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• Raid 1 array won't assemble after power outage. How do I fix this ext4 mirror?

  - by Forkrul Assail

  Two ext4 drives on Raid 1 with mdadm won't reassemble after the power went out for an extended period (UPS drained). After turning the machine back on, mdadm said that the array was degraded, after which it took about 2 days for a full resync, which completed without problems. On trying to remount the array I get: mount: you must specify the filesystem type cat /etc/fstab lines relevant to setup: /dev/md127 /media/mediapool ext4 defaults 0 0 dmesg | tail (on trying to mount) says: [ 1050.818782] EXT3-fs (md127): error: can't find ext3 filesystem on dev md127. [ 1050.849214] EXT4-fs (md127): VFS: Can't find ext4 filesystem [ 1050.944781] FAT-fs (md127): invalid media value (0x00) [ 1050.944782] FAT-fs (md127): Can't find a valid FAT filesystem [ 1058.272787] EXT2-fs (md127): error: can't find an ext2 filesystem on dev md127. cat /proc/mdstat says: Personalities : [raid1] [linear] [multipath] [raid0] [raid6] [raid5] [raid4] [raid10] md127 : active (auto-read-only) raid1 sdj[2] sdi[0] 2930135360 blocks super 1.2 [2/2] [UU] unused devices: <none> fsck /dev/md127 says: fsck from util-linux 2.20.1 e2fsck 1.42 (29-Nov-2011) fsck.ext2: Superblock invalid, trying backup blocks... fsck.ext2: Bad magic number in super-block while trying to open /dev/md127 The superblock could not be read or does not describe a correct ext2 filesystem. If the device is valid and it really contains an ext2 filesystem (and not swap or ufs or something else), then the superblock is corrupt, and you might try running e2fsck with an alternate superblock: e2fsck -b 8193 <device> mdadm -E /dev/sdi gives me: /dev/sdi: Magic : a92b4efc Version : 1.2 Feature Map : 0x0 Array UUID : 37ac1824:eb8a21f6:bd5afd6d:96da6394 Name : sojourn:33 Creation Time : Sat Nov 10 10:43:52 2012 Raid Level : raid1 Raid Devices : 2 Avail Dev Size : 5860271016 (2794.40 GiB 3000.46 GB) Array Size : 2930135360 (2794.39 GiB 3000.46 GB) Used Dev Size : 5860270720 (2794.39 GiB 3000.46 GB) Data Offset : 262144 sectors Super Offset : 8 sectors State : clean Device UUID : 3e6e9a4f:6c07ab3d:22d47fce:13cecfd0 Update Time : Tue Nov 13 20:34:18 2012 Checksum : f7d10db9 - correct Events : 27 Device Role : Active device 0 Array State : AA ('A' == active, '.' == missing) boot@boot ~ $ sudo mdadm -E /dev/sdj /dev/sdj: Magic : a92b4efc Version : 1.2 Feature Map : 0x0 Array UUID : 37ac1824:eb8a21f6:bd5afd6d:96da6394 Name : sojourn:33 Creation Time : Sat Nov 10 10:43:52 2012 Raid Level : raid1 Raid Devices : 2 Avail Dev Size : 5860271016 (2794.40 GiB 3000.46 GB) Array Size : 2930135360 (2794.39 GiB 3000.46 GB) Used Dev Size : 5860270720 (2794.39 GiB 3000.46 GB) Data Offset : 262144 sectors Super Offset : 8 sectors State : clean Device UUID : 7fb84af4:e9295f7b:ede61f27:bec0cb57 Update Time : Tue Nov 13 20:34:18 2012 Checksum : b9d17fef - correct Events : 27 Device Role : Active device 1 Array State : AA ('A' == active, '.' == missing) machine@user ~ dmesg | tail [ 61.785866] init: alsa-restore main process (2736) terminated with status 99 [ 68.433548] eth0: no IPv6 routers present [ 534.142511] EXT4-fs (sdi): ext4_check_descriptors: Block bitmap for group 0 not in group (block 2838187772)! [ 534.142518] EXT4-fs (sdi): group descriptors corrupted! [ 546.418780] EXT2-fs (sdi): error: couldn't mount because of unsupported optional features (240) [ 549.654127] EXT3-fs (sdi): error: couldn't mount because of unsupported optional features (240) Since this is Raid 1 it was suggested that I try and mount or fsck the drives separately. After a long fsck on one drive, it ended with this as tail: Illegal double indirect block (2298566437) in inode 39717736. CLEARED. Illegal block #4231180 (2611866932) in inode 39717736. CLEARED. Error storing directory block information (inode=39717736, block=0, num=1092368): Memory allocation failed Recreate journal? yes Creating journal (32768 blocks): Done. *** journal has been re-created - filesystem is now ext3 again *** The drive however still doesn't want to mount: dmesg | tail [ 170.674659] md: export_rdev(sdc) [ 170.675152] md: export_rdev(sdc) [ 195.275288] md: export_rdev(sdc) [ 195.275876] md: export_rdev(sdc) [ 1338.540092] CE: hpet increased min_delta_ns to 30169 nsec [26125.734105] EXT4-fs (sdc): ext4_check_descriptors: Checksum for group 0 failed (43502!=37987) [26125.734115] EXT4-fs (sdc): group descriptors corrupted! [26182.325371] EXT3-fs (sdc): error: couldn't mount because of unsupported optional features (240) [27083.316519] EXT4-fs (sdc): ext4_check_descriptors: Checksum for group 0 failed (43502!=37987) [27083.316530] EXT4-fs (sdc): group descriptors corrupted! Please help me fix this. I never in my wildest nightmares thought a complete mirror would die this badly. Am I missing something? Suggestions on fixing this? Could someone explain why it would resync after the powerout, only to seemingly nuke the drive? Thanks for reading. Any help much appreciated. I've tried everything I can think of, including booting and filesystem checking with SystemRescue and Ubuntu liveboot discs.

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• Hard drive after PCB swap strange stuff

  - by ramyy

  I’ve done a PCB swap to my HDD. The HDD model is: WD6400AAKS-00A7B2. The original PCB PN matches the new one (first three letter groups), though the cache mismatches (16MB original, 8MB new). The Hardware store that made the swap told me it was hard to do the swap, they have done firmware adaptation. I can see that this firmware version does not match the original, (01.03B01 original, 05.04E05 new). Still I can see that the serial number and model of the drive is correct, the hard drive appeared normal in the BIOS, all the partitions show and everything appears normal. I have encountered three things though, I have left the drive non operated for 2-3 weeks after the swap to avoid corrupting the data or anything else the new PCB might cause, until I buy a new drive and backup the data. I got a drive, and when I powered the old drive manually (I have a laptop, I use a normal desktop power supply and a USB/SATA connector), I heard the motor start and I could hear ticking as if the motor’s somehow struggling to start, and then the motor sound starts again then the ticking, and so on.. I tried powering again it happened again. The third time it started normally and I could see everything normally. I took the chance and copied all the data over to the new drive. When I was done, I powered off the drive (after more than 25 hours of continuous operation), tried to power it up again and it did so normally, and so are the times I powered it up later; but I got very suspicious now. What could be the problem here? And what happened new, it used to power normally after the swap directly? The second thing that happened is that I found size differences with some files; some include movies, songs, (.iso) files for games, and programs. I could find the size is the same, but size on disk is a little more on the new drive for these files. . I’ve tried some of those files (with size differences) they worked fine. They are not too much but still make you suspicious of the integrity of the data copied; one cannot try if all files are working for about (580 GB) worth of data. I tried copying these files on the same partition they exist of the old drive; they are the same in size as when copied to the new drive (allocation unit size not the issue). I took an image of a partition (sector by sector including empty ones) and when I explore it, these file sizes are equal to the original (old drive); I copy them anywhere else their size on disk, increases, i.e becomes equal to the ones I copy from the old drive itself anywhere. Why the size difference and can one trust the integrity of the data?? The third thing is that when I connect my new external USB HDD, the partitions of the old HDD unmount and then mount again. Connected are: (USB mouse + Old HDD) then external HDD. Why that happens?? Considering the following: I compared the SMART reports from after the swap directly and after the copying, no error readings or reallocated sectors where reported. Here they are: http://www.image-share.com/ijpg-1939-219.html I later ran both WD data life guard tests and they came out passed. I’m worried for this drive since I must be sure the data is fine and safe on the new one, and I will consider it backup for the new one, since you can’t trust anything anymore. I hope you can forgive me for the length of the post, but couldn’t ignore any of the details, this hard drive contains very important data to me and I have to deal with the situation with great care.

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• Parallelism in .NET – Part 5, Partitioning of Work

  - by Reed

  When parallelizing any routine, we start by decomposing the problem.  Once the problem is understood, we need to break our work into separate tasks, so each task can be run on a different processing element.  This process is called partitioning. Partitioning our tasks is a challenging feat.  There are opposing forces at work here: too many partitions adds overhead, too few partitions leaves processors idle.  Trying to work the perfect balance between the two extremes is the goal for which we should aim.  Luckily, the Task Parallel Library automatically handles much of this process.  However, there are situations where the default partitioning may not be appropriate, and knowledge of our routines may allow us to guide the framework to making better decisions. First off, I’d like to say that this is a more advanced topic.  It is perfectly acceptable to use the parallel constructs in the framework without considering the partitioning taking place.  The default behavior in the Task Parallel Library is very well-behaved, even for unusual work loads, and should rarely be adjusted.  I have found few situations where the default partitioning behavior in the TPL is not as good or better than my own hand-written partitioning routines, and recommend using the defaults unless there is a strong, measured, and profiled reason to avoid using them.  However, understanding partitioning, and how the TPL partitions your data, helps in understanding the proper usage of the TPL. I indirectly mentioned partitioning while discussing aggregation.  Typically, our systems will have a limited number of Processing Elements (PE), which is the terminology used for hardware capable of processing a stream of instructions.  For example, in a standard Intel i7 system, there are four processor cores, each of which has two potential hardware threads due to Hyperthreading.  This gives us a total of 8 PEs – theoretically, we can have up to eight operations occurring concurrently within our system. In order to fully exploit this power, we need to partition our work into Tasks.  A task is a simple set of instructions that can be run on a PE.  Ideally, we want to have at least one task per PE in the system, since fewer tasks means that some of our processing power will be sitting idle.  A naive implementation would be to just take our data, and partition it with one element in our collection being treated as one task.  When we loop through our collection in parallel, using this approach, we’d just process one item at a time, then reuse that thread to process the next, etc.  There’s a flaw in this approach, however.  It will tend to be slower than necessary, often slower than processing the data serially. The problem is that there is overhead associated with each task.  When we take a simple foreach loop body and implement it using the TPL, we add overhead.  First, we change the body from a simple statement to a delegate, which must be invoked.  In order to invoke the delegate on a separate thread, the delegate gets added to the ThreadPool’s current work queue, and the ThreadPool must pull this off the queue, assign it to a free thread, then execute it.  If our collection had one million elements, the overhead of trying to spawn one million tasks would destroy our performance. The answer, here, is to partition our collection into groups, and have each group of elements treated as a single task.  By adding a partitioning step, we can break our total work into small enough tasks to keep our processors busy, but large enough tasks to avoid overburdening the ThreadPool.  There are two clear, opposing goals here: Always try to keep each processor working, but also try to keep the individual partitions as large as possible. When using Parallel.For, the partitioning is always handled automatically.  At first, partitioning here seems simple.  A naive implementation would merely split the total element count up by the number of PEs in the system, and assign a chunk of data to each processor.  Many hand-written partitioning schemes work in this exactly manner.  This perfectly balanced, static partitioning scheme works very well if the amount of work is constant for each element.  However, this is rarely the case.  Often, the length of time required to process an element grows as we progress through the collection, especially if we’re doing numerical computations.  In this case, the first PEs will finish early, and sit idle waiting on the last chunks to finish.  Sometimes, work can decrease as we progress, since previous computations may be used to speed up later computations.  In this situation, the first chunks will be working far longer than the last chunks.  In order to balance the workload, many implementations create many small chunks, and reuse threads.  This adds overhead, but does provide better load balancing, which in turn improves performance. The Task Parallel Library handles this more elaborately.  Chunks are determined at runtime, and start small.  They grow slowly over time, getting larger and larger.  This tends to lead to a near optimum load balancing, even in odd cases such as increasing or decreasing workloads.  Parallel.ForEach is a bit more complicated, however. When working with a generic IEnumerable<T>, the number of items required for processing is not known in advance, and must be discovered at runtime.  In addition, since we don’t have direct access to each element, the scheduler must enumerate the collection to process it.  Since IEnumerable<T> is not thread safe, it must lock on elements as it enumerates, create temporary collections for each chunk to process, and schedule this out.  By default, it uses a partitioning method similar to the one described above.  We can see this directly by looking at the Visual Partitioning sample shipped by the Task Parallel Library team, and available as part of the Samples for Parallel Programming.  When we run the sample, with four cores and the default, Load Balancing partitioning scheme, we see this: The colored bands represent each processing core.  You can see that, when we started (at the top), we begin with very small bands of color.  As the routine progresses through the Parallel.ForEach, the chunks get larger and larger (seen by larger and larger stripes). Most of the time, this is fantastic behavior, and most likely will out perform any custom written partitioning.  However, if your routine is not scaling well, it may be due to a failure in the default partitioning to handle your specific case.  With prior knowledge about your work, it may be possible to partition data more meaningfully than the default Partitioner. There is the option to use an overload of Parallel.ForEach which takes a Partitioner<T> instance.  The Partitioner<T> class is an abstract class which allows for both static and dynamic partitioning.  By overriding Partitioner<T>.SupportsDynamicPartitions, you can specify whether a dynamic approach is available.  If not, your custom Partitioner<T> subclass would override GetPartitions(int), which returns a list of IEnumerator<T> instances.  These are then used by the Parallel class to split work up amongst processors.  When dynamic partitioning is available, GetDynamicPartitions() is used, which returns an IEnumerable<T> for each partition.  If you do decide to implement your own Partitioner<T>, keep in mind the goals and tradeoffs of different partitioning strategies, and design appropriately. The Samples for Parallel Programming project includes a ChunkPartitioner class in the ParallelExtensionsExtras project.  This provides example code for implementing your own, custom allocation strategies, including a static allocator of a given chunk size.  Although implementing your own Partitioner<T> is possible, as I mentioned above, this is rarely required or useful in practice.  The default behavior of the TPL is very good, often better than any hand written partitioning strategy.

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• Regression testing with Selenium GRID

