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  • Apache2 benchmarks - very poor performance

    - by andrzejp
    I have two servers on which I test the configuration of apache2. The first server: 4GB of RAM, AMD Athlon (tm) 64 X2 Dual Core Processor 5600 + Apache 2.2.3, mod_php, mpm prefork: Settings: Timeout 100 KeepAlive On MaxKeepAliveRequests 150 KeepAliveTimeout 4 <IfModule Mpm_prefork_module> StartServers 7 MinSpareServers 15 MaxSpareServers 30 MaxClients 250 MaxRequestsPerChild 2000 </ IfModule> Compiled in modules: core.c mod_log_config.c mod_logio.c prefork.c http_core.c mod_so.c Second server: 8GB of RAM, Intel (R) Core (TM) i7 CPU [email protected] Apache 2.2.9, **fcgid, mpm worker, suexec** PHP scripts are running via fcgi-wrapper Settings: Timeout 100 KeepAlive On MaxKeepAliveRequests 100 KeepAliveTimeout 4 <IfModule Mpm_worker_module> StartServers 10 MaxClients 200 MinSpareThreads 25 MaxSpareThreads 75 ThreadsPerChild 25 MaxRequestsPerChild 1000 </ IfModule> Compiled in modules: core.c mod_log_config.c mod_logio.c worker.c http_core.c mod_so.c The following test results, which are very strange! New server (dynamic content - php via fcgid+suexec): Server Software: Apache/2.2.9 Server Hostname: XXXXXXXX Server Port: 80 Document Path: XXXXXXX Document Length: 179512 bytes Concurrency Level: 10 Time taken for tests: 0.26276 seconds Complete requests: 1000 Failed requests: 0 Total transferred: 179935000 bytes HTML transferred: 179512000 bytes Requests per second: 38.06 Transfer rate: 6847.88 kb/s received Connnection Times (ms) min avg max Connect: 2 4 54 Processing: 161 257 449 Total: 163 261 503 Old server (dynamic content - mod_php): Server Software: Apache/2.2.3 Server Hostname: XXXXXX Server Port: 80 Document Path: XXXXXX Document Length: 187537 bytes Concurrency Level: 10 Time taken for tests: 173.073 seconds Complete requests: 1000 Failed requests: 22 (Connect: 0, Length: 22, Exceptions: 0) Total transferred: 188003372 bytes HTML transferred: 187546372 bytes Requests per second: 5777.91 Transfer rate: 1086267.40 kb/s received Connnection Times (ms) min avg max Connect: 3 3 28 Processing: 298 1724 26615 Total: 301 1727 26643 Old server: Static content (jpg file) Server Software: Apache/2.2.3 Server Hostname: xxxxxxxxx Server Port: 80 Document Path: /images/top2.gif Document Length: 40486 bytes Concurrency Level: 100 Time taken for tests: 3.558 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 Total transferred: 40864400 bytes HTML transferred: 40557482 bytes Requests per second: 281.09 [#/sec] (mean) Time per request: 355.753 [ms] (mean) Time per request: 3.558 [ms] (mean, across all concurrent requests) Transfer rate: 11217.51 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 3 11 4.5 12 23 Processing: 40 329 61.4 339 1009 Waiting: 6 282 55.2 293 737 Total: 43 340 63.0 351 1020 New server - static content (jpg file) Server Software: Apache/2.2.9 Server Hostname: XXXXX Server Port: 80 Document Path: /images/top2.gif Document Length: 40486 bytes Concurrency Level: 100 Time taken for tests: 3.571531 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 Total transferred: 41282792 bytes HTML transferred: 41030080 bytes Requests per second: 279.99 [#/sec] (mean) Time per request: 357.153 [ms] (mean) Time per request: 3.572 [ms] (mean, across all concurrent requests) Transfer rate: 11287.88 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 2 63 24.8 66 119 Processing: 124 278 31.8 282 391 Waiting: 3 70 28.5 66 164 Total: 126 341 35.9 350 443 I noticed that in the apache error.log is a lot of entries: [notice] mod_fcgid: call /www/XXXXX/public_html/forum/index.php with wrapper /www/php-fcgi-scripts/XXXXXX/php-fcgi-starter What I have omitted, or do not understand? Such a difference in requests per second? Is it possible? What could be the cause?

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  • Delete throws "deleted object would be re-saved by cascade"

    - by Greg
    I have following model: <class name="Person" table="Person" optimistic-lock="version"> <id name="Id" type="Int32" unsaved-value="0"> <generator class="native" /> </id> <!-- plus some properties here --> </class> <class name="Event" table="Event" optimistic-lock="version"> <id name="Id" type="Int32" unsaved-value="0"> <generator class="native" /> </id> <!-- plus some properties here --> </class> <class name="PersonEventRegistration" table="PersonEventRegistration" optimistic-lock="version"> <id name="Id" type="Int32" unsaved-value="0"> <generator class="native" /> </id> <property name="IsComplete" type="Boolean" not-null="true" /> <property name="RegistrationDate" type="DateTime" not-null="true" /> <many-to-one name="Person" class="Person" column="PersonId" foreign-key="FK_PersonEvent_PersonId" cascade="all-delete-orphan" /> <many-to-one name="Event" class="Event" column="EventId" foreign-key="FK_PersonEvent_EventId" cascade="all-delete-orphan" /> </class> There are no properties pointing to PersonEventRegistration either in Person nor in Event. When I try to delete an entry from PersonEventRegistration, I get the following error: "deleted object would be re-saved by cascade" The problem is, I don't store this object in any other collection - the delete code looks like this: public bool UnregisterFromEvent(Person person, Event entry) { var registrationEntry = this.session .CreateCriteria<PersonEventRegistration>() .Add(Restrictions.Eq("Person", person)) .Add(Restrictions.Eq("Event", entry)) .Add(Restrictions.Eq("IsComplete", false)) .UniqueResult<PersonEventRegistration>(); bool result = false; if (null != registrationEntry) { using (ITransaction tx = this.session.BeginTransaction()) { this.session.Delete(registrationEntry); tx.Commit(); result = true; } } return result; } What am I doing wrong here?

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  • Is there a quasi-standard set of attributes to annotate thread safety, immutability etc.?

    - by Eugene Beresovksy
    Except for a blog post here and there, describing the custom attributes someone created, but that do not seem to get any traction - like one describing how to enforce immutability, another one on Documenting Thread Safety, modeling the attributes after JCIP annotations - is there any standard emerging? Anything MS might be planning for the future? This is something that should be standard, if there's to be any chance of interoperability between libraries concurrency-wise. Both for documentation purposes, and also to feed static / dynamic test tools. If MS isn't doing anything in that direction, it could be done on CodePlex - but I couldn't find anything there, either. <opinion>Concurrency and thread safety are really hard in imperative and object-languages like C# and Java, we should try to tame it, until we hopefully switch to more appropriate languages.</opinion>

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  • Utility Objects–Waitfor Delay Coordinator (SQL Server 2008+)

    - by drsql
    Finally… took longer than I had expected when I wrote this a while back, but I had to move my website and get DNS moved before I could post code… When I write code, I do my best to test that code in as many ways as necessary. One of the last types of tests that is necessary is concurrency testing. Concurrency testing is one of the most difficult types of testing because it takes running multiple processes simultaneously and making sure that you get the correct answers multiple times. This is really...(read more)

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  • How can I designed multi-threaded application for larger user base

    - by rokonoid
    Here's my scenario, I need to develop a scalable application. My user base may be over 100K, every user has 10 or more small tasks. Tasks check every minute with a third party application through web services, retrieving data which is written in the database, then the data is forwarded to it's original destination. So here's the processing of a small task: while(true){ Boolean isNewInformationAvailable = checkWhetherInformationIsAvailableOrNot(); If(isNewInformationAvailable ==true){ fetchTheData(); writeToDatabase(); findTheDestination(); deliverTheData(); isAvailable =false; } } Here is the question: as the application is large, how should I approach designing this. I'm going to use Java to write it. Should I use concurrency, and how would you manage the concurrency?

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  • How can I design multi-threaded application for larger user base

    - by rokonoid
    Here's my scenario, I need to develop a scalable application. My user base may be over 100K, every user has 10 or more small tasks. Tasks check every minute with a third party application through web services, retrieving data which is written in the database, then the data is forwarded to it's original destination. So here's the processing of a small task: while(true){ Boolean isNewInformationAvailable = checkWhetherInformationIsAvailableOrNot(); If(isNewInformationAvailable ==true){ fetchTheData(); writeToDatabase(); findTheDestination(); deliverTheData(); isAvailable =false; } } As the application is large, how should I approach designing this? I'm going to use Java to write it. Should I use concurrency, and how would you manage the concurrency?

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  • Insurance Outlook: Just Right of Center

    - by Chuck Johnston Admin
    On Tuesday June 21st, PwC lead a session at the International Insurance Society meeting in Toronto focused on the opportunity in insurance.  The scenarios focusing on globalization, regulation and new areas of insurance opportunity were well defined and thought provoking, but the most interesting part of the session was the audience participation. PwC used a favorite strategic planning tool of mine, scenario planning, to highlight the important financial, political, social and technological dimensions that impact the insurance industry. Using wireless polling keypads, the audience was able to participate in scoring a range of possibilities across each dimension using a 1 to 5 ranking; 1 being generally negative or highly pessimistic scenarios and 5 being very positive or more confident scenarios. The results were then displayed on a screen with a line or "center" in the middle. "Left of center" was defined as being highly cautious and conservative, while "right of center" was defined as a more optimistic outlook for the industry's future. This session was attended by insurance carriers' senior leadership, leading insurance academics, senior regulators, and the occasional insurance technology executive. In general, the average answer fell just right of center, i.e. a little more positive or optimistic than center. Three years ago, after the 2008 financial crisis, I suspect the answers would have skewed more sharply to the left of center. This sense that things are generally getting better for insurers and that there is the potential for positive change pervaded the conference. There is still caution and concern around economic factors, regulation (especially the potential pitfalls of regulatory convergence with banking) and talent management, but in general, the industry outlook is more positive than it's been in several years. Chuck Johnston is vice president of industry strategy, Oracle Insurance. 

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  • Performance issues when using SSD for a developer notebook (WAMP/LAMP stack)?

