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  • Why is Java the lingua franca at so many institutions?

    - by Billy ONeal
    EDIT: This question at first seems to be bashing Java, and I guess at this point it is a bit. However, the bigger point I am trying to make is why any one single language is chosen as the one end all be all solution to all problems. Java happens to be the one that's used so that's the one I had to beat on here, but I'm not intentionality ripping Java a new one :) I don't like Java in most academic settings. I'm not saying the language itself is bad -- it has several extremely desirable aspects, most importantly the ability to run without recompilation on most any platform. Nothing wrong with using the language for Your Next App ^TM. (Not something I would personally do, but that's more because I have less experience with it, rather than it's design being poor) I think it is a waste that high level CS courses are taught using Java as a language. Too many of my co-students cannot program worth a damn, because they don't know how to work in a non-garbage-collected world. They don't fundamentally understand the machines they are programming for. When someone can work outside of a garbage collected world, they can work inside of one, but not vice versa. GC is a tool, not a crutch. But the way it is used to teach computer science students is a as a crutch. Computer science should not teach an entire suite of courses tailored to a single language. Students leave with the idea that all good design is idiomatic Java design, and that Object Oriented Design is the ONE TRUE WAY THAT IS THE ONLY WAY THINGS CAN BE DONE. Other languages, at least one of them not being a garbage collected language, should be used in teaching, in order to give the graduate a better understanding of the machines. It is an embarrassment that somebody with a PHD in CS from a respected institution cannot program their way out of a paper bag. What's worse, is that when I talk to those CS professors who actually do understand how things operate, they share feelings like this, that we're doing a disservice to our students by doing everything in Java. (Note that the above would be the same if I replaced it with any other language, generally using a single language is the problem, not Java itself) In total, I feel I can no longer respect any kind of degree at all -- when I can't see those around me able to program their way out of fizzbuzz problems. Why/how did it get to be this way?

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  • Why can't Java/C# implement RAII?

    - by mike30
    Question: Why can't Java/C# implement RAII? Clarification: I am aware the garbage collector is not deterministic. So with the current language features it is not possible for an object's Dispose() method to be called automatically on scope exit. But could such a deterministic feature be added? My understanding: I feel an implementation of RAII must satisfy two requirements: 1. The lifetime of a resource must be bound to a scope. 2. Implicit. The freeing of the resource must happen without an explicit statement by the programmer. Analogous to a garbage collector freeing memory without an explicit statement. The "implicitness" only needs to occur at point of use of the class. The class library creator must of course explicitly implement a destructor or Dispose() method. Java/C# satisfy point 1. In C# a resource implementing IDisposable can be bound to a "using" scope: void test() { using(Resource r = new Resource()) { r.foo(); }//resource released on scope exit } This does not satisfy point 2. The programmer must explicitly tie the object to a special "using" scope. Programmers can (and do) forget to explicitly tie the resource to a scope, creating a leak. In fact the "using" blocks are converted to try-finally-dispose() code by the compiler. It has the same explicit nature of the try-finally-dispose() pattern. Without an implicit release, the hook to a scope is syntactic sugar. void test() { //Programmer forgot (or was not aware of the need) to explicitly //bind Resource to a scope. Resource r = new Resource(); r.foo(); }//resource leaked!!! I think it is worth creating a language feature in Java/C# allowing special objects that are hooked to the stack via a smart-pointer. The feature would allow you to flag a class as scope-bound, so that it always is created with a hook to the stack. There could be a options for different for different types of smart pointers. class Resource - ScopeBound { /* class details */ void Dispose() { //free resource } } void test() { //class Resource was flagged as ScopeBound so the tie to the stack is implicit. Resource r = new Resource(); //r is a smart-pointer r.foo(); }//resource released on scope exit. I think implicitness is "worth it". Just as the implicitness of garbage collection is "worth it". Explicit using blocks are refreshing on the eyes, but offer no semantic advantage over try-finally-dispose(). Is it impractical to implement such a feature into the Java/C# languages? Could it be introduced without breaking old code?

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  • Is it safe to run multiple XNA ContentManager instances on multiple threads?

    - by Boinst
    My XNA project currently uses one ContentManager instance, and one dedicated background thread for loading all content. I wonder, would it be safe to have multiple ContentManager instances, each in it's own dedicated thread, loading different content at the same time? I'm prompted to ask this question because this article makes the following statement: If there are two textures created at the same time on different threads, they will clobber the other and you will end up with some garbage in the textures. I think that what the author is saying here, is that if I access one ContentManager simultaneously on two threads, I'll get garbage. But what if I have separate ContentManager instances for each thread? If no-one knows the answer already from experience, I'll go ahead and try it and see what happens.

