have seen before. But there are some new role templates in this SDK. Firstly you will have ASP.NET MVC 4 web role available, which means you can create ASP.NET MVC 4 applications for internet, intranet, mobile and WebAPI on the cloud. Then there are two new worker role templates, “Cache Worker Role” and “Worker Role with Service Bus Queue”. “Worker Role with Service Bus Queue” is a worker role which had added necessary references to access the Windows Azure Service Bus Queue. It also have some basic sample code in the worker role class which could read messages from the queue when started. The “Cache Worker Role” is a worker role which has the in-memory distributed cache feature enabled by default. This feature is different than the Windows Azure Caching. It allows the role instance to use its memory as a in-memory distributed cache clusters. By using this feature you can have one or more worker roles as some dedicate cache clusters. Alternatively, you can make part of your web role and worker role’s memory as the cache clusters as well. Let’s just create an ASP.NET MVC 4 Web Role, and click F5 to run it under the local emulator. If you have been working with azure for a while you should know that I need to setup the local storage emulator before running locally if it’s a fresh azure SDK installation. But in this version when we started our azure project the Visual Studio will check if the storage emulator had been initialized. If not, it will run the initializer automatically. And as you can see, in this version the storage emulator relies on the SQL Server 2012 Local DB feature. It will create the emulator database and tables in the default local database. You can set the storage emulator to use a standard SQL Server default instance by using the command “dsinit /instance:.”. The “dsinit” tool now is located at %PROGRAM FILES%\Microsoft SDKs\Windows Azure\Emulator\devstore After the Visual Studio complied and deployed the package our website should be shown in the browser. This is the MVC 4 Web Role home page on my Windows 8 machine in IE10. Another thing you might notice is that, in this version the compute emulator utilizes IIS Express to host the web roles instead of the full IIS. You can add breakpoint in the code and debug, and you can use the local storage emulator to test your code for accessing the storage service. All of them are same as what your are doing now on SDK 1.6. You can switch to use IIS to run your web role in local emulator. Just open the windows azure porject property windows, in the Web page select “Use IIS Web Server”. For more information about this please have a look on Nuno’s blog post. In the role property page in Visual Studio there’s no massive changes. You can configure your role settings such as the endpoints, certificates and local storage, etc.. One thing was added is the Caching tab. Here you can specify enable the caching feature or not, and how much memory you want to use as the cache cluster. I will introduce more details about it in the future posts. The publish and package feature are also no change. You can publish your project to azure directly through Visual Studio 2012, while you can create the package and upload manually. Below is the SDK version of my deployment which is 1.7.30602.1703 in the developer portal.   Summary In this post I introduced about the new Windows Azure SDK 1.7 especially on how it works on the latest Visual Studio 2012 RC. There’s no significant changes in the visual studio tool in this version but some small enhancement such as ASP.NET MVC 4, Cache Worker Role, using SQL 2012 Local DB and IIS Express, etc..   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • General monitoring for SQL Server Analysis Services using Performance Monitor

    - by Testas
    A recent customer engagement required a setup of a monitoring solution for SSAS, due to the time restrictions placed upon this, native Windows Performance Monitor (Perfmon) and SQL Server Profiler Monitoring Tools was used as using a third party tool would have meant the customer providing an additional monitoring server that was not available.I wanted to outline the performance monitoring counters that was used to monitor the system on which SSAS was running. Due to the slow query performance that was occurring during certain scenarios, perfmon was used to establish if any pressure was being placed on the Disk, CPU or Memory subsystem when concurrent connections access the same query, and Profiler to pinpoint how the query was being managed within SSAS, profiler I will leave for another blogThis guide is not designed to provide a definitive list of what should be used when monitoring SSAS, different situations may require the addition or removal of counters as presented by the situation. However I hope that it serves as a good basis for starting your monitoring of SSAS. I would also like to acknowledge Chris Webb’s awesome chapters from “Expert Cube Development” that also helped shape my monitoring strategy:http://cwebbbi.spaces.live.com/blog/cns!7B84B0F2C239489A!6657.entrySimulating ConnectionsTo simulate the additional connections to the SSAS server whilst monitoring, I used ascmd to simulate multiple connections to the typical and worse performing queries that were identified by the customer. A similar sript can be downloaded from codeplex at http://www.codeplex.com/SQLSrvAnalysisSrvcs.     