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  • Problem using python QPID and gevent together [closed]

    - by William Payne
    I have a python script that pulls messages from an Apache QPID queue, and then uses gevent to perform (IO-bound) tasks on those messages in parallel. The queue that this script pulls from was recently changed: I suspect the version of the C++ QPID broker changed, although I cannot verify this at the present time. Now, my process deadlocks and hangs upon QPID queue creation. I strongly suspect that this is a result of an incompatibility with gevent, although I have not done the work yet to produce a minimal example to demonstrate the problem. (Next on my list). Does anybody else have experience of getting gevent and QPID to work together? or Has anybody else seen the same issues?

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  • Use Windbg find argumet passed to a COM+ method

    - by G33kKahuna
    Generated a debug diagnostic dump file for a COM+ application. Upon analysis look like threads deadlocks at line OLE32!SwitchSTA. My symbol path is pointing to msdl.microsoft.com/download/symbols. Is there way to know what arguments were passed to this method? In general, how does one use Windbg to find the input argument value to the method call? thanks in advance

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  • What is causing this SQL 2005 Primary Key Deadlock between two real-time bulk upserts?

    - by skimania
    Here's the scenario: I've got a table called MarketDataCurrent (MDC) that has live updating stock prices. I've got one process called 'LiveFeed' which reads prices streaming from the wire, queues up inserts, and uses a 'bulk upload to temp table then insert/update to MDC table.' (BulkUpsert) I've got another process which then reads this data, computes other data, and then saves the results back into the same table, using a similar BulkUpsert stored proc. Thirdly, there are a multitude of users running a C# Gui polling the MDC table and reading updates from it. Now, during the day when the data is changing rapidly, things run pretty smoothly, but then, after market hours, we've recently started seeing an increasing number of Deadlock exceptions coming out of the database, nowadays we see 10-20 a day. The imporant thing to note here is that these happen when the values are NOT changing. Here's all the relevant info: Table Def: CREATE TABLE [dbo].[MarketDataCurrent]( [MDID] [int] NOT NULL, [LastUpdate] [datetime] NOT NULL, [Value] [float] NOT NULL, [Source] [varchar](20) NULL, CONSTRAINT [PK_MarketDataCurrent] PRIMARY KEY CLUSTERED ( [MDID] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] - stackoverflow wont let me post images until my reputation goes up to 10, so i'll add them as soon as you bump me up, hopefully as a result of this question. ![alt text][1] [1]: http://farm5.static.flickr.com/4049/4690759452_6b94ff7b34.jpg I've got a Sql Profiler Trace Running, catching the deadlocks, and here's what all the graphs look like. stackoverflow wont let me post images until my reputation goes up to 10, so i'll add them as soon as you bump me up, hopefully as a result of this question. ![alt text][2] [2]: http://farm5.static.flickr.com/4035/4690125231_78d84c9e15_b.jpg Process 258 is called the following 'BulkUpsert' stored proc, repeatedly, while 73 is calling the next one: ALTER proc [dbo].[MarketDataCurrent_BulkUpload] @updateTime datetime, @source varchar(10) as begin transaction update c with (rowlock) set LastUpdate = getdate(), Value = t.Value, Source = @source from MarketDataCurrent c INNER JOIN #MDTUP t ON c.MDID = t.mdid where c.lastUpdate < @updateTime and c.mdid not in (select mdid from MarketData where LiveFeedTicker is not null and PriceSource like 'LiveFeed.%') and c.value <> t.value insert into MarketDataCurrent with (rowlock) select MDID, getdate(), Value, @source from #MDTUP where mdid not in (select mdid from MarketDataCurrent with (nolock)) and mdid not in (select mdid from MarketData where LiveFeedTicker is not null and PriceSource like 'LiveFeed.%') commit And the other one: ALTER PROCEDURE [dbo].[MarketDataCurrent_LiveFeedUpload] AS begin transaction -- Update existing mdid UPDATE c WITH (ROWLOCK) SET LastUpdate = t.LastUpdate, Value = t.Value, Source = t.Source FROM MarketDataCurrent c INNER JOIN #TEMPTABLE2 t ON c.MDID = t.mdid; -- Insert new MDID INSERT INTO MarketDataCurrent with (ROWLOCK) SELECT * FROM #TEMPTABLE2 WHERE MDID NOT IN (SELECT MDID FROM MarketDataCurrent with (NOLOCK)) -- Clean up the temp table DELETE #TEMPTABLE2 commit To clarify, those Temp Tables are being created by the C# code on the same connection and are populated using the C# SqlBulkCopy class. To me it looks like it's deadlocking on the PK of the table, so I tried removing that PK and switching to a Unique Constraint instead but that increased the number of deadlocks 10-fold. I'm totally lost as to what to do about this situation and am open to just about any suggestion. HELP!!

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  • how to proxy sql queries (INSERT, UPDATE e.t.c.)

    - by XakRu
    I have installed cluster MYSQL (galley with mariadb) As an application server installed Apache. on a server with Apache installed haproxy which proxies requests from php in this case installed for zabbix server cluster. But faced with deadlocks, now I want to proxy requests WRITE, INSERT, UPDATE to the second server. SELECT queries to the second and third server. I would be happy to see your suggestions. Please do not write: use mysql - proxy. I want to see what program it may to proxy SQL requests. scheme: http://www.gliffy.com/pubdoc/4474830/L.png

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  • SQLAuthority News – A Successful Performance Tuning Seminar at Pune – Dec 4-5, 2010

    - by pinaldave
    This is report to my third of very successful seminar event on SQL Server Performance Tuning. SQL Server Performance Tuning Seminar in Colombo was oversubscribed with total of 35 attendees. You can read the details over here SQLAuthority News – SQL Server Performance Optimizations Seminar – Grand Success – Colombo, Sri Lanka – Oct 4 – 5, 2010. SQL Server Performance Tuning Seminar in Hyderabad was oversubscribed with total of 25 attendees. You can read the details over here SQL SERVER – A Successful Performance Tuning Seminar – Hyderabad – Nov 27-28, 2010. The same Seminar was offered in Pune on December 4,-5, 2010. We had another successful seminar with lots of performance talk. This seminar was attended by 30 attendees. The best part of the seminar was that along with the our agenda, we have talked about following very interesting concepts. Deadlocks Detection and Removal Dynamic SQL and Inline Code SQL Optimizations Multiple OR conditions and performance tuning Dynamic Search Condition Building and Improvement Memory Cache and Improvement Bottleneck Detections – Memory, CPU and IO Beginning Performance Tuning on Production Parametrization Improving already Super Fast Queries Convenience vs. Performance Proper way to create Indexes Hints and Disadvantages I had great time doing the seminar and sharing my performance tricks with all. The highlight of this seminar was I have explained the attendees, how I begin doing performance tuning when I go for Performance Tuning Consultations.   Pinal Dave at SQL Performance Tuning Seminar SQL Server Performance Tuning Seminar Pinal Dave at SQL Performance Tuning Seminar Pinal Dave at SQL Performance Tuning Seminar SQL Server Performance Tuning Seminar SQL Server Performance Tuning Seminar This seminar series are 100% demo oriented and no usual PowerPoint talk. They are created from my experiences of various organizations for performance tuning. I am not planning any more seminar this year as it was great but I am booked currently for next 60 days at various performance tuning engagements. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Training, SQLAuthority News, T SQL, Technology