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  • Option Trading: Getting the most out of the event session options

    - by extended_events
    You can control different aspects of how an event session behaves by setting the event session options as part of the CREATE EVENT SESSION DDL. The default settings for the event session options are designed to handle most of the common event collection situations so I generally recommend that you just use the defaults. Like everything in the real world though, there are going to be a handful of “special cases” that require something different. This post focuses on identifying the special cases and the correct use of the options to accommodate those cases. There is a reason it’s called Default The default session options specify a total event buffer size of 4 MB with a 30 second latency. Translating this into human terms; this means that our default behavior is that the system will start processing events from the event buffer when we reach about 1.3 MB of events or after 30 seconds, which ever comes first. Aside: What’s up with the 1.3 MB, I thought you said the buffer was 4 MB?The Extended Events engine takes the total buffer size specified by MAX_MEMORY (4MB by default) and divides it into 3 equally sized buffers. This is done so that a session can be publishing events to one buffer while other buffers are being processed. There are always at least three buffers; how to get more than three is covered later. Using this configuration, the Extended Events engine can “keep up” with most event sessions on standard workloads. Why is this? The fact is that most events are small, really small; on the order of a couple hundred bytes. Even when you start considering events that carry dynamically sized data (eg. binary, text, etc.) or adding actions that collect additional data, the total size of the event is still likely to be pretty small. This means that each buffer can likely hold thousands of events before it has to be processed. When the event buffers are finally processed there is an economy of scale achieved since most targets support bulk processing of the events so they are processed at the buffer level rather than the individual event level. When all this is working together it’s more likely that a full buffer will be processed and put back into the ready queue before the remaining buffers (remember, there are at least three) are full. I know what you’re going to say: “My server is exceptional! My workload is so massive it defies categorization!” OK, maybe you weren’t going to say that exactly, but you were probably thinking it. The point is that there are situations that won’t be covered by the Default, but that’s a good place to start and this post assumes you’ve started there so that you have something to look at in order to determine if you do have a special case that needs different settings. So let’s get to the special cases… What event just fired?! How about now?! Now?! If you believe the commercial adage from Heinz Ketchup (Heinz Slow Good Ketchup ad on You Tube), some things are worth the wait. This is not a belief held by most DBAs, particularly DBAs who are looking for an answer to a troubleshooting question fast. If you’re one of these anxious DBAs, or maybe just a Program Manager doing a demo, then 30 seconds might be longer than you’re comfortable waiting. If you find yourself in this situation then consider changing the MAX_DISPATCH_LATENCY option for your event session. This option will force the event buffers to be processed based on your time schedule. This option only makes sense for the asynchronous targets since those are the ones where we allow events to build up in the event buffer – if you’re using one of the synchronous targets this option isn’t relevant. Avoid forgotten events by increasing your memory Have you ever had one of those days where you keep forgetting things? That can happen in Extended Events too; we call it dropped events. In order to optimizes for server performance and help ensure that the Extended Events doesn’t block the server if to drop events that can’t be published to a buffer because the buffer is full. You can determine if events are being dropped from a session by querying the dm_xe_sessions DMV and looking at the dropped_event_count field. Aside: Should you care if you’re dropping events?Maybe not – think about why you’re collecting data in the first place and whether you’re really going to miss a few dropped events. For example, if you’re collecting query duration stats over thousands of executions of a query it won’t make a huge difference to miss a couple executions. Use your best judgment. If you find that your session is dropping events it means that the event buffer is not large enough to handle the volume of events that are being published. There are two ways to address this problem. First, you could collect fewer events – examine you session to see if you are over collecting. Do you need all the actions you’ve specified? Could you apply a predicate to be more specific about when you fire the event? Assuming the session is defined correctly, the next option is to change the MAX_MEMORY option to a larger number. Picking the right event buffer size might take some trial and error, but a good place to start is with the number of dropped events compared to the number you’ve collected. Aside: There are three different behaviors for dropping events that you specify using the EVENT_RETENTION_MODE option. The default is to allow single event loss and you should stick with this setting since it is the best choice for keeping the impact on server performance low.You’ll be tempted to use the setting to not lose any events (NO_EVENT_LOSS) – resist this urge since it can result in blocking on the server. If you’re worried that you’re losing events you should be increasing your event buffer memory as described in this section. Some events are too big to fail A less common reason for dropping an event is when an event is so large that it can’t fit into the event buffer. Even though most events are going to be small, you might find a condition that occasionally generates a very large event. You can determine if your session is dropping large events by looking at the dm_xe_sessions DMV once again, this time check the largest_event_dropped_size. If this value is larger than the size of your event buffer [remember, the size of your event buffer, by default, is max_memory / 3] then you need a large event buffer. To specify a large event buffer you set the MAX_EVENT_SIZE option to a value large enough to fit the largest event dropped based on data from the DMV. When you set this option the Extended Events engine will create two buffers of this size to accommodate these large events. As an added bonus (no extra charge) the large event buffer will also be used to store normal events in the cases where the normal event buffers are all full and waiting to be processed. (Note: This is just a side-effect, not the intended use. If you’re dropping many normal events then you should increase your normal event buffer size.) Partitioning: moving your events to a sub-division Earlier I alluded to the fact that you can configure your event session to use more than the standard three event buffers – this is called partitioning and is controlled by the MEMORY_PARTITION_MODE option. The result of setting this option is fairly easy to explain, but knowing when to use it is a bit more art than science. First the science… You can configure partitioning in three ways: None, Per NUMA Node & Per CPU. This specifies the location where sets of event buffers are created with fairly obvious implication. There are rules we follow for sub-dividing the total memory (specified by MAX_MEMORY) between all the event buffers that are specific to the mode used: None: 3 buffers (fixed)Node: 3 * number_of_nodesCPU: 2.5 * number_of_cpus Here are some examples of what this means for different Node/CPU counts: Configuration None Node CPU 2 CPUs, 1 Node 3 buffers 3 buffers 5 buffers 6 CPUs, 2 Node 3 buffers 6 buffers 15 buffers 40 CPUs, 5 Nodes 3 buffers 15 buffers 100 buffers   Aside: Buffer size on multi-processor computersAs the number of Nodes or CPUs increases, the size of the event buffer gets smaller because the total memory is sub-divided into more pieces. The defaults will hold up to this for a while since each buffer set is holding events only from the Node or CPU that it is associated with, but at some point the buffers will get too small and you’ll either see events being dropped or you’ll get an error when you create your session because you’re below the minimum buffer size. Increase the MAX_MEMORY setting to an appropriate number for the configuration. The most likely reason to start partitioning is going to be related to performance. If you notice that running an event session is impacting the performance of your server beyond a reasonably expected level [Yes, there is a reasonably expected level of work required to collect events.] then partitioning might be an answer. Before you partition you might want to check a few other things: Is your event retention set to NO_EVENT_LOSS and causing blocking? (I told you not to do this.) Consider changing your event loss mode or increasing memory. Are you over collecting and causing more work than necessary? Consider adding predicates to events or removing unnecessary events and actions from your session. Are you writing the file target to the same slow disk that you use for TempDB and your other high activity databases? <kidding> <not really> It’s always worth considering the end to end picture – if you’re writing events to a file you can be impacted by I/O, network; all the usual stuff. Assuming you’ve ruled out the obvious (and not so obvious) issues, there are performance conditions that will be addressed by partitioning. For example, it’s possible to have a successful event session (eg. no dropped events) but still see a performance impact because you have many CPUs all attempting to write to the same free buffer and having to wait in line to finish their work. This is a case where partitioning would relieve the contention between the different CPUs and likely reduce the performance impact cause by the event session. There is no DMV you can check to find these conditions – sorry – that’s where the art comes in. This is  largely a matter of experimentation. On the bright side you probably won’t need to to worry about this level of detail all that often. The performance impact of Extended Events is significantly lower than what you may be used to with SQL Trace. You will likely only care about the impact if you are trying to set up a long running event session that will be part of your everyday workload – sessions used for short term troubleshooting will likely fall into the “reasonably expected impact” category. Hey buddy – I think you forgot something OK, there are two options I didn’t cover: STARTUP_STATE & TRACK_CAUSALITY. If you want your event sessions to start automatically when the server starts, set the STARTUP_STATE option to ON. (Now there is only one option I didn’t cover.) I’m going to leave causality for another post since it’s not really related to session behavior, it’s more about event analysis. - Mike Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • Option Trading: Getting the most out of the event session options