File name: ASCMD_StressTestingScripts.zip. Performance MonitorWithin performance monitor,  a counter log was created that contained the list of counters below. The important point to note when running the counter log is that the RUN AS property within the counter log properties should be changed to an account that has rights to the SSAS instance when monitoring MSAS counters. Failure to do so means that the counter log runs under the system account, no errors or warning are given while running the counter log, and it is not until you need to view the MSAS counters that they will not be displayed if run under the default account that has no right to SSAS. If your connection simulation takes hours, this could prove quite frustrating if not done beforehand JThe counters used……  Object Counter Instance Justification System Processor Queue legnth N/A Indicates how many threads are waiting for execution against the processor. If this counter is consistently higher than around 5 when processor utilization approaches 100%, then this is a good indication that there is more work (active threads) available (ready for execution) than the machine's processors are able to handle. System Context Switches/sec N/A Measures how frequently the processor has to switch from user- to kernel-mode to handle a request from a thread running in user mode. The heavier the workload running on your machine, the higher this counter will generally be, but over long term the value of this counter should remain fairly constant. If this counter suddenly starts increasing however, it may be an indicating of a malfunctioning device, especially if the Processor\Interrupts/sec\(_Total) counter on your machine shows a similar unexplained increase Process % Processor Time sqlservr Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process % Processor Time msmdsrv Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process Working Set sqlservr If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Process Working Set msmdsrv If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Processor % Processor Time _Total and individual cores measures the total utilization of your processor by all running processes. If multi-proc then be mindful only an average is provided Processor % Privileged Time _Total To see how the OS is handling basic IO requests. If kernel mode utilization is high, your machine is likely underpowered as it's too busy handling basic OS housekeeping functions to be able to effectively run other applications. Processor % User Time _Total To see how the applications is interacting from a processor perspective, a high percentage utilisation determine that the server is dealing with too many apps and may require increasing thje hardware or scaling out Processor Interrupts/sec _Total  The average rate, in incidents per second, at which the processor received and serviced hardware interrupts. Shoulr be consistant over time but a sudden unexplained increase could indicate a device malfunction which can be confirmed using the System\Context Switches/sec counter Memory Pages/sec N/A Indicates the rate at which pages are read from or written to disk to resolve hard page faults. This counter is a primary indicator of the kinds of faults that cause system-wide delays, this is the primary counter to watch for indication of possible insufficient RAM to meet your server's needs. A good idea here is to configure a perfmon alert that triggers when the number of pages per second exceeds 50 per paging disk on your system. May also want to see the configuration of the page file on the Server Memory Available Mbytes N/A is the amount of physical memory, in bytes, available to processes running on the computer. if this counter is greater than 10% of the actual RAM in your machine then you probably have more than enough RAM. monitor it regularly to see if any downward trend develops, and set an alert to trigger if it drops below 2% of the installed RAM. Physical Disk Disk Transfers/sec for each physical disk If it goes above 10 disk I/Os per second then you've got poor response time for your disk. Physical Disk Idle Time _total If Disk Transfers/sec is above  25 disk I/Os per second use this counter. which measures the percent time that your hard disk is idle during the measurement interval, and if you see this counter fall below 20% then you've likely got read/write requests queuing up for your disk which is unable to service these requests in a timely fashion. Physical Disk Disk queue legnth For the OLAP and SQL physical disk A value that is consistently less than 2 means that the disk system is handling the IO requests against the physical disk Network Interface Bytes Total/sec For the NIC Should be monitored over a period of time to see if there is anb increase/decrease in network utilisation Network Interface Current Bandwidth For the NIC is an estimate of the current bandwidth of the network interface in bits per second (BPS). MSAS 2005: Memory Memory Limit High KB N/A Shows (as a percentage) the high memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Limit Low KB N/A Shows (as a percentage) the low memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Usage KB N/A Displays the memory usage of the server process. MSAS 2005: Memory File Store KB N/A Displays the amount of memory that is reserved for the Cache. Note if total memory limit in the msmdsrv.ini is set to 0, no memory is reserved for the cache MSAS 2005: Storage Engine Query Queries from Cache Direct / sec N/A Displays the rate of queries answered from the cache directly MSAS 2005: Storage Engine Query Queries from Cache Filtered / Sec N/A Displays the Rate of queries answered by filtering existing cache entry. MSAS 2005: Storage Engine Query Queries from File / Sec N/A Displays the Rate of queries answered from files. MSAS 2005: Storage Engine Query Average time /query N/A Displays the average time of a query MSAS 2005: Connection Current connections N/A Displays the number of connections against the SSAS instance MSAS 2005: Connection Requests / sec N/A Displays the rate of query requests per second MSAS 2005: Locks Current Lock Waits N/A Displays thhe number of connections waiting on a lock MSAS 2005: Threads Query Pool job queue Length N/A The number of queries in the job queue MSAS 2005:Proc Aggregations Temp file bytes written/sec N/A Shows the number of bytes of data processed in a temporary file MSAS 2005:Proc Aggregations Temp file rows written/sec N/A Shows the number of bytes of data processed in a temporary file 