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  • The Java Specialist: An Interview with Java Champion Heinz Kabutz

    - by Janice J. Heiss
    Dr. Heinz Kabutz is well known for his Java Specialists’ Newsletter, initiated in November 2000, where he displays his acute grasp of the intricacies of the Java platform for an estimated 70,000 readers; for his work as a consultant; and for his workshops and trainings at his home on the Island of Crete where he has lived since 2006 -- where he is known to curl up on the beach with his laptop to hack away, in between dips in the Mediterranean. Kabutz was born of German parents and raised in Cape Town, South Africa, where he developed a love of programming in junior high school through his explorations on a ZX Spectrum computer. He received a B.S. from the University of Cape Town, and at 25, a Ph.D., both in computer science. He will be leading a two-hour hands-on lab session, HOL6500 – “Finding and Solving Java Deadlocks,” at this year’s JavaOne that will explore what causes deadlocks and how to solve them. Q: Tell us about your JavaOne plans.A: I am arriving on Sunday evening and have just one hands-on-lab to do on Monday morning. This is the first time that a non-Oracle team is doing a HOL at JavaOne under Oracle's stewardship and we are all a bit nervous about how it will turn out. Oracle has been immensely helpful in getting us set up. I have a great team helping me: Kirk Pepperdine, Dario Laverde, Benjamin Evans and Martijn Verburg from jClarity, Nathan Reynolds from Oracle, Henri Tremblay of OCTO Technology and Jeff Genender of Savoir Technologies. Monday will be hard work, but after that, I will hopefully get to network with fellow Java experts, attend interesting sessions and just enjoy San Francisco. Oh, and my kids have already given me a shopping list of things to get, like a GoPro Hero 2 dive housing for shooting those nice videos of Crete. (That's me at the beginning diving down.) Q: What sessions are you attending that we should know about?A: Sometimes the most unusual sessions are the best. I avoid the "big names". They often are spread too thin with all their sessions, which makes it difficult for them to deliver what I would consider deep content. I also avoid entertainers who might be good at presenting but who do not say that much.In 2010, I attended a session by Vladimir Yaroslavskiy where he talked about sorting. Although he struggled to speak English, what he had to say was spectacular. There was hardly anybody in the room, having not heard of Vladimir before. To me that was the highlight of 2010. Funnily enough, he was supposed to speak with Joshua Bloch, but if you remember, Google cancelled. If Bloch has been there, the room would have been packed to capacity.Q: Give us an update on the Java Specialists’ Newsletter.A: The Java Specialists' Newsletter continues being read by an elite audience around the world. The apostrophe in the name is significant.  It is a newsletter for Java specialists. When I started it twelve years ago, I was trying to find non-obvious things in Java to write about. Things that would be interesting to an advanced audience.As an April Fool's joke, I told my readers in Issue 44 that subscribing would remain free, but that they would have to pay US$5 to US$7 depending on their geographical location. I received quite a few angry emails from that one. I would have not earned that much from unsubscriptions. Most readers stay for a very long time.After Oracle bought Sun, the Java community held its breath for about two years whilst Oracle was figuring out what to do with Java. For a while, we were quite concerned that there was not much progress shown by Oracle. My newsletter still continued, but it was quite difficult finding new things to write about. We have probably about 70,000 readers, which is quite a small number for a Java publication. However, our readers are the top in the Java industry. So I don't mind having "only" 70000 readers, as long as they are the top 0.7%.Java concurrency is a very important topic that programmers think they should know about, but often neglect to fully understand. I continued writing about that and made some interesting discoveries. For example, in Issue 165, I showed how we can get thread starvation with the ReadWriteLock. This was a bug in Java 5, which was corrected in Java 6, but perhaps a bit too much. Whereas we could get starvation of writers in Java 5, in Java 6 we could now get starvation of readers. All of these interesting findings make their way into my courseware to help companies avoid these pitfalls.Another interesting discovery was how polymorphism works in the Server HotSpot compiler in Issue 157 and Issue 158. HotSpot can inline methods from interfaces that have only one implementation class in the JVM. When a new subclass is instantiated and called for the first time, the JVM will undo the previous optimization and re-optimize differently.Here is a little memory puzzle for your readers: public class JavaMemoryPuzzle {  private final int dataSize =      (int) (Runtime.getRuntime().maxMemory() * 0.6);  public void f() {    {      byte[] data = new byte[dataSize];    }    byte[] data2 = new byte[dataSize];  }  public static void main(String[] args) {    JavaMemoryPuzzle jmp = new JavaMemoryPuzzle();    jmp.f();  }}When you run this you will always get an OutOfMemoryError, even though the local variable data is no longer visible outside of the code block.So here comes the puzzle, that I'd like you to ponder a bit. If you very politely ask the VM to release memory, then you don't get an OutOfMemoryError: public class JavaMemoryPuzzlePolite {  private final int dataSize =      (int) (Runtime.getRuntime().maxMemory() * 0.6);  public void f() {    {      byte[] data = new byte[dataSize];    }    for(int i=0; i<10; i++) {      System.out.println("Please be so kind and release memory");    }    byte[] data2 = new byte[dataSize];  }  public static void main(String[] args) {    JavaMemoryPuzzlePolite jmp = new JavaMemoryPuzzlePolite();    jmp.f();    System.out.println("No OutOfMemoryError");  }}Why does this work? When I published this in my newsletter, I received over 400 emails from excited readers around the world, most of whom sent me the wrong explanation. After the 300th wrong answer, my replies became unfortunately a bit curt. Have a look at Issue 174 for a detailed explanation, but before you do, put on your thinking caps and try to figure it out yourself. Q: What do you think Java developers should know that they currently do not know?A: They should definitely get to know more about concurrency. It is a tough subject that most programmers try to avoid. Unfortunately we do come in contact with it. And when we do, we need to know how to protect ourselves and how to solve tricky system errors.Knowing your IDE is also useful. Most IDEs have a ton of shortcuts, which can make you a lot more productive in moving code around. Another thing that is useful is being able to read GC logs. Kirk Pepperdine has a great talk at JavaOne that I can recommend if you want to learn more. It's this: CON5405 – “Are Your Garbage Collection Logs Speaking to You?” Q: What are you looking forward to in Java 8?A: I'm quite excited about lambdas, though I must confess that I have not studied them in detail yet. Maurice Naftalin's Lambda FAQ is quite a good start to document what you can do with them. I'm looking forward to finding all the interesting bugs that we will now get due to lambdas obscuring what is really going on underneath, just like we had with generics.I am quite impressed with what the team at Oracle did with OpenJDK's performance. A lot of the benchmarks now run faster.Hopefully Java 8 will come with JSR 310, the Date and Time API. It still boggles my mind that such an important API has been left out in the cold for so long.What I am not looking forward to is losing perm space. Even though some systems run out of perm space, at least the problem is contained and they usually manage to work around it. In most cases, this is due to a memory leak in that region of memory. Once they bundle perm space with the old generation, I predict that memory leaks in perm space will be harder to find. More contracts for us, but also more pain for our customers. Originally published on blogs.oracle.com/javaone.