    - by extended_events
    You can control different aspects of how an event session behaves by setting the event session options as part of the CREATE EVENT SESSION DDL. The default settings for the event session options are designed to handle most of the common event collection situations so I generally recommend that you just use the defaults. Like everything in the real world though, there are going to be a handful of “special cases” that require something different. This post focuses on identifying the special cases and the correct use of the options to accommodate those cases. There is a reason it’s called Default The default session options specify a total event buffer size of 4 MB with a 30 second latency. Translating this into human terms; this means that our default behavior is that the system will start processing events from the event buffer when we reach about 1.3 MB of events or after 30 seconds, which ever comes first. Aside: What’s up with the 1.3 MB, I thought you said the buffer was 4 MB?The Extended Events engine takes the total buffer size specified by MAX_MEMORY (4MB by default) and divides it into 3 equally sized buffers. This is done so that a session can be publishing events to one buffer while other buffers are being processed. There are always at least three buffers; how to get more than three is covered later. Using this configuration, the Extended Events engine can “keep up” with most event sessions on standard workloads. Why is this? The fact is that most events are small, really small; on the order of a couple hundred bytes. Even when you start considering events that carry dynamically sized data (eg. binary, text, etc.) or adding actions that collect additional data, the total size of the event is still likely to be pretty small. This means that each buffer can likely hold thousands of events before it has to be processed. When the event buffers are finally processed there is an economy of scale achieved since most targets support bulk processing of the events so they are processed at the buffer level rather than the individual event level. When all this is working together it’s more likely that a full buffer will be processed and put back into the ready queue before the remaining buffers (remember, there are at least three) are full. I know what you’re going to say: “My server is exceptional! My workload is so massive it defies categorization!” OK, maybe you weren’t going to say that exactly, but you were probably thinking it. The point is that there are situations that won’t be covered by the Default, but that’s a good place to start and this post assumes you’ve started there so that you have something to look at in order to determine if you do have a special case that needs different settings. So let’s get to the special cases… What event just fired?! How about now?! Now?! If you believe the commercial adage from Heinz Ketchup (Heinz Slow Good Ketchup ad on You Tube), some things are worth the wait. This is not a belief held by most DBAs, particularly DBAs who are looking for an answer to a troubleshooting question fast. If you’re one of these anxious DBAs, or maybe just a Program Manager doing a demo, then 30 seconds might be longer than you’re comfortable waiting. If you find yourself in this situation then consider changing the MAX_DISPATCH_LATENCY option for your event session. This option will force the event buffers to be processed based on your time schedule. This option only makes sense for the asynchronous targets since those are the ones where we allow events to build up in the event buffer – if you’re using one of the synchronous targets this option isn’t relevant. Avoid forgotten events by increasing your memory Have you ever had one of those days where you keep forgetting things? That can happen in Extended Events too; we call it dropped events. In order to optimizes for server performance and help ensure that the Extended Events doesn’t block the server if to drop events that can’t be published to a buffer because the buffer is full. You can determine if events are being dropped from a session by querying the dm_xe_sessions DMV and looking at the dropped_event_count field. Aside: Should you care if you’re dropping events?Maybe not – think about why you’re collecting data in the first place and whether you’re really going to miss a few dropped events. For example, if you’re collecting query duration stats over thousands of executions of a query it won’t make a huge difference to miss a couple executions. Use your best judgment. If you find that your session is dropping events it means that the event buffer is not large enough to handle the volume of events that are being published. There are two ways to address this problem. First, you could collect fewer events – examine you session to see if you are over collecting. Do you need all the actions you’ve specified? Could you apply a predicate to be more specific about when you fire the event? Assuming the session is defined correctly, the next option is to change the MAX_MEMORY option to a larger number. Picking the right event buffer size might take some trial and error, but a good place to start is with the number of dropped events compared to the number you’ve collected. Aside: There are three different behaviors for dropping events that you specify using the EVENT_RETENTION_MODE option. The default is to allow single event loss and you should stick with this setting since it is the best choice for keeping the impact on server performance low.You’ll be tempted to use the setting to not lose any events (NO_EVENT_LOSS) – resist this urge since it can result in blocking on the server. If you’re worried that you’re losing events you should be increasing your event buffer memory as described in this section. Some events are too big to fail A less common reason for dropping an event is when an event is so large that it can’t fit into the event buffer. Even though most events are going to be small, you might find a condition that occasionally generates a very large event. You can determine if your session is dropping large events by looking at the dm_xe_sessions DMV once again, this time check the largest_event_dropped_size. If this value is larger than the size of your event buffer [remember, the size of your event buffer, by default, is max_memory / 3] then you need a large event buffer. To specify a large event buffer you set the MAX_EVENT_SIZE option to a value large enough to fit the largest event dropped based on data from the DMV. When you set this option the Extended Events engine will create two buffers of this size to accommodate these large events. As an added bonus (no extra charge) the large event buffer will also be used to store normal events in the cases where the normal event buffers are all full and waiting to be processed. (Note: This is just a side-effect, not the intended use. If you’re dropping many normal events then you should increase your normal event buffer size.) Partitioning: moving your events to a sub-division Earlier I alluded to the fact that you can configure your event session to use more than the standard three event buffers – this is called partitioning and is controlled by the MEMORY_PARTITION_MODE option. The result of setting this option is fairly easy to explain, but knowing when to use it is a bit more art than science. First the science… You can configure partitioning in three ways: None, Per NUMA Node & Per CPU. This specifies the location where sets of event buffers are created with fairly obvious implication. There are rules we follow for sub-dividing the total memory (specified by MAX_MEMORY) between all the event buffers that are specific to the mode used: None: 3 buffers (fixed)Node: 3 * number_of_nodesCPU: 2.5 * number_of_cpus Here are some examples of what this means for different Node/CPU counts: Configuration None Node CPU 2 CPUs, 1 Node 3 buffers 3 buffers 5 buffers 6 CPUs, 2 Node 3 buffers 6 buffers 15 buffers 40 CPUs, 5 Nodes 3 buffers 15 buffers 100 buffers   Aside: Buffer size on multi-processor computersAs the number of Nodes or CPUs increases, the size of the event buffer gets smaller because the total memory is sub-divided into more pieces. The defaults will hold up to this for a while since each buffer set is holding events only from the Node or CPU that it is associated with, but at some point the buffers will get too small and you’ll either see events being dropped or you’ll get an error when you create your session because you’re below the minimum buffer size. Increase the MAX_MEMORY setting to an appropriate number for the configuration. The most likely reason to start partitioning is going to be related to performance. If you notice that running an event session is impacting the performance of your server beyond a reasonably expected level [Yes, there is a reasonably expected level of work required to collect events.] then partitioning might be an answer. Before you partition you might want to check a few other things: Is your event retention set to NO_EVENT_LOSS and causing blocking? (I told you not to do this.) Consider changing your event loss mode or increasing memory. Are you over collecting and causing more work than necessary? Consider adding predicates to events or removing unnecessary events and actions from your session. Are you writing the file target to the same slow disk that you use for TempDB and your other high activity databases? <kidding> <not really> It’s always worth considering the end to end picture – if you’re writing events to a file you can be impacted by I/O, network; all the usual stuff. Assuming you’ve ruled out the obvious (and not so obvious) issues, there are performance conditions that will be addressed by partitioning. For example, it’s possible to have a successful event session (eg. no dropped events) but still see a performance impact because you have many CPUs all attempting to write to the same free buffer and having to wait in line to finish their work. This is a case where partitioning would relieve the contention between the different CPUs and likely reduce the performance impact cause by the event session. There is no DMV you can check to find these conditions – sorry – that’s where the art comes in. This is  largely a matter of experimentation. On the bright side you probably won’t need to to worry about this level of detail all that often. The performance impact of Extended Events is significantly lower than what you may be used to with SQL Trace. You will likely only care about the impact if you are trying to set up a long running event session that will be part of your everyday workload – sessions used for short term troubleshooting will likely fall into the “reasonably expected impact” category. Hey buddy – I think you forgot something OK, there are two options I didn’t cover: STARTUP_STATE & TRACK_CAUSALITY. If you want your event sessions to start automatically when the server starts, set the STARTUP_STATE option to ON. (Now there is only one option I didn’t cover.) I’m going to leave causality for another post since it’s not really related to session behavior, it’s more about event analysis. - Mike Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • Retro Video Game Collection