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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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  • Announcing: Improvements to the Windows Azure Portal

    - by ScottGu
    Earlier today we released a number of enhancements to the new Windows Azure Management Portal.  These new capabilities include: Service Bus Management and Monitoring Support for Managing Co-administrators Import/Export support for SQL Databases Virtual Machine Experience Enhancements Improved Cloud Service Status Notifications Media Services Monitoring Support Storage Container Creation and Access Control Support All of these improvements are now live in production and available to start using immediately.  Below are more details on them: Service Bus Management and Monitoring The new Windows Azure Management Portal now supports Service Bus management and monitoring. Service Bus provides rich messaging infrastructure that can sit between applications (or between cloud and on-premise environments) and allow them to communicate in a loosely coupled way for improved scale and resiliency. With the new Service Bus experience, you can now create and manage Service Bus Namespaces, Queues, Topics, Relays and Subscriptions. You can also get rich monitoring for Service Bus Queues, Topics and Subscriptions. To create a Service Bus namespace, you can now select the “Service Bus” tab in the Windows Azure portal and then simply select the CREATE command: Doing so will bring up a new “Create a Namespace” dialog that allows you to name and create a new Service Bus Namespace: Once created, you can obtain security credentials associated with the Namespace via the ACCESS KEY command. This gives you the ability to obtain the connection string associated with the service namespace. You can copy and paste these values into any application that requires these credentials: It is also now easy to create Service Bus Queues and Topics via the NEW experience in the portal drawer.  Simply click the NEW command and navigate to the “App Services” category to create a new Service Bus entity: Once you provision a new Queue or Topic it can be managed in the portal.  Clicking on a namespace will display all queues and topics within it: Clicking on an item in the list will allow you to drill down into a dashboard view that allows you to monitor the activity and traffic within it, as well as perform operations on it. For example, below is a view of an “orders” queue – note how we now surface both the incoming and outgoing message flow rate, as well as the total queue length and queue size: To monitor pub/sub subscriptions you can use the ADD METRICS command within a topic and select a specific subscription to monitor. Support for Managing Co-Administrators You can now add co-administrators for your Windows Azure subscription using the new Windows Azure Portal. This allows you to share management of your Windows Azure services with other users. Subscription co-administrators share the same administrative rights and permissions that service administrator have - except a co-administrator cannot change or view billing details about the account, nor remove the service administrator from a subscription. In the SETTINGS section, click on the ADMINISTRATORS tab, and select the ADD button to add a co-administrator to your subscription: To add a co-administrator, you specify the email address for a Microsoft account (formerly Windows Live ID) or an organizational account, and choose the subscription you want to add them to: You can later update the subscriptions that the co-administrator has access to by clicking on the EDIT button, and then select or deselect the subscriptions to which they belong. Import/Export Support for SQL Databases The Windows Azure administration portal now supports importing and exporting SQL Databases to/from Blob Storage.  Databases can be imported/exported to blob storage using the same BACPAC file format that is supported with SQL Server 2012.  Among other benefits, this makes it easy to copy and migrate databases between on-premise and cloud environments. SQL Databases now have an EXPORT command in the bottom drawer that when pressed will prompt you to save your database to a Windows Azure storage container: The UI allows you to choose an existing storage account or create a new one, as well as the name of the BACPAC file to persist in blob storage: You can also now import and create a new SQL Database by using the NEW command.  This will prompt you to select the storage container and file to import the database from: The Windows Azure Portal enables you to monitor the progress of import and export operations. If you choose to log out of the portal, you can come back later and check on the status of all of the operations in the new history tab of the SQL Database server – this shows your entire import and export history and the status (success/fail) of each: Enhancements to the Virtual Machine Experience One of the common pain-points we have heard from customers using the preview of our new Virtual Machine support has been the inability to delete the associated VHDs when a VM instance (or VM drive) gets deleted. Prior to today’s release the VHDs would continue to be in your storage account and accumulate storage charges. You can now navigate to the Disks tab within the Virtual Machine extension, select a VM disk to delete, and click the DELETE DISK command: When you click the DELETE DISK button you have the option to delete the disk + associated .VHD file (completely clearing it from storage).  Alternatively you can delete the disk but still retain a .VHD copy of it in storage. Improved Cloud Service Status Notifications The Windows Azure portal now exposes more information of the health status of role instances.  If any of the instances are in a non-running state, the status at the top of the dashboard will summarize the status (and update automatically as the role health changes): Clicking the instance hyperlink within this status summary view will navigate you to a detailed role instance view, and allow you to get more detailed health status of each of the instances.  The portal has been updated to provide more specific status information within this detailed view – giving you better visibility into the health of your app: Monitoring Support for Media Services Windows Azure Media Services allows you to create media processing jobs (for example: encoding media files) in your Windows Azure Media Services account. In the Windows Azure Portal, you can now monitor the number of encoding jobs that are queued up for processing as well as active, failed and queued tasks for encoding jobs. On your media services account dashboard, you can visualize the monitoring data for last 6 hours, 24 hours or 7 days. Storage Container Creation and Access Control Support You can now create Windows Azure Storage storage containers from within the Windows Azure Portal.  After selecting a storage account, you can navigate to the CONTAINERS tab and click the ADD CONTAINER command: This will display a dialog that lets you name the new container and control access to it: You can also update the access setting as well as container metadata of existing containers by selecting one and then using the new EDIT CONTAINER command: This will then bring up the edit container dialog that allows you to change and save its settings: In addition to creating and editing containers, you can click on them within the portal to drill-in and view blobs within them.  Summary The above features are all now live in production and available to use immediately.  If you don’t already have a Windows Azure account, you can sign-up for a free trial and start using them today.  Visit the Windows Azure Developer Center to learn more about how to build apps with it. We’ll have even more new features and enhancements coming later this month – including support for the recent Windows Server 2012 and .NET 4.5 releases (we will enable new web and worker role images with Windows Server 2012 and .NET 4.5, and support .NET 4.5 with Websites).  Keep an eye out on my blog for details as these new features become available. 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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  • T4 Performance Counters explained