  - by Ben Adderson

  A lot of software teams out there are tasked with supporting and maintaining systems that have grown organically over time, and the web team here at Red Gate is no exception. We're about to embark on our first significant refactoring endeavour for some time, and as such its clearly paramount that the code be tested thoroughly for regressions. Unfortunately we currently find ourselves with a codebase that isn't very testable - the three layers (database, business logic and UI) are currently tightly coupled. This leaves us with the unfortunate problem that, in order to confidently refactor the code, we need unit tests. But in order to write unit tests, we need to refactor the code :S To try and ease the initial pain of decoupling these layers, I've been looking into the idea of using UI automation to provide a sort of system-level regression test suite. The idea being that these tests can help us identify regressions whilst we work towards a more testable codebase, at which point the more traditional combination of unit and integration tests can take over. Ending up with a strong battery of UI tests is also a nice bonus :) Following on from my previous posts (here, here and here) I knew I wanted to use Selenium. I also figured that this would be a good excuse to put my xUnit [Browser] attribute to good use. Pretty quickly, I had a raft of tests that looked like the following (this particular example uses Reflector Pro). In a nut shell the test traverses our shopping cart and, for a particular combination of number of users and months of support, checks that the price calculations all come up with the correct values. [BrowserTheory] [Browser(Browsers.Firefox3_6, "http://www.red-gate.com")] public void Purchase1UserLicenceNoSupport(SeleniumProvider seleniumProvider) {     //Arrange     _browser = seleniumProvider.GetBrowser();     _browser.Open("http://www.red-gate.com/dynamic/shoppingCart/ProductOption.aspx?Product=ReflectorPro");                  //Act     _browser = ShoppingCartHelpers.TraverseShoppingCart(_browser, 1, 0, ".NET Reflector Pro");     //Assert     var priceResult = PriceHelpers.GetNewPurchasePrice(db, "ReflectorPro", 1, 0, Currencies.Euros);         Assert.Equal(priceResult.Price, _browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl01_Price"));     Assert.Equal(priceResult.Tax, _browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Tax"));     Assert.Equal(priceResult.Total, _browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Total")); } These tests are pretty concise, with much of the common code in the TraverseShoppingCart() and GetNewPurchasePrice() methods. The (inevitable) problem arose when it came to execute these tests en masse. Selenium is a very slick tool, but it can't mask the fact that UI automation is very slow. To give you an idea, the set of cases that covers all of our products, for all combinations of users and support, came to 372 tests (for now only considering purchases in dollars). In the world of automated integration tests, that's a very manageable number. For unit tests, it's a trifle. However for UI automation, those 372 tests were taking just over two hours to run. Two hours may not sound like a lot, but those cases only cover one of the three currencies we deal with, and only one of the many different ways our systems can be asked to calculate a price. It was already pretty clear at this point that in order for this approach to be viable, I was going to have to find a way to speed things up. Up to this point I had been using Selenium Remote Control to automate Firefox, as this was the approach I had used previously and it had worked well. Fortunately,  the guys at SeleniumHQ also maintain a tool for executing multiple Selenium RC tests in parallel: Selenium Grid. Selenium Grid uses a central 'hub' to handle allocation of Selenium tests to individual RCs. The Remote Controls simply register themselves with the hub when they start, and then wait to be assigned work. The (for me) really clever part is that, as far as the client driver library is concerned, the grid hub looks exactly the same as a vanilla remote control. To create a new browser session against Selenium RC, the following C# code suffices: new DefaultSelenium("localhost", 4444, "*firefox", "http://www.red-gate.com"); This assumes that the RC is running on the local machine, and is listening on port 4444 (the default). Assuming the hub is running on your local machine, then to create a browser session in Selenium Grid, via the hub rather than directly against the control, the code is exactly the same! Behind the scenes, the hub will take this request and hand it off to one of the registered RCs that provides the "*firefox" execution environment. It will then pass all communications back and forth between the test runner and the remote control transparently. This makes running existing RC tests on a Selenium Grid a piece of cake, as the developers intended. For a more detailed description of exactly how Selenium Grid works, see this page. Once I had a test environment capable of running multiple tests in parallel, I needed a test runner capable of doing the same. Unfortunately, this does not currently exist for xUnit (boo!). MbUnit on the other hand, has the concept of concurrent execution baked right into the framework. So after swapping out my assembly references, and fixing up the resulting mismatches in assertions, my example test now looks like this: [Test] public void Purchase1UserLicenceNoSupport() {    //Arrange    ISelenium browser = BrowserHelpers.GetBrowser();    var db = DbHelpers.GetWebsiteDBDataContext();    browser.Start();    browser.Open("http://www.red-gate.com/dynamic/shoppingCart/ProductOption.aspx?Product=ReflectorPro");                 //Act     browser = ShoppingCartHelpers.TraverseShoppingCart(browser, 1, 0, ".NET Reflector Pro");    var priceResult = PriceHelpers.GetNewPurchasePrice(db, "ReflectorPro", 1, 0, Currencies.Euros);    //Assert     Assert.AreEqual(priceResult.Price, browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl01_Price"));     Assert.AreEqual(priceResult.Tax, browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Tax"));     Assert.AreEqual(priceResult.Total, browser.GetText("ctl00_content_InvoiceShoppingItemRepeater_ctl02_Total")); } This is pretty much the same as the xUnit version. The exceptions are that the attributes have changed,  the //Arrange phase now has to handle setting up the ISelenium object, as the attribute that previously did this has gone away, and the test now sets up its own database connection. Previously I was using a shared database connection, but this approach becomes more complicated when tests are being executed concurrently. To avoid complexity each test has its own connection, which it is responsible for closing. For the sake of readability, I snipped out the code that closes the browser session and the db connection at the end of the test. With all that done, there was only one more step required before the tests would execute concurrently. It is necessary to tell the test runner which tests are eligible to run in parallel, via the [Parallelizable] attribute. This can be done at the test, fixture or assembly level. Since I wanted to run all tests concurrently, I marked mine at the assembly level in the AssemblyInfo.cs using the following: [assembly: DegreeOfParallelism(3)] [assembly: Parallelizable(TestScope.All)] The second attribute marks all tests in the assembly as [Parallelizable], whilst the first tells the test runner how many concurrent threads to use when executing the tests. I set mine to three since I was using 3 RCs in separate VMs. With everything now in place, I fired up the Icarus* test runner that comes with MbUnit. Executing my 372 tests three at a time instead of one at a time reduced the running time from 2 hours 10 minutes, to 55 minutes, that's an improvement of about 58%! I'd like to have seen an improvement of 66%, but I can understand that either inefficiencies in the hub code, my test environment or the test runner code (or some combination of all three most likely) contributes to a slightly diminished improvement. That said, I'd love to hear about any experience you have in upping this efficiency. Ultimately though, it was a saving that was most definitely worth having. It makes regression testing via UI automation a far more plausible prospect. The other obvious point to make is that this approach scales far better than executing tests serially. So if ever we need to improve performance, we just register additional RC's with the hub, and up the DegreeOfParallelism. *This was just my personal preference for a GUI runner. The MbUnit/Gallio installer also provides a command line runner, a TestDriven.net runner, and a Resharper 4.5 runner. For now at least, Resharper 5 isn't supported.

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• SQL SERVER – SSMS: Disk Usage Report

  - by Pinal Dave

  Let us start with humor!  I think we the series on various reports, we come to a logical point. We covered all the reports at server level. This means the reports we saw were targeted towards activities that are related to instance level operations. These are mostly like how a doctor diagnoses a patient. At this point I am reminded of a dialog which I read somewhere: Patient: Doc, It hurts when I touch my head. Doc: Ok, go on. What else have you experienced? Patient: It hurts even when I touch my eye, it hurts when I touch my arms, it even hurts when I touch my feet, etc. Doc: Hmmm … Patient: I feel it hurts when I touch anywhere in my body. Doc: Ahh … now I get it. You need a plaster to your finger John. Sometimes the server level gives an indicator to what is happening in the system, but we need to get to the root cause for a specific database. So, this is the first blog in series where we would start discussing about database level reports. To launch database level reports, expand selected server in Object Explorer, expand the Databases folder, and then right-click any database for which we want to look at reports. From the menu, select Reports, then Standard Reports, and then any of database level reports. In this blog, we would talk about four “disk” reports because they are similar: Disk Usage Disk Usage by Top Tables Disk Usage by Table Disk Usage by Partition Disk Usage This report shows multiple information about the database. Let us discuss them one by one.  We have divided the output into 5 different sections. Section 1 shows the high level summary of the database. It shows the space used by database files (mdf and ldf). Under the hood, the report uses, various DMVs and DBCC Commands, it is using sys.data_spaces and DBCC SHOWFILESTATS. Section 2 and 3 are pie charts. One for data file allocation and another for the transaction log file. Pie chart for “Data Files Space Usage (%)” shows space consumed data, indexes, allocated to the SQL Server database, and unallocated space which is allocated to the SQL Server database but not yet filled with anything. “Transaction Log Space Usage (%)” used DBCC SQLPERF (LOGSPACE) and shows how much empty space we have in the physical transaction log file. Section 4 shows the data from Default Trace and looks at Event IDs 92, 93, 94, 95 which are for “Data File Auto Grow”, “Log File Auto Grow”, “Data File Auto Shrink” and “Log File Auto Shrink” respectively. Here is an expanded view for that section. If default trace is not enabled, then this section would be replaced by the message “Trace Log is disabled” as highlighted below. Section 5 of the report uses DBCC SHOWFILESTATS to get information. Here is the enhanced version of that section. This shows the physical layout of the file. In case you have In-Memory Objects in the database (from SQL Server 2014), then report would show information about those as well. Here is the screenshot taken for a different database, which has In-Memory table. I have highlighted new things which are only shown for in-memory database. The new sections which are highlighted above are using sys.dm_db_xtp_checkpoint_files, sys.database_files and sys.data_spaces. The new type for in-memory OLTP is ‘FX’ in sys.data_space. The next set of reports is targeted to get information about a table and its storage. These reports can answer questions like: Which is the biggest table in the database? How many rows we have in table? Is there any table which has a lot of reserved space but its unused? Which partition of the table is having more data? Disk Usage by Top Tables This report provides detailed data on the utilization of disk space by top 1000 tables within the Database. The report does not provide data for memory optimized tables. Disk Usage by Table This report is same as earlier report with few difference. First Report shows only 1000 rows First Report does order by values in DMV sys.dm_db_partition_stats whereas second one does it based on name of the table. Both of the reports have interactive sort facility. We can click on any column header and change the sorting order of data. Disk Usage by Partition This report shows the distribution of the data in table based on partition in the table. This is so similar to previous output with the partition details now. Here is the query taken from profiler. SELECT row_number() OVER (ORDER BY a1.used_page_count DESC, a1.index_id) AS row_number ,      (dense_rank() OVER (ORDER BY a5.name, a2.name))%2 AS l1 ,      a1.OBJECT_ID ,      a5.name AS [schema] ,       a2.name ,       a1.index_id ,       a3.name AS index_name ,       a3.type_desc ,       a1.partition_number ,       a1.used_page_count * 8 AS total_used_pages ,       a1.reserved_page_count * 8 AS total_reserved_pages ,       a1.row_count FROM sys.dm_db_partition_stats a1 INNER JOIN sys.all_objects a2  ON ( a1.OBJECT_ID = a2.OBJECT_ID) AND a1.OBJECT_ID NOT IN (SELECT OBJECT_ID FROM sys.tables WHERE is_memory_optimized = 1) INNER JOIN sys.schemas a5 ON (a5.schema_id = a2.schema_id) LEFT OUTER JOIN  sys.indexes a3  ON ( (a1.OBJECT_ID = a3.OBJECT_ID) AND (a1.index_id = a3.index_id) ) WHERE (SELECT MAX(DISTINCT partition_number) FROM sys.dm_db_partition_stats a4 WHERE (a4.OBJECT_ID = a1.OBJECT_ID)) >= 1 AND a2.TYPE <> N'S' AND  a2.TYPE <> N'IT' ORDER BY a5.name ASC, a2.name ASC, a1.index_id, a1.used_page_count DESC, a1.partition_number Using all of the above reports, you should be able to get the usage of database files and also space used by tables. I think this is too much disk information for a single blog and I hope you have used them in the past to get data. Do let me know if you found anything interesting using these reports in your environments. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL Tagged: SQL Reports

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• techniques for an AI for a highly cramped turn-based tactics game

  - by Adam M.

  I'm trying to write an AI for a tactics game in the vein of Final Fantasy Tactics or Vandal Hearts. I can't change the game rules in any way, only upgrade the AI. I have experience programming AI for classic board games (basically minimax and its variants), but I think the branching factor is too great for the approach to be reasonable here. I'll describe the game and some current AI flaws that I'd like to fix. I'd like to hear ideas for applicable techniques. I'm a decent enough programmer, so I only need the ideas, not an implementation (though that's always appreciated). I'd rather not expend effort chasing (too many) dead ends, so although speculation and brainstorming are good and probably helpful, I'd prefer to hear from somebody with actual experience solving this kind of problem. For those who know it, the game is the land battle mini-game in Sid Meier's Pirates! (2004) and you can skim/skip the next two paragraphs. For those who don't, here's briefly how it works. The battle is turn-based and takes place on a 16x16 grid. There are three terrain types: clear (no hindrance), forest (hinders movement, ranged attacks, and sight), and rock (impassible, but does not hinder attacks or sight). The map is randomly generated with roughly equal amounts of each type of terrain. Because there are many rock and forest tiles, movement is typically very cramped. This is tactically important. The terrain is not flat; higher terrain gives minor bonuses. The terrain is known to both sides. The player is always the attacker and the AI is always the defender, so it's perfectly valid for the AI to set up a defensive position and just wait. The player wins by killing all defenders or by getting a unit to the city gates (a tile on the other side of the map). There are very few units on each side, usually 4-8. Because of this, it's crucial not to take damage without gaining some advantage from it. Units can take multiple actions per turn. All units on one side move before any units on the other side. Order of execution is important, and interleaving of actions between units is often useful. Units have melee and ranged attacks. Melee attacks vary widely in strength; ranged attacks have the same strength but vary in range. The main challenges I face are these: Lots of useful move combinations start with a "useless" move that gains no immediate advantage, or even loses advantage, in order to set up a powerful flank attack in the future. And, since the player units are stronger and have longer range, the AI pretty much always has to take some losses before they can start to gain kills. The AI must be able to look ahead to distinguish between sacrificial actions that provide a future benefit and those that don't. Because the terrain is so cramped, most of the tactics come down to achieving good positioning with multiple units that work together to defend an area. For instance, two defenders can often dominate a narrow pass by positioning themselves so an enemy unit attempting to pass must expose itself to a flank attack. But one defender in the same pass would be useless, and three units can defend a slightly larger pass. Etc. The AI should be able to figure out where the player must go to reach the city gates and how to best position its few units to cover the approaches, shifting, splitting, or combining them appropriately as the player moves. Because flank attacks are extremely deadly (and engineering flank attacks is key to the player strategy), the AI should be competent at moving its units so that they cover each other's flanks unless the sacrifice of a unit would give a substantial benefit. They should also be able to force flank attacks on players, for instance by threatening a unit from two different directions such that responding to one threat exposes the flank to the other. The AI should attack if possible, but sometimes there are no good ways to approach the player's position. In that case, the AI should be able to recognize this and set up a defensive position of its own. But the AI shouldn't be vulnerable to a trivial exploit where the player repeatedly opens and closes a hole in his defense and shoots at the AI as it approaches and retreats. That is, the AI should ideally be able to recognize that the player is capable of establishing a solid defense of an area, even if the defense is not currently in place. (I suppose if a good unit allocation algorithm existed, as needed for the second bullet point, the AI could run it on the player units to see where they could defend.) Because it's important to choose a good order of action and interleave actions between units, it's not as simple as just finding the best move for each unit in turn. All of these can be accomplished with a minimax search in theory, but the search space is too large, so specialized techniques are needed. I thought about techniques such as influence mapping, but I don't see how to use the technique to great effect. I thought about assigning goals to the units. This can help them work together in some limited way, and the problem of "how do I accomplish this goal?" is easier to solve than "how do I win this battle?", but assigning good goals is a hard problem in itself, because it requires knowing whether the goal is achievable and whether it's a good use of resources. So, does anyone have specific ideas for techniques that can help cleverize this AI? Update: I found a related question on Stackoverflow: http://stackoverflow.com/questions/3133273/ai-for-a-final-fantasy-tactics-like-game The selected answer gives a decent approach to choosing between alternative actions, but it doesn't seem to have much ability to look into the future and discern beneficial sacrifices from wasteful ones. It also focuses on a single unit at a time and it's not clear how it could be extended to support cooperation between units in defending or attacking.

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• Parent Objects

  - by Ali Bahrami

  Support for Parent Objects was added in Solaris 11 Update 1. The following material is adapted from the PSARC arc case, and the Solaris Linker and Libraries Manual. A "plugin" is a shared object, usually loaded via dlopen(), that is used by a program in order to allow the end user to add functionality to the program. Examples of plugins include those used by web browsers (flash, acrobat, etc), as well as mdb and elfedit modules. The object that loads the plugin at runtime is called the "parent object". Unlike most object dependencies, the parent is not identified by name, but by its status as the object doing the load. Historically, building a good plugin is has been more complicated than it should be: A parent and its plugin usually share a 2-way dependency: The plugin provides one or more routines for the parent to call, and the parent supplies support routines for use by the plugin for things like memory allocation and error reporting. It is a best practice to build all objects, including plugins, with the -z defs option, in order to ensure that the object specifies all of its dependencies, and is self contained. However: The parent is usually an executable, which cannot be linked to via the usual library mechanisms provided by the link editor. Even if the parent is a shared object, which could be a normal library dependency to the plugin, it may be desirable to build plugins that can be used by more than one parent, in which case embedding a dependency NEEDED entry for one of the parents is undesirable. The usual way to build a high quality plugin with -z defs uses a special mapfile provided by the parent. This mapfile defines the parent routines, specifying the PARENT attribute (see example below). This works, but is inconvenient, and error prone. The symbol table in the parent already describes what it makes available to plugins — ideally the plugin would obtain that information directly rather than from a separate mapfile. The new -z parent option to ld allows a plugin to link to the parent and access the parent symbol table. This differs from a typical dependency: No NEEDED record is created. The relationship is recorded as a logical connection to the parent, rather than as an explicit object name However, it operates in the same manner as any other dependency in terms of making symbols available to the plugin. When the -z parent option is used, the link-editor records the basename of the parent object in the dynamic section, using the new tag DT_SUNW_PARENT. This is an informational tag, which is not used by the runtime linker to locate the parent, but which is available for diagnostic purposes. The ld(1) manpage documentation for the -z parent option is: -z parent=object Specifies a "parent object", which can be an executable or shared object, against which to link the output object. This option is typically used when creating "plugin" shared objects intended to be loaded by an executable at runtime via the dlopen() function. The symbol table from the parent object is used to satisfy references from the plugin object. The use of the -z parent option makes symbols from the object calling dlopen() available to the plugin. Example For this example, we use a main program, and a plugin. The parent provides a function named parent_callback() for the plugin to call. The plugin provides a function named plugin_func() to the parent: % cat main.c #include <stdio.h> #include <dlfcn.h> #include <link.h> void parent_callback(void) { printf("plugin_func() has called parent_callback()\n"); } int main(int argc, char **argv) { typedef void plugin_func_t(void); void *hdl; plugin_func_t *plugin_func; if (argc != 2) { fprintf(stderr, "usage: main plugin\n"); return (1); } if ((hdl = dlopen(argv[1], RTLD_LAZY)) == NULL) { fprintf(stderr, "unable to load plugin: %s\n", dlerror()); return (1); } plugin_func = (plugin_func_t *) dlsym(hdl, "plugin_func"); if (plugin_func == NULL) { fprintf(stderr, "unable to find plugin_func: %s\n", dlerror()); return (1); } (*plugin_func)(); return (0); } % cat plugin.c #include <stdio.h> extern void parent_callback(void); void plugin_func(void) { printf("parent has called plugin_func() from plugin.so\n"); parent_callback(); } Building this in the traditional manner, without -zdefs: % cc -o main main.c % cc -G -o plugin.so plugin.c % ./main ./plugin.so parent has called plugin_func() from plugin.so plugin_func() has called parent_callback() As noted above, when building any shared object, the -z defs option is recommended, in order to ensure that the object is self contained and specifies all of its dependencies. However, the use of -z defs prevents the plugin object from linking due to the unsatisfied symbol from the parent object: % cc -zdefs -G -o plugin.so plugin.c Undefined first referenced symbol in file parent_callback plugin.o ld: fatal: symbol referencing errors. No output written to plugin.so A mapfile can be used to specify to ld that the parent_callback symbol is supplied by the parent object. % cat plugin.mapfile $mapfile_version 2 SYMBOL_SCOPE { global: parent_callback { FLAGS = PARENT }; }; % cc -zdefs -Mplugin.mapfile -G -o plugin.so plugin.c However, the -z parent option to ld is the most direct solution to this problem, allowing the plugin to actually link against the parent object, and obtain the available symbols from it. An added benefit of using -z parent instead of a mapfile, is that the name of the parent object is recorded in the dynamic section of the plugin, and can be displayed by the file utility: % cc -zdefs -zparent=main -G -o plugin.so plugin.c % elfdump -d plugin.so | grep PARENT [0] SUNW_PARENT 0xcc main % file plugin.so plugin.so: ELF 32-bit LSB dynamic lib 80386 Version 1, parent main, dynamically linked, not stripped % ./main ./plugin.so parent has called plugin_func() from plugin.so plugin_func() has called parent_callback() We can also observe this in elfedit plugins on Solaris systems running Solaris 11 Update 1 or newer: % file /usr/lib/elfedit/dyn.so /usr/lib/elfedit/dyn.so: ELF 32-bit LSB dynamic lib 80386 Version 1, parent elfedit, dynamically linked, not stripped, no debugging information available Related Other Work The GNU ld has an option named --just-symbols that can be used in a similar manner: --just-symbols=filename Read symbol names and their addresses from filename, but do not relocate it or include it in the output. This allows your output file to refer symbolically to absolute locations of memory defined in other programs. You may use this option more than once. -z parent is a higher level operation aimed specifically at simplifying the construction of high quality plugins. Although it employs the same operation, it differs from --just symbols in 2 significant ways: There can only be one parent. The parent is recorded in the created object, and can be displayed by 'file', or other similar tools.