    - by Matt Christian
    Recently I've decided, in true nerd fashion, to collect either comic books or video games.  Considering I'm much more versed in the technological arts and not in ACTUAL art, I thought collecting old video games would be an interesting venture.  After all, I am a self-described compulsive shopper (my bank statement at the end of the month has a purchase every few days).  (Don't worry, I'm not in debt and still pay my bills on time!) I went to a local video game store in Stevens Point called Gaming Generations which is a neat little shop with loads of old games for great prices.  For example, any NES cartridge on the shelf (not behind glass) is, at most, $4.99 with the cheaper ones around $1.99.  During my first round at GG, I picked up the following: NES: - Fester's Quest - Adventures of Link (Zelda 2, grey cart) - Little Nemo - Total Recall - The Goonies 2 PSX: - Galerians N64: - Mission: Impossible - Hybrid Heaven I was a little cautious, would I even like collecting old games?  As soon as I popped a few of those games in I knew right away the answer was an astounding YES!  Not only is it fun to bring back memories of all these old games, but searching for them in stores is also a blast and saying 'I have that one, I need the second one.' After finding such joy in buying these games, I decided to go search through 4-5 stores in Wausau for old games as well.  While the prices were a bit higher and selection smaller, the search was still fun.  I found the following: NES: - Maniac Mansion - T&C Surf - Chip N Dale: Rescue Rangers - TMNT (the first one) - Mission: Impossible N64: - Turok - Turok 2 Genesis: - Sonic the Hedgehog Dreamcast: - Shenmue And I found a Gamegear for $5!  Now I just need to find games for it... Tonight I will go on one more small expedition into the used, once again stopping at GG and another second hand store to see if I can find any items for my collection.