    - by András Szepesházi
    I'm a web application developer using my notebook as a standalone development environment (WAMP stack). I just switched from a Core2-duo Vista 32 bit notebook with 2Gb RAM and SATA HDD, to an i5-2520M Win7 64 bit with 4Gb RAM and 128 GB SDD (Corsair P3 128). My initial experience was what I expected, fast boot, quick load of all the applications (Eclipse takes now 5 seconds as opposed to 30s on my old notebook), overall great experience. Then I started to build up my development stack, both as LAMP (using VirtualBox with a debian guest) and WAMP (windows native apache + mysql + php). I wanted to compare those two. This still all worked great out, then I started to pull in my projects to these stacks. And here came the nasty surprise, one of those projects produced a lot worse response times than on my old notebook (that was true for both the VirtualBox and WAMP stack). Apache, php and mysql configurations were practically identical in all environments. I started to do a lot of benchmarking and profiling, and here is what I've found: All general benchmarks (Performance Test 7.0, HDTune Pro, wPrime2 and some more) gave a big advantage to the new notebook. Nothing surprising here. Disc specific tests showed that read/write operations peaked around 380M/160M for the SSD, and all the different sized block operations also performed very well. Started apache performance benchmarking with Apache Benchmark for a small static html file (10 concurrent threads, 500 iterations). Old notebook: min 47ms, median 111ms, max 156ms New WAMP stack: min 71ms, median 135ms, max 296ms New LAMP stack (in VirtualBox): min 6ms, median 46ms, max 175ms Right here I don't get why the native WAMP stack performed so bad, but at least the LAMP environment brought the expected speed. Apache performance measurement for non-cached php content. The php runs a loop of 1000 and generates sha1(uniqid()) inisde. Again, 10 concurrent threads, 500 iterations were used for the benchmark. Old notebook: min 0ms, median 39ms, max 218ms New WAMP stack: min 20ms, median 61ms, max 186ms New LAMP stack (in VirtualBox): min 124ms, median 704ms, max 2463ms What the hell? The new LAMP performed miserably, and even the new native WAMP was outperformed by the old notebook. php + mysql test. The test consists of connecting to a database and reading a single record form a table using INNER JOIN on 3 more (indexed) tables, repeated 100 times within a loop. Databases were identical. 10 concurrent threads, 100 iterations were used for the benchmark. Old notebook: min 1201ms, median 1734ms, max 3728ms New WAMP stack: min 367ms, median 675ms, max 1893ms New LAMP stack (in VirtualBox): min 1410ms, median 3659ms, max 5045ms And the same test with concurrency set to 1 (instead of 10): Old notebook: min 1201ms, median 1261ms, max 1357ms New WAMP stack: min 399ms, median 483ms, max 539ms New LAMP stack (in VirtualBox): min 285ms, median 348ms, max 444ms Strictly for my purposes, as I'm using a self contained development environment (= low concurrency) I could be satisfied with the second test's result. Though I have no idea why the VirtualBox environment performed so bad with higher concurrency. Finally I performed a test of including many php files. The application that I mentioned at the beginning, the one that was performing so bad, has a heavy bootstrap, loads hundreds of small library and configuration files while initializing. So this test does nothing else just includes about 100 files. Concurrency set to 1, 100 iterations: Old notebook: min 140ms, median 168ms, max 406ms New WAMP stack: min 434ms, median 488ms, max 604ms New LAMP stack (in VirtualBox): min 413ms, median 1040ms, max 1921ms Even if I consider that VirtualBox reached those files via shared folders, and that slows things down a bit, I still don't see how could the old notebook outperform so heavily both new configurations. And I think this is the real root of the slow performance, as the application uses even more includes, and the whole bootstrap will occur several times within a page request (for each ajax call, for example). To sum it up, here I am with a brand new high-performance notebook that loads the same page in 20 seconds, that my old notebook can do in 5-7 seconds. Needless to say, I'm not a very happy person right now. Why do you think I experience these poor performance values? What are my options to remedy this situation?

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  • OSError : [Errno 38] Function not implemented - Django Celery implementation

    - by Jordan Messina
    I installed django-celery and I tried to start up the worker server but I get an OSError that a function isn't implemented. I'm running CentOS release 5.4 (Final) on a VPS: . broker -> amqp://guest@localhost:5672/ . queues -> . celery -> exchange:celery (direct) binding:celery . concurrency -> 4 . loader -> djcelery.loaders.DjangoLoader . logfile -> [stderr]@WARNING . events -> OFF . beat -> OFF [2010-07-22 17:10:01,364: WARNING/MainProcess] Traceback (most recent call last): [2010-07-22 17:10:01,364: WARNING/MainProcess] File "manage.py", line 11, in <module> [2010-07-22 17:10:01,364: WARNING/MainProcess] execute_manager(settings) [2010-07-22 17:10:01,364: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/__init__.py", line 438, in execute_manager [2010-07-22 17:10:01,364: WARNING/MainProcess] utility.execute() [2010-07-22 17:10:01,364: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/__init__.py", line 379, in execute [2010-07-22 17:10:01,365: WARNING/MainProcess] self.fetch_command(subcommand).run_from_argv(self.argv) [2010-07-22 17:10:01,365: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/base.py", line 191, in run_from_argv [2010-07-22 17:10:01,365: WARNING/MainProcess] self.execute(*args, **options.__dict__) [2010-07-22 17:10:01,365: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django/core/management/base.py", line 218, in execute [2010-07-22 17:10:01,365: WARNING/MainProcess] output = self.handle(*args, **options) [2010-07-22 17:10:01,365: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/django_celery-2.0.0-py2.6.egg/djcelery/management/commands/celeryd.py", line 22, in handle [2010-07-22 17:10:01,366: WARNING/MainProcess] run_worker(**options) [2010-07-22 17:10:01,366: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/bin/celeryd.py", line 385, in run_worker [2010-07-22 17:10:01,366: WARNING/MainProcess] return Worker(**options).run() [2010-07-22 17:10:01,366: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/bin/celeryd.py", line 218, in run [2010-07-22 17:10:01,366: WARNING/MainProcess] self.run_worker() [2010-07-22 17:10:01,366: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/bin/celeryd.py", line 312, in run_worker [2010-07-22 17:10:01,367: WARNING/MainProcess] worker.start() [2010-07-22 17:10:01,367: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/worker/__init__.py", line 206, in start [2010-07-22 17:10:01,367: WARNING/MainProcess] component.start() [2010-07-22 17:10:01,367: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/concurrency/processes/__init__.py", line 54, in start [2010-07-22 17:10:01,367: WARNING/MainProcess] maxtasksperchild=self.maxtasksperchild) [2010-07-22 17:10:01,367: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/concurrency/processes/pool.py", line 448, in __init__ [2010-07-22 17:10:01,368: WARNING/MainProcess] self._setup_queues() [2010-07-22 17:10:01,368: WARNING/MainProcess] File "/usr/local/lib/python2.6/site-packages/celery-2.0.1-py2.6.egg/celery/concurrency/processes/pool.py", line 564, in _setup_queues [2010-07-22 17:10:01,368: WARNING/MainProcess] self._inqueue = SimpleQueue() [2010-07-22 17:10:01,368: WARNING/MainProcess] File "/usr/local/lib/python2.6/multiprocessing/queues.py", line 315, in __init__ [2010-07-22 17:10:01,368: WARNING/MainProcess] self._rlock = Lock() [2010-07-22 17:10:01,368: WARNING/MainProcess] File "/usr/local/lib/python2.6/multiprocessing/synchronize.py", line 117, in __init__ [2010-07-22 17:10:01,369: WARNING/MainProcess] SemLock.__init__(self, SEMAPHORE, 1, 1) [2010-07-22 17:10:01,369: WARNING/MainProcess] File "/usr/local/lib/python2.6/multiprocessing/synchronize.py", line 49, in __init__ [2010-07-22 17:10:01,369: WARNING/MainProcess] sl = self._semlock = _multiprocessing.SemLock(kind, value, maxvalue) [2010-07-22 17:10:01,369: WARNING/MainProcess] OSError [2010-07-22 17:10:01,369: WARNING/MainProcess] : [2010-07-22 17:10:01,369: WARNING/MainProcess] [Errno 38] Function not implemented Am I just totally screwed and should use a new kernel that has this implemented or is there an easy way to resolve this?

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  • Coherence Data Guarantees for Data Reads - Basic Terminology

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

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  • The case of the phantom ADF developer (and other yarns)