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  • The Java Specialist: An Interview with Java Champion Heinz Kabutz

    - by Janice J. Heiss
    Dr. Heinz Kabutz is well known for his Java Specialists’ Newsletter, initiated in November 2000, where he displays his acute grasp of the intricacies of the Java platform for an estimated 70,000 readers; for his work as a consultant; and for his workshops and trainings at his home on the Island of Crete where he has lived since 2006 -- where he is known to curl up on the beach with his laptop to hack away, in between dips in the Mediterranean. Kabutz was born of German parents and raised in Cape Town, South Africa, where he developed a love of programming in junior high school through his explorations on a ZX Spectrum computer. He received a B.S. from the University of Cape Town, and at 25, a Ph.D., both in computer science. He will be leading a two-hour hands-on lab session, HOL6500 – “Finding and Solving Java Deadlocks,” at this year’s JavaOne that will explore what causes deadlocks and how to solve them. Q: Tell us about your JavaOne plans.A: I am arriving on Sunday evening and have just one hands-on-lab to do on Monday morning. This is the first time that a non-Oracle team is doing a HOL at JavaOne under Oracle's stewardship and we are all a bit nervous about how it will turn out. Oracle has been immensely helpful in getting us set up. I have a great team helping me: Kirk Pepperdine, Dario Laverde, Benjamin Evans and Martijn Verburg from jClarity, Nathan Reynolds from Oracle, Henri Tremblay of OCTO Technology and Jeff Genender of Savoir Technologies. Monday will be hard work, but after that, I will hopefully get to network with fellow Java experts, attend interesting sessions and just enjoy San Francisco. Oh, and my kids have already given me a shopping list of things to get, like a GoPro Hero 2 dive housing for shooting those nice videos of Crete. (That's me at the beginning diving down.) Q: What sessions are you attending that we should know about?A: Sometimes the most unusual sessions are the best. I avoid the "big names". They often are spread too thin with all their sessions, which makes it difficult for them to deliver what I would consider deep content. I also avoid entertainers who might be good at presenting but who do not say that much.In 2010, I attended a session by Vladimir Yaroslavskiy where he talked about sorting. Although he struggled to speak English, what he had to say was spectacular. There was hardly anybody in the room, having not heard of Vladimir before. To me that was the highlight of 2010. Funnily enough, he was supposed to speak with Joshua Bloch, but if you remember, Google cancelled. If Bloch has been there, the room would have been packed to capacity.Q: Give us an update on the Java Specialists’ Newsletter.A: The Java Specialists' Newsletter continues being read by an elite audience around the world. The apostrophe in the name is significant.  It is a newsletter for Java specialists. When I started it twelve years ago, I was trying to find non-obvious things in Java to write about. Things that would be interesting to an advanced audience.As an April Fool's joke, I told my readers in Issue 44 that subscribing would remain free, but that they would have to pay US$5 to US$7 depending on their geographical location. I received quite a few angry emails from that one. I would have not earned that much from unsubscriptions. Most readers stay for a very long time.After Oracle bought Sun, the Java community held its breath for about two years whilst Oracle was figuring out what to do with Java. For a while, we were quite concerned that there was not much progress shown by Oracle. My newsletter still continued, but it was quite difficult finding new things to write about. We have probably about 70,000 readers, which is quite a small number for a Java publication. However, our readers are the top in the Java industry. So I don't mind having "only" 70000 readers, as long as they are the top 0.7%.Java concurrency is a very important topic that programmers think they should know about, but often neglect to fully understand. I continued writing about that and made some interesting discoveries. For example, in Issue 165, I showed how we can get thread starvation with the ReadWriteLock. This was a bug in Java 5, which was corrected in Java 6, but perhaps a bit too much. Whereas we could get starvation of writers in Java 5, in Java 6 we could now get starvation of readers. All of these interesting findings make their way into my courseware to help companies avoid these pitfalls.Another interesting discovery was how polymorphism works in the Server HotSpot compiler in Issue 157 and Issue 158. HotSpot can inline methods from interfaces that have only one implementation class in the JVM. When a new subclass is instantiated and called for the first time, the JVM will undo the previous optimization and re-optimize differently.Here is a little memory puzzle for your readers: public class JavaMemoryPuzzle {  private final int dataSize =      (int) (Runtime.getRuntime().maxMemory() * 0.6);  public void f() {    {      byte[] data = new byte[dataSize];    }    byte[] data2 = new byte[dataSize];  }  public static void main(String[] args) {    JavaMemoryPuzzle jmp = new JavaMemoryPuzzle();    jmp.f();  }}When you run this you will always get an OutOfMemoryError, even though the local variable data is no longer visible outside of the code block.So here comes the puzzle, that I'd like you to ponder a bit. If you very politely ask the VM to release memory, then you don't get an OutOfMemoryError: public class JavaMemoryPuzzlePolite {  private final int dataSize =      (int) (Runtime.getRuntime().maxMemory() * 0.6);  public void f() {    {      byte[] data = new byte[dataSize];    }    for(int i=0; i<10; i++) {      System.out.println("Please be so kind and release memory");    }    byte[] data2 = new byte[dataSize];  }  public static void main(String[] args) {    JavaMemoryPuzzlePolite jmp = new JavaMemoryPuzzlePolite();    jmp.f();    System.out.println("No OutOfMemoryError");  }}Why does this work? When I published this in my newsletter, I received over 400 emails from excited readers around the world, most of whom sent me the wrong explanation. After the 300th wrong answer, my replies became unfortunately a bit curt. Have a look at Issue 174 for a detailed explanation, but before you do, put on your thinking caps and try to figure it out yourself. Q: What do you think Java developers should know that they currently do not know?A: They should definitely get to know more about concurrency. It is a tough subject that most programmers try to avoid. Unfortunately we do come in contact with it. And when we do, we need to know how to protect ourselves and how to solve tricky system errors.Knowing your IDE is also useful. Most IDEs have a ton of shortcuts, which can make you a lot more productive in moving code around. Another thing that is useful is being able to read GC logs. Kirk Pepperdine has a great talk at JavaOne that I can recommend if you want to learn more. It's this: CON5405 – “Are Your Garbage Collection Logs Speaking to You?” Q: What are you looking forward to in Java 8?A: I'm quite excited about lambdas, though I must confess that I have not studied them in detail yet. Maurice Naftalin's Lambda FAQ is quite a good start to document what you can do with them. I'm looking forward to finding all the interesting bugs that we will now get due to lambdas obscuring what is really going on underneath, just like we had with generics.I am quite impressed with what the team at Oracle did with OpenJDK's performance. A lot of the benchmarks now run faster.Hopefully Java 8 will come with JSR 310, the Date and Time API. It still boggles my mind that such an important API has been left out in the cold for so long.What I am not looking forward to is losing perm space. Even though some systems run out of perm space, at least the problem is contained and they usually manage to work around it. In most cases, this is due to a memory leak in that region of memory. Once they bundle perm space with the old generation, I predict that memory leaks in perm space will be harder to find. More contracts for us, but also more pain for our customers.