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  • Can't shrink Windows Boot NTFS disk: ERROR(5): Could not map attribute 0x80 in inode, Input/output error

    - by arcyqwerty
    Ubuntu 12.04 LTS, all updates current as of 7/3/2012 gksudo gparted Shrink /dev/sda2 from 367GB to 307GB GParted 0.11.0 --enable-libparted-dmraid Libparted 2.3 Shrink /dev/sda2 from 367.00 GiB to 307.00 GiB 00:32:57 ( ERROR ) calibrate /dev/sda2 00:00:00 ( SUCCESS ) path: /dev/sda2 start: 20,484,096 end: 790,142,975 size: 769,658,880 (367.00 GiB) check file system on /dev/sda2 for errors and (if possible) fix them 00:00:53 ( SUCCESS ) ntfsresize -P -i -f -v /dev/sda2 ntfsresize v2012.1.15AR.1 (libntfs-3g) Device name : /dev/sda2 NTFS volume version: 3.1 Cluster size : 4096 bytes Current volume size: 394065338880 bytes (394066 MB) Current device size: 394065346560 bytes (394066 MB) Checking for bad sectors ... Checking filesystem consistency ... Accounting clusters ... Space in use : 327950 MB (83.2%) Collecting resizing constraints ... Estimating smallest shrunken size supported ... File feature Last used at By inode $MFT : 389998 MB 0 Multi-Record : 394061 MB 386464 $MFTMirr : 314823 MB 1 Compressed : 394064 MB 1019521 Sparse : 330887 MB 752454 Ordinary : 393297 MB 706060 You might resize at 327949758464 bytes or 327950 MB (freeing 66116 MB). Please make a test run using both the -n and -s options before real resizing! shrink file system 00:32:04 ( ERROR ) run simulation 00:32:04 ( ERROR ) ntfsresize -P --force --force /dev/sda2 -s 329640837119 --no-action ntfsresize v2012.1.15AR.1 (libntfs-3g) Device name : /dev/sda2 NTFS volume version: 3.1 Cluster size : 4096 bytes Current volume size: 394065338880 bytes (394066 MB) Current device size: 394065346560 bytes (394066 MB) New volume size : 329640829440 bytes (329641 MB) Checking filesystem consistency ... Accounting clusters ... Space in use : 327950 MB (83.2%) Collecting resizing constraints ... Needed relocations : 13300525 (54479 MB) Schedule chkdsk for NTFS consistency check at Windows boot time ... Resetting $LogFile ... (this might take a while) Relocating needed data ... Updating $BadClust file ... Updating $Bitmap file ... ERROR(5): Could not map attribute 0x80 in inode 1667593: Input/output error ======================================== Windows has run chkdsk successfully (on boot) several times now

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  • Short Look at Frends Helium 2.0 Beta