    - by Chris Muir
    A few years of ADF experience means I see common mistakes made by different developers, some I regularly make myself.  This post is designed to assist beginners to Oracle JDeveloper Application Development Framework (ADF) avoid a common ADF pitfall, the case of the phantom ADF developer [add Scooby-Doo music here]. ADF Business Components - triggers, default table values and instead of views. Oracle's JDeveloper tutorials help with the A-B-Cs of ADF development, typically built on the nice 'n safe demo schema provided by with the Oracle database such as the HR demo schema. However it's not too long until ADF beginners, having built up some confidence from learning with the tutorials and vanilla demo schemas, start building ADF Business Components based upon their own existing database schema objects.  This is where unexpected problems can sneak in. The crime Developers may encounter a surprising error at runtime when editing a record they just created or updated and committed to the database, based on their own existing tables, namely the error: JBO-25014: Another user has changed the row with primary key oracle.jbo.Key[x] ...where X is the primary key value of the row at hand.  In a production environment with multiple users this error may be legit, one of the other users has updated the row since you queried it.  Yet in a development environment this error is just plain confusing.  If developers are isolated in their own database, creating and editing records they know other users can't possibly be working with, or all the other developers have gone home for the day, how is this error possible? There are no other users?  It must be the phantom ADF developer! [insert dramatic music here] The following picture is what you'll see in the Business Component Browser, and you'll receive a similar error message via an ADF Faces page: A false conclusion What can possibly cause this issue if it isn't our phantom ADF developer?  Doesn't ADF BC implement record locking, locking database records when the row is modified in the ADF middle-tier by a user?  How can our phantom ADF developer even take out a lock if this is the case?  Maybe ADF has a bug, maybe ADF isn't implementing record locking at all?  Shouldn't we see the error "JBO-26030: Failed to lock the record, another user holds the lock" as we attempt to modify the record, why do we see JBO-25014? : Let's verify that ADF is in fact issuing the correct SQL LOCK-FOR-UPDATE statement to the database. First we need to verify ADF's locking strategy.  It is determined by the Application Module's jbo.locking.mode property.  The default (as of JDev 11.1.1.4.0 if memory serves me correct) and recommended value is optimistic, and the other valid value is pessimistic. Next we need a mechanism to check that ADF is issuing the LOCK statements to the database.  We could ask DBAs to monitor locks with OEM, but optimally we'd rather not involve overworked DBAs in this process, so instead we can use the ADF runtime setting –Djbo.debugoutput=console.  At runtime this options turns on instrumentation within the ADF BC layer, which among a lot of extra detail displayed in the log window, will show the actual SQL statement issued to the database, including the LOCK statement we're looking to confirm. Setting our locking mode to pessimistic, opening the Business Components Browser of a JSF page allowing us to edit a record, say the CHARGEABLE field within a BOOKINGS record where BOOKING_NO = 1206, upon editing the record see among others the following log entries: [421] Built select: 'SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings'[422] Executing LOCK...SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings WHERE BOOKING_NO=:1 FOR UPDATE NOWAIT[423] Where binding param 1: 1206  As can be seen on line 422, in fact a LOCK-FOR-UPDATE is indeed issued to the database.  Later when we commit the record we see: [441] OracleSQLBuilder: SAVEPOINT 'BO_SP'[442] OracleSQLBuilder Executing, Lock 1 DML on: BOOKINGS (Update)[443] UPDATE buf Bookings>#u SQLStmtBufLen: 210, actual=62[444] UPDATE BOOKINGS Bookings SET CHARGEABLE=:1 WHERE BOOKING_NO=:2[445] Update binding param 1: N[446] Where binding param 2: 1206[447] BookingsView1 notify COMMIT ... [448] _LOCAL_VIEW_USAGE_model_Bookings_ResourceTypesView1 notify COMMIT ... [449] EntityCache close prepared statement ....and as a result the changes are saved to the database, and the lock is released. Let's see what happens when we use the optimistic locking mode, this time to change the same BOOKINGS record CHARGEABLE column again.  As soon as we edit the record we see little activity in the logs, nothing to indicate any SQL statement, let alone a LOCK has been taken out on the row. However when we save our records by issuing a commit, the following is recorded in the logs: [509] OracleSQLBuilder: SAVEPOINT 'BO_SP'[510] OracleSQLBuilder Executing doEntitySelect on: BOOKINGS (true)[511] Built select: 'SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings'[512] Executing LOCK...SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings WHERE BOOKING_NO=:1 FOR UPDATE NOWAIT[513] Where binding param 1: 1205[514] OracleSQLBuilder Executing, Lock 2 DML on: BOOKINGS (Update)[515] UPDATE buf Bookings>#u SQLStmtBufLen: 210, actual=62[516] UPDATE BOOKINGS Bookings SET CHARGEABLE=:1 WHERE BOOKING_NO=:2[517] Update binding param 1: Y[518] Where binding param 2: 1205[519] BookingsView1 notify COMMIT ... [520] _LOCAL_VIEW_USAGE_model_Bookings_ResourceTypesView1 notify COMMIT ... [521] EntityCache close prepared statement Again even though we're seeing the midtier delay the LOCK statement until commit time, it is in fact occurring on line 412, and released as part of the commit issued on line 419.  Therefore with either optimistic or pessimistic locking a lock is indeed issued. Our conclusion at this point must be, unless there's the unlikely cause the LOCK statement is never really hitting the database, or the even less likely cause the database has a bug, then ADF does in fact take out a lock on the record before allowing the current user to update it.  So there's no way our phantom ADF developer could even modify the record if he tried without at least someone receiving a lock error. Hmm, we can only conclude the locking mode is a red herring and not the true cause of our problem.  Who is the phantom? At this point we'll need to conclude that the error message "JBO-25014: Another user has changed" is somehow legit, even though we don't understand yet what's causing it. This leads onto two further questions, how does ADF know another user has changed the row, and what's been changed anyway? To answer the first question, how does ADF know another user has changed the row, the Fusion Guide's section 4.10.11 How to Protect Against Losing Simultaneous Updated Data , that details the Entity Object Change-Indicator property, gives us the answer: At runtime the framework provides automatic "lost update" detection for entity objects to ensure that a user cannot unknowingly modify data that another user has updated and committed in the meantime. Typically, this check is performed by comparing the original values of each persistent entity attribute against the corresponding current column values in the database at the time the underlying row is locked. Before updating a row, the entity object verifies that the row to be updated is still consistent with the current state of the database.  The guide further suggests to make this solution more efficient: You can make the lost update detection more efficient by identifying any attributes of your entity whose values you know will be updated whenever the entity is modified. Typical candidates include a version number column or an updated date column in the row.....To detect whether the row has been modified since the user queried it in the most efficient way, select the Change Indicator option to compare only the change-indicator attribute values. We now know that ADF BC doesn't use the locking mechanism at all to protect the current user against updates, but rather it keeps a copy of the original record fetched, separate to the user changed version of the record, and it compares the original record against the one in the database when the lock is taken out.  If values don't match, be it the default compare-all-columns behaviour, or the more efficient Change Indicator mechanism, ADF BC will throw the JBO-25014 error. This leaves one last question.  Now we know the mechanism under which ADF identifies a changed row, what we don't know is what's changed and who changed it? The real culprit What's changed?  We know the record in the mid-tier has been changed by the user, however ADF doesn't use the changed record in the mid-tier to compare to the database record, but rather a copy of the original record before it was changed.  This leaves us to conclude the database record has changed, but how and by who? There are three potential causes: Database triggers The database trigger among other uses, can be configured to fire PLSQL code on a database table insert, update or delete.  In particular in an insert or update the trigger can override the value assigned to a particular column.  The trigger execution is actioned by the database on behalf of the user initiating the insert or update action. Why this causes the issue specific to our ADF use, is when we insert or update a record in the database via ADF, ADF keeps a copy of the record written to the database.  However the cached record is instantly out of date as the database triggers have modified the record that was actually written to the database.  Thus when we update the record we just inserted or updated for a second time to the database, ADF compares its original copy of the record to that in the database, and it detects the record has been changed – giving us JBO-25014. This is probably the most common cause of this problem. Default values A second reason this issue can occur is another database feature, default column values.  When creating a database table the schema designer can define default values for specific columns.  For example a CREATED_BY column could be set to SYSDATE, or a flag column to Y or N.  Default values are only used by the database when a user inserts a new record and the specific column is assigned NULL.  The database in this case will overwrite the column with the default value. As per the database trigger section, it then becomes apparent why ADF chokes on this feature, though it can only specifically occur in an insert-commit-update-commit scenario, not the update-commit-update-commit scenario. Instead of trigger views I must admit I haven't double checked this scenario but it seems plausible, that of the Oracle database's instead of trigger view (sometimes referred to as instead of views).  A view in the database is based on a query, and dependent on the queries complexity, may support insert, update and delete functionality to a limited degree.  In order to support fully insertable, updateable and deletable views, Oracle introduced the instead of view, that gives the view designer the ability to not only define the view query, but a set of programmatic PLSQL triggers where the developer can define their own logic for inserts, updates and deletes. While this provides the database programmer a very powerful feature, it can cause issues for our ADF application.  On inserting or updating a record in the instead of view, the record and it's data that goes in is not necessarily the data that comes out when ADF compares the records, as the view developer has the option to practically do anything with the incoming data, including throwing it away or pushing it to tables which aren't used by the view underlying query for fetching the data. Readers are at this point reminded that this article is specifically about how the JBO-25014 error occurs in the context of 1 developer on an isolated database.  The article is not considering how the error occurs in a production environment where there are multiple users who can cause this error in a legitimate fashion.  Assuming none of the above features are the cause of the problem, and optimistic locking is turned on (this error is not possible if pessimistic locking is the default mode *and* none of the previous causes are possible), JBO-25014 is quite feasible in a production ADF application if 2 users modify the same record. At this point under project timelines pressure, the obvious fix for developers is to drop both database triggers and default values from the underlying tables.  However we must be careful that these legacy constructs aren't used and assumed to be in place by other legacy systems.  Dropping the database triggers or default value that the existing Oracle Forms  applications assumes and requires to be in place could cause unexpected behaviour and bugs in the Forms application.  Proficient software engineers would recognize such a change may require a partial or full regression test of the existing legacy system, a potentially costly and timely exercise, not ideal. Solving the mystery once and for all Luckily ADF has built in functionality to deal with this issue, though it's not a surprise, as Oracle as the author of ADF also built the database, and are fully aware of the Oracle database's feature set.  At the Entity Object attribute level, the Refresh After Insert and Refresh After Update properties.  Simply selecting these instructs ADF BC after inserting or updating a record to the database, to expect the database to modify the said attributes, and read a copy of the changed attributes back into its cached mid-tier record.  Thus next time the developer modifies the current record, the comparison between the mid-tier record and the database record match, and JBO-25014: Another user has changed" is no longer an issue. [Post edit - as per the comment from Oracle's Steven Davelaar below, as he correctly points out the above solution will not work for instead-of-triggers views as it relies on SQL RETURNING clause which is incompatible with this type of view] Alternatively you can set the Change Indicator on one of the attributes.  This will work as long as the relating column for the attribute in the database itself isn't inadvertently updated.  In turn you're possibly just masking the issue rather than solving it, because if another developer turns the Change Indicator back on the original issue will return.

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  • Long-running transactions structured approach

    - by disown
    I'm looking for a structured approach to long-running (hours or more) transactions. As mentioned here, these type of interactions are usually handled by optimistic locking and manual merge strategies. It would be very handy to have some more structured approach to this type of problem using standard transactions. Various long-running interactions such as user registration, order confirmation etc. all have transaction-like semantics, and it is both error-prone and tedious to invent your own fragile manual roll-back and/or time-out/clean-up strategies. Taking a RDBMS as an example, I realize that it would be a major performance cost associated with keeping all the transactions open. As an alternative, I could imagine having a database supporting two isolation levels/strategies simultaneously, one for short-running and one for long-running conversations. Long-running conversations could then for instance have more strict limitations on data access to facilitate them taking more time (read-only semantics on some data, optimistic locking semantics etc). Are there any solutions which could do something similar?