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• Thread placement policies on NUMA systems - update

  - by Dave

  In a prior blog entry I noted that Solaris used a "maximum dispersal" placement policy to assign nascent threads to their initial processors. The general idea is that threads should be placed as far away from each other as possible in the resource topology in order to reduce resource contention between concurrently running threads. This policy assumes that resource contention -- pipelines, memory channel contention, destructive interference in the shared caches, etc -- will likely outweigh (a) any potential communication benefits we might achieve by packing our threads more densely onto a subset of the NUMA nodes, and (b) benefits of NUMA affinity between memory allocated by one thread and accessed by other threads. We want our threads spread widely over the system and not packed together. Conceptually, when placing a new thread, the kernel picks the least loaded node NUMA node (the node with lowest aggregate load average), and then the least loaded core on that node, etc. Furthermore, the kernel places threads onto resources -- sockets, cores, pipelines, etc -- without regard to the thread's process membership. That is, initial placement is process-agnostic. Keep reading, though. This description is incorrect. On Solaris 10 on a SPARC T5440 with 4 x T2+ NUMA nodes, if the system is otherwise unloaded and we launch a process that creates 20 compute-bound concurrent threads, then typically we'll see a perfect balance with 5 threads on each node. We see similar behavior on an 8-node x86 x4800 system, where each node has 8 cores and each core is 2-way hyperthreaded. So far so good; this behavior seems in agreement with the policy I described in the 1st paragraph. I recently tried the same experiment on a 4-node T4-4 running Solaris 11. Both the T5440 and T4-4 are 4-node systems that expose 256 logical thread contexts. To my surprise, all 20 threads were placed onto just one NUMA node while the other 3 nodes remained completely idle. I checked the usual suspects such as processor sets inadvertently left around by colleagues, processors left offline, and power management policies, but the system was configured normally. I then launched multiple concurrent instances of the process, and, interestingly, all the threads from the 1st process landed on one node, all the threads from the 2nd process landed on another node, and so on. This happened even if I interleaved thread creating between the processes, so I was relatively sure the effect didn't related to thread creation time, but rather that placement was a function of process membership. I this point I consulted the Solaris sources and talked with folks in the Solaris group. The new Solaris 11 behavior is intentional. The kernel is no longer using a simple maximum dispersal policy, and thread placement is process membership-aware. Now, even if other nodes are completely unloaded, the kernel will still try to pack new threads onto the home lgroup (socket) of the primordial thread until the load average of that node reaches 50%, after which it will pick the next least loaded node as the process's new favorite node for placement. On the T4-4 we have 64 logical thread contexts (strands) per socket (lgroup), so if we launch 48 concurrent threads we will find 32 placed on one node and 16 on some other node. If we launch 64 threads we'll find 32 and 32. That means we can end up with our threads clustered on a small subset of the nodes in a way that's quite different that what we've seen on Solaris 10. So we have a policy that allows process-aware packing but reverts to spreading threads onto other nodes if a node becomes too saturated. It turns out this policy was enabled in Solaris 10, but certain bugs suppressed the mixed packing/spreading behavior. There are configuration variables in /etc/system that allow us to dial the affinity between nascent threads and their primordial thread up and down: see lgrp_expand_proc_thresh, specifically. In the OpenSolaris source code the key routine is mpo_update_tunables(). This method reads the /etc/system variables and sets up some global variables that will subsequently be used by the dispatcher, which calls lgrp_choose() in lgrp.c to place nascent threads. Lgrp_expand_proc_thresh controls how loaded an lgroup must be before we'll consider homing a process's threads to another lgroup. Tune this value lower to have it spread your process's threads out more. To recap, the 'new' policy is as follows. Threads from the same process are packed onto a subset of the strands of a socket (50% for T-series). Once that socket reaches the 50% threshold the kernel then picks another preferred socket for that process. Threads from unrelated processes are spread across sockets. More precisely, different processes may have different preferred sockets (lgroups). Beware that I've simplified and elided details for the purposes of explication. The truth is in the code. Remarks: It's worth noting that initial thread placement is just that. If there's a gross imbalance between the load on different nodes then the kernel will migrate threads to achieve a better and more even distribution over the set of available nodes. Once a thread runs and gains some affinity for a node, however, it becomes "stickier" under the assumption that the thread has residual cache residency on that node, and that memory allocated by that thread resides on that node given the default "first-touch" page-level NUMA allocation policy. Exactly how the various policies interact and which have precedence under what circumstances could the topic of a future blog entry. The scheduler is work-conserving. The x4800 mentioned above is an interesting system. Each of the 8 sockets houses an Intel 7500-series processor. Each processor has 3 coherent QPI links and the system is arranged as a glueless 8-socket twisted ladder "mobius" topology. Nodes are either 1 or 2 hops distant over the QPI links. As an aside the mapping of logical CPUIDs to physical resources is rather interesting on Solaris/x4800. On SPARC/Solaris the CPUID layout is strictly geographic, with the highest order bits identifying the socket, the next lower bits identifying the core within that socket, following by the pipeline (if present) and finally the logical thread context ("strand") on the core. But on Solaris on the x4800 the CPUID layout is as follows. [6:6] identifies the hyperthread on a core; bits [5:3] identify the socket, or package in Intel terminology; bits [2:0] identify the core within a socket. Such low-level details should be of interest only if you're binding threads -- a bad idea, the kernel typically handles placement best -- or if you're writing NUMA-aware code that's aware of the ambient placement and makes decisions accordingly. Solaris introduced the so-called critical-threads mechanism, which is expressed by putting a thread into the FX scheduling class at priority 60. The critical-threads mechanism applies to placement on cores, not on sockets, however. That is, it's an intra-socket policy, not an inter-socket policy. Solaris 11 introduces the Power Aware Dispatcher (PAD) which packs threads instead of spreading them out in an attempt to be able to keep sockets or cores at lower power levels. Maximum dispersal may be good for performance but is anathema to power management. PAD is off by default, but power management polices constitute yet another confounding factor with respect to scheduling and dispatching. If your threads communicate heavily -- one thread reads cache lines last written by some other thread -- then the new dense packing policy may improve performance by reducing traffic on the coherent interconnect. On the other hand if your threads in your process communicate rarely, then it's possible the new packing policy might result on contention on shared computing resources. Unfortunately there's no simple litmus test that says whether packing or spreading is optimal in a given situation. The answer varies by system load, application, number of threads, and platform hardware characteristics. Currently we don't have the necessary tools and sensoria to decide at runtime, so we're reduced to an empirical approach where we run trials and try to decide on a placement policy. The situation is quite frustrating. Relatedly, it's often hard to determine just the right level of concurrency to optimize throughput. (Understanding constructive vs destructive interference in the shared caches would be a good start. We could augment the lines with a small tag field indicating which strand last installed or accessed a line. Given that, we could augment the CPU with performance counters for misses where a thread evicts a line it installed vs misses where a thread displaces a line installed by some other thread.)

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• ANTS Memory Profiler 7.0

  - by James Michael Hare

  I had always been a fan of ANTS products (Reflector is absolutely invaluable, and their performance profiler is great as well – very easy to use!), so I was curious to see what the ANTS Memory Profiler could show me. Background While a performance profiler will track how much time is typically spent in each unit of code, a memory profiler gives you much more detail on how and where your memory is being consumed and released in a program. As an example, I’d been working on a data access layer at work to call a market data web service.  This web service would take a list of symbols to quote and would return back the quote data.  To help consolidate the thousands of web requests per second we get and reduce load on the web services, we implemented a 5-second cache of quote data.  Not quite long enough to where customers will typically notice a quote go “stale”, but just long enough to be able to collapse multiple quote requests for the same symbol in a short period of time. A 5-second cache may not sound like much, but it actually pays off by saving us roughly 42% of our web service calls, while still providing relatively up-to-date information.  The question is whether or not the extra memory involved in maintaining the cache was worth it, so I decided to fire up the ANTS Memory Profiler and take a look at memory usage. First Impressions The main thing I’ve always loved about the ANTS tools is their ease of use.  Pretty much everything is right there in front of you in a way that makes it easy for you to find what you need with little digging required.  I’ve worked with other, older profilers before (that shall remain nameless other than to hint it was created by a very large chip maker) where it was a mind boggling experience to figure out how to do simple tasks. Not so with AMP.  The opening dialog is very straightforward.  You can choose from here whether to debug an executable, a web application (either in IIS or from VS’s web development server), windows services, etc. So I chose a .NET Executable and navigated to the build location of my test harness.  Then began profiling. At this point while the application is running, you can see a chart of the memory as it ebbs and wanes with allocations and collections.  At any given point in time, you can take snapshots (to compare states) zoom in, or choose to stop at any time.  Snapshots Taking a snapshot also gives you a breakdown of the managed memory heaps for each generation so you get an idea how many objects are staying around for extended periods of time (as an object lives and survives collections, it gets promoted into higher generations where collection becomes less frequent). Generating a snapshot brings up an analysis view with very handy graphs that show your generation sizes.  Almost all my memory is in Generation 1 in the managed memory component of the first graph, which is good news to me, because Gen 2 collections are much rarer.  I once3 made the mistake once of caching data for 30 minutes and found it didn’t get collected very quick after I released my reference because it had been promoted to Gen 2 – doh! Analysis It looks like (from the second pie chart) that the majority of the allocations were in the string class.  This also is expected for me because the majority of the memory allocated is in the web service responses, so it doesn’t seem the entities I’m adapting to (to prevent being too tightly coupled to the web service proxy classes, which can change easily out from under me) aren’t taking a significant portion of memory. I also appreciate that they have clear summary text in key places such as “No issues with large object heap fragmentation were detected”.  For novice users, this type of summary information can be critical to getting them to use a tool and develop a good working knowledge of it. There is also a handy link at the bottom for “What to look for on the summary” which loads a web page of help on key points to look for. Clicking over to the session overview, it’s easy to compare the samples at each snapshot to see how your memory is growing, shrinking, or staying relatively the same.  Looking at my snapshots, I’m pretty happy with the fact that memory allocation and heap size seems to be fairly stable and in control: Once again, you can check on the large object heap, generation one heap, and generation two heap across each snapshot to spot trends. Back on the analysis tab, we can go to the [Class List] button to get an idea what classes are making up the majority of our memory usage.  As was little surprise to me, System.String was the clear majority of my allocations, though I found it surprising that the System.Reflection.RuntimeMehtodInfo came in second.  I was curious about this, so I selected it and went into the [Instance Categorizer].  This view let me see where these instances to RuntimeMehtodInfo were coming from. So I scrolled back through the graph, and discovered that these were being held by the System.ServiceModel.ChannelFactoryRefCache and I was satisfied this was just an artifact of my WCF proxy. I also like that down at the bottom of the Instance Categorizer it gives you a series of filters and offers to guide you on which filter to use based on the problem you are trying to find.  For example, if I suspected a memory leak, I might try to filter for survivors in growing classes.  This means that for instances of a class that are growing in memory (more are being created than cleaned up), which ones are survivors (not collected) from garbage collection.  This might allow me to drill down and find places where I’m holding onto references by mistake and not freeing them! Finally, if you want to really see all your instances and who is holding onto them (preventing collection), you can go to the “Instance Retention Graph” which creates a graph showing what references are being held in memory and who is holding onto them. Visual Studio Integration Of course, VS has its own profiler built in – and for a free bundled profiler it is quite capable – but AMP gives a much cleaner and easier-to-use experience, and when you install it you also get the option of letting it integrate directly into VS. So once you go back into VS after installation, you’ll notice an ANTS menu which lets you launch the ANTS profiler directly from Visual Studio.   Clicking on one of these options fires up the project in the profiler immediately, allowing you to get right in.  It doesn’t integrate with the Visual Studio windows themselves (like the VS profiler does), but still the plethora of information it provides and the clear and concise manner in which it presents it makes it well worth it. Summary If you like the ANTS series of tools, you shouldn’t be disappointed with the ANTS Memory Profiler.  It was so easy to use that I was able to jump in with very little product knowledge and get the information I was looking it for. I’ve used other profilers before that came with 3-inch thick tomes that you had to read in order to get anywhere with the tool, and this one is not like that at all.  It’s built for your everyday developer to get in and find their problems quickly, and I like that! Tweet Technorati Tags: Influencers,ANTS,Memory,Profiler

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• I see no LOBs!