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  • SQL Server Database Settings

    - by rbishop
    For those using Data Relationship Management on Oracle DB this does not apply, but for those using Microsoft SQL Server it is highly recommended that you run with Snapshot Isolation Mode. The Data Governance module will not function correctly without this mode enabled. All new Data Relationship Management repositories are created with this mode enabled by default. This mode makes SQL Server (2005+) behave more like Oracle DB where readers simply see older versions of rows while a write is in progress, instead of readers being blocked by locks while a write takes place. Many common sources of deadlocks are eliminated. For example, if one user starts a 5 minute transaction updating half the rows in a table, without snapshot isolation everyone else reading the table will be blocked waiting. With snapshot isolation, they will see the rows as they were before the write transaction started. Conversely, if the readers had started first, the writer won't be stuck waiting for them to finish reading... the writes can begin immediately without affecting the current transactions. To make this change, make sure no one is using the target database (eg: put it into single-user mode), then run these commands: ALTER DATABASE [DB] SET ALLOW_SNAPSHOT_ISOLATION ONALTER DATABASE [DB] SET READ_COMMITTED_SNAPSHOT ON Please make sure you coordinate with your DBA team to ensure tempdb is appropriately setup to support snapshot isolation mode, as the extra row versions are stored in tempdb until the transactions are committed. Let me take this opportunity to extremely strongly highly recommend that you use solid state storage for your databases with appropriate iSCSI, FiberChannel, or SAN bandwidth. The performance gains are significant and there is no excuse for not using 100% solid state storage in 2013. Actually unless you need to store petabytes of archival data, there is no excuse for using hard drives in any systems, whether laptops, desktops, application servers, or database servers. The productivity benefits alone are tremendous, not to mention power consumption, heat, etc.

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  • How exactly to implement multiple threads in a game

    - by xerwin
    So I recently started learning Java, and having a interest in playing games as well as developing them, naturally I want to create game in Java. I have experience with games in C# and C++ but all of them were single-threaded simple games. But now, I learned how easy it is to make threads in Java, I want to take things to the next level. I started thinking about how would I actually implement threading in a game. I read couple of articles that say the same thing "Usually you have thread for rendering, for updating game logic, for AI, ..." but I haven't (or didn't look hard enough) found example of implementation. My idea how to make implementation is something like this (example for AI) public class AIThread implements Runnable{ private List<AI> ai; private Player player; /*...*/ public void run() { for (int i = 0; i < ai.size(); i++){ ai.get(i).update(player); } Thread.sleep(/* sleep until the next game "tick" */); } } I think this could work. If I also had a rendering and updating thread list of AI in both those threads, since I need to draw the AI and I need to calculate the logic between player and AI(But that could be moved to AIThread, but as an example) . Coming from C++ I'm used to do thing elegantly and efficiently, and this seems like neither of those. So what would be the correct way to handle this? Should I just keep multiple copies of resources in each thread or should I have the resources on one spot, declared with synchronized keyword? I'm afraid that could cause deadlocks, but I'm not yet qualified enough to know when a code will produce deadlock.

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  • How should I implement multiple threads in a game? [duplicate]

    - by xerwin
    This question already has an answer here: Multi-threaded games best practices. One thread for 'logic', one for rendering, or more? 6 answers So I recently started learning Java, and having a interest in playing games as well as developing them, naturally I want to create game in Java. I have experience with games in C# and C++ but all of them were single-threaded simple games. But now, I learned how easy it is to make threads in Java, I want to take things to the next level. I started thinking about how would I actually implement threading in a game. I read couple of articles that say the same thing "Usually you have thread for rendering, for updating game logic, for AI, ..." but I haven't (or didn't look hard enough) found example of implementation. My idea how to make implementation is something like this (example for AI) public class AIThread implements Runnable{ private List<AI> ai; private Player player; /*...*/ public void run() { for (int i = 0; i < ai.size(); i++){ ai.get(i).update(player); } Thread.sleep(/* sleep until the next game "tick" */); } } I think this could work. If I also had a rendering and updating thread list of AI in both those threads, since I need to draw the AI and I need to calculate the logic between player and AI(But that could be moved to AIThread, but as an example) . Coming from C++ I'm used to do thing elegantly and efficiently, and this seems like neither of those. So what would be the correct way to handle this? Should I just keep multiple copies of resources in each thread or should I have the resources on one spot, declared with synchronized keyword? I'm afraid that could cause deadlocks, but I'm not yet qualified enough to know when a code will produce deadlock.

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  • Inside the Concurrent Collections: ConcurrentDictionary