    - by user13346607
    Now that T4 is out for a few month some people might have wondered what details of the new pipeline you can monitor. A "cpustat -h" lists a lot of events that can be monitored, and only very few are self-explanatory. I will try to give some insight on all of them, some of these "PIC events" require an in-depth knowledge of T4 pipeline. Over time I will try to explain these, for the time being these events should simply be ignored. (Side note: some counters changed from tape-out 1.1 (*only* used in the T4 beta program) to tape-out 1.2 (used in the systems shipping today) The table only lists the tape-out 1.2 counters) 0 0 1 1058 6033 Oracle Microelectronics 50 14 7077 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;} pic name (cpustat) Prose Comment Sel-pipe-drain-cycles, Sel-0-[wait|ready], Sel-[1,2] Sel-0-wait counts cycles a strand waits to be selected. Some reasons can be counted in detail; these are: Sel-0-ready: Cycles a strand was ready but not selected, that can signal pipeline oversubscription Sel-1: Cycles only one instruction or µop was selected Sel-2: Cycles two instructions or µops were selected Sel-pipe-drain-cycles: cf. PRM footnote 8 to table 10.2 Pick-any, Pick-[0|1|2|3] Cycles one, two, three, no or at least one instruction or µop is picked Instr_FGU_crypto Number of FGU or crypto instructions executed on that vcpu Instr_ld dto. for load Instr_st dto. for store SPR_ring_ops dto. for SPR ring ops Instr_other dto. for all other instructions not listed above, PRM footnote 7 to table 10.2 lists the instructions Instr_all total number of instructions executed on that vcpu Sw_count_intr Nr of S/W count instructions on that vcpu (sethi %hi(fc000),%g0 (whatever that is))  Atomics nr of atomic ops, which are LDSTUB/a, CASA/XA, and SWAP/A SW_prefetch Nr of PREFETCH or PREFETCHA instructions Block_ld_st Block loads or store on that vcpu IC_miss_nospec, IC_miss_[L2_or_L3|local|remote]\ _hit_nospec Various I$ misses, distinguished by where they hit. All of these count per thread, but only primary events: T4 counts only the first occurence of an I$ miss on a core for a certain instruction. If one strand misses in I$ this miss is counted, but if a second strand on the same core misses while the first miss is being resolved, that second miss is not counted This flavour of I$ misses counts only misses that are caused by instruction that really commit (note the "_nospec") BTC_miss Branch target cache miss ITLB_miss ITLB misses (synchronously counted) ITLB_miss_asynch dto. but asynchronously [I|D]TLB_fill_\ [8KB|64KB|4MB|256MB|2GB|trap] H/W tablewalk events that fill ITLB or DTLB with translation for the corresponding page size. The “_trap” event occurs if the HWTW was not able to fill the corresponding TLB IC_mtag_miss, IC_mtag_miss_\ [ptag_hit|ptag_miss|\ ptag_hit_way_mismatch] I$ micro tag misses, with some options for drill down Fetch-0, Fetch-0-all fetch-0 counts nr of cycles nothing was fetched for this particular strand, fetch-0-all counts cycles nothing was fetched for all strands on a core Instr_buffer_full Cycles the instruction buffer for a strand was full, thereby preventing any fetch BTC_targ_incorrect Counts all occurences of wrongly predicted branch targets from the BTC [PQ|ROB|LB|ROB_LB|SB|\ ROB_SB|LB_SB|RB_LB_SB|\ DTLB_miss]\ _tag_wait ST_q_tag_wait is listed under sl=20. These counters monitor pipeline behaviour therefore they are not strand specific: PQ_...: cycles Rename stage waits for a Pick Queue tag (might signal memory bound workload for single thread mode, cf. Mail from Richard Smith) ROB_...: cycles Select stage waits for a ROB (ReOrderBuffer) tag LB_...: cycles Select stage waits for a Load Buffer tag SB_...: cycles Select stage waits for Store Buffer tag combinations of the above are allowed, although some of these events can overlap, the counter will only be incremented once per cycle if any of these occur DTLB_...: cycles load or store instructions wait at Pick stage for a DTLB miss tag [ID]TLB_HWTW_\ [L2_hit|L3_hit|L3_miss|all] Counters for HWTW accesses caused by either DTLB or ITLB misses. Canbe further detailed by where they hit IC_miss_L2_L3_hit, IC_miss_local_remote_remL3_hit, IC_miss I$ prefetches that were dropped because they either miss in L2$ or L3$ This variant counts misses regardless if the causing instruction commits or not DC_miss_nospec, DC_miss_[L2_L3|local|remote_L3]\ _hit_nospec D$ misses either in general or detailed by where they hit cf. the explanation for the IC_miss in two flavours for an explanation of _nospec and the reasoning for two DC_miss counters DTLB_miss_asynch counts all DTLB misses asynchronously, there is no way to count them synchronously DC_pref_drop_DC_hit, SW_pref_drop_[DC_hit|buffer_full] L1-D$ h/w prefetches that were dropped because of a D$ hit, counted per core. The others count software prefetches per strand [Full|Partial]_RAW_hit_st_[buf|q] Count events where a load wants to get data that has not yet been stored, i. e. it is still inside the pipeline. The data might be either still in the store buffer or in the store queue. If the load's data matches in the SB and in the store queue the data in buffer takes precedence of course since it is younger [IC|DC]_evict_invalid, [IC|DC|L1]_snoop_invalid, [IC|DC|L1]_invalid_all Counter for invalidated cache evictions per core St_q_tag_wait Number of cycles pipeline waits for a store queue tag, of course counted per core Data_pref_[drop_L2|drop_L3|\ hit_L2|hit_L3|\ hit_local|hit_remote] Data prefetches that can be further detailed by either why they were dropped or where they did hit St_hit_[L2|L3], St_L2_[local|remote]_C2C, St_local, St_remote Store events distinguished by where they hit or where they cause a L2 cache-to-cache transfer, i.e. either a transfer from another L2$ on the same die or from a different die DC_miss, DC_miss_\ [L2_L3|local|remote]_hit D$ misses either in general or detailed by where they hit cf. the explanation for the IC_miss in two flavours for an explanation of _nospec and the reasoning for two DC_miss counters L2_[clean|dirty]_evict Per core clean or dirty L2$ evictions L2_fill_buf_full, L2_wb_buf_full, L2_miss_buf_full Per core L2$ buffer events, all count number of cycles that this state was present L2_pipe_stall Per core cycles pipeline stalled because of L2$ Branches Count branches (Tcc, DONE, RETRY, and SIT are not counted as branches) Br_taken Counts taken branches (Tcc, DONE, RETRY, and SIT are not counted as branches) Br_mispred, Br_dir_mispred, Br_trg_mispred, Br_trg_mispred_\ [far_tbl|indir_tbl|ret_stk] Counter for various branch misprediction events.  Cycles_user counts cycles, attribute setting hpriv, nouser, sys controls addess space to count in Commit-[0|1|2], Commit-0-all, Commit-1-or-2 Number of times either no, one, or two µops commit for a strand. Commit-0-all counts number of times no µop commits for the whole core, cf. footnote 11 to table 10.2 in PRM for a more detailed explanation on how this counters interacts with the privilege levels