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  • PTLQueue : a scalable bounded-capacity MPMC queue

    - by Dave
    Title: Fast concurrent MPMC queue -- I've used the following concurrent queue algorithm enough that it warrants a blog entry. I'll sketch out the design of a fast and scalable multiple-producer multiple-consumer (MPSC) concurrent queue called PTLQueue. The queue has bounded capacity and is implemented via a circular array. Bounded capacity can be a useful property if there's a mismatch between producer rates and consumer rates where an unbounded queue might otherwise result in excessive memory consumption by virtue of the container nodes that -- in some queue implementations -- are used to hold values. A bounded-capacity queue can provide flow control between components. Beware, however, that bounded collections can also result in resource deadlock if abused. The put() and take() operators are partial and wait for the collection to become non-full or non-empty, respectively. Put() and take() do not allocate memory, and are not vulnerable to the ABA pathologies. The PTLQueue algorithm can be implemented equally well in C/C++ and Java. Partial operators are often more convenient than total methods. In many use cases if the preconditions aren't met, there's nothing else useful the thread can do, so it may as well wait via a partial method. An exception is in the case of work-stealing queues where a thief might scan a set of queues from which it could potentially steal. Total methods return ASAP with a success-failure indication. (It's tempting to describe a queue or API as blocking or non-blocking instead of partial or total, but non-blocking is already an overloaded concurrency term. Perhaps waiting/non-waiting or patient/impatient might be better terms). It's also trivial to construct partial operators by busy-waiting via total operators, but such constructs may be less efficient than an operator explicitly and intentionally designed to wait. A PTLQueue instance contains an array of slots, where each slot has volatile Turn and MailBox fields. The array has power-of-two length allowing mod/div operations to be replaced by masking. We assume sensible padding and alignment to reduce the impact of false sharing. (On x86 I recommend 128-byte alignment and padding because of the adjacent-sector prefetch facility). Each queue also has PutCursor and TakeCursor cursor variables, each of which should be sequestered as the sole occupant of a cache line or sector. You can opt to use 64-bit integers if concerned about wrap-around aliasing in the cursor variables. Put(null) is considered illegal, but the caller or implementation can easily check for and convert null to a distinguished non-null proxy value if null happens to be a value you'd like to pass. Take() will accordingly convert the proxy value back to null. An advantage of PTLQueue is that you can use atomic fetch-and-increment for the partial methods. We initialize each slot at index I with (Turn=I, MailBox=null). Both cursors are initially 0. All shared variables are considered "volatile" and atomics such as CAS and AtomicFetchAndIncrement are presumed to have bidirectional fence semantics. Finally T is the templated type. I've sketched out a total tryTake() method below that allows the caller to poll the queue. tryPut() has an analogous construction. Zebra stripping : alternating row colors for nice-looking code listings. See also google code "prettify" : https://code.google.com/p/google-code-prettify/ Prettify is a javascript module that yields the HTML/CSS/JS equivalent of pretty-print. -- pre:nth-child(odd) { background-color:#ff0000; } pre:nth-child(even) { background-color:#0000ff; } border-left: 11px solid #ccc; margin: 1.7em 0 1.7em 0.3em; background-color:#BFB; font-size:12px; line-height:65%; " // PTLQueue : Put(v) : // producer : partial method - waits as necessary assert v != null assert Mask = 1 && (Mask & (Mask+1)) == 0 // Document invariants // doorway step // Obtain a sequence number -- ticket // As a practical concern the ticket value is temporally unique // The ticket also identifies and selects a slot auto tkt = AtomicFetchIncrement (&PutCursor, 1) slot * s = &Slots[tkt & Mask] // waiting phase : // wait for slot's generation to match the tkt value assigned to this put() invocation. // The "generation" is implicitly encoded as the upper bits in the cursor // above those used to specify the index : tkt div (Mask+1) // The generation serves as an epoch number to identify a cohort of threads // accessing disjoint slots while s-Turn != tkt : Pause assert s-MailBox == null s-MailBox = v // deposit and pass message Take() : // consumer : partial method - waits as necessary auto tkt = AtomicFetchIncrement (&TakeCursor,1) slot * s = &Slots[tkt & Mask] // 2-stage waiting : // First wait for turn for our generation // Acquire exclusive "take" access to slot's MailBox field // Then wait for the slot to become occupied while s-Turn != tkt : Pause // Concurrency in this section of code is now reduced to just 1 producer thread // vs 1 consumer thread. // For a given queue and slot, there will be most one Take() operation running // in this section. // Consumer waits for producer to arrive and make slot non-empty // Extract message; clear mailbox; advance Turn indicator // We have an obvious happens-before relation : // Put(m) happens-before corresponding Take() that returns that same "m" for T v = s-MailBox if v != null : s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 // unlock slot to admit next producer and consumer return v Pause tryTake() : // total method - returns ASAP with failure indication for auto tkt = TakeCursor slot * s = &Slots[tkt & Mask] if s-Turn != tkt : return null T v = s-MailBox // presumptive return value if v == null : return null // ratify tkt and v values and commit by advancing cursor if CAS (&TakeCursor, tkt, tkt+1) != tkt : continue s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 return v The basic idea derives from the Partitioned Ticket Lock "PTL" (US20120240126-A1) and the MultiLane Concurrent Bag (US8689237). The latter is essentially a circular ring-buffer where the elements themselves are queues or concurrent collections. You can think of the PTLQueue as a partitioned ticket lock "PTL" augmented to pass values from lock to unlock via the slots. Alternatively, you could conceptualize of PTLQueue as a degenerate MultiLane bag where each slot or "lane" consists of a simple single-word MailBox instead of a general queue. Each lane in PTLQueue also has a private Turn field which acts like the Turn (Grant) variables found in PTL. Turn enforces strict FIFO ordering and restricts concurrency on the slot mailbox field to at most one simultaneous put() and take() operation. PTL uses a single "ticket" variable and per-slot Turn (grant) fields while MultiLane has distinct PutCursor and TakeCursor cursors and abstract per-slot sub-queues. Both PTL and MultiLane advance their cursor and ticket variables with atomic fetch-and-increment. PTLQueue borrows from both PTL and MultiLane and has distinct put and take cursors and per-slot Turn fields. Instead of a per-slot queues, PTLQueue uses a simple single-word MailBox field. PutCursor and TakeCursor act like a pair of ticket locks, conferring "put" and "take" access to a given slot. PutCursor, for instance, assigns an incoming put() request to a slot and serves as a PTL "Ticket" to acquire "put" permission to that slot's MailBox field. To better explain the operation of PTLQueue we deconstruct the operation of put() and take() as follows. Put() first increments PutCursor obtaining a new unique ticket. That ticket value also identifies a slot. Put() next waits for that slot's Turn field to match that ticket value. This is tantamount to using a PTL to acquire "put" permission on the slot's MailBox field. Finally, having obtained exclusive "put" permission on the slot, put() stores the message value into the slot's MailBox. Take() similarly advances TakeCursor, identifying a slot, and then acquires and secures "take" permission on a slot by waiting for Turn. Take() then waits for the slot's MailBox to become non-empty, extracts the message, and clears MailBox. Finally, take() advances the slot's Turn field, which releases both "put" and "take" access to the slot's MailBox. Note the asymmetry : put() acquires "put" access to the slot, but take() releases that lock. At any given time, for a given slot in a PTLQueue, at most one thread has "put" access and at most one thread has "take" access. This restricts concurrency from general MPMC to 1-vs-1. We have 2 ticket locks -- one for put() and one for take() -- each with its own "ticket" variable in the form of the corresponding cursor, but they share a single "Grant" egress variable in the form of the slot's Turn variable. Advancing the PutCursor, for instance, serves two purposes. First, we obtain a unique ticket which identifies a slot. Second, incrementing the cursor is the doorway protocol step to acquire the per-slot mutual exclusion "put" lock. The cursors and operations to increment those cursors serve double-duty : slot-selection and ticket assignment for locking the slot's MailBox field. At any given time a slot MailBox field can be in one of the following states: empty with no pending operations -- neutral state; empty with one or more waiting take() operations pending -- deficit; occupied with no pending operations; occupied with one or more waiting put() operations -- surplus; empty with a pending put() or pending put() and take() operations -- transitional; or occupied with a pending take() or pending put() and take() operations -- transitional. The partial put() and take() operators can be implemented with an atomic fetch-and-increment operation, which may confer a performance advantage over a CAS-based loop. In addition we have independent PutCursor and TakeCursor cursors. Critically, a put() operation modifies PutCursor but does not access the TakeCursor and a take() operation modifies the TakeCursor cursor but does not access the PutCursor. This acts to reduce coherence traffic relative to some other queue designs. It's worth noting that slow threads or obstruction in one slot (or "lane") does not impede or obstruct operations in other slots -- this gives us some degree of obstruction isolation. PTLQueue is not lock-free, however. The implementation above is expressed with polite busy-waiting (Pause) but it's trivial to implement per-slot parking and unparking to deschedule waiting threads. It's also easy to convert the queue to a more general deque by replacing the PutCursor and TakeCursor cursors with Left/Front and Right/Back cursors that can move either direction. Specifically, to push and pop from the "left" side of the deque we would decrement and increment the Left cursor, respectively, and to push and pop from the "right" side of the deque we would increment and decrement the Right cursor, respectively. We used a variation of PTLQueue for message passing in our recent OPODIS 2013 paper. ul { list-style:none; padding-left:0; padding:0; margin:0; margin-left:0; } ul#myTagID { padding: 0px; margin: 0px; list-style:none; margin-left:0;} -- -- There's quite a bit of related literature in this area. I'll call out a few relevant references: Wilson's NYU Courant Institute UltraComputer dissertation from 1988 is classic and the canonical starting point : Operating System Data Structures for Shared-Memory MIMD Machines with Fetch-and-Add. Regarding provenance and priority, I think PTLQueue or queues effectively equivalent to PTLQueue have been independently rediscovered a number of times. See CB-Queue and BNPBV, below, for instance. But Wilson's dissertation anticipates the basic idea and seems to predate all the others. Gottlieb et al : Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors Orozco et al : CB-Queue in Toward high-throughput algorithms on many-core architectures which appeared in TACO 2012. Meneghin et al : BNPVB family in Performance evaluation of inter-thread communication mechanisms on multicore/multithreaded architecture Dmitry Vyukov : bounded MPMC queue (highly recommended) Alex Otenko : US8607249 (highly related). John Mellor-Crummey : Concurrent queues: Practical fetch-and-phi algorithms. Technical Report 229, Department of Computer Science, University of Rochester Thomasson : FIFO Distributed Bakery Algorithm (very similar to PTLQueue). Scott and Scherer : Dual Data Structures I'll propose an optimization left as an exercise for the reader. Say we wanted to reduce memory usage by eliminating inter-slot padding. Such padding is usually "dark" memory and otherwise unused and wasted. But eliminating the padding leaves us at risk of increased false sharing. Furthermore lets say it was usually the case that the PutCursor and TakeCursor were numerically close to each other. (That's true in some use cases). We might still reduce false sharing by incrementing the cursors by some value other than 1 that is not trivially small and is coprime with the number of slots. Alternatively, we might increment the cursor by one and mask as usual, resulting in a logical index. We then use that logical index value to index into a permutation table, yielding an effective index for use in the slot array. The permutation table would be constructed so that nearby logical indices would map to more distant effective indices. (Open question: what should that permutation look like? Possibly some perversion of a Gray code or De Bruijn sequence might be suitable). As an aside, say we need to busy-wait for some condition as follows : "while C == 0 : Pause". Lets say that C is usually non-zero, so we typically don't wait. But when C happens to be 0 we'll have to spin for some period, possibly brief. We can arrange for the code to be more machine-friendly with respect to the branch predictors by transforming the loop into : "if C == 0 : for { Pause; if C != 0 : break; }". Critically, we want to restructure the loop so there's one branch that controls entry and another that controls loop exit. A concern is that your compiler or JIT might be clever enough to transform this back to "while C == 0 : Pause". You can sometimes avoid this by inserting a call to a some type of very cheap "opaque" method that the compiler can't elide or reorder. On Solaris, for instance, you could use :"if C == 0 : { gethrtime(); for { Pause; if C != 0 : break; }}". It's worth noting the obvious duality between locks and queues. If you have strict FIFO lock implementation with local spinning and succession by direct handoff such as MCS or CLH,then you can usually transform that lock into a queue. Hidden commentary and annotations - invisible : * And of course there's a well-known duality between queues and locks, but I'll leave that topic for another blog post. * Compare and contrast : PTLQ vs PTL and MultiLane * Equivalent : Turn; seq; sequence; pos; position; ticket * Put = Lock; Deposit Take = identify and reserve slot; wait; extract & clear; unlock * conceptualize : Distinct PutLock and TakeLock implemented as ticket lock or PTL Distinct arrival cursors but share per-slot "Turn" variable provides exclusive role-based access to slot's mailbox field put() acquires exclusive access to a slot for purposes of "deposit" assigns slot round-robin and then acquires deposit access rights/perms to that slot take() acquires exclusive access to slot for purposes of "withdrawal" assigns slot round-robin and then acquires withdrawal access rights/perms to that slot At any given time, only one thread can have withdrawal access to a slot at any given time, only one thread can have deposit access to a slot Permissible for T1 to have deposit access and T2 to simultaneously have withdrawal access * round-robin for the purposes of; role-based; access mode; access role mailslot; mailbox; allocate/assign/identify slot rights; permission; license; access permission; * PTL/Ticket hybrid Asymmetric usage ; owner oblivious lock-unlock pairing K-exclusion add Grant cursor pass message m from lock to unlock via Slots[] array Cursor performs 2 functions : + PTL ticket + Assigns request to slot in round-robin fashion Deconstruct protocol : explication put() : allocate slot in round-robin fashion acquire PTL for "put" access store message into slot associated with PTL index take() : Acquire PTL for "take" access // doorway step seq = fetchAdd (&Grant, 1) s = &Slots[seq & Mask] // waiting phase while s-Turn != seq : pause Extract : wait for s-mailbox to be full v = s-mailbox s-mailbox = null Release PTL for both "put" and "take" access s-Turn = seq + Mask + 1 * Slot round-robin assignment and lock "doorway" protocol leverage the same cursor and FetchAdd operation on that cursor FetchAdd (&Cursor,1) + round-robin slot assignment and dispersal + PTL/ticket lock "doorway" step waiting phase is via "Turn" field in slot * PTLQueue uses 2 cursors -- put and take. Acquire "put" access to slot via PTL-like lock Acquire "take" access to slot via PTL-like lock 2 locks : put and take -- at most one thread can access slot's mailbox Both locks use same "turn" field Like multilane : 2 cursors : put and take slot is simple 1-capacity mailbox instead of queue Borrow per-slot turn/grant from PTL Provides strict FIFO Lock slot : put-vs-put take-vs-take at most one put accesses slot at any one time at most one put accesses take at any one time reduction to 1-vs-1 instead of N-vs-M concurrency Per slot locks for put/take Release put/take by advancing turn * is instrumental in ... * P-V Semaphore vs lock vs K-exclusion * See also : FastQueues-excerpt.java dice-etc/queue-mpmc-bounded-blocking-circular-xadd/ * PTLQueue is the same as PTLQB - identical * Expedient return; ASAP; prompt; immediately * Lamport's Bakery algorithm : doorway step then waiting phase Threads arriving at doorway obtain a unique ticket number Threads enter in ticket order * In the terminology of Reed and Kanodia a ticket lock corresponds to the busy-wait implementation of a semaphore using an eventcount and a sequencer It can also be thought of as an optimization of Lamport's bakery lock was designed for fault-tolerance rather than performance Instead of spinning on the release counter, processors using a bakery lock repeatedly examine the tickets of their peers --