    - by Simon Cooper
    Using locks to implement a thread-safe collection is rather like using a sledgehammer - unsubtle, easy to understand, and tends to make any other tool redundant. Unlike the previous two collections I looked at, ConcurrentStack and ConcurrentQueue, ConcurrentDictionary uses locks quite heavily. However, it is careful to wield locks only where necessary to ensure that concurrency is maximised. This will, by necessity, be a higher-level look than my other posts in this series, as there is quite a lot of code and logic in ConcurrentDictionary. Therefore, I do recommend that you have ConcurrentDictionary open in a decompiler to have a look at all the details that I skip over. The problem with locks There's several things to bear in mind when using locks, as encapsulated by the lock keyword in C# and the System.Threading.Monitor class in .NET (if you're unsure as to what lock does in C#, I briefly covered it in my first post in the series): Locks block threads The most obvious problem is that threads waiting on a lock can't do any work at all. No preparatory work, no 'optimistic' work like in ConcurrentQueue and ConcurrentStack, nothing. It sits there, waiting to be unblocked. This is bad if you're trying to maximise concurrency. Locks are slow Whereas most of the methods on the Interlocked class can be compiled down to a single CPU instruction, ensuring atomicity at the hardware level, taking out a lock requires some heavy lifting by the CLR and the operating system. There's quite a bit of work required to take out a lock, block other threads, and wake them up again. If locks are used heavily, this impacts performance. Deadlocks When using locks there's always the possibility of a deadlock - two threads, each holding a lock, each trying to aquire the other's lock. Fortunately, this can be avoided with careful programming and structured lock-taking, as we'll see. So, it's important to minimise where locks are used to maximise the concurrency and performance of the collection. Implementation As you might expect, ConcurrentDictionary is similar in basic implementation to the non-concurrent Dictionary, which I studied in a previous post. I'll be using some concepts introduced there, so I recommend you have a quick read of it. So, if you were implementing a thread-safe dictionary, what would you do? The naive implementation is to simply have a single lock around all methods accessing the dictionary. This would work, but doesn't allow much concurrency. Fortunately, the bucketing used by Dictionary allows a simple but effective improvement to this - one lock per bucket. This allows different threads modifying different buckets to do so in parallel. Any thread making changes to the contents of a bucket takes the lock for that bucket, ensuring those changes are thread-safe. The method that maps each bucket to a lock is the GetBucketAndLockNo method: private void GetBucketAndLockNo( int hashcode, out int bucketNo, out int lockNo, int bucketCount) { // the bucket number is the hashcode (without the initial sign bit) // modulo the number of buckets bucketNo = (hashcode & 0x7fffffff) % bucketCount; // and the lock number is the bucket number modulo the number of locks lockNo = bucketNo % m_locks.Length; } However, this does require some changes to how the buckets are implemented. The 'implicit' linked list within a single backing array used by the non-concurrent Dictionary adds a dependency between separate buckets, as every bucket uses the same backing array. Instead, ConcurrentDictionary uses a strict linked list on each bucket: This ensures that each bucket is entirely separate from all other buckets; adding or removing an item from a bucket is independent to any changes to other buckets. Modifying the dictionary All the operations on the dictionary follow the same basic pattern: void AlterBucket(TKey key, ...) { int bucketNo, lockNo; 1: GetBucketAndLockNo( key.GetHashCode(), out bucketNo, out lockNo, m_buckets.Length); 2: lock (m_locks[lockNo]) { 3: Node headNode = m_buckets[bucketNo]; 4: Mutate the node linked list as appropriate } } For example, when adding another entry to the dictionary, you would iterate through the linked list to check whether the key exists already, and add the new entry as the head node. When removing items, you would find the entry to remove (if it exists), and remove the node from the linked list. Adding, updating, and removing items all follow this pattern. Performance issues There is a problem we have to address at this point. If the number of buckets in the dictionary is fixed in the constructor, then the performance will degrade from O(1) to O(n) when a large number of items are added to the dictionary. As more and more items get added to the linked lists in each bucket, the lookup operations will spend most of their time traversing a linear linked list. To fix this, the buckets array has to be resized once the number of items in each bucket has gone over a certain limit. (In ConcurrentDictionary this limit is when the size of the largest bucket is greater than the number of buckets for each lock. This check is done at the end of the TryAddInternal method.) Resizing the bucket array and re-hashing everything affects every bucket in the collection. Therefore, this operation needs to take out every lock in the collection. Taking out mutiple locks at once inevitably summons the spectre of the deadlock; two threads each hold a lock, and each trying to acquire the other lock. How can we eliminate this? Simple - ensure that threads never try to 'swap' locks in this fashion. When taking out multiple locks, always take them out in the same order, and always take out all the locks you need before starting to release them. In ConcurrentDictionary, this is controlled by the AcquireLocks, AcquireAllLocks and ReleaseLocks methods. Locks are always taken out and released in the order they are in the m_locks array, and locks are all released right at the end of the method in a finally block. At this point, it's worth pointing out that the locks array is never re-assigned, even when the buckets array is increased in size. The number of locks is fixed in the constructor by the concurrencyLevel parameter. This simplifies programming the locks; you don't have to check if the locks array has changed or been re-assigned before taking out a lock object. And you can be sure that when a thread takes out a lock, another thread isn't going to re-assign the lock array. This would create a new series of lock objects, thus allowing another thread to ignore the existing locks (and any threads controlling them), breaking thread-safety. Consequences of growing the array Just because we're using locks doesn't mean that race conditions aren't a problem. We can see this by looking at the GrowTable method. The operation of this method can be boiled down to: private void GrowTable(Node[] buckets) { try { 1: Acquire first lock in the locks array // this causes any other thread trying to take out // all the locks to block because the first lock in the array // is always the one taken out first // check if another thread has already resized the buckets array // while we were waiting to acquire the first lock 2: if (buckets != m_buckets) return; 3: Calculate the new size of the backing array 4: Node[] array = new array[size]; 5: Acquire all the remaining locks 6: Re-hash the contents of the existing buckets into array 7: m_buckets = array; } finally { 8: Release all locks } } As you can see, there's already a check for a race condition at step 2, for the case when the GrowTable method is called twice in quick succession on two separate threads. One will successfully resize the buckets array (blocking the second in the meantime), when the second thread is unblocked it'll see that the array has already been resized & exit without doing anything. There is another case we need to consider; looking back at the AlterBucket method above, consider the following situation: Thread 1 calls AlterBucket; step 1 is executed to get the bucket and lock numbers. Thread 2 calls GrowTable and executes steps 1-5; thread 1 is blocked when it tries to take out the lock in step 2. Thread 2 re-hashes everything, re-assigns the buckets array, and releases all the locks (steps 6-8). Thread 1 is unblocked and continues executing, but the calculated bucket and lock numbers are no longer valid. Between calculating the correct bucket and lock number and taking out the lock, another thread has changed where everything is. Not exactly thread-safe. Well, a similar problem was solved in ConcurrentStack and ConcurrentQueue by storing a local copy of the state, doing the necessary calculations, then checking if that state is still valid. We can use a similar idea here: void AlterBucket(TKey key, ...) { while (true) { Node[] buckets = m_buckets; int bucketNo, lockNo; GetBucketAndLockNo( key.GetHashCode(), out bucketNo, out lockNo, buckets.Length); lock (m_locks[lockNo]) { // if the state has changed, go back to the start if (buckets != m_buckets) continue; Node headNode = m_buckets[bucketNo]; Mutate the node linked list as appropriate } break; } } TryGetValue and GetEnumerator And so, finally, we get onto TryGetValue and GetEnumerator. I've left these to the end because, well, they don't actually use any locks. How can this be? Whenever you change a bucket, you need to take out the corresponding lock, yes? Indeed you do. However, it is important to note that TryGetValue and GetEnumerator don't actually change anything. Just as immutable objects are, by definition, thread-safe, read-only operations don't need to take out a lock because they don't change anything. All lockless methods can happily iterate through the buckets and linked lists without worrying about locking anything. However, this does put restrictions on how the other methods operate. Because there could be another thread in the middle of reading the dictionary at any time (even if a lock is taken out), the dictionary has to be in a valid state at all times. Every change to state has to be made visible to other threads in a single atomic operation (all relevant variables are marked volatile to help with this). This restriction ensures that whatever the reading threads are doing, they never read the dictionary in an invalid state (eg items that should be in the collection temporarily removed from the linked list, or reading a node that has had it's key & value removed before the node itself has been removed from the linked list). Fortunately, all the operations needed to change the dictionary can be done in that way. Bucket resizes are made visible when the new array is assigned back to the m_buckets variable. Any additions or modifications to a node are done by creating a new node, then splicing it into the existing list using a single variable assignment. Node removals are simply done by re-assigning the node's m_next pointer. Because the dictionary can be changed by another thread during execution of the lockless methods, the GetEnumerator method is liable to return dirty reads - changes made to the dictionary after GetEnumerator was called, but before the enumeration got to that point in the dictionary. It's worth listing at this point which methods are lockless, and which take out all the locks in the dictionary to ensure they get a consistent view of the dictionary: Lockless: TryGetValue GetEnumerator The indexer getter ContainsKey Takes out every lock (lockfull?): Count IsEmpty Keys Values CopyTo ToArray Concurrent principles That covers the overall implementation of ConcurrentDictionary. I haven't even begun to scratch the surface of this sophisticated collection. That I leave to you. However, we've looked at enough to be able to extract some useful principles for concurrent programming: Partitioning When using locks, the work is partitioned into independant chunks, each with its own lock. Each partition can then be modified concurrently to other partitions. Ordered lock-taking When a method does need to control the entire collection, locks are taken and released in a fixed order to prevent deadlocks. Lockless reads Read operations that don't care about dirty reads don't take out any lock; the rest of the collection is implemented so that any reading thread always has a consistent view of the collection. That leads us to the final collection in this little series - ConcurrentBag. Lacking a non-concurrent analogy, it is quite different to any other collection in the class libraries. Prepare your thinking hats!