  - by Paul White

  Is it possible to see LOB (large object) logical reads from STATISTICS IO output on a table with no LOB columns? I was asked this question today by someone who had spent a good fraction of their afternoon trying to work out why this was occurring – even going so far as to re-run DBCC CHECKDB to see if any corruption had taken place.  The table in question wasn’t particularly pretty – it had grown somewhat organically over time, with new columns being added every so often as the need arose.  Nevertheless, it remained a simple structure with no LOB columns – no TEXT or IMAGE, no XML, no MAX types – nothing aside from ordinary INT, MONEY, VARCHAR, and DATETIME types.  To add to the air of mystery, not every query that ran against the table would report LOB logical reads – just sometimes – but when it did, the query often took much longer to execute. Ok, enough of the pre-amble.  I can’t reproduce the exact structure here, but the following script creates a table that will serve to demonstrate the effect: IF OBJECT_ID(N'dbo.Test', N'U') IS NOT NULL DROP TABLE dbo.Test GO CREATE TABLE dbo.Test ( row_id NUMERIC IDENTITY NOT NULL,   col01 NVARCHAR(450) NOT NULL, col02 NVARCHAR(450) NOT NULL, col03 NVARCHAR(450) NOT NULL, col04 NVARCHAR(450) NOT NULL, col05 NVARCHAR(450) NOT NULL, col06 NVARCHAR(450) NOT NULL, col07 NVARCHAR(450) NOT NULL, col08 NVARCHAR(450) NOT NULL, col09 NVARCHAR(450) NOT NULL, col10 NVARCHAR(450) NOT NULL, CONSTRAINT [PK dbo.Test row_id] PRIMARY KEY CLUSTERED (row_id) ) ; The next script loads the ten variable-length character columns with one-character strings in the first row, two-character strings in the second row, and so on down to the 450th row: WITH Numbers AS ( -- Generates numbers 1 - 450 inclusive SELECT TOP (450) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) INSERT dbo.Test WITH (TABLOCKX) SELECT REPLICATE(N'A', N.n), REPLICATE(N'B', N.n), REPLICATE(N'C', N.n), REPLICATE(N'D', N.n), REPLICATE(N'E', N.n), REPLICATE(N'F', N.n), REPLICATE(N'G', N.n), REPLICATE(N'H', N.n), REPLICATE(N'I', N.n), REPLICATE(N'J', N.n) FROM Numbers AS N ORDER BY N.n ASC ; Once those two scripts have run, the table contains 450 rows and 10 columns of data like this: Most of the time, when we query data from this table, we don’t see any LOB logical reads, for example: -- Find the maximum length of the data in -- column 5 for a range of rows SELECT result = MAX(DATALENGTH(T.col05)) FROM dbo.Test AS T WHERE row_id BETWEEN 50 AND 100 ; But with a different query… -- Read all the data in column 1 SELECT result = MAX(DATALENGTH(T.col01)) FROM dbo.Test AS T ; …suddenly we have 49 LOB logical reads, as well as the ‘normal’ logical reads we would expect. The Explanation If we had tried to create this table in SQL Server 2000, we would have received a warning message to say that future INSERT or UPDATE operations on the table might fail if the resulting row exceeded the in-row storage limit of 8060 bytes.  If we needed to store more data than would fit in an 8060 byte row (including internal overhead) we had to use a LOB column – TEXT, NTEXT, or IMAGE.  These special data types store the large data values in a separate structure, with just a small pointer left in the original row. Row Overflow SQL Server 2005 introduced a feature called row overflow, which allows one or more variable-length columns in a row to move to off-row storage if the data in a particular row would otherwise exceed 8060 bytes.  You no longer receive a warning when creating (or altering) a table that might need more than 8060 bytes of in-row storage; if SQL Server finds that it can no longer fit a variable-length column in a particular row, it will silently move one or more of these columns off the row into a separate allocation unit. Only variable-length columns can be moved in this way (for example the (N)VARCHAR, VARBINARY, and SQL_VARIANT types).  Fixed-length columns (like INTEGER and DATETIME for example) never move into ‘row overflow’ storage.  The decision to move a column off-row is done on a row-by-row basis – so data in a particular column might be stored in-row for some table records, and off-row for others. In general, if SQL Server finds that it needs to move a column into row-overflow storage, it moves the largest variable-length column record for that row.  Note that in the case of an UPDATE statement that results in the 8060 byte limit being exceeded, it might not be the column that grew that is moved! Sneaky LOBs Anyway, that’s all very interesting but I don’t want to get too carried away with the intricacies of row-overflow storage internals.  The point is that it is now possible to define a table with non-LOB columns that will silently exceed the old row-size limit and result in ordinary variable-length columns being moved to off-row storage.  Adding new columns to a table, expanding an existing column definition, or simply storing more data in a column than you used to – all these things can result in one or more variable-length columns being moved off the row. Note that row-overflow storage is logically quite different from old-style LOB and new-style MAX data type storage – individual variable-length columns are still limited to 8000 bytes each – you can just have more of them now.  Having said that, the physical mechanisms involved are very similar to full LOB storage – a column moved to row-overflow leaves a 24-byte pointer record in the row, and the ‘separate storage’ I have been talking about is structured very similarly to both old-style LOBs and new-style MAX types.  The disadvantages are also the same: when SQL Server needs a row-overflow column value it needs to follow the in-row pointer a navigate another chain of pages, just like retrieving a traditional LOB. And Finally… In the example script presented above, the rows with row_id values from 402 to 450 inclusive all exceed the total in-row storage limit of 8060 bytes.  A SELECT that references a column in one of those rows that has moved to off-row storage will incur one or more lob logical reads as the storage engine locates the data.  The results on your system might vary slightly depending on your settings, of course; but in my tests only column 1 in rows 402-450 moved off-row.  You might like to play around with the script – updating columns, changing data type lengths, and so on – to see the effect on lob logical reads and which columns get moved when.  You might even see row-overflow columns moving back in-row if they are updated to be smaller (hint: reduce the size of a column entry by at least 1000 bytes if you hope to see this). Be aware that SQL Server will not warn you when it moves ‘ordinary’ variable-length columns into overflow storage, and it can have dramatic effects on performance.  It makes more sense than ever to choose column data types sensibly.  If you make every column a VARCHAR(8000) or NVARCHAR(4000), and someone stores data that results in a row needing more than 8060 bytes, SQL Server might turn some of your column data into pseudo-LOBs – all without saying a word. Finally, some people make a distinction between ordinary LOBs (those that can hold up to 2GB of data) and the LOB-like structures created by row-overflow (where columns are still limited to 8000 bytes) by referring to row-overflow LOBs as SLOBs.  I find that quite appealing, but the ‘S’ stands for ‘small’, which makes expanding the whole acronym a little daft-sounding…small large objects anyone? © Paul White 2011 email: [email protected] twitter: @SQL_Kiwi

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• Oracle EBS?????(Order->AR)

  - by Pan.Tian

  ???? ??:Order Management > Orders,Returns > Sales Orders ???????,??,????,???? ???????,????,??... ??Book Order,??Book??,????????Status??????“Booked”,???????"Awaiting Shipping",?????????,??????????????? ??:??Book??,????????????,????Shipping Transactions Form,????,?????????Line Status?Ready to Release,Next Step?Pick Release Pick Release ??:Order Management > Shipping > Release Sales Orders > Release Sales Orders Pick Release????(?????????).?Order  Number?????????? Auto Pick Confirm???No Auto Allocate???N Auto Allocate?Auto Pick Confirm??????Yes,???????????,??????No,???Yes??,?????Allocate?Pick Confirm??,??????????? ??????????Pick  Release,”Concurrent“??Pick Release?????Concurrent Request???,"Execute Now"????????Pick Release,??????????????User,??????Concurrent??? Pick Release?????????Pick Release?????Pick Wave??Move Order,??Move Order????????????????????(Staging),????INV??????????? INV_MOVE_ORDER_PUB.CREATE_MOVE_ORDER_HEADER???Move Order??(??Pick Release?????????????:Pick Release Process) ????????,?Pick Release??,?????????????Reservation(??),?????????Soft Reservations,?????????????,????Org?????????? ??:????,Shipping Transaction?Line Status?"Released to Warehouse",Next Step?"Transact Move Order";????????Booked,?????”Awaiting Shipping“? Pick Confirm Pick Confirm(????)????????Transact Move Order????,?Allocate????,?Transact Move Order. ??:Inventory > Move Orders > Transact Move Orders ????,Pick Wave??Tab,????? ??TMO????,??Allocate,Allocate?????????Picking Rule?????,??????Suggestion????,Suggestion?????? MTL_MATERIAL_TRANSACTIONS_TEMP?(?Pending Transactions)? ????Allocate??,??????Allocation????Single,Multiple??None???,Single??, ??????????Suggestion?Transaction??,Multiple???????;None??????Suggestion? ?(????????????????) ????????Transact??Move Order ?Transact??,Inventory Transaction Manager ???Suggestion Transactions(MMTT),???????????????,??????Subinventory??????(Staging)??? Transction???Material Transaction?Form????? ????Reservation??,?Transact??,???????,Reservation????????,????Sub,locator???? ??:????,Shipping Transaction?Line Status?"Staged/Pick Confirmed",Next Step?"Ship Confirm/Close Trip Stop";????????Booked,??????”Picked“? Ship Confirm Deliveries ??:Order Management > Shipping > Transactions ???Delivery??,??Ship Confirm(????),????Pick Release???,????Autocreate Delivery,???????Define Shipping Parameters????????,??shipping parameters???????,?????????Ship Confirm?????Action->Auto-create Deliveries. Delivery????????????????,????????.... Delivery??,??Ship Confirm???,???????,"Defer Interface"?????,?????????Interface Trip Stop SRS,????Defer Interface,?OK? Delivery was successfully confirmed!!! Ship Confirm????????????MTL_TRANSACTIONS_INTERFACE??,??MTI??????Sales Order Issue,??????????Interface Trip Stop???,???MTI??MMT??? ??:????,Shipping Transaction?Line Status?"Shipped",Next Step?"Run Interfaces";????????Booked,??????”Shipped“? Interface Trip Stop - SRS ?????Ship Confirm??????Defer Interface,??????????????Interface Trip Stop - SRS? ??:Order Management > Shipping > Interface > Run > Request:Interface Trip Stop - SRS Interface Trip Stop????????:Inventory Interface  SRS(????????)? Order Management Interface  SRS(?????????????AR??)? Inventory Interface  SRS???Shipping Transaction??????MTI,??INV Manager????MTI????MMT??,??Sales Order Issue?transaction??????,???????????Reservation????Inventory Interface  SRS?????,???WSH_DELIVERY_DETAILS??INV_INTERFACED_FLAG???Y? Order Management Interface - SRS??Inventory Interface  SRS?????,??Request?????????????AR??,OM Interface????????WSH_DELIVERY_DETAILS??OE_INTERFACED_FLAG?Y? ??:????,Shipping Transaction?Line Status?"Interfaced",Next Step?"Not Applicable";????????Booked,??????”Shipped“? Workflow background Process ??:Inventory > Workflow Background Engine Item Type:OM Order Line Process Deferred:Yes Process Timeout:No ??program????Deffered???workflow,Workflow Background Process???,???????Order????RA Interface???(RA_INTERFACE_LINES_ALL,RA_INTERFACE_SALESCREDITS_ALL,RA_Interface_distribution) ????????SQL???RA Interface??: 1.SELECT * FROM RA_INTERFACE_LINES_ALL WHERE sales_order = '65961'; 2.SELECT * FROM RA_INTERFACE_SALESCREDITS_ALL WHERE INTERFACE_LINE_ID IN (SELECT INTERFACE_LINE_ID FROM RA_INTERFACE_LINES_ALL WHERE sales_order = '65961' ); 3.SELECT * FROM RA_INTERFACE_DISTRIBUTIONS_ALL WHERE INTERFACE_LINE_ID IN (SELECT INTERFACE_LINE_ID FROM RA_INTERFACE_LINES_ALL WHERE sales_order = '65961' ); ?????RA Interface??,??OE_ORDER_LINES_ALL?INVOICE_INTERFACE_STATUS_CODE????? Yes,INVOICED_QUANTITY?????????????????????????Closed,????????Booked? AutoInvoice ????AR?? ??:Account Receivable > Interface > AutoInvoice Name:Autoinvoice Master Program Invoice Source:Order Entry Default Day:???? ???,?request????”Autoinvoice Import Program“???? ???,????Auto Invoice Program????RA?interface?,?????????????,???????AR???? (RA_CUSTOMER_TRX_ALL,RA_CUSTOMER_TRX_LINES,AR_PAYMENT_SCHEDULES). ?????? Order > Action > Additional Information > Invoices/Credit Memos????????,???????SQL?????AR??, SELECT ooha.order_number , oola.line_number so_line_number , oola.ordered_item , oola.ordered_quantity * oola.unit_selling_price so_extended_price , rcta.trx_number invoice_number , rcta.trx_date , rctla.line_number inv_line_number , rctla.unit_selling_price inv_unit_selling_price FROM oe_order_headers_all ooha , oe_order_lines_all oola , ra_customer_trx_all rcta , ra_customer_trx_lines_all rctla WHERE ooha.header_id = oola.header_id AND rcta.customer_trx_id = rctla.customer_trx_id AND rctla.interface_line_attribute6 = TO_CHAR (oola.line_id) AND rctla.interface_line_attribute1 = TO_CHAR (ooha.order_number) AND order_number = :p_order_number; ??Autoinvoice Import Program???error???,?????RA_INTERFACE_ERRORS_ALL?Message_text??,???????? Closing the Order ?????????,?????????(Close??Cancel)?0.5?,??????Workflow Background Process??????? ????????:you can wait until month-end and the “Order Flow – Generic” workflow will close it for you. Order&Shipping Transactions Status Summary Step Order Header Status Order Line Status Order Flow Workflow Status (Order Header) Line Flow Workflow Status (Order Line) Shipping Transaction  Status(RELEASED_STATUS in WDD) 1. Enter an Order Entered Entered Book Order Manual Enter – Line                              N/A 2. Book the Order Booked Awaiting Shipping Close Order Schedule ->Create Supply ->Ship – Line                       Ready to Release(R) 3. Pick the Order Booked Picked Close Order Ship – Line 1.Released to Warehouse(S)(Pick Release but not pick confirm) 2.Staged/Pick Confirmed(Y)(After pick confirm) 4. Ship the Order Booked Shipped Close Order Fulfill – Deferred 1.Shipped(After ship confirm) 2.Interfaced(C)(After ITS) Booked Closed Close Order Fulfill ->Invoice Interface ->Close Line -> End 5. Close the Order Closed Closed End End ????,shipping txn???,??????????:http://blog.csdn.net/pan_tian/article/details/7696528 ======EOF======

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• SQL Server and Hyper-V Dynamic Memory Part 2

  - by SQLOS Team

  Part 1 of this series was an introduction and overview of Hyper-V Dynamic Memory. This part looks at SQL Server memory management and how the SQL engine responds to changing OS memory conditions.   Part 2: SQL Server Memory Management As with any Windows process, sqlserver.exe has a virtual address space (VAS) of 4GB on 32-bit and 8TB in 64-bit editions. Pages in its VAS are mapped to pages in physical memory when the memory is committed and referenced for the first time. The collection of VAS pages that have been recently referenced is known as the Working Set. How and when SQL Server allocates virtual memory and grows its working set depends on the memory model it uses. SQL Server supports three basic memory models:   1. Conventional Memory Model   The Conventional model is the default SQL Server memory model and has the following properties: - Dynamic - can grow or shrink its working set in response to load and external (operating system) memory conditions. - OS uses 4K pages – (not to be confused with SQL Server “pages” which are 8K regions of committed memory).- Pageable - Can be paged out to disk by the operating system.   2. Locked Page Model The locked page memory model is set when SQL Server is started with "Lock Pages in Memory" privilege*. It has the following characteristics: - Dynamic - can grow or shrink its working set in the same way as the Conventional model.- OS uses 4K pages - Non-Pageable – When memory is committed it is locked in memory, meaning that it will remain backed by physical memory and will not be paged out by the operating system. A common misconception is to interpret "locked" as non-dynamic. A SQL Server instance using the locked page memory model will grow and shrink (allocate memory and release memory) in response to changing workload and OS memory conditions in the same way as it does with the conventional model.   This is an important consideration when we look at Hyper-V Dynamic Memory – “locked” memory works perfectly well with “dynamic” memory.   * Note in “Denali” (Standard Edition and above), and in SQL 2008 R2 64-bit (Enterprise and above editions) the Lock Pages in Memory privilege is all that is required to set this model. In 2008 R2 64-Bit standard edition it also requires trace flag 845 to be set, in 2008 R2 32-bit editions it requires sp_configure 'awe enabled' 1.   3. Large Page Model The Large page model is set using trace flag 834 and potentially offers a small performance boost for systems that are configured with large pages. It is characterized by: - Static - memory is allocated at startup and does not change. - OS uses large (>2MB) pages - Non-Pageable The large page model is supported with Hyper-V Dynamic Memory (and Hyper-V also supports large pages), but you get no benefit from using Dynamic Memory with this model since SQL Server memory does not grow or shrink. The rest of this article will focus on the locked and conventional SQL Server memory models.   When does SQL Server grow? For “dynamic” configurations (Conventional and Locked memory models), the sqlservr.exe process grows – allocates and commits memory from the OS – in response to a workload. As much memory is allocated as is required to optimally run the query and buffer data for future queries, subject to limitations imposed by:   - SQL Server max server memory setting. If this configuration option is set, the buffer pool is not allowed to grow to more than this value. In SQL Server 2008 this value represents single page allocations, and in “Denali” it represents any size page allocations and also managed CLR procedure allocations.   - Memory signals from OS. The operating system sets a signal on memory resource notification objects to indicate whether it has memory available or whether it is low on available memory. If there is only 32MB free for every 4GB of memory a low memory signal is set, which continues until 64MB/4GB is free. If there is 96MB/4GB free the operating system sets a high memory signal. SQL Server only allocates memory when the high memory signal is set.   To summarize, for SQL Server to grow you need three conditions: a workload, max server memory setting higher than the current allocation, high memory signals from the OS.    When does SQL Server shrink caches? SQL Server as a rule does not like to return memory to the OS, but it will shrink its caches in response to memory pressure. Memory pressure can be divided into “internal” and “external”.   - External memory pressure occurs when the operating system is running low on memory and low memory signals are set. The SQL Server Resource Monitor checks for low memory signals approximately every 5 seconds and it will attempt to free memory until the signals stop.   To free memory SQL Server does the following: ·         Frees unused memory. ·         Notifies Memory Manager Clients to release memory o   Caches – Free unreferenced cache objects. o   Buffer pool - Based on oldest access times.   The freed memory is released back to the operating system. This process continues until the low memory resource notifications stop.    - Internal memory pressure occurs when the size of different caches and allocations increase but the SQL Server process needs to keep its total memory within a target value. For example if max server memory is set and certain caches are growing large, it will cause SQL to free memory for re-use internally, but not to release memory back to the OS. If you lower the value of max server memory you will generate internal memory pressure that will cause SQL to release memory back to the OS.    Memory pressure handling has not changed much since SQL 2005 and it was described in detail in a blog post by Slava Oks.   Note that SQL Server Express is an exception to the above behavior. Unlike other editions it does not assume it is the most important process running on the system but tries to be more “desktop” friendly. It will empty its working set after a period of inactivity.   How does SQL Server respond to changing OS memory?    In SQL Server 2005 support for Hot-Add memory was introduced. This feature, available in Enterprise and above editions, allows the server to make use of any extra physical memory that was added after SQL Server started. Being able to add physical memory when the system is running is limited to specialized hardware, but with the Hyper-V Dynamic Memory feature, when new memory is allocated to a guest virtual machine, it looks like hot-add physical memory to the guest. What this means is that thanks to the hot-add memory feature, SQL Server 2005 and higher can dynamically grow if more “physical” memory is granted to a guest VM by Hyper-V dynamic memory.   SQL Server checks OS memory every second and dynamically adjusts its “target” (based on available OS memory and max server memory) accordingly.   In “Denali” Standard Edition will also have sqlserver.exe support for hot-add memory when running virtualized (i.e. detecting and acting on Hyper-V Dynamic Memory allocations).   How does a SQL Server workload in a guest VM impact Hyper-V dynamic memory scheduling?   When a SQL workload causes the sqlserver.exe process to grow its working set, the Hyper-V memory scheduler will detect memory pressure in the guest VM and add memory to it. SQL Server will then detect the extra memory and grow according to workload demand. In our tests we have seen this feedback process cause a guest VM to grow quickly in response to SQL workload - we are still working on characterizing this ramp-up.    How does SQL Server respond when Hyper-V removes memory from a guest VM through ballooning?   If pressure from other VM's cause Hyper-V Dynamic Memory to take memory away from a VM through ballooning (allocating memory with a virtual device driver and returning it to the host OS), Windows Memory Manager will page out unlocked portions of memory and signal low resource notification events. When SQL Server detects these events it will shrink memory until the low memory notifications stop (see cache shrinking description above).    This raises another question. Can we make SQL Server release memory more readily and hence behave more "dynamically" without compromising performance? In certain circumstances where the application workload is predictable it may be possible to have a job which varies "max server memory" according to need, lowering it when the engine is inactive and raising it before a period of activity. This would have limited applicaability but it is something we're looking into.   What Memory Management changes are there in SQL Server “Denali”?   In SQL Server “Denali” (aka SQL11) the Memory Manager has been re-written to be more efficient. The main changes are summarized in this post. An important change with respect to Hyper-V Dynamic Memory support is that now the max server memory setting includes any size page allocations and managed CLR procedure allocations it now represents a closer approximation to total sqlserver.exe memory usage. This makes it easier to calculate a value for max server memory, which becomes important when configuring virtual machines to work well with Hyper-V Dynamic Memory Startup and Maximum RAM settings.   Another important change is no more AWE or hot-add support for 32-bit edition. This means if you're running a 32-bit edition of Denali you're limited to a 4GB address space and will not be able to take advantage of dynamically added OS memory that wasn't present when SQL Server started (though Hyper-V Dynamic Memory is still a supported configuration).   In part 3 we’ll develop some best practices for configuring and using SQL Server with Dynamic Memory. Originally posted at http://blogs.msdn.com/b/sqlosteam/