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  • RHEL - blocked FC remote port time out: saving binding

    - by Dev G
    My Server went into a faulty state since the database could not write on the partition. I found out that the partition went into Read Only mode. Finally to fix it, I had to do a hard reboot. Linux 2.6.18-164.el5PAE #1 SMP Tue Aug 18 15:59:11 EDT 2009 i686 i686 i386 GNU/Linux /var/log/messages Oct 31 00:56:45 ota3g1 Had[17275]: VCS ERROR V-16-1-10214 Concurrency Violation:CurrentCount increased above 1 for failover group sg_network Oct 31 00:57:05 ota3g1 Had[17275]: VCS CRITICAL V-16-1-50086 CPU usage on ota3g1.mtsallstream.com is 100% Oct 31 01:01:47 ota3g1 Had[17275]: VCS ERROR V-16-1-10214 Concurrency Violation:CurrentCount increased above 1 for failover group sg_network Oct 31 01:06:50 ota3g1 Had[17275]: VCS ERROR V-16-1-10214 Concurrency Violation:CurrentCount increased above 1 for failover group sg_network Oct 31 01:11:52 ota3g1 Had[17275]: VCS ERROR V-16-1-10214 Concurrency Violation:CurrentCount increased above 1 for failover group sg_network Oct 31 01:12:10 ota3g1 kernel: lpfc 0000:29:00.1: 1:1305 Link Down Event x2 received Data: x2 x20 x80000 x0 x0 Oct 31 01:12:10 ota3g1 kernel: lpfc 0000:29:00.1: 1:1303 Link Up Event x3 received Data: x3 x1 x10 x1 x0 x0 0 Oct 31 01:12:12 ota3g1 kernel: lpfc 0000:29:00.1: 1:1305 Link Down Event x4 received Data: x4 x20 x80000 x0 x0 Oct 31 01:12:40 ota3g1 kernel: rport-8:0-0: blocked FC remote port time out: saving binding Oct 31 01:12:40 ota3g1 kernel: lpfc 0000:29:00.1: 1:(0):0203 Devloss timeout on WWPN 20:25:00:a0:b8:74:f5:65 NPort x0000e4 Data: x0 x7 x0 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 38617577 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 283532153 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 90825 Oct 31 01:12:40 ota3g1 kernel: Aborting journal on device dm-16. Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 868841 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: Aborting journal on device dm-10. Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 37759889 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 283349449 Oct 31 01:12:40 ota3g1 kernel: printk: 6 messages suppressed. Oct 31 01:12:40 ota3g1 kernel: Aborting journal on device dm-12. Oct 31 01:12:40 ota3g1 kernel: EXT3-fs error (device dm-12) in ext3_reserve_inode_write: Journal has aborted Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-16, logical block 1545 Oct 31 01:12:40 ota3g1 kernel: lost page write due to I/O error on dm-16 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 12745 Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-10, logical block 1545 Oct 31 01:12:40 ota3g1 kernel: EXT3-fs error (device dm-16) in ext3_reserve_inode_write: Journal has aborted Oct 31 01:12:40 ota3g1 kernel: lost page write due to I/O error on dm-10 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 37749121 Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-12, logical block 0 Oct 31 01:12:40 ota3g1 kernel: lost page write due to I/O error on dm-12 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: EXT3-fs error (device dm-12) in ext3_dirty_inode: Journal has aborted Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 37757897 Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-12, logical block 1097 Oct 31 01:12:40 ota3g1 kernel: lost page write due to I/O error on dm-12 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 283337089 Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-16, logical block 0 Oct 31 01:12:40 ota3g1 kernel: lost page write due to I/O error on dm-16 Oct 31 01:12:40 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:40 ota3g1 kernel: EXT3-fs error (device dm-16) in ext3_dirty_inode: Journal has aborted Oct 31 01:12:40 ota3g1 kernel: end_request: I/O error, dev sdi, sector 37749121 Oct 31 01:12:40 ota3g1 kernel: Buffer I/O error on device dm-12, logical block 0 Oct 31 01:12:41 ota3g1 kernel: lost page write due to I/O error on dm-12 Oct 31 01:12:41 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 Oct 31 01:12:41 ota3g1 kernel: end_request: I/O error, dev sdi, sector 283337089 Oct 31 01:12:41 ota3g1 kernel: Buffer I/O error on device dm-16, logical block 0 Oct 31 01:12:41 ota3g1 kernel: lost page write due to I/O error on dm-16 Oct 31 01:12:41 ota3g1 kernel: sd 8:0:0:4: SCSI error: return code = 0x00010000 df -h Filesystem Size Used Avail Use% Mounted on /dev/mapper/cciss-root 4.9G 730M 3.9G 16% / /dev/mapper/cciss-home 9.7G 1.2G 8.1G 13% /home /dev/mapper/cciss-var 9.7G 494M 8.8G 6% /var /dev/mapper/cciss-usr 15G 2.6G 12G 19% /usr /dev/mapper/cciss-tmp 3.9G 153M 3.6G 5% /tmp /dev/sda1 996M 43M 902M 5% /boot tmpfs 5.9G 0 5.9G 0% /dev/shm /dev/mapper/cciss-product 25G 16G 7.4G 68% /product /dev/mapper/cciss-opt 20G 4.5G 14G 25% /opt /dev/mapper/dg_db1-vol_db1_system 18G 2.2G 15G 14% /database/OTADB/sys /dev/mapper/dg_db1-vol_db1_undo 18G 5.8G 12G 35% /database/OTADB/undo /dev/mapper/dg_db1-vol_db1_redo 8.9G 4.3G 4.2G 51% /database/OTADB/redo /dev/mapper/dg_db1-vol_db1_sgbd 8.9G 654M 7.8G 8% /database/OTADB/admin /dev/mapper/dg_db1-vol_db1_arch 98G 24G 69G 26% /database/OTADB/arch /dev/mapper/dg_db1-vol_db1_indexes 240G 14G 214G 6% /database/OTADB/index /dev/mapper/dg_db1-vol_db1_data 275G 47G 215G 18% /database/OTADB/data /dev/mapper/dg_dbrman-vol_db_rman 8.9G 351M 8.1G 5% /database/RMAN /dev/mapper/dg_app1-vol_app1 151G 113G 31G 79% /files/ota /etc/fstab /dev/cciss/root / ext3 defaults 1 1 /dev/cciss/home /home ext3 defaults 1 2 /dev/cciss/var /var ext3 defaults 1 2 /dev/cciss/usr /usr ext3 defaults 1 2 /dev/cciss/tmp /tmp ext3 defaults 1 2 LABEL=/boot /boot ext3 defaults 1 2 tmpfs /dev/shm tmpfs defaults 0 0 devpts /dev/pts devpts gid=5,mode=620 0 0 sysfs /sys sysfs defaults 0 0 proc /proc proc defaults 0 0 /dev/cciss/swap swap swap defaults 0 0 /dev/cciss/product /product ext3 defaults 1 2 /dev/cciss/opt /opt ext3 defaults 1 2 /dev/dg_db1/vol_db1_system /database/OTADB/sys ext3 defaults 1 2 /dev/dg_db1/vol_db1_undo /database/OTADB/undo ext3 defaults 1 2 /dev/dg_db1/vol_db1_redo /database/OTADB/redo ext3 defaults 1 2 /dev/dg_db1/vol_db1_sgbd /database/OTADB/admin ext3 defaults 1 2 /dev/dg_db1/vol_db1_arch /database/OTADB/arch ext3 defaults 1 2 /dev/dg_db1/vol_db1_indexes /database/OTADB/index ext3 defaults 1 2 /dev/dg_db1/vol_db1_data /database/OTADB/data ext3 defaults 1 2 /dev/dg_dbrman/vol_db_rman /database/RMAN ext3 defaults 1 2 /dev/dg_app1/vol_app1 /files/ota ext3 defaults 1 2 Thanks for all the help.