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  • SQL Server Table Polling by Multiple Subscribers

    - by Daniel Hester
    Background Designing Stored Procedures that are safe for multiple subscribers (to call simultaneously) can be challenging.  For example let’s say that you want multiple worker processes to poll a shared work queue that’s encapsulated as a SQL Table. This is a common scenario and through experience you’ll find that you want to use Table Hints to prevent unwanted locking when performing simultaneous queries on the same table. There are three table hints to consider: NOLOCK, READPAST and UPDLOCK. Both NOLOCK and READPAST table hints allow you to SELECT from a table without placing a LOCK on that table. However, SELECTs with the READPAST hint will ignore any records that are locked due to being updated/inserted (or otherwise “dirty”), whereas a SELECT with NOLOCK ignores all locks including dirty reads. For the initial update of the flag (that marks the record as available for subscription) I don’t use the NOLOCK Table Hint because I want to be sensitive to the “active” records in the table and I want to exclude them.  I use an Update Lock (UPDLOCK) in conjunction with a WHERE clause that uses a sub-select with a READPAST Table Hint in order to explicitly lock the records I’m updating (UPDLOCK) but not place a lock on the table when selecting the records that I’m going to update (READPAST). UPDATES should be allowed to lock the rows affected because we’re probably changing a flag on a record so that it is not included in a SELECT from another subscriber. On the UPDATE statement we should explicitly use the UPDLOCK to guard against lock escalation. A SELECT to check for the next record(s) to process can result in a shared read lock being held by more than one subscriber polling the shared work queue (SQL table). It is expected that more than one worker process (or server) might try to process the same new record(s) at the same time. When each process then tries to obtain the update lock, none of them can because another process has a shared read lock in place. Thus without the UPDLOCK hint the result would be a lock escalation deadlock; however with the UPDLOCK hint this condition is mitigated against. Note that using the READPAST table hint requires that you also set the ISOLATION LEVEL of the transaction to be READ COMMITTED (rather than the default of SERIALIZABLE). Guidance In the Stored Procedure that returns records to the multiple subscribers: Perform the UPDATE first. Change the flag that makes the record available to subscribers.  Additionally, you may want to update a LastUpdated datetime field in order to be able to check for records that “got stuck” in an intermediate state or for other auditing purposes. In the UPDATE statement use the (UPDLOCK) Table Hint on the UPDATE statement to prevent lock escalation. In the UPDATE statement also use a WHERE Clause that uses a sub-select with a (READPAST) Table Hint to select the records that you’re going to update. In the UPDATE statement use the OUTPUT clause in conjunction with a Temporary Table to isolate the record(s) that you’ve just updated and intend to return to the subscriber. This is the fastest way to update the record(s) and to get the records’ identifiers within the same operation. Finally do a set-based SELECT on the main Table (using the Temporary Table to identify the records in the set) with either a READPAST or NOLOCK table hint.  Use NOLOCK if there are other processes (besides the multiple subscribers) that might be changing the data that you want to return to the multiple subscribers; or use READPAST if you're sure there are no other processes (besides the multiple subscribers) that might be updating column data in the table for other purposes (e.g. changes to a person’s last name).  NOLOCK is generally the better fit in this part of the scenario. See the following as an example: CREATE PROCEDURE [dbo].[usp_NewCustomersSelect] AS BEGIN -- OVERRIDE THE DEFAULT ISOLATION LEVEL SET TRANSACTION ISOLATION LEVEL READ COMMITTED -- SET NOCOUNT ON SET NOCOUNT ON -- DECLARE TEMP TABLE -- Note that this example uses CustomerId as an identifier; -- you could just use the Identity column Id if that’s all you need. DECLARE @CustomersTempTable TABLE ( CustomerId NVARCHAR(255) ) -- PERFORM UPDATE FIRST -- [Customers] is the name of the table -- [Id] is the Identity Column on the table -- [CustomerId] is the business document key used to identify the -- record globally, i.e. in other systems or across SQL tables -- [Status] is INT or BIT field (if the status is a binary state) -- [LastUpdated] is a datetime field used to record the time of the -- last update UPDATE [Customers] WITH (UPDLOCK) SET [Status] = 1, [LastUpdated] = GETDATE() OUTPUT [INSERTED].[CustomerId] INTO @CustomersTempTable WHERE ([Id] = (SELECT TOP 100 [Id] FROM [Customers] WITH (READPAST) WHERE ([Status] = 0) ORDER BY [Id] ASC)) -- PERFORM SELECT FROM ENTITY TABLE SELECT [C].[CustomerId], [C].[FirstName], [C].[LastName], [C].[Address1], [C].[Address2], [C].[City], [C].[State], [C].[Zip], [C].[ShippingMethod], [C].[Id] FROM [Customers] AS [C] WITH (NOLOCK), @CustomersTempTable AS [TEMP] WHERE ([C].[CustomerId] = [TEMP].[CustomerId]) END In a system that has been designed to have multiple status values for records that need to be processed in the Work Queue it is necessary to have a “Watch Dog” process by which “stale” records in intermediate states (such as “In Progress”) are detected, i.e. a [Status] of 0 = New or Unprocessed; a [Status] of 1 = In Progress; a [Status] of 2 = Processed; etc.. Thus, if you have a business rule that states that the application should only process new records if all of the old records have been processed successfully (or marked as an error), then it will be necessary to build a monitoring process to detect stalled or stale records in the Work Queue, hence the use of the LastUpdated column in the example above. The Status field along with the LastUpdated field can be used as the criteria to detect stalled / stale records. It is possible to put this watchdog logic into the stored procedure above, but I would recommend making it a separate monitoring function. In writing the stored procedure that checks for stale records I would recommend using the same kind of lock semantics as suggested above. The example below looks for records that have been in the “In Progress” state ([Status] = 1) for greater than 60 seconds: CREATE PROCEDURE [dbo].[usp_NewCustomersWatchDog] AS BEGIN -- TO OVERRIDE THE DEFAULT ISOLATION LEVEL SET TRANSACTION ISOLATION LEVEL READ COMMITTED -- SET NOCOUNT ON SET NOCOUNT ON DECLARE @MaxWait int; SET @MaxWait = 60 IF EXISTS (SELECT 1 FROM [dbo].[Customers] WITH (READPAST) WHERE ([Status] = 1) AND (DATEDIFF(s, [LastUpdated], GETDATE()) > @MaxWait)) BEGIN SELECT 1 AS [IsWatchDogError] END ELSE BEGIN SELECT 0 AS [IsWatchDogError] END END Downloads The zip file below contains two SQL scripts: one to create a sample database with the above stored procedures and one to populate the sample database with 10,000 sample records.  I am very grateful to Red-Gate software for their excellent SQL Data Generator tool which enabled me to create these sample records in no time at all. References http://msdn.microsoft.com/en-us/library/ms187373.aspx http://www.techrepublic.com/article/using-nolock-and-readpast-table-hints-in-sql-server/6185492 http://geekswithblogs.net/gwiele/archive/2004/11/25/15974.aspx http://grounding.co.za/blogs/romiko/archive/2009/03/09/biztalk-sql-receive-location-deadlocks-dirty-reads-and-isolation-levels.aspx

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  • Committed JDO writes do not apply on local GAE HRD, or possibly reused transaction