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• Move Files from a Failing PC with an Ubuntu Live CD

  - by Trevor Bekolay

  You’ve loaded the Ubuntu Live CD to salvage files from a failing system, but where do you store the recovered files? We’ll show you how to store them on external drives, drives on the same PC, a Windows home network, and other locations. We’ve shown you how to recover data like a forensics expert, but you can’t store recovered files back on your failed hard drive! There are lots of ways to transfer the files you access from an Ubuntu Live CD to a place that a stable Windows machine can access them. We’ll go through several methods, starting each section from the Ubuntu desktop – if you don’t yet have an Ubuntu Live CD, follow our guide to creating a bootable USB flash drive, and then our instructions for booting into Ubuntu. If your BIOS doesn’t let you boot using a USB flash drive, don’t worry, we’ve got you covered! Use a Healthy Hard Drive If your computer has more than one hard drive, or your hard drive is healthy and you’re in Ubuntu for non-recovery reasons, then accessing your hard drive is easy as pie, even if the hard drive is formatted for Windows. To access a hard drive, it must first be mounted. To mount a healthy hard drive, you just have to select it from the Places menu at the top-left of the screen. You will have to identify your hard drive by its size. Clicking on the appropriate hard drive mounts it, and opens it in a file browser. You can now move files to this hard drive by drag-and-drop or copy-and-paste, both of which are done the same way they’re done in Windows. Once a hard drive, or other external storage device, is mounted, it will show up in the /media directory. To see a list of currently mounted storage devices, navigate to /media by clicking on File System in a File Browser window, and then double-clicking on the media folder. Right now, our media folder contains links to the hard drive, which Ubuntu has assigned a terribly uninformative label, and the PLoP Boot Manager CD that is currently in the CD-ROM drive. Connect a USB Hard Drive or Flash Drive An external USB hard drive gives you the advantage of portability, and is still large enough to store an entire hard disk dump, if need be. Flash drives are also very quick and easy to connect, though they are limited in how much they can store. When you plug a USB hard drive or flash drive in, Ubuntu should automatically detect it and mount it. It may even open it in a File Browser automatically. Since it’s been mounted, you will also see it show up on the desktop, and in the /media folder. Once it’s been mounted, you can access it and store files on it like you would any other folder in Ubuntu. If, for whatever reason, it doesn’t mount automatically, click on Places in the top-left of your screen and select your USB device. If it does not show up in the Places list, then you may need to format your USB drive. To properly remove the USB drive when you’re done moving files, right click on the desktop icon or the folder in /media and select Safely Remove Drive. If you’re not given that option, then Eject or Unmount will effectively do the same thing. Connect to a Windows PC on your Local Network If you have another PC or a laptop connected through the same router (wired or wireless) then you can transfer files over the network relatively quickly. To do this, we will share one or more folders from the machine booted up with the Ubuntu Live CD over the network, letting our Windows PC grab the files contained in that folder. As an example, we’re going to share a folder on the desktop called ToShare. Right-click on the folder you want to share, and click Sharing Options. A Folder Sharing window will pop up. Check the box labeled Share this folder. A window will pop up about the sharing service. Click the Install service button. Some files will be downloaded, and then installed. When they’re done installing, you’ll be appropriately notified. You will be prompted to restart your session. Don’t worry, this won’t actually log you out, so go ahead and press the Restart session button. The Folder Sharing window returns, with Share this folder now checked. Edit the Share name if you’d like, and add checkmarks in the two checkboxes below the text fields. Click Create Share. Nautilus will ask your permission to add some permissions to the folder you want to share. Allow it to Add the permissions automatically. The folder is now shared, as evidenced by the new arrows above the folder’s icon. At this point, you are done with the Ubuntu machine. Head to your Windows PC, and open up Windows Explorer. Click on Network in the list on the left, and you should see a machine called UBUNTU in the right pane. Note: This example is shown in Windows 7; the same steps should work for Windows XP and Vista, but we have not tested them. Double-click on UBUNTU, and you will see the folder you shared earlier! As well as any other folders you’ve shared from Ubuntu. Double click on the folder you want to access, and from there, you can move the files from the machine booted with Ubuntu to your Windows PC. Upload to an Online Service There are many services online that will allow you to upload files, either temporarily or permanently. As long as you aren’t transferring an entire hard drive, these services should allow you to transfer your important files from the Ubuntu environment to any other machine with Internet access. We recommend compressing the files that you want to move, both to save a little bit of bandwidth, and to save time clicking on files, as uploading a single file will be much less work than a ton of little files. To compress one or more files or folders, select them, and then right-click on one of the members of the group. Click Compress…. Give the compressed file a suitable name, and then select a compression format. We’re using .zip because we can open it anywhere, and the compression rate is acceptable. Click Create and the compressed file will show up in the location selected in the Compress window. Dropbox If you have a Dropbox account, then you can easily upload files from the Ubuntu environment to Dropbox. There is no explicit limit on the size of file that can be uploaded to Dropbox, though a free account begins with a total limit of 2 GB of files in total. Access your account through Firefox, which can be opened by clicking on the Firefox logo to the right of the System menu at the top of the screen. Once into your account, press the Upload button on top of the main file list. Because Flash is not installed in the Live CD environment, you will have to switch to the basic uploader. Click Browse…find your compressed file, and then click Upload file. Depending on the size of the file, this could take some time. However, once the file has been uploaded, it should show up on any computer connected through Dropbox in a matter of minutes. Google Docs Google Docs allows the upload of any type of file – making it an ideal place to upload files that we want to access from another computer. While your total allocation of space varies (mine is around 7.5 GB), there is a per-file maximum of 1 GB. Log into Google Docs, and click on the Upload button at the top left of the page. Click Select files to upload and select your compressed file. For safety’s sake, uncheck the checkbox concerning converting files to Google Docs format, and then click Start upload. Go Online – Through FTP If you have access to an FTP server – perhaps through your web hosting company, or you’ve set up an FTP server on a different machine – you can easily access the FTP server in Ubuntu and transfer files. Just make sure you don’t go over your quota if you have one. You will need to know the address of the FTP server, as well as the login information. Click on Places > Connect to Server… Choose the FTP (with login) Service type, and fill in your information. Adding a bookmark is optional, but recommended. You will be asked for your password. You can choose to remember it until you logout, or indefinitely. You can now browse your FTP server just like any other folder. Drop files into the FTP server and you can retrieve them from any computer with an Internet connection and an FTP client. Conclusion While at first the Ubuntu Live CD environment may seem claustrophobic, it has a wealth of options for connecting to peripheral devices, local computers, and machines on the Internet – and this article has only scratched the surface. Whatever the storage medium, Ubuntu’s got an interface for it! Similar Articles Productive Geek Tips Backup Your Windows Live Writer SettingsMove a Window Without Clicking the Titlebar in UbuntuRecover Deleted Files on an NTFS Hard Drive from a Ubuntu Live CDCreate a Bootable Ubuntu USB Flash Drive the Easy WayReset Your Ubuntu Password Easily from the Live CD TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Acronis Online Backup DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows Tech Fanboys Field Guide Check these Awesome Chrome Add-ons iFixit Offers Gadget Repair Manuals Online Vista style sidebar for Windows 7 Create Nice Charts With These Web Based Tools Track Daily Goals With 42Goals

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• C#: String Concatenation vs Format vs StringBuilder

  - by James Michael Hare

  I was looking through my groups’ C# coding standards the other day and there were a couple of legacy items in there that caught my eye.  They had been passed down from committee to committee so many times that no one even thought to second guess and try them for a long time.  It’s yet another example of how micro-optimizations can often get the best of us and cause us to write code that is not as maintainable as it could be for the sake of squeezing an extra ounce of performance out of our software. So the two standards in question were these, in paraphrase: Prefer StringBuilder or string.Format() to string concatenation. Prefer string.Equals() with case-insensitive option to string.ToUpper().Equals(). Now some of you may already know what my results are going to show, as these items have been compared before on many blogs, but I think it’s always worth repeating and trying these yourself.  So let’s dig in. The first test was a pretty standard one.  When concattenating strings, what is the best choice: StringBuilder, string concattenation, or string.Format()? So before we being I read in a number of iterations from the console and a length of each string to generate.  Then I generate that many random strings of the given length and an array to hold the results.  Why am I so keen to keep the results?  Because I want to be able to snapshot the memory and don’t want garbage collection to collect the strings, hence the array to keep hold of them.  I also didn’t want the random strings to be part of the allocation, so I pre-allocate them and the array up front before the snapshot.  So in the code snippets below: num – Number of iterations. strings – Array of randomly generated strings. results – Array to hold the results of the concatenation tests. timer – A System.Diagnostics.Stopwatch() instance to time code execution. start – Beginning memory size. stop – Ending memory size. after – Memory size after final GC. So first, let’s look at the concatenation loop: 1: // build num strings using concattenation. 2: for (int i = 0; i < num; i++) 3: { 4: results[i] = "This is test #" + i + " with a result of " + strings[i]; 5: } Pretty standard, right?  Next for string.Format(): 1: // build strings using string.Format() 2: for (int i = 0; i < num; i++) 3: { 4: results[i] = string.Format("This is test #{0} with a result of {1}", i, strings[i]); 5: }   Finally, StringBuilder: 1: // build strings using StringBuilder 2: for (int i = 0; i < num; i++) 3: { 4: var builder = new StringBuilder(); 5: builder.Append("This is test #"); 6: builder.Append(i); 7: builder.Append(" with a result of "); 8: builder.Append(strings[i]); 9: results[i] = builder.ToString(); 10: } So I take each of these loops, and time them by using a block like this: 1: // get the total amount of memory used, true tells it to run GC first. 2: start = System.GC.GetTotalMemory(true); 3:  4: // restart the timer 5: timer.Reset(); 6: timer.Start(); 7:  8: // *** code to time and measure goes here. *** 9:  10: // get the current amount of memory, stop the timer, then get memory after GC. 11: stop = System.GC.GetTotalMemory(false); 12: timer.Stop(); 13: other = System.GC.GetTotalMemory(true); So let’s look at what happens when I run each of these blocks through the timer and memory check at 500,000 iterations: 1: Operator + - Time: 547, Memory: 56104540/55595960 - 500000 2: string.Format() - Time: 749, Memory: 57295812/55595960 - 500000 3: StringBuilder - Time: 608, Memory: 55312888/55595960 – 500000   Egad!  string.Format brings up the rear and + triumphs, well, at least in terms of speed.  The concat burns more memory than StringBuilder but less than string.Format().  This shows two main things: StringBuilder is not always the panacea many think it is. The difference between any of the three is miniscule! The second point is extremely important!  You will often here people who will grasp at results and say, “look, operator + is 10% faster than StringBuilder so always use StringBuilder.”  Statements like this are a disservice and often misleading.  For example, if I had a good guess at what the size of the string would be, I could have preallocated my StringBuffer like so:   1: for (int i = 0; i < num; i++) 2: { 3: // pre-declare StringBuilder to have 100 char buffer. 4: var builder = new StringBuilder(100); 5: builder.Append("This is test #"); 6: builder.Append(i); 7: builder.Append(" with a result of "); 8: builder.Append(strings[i]); 9: results[i] = builder.ToString(); 10: }   Now let’s look at the times: 1: Operator + - Time: 551, Memory: 56104412/55595960 - 500000 2: string.Format() - Time: 753, Memory: 57296484/55595960 - 500000 3: StringBuilder - Time: 525, Memory: 59779156/55595960 - 500000   Whoa!  All of the sudden StringBuilder is back on top again!  But notice, it takes more memory now.  This makes perfect sense if you examine the IL behind the scenes.  Whenever you do a string concat (+) in your code, it examines the lengths of the arguments and creates a StringBuilder behind the scenes of the appropriate size for you. But even IF we know the approximate size of our StringBuilder, look how much less readable it is!  That’s why I feel you should always take into account both readability and performance.  After all, consider all these timings are over 500,000 iterations.   That’s at best  0.0004 ms difference per call which is neglidgable at best.  The key is to pick the best tool for the job.  What do I mean?  Consider these awesome words of wisdom: Concatenate (+) is best at concatenating.  StringBuilder is best when you need to building. Format is best at formatting. Totally Earth-shattering, right!  But if you consider it carefully, it actually has a lot of beauty in it’s simplicity.  Remember, there is no magic bullet.  If one of these always beat the others we’d only have one and not three choices. The fact is, the concattenation operator (+) has been optimized for speed and looks the cleanest for joining together a known set of strings in the simplest manner possible. StringBuilder, on the other hand, excels when you need to build a string of inderterminant length.  Use it in those times when you are looping till you hit a stop condition and building a result and it won’t steer you wrong. String.Format seems to be the looser from the stats, but consider which of these is more readable.  Yes, ignore the fact that you could do this with ToString() on a DateTime.  1: // build a date via concatenation 2: var date1 = (month < 10 ? string.Empty : "0") + month + '/' 3: + (day < 10 ? string.Empty : "0") + '/' + year; 4:  5: // build a date via string builder 6: var builder = new StringBuilder(10); 7: if (month < 10) builder.Append('0'); 8: builder.Append(month); 9: builder.Append('/'); 10: if (day < 10) builder.Append('0'); 11: builder.Append(day); 12: builder.Append('/'); 13: builder.Append(year); 14: var date2 = builder.ToString(); 15:  16: // build a date via string.Format 17: var date3 = string.Format("{0:00}/{1:00}/{2:0000}", month, day, year); 18:  So the strength in string.Format is that it makes constructing a formatted string easy to read.  Yes, it’s slower, but look at how much more elegant it is to do zero-padding and anything else string.Format does. So my lesson is, don’t look for the silver bullet!  Choose the best tool.  Micro-optimization almost always bites you in the end because you’re sacrificing readability for performance, which is almost exactly the wrong choice 90% of the time. I love the rules of optimization.  They’ve been stated before in many forms, but here’s how I always remember them: For Beginners: Do not optimize. For Experts: Do not optimize yet. It’s so true.  Most of the time on today’s modern hardware, a micro-second optimization at the sake of readability will net you nothing because it won’t be your bottleneck.  Code for readability, choose the best tool for the job which will usually be the most readable and maintainable as well.  Then, and only then, if you need that extra performance boost after profiling your code and exhausting all other options… then you can start to think about optimizing.

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• EM12c Release 4: Cloud Control to Major Tom...