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  • Michael Crump&rsquo;s notes for 70-563 PRO &ndash; Designing and Developing Windows Applications usi

    - by mbcrump
    TIME TO GO PRO! This is my notes for 70-563 PRO – Designing and Developing Windows Applications using .NET Framework 3.5 I created it using several resources (various certification web sites, msdn, official ms 70-548 book). The reason that I created this review is because a) I am taking the exam. b) MS did not create a book for this exam. Use the(MS 70-548)book. c) To make sure I am familiar with each before the exam. I hope that it provides a good start for your own notes. I hope that someone finds this useful. At least, it will give you a starting point of what to expect to know on the PRO exam. Also, for those wondering, the PRO exam does contains very little code. It is basically all theory. 1. Validation Controls – How to prevent users from entering invalid data on forms. (MaskedTextBox control and RegEx) 2. ServiceController – used to start and control the behavior of existing services. 3. User Feedback (know winforms Status Bar, Tool Tips, Color, Error Provider, Context-Sensitive and Accessibility) 4. Specific (derived) exceptions must be handled before general (base class) exceptions. By moving the exception handling for the base type Exception to after exception handling of ArgumentNullException, all ArgumentNullException thrown by the Helper method will be caught and logged correctly. 5. A heartbeat method is a method exposed by a Web service that allows external applications to check on the status of the service. 6. New users must master key tasks quickly. Giving these tasks context and appropriate detail will help. However, advanced users will demand quicker paths. Shortcuts, accelerators, or toolbar buttons will speed things along for the advanced user. 7. MSBuild uses project files to instruct the build engine what to build and how to build it. MSBuild project files are XML files that adhere to the MSBuild XML schema. The MSBuild project files contain complete file, build action, and dependency information for each individual projects. 8. Evaluating whether or not to fix a bug involves a triage process. You must identify the bug's impact, set the priority, categorize it, and assign a developer. Many times the person doing the triage work will assign the bug to a developer for further investigation. In fact, the workflow for the bug work item inside of Team System supports this step. Developers are often asked to assess the impact of a given bug. This assessment helps the person doing the triage make a decision on how to proceed. When assessing the impact of a bug, you should consider time and resources to fix it, bug risk, and impacts of the bug. 9. In large projects it is generally impossible and unfeasible to fix all bugs because of the impact on schedule and budget. 10. Code reviews should be conducted by a technical lead or a technical peer. 11. Testing Applications 12. WCF Services – application state 13. SQL Server 2005 / 2008 Express Edition – reliable storage of data / Microsoft SQL Server 3.5 Compact Database– used for client computers to retrieve and save data from a shared location. 14. SQL Server 2008 Compact Edition – used for minimum possible memory and can synchronize data with a corporate SQL Server 2008 Database. Supports offline user and minimum dependency on external components. 15. MDI and SDI Forms (specifically IsMDIContainer) 16. GUID – in the case of data warehousing, it is important to define unique keys. 17. Encrypting / Security Data 18. Understanding of Isolated Storage/Proper location to store items 19. LINQ to SQL 20. Multithreaded access 21. ADO.NET Entity Framework model 22. Marshal.ReleaseComObject 23. Common User Interface Layout (ComboBox, ListBox, Listview, MaskedTextBox, TextBox, RichTextBox, SplitContainer, TableLayoutPanel, TabControl) 24. DataSets Class - http://msdn.microsoft.com/en-us/library/system.data.dataset%28VS.71%29.aspx 25. SQL Server 2008 Reporting Services (SSRS) 26. SystemIcons.Shield (Vista UAC) 27. Leverging stored procedures to perform data manipulation for a database schema that can change. 28. DataContext 29. Microsoft Windows Installer Packages, ClickOnce(bootstrapping features), XCopy. 30. Client Application Services – will authenticate users by using the same data source as a ASP.NET web application. 31. SQL Server 2008 Caching 32. StringBuilder 33. Accessibility Guidelines for Windows Applications http://msdn.microsoft.com/en-us/library/ms228004.aspx 34. Logging erros 35. Testing performance related issues. 36. Role Based Security, GenericIdentity and GenericPrincipal 37. System.Net.CookieContainer will store session data for webapps (see isolated storage for winforms) 38. .NET CLR Profiler tool will identify objects that cause performance issues. 39. ADO.NET Synchronization (SyncGroup) 40. Globalization - CultureInfo 41. IDisposable Interface- reports on several questions relating to this. 42. Adding timestamps to determine whether data has changed or not. 43. Converting applications to .NET Framework 3.5 44. MicrosoftReportViewer 45. Composite Controls 46. Windows Vista KNOWN folders. 47. Microsoft Sync Framework 48. TypeConverter -Provides a unified way of converting types of values to other types, as well as for accessing standard values and sub properties. http://msdn.microsoft.com/en-us/library/system.componentmodel.typeconverter.aspx 49. Concurrency control mechanisms The main categories of concurrency control mechanisms are: Optimistic - Delay the checking of whether a transaction meets the isolation rules (e.g., serializability and recoverability) until its end, without blocking any of its (read, write) operations, and then abort a transaction, if the desired rules are violated. Pessimistic - Block operations of a transaction, if they may cause violation of the rules. Semi-optimistic - Block operations in some situations, and do not block in other situations, while delaying rules checking to transaction's end, as done with optimistic. 50. AutoResetEvent 51. Microsoft Messaging Queue (MSMQ) 4.0 52. Bulk imports 53. KeyDown event of controls 54. WPF UI components 55. UI process layer 56. GAC (installing, removing and queuing) 57. Use a local database cache to reduce the network bandwidth used by applications. 58. Sound can easily be annoying and distracting to users, so use it judiciously. Always give users the option to turn sound off. Because a user might have sound off, never convey important information through sound alone.

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  • array and array_view from amp.h