    - by mipsen
    Pekka from Frends gave me the opportunity to have a look at the beta-version of their Helium 2.0. For all of you, who don't know the tool: Helium is a web-application that collects management-data from BizTalk which you usually have to tediously collect yourself, like performance-data (throttling, throughput (like completed Orchestrations/hour), other perfomance-counters) and data about the state of BTS-Applications and presents the data in clearly structured diagrams and overviews which (often) even allow drill-down.  Installing Helium 2 was quite easy. It comes as an msi-file which creates the web-application on IIS. Aditionally a windows-service is deployt which acts as an agent for sending alert-e-mails and collecting data. What I missed during installation was a link to the created web-app at the end, but the link can be found under Program Files/Frends... On the start-page Helium shows two sections: An overview about the BTS-Apps (Running?, suspended messages?) Basic perfomance-data You can drill-down into the BTS-Apps further, to see ReceiveLocations, Orchestrations and SendPorts. And then a very nice feature can be activated: You can set a monitor to each of the ports and/or orchestrations and have an e-mail sent when a threshold of executions/day or hour is not met. I think this is a great idea. The following screeshot shows the configuration of this option. Conclusion: Helium is a useful monitoring  tool for BTS-operations that might save a lot of time for collecting data, writing a tool yourself or documentation for the operations-staff where to find the data. Pros: Simple installation Most important data for BTS-operations in one place Monitor for alerts, if throughput is not met Nice Web-UI Reasonable price Cons: Additional Perormance-counters cannot be added Im am not sure when the final version is to be shipped, but you can see that on Frend's homepage soon, I guess... A trial version is available here

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  • SteelSeries & JavaFX External Control via Tinkerforge

    - by Geertjan
    The first photo shows me controling a JavaFX chart by rotating a Tinkerforge device, while the other two are of me using code by Christian Pohl from Nordhorn, Germany, to show components from Gerrit Grunwald's SteelSeries library being externally controled by Tinkerforge devices: What these examples show is that you can have a robot (i.e., an external device), of some kind, that can produce output that can be visualized via JavaFX charts and SteelSeries components. For example, imagine a robot that moves around while collecting data on the current temperature throughout a building. That's possible because a temperature device is part of Tinkerforge, just like the rotating device and distance device shown in the photos above. The temperature data collected by the robot would be displayed in various ways on a computer via, for example, JavaFX charts and SteelSeries components. From there, reports could be produced and adjustments could be made to the robot while it continues moving around collecting temperature data. The fact that Tinkerforge has Wifi support makes a scenario such as described here completely possible to implement. And all of this can be programmed in Java, without very much work, since the Java bindings for Tinkerforge are simple to use, such as shown in yesterday's blog entry.

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  • How to actually defragment a JFFS2 filesystem

    - by Julie in Austin
    I have searched all over the Internet, including on a number of StackExchange forums, for a workable method for defragmenting a JFFS2 filesystem and cannot find an answer. The system in question has a 256MB NAND flash part. It is being accessed as a MTD device which is divided into three partitions. The third partition is where the root file system is being stored as a JFFS2 file system. The issue is that writes to the root file system have non-deterministic performance due to the usual issues of the JFFS2 garbage collector deciding to run at the worst possible times. When that happens, the product is hung for some unknown length of time while the garbage collector (and pdflush) run. Changing the file system isn't an option. The solution needs to be something that can run during off-hours that after having been run results in more predictable write performance. Right now I am working on a program that will attempt to force the garbage collector to run, then delete the file with the hope that all of the freed nodes are suddenly more readily available and make writes perform better. Thoughts?

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  • Java heap keeps on shrinking! What is happening in this graph of heap size?

    - by chillitom
    Hi Guys, This is a screen shot of a JVM (win64, 6u17) running ActiveMQ, after every garbage collection the heap size is reducing. As the heap size reduces garbage collection gets more frequent and the heap reduces more quickly. Eventually the VM locks up as it's spending all it's time in GC. -Xms is the default and -Xmx is 2048mb. What is happening!!? How can I avoid this? http://imagebin.org/92614 n.b originally posted on serverfault.com, moved to stackoverflow.com as requested

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  • Destructor - does it get called if the app crashes

    - by Antonio Nakic Alfirevic
    Does a destructor get called if the app crashes? If it's an unhandled exception I'm guessing it does, but what about more serious errors, or something like a user killing the application process? And a few more potentially dumb questions: what happens to all the objects in an app when the app exits and all finalizers have been executed - do the objects get garbage collected or are they somehow all "unloaded" with the process or appdomain? is the garbage collector part of each application (runs in the same process) or is it independent?

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  • What's the life-time of a thread-local value in Python?

    - by Carlos Valiente
    import threading mydata = threading.local() def run(): # When will the garbage collector be able to destroy the object created # here? After the thread exits from ``run()``? After ``join()`` is called? # Or will it survive the thread in which it was created, and live until # ``mydata`` is garbage-collected? mydata.foo = object() t = threading.Thread(target=run) t.start() t.join()

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  • Analysing objects generated by a Java application in between GCs.

    - by elec
    Is there a tool which could be used to analyse the objects being created between two separate garbage collection run (= number of objects created and their type) ? Heapdumps dont really work here as they perform a GC when they're invoked (or at least that's what I observed everytime so far), and I want to see which objects are collected by the GC, not which objects are left after the GC run, if that makes sense. ...or is it possible somehow to inspect the nature and size of objects being collected by the garbage collector ?