    - by eeeeaaii
    I'm using JDO 2.3 on app engine. I was using the Master/Slave datastore for local testing and recently switched over to using the HRD datastore for local testing, and parts of my app are breaking (which is to be expected). One part of the app that's breaking is where it sends a lot of writes quickly - that is because of the 1-second limit thing, it's failing with a concurrent modification exception. Okay, so that's also to be expected, so I have the browser retry the writes again later when they fail (maybe not the best hack but I'm just trying to get it working quickly). But a weird thing is happening. Some of the writes which should be succeeding (the ones that DON'T get the concurrent modification exception) are also failing, even though the commit phase completes and the request returns my success code. I can see from the log that the retried requests are working okay, but these other requests that seem to have committed on the first try are, I guess, never "applied." But from what I read about the Apply phase, writing again to that same entity should force the apply... but it doesn't. Code follows. Some things to note: I am attempting to use automatic JDO caching. So this is where JDO uses memcache under the covers. This doesn't actually work unless you wrap everything in a transaction. all the requests are doing is reading a string out of an entity, modifying part of the string, and saving that string back to the entity. If these requests weren't in transactions, you'd of course have the "dirty read" problem. But with transactions, isolation is supposed to be at the level of "serializable" so I don't see what's happening here. the entity being modified is a root entity (not in a group) I have cross-group transactions enabled Another weird thing is happening. If the concurrent modification thing happens, and I subsequently edit more than 5 more entities (this is the max for cross-group transactions), then nothing happens right away, but when I stop and restart the server I get "IllegalArgumentException: operating on too many entity groups in a single transaction". Could it be possible that the PMF is returning the same PersistenceManager every time, or the PM is reusing the same transaction every time? I don't see how I could possibly get the above error otherwise. The code inside the transaction just edits one root entity. I can't think of any other way that GAE would give me the "too many entity groups" error. The relevant code (this is a simplified version) PersistenceManager pm = PMF.getManager(); Transaction tx = pm.currentTransaction(); String responsetext = ""; try { tx.begin(); // I have extra calls to "makePersistent" because I found that relying // on pm.close didn't always write the objects to cache, maybe that // was only a DataNucleus 1.x issue though Key userkey = obtainUserKeyFromCookie(); User u = pm.getObjectById(User.class, userkey); pm.makePersistent(u); // to make sure it gets cached for next time Key mapkey = obtainMapKeyFromQueryString(); // this is NOT a java.util.Map, just FYI Map currentmap = pm.getObjectById(Map.class, mapkey); Text mapData = currentmap.getMapData(); // mapData is JSON stored in the entity Text newMapData = parseModifyAndReturn(mapData); // transform the map currentmap.setMapData(newMapData); // mutate the Map object pm.makePersistent(currentmap); // make sure to persist so there is a cache hit tx.commit(); responsetext = "OK"; } catch (JDOCanRetryException jdoe) { // log jdoe responsetext = "RETRY"; } catch (Exception e) { // log e responsetext = "ERROR"; } finally { if (tx.isActive()) { tx.rollback(); } pm.close(); } resp.getWriter().println(responsetext); EDIT: so I have verified that it fails after exactly 5 transactions. Here's what I do: I create a Foo (root entity), do a bunch of concurrent operations on that Foo, and some fail and get retried, and some commit but don't apply (as described above). Then, I start creating more Foos, and do a few operations on those new Foos. If I only create four Foos, stopping and restarting app engine does NOT give me the IllegalArgumentException. However if I create five Foos (which is the limit for cross-group transactions), then when I stop and restart app engine, I do get the exception. So it seems that somehow these new Foos I am creating are counting toward the limit of 5 max entities per transaction, even though they are supposed to be handled by separate transactions. It's as if a transaction is still open and is being reused by the servlet when it handles the new requests for the 2nd through 5th Foos. EDIT2: it looks like the IllegalArgument thing is independent of the other bug. In other words, it always happens when I create five Foos, even if I don't get the concurrent modification exception. I don't know if it's a symptom of the same problem or if it's unrelated. EDIT3: I found out what was causing the (unrelated) IllegalArgumentException, it was a dumb mistake on my part. But the other issue is still happening. EDIT4: added pseudocode for the datastore access EDIT5: I am pretty sure I know why this is happening, but I will still award the bounty to anyone who can confirm it. Basically, I think the problem is that transactions are not really implemented in the local version of the datastore. References: https://groups.google.com/forum/?fromgroups=#!topic/google-appengine-java/gVMS1dFSpcU https://groups.google.com/forum/?fromgroups=#!topic/google-appengine-java/deGasFdIO-M https://groups.google.com/forum/?hl=en&fromgroups=#!msg/google-appengine-java/4YuNb6TVD6I/gSttMmHYwo0J Because transactions are not implemented, rollback is essentially a no-op. Therefore, I get a dirty read when two transactions try to modify the record at the same time. In other words, A reads the data and B reads the data at the same time. A attempts to modify the data, and B attempts to modify a different part of the data. A writes to the datastore, then B writes, obliterating A's changes. Then B is "rolled back" by app engine, but since rollbacks are a no-op when running on the local datastore, B's changes stay, and A's do not. Meanwhile, since B is the thread that threw the exception, the client retries B, but does not retry A (since A was supposedly the transaction that succeeded).

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  • WebLogic Server Performance and Tuning: Part I - Tuning JVM

    - by Gokhan Gungor
    Each WebLogic Server instance runs in its own dedicated Java Virtual Machine (JVM) which is their runtime environment. Every Admin Server in any domain executes within a JVM. The same also applies for Managed Servers. WebLogic Server can be used for a wide variety of applications and services which uses the same runtime environment and resources. Oracle WebLogic ships with 2 different JVM, HotSpot and JRocket but you can choose which JVM you want to use. JVM is designed to optimize itself however it also provides some startup options to make small changes. There are default values for its memory and garbage collection. In real world, you will not want to stick with the default values provided by the JVM rather want to customize these values based on your applications which can produce large gains in performance by making small changes with the JVM parameters. We can tell the garbage collector how to delete garbage and we can also tell JVM how much space to allocate for each generation (of java Objects) or for heap. Remember during the garbage collection no other process is executed within the JVM or runtime, which is called STOP THE WORLD which can affect the overall throughput. Each JVM has its own memory segment called Heap Memory which is the storage for java Objects. These objects can be grouped based on their age like young generation (recently created objects) or old generation (surviving objects that have lived to some extent), etc. A java object is considered garbage when it can no longer be reached from anywhere in the running program. Each generation has its own memory segment within the heap. When this segment gets full, garbage collector deletes all the objects that are marked as garbage to create space. When the old generation space gets full, the JVM performs a major collection to remove the unused objects and reclaim their space. A major garbage collect takes a significant amount of time and can affect system performance. When we create a managed server either on the same machine or on remote machine it gets its initial startup parameters from $DOMAIN_HOME/bin/setDomainEnv.sh/cmd file. By default two parameters are set:     Xms: The initial heapsize     Xmx: The max heapsize Try to set equal initial and max heapsize. The startup time can be a little longer but for long running applications it will provide a better performance. When we set -Xms512m -Xmx1024m, the physical heap size will be 512m. This means that there are pages of memory (in the state of the 512m) that the JVM does not explicitly control. It will be controlled by OS which could be reserve for the other tasks. In this case, it is an advantage if the JVM claims the entire memory at once and try not to spend time to extend when more memory is needed. Also you can use -XX:MaxPermSize (Maximum size of the permanent generation) option for Sun JVM. You should adjust the size accordingly if your application dynamically load and unload a lot of classes in order to optimize the performance. You can set the JVM options/heap size from the following places:     Through the Admin console, in the Server start tab     In the startManagedWeblogic script for the managed servers     $DOMAIN_HOME/bin/startManagedWebLogic.sh/cmd     JAVA_OPTIONS="-Xms1024m -Xmx1024m" ${JAVA_OPTIONS}     In the setDomainEnv script for the managed servers and admin server (domain wide)     USER_MEM_ARGS="-Xms1024m -Xmx1024m" When there is free memory available in the heap but it is too fragmented and not contiguously located to store the object or when there is actually insufficient memory we can get java.lang.OutOfMemoryError. We should create Thread Dump and analyze if that is possible in case of such error. The second option we can use to produce higher throughput is to garbage collection. We can roughly divide GC algorithms into 2 categories: parallel and concurrent. Parallel GC stops the execution of all the application and performs the full GC, this generally provides better throughput but also high latency using all the CPU resources during GC. Concurrent GC on the other hand, produces low latency but also low throughput since it performs GC while application executes. The JRockit JVM provides some useful command-line parameters that to control of its GC scheme like -XgcPrio command-line parameter which takes the following options; XgcPrio:pausetime (To minimize latency, parallel GC) XgcPrio:throughput (To minimize throughput, concurrent GC ) XgcPrio:deterministic (To guarantee maximum pause time, for real time systems) Sun JVM has similar parameters (like  -XX:UseParallelGC or -XX:+UseConcMarkSweepGC) to control its GC scheme. We can add -verbosegc -XX:+PrintGCDetails to monitor indications of a problem with garbage collection. Try configuring JVM’s of all managed servers to execute in -server mode to ensure that it is optimized for a server-side production environment.