    - by Daniel Moth
    This is a very long post, but it also covers what are probably the classes (well, array_view at least) that you will use the most with C++ AMP, so I hope you enjoy it! Overview The concurrency::array and concurrency::array_view template classes represent multi-dimensional data of type T, of N dimensions, specified at compile time (and you can later access the number of dimensions via the rank property). If N is not specified, it is assumed that it is 1 (i.e. single-dimensional case). They are rectangular (not jagged). The difference between them is that array is a container of data, whereas array_view is a wrapper of a container of data. So in that respect, array behaves like an STL container, whereas the closest thing an array_view behaves like is an STL iterator (albeit with random access and allowing you to view more than one element at a time!). The data in the array (whether provided at creation time or added later) resides on an accelerator (which is specified at creation time either explicitly by the developer, or set to the default accelerator at creation time by the runtime) and is laid out contiguously in memory. The data provided to the array_view is not stored by/in the array_view, because the array_view is simply a view over the real source (which can reside on the CPU or other accelerator). The underlying data is copied on demand to wherever the array_view is accessed. Elements which differ by one in the least significant dimension of the array_view are adjacent in memory. array objects must be captured by reference into the lambda you pass to the parallel_for_each call, whereas array_view objects must be captured by value (into the lambda you pass to the parallel_for_each call). Creating array and array_view objects and relevant properties You can create array_view objects from other array_view objects of the same rank and element type (shallow copy, also possible via assignment operator) so they point to the same underlying data, and you can also create array_view objects over array objects of the same rank and element type e.g.   array_view<int,3> a(b); // b can be another array or array_view of ints with rank=3 Note: Unlike the constructors above which can be called anywhere, the ones in the rest of this section can only be called from CPU code. You can create array objects from other array objects of the same rank and element type (copy and move constructors) and from other array_view objects, e.g.   array<float,2> a(b); // b can be another array or array_view of floats with rank=2 To create an array from scratch, you need to at least specify an extent object, e.g. array<int,3> a(myExtent);. Note that instead of an explicit extent object, there are convenience overloads when N<=3 so you can specify 1-, 2-, 3- integers (dependent on the array's rank) and thus have the extent created for you under the covers. At any point, you can access the array's extent thought the extent property. The exact same thing applies to array_view (extent as constructor parameters, incl. convenience overloads, and property). While passing only an extent object to create an array is enough (it means that the array will be written to later), it is not enough for the array_view case which must always wrap over some other container (on which it relies for storage space and actual content). So in addition to the extent object (that describes the shape you'd like to be viewing/accessing that data through), to create an array_view from another container (e.g. std::vector) you must pass in the container itself (which must expose .data() and a .size() methods, e.g. like std::array does), e.g.   array_view<int,2> aaa(myExtent, myContainerOfInts); Similarly, you can create an array_view from a raw pointer of data plus an extent object. Back to the array case, to optionally initialize the array with data, you can pass an iterator pointing to the start (and optionally one pointing to the end of the source container) e.g.   array<double,1> a(5, myVector.begin(), myVector.end()); We saw that arrays are bound to an accelerator at creation time, so in case you don’t want the C++ AMP runtime to assign the array to the default accelerator, all array constructors have overloads that let you pass an accelerator_view object, which you can later access via the accelerator_view property. Note that at the point of initializing an array with data, a synchronous copy of the data takes place to the accelerator, and then to copy any data back we'll see that an explicit copy call is required. This does not happen with the array_view where copying is on demand... refresh and synchronize on array_view Note that in the previous section on constructors, unlike the array case, there was no overload that accepted an accelerator_view for array_view. That is because the array_view is simply a wrapper, so the allocation of the data has already taken place before you created the array_view. When you capture an array_view variable in your call to parallel_for_each, the copy of data between the non-CPU accelerator and the CPU takes place on demand (i.e. it is implicit, versus the explicit copy that has to happen with the array). There are some subtleties to the on-demand-copying that we cover next. The assumption when using an array_view is that you will continue to access the data through the array_view, and not through the original underlying source, e.g. the pointer to the data that you passed to the array_view's constructor. So if you modify the data through the array_view on the GPU, the original pointer on the CPU will not "know" that, unless one of two things happen: you access the data through the array_view on the CPU side, i.e. using indexing that we cover below you explicitly call the array_view's synchronize method on the CPU (this also gets called in the array_view's destructor for you) Conversely, if you make a change to the underlying data through the original source (e.g. the pointer), the array_view will not "know" about those changes, unless you call its refresh method. Finally, note that if you create an array_view of const T, then the data is copied to the accelerator on demand, but it does not get copied back, e.g.   array_view<const double, 5> myArrView(…); // myArrView will not get copied back from GPU There is also a similar mechanism to achieve the reverse, i.e. not to copy the data of an array_view to the GPU. copy_to, data, and global copy/copy_async functions Both array and array_view expose two copy_to overloads that allow copying them to another array, or to another array_view, and these operations can also be achieved with assignment (via the = operator overloads). Also both array and array_view expose a data method, to get a raw pointer to the underlying data of the array or array_view, e.g. float* f = myArr.data();. Note that for array_view, this only works when the rank is equal to 1, due to the data only being contiguous in one dimension as covered in the overview section. Finally, there are a bunch of global concurrency::copy functions returning void (and corresponding concurrency::copy_async functions returning a future) that allow copying between arrays and array_views and iterators etc. Just browse intellisense or amp.h directly for the full set. Note that for array, all copying described throughout this post is deep copying, as per other STL container expectations. You can never have two arrays point to the same data. indexing into array and array_view plus projection Reading or writing data elements of an array is only legal when the code executes on the same accelerator as where the array was bound to. In the array_view case, you can read/write on any accelerator, not just the one where the original data resides, and the data gets copied for you on demand. In both cases, the way you read and write individual elements is via indexing as described next. To access (or set the value of) an element, you can index into it by passing it an index object via the subscript operator. Furthermore, if the rank is 3 or less, you can use the function ( ) operator to pass integer values instead of having to use an index object. e.g. array<float,2> arr(someExtent, someIterator); //or array_view<float,2> arr(someExtent, someContainer); index<2> idx(5,4); float f1 = arr[idx]; float f2 = arr(5,4); //f2 ==f1 //and the reverse for assigning, e.g. arr(idx[0], 7) = 6.9; Note that for both array and array_view, regardless of rank, you can also pass a single integer to the subscript operator which results in a projection of the data, and (for both array and array_view) you get back an array_view of rank N-1 (or if the rank was 1, you get back just the element at that location). Not Covered In this already very long post, I am not going to cover three very cool methods (and related overloads) that both array and array_view expose: view_as, section, reinterpret_as. We'll revisit those at some point in the future, probably on the team blog. Comments about this post by Daniel Moth welcome at the original blog.

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  • State Changes in a Component Based Architecture [closed]

    - by Maxem
    I'm currently working on a game and using the naive component based architecture thingie (Entities are a bag of components, entity.Update() calls Update on each updateable component), while the addition of new features is really simple, it makes a few things really difficult: a) multithreading / currency b) networking c) unit testing. Multithreading / Concurrency is difficult because I basically have to do poor mans concurrency (running the entity updates in separate threads while locking only stuff that crashes (like lists) and ignoring the staleness of read state (some states are already updated, others aren't)) Networking: There are no explicit state changes that I could efficiently push over the net. Unit testing: All updates may or may not conflict, so automated testing is at least awkward. I was thinking about these issues a bit and would like your input on these changes / idea: Switch from the naive cba to a cba with sub systems that work on lists of components Make all state changes explicit Combine 1 and 2 :p Example world update: statePostProcessing.Wait() // ensure that post processing has finished Apply(postProcessedState) state = new StateBag() Concurrently( () => LifeCycleSubSystem.Update(state), // populates the state bag () => MovementSubSystem.Update(state), // populates the state bag .... }) statePostProcessing = Future(() => PostProcess(state)) statePostProcessing.Start() // Tick is finished, the post processing happens in the background So basically the changes are (consistently) based on the data for the last tick; the post processing can a) generate network packages and b) fix conflicts / remove useless changes (example: entity has been destroyed - ignore movement etc.). EDIT: To clarify the granularity of the state changes: If I save these post processed state bags and apply them to an empty world, I see exactly what has happened in the game these state bags originated from - "Free" replay capability. EDIT2: I guess I should have used the term Event instead of State Change and point out that I kind of want to use the Event Sourcing pattern

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  • Some Common Mistakes in SEO

    SEO is the main essence of online revenues as well as optimistic turnover in a particular organization. It drags away the online traffic and hikes the ranking possibility of the websites in the major search engines.

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  • Why mysql 5.5 slower than 5.1 (linux,using mysqlslap)

    - by Zenofo
    my.cnf (5.5 and 5.1 is the same) : back_log=200 max_connections=512 max_connect_errors=999999 key_buffer=512M max_allowed_packet=8M table_cache=512 sort_buffer=8M read_buffer_size=8M thread_cache=8 thread_concurrency=4 myisam_sort_buffer_size=128M interactive_timeout=28800 wait_timeout=7200 mysql 5.5: ..mysql5.5/bin/mysqlslap -a --concurrency=10 --number-of-queries 5000 --iterations=5 -S /tmp/mysql_5.5.sock --engine=innodb Benchmark Running for engine innodb Average number of seconds to run all queries: 15.156 seconds Minimum number of seconds to run all queries: 15.031 seconds Maximum number of seconds to run all queries: 15.296 seconds Number of clients running queries: 10 Average number of queries per client: 500 mysql5.1: ..mysql5.5/bin/mysqlslap -a --concurrency=10 --number-of-queries 5000 --iterations=5 -S /tmp/mysql_5.1.sock --engine=innodb Benchmark Running for engine innodb Average number of seconds to run all queries: 13.252 seconds Minimum number of seconds to run all queries: 13.019 seconds Maximum number of seconds to run all queries: 13.480 seconds Number of clients running queries: 10 Average number of queries per client: 500 Why mysql 5.5 slower than 5.1 ? BTW:I'm tried mysql5.5/bin/mysqlslap and mysql5.1/bin/mysqlslap,result is the same

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  • Should I amortize scripting cost via bytecode analysis or multithreading?

    - by user18983
    I'm working on a game sort of thing where users can write arbitrary code for individual agents, and I'm trying to decide the best way to divide up computation time. The simplest option would be to give each agent a set amount of time and skip their turn if it elapses without an action being decided upon, but I would like people to be able to write their agents decision functions without having to think too much about how long its taking unless they really want to. The two approaches I'm considering are giving each agent a set number of bytecode instructions (taking cost into account) each timestep, and making players deal with the consequences of the game state changing between blocks of computation (as with Battlecode) or giving each agent it's own thread and giving each thread equal time on the processor. I'm about equally knowledgeable on both concurrency and bytecode stuff, which is to say not very, so I'm wondering which approach would be best. I have a clearer idea of how I'd structure things if I used bytecode, but less certainty about how to actually implement the analysis. I'm pretty sure I can work up a concurrency based system without much trouble, but I worry it will be messier with more overhead and will add unnecessary complexity to the project.