  - by abulloch

  With the latest release of Enterprise Manager 12c, Release 4 (12.1.0.4) the EM development team has added new functionality to assist the EM Administrator to monitor the health of the EM infrastructure.   Taking feedback delivered from customers directly and through customer advisory boards some nice enhancements have been made to the “Manage Cloud Control” sections of the UI, commonly known in the EM community as “the MTM pages” (MTM stands for Monitor the Monitor).  This part of the EM Cloud Control UI is viewed by many as the mission control for EM Administrators. In this post we’ll highlight some of the new information that’s on display in these redesigned pages and explain how the information they present can help EM administrators identify potential bottlenecks or issues with the EM infrastructure. The first page we’ll take a look at is the newly designed Repository information page.  You can get to this from the main Setup menu, through Manage Cloud Control, then Repository.  Once this page loads you’ll see the new layout that includes 3 tabs containing more drill-down information. The Repository Tab The first tab, Repository, gives you a series of 6 panels or regions on screen that display key information that the EM Administrator needs to review from time to time to ensure that their infrastructure is in good health. Rather than go through every panel let’s call out a few and let you explore the others later yourself on your own EM site.  Firstly, we have the Repository Details panel. At a glance the EM Administrator can see the current version of the EM repository database and more critically, three important elements of information relating to availability and reliability :- Is the database in Archive Log mode ? Is the database using Flashback ? When was the last database backup taken ? In this test environment above the answers are not too worrying, however, Production environments should have at least Archivelog mode enabled, Flashback is a nice feature to enable prior to upgrades (for fast rollback) and all Production sites should have a backup.  In this case the backup information in the Control file indicates there’s been no recorded backups taken. The next region of interest to note on this page shows key information around the Repository configuration, specifically, the initialisation parameters (from the spfile). If you’re storing your EM Repository in a Cluster Database you can view the parameters on each individual instance using the Instance Name drop-down selector in the top right of the region. Additionally, you’ll note there is now a check performed on the active configuration to ensure that you’re using, at the very least, Oracle minimum recommended values.  Should the values in your EM Repository not meet these requirements it will be flagged in this table with a red X for non-compliance.  You can of-course change these values within EM by selecting the Database target and modifying the parameters in the spfile (and optionally, the run-time values if the parameter allows dynamic changes). The last region to call out on this page before moving on is the new look Repository Scheduler Job Status region. This region is an update of a similar region seen on previous releases of the MTM pages in Cloud Control but there’s some important new functionality that’s been added that customers have requested. First-up - Restarting Repository Jobs.  As you can see from the graphic, you can now optionally select a job (by selecting the row in the UI table element) and click on the Restart Job button to take care of any jobs which have stopped or stalled for any reason.  Previously this needed to be done at the command line using EMDIAG or through a PL/SQL package invocation.  You can now take care of this directly from within the UI. Next, you’ll see that a feature has been added to allow the EM administrator to customise the run-time for some of the background jobs that run in the Repository.  We heard from some customers that ensuring these jobs don’t clash with Production backups, etc is a key requirement.  This new functionality allows you to select the pencil icon to edit the schedule time for these more resource intensive background jobs and modify the schedule to avoid clashes like this. Moving onto the next tab, let’s select the Metrics tab. The Metrics Tab There’s some big changes here, this page contains new information regions that help the Administrator understand the direct impact the in-bound metric flows are having on the EM Repository.  Many customers have provided feedback that they are in the dark about the impact of adding new targets or large numbers of new hosts or new target types into EM and the impact this has on the Repository.  This page helps the EM Administrator get to grips with this.  Let’s take a quick look at two regions on this page. First-up there’s a bubble chart showing a comprehensive view of the top resource consumers of metric data, over the last 30 days, charted as the number of rows loaded against the number of collections for the metric.  The size of the bubble indicates a relative volume.  You can see from this example above that a quick glance shows that Host metrics are the largest inbound flow into the repository when measured by number of rows.  Closely following behind this though are a large number of collections for Oracle Weblogic Server and Application Deployment.  Taken together the Host Collections is around 0.7Mb of data.  The total information collection for Weblogic Server and Application Deployments is 0.38Mb and 0.37Mb respectively. If you want to get this information breakdown on the volume of data collected simply hover over the bubble in the chart and you’ll get a floating tooltip showing the information. Clicking on any bubble in the chart takes you one level deeper into a drill-down of the Metric collection. Doing this reveals the individual metric elements for these target types and again shows a representation of the relative cost - in terms of Number of Rows, Number of Collections and Storage cost of data for each Metric type. Looking at another panel on this page we can see a different view on this data. This view shows a view of the Top N metrics (the drop down allows you to select 10, 15 or 20) and sort them by volume of data.  In the case above we can see the largest metric collection (by volume) in this case (over the last 30 days) is the information about OS Registered Software on a Host target. Taken together, these two regions provide a powerful tool for the EM Administrator to understand the potential impact of any new targets that have been discovered and promoted into management by EM12c.  It’s a great tool for identifying the cause of a sudden increase in Repository storage consumption or Redo log and Archive log generation. Using the information on this page EM Administrators can take action to mitigate any load impact by deploying monitoring templates to the targets causing most load if appropriate.   The last tab we’ll look at on this page is the Schema tab. The Schema Tab Selecting this tab brings up a window onto the SYSMAN schema with a focus on Space usage in the EM Repository.  Understanding what tablespaces are growing, at what rate, is essential information for the EM Administrator to stay on top of managing space allocations for the EM Repository so that it works as efficiently as possible and performs well for the users.  Not least because ensuring storage is managed well ensures continued availability of EM for monitoring purposes. The first region to highlight here shows the trend of space usage for the tablespaces in the EM Repository over time.  You can see the upward trend here showing that storage in the EM Repository is being consumed on an upward trend over the last few days here. This is normal as this EM being used here is brand new with Agents being added daily to bring targets into monitoring.  If your Enterprise Manager configuration has reached a steady state over a period of time where the number of new inbound targets is relatively small, the metric collection settings are fairly uniform and standardised (using Templates and Template Collections) you’re likely to see a trend of space allocation that plateau’s. The table below the trend chart shows the Top 20 Tables/Indexes sorted descending by order of space consumed.  You can switch the trend view chart and corresponding detail table by choosing a different tablespace in the EM Repository using the drop-down picker on the top right of this region. The last region to highlight on this page is the region showing information about the Purge policies in effect in the EM Repository. This information is useful to illustrate to EM Administrators the default purge policies in effect for the different categories of information available in the EM Repository.  Of course, it’s also been a long requested feature to have the ability to modify these default retention periods.  You can also do this using this screen.  As there are interdependencies between some data elements you can’t modify retention policies on a feature by feature basis.  Instead, retention policies take categories of information and bundles them together in Groups.  Retention policies are modified at the Group Level.  Understanding the impact of this really deserves a blog post all on it’s own as modifying these can have a significant impact on both the EM Repository’s storage footprint and it’s performance.  For now, we’re just highlighting the features visibility on these new pages. As a user of EM12c we hope the new features you see here address some of the feedback that’s been given on these pages over the past few releases.  We’ll look out for any comments or feedback you have on these pages ! 

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• How to disable Mac OS X from using swap when there still is "Inactive" memory?

  - by Motin

  A common phenomena in my day to day usage (and several other's according to various posts throughout the internet) of OS X, the system seems to become slow whenever there is no more "Free" memory available. Supposedly, this is due to swapping, since heavy disk activity is apparent and that vm_stat reports many pageouts. (Correct me from wrong) However, the amount of "Inactive" ram is typically around 12.5%-25% of all available memory (^1.) when swapping starts/occurs/ends. According to http://support.apple.com/kb/ht1342 : Inactive memory This information in memory is not actively being used, but was recently used. For example, if you've been using Mail and then quit it, the RAM that Mail was using is marked as Inactive memory. This Inactive memory is available for use by another application, just like Free memory. However, if you open Mail before its Inactive memory is used by a different application, Mail will open quicker because its Inactive memory is converted to Active memory, instead of loading Mail from the slower hard disk. And according to http://developer.apple.com/library/mac/#documentation/Performance/Conceptual/ManagingMemory/Articles/AboutMemory.html : The inactive list contains pages that are currently resident in physical memory but have not been accessed recently. These pages contain valid data but may be released from memory at any time. So, basically: When a program has quit, it's memory becomes marked as Inactive and should be claimable at any time. Still, OS X will prefer to start swapping out memory to the Swap file instead of just claiming this memory, whenever the "Free" memory gets to low. Why? What is the advantage of this behavior over, say, instantly releasing Inactive memory and not even touch the swap file? Some sources (^2.) indicate that OS X would page out the "Inactive" memory to swap before releasing it, but that doesn't make sense now does it if the memory may be released from memory at any time? Swapping is expensive, releasing is cheap, right? Can this behavior be changed using some preference or known hack? (Preferably one that doesn't include disabling swap/dynamic_pager altogether and restarting...) I do appreciate the purge command, as well as the concept of Repairing disk permissions to force some Free memory, but those are ways to painfully force more Free memory than to actually fixing the swap/release decision logic... Btw a similar question was asked here: http://forums.macnn.com/90/mac-os-x/434650/why-does-os-x-swap-when/ and here: http://hintsforums.macworld.com/showthread.php?t=87688 but even though the OPs re-asked the core question, none of the replies addresses an answer to it... ^1. UPDATE 17-mar-2012 Since I first posted this question, I have gone from 4gb to 8gb of installed ram, and the problem remains. The amount of "Inactive" ram was 0.5gb-1.0gb before and is now typically around 1.0-2.0GB when swapping starts/occurs/ends, ie it seems that around 12.5%-25% of the ram is preserved as Inactive by osx kernel logic. ^2. For instance http://apple.stackexchange.com/questions/4288/what-does-it-mean-if-i-have-lots-of-inactive-memory-at-the-end-of-a-work-day : Once all your memory is used (free memory is 0), the OS will write out inactive memory to the swapfile to make more room in active memory. UPDATE 17-mar-2012 Here is a round-up of the methods that have been suggested to help so far: The purge command "Used to approximate initial boot conditions with a cold disk buffer cache for performance analysis. It does not affect anonymous memory that has been allocated through malloc, vm_allocate, etc". This is useful to prevent osx to swap-out the disk cache (which is ridiculous that osx actually does so in the first place), but with the downside that the disk cache is released, meaning that if the disk cache was not about to be swapped out, one would simply end up with a cold disk buffer cache, probably affecting performance negatively. The FreeMemory app and/or Repairing disk permissions to force some Free memory Doesn't help releasing any memory, only moving some gigabytes of memory contents from ram to the hd. In the end, this causes lots of swap-ins when I attempt to use the applications that were open while freeing memory, as a lot of its vm is now on swap. Speeding up swap-allocation using dynamicpagerwrapper Seems a good thing to do in order to speed up swap-usage, but does not address the problem of osx swapping in the first place while there is still inactive memory. Disabling swap by disabling dynamicpager and restarting This will force osx not to use swap to the price of the system hanging when all memory is used. Not a viable alternative... Disabling swap using a hacked dynamicpager Similar to disabling dynamicpager above, some excerpts from the comments to the blog post indicate that this is not a viable solution: "The Inactive Memory is high as usual". "when your system is running out of memory, the whole os hangs...", "if you consume the whole amount of memory of the mac, the machine will likely hang" To sum up, I am still unaware of a way of disabling Mac OS X from using swap when there still is "Inactive" memory. If it isn't possible, maybe at least there is an explanation somewhere of why osx prefers to swap out memory that may be released from memory at any time?

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• 10 Reasons Why Java is the Top Embedded Platform

  - by Roger Brinkley

  With the release of Oracle ME Embedded 3.2 and Oracle Java Embedded Suite, Java is now ready to fully move into the embedded developer space, what many have called the "Internet of Things". Here are 10 reasons why Java is the top embedded platform. 1. Decouples software development from hardware development cycle Development is typically split between both hardware and software in a traditional design flow . This leads to complicated co-design and requires prototype hardware to be built. This parallel and interdependent hardware / software design process typically leads to two or more re-development phases. With Embedded Java, all specific work is carried out in software, with the (processor) hardware implementation fully decoupled. This with eliminate or at least reduces the need for re-spins of software or hardware and the original development efforts can be carried forward directly into product development and validation. 2. Development and testing can be done (mostly) using standard desktop systems through emulation Because the software and hardware are decoupled it now becomes easier to test the software long before it reaches the hardware through hardware emulation. Emulation is the ability of a program in an electronic device to imitate another program or device. In the past Java tools like the Java ME SDK and the SunSPOTs Solarium provided developers with emulation for a complete set of mobile telelphones and SunSpots. This often included network interaction or in the case of SunSPOTs radio communication. What emulation does is speed up the development cycle by refining the software development process without the need of hardware. The software is fixed, redefined, and refactored without the timely expense of hardware testing. With tools like the Java ME 3.2 SDK, Embedded Java applications can be be quickly developed on Windows based platforms. In the end of course developers should do a full set of testing on the hardware as incompatibilities between emulators and hardware will exist, but the amount of time to do this should be significantly reduced. 3. Highly productive language, APIs, runtime, and tools mean quick time to market Charles Nutter probably said it best in twitter blog when he tweeted, "Every time I see a piece of C code I need to port, my heart dies a little. Then I port it to 1/4 as much Java, and feel better." The Java environment is a very complex combination of a Java Virtual Machine, the Java Language, and it's robust APIs. Combine that with the Java ME SDK for small devices or just Netbeans for the larger devices and you have a development environment where development time is reduced significantly meaning the product can be shipped sooner. Of course this is assuming that the engineers don't get slap happy adding new features given the extra time they'll have.  4. Create high-performance, portable, secure, robust, cross-platform applications easily The latest JIT compilers for the Oracle JVM approach the speed of C/C++ code, and in some memory allocation intensive circumstances, exceed it. And specifically for the embedded devices both ME Embedded and SE Embedded have been optimized for the smaller footprints.  In portability Java uses Bytecode to make the language platform independent. This creates a write once run anywhere environment that allows you to develop on one platform and execute on others and avoids a platform vendor lock in. For security, Java achieves protection by confining a Java program to a Java execution environment and not allowing it to access other parts of computer.  In variety of systems the program must execute reliably to be robust. Finally, Oracle Java ME Embedded is a cross-industry and cross-platform product optimized in release version 3.2 for chipsets based on the ARM architectures. Similarly Oracle Java SE Embedded works on a variety of ARM V5, V6, and V7, X86 and Power Architecture Linux. 5. Java isolates your apps from language and platform variations (e.g. C/C++, kernel, libc differences) This has been a key factor in Java from day one. Developers write to Java and don't have to worry about underlying differences in the platform variations. Those platform variations are being managed by the JVM. Gone are the C/C++ problems like memory corruptions, stack overflows, and other such bugs which are extremely difficult to isolate. Of course this doesn't imply that you won't be able to get away from native code completely. There could be some situations where you have to write native code in either assembler or C/C++. But those instances should be limited. 6. Most popular embedded processors supported allowing design flexibility Java SE Embedded is now available on ARM V5, V6, and V7 along with Linux on X86 and Power Architecture platforms. Java ME Embedded is available on system based on ARM architecture SOCs with low memory footprints and a device emulation environment for x86/Windows desktop computers, integrated with the Java ME SDK 3.2. A standard binary of Oracle Java ME Embedded 3.2 for ARM KEIL development boards based on ARM Cortex M-3/4 (KEIL MCBSTM32F200 using ST Micro SOC STM32F207IG) will soon be available for download from the Oracle Technology Network (OTN). 7. Support for key embedded features (low footprint, power mgmt., low latency, etc) All embedded devices by there very nature are constrained in some way. Economics may dictate a device with a less RAM and ROM. The CPU needs can dictate a less powerful device. Power consumption is another major resource in some embedded devices as connecting to consistent power source not always desirable or possible. For others they have to constantly on. Often many of these systems are headless (in the embedded space it's almost always Halloween).  For memory resources ,Java ME Embedded can run in environment as low as 130KB RAM/350KB ROM for a minimal, customized configuration up to 700KB RAM/1500KB ROM for the full, standard configuration. Java SE Embedded is designed for environments starting at 32MB RAM/39MB  ROM. Key functionality of embedded devices such as auto-start and recovery, flexible networking are fully supported. And while Java SE Embedded has been optimized for mid-range to high-end embedded systems, Java ME Embedded is a Java runtime stack optimized for small embedded systems. It provides a robust and flexible application platform with dedicated embedded functionality for always-on, headless (no graphics/UI), and connected devices. 8. Leverage huge Java developer ecosystem (expertise, existing code) There are over 9 million developers in world that work on Java, and while not all of them work on embedded systems, their wealth of expertise in developing applications is immense. In short, getting a java developer to work on a embedded system is pretty easy, you probably have a java developer living in your subdivsion.  Then of course there is the wealth of existing code. The Java Embedded Community on Java.net is central gathering place for embedded Java developers. Conferences like Embedded Java @ JavaOne and the a variety of hardware vendor conferences like Freescale Technlogy Forums offer an excellent opportunity for those interested in embedded systems. 9. Easily create end-to-end solutions integrated with Java back-end services In the "Internet of Things" things aren't on an island doing an single task. For instance and embedded drink dispenser doesn't just dispense a beverage, but could collect money from a credit card and also send information about current sales. Similarly, an embedded house power monitoring system doesn't just manage the power usage in a house, but can also send that data back to the power company. In both cases it isn't about the individual thing, but monitoring a collection of  things. How much power did your block, subdivsion, area of town, town, county, state, nation, world use? How many Dr Peppers were purchased from thing1, thing2, thingN? The point is that all this information can be collected and transferred securely  (and believe me that is key issue that Java fully supports) to back end services for further analysis. And what better back in service exists than a Java back in service. It's interesting to note that on larger embedded platforms that support the Java Embedded Suite some of the analysis might be done on the embedded device itself as JES has a glassfish server and Java Database as part of the installation. The result is an end to end Java solution. 10. Solutions from constrained devices to server-class systems Just take a look at some of the embedded Java systems that have already been developed and you'll see a vast range of solutions. Livescribe pen, Kindle, each and every Blu-Ray player, Cisco's Advanced VOIP phone, KronosInTouch smart time clock, EnergyICT smart metering, EDF's automated meter management, Ricoh Printers, and Stanford's automated car  are just a few of the list of embedded Java implementation that continues to grow. Conclusion Now if your a Java Developer you probably look at some of the 10 reasons and say "duh", but for the embedded developers this is should be an eye opening list. And with the release of ME Embedded 3.2 and the Java Embedded Suite the embedded developers life is now a whole lot easier. For the Java developer your employment opportunities are about to increase. For both it's a great time to start developing Java for the "Internet of Things".