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  • .NET Code Evolution

    - by Alois Kraus
    Originally posted on: http://geekswithblogs.net/akraus1/archive/2013/07/24/153504.aspxAt my day job I do look at a lot of code written by other people. Most of the code is quite good and some is even a masterpiece. And there is also code which makes you think WTF… oh it was written by me. Hm not so bad after all. There are many excuses reasons for bad code. Most often it is time pressure followed by not enough ambition (who cares) or insufficient training. Normally I do care about code quality quite a lot which makes me a (perceived) slow worker who does write many tests and refines the code quite a lot because of the design deficiencies. Most of the deficiencies I do find by putting my design under stress while checking for invariants. It does also help a lot to step into the code with a debugger (sometimes also Windbg). I do this much more often when my tests are red. That way I do get a much better understanding what my code really does and not what I think it should be doing. This time I do want to show you how code can evolve over the years with different .NET Framework versions. Once there was  time where .NET 1.1 was new and many C++ programmers did switch over to get rid of not initialized pointers and memory leaks. There were also nice new data structures available such as the Hashtable which is fast lookup table with O(1) time complexity. All was good and much code was written since then. At 2005 a new version of the .NET Framework did arrive which did bring many new things like generics and new data structures. The “old” fashioned way of Hashtable were coming to an end and everyone used the new Dictionary<xx,xx> type instead which was type safe and faster because the object to type conversion (aka boxing) was no longer necessary. I think 95% of all Hashtables and dictionaries use string as key. Often it is convenient to ignore casing to make it easy to look up values which the user did enter. An often followed route is to convert the string to upper case before putting it into the Hashtable. Hashtable Table = new Hashtable(); void Add(string key, string value) { Table.Add(key.ToUpper(), value); } This is valid and working code but it has problems. First we can pass to the Hashtable a custom IEqualityComparer to do the string matching case insensitive. Second we can switch over to the now also old Dictionary type to become a little faster and we can keep the the original keys (not upper cased) in the dictionary. Dictionary<string, string> DictTable = new Dictionary<string, string>(StringComparer.OrdinalIgnoreCase); void AddDict(string key, string value) { DictTable.Add(key, value); } Many people do not user the other ctors of Dictionary because they do shy away from the overhead of writing their own comparer. They do not know that .NET has for strings already predefined comparers at hand which you can directly use. Today in the many core area we do use threads all over the place. Sometimes things break in subtle ways but most of the time it is sufficient to place a lock around the offender. Threading has become so mainstream that it may sound weird that in the year 2000 some guy got a huge incentive for the idea to reduce the time to process calibration data from 12 hours to 6 hours by using two threads on a dual core machine. Threading does make it easy to become faster at the expense of correctness. Correct and scalable multithreading can be arbitrarily hard to achieve depending on the problem you are trying to solve. Lets suppose we want to process millions of items with two threads and count the processed items processed by all threads. A typical beginners code might look like this: int Counter; void IJustLearnedToUseThreads() { var t1 = new Thread(ThreadWorkMethod); t1.Start(); var t2 = new Thread(ThreadWorkMethod); t2.Start(); t1.Join(); t2.Join(); if (Counter != 2 * Increments) throw new Exception("Hmm " + Counter + " != " + 2 * Increments); } const int Increments = 10 * 1000 * 1000; void ThreadWorkMethod() { for (int i = 0; i < Increments; i++) { Counter++; } } It does throw an exception with the message e.g. “Hmm 10.222.287 != 20.000.000” and does never finish. The code does fail because the assumption that Counter++ is an atomic operation is wrong. The ++ operator is just a shortcut for Counter = Counter + 1 This does involve reading the counter from a memory location into the CPU, incrementing value on the CPU and writing the new value back to the memory location. When we do look at the generated assembly code we will see only inc dword ptr [ecx+10h] which is only one instruction. Yes it is one instruction but it is not atomic. All modern CPUs have several layers of caches (L1,L2,L3) which try to hide the fact how slow actual main memory accesses are. Since cache is just another word for redundant copy it can happen that one CPU does read a value from main memory into the cache, modifies it and write it back to the main memory. The problem is that at least the L1 cache is not shared between CPUs so it can happen that one CPU does make changes to values which did change in meantime in the main memory. From the exception you can see we did increment the value 20 million times but half of the changes were lost because we did overwrite the already changed value from the other thread. This is a very common case and people do learn to protect their  data with proper locking.   void Intermediate() { var time = Stopwatch.StartNew(); Action acc = ThreadWorkMethod_Intermediate; var ar1 = acc.BeginInvoke(null, null); var ar2 = acc.BeginInvoke(null, null); ar1.AsyncWaitHandle.WaitOne(); ar2.AsyncWaitHandle.WaitOne(); if (Counter != 2 * Increments) throw new Exception(String.Format("Hmm {0:N0} != {1:N0}", Counter, 2 * Increments)); Console.WriteLine("Intermediate did take: {0:F1}s", time.Elapsed.TotalSeconds); } void ThreadWorkMethod_Intermediate() { for (int i = 0; i < Increments; i++) { lock (this) { Counter++; } } } This is better and does use the .NET Threadpool to get rid of manual thread management. It does give the expected result but it can result in deadlocks because you do lock on this. This is in general a bad idea since it can lead to deadlocks when other threads use your class instance as lock object. It is therefore recommended to create a private object as lock object to ensure that nobody else can lock your lock object. When you read more about threading you will read about lock free algorithms. They are nice and can improve performance quite a lot but you need to pay close attention to the CLR memory model. It does make quite weak guarantees in general but it can still work because your CPU architecture does give you more invariants than the CLR memory model. For a simple counter there is an easy lock free alternative present with the Interlocked class in .NET. As a general rule you should not try to write lock free algos since most likely you will fail to get it right on all CPU architectures. void Experienced() { var time = Stopwatch.StartNew(); Task t1 = Task.Factory.StartNew(ThreadWorkMethod_Experienced); Task t2 = Task.Factory.StartNew(ThreadWorkMethod_Experienced); t1.Wait(); t2.Wait(); if (Counter != 2 * Increments) throw new Exception(String.Format("Hmm {0:N0} != {1:N0}", Counter, 2 * Increments)); Console.WriteLine("Experienced did take: {0:F1}s", time.Elapsed.TotalSeconds); } void ThreadWorkMethod_Experienced() { for (int i = 0; i < Increments; i++) { Interlocked.Increment(ref Counter); } } Since time does move forward we do not use threads explicitly anymore but the much nicer Task abstraction which was introduced with .NET 4 at 2010. It is educational to look at the generated assembly code. The Interlocked.Increment method must be called which does wondrous things right? Lets see: lock inc dword ptr [eax] The first thing to note that there is no method call at all. Why? Because the JIT compiler does know very well about CPU intrinsic functions. Atomic operations which do lock the memory bus to prevent other processors to read stale values are such things. Second: This is the same increment call prefixed with a lock instruction. The only reason for the existence of the Interlocked class is that the JIT compiler can compile it to the matching CPU intrinsic functions which can not only increment by one but can also do an add, exchange and a combined compare and exchange operation. But be warned that the correct usage of its methods can be tricky. If you try to be clever and look a the generated IL code and try to reason about its efficiency you will fail. Only the generated machine code counts. Is this the best code we can write? Perhaps. It is nice and clean. But can we make it any faster? Lets see how good we are doing currently. Level Time in s IJustLearnedToUseThreads Flawed Code Intermediate 1,5 (lock) Experienced 0,3 (Interlocked.Increment) Master 0,1 (1,0 for int[2]) That lock free thing is really a nice thing. But if you read more about CPU cache, cache coherency, false sharing you can do even better. int[] Counters = new int[12]; // Cache line size is 64 bytes on my machine with an 8 way associative cache try for yourself e.g. 64 on more modern CPUs void Master() { var time = Stopwatch.StartNew(); Task t1 = Task.Factory.StartNew(ThreadWorkMethod_Master, 0); Task t2 = Task.Factory.StartNew(ThreadWorkMethod_Master, Counters.Length - 1); t1.Wait(); t2.Wait(); Counter = Counters[0] + Counters[Counters.Length - 1]; if (Counter != 2 * Increments) throw new Exception(String.Format("Hmm {0:N0} != {1:N0}", Counter, 2 * Increments)); Console.WriteLine("Master did take: {0:F1}s", time.Elapsed.TotalSeconds); } void ThreadWorkMethod_Master(object number) { int index = (int) number; for (int i = 0; i < Increments; i++) { Counters[index]++; } } The key insight here is to use for each core its own value. But if you simply use simply an integer array of two items, one for each core and add the items at the end you will be much slower than the lock free version (factor 3). Each CPU core has its own cache line size which is something in the range of 16-256 bytes. When you do access a value from one location the CPU does not only fetch one value from main memory but a complete cache line (e.g. 16 bytes). This means that you do not pay for the next 15 bytes when you access them. This can lead to dramatic performance improvements and non obvious code which is faster although it does have many more memory reads than another algorithm. So what have we done here? We have started with correct code but it was lacking knowledge how to use the .NET Base Class Libraries optimally. Then we did try to get fancy and used threads for the first time and failed. Our next try was better but it still had non obvious issues (lock object exposed to the outside). Knowledge has increased further and we have found a lock free version of our counter which is a nice and clean way which is a perfectly valid solution. The last example is only here to show you how you can get most out of threading by paying close attention to your used data structures and CPU cache coherency. Although we are working in a virtual execution environment in a high level language with automatic memory management it does pay off to know the details down to the assembly level. Only if you continue to learn and to dig deeper you can come up with solutions no one else was even considering. I have studied particle physics which does help at the digging deeper part. Have you ever tried to solve Quantum Chromodynamics equations? Compared to that the rest must be easy ;-). Although I am no longer working in the Science field I take pride in discovering non obvious things. This can be a very hard to find bug or a new way to restructure data to make something 10 times faster. Now I need to get some sleep ….