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  • Partition Wise Joins

    - by jean-pierre.dijcks
    Some say they are the holy grail of parallel computing and PWJ is the basis for a shared nothing system and the only join method that is available on a shared nothing system (yes this is oversimplified!). The magic in Oracle is of course that is one of many ways to join data. And yes, this is the old flexibility vs. simplicity discussion all over, so I won't go there... the point is that what you must do in a shared nothing system, you can do in Oracle with the same speed and methods. The Theory A partition wise join is a join between (for simplicity) two tables that are partitioned on the same column with the same partitioning scheme. In shared nothing this is effectively hard partitioning locating data on a specific node / storage combo. In Oracle is is logical partitioning. If you now join the two tables on that partitioned column you can break up the join in smaller joins exactly along the partitions in the data. Since they are partitioned (grouped) into the same buckets, all values required to do the join live in the equivalent bucket on either sides. No need to talk to anyone else, no need to redistribute data to anyone else... in short, the optimal join method for parallel processing of two large data sets. PWJ's in Oracle Since we do not hard partition the data across nodes in Oracle we use the Partitioning option to the database to create the buckets, then set the Degree of Parallelism (or run Auto DOP - see here) and get our PWJs. The main questions always asked are: How many partitions should I create? What should my DOP be? In a shared nothing system the answer is of course, as many partitions as there are nodes which will be your DOP. In Oracle we do want you to look at the workload and concurrency, and once you know that to understand the following rules of thumb. Within Oracle we have more ways of joining of data, so it is important to understand some of the PWJ ideas and what it means if you have an uneven distribution across processes. Assume we have a simple scenario where we partition the data on a hash key resulting in 4 hash partitions (H1 -H4). We have 2 parallel processes that have been tasked with reading these partitions (P1 - P2). The work is evenly divided assuming the partitions are the same size and we can scan this in time t1 as shown below. Now assume that we have changed the system and have a 5th partition but still have our 2 workers P1 and P2. The time it takes is actually 50% more assuming the 5th partition has the same size as the original H1 - H4 partitions. In other words to scan these 5 partitions, the time t2 it takes is not 1/5th more expensive, it is a lot more expensive and some other join plans may now start to look exciting to the optimizer. Just to post the disclaimer, it is not as simple as I state it here, but you get the idea on how much more expensive this plan may now look... Based on this little example there are a few rules of thumb to follow to get the partition wise joins. First, choose a DOP that is a factor of two (2). So always choose something like 2, 4, 8, 16, 32 and so on... Second, choose a number of partitions that is larger or equal to 2* DOP. Third, make sure the number of partitions is divisible through 2 without orphans. This is also known as an even number... Fourth, choose a stable partition count strategy, which is typically hash, which can be a sub partitioning strategy rather than the main strategy (range - hash is a popular one). Fifth, make sure you do this on the join key between the two large tables you want to join (and this should be the obvious one...). Translating this into an example: DOP = 8 (determined based on concurrency or by using Auto DOP with a cap due to concurrency) says that the number of partitions >= 16. Number of hash (sub) partitions = 32, which gives each process four partitions to work on. This number is somewhat arbitrary and depends on your data and system. In this case my main reasoning is that if you get more room on the box you can easily move the DOP for the query to 16 without repartitioning... and of course it makes for no leftovers on the table... And yes, we recommend up-to-date statistics. And before you start complaining, do read this post on a cool way to do stats in 11.

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  • Plagued by multithreaded bugs

    - by koncurrency
    On my new team that I manage, the majority of our code is platform, TCP socket, and http networking code. All C++. Most of it originated from other developers that have left the team. The current developers on the team are very smart, but mostly junior in terms of experience. Our biggest problem: multi-threaded concurrency bugs. Most of our class libraries are written to be asynchronous by use of some thread pool classes. Methods on the class libraries often enqueue long running taks onto the thread pool from one thread and then the callback methods of that class get invoked on a different thread. As a result, we have a lot of edge case bugs involving incorrect threading assumptions. This results in subtle bugs that go beyond just having critical sections and locks to guard against concurrency issues. What makes these problems even harder is that the attempts to fix are often incorrect. Some mistakes I've observed the team attempting (or within the legacy code itself) includes something like the following: Common mistake #1 - Fixing concurrency issue by just put a lock around the shared data, but forgetting about what happens when methods don't get called in an expected order. Here's a very simple example: void Foo::OnHttpRequestComplete(statuscode status) { m_pBar->DoSomethingImportant(status); } void Foo::Shutdown() { m_pBar->Cleanup(); delete m_pBar; m_pBar=nullptr; } So now we have a bug in which Shutdown could get called while OnHttpNetworkRequestComplete is occuring on. A tester finds the bug, captures the crash dump, and assigns the bug to a developer. He in turn fixes the bug like this. void Foo::OnHttpRequestComplete(statuscode status) { AutoLock lock(m_cs); m_pBar->DoSomethingImportant(status); } void Foo::Shutdown() { AutoLock lock(m_cs); m_pBar->Cleanup(); delete m_pBar; m_pBar=nullptr; } The above fix looks good until you realize there's an even more subtle edge case. What happens if Shutdown gets called before OnHttpRequestComplete gets called back? The real world examples my team has are even more complex, and the edge cases are even harder to spot during the code review process. Common Mistake #2 - fixing deadlock issues by blindly exiting the lock, wait for the other thread to finish, then re-enter the lock - but without handling the case that the object just got updated by the other thread! Common Mistake #3 - Even though the objects are reference counted, the shutdown sequence "releases" it's pointer. But forgets to wait for the thread that is still running to release it's instance. As such, components are shutdown cleanly, then spurious or late callbacks are invoked on an object in an state not expecting any more calls. There are other edge cases, but the bottom line is this: Multithreaded programming is just plain hard, even for smart people. As I catch these mistakes, I spend time discussing the errors with each developer on developing a more appropriate fix. But I suspect they are often confused on how to solve each issue because of the enormous amount of legacy code that the "right" fix will involve touching. We're going to be shipping soon, and I'm sure the patches we're applying will hold for the upcoming release. Afterwards, we're going to have some time to improve the code base and refactor where needed. We won't have time to just re-write everything. And the majority of the code isn't all that bad. But I'm looking to refactor code such that threading issues can be avoided altogether. One approach I am considering is this. For each significant platform feature, have a dedicated single thread where all events and network callbacks get marshalled onto. Similar to COM apartment threading in Windows with use of a message loop. Long blocking operations could still get dispatched to a work pool thread, but the completion callback is invoked on on the component's thread. Components could possibly even share the same thread. Then all the class libraries running inside the thread can be written under the assumption of a single threaded world. Before I go down that path, I am also very interested if there are other standard techniques or design patterns for dealing with multithreaded issues. And I have to emphasize - something beyond a book that describes the basics of mutexes and semaphores. What do you think? I am also interested in any other approaches to take towards a refactoring process. Including any of the following: Literature or papers on design patterns around threads. Something beyond an introduction to mutexes and semaphores. We don't need massive parallelism either, just ways to design an object model so as to handle asynchronous events from other threads correctly. Ways to diagram the threading of various components, so that it will be easy to study and evolve solutions for. (That is, a UML equivalent for discussing threads across objects and classes) Educating your development team on the issues with multithreaded code. What would you do?

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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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  • save the transient instance before flushing

    - by eugenn
    Exception: object references an unsaved transient instance - save the transient instance before flushing: Child How to reproduce issue: 1. Hibernate is load the entity "Parent". The property "child" is null 2. The "Parent" is rendered on the screen and after that the "child" property is auto instantiated. So I have the following graph: Parent.child != null Parent.child.childId = null Parent.child.childKey = "" Parent.child.childName = "" Question: How I could to force the Hibernate to ignore updating or inserting Child entity WHEN childId = null? If childId != null I would like just create relation. <hibernate-mapping> <class name="com.test.Parent" entity-name="ParentObject" table="parent" dynamic-insert="false" dynamic-update="true" optimistic-lock="version"> <id name="rowId" type="long"> <column name="RowID" /> <generator class="native" /> </id> <version name="versionSequence" type="integer" unsaved-value="null" generated="never" insert="false"> <column name="VersionSequence" /> </version> <many-to-one name="child" entity-name="Child" fetch="select" optimistic-lock="true" embed-xml="false" update="true" insert="false"> <column name="ChildID" /> </many-to-one> <property name="dateCreated" type="timestamp"> <column name="DateCreated" length="0" /> </property> <property name="dateUpdated" type="timestamp" update="false"> <column name="DateUpdated" length="0" /> </property> </class> </hibernate-mapping> <hibernate-mapping> <class name="com.Child" entity-name="Child" table="Child" dynamic-insert="false" dynamic-update="true" optimistic-lock="version"> <id name="childId" type="long" > <column name="ChildID" /> <generator class="native" /> </id> <version name="versionSequence" type="integer" insert="false" generated="never" > <column name="VersionSequence" /> </version> <property name="childKey" type="string" > <column name="ChildKey" length="20" /> </property> <property name="childName" type="string" > <column name="ChildName" length="30" /> </property> <property name="childNumber" type="string" > <column name="ChildNumber" /> </property> <property name="dateCreated" type="timestamp"> <column name="DateCreated" /> </property> <property name="dateUpdated" type="timestamp" update="false"> <column name="DateUpdated" /> </property> </class> </hibernate-mapping>

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  • How to reconcile my support of open-source software and need to feed and house myself?

    - by Guzba
    I have a bit of a dilemma and wanted to get some other developers' opinions on it and maybe some guidance. So I have created a 2D game for Android from the ground up, learning and re factoring as I went along. I did it for the experience, and have been proud of the results. I released it for free as ad supported with AdMob not really expecting much out of it, but curious to see what would happen. Its been a few of months now since release, and it has become very popular (250k downloads!). Additionally, the ad revenue is great and is driving me to make more good games and even allowing me to work less so that I can focus on my own works. When I originally began working on the game, I was pretty new to concurrency and completely new to Android (had Java experience though). The standard advice I got for starting an Android game was to look at the sample games from Google (Snake, Lunar Lander, ...) so I did. In my opinion, these Android sample games from Google are decent to see in general what your code should look like, but not actually all that great to follow. This is because some of their features don't work (saving game state), the concurrency is both unexplained and cumbersome (there is no real separation between the game thread and the UI thread since they sync lock each other out all the time and the UI thread runs game thread code). This made it difficult for me as a newbie to concurrency to understand how it was organized and what was really running what code. Here is my dilemma: After spending this past few months slowly improving my code, I feel that it could be very beneficial to developers who are in the same position that I was in when I started. (Since it is not a complex game, but clearly coded in my opinion.) I want to open up the source so that others can learn from it but don't want to lose my ad revenue stream, which, if I did open the source, I fear I would when people released versions with the ad stripped, or minor tweaks that would fragment my audience, etc. I am a CS undergrad major in college and this money is giving me the freedom to work less at summer job, thus giving me the time and will to work on more of my own projects and improving my own skills while still paying the bills. So what do I do? Open the source at personal sacrifice for the greater good, or keep it closed and be a sort of hypocritical supporter of open source?

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