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  • Build Explorer version 1.1 for Visual Studio Team Explorer is released

    - by terje
    Our free extension to Visual Studio , the folder based Build Explorer Version 1.1 has now been released, and uploaded to the Visual Studio Gallery and Codeplex. We have collected up a few changes and some bugs, as follows: Changes: Queue Default Builds can now be optionally fully enabled, fully disabled or enabled just for leaf nodes (=disabled for folders).  If you got a large number of builds it was pretty scary to be able to launch all of them with just one click.  However, it is nice to avoid having the dialog box up when you want to just run off a single build.  That’s the reasoning between the 3rd choice here. Auto fill-in of the builds at start up and refresh  This was a request that came up a lot, and which was also irritating to us.  When the Team Project is opened, the Build explorer will start by itself and fill up it’s tree. So you don’t need to click the node anymore. There was also quite a bit of flashing when the tree filled up, this has been reduced to just a single top level fill before it collapses the node. The speed of the buildup of the tree has also been increased. The “All Build Definitions” node is now shown on top of the list Login box appeared in certain cross domain situations. This was a fix for the TF30063 authentication problem we had in the beginning.  Hopefully the new code has that fixed properly so that both the login box and the TF30063 are gone forever.  Our testing so far seems to indicate it works.  If anyone gets a real problem here there are two workarounds: 1) Turn off the auto refresh to reduce the issue. If this doesn’t fix it, then 2) please reinstall the former version (go to the codeplex download site if you don’t have it anymore)  Write a comment to this blog post with a description of what happens, and I will send a temporary fix asap. Bug fixes: The folder name matching was case sensitive, so “Application.CI” and “application.CI” created two different folders.  View all builds not shown for leaf odes, and view builds didn’t work in all cases.  There was some inconsistencies here which have been fixed. Partly fixed:  The context menu to queue a new build for disabled builds should be removed, but that was a difficult one, and is still on the list, but the command will not do anything for a disabled build. Using the Queue Default Builds on a folder, and if it had some disabled builds below an error box appeared and ruined the whole experience. As a result of these fixes there has been introduced some new options, as shown below:   The two first settings, the Separator symbol and the options for how to handle Queuing of default builds are set per Team Project, and is stored in the TFS source control under the BuildProcessTemplates folder, with the name Inmeta.VisualStudio.BuildExplorer.Settings.xml The next two settings need some explanations.  They handle the behavior for the auto update of the build folders.  First, these are stored in the local registry per user, at the key HKEY_CURRENT_USER/Software\Inmeta\BuildExplorer. The first option Use Timed Refresh at Startup, if turned off, you will need to click the node as it is done in Version 1.0.  The second option is a timed value, the time after the Build explorer node is created and until the scanning of the Build folders start.  It is assumed that this is enough, and the tests so far indicates this.  If you have very many builds and you see that the explorer don’t get them all, try to increase this value, and of course, notify me of your case, either here or on the Visual Gallery site.

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  • Using NServiceBus behind a custom web service

    - by Michael Stephenson
    In this post I'd like to talk about an architecture scenario we had recently and how we were able to utilise NServiceBus to help us address this problem. Scenario Cognos is a reporting system used by one of my clients. A while back we developed a web service façade to allow line of business applications to be able to access reports from Cognos to support their various functions. The service was intended to provide access to reports which were quick running reports or pre-generated reports which could be accessed real-time on demand. One of the key aims of the web service was to provide a simple generic interface to allow applications to get any report without needing to worry about the complex .net SDK for Cognos. The web service also supported multi-hop kerberos delegation so that report data could be accesses under the context of the end user. This service was working well for a period of time. The Problem The problem we encountered was that reports were now also required to be available to batch processes. The original design was optimised for low latency so users would enjoy a positive experience, however when the batch processes started to request 250+ concurrent reports over an extended period of time you can begin to imagine the sorts of problems that come into play. The key problems this new scenario caused are: Users may be affected and the latency of on demand reports was significantly slower The Cognos infrastructure was not scaled sufficiently to be able to cope with these long peaks of load From a cost perspective it just isn't feasible to scale the Cognos infrastructure to be able to handle the load when it is only for a couple of hour window each night. We really needed to introduce a second pattern for accessing this service which would support high through-put scenarios. We also had little control over the batch process in terms of being able to throttle its load. We could however make some changes to the way it accessed the reports. The Approach My idea was to introduce a throttling mechanism between the Web Service Façade and Cognos. This would allow the batch processes to push reports requests hard at the web service which we were confident the web service can handle. The web service would then queue these requests and process them behind the scenes and make a call back to the batch application to provide the report once it had been accessed. In terms of technology we had some limitations because we were not able to use WCF or IIS7 where the MSMQ-Activated WCF services could have helped, but we did have MSMQ as an option and I thought NServiceBus could do just the job to help us here. The flow of how this would work was as follows: The batch applications would send a request for a report to the web service The web service uses NServiceBus to send the message to a Queue The NServiceBus Generic Host is running as a windows service with a message handler which subscribes to these messages The message handler gets the message, accesses the report from Cognos The message handler calls back to the original batch application, this is decoupled because the calling application provides a call back url The report gets into the batch application and is processed as normal This approach looks something like the below diagram: The key points are an application wanting to take advantage of the batch driven reports needs to do the following: Implement our call back contract Make a call to the service providing a call back url Provide a correlation ID so it knows how to tie each response back to its request What does NServiceBus offer in this solution So this scenario is not the typical messaging service bus type of solution people implement with NServiceBus, but it did offer the following: Simplified interaction with MSMQ Offered the ability to configure the number of processes working through the queue so we could find a balance between load on Cognos versus the applications end to end processing time NServiceBus offers retries and a way to manage failed messages NServiceBus offers a high availability setup The simple thing is that NServiceBus gave us the platform to build the solution on. We just implemented a message handler which functionally processed a message and we could rely on NServiceBus to do all of the hard work around managing the queues and all of the lower level things that would have took ages to write to any kind of robust level. Conclusion With this approach we were able to deal with a fairly significant performance issue with out too much rework. Hopefully this write up gives people some insight into ideas on how to leverage the excellent NServiceBus framework to help solve integration and high through-put scenarios.