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  • More Fun with C# Iterators and Generators

    - by James Michael Hare
    In my last post, I talked quite a bit about iterators and how they can be really powerful tools for filtering a list of items down to a subset of items.  This had both pros and cons over returning a full collection, which, in summary, were:   Pros: If traversal is only partial, does not have to visit rest of collection. If evaluation method is costly, only incurs that cost on elements visited. Adds little to no garbage collection pressure.    Cons: Very slight performance impact if you know caller will always consume all items in collection. And as we saw in the last post, that con for the cost was very, very small and only really became evident on very tight loops consuming very large lists completely.    One of the key items to note, though, is the garbage!  In the traditional (return a new collection) method, if you have a 1,000,000 element collection, and wish to transform or filter it in some way, you have to allocate space for that copy of the collection.  That is, say you have a collection of 1,000,000 items and you want to double every item in the collection.  Well, that means you have to allocate a collection to hold those 1,000,000 items to return, which is a lot especially if you are just going to use it once and toss it.   Iterators, though, don't have this problem.  Each time you visit the node, it would return the doubled value of the node (in this example) and not allocate a second collection of 1,000,000 doubled items.  Do you see the distinction?  In both cases, we're consuming 1,000,000 items.  But in one case we pass back each doubled item which is just an int (for example's sake) on the stack and in the other case, we allocate a list containing 1,000,000 items which then must be garbage collected.   So iterators in C# are pretty cool, eh?  Well, here's one more thing a C# iterator can do that a traditional "return a new collection" transformation can't!   It can return **unbounded** collections!   I know, I know, that smells a lot like an infinite loop, eh?  Yes and no.  Basically, you're relying on the caller to put the bounds on the list, and as long as the caller doesn't you keep going.  Consider this example:   public static class Fibonacci {     // returns the infinite fibonacci sequence     public static IEnumerable<int> Sequence()     {         int iteration = 0;         int first = 1;         int second = 1;         int current = 0;         while (true)         {             if (iteration++ < 2)             {                 current = 1;             }             else             {                 current = first + second;                 second = first;                 first = current;             }             yield return current;         }     } }   Whoa, you say!  Yes, that's an infinite loop!  What the heck is going on there?  Yes, that was intentional.  Would it be better to have a fibonacci sequence that returns only a specific number of items?  Perhaps, but that wouldn't give you the power to defer the execution to the caller.   The beauty of this function is it is as infinite as the sequence itself!  The fibonacci sequence is unbounded, and so is this method.  It will continue to return fibonacci numbers for as long as you ask for them.  Now that's not something you can do with a traditional method that would return a collection of ints representing each number.  In that case you would eventually run out of memory as you got to higher and higher numbers.  This method, though, never runs out of memory.   Now, that said, you do have to know when you use it that it is an infinite collection and bound it appropriately.  Fortunately, Linq provides a lot of these extension methods for you!   Let's say you only want the first 10 fibonacci numbers:       foreach(var fib in Fibonacci.Sequence().Take(10))     {         Console.WriteLine(fib);     }   Or let's say you only want the fibonacci numbers that are less than 100:       foreach(var fib in Fibonacci.Sequence().TakeWhile(f => f < 100))     {         Console.WriteLine(fib);     }   So, you see, one of the nice things about iterators is their power to work with virtually any size (even infinite) collections without adding the garbage collection overhead of making new collections.    You can also do fun things like this to make a more "fluent" interface for for loops:   // A set of integer generator extension methods public static class IntExtensions {     // Begins counting to inifity, use To() to range this.     public static IEnumerable<int> Every(this int start)     {         // deliberately avoiding condition because keeps going         // to infinity for as long as values are pulled.         for (var i = start; ; ++i)         {             yield return i;         }     }     // Begins counting to infinity by the given step value, use To() to     public static IEnumerable<int> Every(this int start, int byEvery)     {         // deliberately avoiding condition because keeps going         // to infinity for as long as values are pulled.         for (var i = start; ; i += byEvery)         {             yield return i;         }     }     // Begins counting to inifity, use To() to range this.     public static IEnumerable<int> To(this int start, int end)     {         for (var i = start; i <= end; ++i)         {             yield return i;         }     }     // Ranges the count by specifying the upper range of the count.     public static IEnumerable<int> To(this IEnumerable<int> collection, int end)     {         return collection.TakeWhile(item => item <= end);     } }   Note that there are two versions of each method.  One that starts with an int and one that starts with an IEnumerable<int>.  This is to allow more power in chaining from either an existing collection or from an int.  This lets you do things like:   // count from 1 to 30 foreach(var i in 1.To(30)) {     Console.WriteLine(i); }     // count from 1 to 10 by 2s foreach(var i in 0.Every(2).To(10)) {     Console.WriteLine(i); }     // or, if you want an infinite sequence counting by 5s until something inside breaks you out... foreach(var i in 0.Every(5)) {     if (someCondition)     {         break;     }     ... }     Yes, those are kinda play functions and not particularly useful, but they show some of the power of generators and extension methods to form a fluid interface.   So what do you think?  What are some of your favorite generators and iterators?

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  • Silverlight DataForm Memory Leak

    - by Andrew Garrison
    Some Background I have noticed that setting the EditTemplate of a DataForm (from the Silverlight Toolkit) can cause the DataForm to not be garbage collected. Consequently, the parent control of the DataForm cannot be garbage collected either, causing a very significant memory leak. Here's some XAML which demonstrates the case. <toolkit:DataForm HorizontalAlignment="Stretch" Margin="10" VerticalAlignment="Stretch"> <toolkit:DataForm.EditTemplate> <DataTemplate> <toolkit:DataField Label="Dummy Binding:"> <TextBox Text="{Binding DummyBinding, Mode=TwoWay}" /> </toolkit:DataField> </DataTemplate> </toolkit:DataForm.EditTemplate> </toolkit:DataForm> I have opened an issue on CodePlex. The isssue has an attachment which has a project which desmonstrates the case. So, My Question Is Has anyone else encountered this issue? More importantly, does anyone know of any workarounds? How can I force this DataForm to be garbage collected?