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  • Writing an Iron Python debugger

    - by Kragen
    As a learning exercise I'm writing myself a simple extension / plugin / macro framework using IronPython - I've gotten the basics working but I'd like to add some basic debugging support to make my script editor easier to work with. I've been hunting around on the internet a bit and I've found a couple of good resources on writing managed debuggers (including Mike Stall's excellent .Net Debugging blog and the MSDN documentaiton on the CLR Debugging API) - I understand that IronPython is essentially IL however apart from that I'm a tad lost on how to get started, in particular: Are there any significant differences between debugging a dynamic language (such as IronPython) to a static one (such as C#)? Do I need to execute my script in a special way to get IronPython to output suitable debugging information? Is debugging a script running inside the current process going to cause deadlocks, or does IronPython execute my script in a child process? Am I better off looking into how to produce a simple C# debugger first to get the general idea? (I'm not interested in the GUI aspect of making a debugger for now - I've already got a pretty good idea of how this might work)

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  • Deciding between Apache Commons exec or ProcessBuilder

    - by Moev4
    I am trying to decide as to whether to use ProcessBuilder or Commons exec, My requirements are that I am simply trying to create a daemon process whose stdout/stdin/stderr I do not care about. In addition I want to execute a kill to destroy this process when the time comes. I am using Java on Linux. I know that both have their pains and pitfalls (such as being sure to use separate thread to swallow streams can lead to blocking or deadlocks, and closing the streams so not to leave open files hanging around)and wanted to know if anyone had suggestions one way or the other as well as any good resources to follow.

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  • What static analysis tools are available for C#?

    - by Paul Mrozowski
    What tools are there available for static analysis against C# code? I know about FxCop and StyleCop. Are there others? I've run across NStatic before but it's been in development for what seems like forever - it's looking pretty slick from what little I've seen of it, so it would be nice if it would ever see the light of day. Along these same lines (this is primarily my interest for static analysis), tools for testing code for multithreading issues (deadlocks, race conditions, etc.) also seem a bit scarce. Typemock Racer just popped up so I'll be looking at that. Anything beyond this? Real-life opinions about tools you've used are appreciated.

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  • What threading analysis tools do you recommend?

    - by glutz78
    My primary IDE is Visual Studio 2005 and I have a large C/C++ project. I'm interested in what thread analysis tools are recommended. By that I mean, I want a tool, static or dynamic, to help find race conditions, deadlocks, and the like. So far I've casually researched the following: 1. Intel Thread Checker: I don't believe that it ties into VS 2005? 2. Valgrind/Helgrind: free. 3. Coverity: this is a costly tool if i understand correctly. Anyone have experience with any of these or other? I'd much appreciate any advice. Thank you.