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• Ubuntu 11.10 - Everytime i am trying to connect to my box using SSH, its failing not connecting

  - by YumYumYum

  From any other PC doing SSH to my Ubuntu 11.10,is failing. Even the SSH is running: Other PC: retrying over and over $ ping 192.168.0.128 PING 192.168.0.128 (192.168.0.128) 56(84) bytes of data. From 192.168.0.226 icmp_seq=1 Destination Host Unreachable From 192.168.0.226 icmp_seq=2 Destination Host Unreachable From 192.168.0.226 icmp_seq=3 Destination Host Unreachable From 192.168.0.226 icmp_seq=4 Destination Host Unreachable $ sudo service iptables stop Stopping iptables (via systemctl): [ OK ] $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host $ ssh [email protected] Connection closed by 192.168.0.128 $ ssh [email protected] [email protected]'s password: Connection closed by UNKNOWN $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host Follow up: -- checked cable -- using cable tester and other detectors -- no problem found in cable -- used random 10 cables -- adapter is not broken -- checked it using circuit tester by opening the system (card is new so its not network adapter card problem) -- leds are OK showing -- used LiveCD and did same ping test was having same problem -- disabled ipv6 100% to make sure its not the cause -- disabled iptables 100% so its also not the issue -- some more info $ sudo killall dnsmasq -- did not solved the problem -- -- like many other Q/A was suggesting this same --- $ iptables --list Chain INPUT (policy ACCEPT) target prot opt source destination Chain FORWARD (policy ACCEPT) target prot opt source destination Chain OUTPUT (policy ACCEPT) target prot opt source destination $ netstat -nr Kernel IP routing table Destination Gateway Genmask Flags MSS Window irtt Iface 0.0.0.0 192.168.0.1 0.0.0.0 UG 0 0 0 eth0 169.254.0.0 0.0.0.0 255.255.0.0 U 0 0 0 eth0 192.168.0.0 0.0.0.0 255.255.255.0 U 0 0 0 eth0 $ ssh -vvv [email protected] OpenSSH_5.6p1, OpenSSL 1.0.0j-fips 10 May 2012 debug1: Reading configuration data /etc/ssh/ssh_config debug1: Applying options for * debug2: ssh_connect: needpriv 0 debug1: Connecting to 192.168.0.128 [192.168.0.128] port 22. debug1: Connection established. debug3: Not a RSA1 key file /home/sun/.ssh/id_rsa. debug2: key_type_from_name: unknown key type '-----BEGIN' debug3: key_read: missing keytype debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug2: key_type_from_name: unknown key type '-----END' debug3: key_read: missing keytype debug1: identity file /home/sun/.ssh/id_rsa type 1 debug1: identity file /home/sun/.ssh/id_rsa-cert type -1 debug1: identity file /home/sun/.ssh/id_dsa type -1 debug1: identity file /home/sun/.ssh/id_dsa-cert type -1 debug1: Remote protocol version 2.0, remote software version OpenSSH_5.8p1 Debian-7ubuntu1 debug1: match: OpenSSH_5.8p1 Debian-7ubuntu1 pat OpenSSH* debug1: Enabling compatibility mode for protocol 2.0 debug1: Local version string SSH-2.0-OpenSSH_5.6 debug2: fd 3 setting O_NONBLOCK debug1: SSH2_MSG_KEXINIT sent debug1: SSH2_MSG_KEXINIT received debug2: kex_parse_kexinit: diffie-hellman-group-exchange-sha256,diffie-hellman-group-exchange-sha1,diffie-hellman-group14-sha1,diffie-hellman-group1-sha1 debug2: kex_parse_kexinit: [email protected],[email protected],[email protected],[email protected],ssh-rsa,ssh-dss debug2: kex_parse_kexinit: aes128-ctr,aes192-ctr,aes256-ctr,arcfour256,arcfour128,aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,aes192-cbc,aes256-cbc,arcfour,[email protected] debug2: kex_parse_kexinit: aes128-ctr,aes192-ctr,aes256-ctr,arcfour256,arcfour128,aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,aes192-cbc,aes256-cbc,arcfour,[email protected] debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: none,[email protected],zlib debug2: kex_parse_kexinit: none,[email protected],zlib debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: first_kex_follows 0 debug2: kex_parse_kexinit: reserved 0 debug2: kex_parse_kexinit: ecdh-sha2-nistp256,ecdh-sha2-nistp384,ecdh-sha2-nistp521,diffie-hellman-group-exchange-sha256,diffie-hellman-group-exchange-sha1,diffie-hellman-group14-sha1,diffie-hellman-group1-sha1 debug2: kex_parse_kexinit: ssh-rsa,ssh-dss,ecdsa-sha2-nistp256 debug2: kex_parse_kexinit: aes128-ctr,aes192-ctr,aes256-ctr,arcfour256,arcfour128,aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,aes192-cbc,aes256-cbc,arcfour,[email protected] debug2: kex_parse_kexinit: aes128-ctr,aes192-ctr,aes256-ctr,arcfour256,arcfour128,aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,aes192-cbc,aes256-cbc,arcfour,[email protected] debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: none,[email protected] debug2: kex_parse_kexinit: none,[email protected] debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: first_kex_follows 0 debug2: kex_parse_kexinit: reserved 0 debug2: mac_setup: found hmac-md5 debug1: kex: server->client aes128-ctr hmac-md5 none debug2: mac_setup: found hmac-md5 debug1: kex: client->server aes128-ctr hmac-md5 none debug1: SSH2_MSG_KEX_DH_GEX_REQUEST(1024<1024<8192) sent debug1: expecting SSH2_MSG_KEX_DH_GEX_GROUP debug2: dh_gen_key: priv key bits set: 118/256 debug2: bits set: 539/1024 debug1: SSH2_MSG_KEX_DH_GEX_INIT sent debug1: expecting SSH2_MSG_KEX_DH_GEX_REPLY debug3: check_host_in_hostfile: host 192.168.0.128 filename /home/sun/.ssh/known_hosts debug3: check_host_in_hostfile: host 192.168.0.128 filename /home/sun/.ssh/known_hosts debug3: check_host_in_hostfile: match line 139 debug1: Host '192.168.0.128' is known and matches the RSA host key. debug1: Found key in /home/sun/.ssh/known_hosts:139 debug2: bits set: 544/1024 debug1: ssh_rsa_verify: signature correct debug2: kex_derive_keys debug2: set_newkeys: mode 1 debug1: SSH2_MSG_NEWKEYS sent debug1: expecting SSH2_MSG_NEWKEYS debug2: set_newkeys: mode 0 debug1: SSH2_MSG_NEWKEYS received debug1: Roaming not allowed by server debug1: SSH2_MSG_SERVICE_REQUEST sent debug2: service_accept: ssh-userauth debug1: SSH2_MSG_SERVICE_ACCEPT received debug2: key: /home/sun/.ssh/id_rsa (0x213db960) debug2: key: /home/sun/.ssh/id_dsa ((nil)) debug1: Authentications that can continue: publickey,password debug3: start over, passed a different list publickey,password debug3: preferred gssapi-keyex,gssapi-with-mic,publickey,keyboard-interactive,password debug3: authmethod_lookup publickey debug3: remaining preferred: keyboard-interactive,password debug3: authmethod_is_enabled publickey debug1: Next authentication method: publickey debug1: Offering RSA public key: /home/sun/.ssh/id_rsa debug3: send_pubkey_test debug2: we sent a publickey packet, wait for reply debug1: Authentications that can continue: publickey,password debug1: Trying private key: /home/sun/.ssh/id_dsa debug3: no such identity: /home/sun/.ssh/id_dsa debug2: we did not send a packet, disable method debug3: authmethod_lookup password debug3: remaining preferred: ,password debug3: authmethod_is_enabled password debug1: Next authentication method: password [email protected]'s password: debug3: packet_send2: adding 64 (len 60 padlen 4 extra_pad 64) debug2: we sent a password packet, wait for reply debug1: Authentication succeeded (password). Authenticated to 192.168.0.128 ([192.168.0.128]:22). debug1: channel 0: new [client-session] debug3: ssh_session2_open: channel_new: 0 debug2: channel 0: send open debug1: Requesting [email protected] debug1: Entering interactive session. debug2: callback start debug2: client_session2_setup: id 0 debug2: channel 0: request pty-req confirm 1 debug1: Sending environment. debug3: Ignored env ORBIT_SOCKETDIR debug3: Ignored env XDG_SESSION_ID debug3: Ignored env HOSTNAME debug3: Ignored env GIO_LAUNCHED_DESKTOP_FILE_PID debug3: Ignored env IMSETTINGS_INTEGRATE_DESKTOP debug3: Ignored env GPG_AGENT_INFO debug3: Ignored env TERM debug3: Ignored env HARDWARE_PLATFORM debug3: Ignored env SHELL debug3: Ignored env DESKTOP_STARTUP_ID debug3: Ignored env HISTSIZE debug3: Ignored env XDG_SESSION_COOKIE debug3: Ignored env GJS_DEBUG_OUTPUT debug3: Ignored env WINDOWID debug3: Ignored env GNOME_KEYRING_CONTROL debug3: Ignored env QTDIR debug3: Ignored env QTINC debug3: Ignored env GJS_DEBUG_TOPICS debug3: Ignored env IMSETTINGS_MODULE debug3: Ignored env USER debug3: Ignored env LS_COLORS debug3: Ignored env SSH_AUTH_SOCK debug3: Ignored env USERNAME debug3: Ignored env SESSION_MANAGER debug3: Ignored env GIO_LAUNCHED_DESKTOP_FILE debug3: Ignored env PATH debug3: Ignored env MAIL debug3: Ignored env DESKTOP_SESSION debug3: Ignored env QT_IM_MODULE debug3: Ignored env PWD debug1: Sending env XMODIFIERS = @im=none debug2: channel 0: request env confirm 0 debug1: Sending env LANG = en_US.utf8 debug2: channel 0: request env confirm 0 debug3: Ignored env KDE_IS_PRELINKED debug3: Ignored env GDM_LANG debug3: Ignored env KDEDIRS debug3: Ignored env GDMSESSION debug3: Ignored env SSH_ASKPASS debug3: Ignored env HISTCONTROL debug3: Ignored env HOME debug3: Ignored env SHLVL debug3: Ignored env GDL_PATH debug3: Ignored env GNOME_DESKTOP_SESSION_ID debug3: Ignored env LOGNAME debug3: Ignored env QTLIB debug3: Ignored env CVS_RSH debug3: Ignored env DBUS_SESSION_BUS_ADDRESS debug3: Ignored env LESSOPEN debug3: Ignored env WINDOWPATH debug3: Ignored env XDG_RUNTIME_DIR debug3: Ignored env DISPLAY debug3: Ignored env G_BROKEN_FILENAMES debug3: Ignored env COLORTERM debug3: Ignored env XAUTHORITY debug3: Ignored env _ debug2: channel 0: request shell confirm 1 debug2: fd 3 setting TCP_NODELAY debug2: callback done debug2: channel 0: open confirm rwindow 0 rmax 32768 debug2: channel_input_status_confirm: type 99 id 0 debug2: PTY allocation request accepted on channel 0 debug2: channel 0: rcvd adjust 2097152 debug2: channel_input_status_confirm: type 99 id 0 debug2: shell request accepted on channel 0 Welcome to Ubuntu 11.10 (GNU/Linux 3.0.0-12-generic x86_64) * Documentation: https://help.ubuntu.com/ 297 packages can be updated. 92 updates are security updates. New release '12.04 LTS' available. Run 'do-release-upgrade' to upgrade to it. Last login: Fri Jun 8 07:45:15 2012 from 192.168.0.226 sun@SystemAX51:~$ ping 19<--------Lost connection again-------------- Tail follow: -- dmesg is showing a very abnormal logs, like Ubuntu is automatically bringing the eth0 up, where eth0 is getting also auto down. [ 2025.897511] r8169 0000:02:00.0: eth0: link up [ 2029.347649] r8169 0000:02:00.0: eth0: link up [ 2030.775556] r8169 0000:02:00.0: eth0: link up [ 2038.242203] r8169 0000:02:00.0: eth0: link up [ 2057.267801] r8169 0000:02:00.0: eth0: link up [ 2062.871770] r8169 0000:02:00.0: eth0: link up [ 2082.479712] r8169 0000:02:00.0: eth0: link up [ 2285.630797] r8169 0000:02:00.0: eth0: link up [ 2308.417640] r8169 0000:02:00.0: eth0: link up [ 2480.948290] r8169 0000:02:00.0: eth0: link up [ 2824.884798] r8169 0000:02:00.0: eth0: link up [ 3030.022183] r8169 0000:02:00.0: eth0: link up [ 3306.587353] r8169 0000:02:00.0: eth0: link up [ 3523.566881] r8169 0000:02:00.0: eth0: link up [ 3619.839585] r8169 0000:02:00.0: eth0: link up [ 3682.154393] nf_conntrack version 0.5.0 (16384 buckets, 65536 max) [ 3899.866854] r8169 0000:02:00.0: eth0: link up [ 4723.978269] r8169 0000:02:00.0: eth0: link up [ 4807.415682] r8169 0000:02:00.0: eth0: link up [ 5101.865686] r8169 0000:02:00.0: eth0: link up How do i fix it? -- http://ubuntuforums.org/showthread.php?t=1959794 -- apt-get install openipml openhpi-plugin-ipml

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• ubuntu ssh does not connect

  - by bocca

  SSH won't be able to establish a connection to our server Here's the output of ssh -vvv: ssh -v -v -v 11.11.11.11 OpenSSH_5.1p1 Debian-6ubuntu2, OpenSSL 0.9.8g 19 Oct 2007 debug1: Reading configuration data /etc/ssh/ssh_config debug1: Applying options for * debug2: ssh_connect: needpriv 0 debug1: Connecting to 11.11.11.11 [11.11.11.11] port 22. debug1: Connection established. debug1: permanently_set_uid: 0/0 debug1: identity file /root/.ssh/identity type -1 debug1: identity file /root/.ssh/id_rsa type -1 debug1: identity file /root/.ssh/id_dsa type -1 debug1: Remote protocol version 2.0, remote software version OpenSSH_5.1p1 Debian-5ubuntu1 debug1: match: OpenSSH_5.1p1 Debian-5ubuntu1 pat OpenSSH* debug1: Enabling compatibility mode for protocol 2.0 debug1: Local version string SSH-2.0-OpenSSH_5.1p1 Debian-6ubuntu2 debug2: fd 3 setting O_NONBLOCK debug1: SSH2_MSG_KEXINIT sent debug1: SSH2_MSG_KEXINIT received debug2: kex_parse_kexinit: diffie-hellman-group-exchange-sha256,diffie-hellman-group-exchange-sha1,diffie-hellman-group14-sha1,diffie-hellman-group1-sha1 debug2: kex_parse_kexinit: ssh-rsa,ssh-dss debug2: kex_parse_kexinit: aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,arcfour128,arcfour256,arcfour,aes192-cbc,aes256-cbc,[email protected],aes128-ctr,aes192-ctr,aes256-ctr debug2: kex_parse_kexinit: aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,arcfour128,arcfour256,arcfour,aes192-cbc,aes256-cbc,[email protected],aes128-ctr,aes192-ctr,aes256-ctr debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: none,[email protected],zlib debug2: kex_parse_kexinit: none,[email protected],zlib debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: first_kex_follows 0 debug2: kex_parse_kexinit: reserved 0 debug2: kex_parse_kexinit: diffie-hellman-group-exchange-sha256,diffie-hellman-group-exchange-sha1,diffie-hellman-group14-sha1,diffie-hellman-group1-sha1 debug2: kex_parse_kexinit: ssh-rsa,ssh-dss debug2: kex_parse_kexinit: aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,arcfour128,arcfour256,arcfour,aes192-cbc,aes256-cbc,[email protected],aes128-ctr,aes192-ctr,aes256-ctr debug2: kex_parse_kexinit: aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,arcfour128,arcfour256,arcfour,aes192-cbc,aes256-cbc,[email protected],aes128-ctr,aes192-ctr,aes256-ctr debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: none,[email protected] debug2: kex_parse_kexinit: none,[email protected] debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: first_kex_follows 0 debug2: kex_parse_kexinit: reserved 0 debug2: mac_setup: found hmac-md5 debug1: kex: server->client aes128-cbc hmac-md5 none debug2: mac_setup: found hmac-md5 debug1: kex: client->server aes128-cbc hmac-md5 none debug1: SSH2_MSG_KEX_DH_GEX_REQUEST(1024<1024<8192) sent debug1: expecting SSH2_MSG_KEX_DH_GEX_GROUP debug2: dh_gen_key: priv key bits set: 133/256 debug2: bits set: 486/1024 debug1: SSH2_MSG_KEX_DH_GEX_INIT sent debug1: expecting SSH2_MSG_KEX_DH_GEX_REPLY debug3: check_host_in_hostfile: filename /root/.ssh/known_hosts debug3: check_host_in_hostfile: match line 1 debug1: Host '11.11.11.11' is known and matches the RSA host key. debug1: Found key in /root/.ssh/known_hosts:1 debug2: bits set: 497/1024 debug1: ssh_rsa_verify: signature correct debug2: kex_derive_keys debug2: set_newkeys: mode 1 debug1: SSH2_MSG_NEWKEYS sent debug1: expecting SSH2_MSG_NEWKEYS debug2: set_newkeys: mode 0 debug1: SSH2_MSG_NEWKEYS received debug1: SSH2_MSG_SERVICE_REQUEST sent debug2: service_accept: ssh-userauth debug1: SSH2_MSG_SERVICE_ACCEPT received debug2: key: /root/.ssh/identity ((nil)) debug2: key: /root/.ssh/id_rsa ((nil)) debug2: key: /root/.ssh/id_dsa ((nil)) debug1: Authentications that can continue: publickey,password debug3: start over, passed a different list publickey,password debug3: preferred gssapi-keyex,gssapi-with-mic,gssapi,publickey,keyboard-interactive,password debug3: authmethod_lookup publickey debug3: remaining preferred: keyboard-interactive,password debug3: authmethod_is_enabled publickey debug1: Next authentication method: publickey debug1: Trying private key: /root/.ssh/identity debug3: no such identity: /root/.ssh/identity debug1: Trying private key: /root/.ssh/id_rsa debug3: no such identity: /root/.ssh/id_rsa debug1: Trying private key: /root/.ssh/id_dsa debug3: no such identity: /root/.ssh/id_dsa debug2: we did not send a packet, disable method debug3: authmethod_lookup password debug3: remaining preferred: ,password debug3: authmethod_is_enabled password debug1: Next authentication method: password [email protected]'s password: debug3: packet_send2: adding 64 (len 57 padlen 7 extra_pad 64) debug2: we sent a password packet, wait for reply debug1: Authentication succeeded (password). debug1: channel 0: new [client-session] debug3: ssh_session2_open: channel_new: 0 debug2: channel 0: send open debug1: Requesting [email protected] debug1: Entering interactive session. debug2: callback start debug2: client_session2_setup: id 0 debug2: channel 0: request pty-req confirm 1 debug3: tty_make_modes: ospeed 38400 debug3: tty_make_modes: ispeed 38400 debug1: Sending environment. debug3: Ignored env ORBIT_SOCKETDIR debug3: Ignored env SSH_AGENT_PID debug3: Ignored env SHELL debug3: Ignored env TERM debug3: Ignored env XDG_SESSION_COOKIE debug3: Ignored env GTK_RC_FILES debug3: Ignored env WINDOWID debug3: Ignored env USER debug3: Ignored env LS_COLORS debug3: Ignored env GNOME_KEYRING_SOCKET debug3: Ignored env SSH_AUTH_SOCK debug3: Ignored env USERNAME debug3: Ignored env SESSION_MANAGER debug3: Ignored env MAIL debug3: Ignored env PATH debug3: Ignored env DESKTOP_SESSION debug3: Ignored env PWD debug3: Ignored env GDM_KEYBOARD_LAYOUT debug3: Ignored env GNOME_KEYRING_PID debug1: Sending env LANG = en_CA.UTF-8 debug2: channel 0: request env confirm 0 debug3: Ignored env GDM_LANG debug3: Ignored env GDMSESSION debug3: Ignored env HISTCONTROL debug3: Ignored env SPEECHD_PORT debug3: Ignored env HOME debug3: Ignored env SHLVL debug3: Ignored env GNOME_DESKTOP_SESSION_ID debug3: Ignored env LOGNAME debug3: Ignored env XDG_DATA_DIRS debug3: Ignored env DBUS_SESSION_BUS_ADDRESS debug3: Ignored env LESSOPEN debug3: Ignored env DISPLAY debug3: Ignored env LESSCLOSE debug3: Ignored env XAUTHORITY debug3: Ignored env COLORTERM debug3: Ignored env _ debug2: channel 0: request shell confirm 1 debug2: fd 3 setting TCP_NODELAY debug2: callback done debug2: channel 0: open confirm rwindow 0 rmax 32768 debug2: channel_input_confirm: type 99 id 0 debug2: PTY allocation request accepted on channel 0 debug2: channel 0: rcvd adjust 2097152 debug2: channel_input_confirm: type 99 id 0 debug2: shell request accepted on channel 0