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  • AS3 Memory Conservation (Loaders/BitmapDatas/Bitmaps/Sprites)

    - by rinogo
    I'm working on reducing the memory requirements of my AS3 app. I understand that once there are no remaining references to an object, it is flagged as being a candidate for garbage collection. Is it even worth it to try to remove references to Loaders that are no longer actively in use? My first thought is that it is not worth it. Here's why: My Sprites need perpetual references to the Bitmaps they display (since the Sprites are always visible in my app). So, the Bitmaps cannot be garbage collected. The Bitmaps rely upon BitmapData objects for their data, so we can't get rid of them. (Up until this point it's all pretty straightforward). Here's where I'm unsure of what's going on: Does a BitmapData have a reference to the data loaded by the Loader? In other words, is BitmapData essentially just a wrapper that has a reference to loader.content, or is the data copied from loader.content to BitmapData? If a reference is maintained, then I don't get anything by garbage collecting my loaders... Thoughts?

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  • Accessing global variable in multithreaded Tomcat server

    - by jwegan
    I have a singleton object that I construct like thus: private static volatile KeyMapper mapper = null; public static KeyMapper getMapper() { if(mapper == null) { synchronized(Utils.class) { if(mapper == null) { mapper = new LocalMemoryMapper(); } } } return mapper; } The class KeyMapper is basically a synchronized wrapper to HashMap with only two functions, one to add a mapping and one to remove a mapping. When running in Tomcat 6.24 on my 32bit Windows machine everything works fine. However when running on a 64 bit Linux machine (CentOS 5.4 with OpenJDK 1.6.0-b09) I add one mapping and print out the size of the HashMap used by KeyMapper to verify the mapping got added (i.e. verify size = 1). Then I try to retrieve the mapping with another request and I keep getting null and when I checked the size of the HashMap it was 0. I'm confident the mapping isn't accidentally being removed since I've commented out all calls to remove (and I don't use clear or any other mutators, just get and put). The requests are going through Tomcat 6.24 (configured to use 200 threads with a minimum of 4 threads) and I passed -Xnoclassgc to the jvm to ensure the class isn't inadvertently getting garbage collected (jvm is also running in -server mode). I also added a finalize method to KeyMapper to print to stderr if it ever gets garbage collected to verify that it wasn't being garbage collected. I'm at my wits end and I can't figure out why one minute the entry in HashMap is there and the next it isn't :(

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  • unicode convertion problem

    - by bhoomi-nature
    Hai frnds i am bhoomi new to php,i am having having below problem in my coding please can you help anyone 1.first i want to open one word document which is having content and i wann to edit it 2.for that i am opening word document from the server and at that time its opening with garbage value(i thing its not converting to utf8 format) 3.wen i delete that garbage value and insert something from textarea to that file it is going to insert and next time onwords its its getting open properly. 4.actually i wann that doc file should open with english words wats there in that doc instead of garbage value..first time opening only its giving problem. i am using below code for that please do the needful $filename = 'test.doc'; if(isset($_REQUEST['Submit'])){ $somecontent = stripslashes($_POST['somecontent']); // Let's make sure the file exists and is writable first. if (is_writable($filename)) { // In our example we're opening $filename in append mode. // The file pointer is at the bottom of the file hence // that's where $somecontent will go when we fwrite() it. if (!$handle = fopen($filename, 'w')) { echo "Cannot open file ($filename)"; exit; } // Write $somecontent to our opened file. if (fwrite($handle, $somecontent) === FALSE) { echo "Cannot write to file ($filename)"; exit; } echo "Success, wrote ($somecontent) to file ($filename) - Continue - "; fclose($handle); } else { echo "The file $filename is not writable"; } } else{ // get contents of a file into a string $handle = fopen($filename, 'r'); $somecontent = fread($handle, filesize($filename)); ? Edit file

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  • Add a Flight Full of Color to Your Desktop with the Beautiful Birds Theme for Windows 7

    - by Asian Angel
    Do you enjoy looking at and collecting pictures of beautifully colored birds? Then brighten up your desktop with the grace and gorgeous plumage of swans, flamingoes, peacocks, and other exotic birds with this wonderful theme for Windows 7. Note: The theme comes with seventeen awesome wallpapers full of brightly colored avian goodness. Download the Beautiful Birds Theme [Windows 7 Personalization Gallery] How To Encrypt Your Cloud-Based Drive with BoxcryptorHTG Explains: Photography with Film-Based CamerasHow to Clean Your Dirty Smartphone (Without Breaking Something)

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  • How do I run a successful Ubuntu Hour?

    - by Darcy Casselman
    I'm taking my be-stickered laptop to a coffee shop tonight for an Ubuntu Hour. I've let a bunch of local LUG people know about it. How can I ensure people come away from it feeling like the experience was valuable? Is there something you've done that was particularly successful? There is a wiki page about Ubuntu Hours which is very helpful. I'm interested in collecting best practices from the community.

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  • Alexa Traffic Rankings Continuously Inaccurate - Room For Improvement

    There is growing news on the internet that the Alexa Traffic Rankings are not accurate and cannot be used to boast traffic rankings for the average site owner. The main flaw that I have found with Alexa, is their main method of collecting user traffic data, which is mainly through an Alexa Toolbar that collects data from users' browsers that have the toolbars installed. This is my experiment and the results.

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