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

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

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  • Convert .3GP and .3G2 Files to AVI / MPEG for Free

    - by DigitalGeekery
    3GP and .3G2 are common video capture formats used on many mobile phones, but they may not be supported by your favorite media player. Today we’ll show you a quick and easy way to convert those files to AVI or MPG format with the free Windows application, Pazera Free 3GP to AVI Converter. Download the Pazera Free 3GP to AVI Converter. You’ll have to unzip the download folder, but there is no need to install the application. Just double-click the 3gptoavi.exe file to run the application. To add your 3GP or 3G2 files to the queue to be converted, click on the Add files  button at the top left. Browse for your file, and click Open.   Your video will be added to the Queue. You can add multiple files to the queue and convert them all at one time.   Most users will find it preferable to use one of the pre-configured profiles for their conversion settings. To load a profile, choose one from the Profile drop down list and then click the Load button. You will see the profile update the settings in the panels at the bottom of the application. We tested Pazera Free 3GP to AVI Converter with 3GP files recorded on a Motorola Droid, and found the AVI H.264 Very High Q. profile to return the best results for AVI output, and the MPG – DVD NTSC: MPEG-2 the best results for MPG output. Other profiles produced smaller file sizes, but at a cost of reduced quality video output.   More advanced users may tweak video and audio settings to their liking in the lower panels. Click on the AVI button under Output file format / Video settings to adjust settings AVI… Or the MPG button to adjust the settings for MPG output. By default, the converted file will be output to the same location as the input directory. You can change it by clicking the text box input radio button and browsing for a different folder. When you’ve chosen your settings, click Convert to begin the conversion process.   A conversion output box will open and display the progress. When finished, click Close. Now you’re ready to enjoy your video in your favorite media player. Pazera Free 3GP to AVI Converter isn’t the most robust media conversion tool, but it does what it is intended to do. It handles the task of 3GP to AVI / MPG conversion very well. It’s easy enough for the beginner to manage without much trouble, but also has enough options to please more experienced users. Download Pazera Free 3GP to AVI Converter Similar Articles Productive Geek Tips How To Convert Video Files to MP3 with VLCEasily Change Audio File Formats with XRECODEConvert PDF Files to Word Documents and Other FormatsConvert Video and Remove Commercials in Windows 7 Media Center with MCEBuddy 1.1Compress Large Video Files with DivX / Xvid and AutoGK TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 Install, Remove and HIDE Fonts in Windows 7 Need Help with Your Home Network? Awesome Lyrics Finder for Winamp & Windows Media Player Download Videos from Hulu Pixels invade Manhattan Convert PDF files to ePub to read on your iPad

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  • Extract Audio from a Video File with Pazera Free Audio Extractor

    - by DigitalGeekery
    Have you ever wanted to extract some or all of the audio from a video file?  Today we’ll take a look at Pazera Free Audio Extractor. A simple audio converter that specializes in that very task. Download the Pazera Free Audio Extractor. (See download link below) You’ll need to unzip the download folder, but there is no need to install the application. Simply double-click the AudioExtractor.exe file to run the application. To add your video files to the queue to be converted, click on the Add files  button at the top left. You can add multiple files to the queue and convert them all at one time. Browse for your video file, and click Open.   Your video will be added to the Queue for processing.   Under Output directory you can choose to output to a folder of your choice. Outputting to the same folder as the input folder is the default.   Pazera Free Audio Extractor includes pre-configured profiles that will simplify the process of choosing conversion settings. To load a profile, choose one from the Profile drop down list and then click the Load button. You can choose to output to MP3, AAC, AC3, WMA, FLAC, OGG or WAV file format.   You will see the profile update the Audio settings in the panels at the lower left of the application. If you wish, you may also select your own custom settings. Advanced Settings The Advanced settings can be used if you want to extract only a portion of the the audio, such as a clip of dialog or a song from a movie. To extract only a portion of the audio, set the start time by selecting the Start time offset check box, then entering the time in the video clip where the audio begins. To set the end time, begin by selecting the Duration check box. Now, you can either select the Duration radio button and enter the amount of time for which you would like to extract the audio, or you can select the End time offset radio button and enter the time in the video clip where the audio ends. When you are ready to convert, click the CONVERT button on the menu at the top of the screen.   An output box will open and display the conversion progress. When finished, click Close.   Now you are ready to enjoy your audio clip. Pazera Free Audio Extractor is a basic audio tool that is easy enough for everyone to use. It runs on Windows only and supports most common video formats including AVI, FLV, MP4, MPG, MOV, 3GP, and WMV. Download Free Audio Extractor 1.3 Similar Articles Productive Geek Tips Eufony Free Audio Player – Resource Gentle Audio PlayerConvert .3GP and .3G2 Files to AVI / MPEG for FreeTurn Off Auto-Play of Audio and Video CDs and DVDs in UbuntuHow to Make/Edit a movie with Windows Movie Maker in Windows VistaEasily Change Audio File Formats with XRECODE TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 Use Printflush to Solve Printing Problems Icelandic Volcano Webcams Open Multiple Links At One Go NachoFoto Searches Images in Real-time Office 2010 Product Guides Google Maps Place marks – Pizza, Guns or Strip Clubs

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