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  • Lock Question - When is an Update (U) lock issued?

    - by Randy Minder
    We are trying to resolve a deadlock problem. The transaction that is getting rolled back is attempting to issue an Update (U) lock on a resource that another transaction has an Exclusive (X) lock on. According to Books Online (http://msdn.microsoft.com/en-us/library/ms175519.aspx), an Update lock is supposed to prevent deadlocks, not cause them. So, my question is, why/when is an Update lock applied to a resource? We're a little confused about this because the resource that is attempting to have the Update lock applied to will not be updated by the process that is having the transaction rolled back. Thanks for your help on this. Randy

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  • Problems when going from SQL 2005 to SQL 2008

    - by Nezdet
    Hi! I did go over from SQL server 2005 to 2008. Doing that gave me some problems with the fulltext search. This site is based on Fulltext search. It occurs more deadlocks, the search is slower and sometimes it return empty lists, don't know why. A lot of people has been writning about they having this problem with 2008. But I haven'tgot any solutions why 2005 worked better for my program.. PLS help me out!

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  • sql server deadlock issue on a single table..................

    - by devendra
    I have one table let's say "xxx "in sql server 2000 . One .exe is inserting data into that table "xxx" through sql job. But once data is inserted , one stored procedure is reading the data from that "xxx table "and inserting/updating into other two tables and updating back the status into the same "xxx" table. Now, client says that multiple deadlocks are occuring on that "xxx" table . please kindly anyone sdvice me the resolution steps to be taken to resolve this deadlock issue and how to identify it step by step............. Thanks in advance..... XXX

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  • Unresponsive UI when using BeginInvoke

    - by Kazoom
    Bckground I have a networked application written in C#. my server program has a UI and several communication threads, that read from tcp sockets and display messages on controller UI. Communication with each client is done through a seprate thread. When i recieve some stream of messages from one client , the thread for that client writes on UI, which is a richtextbox on a Form. I call SetTextHelper(string text) method of the form. which looks like this private delegate void MyTextUpdateHandler(string text); public void SetTextHelper(string text) { BeginInvoke(new MyTextUpdateHandler(SetText), new object[] { text }); } public setText(string text) { richtext.Text= text; } Question - If i use BeginInvoke my UI is entirely unresponsive when i m writing large stream of data to UI - Invoke solves that problem, but i read that for multi threaded environment where many thereads are sharing same resource Invoke can lead to deadlocks I share the common ichtextbox between around 16 threads - What would be a good desing for my situation?

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  • Write to pipe deadlocking program

    - by avs3323
    Hi, I am having a problem in my program that uses pipes. What I am doing is using pipes along with fork/exec to send data to another process What I have is something like this: //pipes are created up here if(fork() == 0) //child process { ... execlp(...); } else { ... fprintf(stderr, "Writing to pipe now\n"); write(pipe, buffer, BUFFER_SIZE); fprintf(stderr, "Wrote to pipe!"); ... } This works fine for most messages, but when the message is very large, the write into the pipe deadlocks. I think the pipe might be full, but I do not know how to clear it. I tried using fsync but that didn't work. Can anyone help me?

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  • Emulating a transaction-safe SEQUENCE in MySQL

    - by Michael Pliskin
    We're using MySQL with InnoDB storage engine and transactions a lot, and we've run into a problem: we need a nice way to emulate Oracle's SEQUENCEs in MySQL. The requirements are: - concurrency support - transaction safety - max performance (meaning minimizing locks and deadlocks) We don't care if some of the values won't be used, i.e. gaps in sequence are ok. There is an easy way to archieve that by creating a separate InnoDB table with a counter, however this means it will take part in transaction and will introduce locks and waiting. I am thinking to try a MyISAM table with manual locks, any other ideas or best practices?

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  • HPC Server Dynamic Job Scheduling: when jobs spawn jobs

    - by JoshReuben
    HPC Job Types HPC has 3 types of jobs http://technet.microsoft.com/en-us/library/cc972750(v=ws.10).aspx · Task Flow – vanilla sequence · Parametric Sweep – concurrently run multiple instances of the same program, each with a different work unit input · MPI – message passing between master & slave tasks But when you try go outside the box – job tasks that spawn jobs, blocking the parent task – you run the risk of resource starvation, deadlocks, and recursive, non-converging or exponential blow-up. The solution to this is to write some performance monitoring and job scheduling code. You can do this in 2 ways: manually control scheduling - allocate/ de-allocate resources, change job priorities, pause & resume tasks , restrict long running tasks to specific compute clusters Semi-automatically - set threshold params for scheduling. How – Control Job Scheduling In order to manage the tasks and resources that are associated with a job, you will need to access the ISchedulerJob interface - http://msdn.microsoft.com/en-us/library/microsoft.hpc.scheduler.ischedulerjob_members(v=vs.85).aspx This really allows you to control how a job is run – you can access & tweak the following features: max / min resource values whether job resources can grow / shrink, and whether jobs can be pre-empted, whether the job is exclusive per node the creator process id & the job pool timestamp of job creation & completion job priority, hold time & run time limit Re-queue count Job progress Max/ min Number of cores, nodes, sockets, RAM Dynamic task list – can add / cancel jobs on the fly Job counters When – poll perf counters Tweaking the job scheduler should be done on the basis of resource utilization according to PerfMon counters – HPC exposes 2 Perf objects: Compute Clusters, Compute Nodes http://technet.microsoft.com/en-us/library/cc720058(v=ws.10).aspx You can monitor running jobs according to dynamic thresholds – use your own discretion: Percentage processor time Number of running jobs Number of running tasks Total number of processors Number of processors in use Number of processors idle Number of serial tasks Number of parallel tasks Design Your algorithms correctly Finally , don’t assume you have unlimited compute resources in your cluster – design your algorithms with the following factors in mind: · Branching factor - http://en.wikipedia.org/wiki/Branching_factor - dynamically optimize the number of children per node · cutoffs to prevent explosions - http://en.wikipedia.org/wiki/Limit_of_a_sequence - not all functions converge after n attempts. You also need a threshold of good enough, diminishing returns · heuristic shortcuts - http://en.wikipedia.org/wiki/Heuristic - sometimes an exhaustive search is impractical and short cuts are suitable · Pruning http://en.wikipedia.org/wiki/Pruning_(algorithm) – remove / de-prioritize unnecessary tree branches · avoid local minima / maxima - http://en.wikipedia.org/wiki/Local_minima - sometimes an algorithm cant converge because it gets stuck in a local saddle – try simulated annealing, hill climbing or genetic algorithms to get out of these ruts   watch out for rounding errors – http://en.wikipedia.org/wiki/Round-off_error - multiple iterations can in parallel can quickly amplify & blow up your algo ! Use an epsilon, avoid floating point errors,  truncations, approximations Happy Coding !

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