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• Everytime i am trying to connect to my box using SSH, its failing not connecting

  - by YumYumYum

  From any other PC doing SSH to my Ubuntu 11.10,is failing. My network setup: Telenet ISP (Belgium) Fiber cable < RJ45 cable straight to Ubuntu PC Even the SSH is running: Other PC: retrying over and over $ ping 192.168.0.128 PING 192.168.0.128 (192.168.0.128) 56(84) bytes of data. From 192.168.0.226 icmp_seq=1 Destination Host Unreachable From 192.168.0.226 icmp_seq=2 Destination Host Unreachable From 192.168.0.226 icmp_seq=3 Destination Host Unreachable From 192.168.0.226 icmp_seq=4 Destination Host Unreachable $ sudo service iptables stop Stopping iptables (via systemctl): [ OK ] $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host $ ssh [email protected] Connection closed by 192.168.0.128 $ ssh [email protected] [email protected]'s password: Connection closed by UNKNOWN $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host $ ssh [email protected] ssh: connect to host 192.168.0.128 port 22: No route to host Follow up: -- checked cable -- using cable tester and other detectors -- no problem found in cable -- used random 10 cables -- adapter is not broken -- checked it using circuit tester by opening the system (card is new so its not network adapter card problem) -- leds are OK showing -- used LiveCD and did same ping test was having same problem -- disabled ipv6 100% to make sure its not the cause -- disabled iptables 100% so its also not the issue -- some more info $ nmap 192.168.0.128 Starting Nmap 5.50 ( http://nmap.org ) at 2012-06-08 19:11 CEST Nmap scan report for 192.168.0.128 Host is up (0.00045s latency). All 1000 scanned ports on 192.168.0.128 are closed (842) or filtered (158) Nmap done: 1 IP address (1 host up) scanned in 6.86 seconds ubuntu@ubuntu:~$ netstat -aunt | head Active Internet connections (servers and established) Proto Recv-Q Send-Q Local Address Foreign Address State tcp 0 0 127.0.0.1:631 0.0.0.0:* LISTEN tcp 0 1 192.168.0.128:58616 74.125.132.99:80 FIN_WAIT1 tcp 0 0 192.168.0.128:56749 199.7.57.72:80 ESTABLISHED tcp 0 1 192.168.0.128:58614 74.125.132.99:80 FIN_WAIT1 tcp 0 0 192.168.0.128:49916 173.194.65.113:443 ESTABLISHED tcp 0 1 192.168.0.128:45699 64.34.119.101:80 SYN_SENT tcp 0 0 192.168.0.128:48404 64.34.119.12:80 ESTABLISHED tcp 0 0 192.168.0.128:54161 67.201.31.70:80 TIME_WAIT $ sudo killall dnsmasq -- did not solved the problem -- -- like many other Q/A was suggesting this same --- $ iptables --list Chain INPUT (policy ACCEPT) target prot opt source destination Chain FORWARD (policy ACCEPT) target prot opt source destination Chain OUTPUT (policy ACCEPT) target prot opt source destination $ netstat -nr Kernel IP routing table Destination Gateway Genmask Flags MSS Window irtt Iface 0.0.0.0 192.168.0.1 0.0.0.0 UG 0 0 0 eth0 169.254.0.0 0.0.0.0 255.255.0.0 U 0 0 0 eth0 192.168.0.0 0.0.0.0 255.255.255.0 U 0 0 0 eth0 $ ssh -vvv [email protected] OpenSSH_5.6p1, OpenSSL 1.0.0j-fips 10 May 2012 debug1: Reading configuration data /etc/ssh/ssh_config debug1: Applying options for * debug2: ssh_connect: needpriv 0 debug1: Connecting to 192.168.0.128 [192.168.0.128] port 22. debug1: Connection established. debug3: Not a RSA1 key file /home/sun/.ssh/id_rsa. debug2: key_type_from_name: unknown key type '-----BEGIN' debug3: key_read: missing keytype debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug3: key_read: missing whitespace debug2: key_type_from_name: unknown key type '-----END' debug3: key_read: missing keytype debug1: identity file /home/sun/.ssh/id_rsa type 1 debug1: identity file /home/sun/.ssh/id_rsa-cert type -1 debug1: identity file /home/sun/.ssh/id_dsa type -1 debug1: identity file /home/sun/.ssh/id_dsa-cert type -1 debug1: Remote protocol version 2.0, remote software version OpenSSH_5.8p1 Debian-7ubuntu1 debug1: match: OpenSSH_5.8p1 Debian-7ubuntu1 pat OpenSSH* debug1: Enabling compatibility mode for protocol 2.0 debug1: Local version string SSH-2.0-OpenSSH_5.6 debug2: fd 3 setting O_NONBLOCK debug1: SSH2_MSG_KEXINIT sent debug1: SSH2_MSG_KEXINIT received debug2: kex_parse_kexinit: diffie-hellman-group-exchange-sha256,diffie-hellman-group-exchange-sha1,diffie-hellman-group14-sha1,diffie-hellman-group1-sha1 debug2: kex_parse_kexinit: [email protected],[email protected],[email protected],[email protected],ssh-rsa,ssh-dss debug2: kex_parse_kexinit: aes128-ctr,aes192-ctr,aes256-ctr,arcfour256,arcfour128,aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,aes192-cbc,aes256-cbc,arcfour,[email protected] debug2: kex_parse_kexinit: aes128-ctr,aes192-ctr,aes256-ctr,arcfour256,arcfour128,aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,aes192-cbc,aes256-cbc,arcfour,[email protected] debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: none,[email protected],zlib debug2: kex_parse_kexinit: none,[email protected],zlib debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: first_kex_follows 0 debug2: kex_parse_kexinit: reserved 0 debug2: kex_parse_kexinit: ecdh-sha2-nistp256,ecdh-sha2-nistp384,ecdh-sha2-nistp521,diffie-hellman-group-exchange-sha256,diffie-hellman-group-exchange-sha1,diffie-hellman-group14-sha1,diffie-hellman-group1-sha1 debug2: kex_parse_kexinit: ssh-rsa,ssh-dss,ecdsa-sha2-nistp256 debug2: kex_parse_kexinit: aes128-ctr,aes192-ctr,aes256-ctr,arcfour256,arcfour128,aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,aes192-cbc,aes256-cbc,arcfour,[email protected] debug2: kex_parse_kexinit: aes128-ctr,aes192-ctr,aes256-ctr,arcfour256,arcfour128,aes128-cbc,3des-cbc,blowfish-cbc,cast128-cbc,aes192-cbc,aes256-cbc,arcfour,[email protected] debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: hmac-md5,hmac-sha1,[email protected],hmac-ripemd160,[email protected],hmac-sha1-96,hmac-md5-96 debug2: kex_parse_kexinit: none,[email protected] debug2: kex_parse_kexinit: none,[email protected] debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: debug2: kex_parse_kexinit: first_kex_follows 0 debug2: kex_parse_kexinit: reserved 0 debug2: mac_setup: found hmac-md5 debug1: kex: server->client aes128-ctr hmac-md5 none debug2: mac_setup: found hmac-md5 debug1: kex: client->server aes128-ctr hmac-md5 none debug1: SSH2_MSG_KEX_DH_GEX_REQUEST(1024<1024<8192) sent debug1: expecting SSH2_MSG_KEX_DH_GEX_GROUP debug2: dh_gen_key: priv key bits set: 118/256 debug2: bits set: 539/1024 debug1: SSH2_MSG_KEX_DH_GEX_INIT sent debug1: expecting SSH2_MSG_KEX_DH_GEX_REPLY debug3: check_host_in_hostfile: host 192.168.0.128 filename /home/sun/.ssh/known_hosts debug3: check_host_in_hostfile: host 192.168.0.128 filename /home/sun/.ssh/known_hosts debug3: check_host_in_hostfile: match line 139 debug1: Host '192.168.0.128' is known and matches the RSA host key. debug1: Found key in /home/sun/.ssh/known_hosts:139 debug2: bits set: 544/1024 debug1: ssh_rsa_verify: signature correct debug2: kex_derive_keys debug2: set_newkeys: mode 1 debug1: SSH2_MSG_NEWKEYS sent debug1: expecting SSH2_MSG_NEWKEYS debug2: set_newkeys: mode 0 debug1: SSH2_MSG_NEWKEYS received debug1: Roaming not allowed by server debug1: SSH2_MSG_SERVICE_REQUEST sent debug2: service_accept: ssh-userauth debug1: SSH2_MSG_SERVICE_ACCEPT received debug2: key: /home/sun/.ssh/id_rsa (0x213db960) debug2: key: /home/sun/.ssh/id_dsa ((nil)) debug1: Authentications that can continue: publickey,password debug3: start over, passed a different list publickey,password debug3: preferred gssapi-keyex,gssapi-with-mic,publickey,keyboard-interactive,password debug3: authmethod_lookup publickey debug3: remaining preferred: keyboard-interactive,password debug3: authmethod_is_enabled publickey debug1: Next authentication method: publickey debug1: Offering RSA public key: /home/sun/.ssh/id_rsa debug3: send_pubkey_test debug2: we sent a publickey packet, wait for reply debug1: Authentications that can continue: publickey,password debug1: Trying private key: /home/sun/.ssh/id_dsa debug3: no such identity: /home/sun/.ssh/id_dsa debug2: we did not send a packet, disable method debug3: authmethod_lookup password debug3: remaining preferred: ,password debug3: authmethod_is_enabled password debug1: Next authentication method: password [email protected]'s password: debug3: packet_send2: adding 64 (len 60 padlen 4 extra_pad 64) debug2: we sent a password packet, wait for reply debug1: Authentication succeeded (password). Authenticated to 192.168.0.128 ([192.168.0.128]:22). debug1: channel 0: new [client-session] debug3: ssh_session2_open: channel_new: 0 debug2: channel 0: send open debug1: Requesting [email protected] debug1: Entering interactive session. debug2: callback start debug2: client_session2_setup: id 0 debug2: channel 0: request pty-req confirm 1 debug1: Sending environment. debug3: Ignored env ORBIT_SOCKETDIR debug3: Ignored env XDG_SESSION_ID debug3: Ignored env HOSTNAME debug3: Ignored env GIO_LAUNCHED_DESKTOP_FILE_PID debug3: Ignored env IMSETTINGS_INTEGRATE_DESKTOP debug3: Ignored env GPG_AGENT_INFO debug3: Ignored env TERM debug3: Ignored env HARDWARE_PLATFORM debug3: Ignored env SHELL debug3: Ignored env DESKTOP_STARTUP_ID debug3: Ignored env HISTSIZE debug3: Ignored env XDG_SESSION_COOKIE debug3: Ignored env GJS_DEBUG_OUTPUT debug3: Ignored env WINDOWID debug3: Ignored env GNOME_KEYRING_CONTROL debug3: Ignored env QTDIR debug3: Ignored env QTINC debug3: Ignored env GJS_DEBUG_TOPICS debug3: Ignored env IMSETTINGS_MODULE debug3: Ignored env USER debug3: Ignored env LS_COLORS debug3: Ignored env SSH_AUTH_SOCK debug3: Ignored env USERNAME debug3: Ignored env SESSION_MANAGER debug3: Ignored env GIO_LAUNCHED_DESKTOP_FILE debug3: Ignored env PATH debug3: Ignored env MAIL debug3: Ignored env DESKTOP_SESSION debug3: Ignored env QT_IM_MODULE debug3: Ignored env PWD debug1: Sending env XMODIFIERS = @im=none debug2: channel 0: request env confirm 0 debug1: Sending env LANG = en_US.utf8 debug2: channel 0: request env confirm 0 debug3: Ignored env KDE_IS_PRELINKED debug3: Ignored env GDM_LANG debug3: Ignored env KDEDIRS debug3: Ignored env GDMSESSION debug3: Ignored env SSH_ASKPASS debug3: Ignored env HISTCONTROL debug3: Ignored env HOME debug3: Ignored env SHLVL debug3: Ignored env GDL_PATH debug3: Ignored env GNOME_DESKTOP_SESSION_ID debug3: Ignored env LOGNAME debug3: Ignored env QTLIB debug3: Ignored env CVS_RSH debug3: Ignored env DBUS_SESSION_BUS_ADDRESS debug3: Ignored env LESSOPEN debug3: Ignored env WINDOWPATH debug3: Ignored env XDG_RUNTIME_DIR debug3: Ignored env DISPLAY debug3: Ignored env G_BROKEN_FILENAMES debug3: Ignored env COLORTERM debug3: Ignored env XAUTHORITY debug3: Ignored env _ debug2: channel 0: request shell confirm 1 debug2: fd 3 setting TCP_NODELAY debug2: callback done debug2: channel 0: open confirm rwindow 0 rmax 32768 debug2: channel_input_status_confirm: type 99 id 0 debug2: PTY allocation request accepted on channel 0 debug2: channel 0: rcvd adjust 2097152 debug2: channel_input_status_confirm: type 99 id 0 debug2: shell request accepted on channel 0 Welcome to Ubuntu 11.10 (GNU/Linux 3.0.0-12-generic x86_64) * Documentation: https://help.ubuntu.com/ 297 packages can be updated. 92 updates are security updates. New release '12.04 LTS' available. Run 'do-release-upgrade' to upgrade to it. Last login: Fri Jun 8 07:45:15 2012 from 192.168.0.226 sun@SystemAX51:~$ ping 19<--------Lost connection again-------------- Tail follow: -- dmesg is showing a very abnormal logs, like Ubuntu is automatically bringing the eth0 up, where eth0 is getting also auto down. [ 2025.897511] r8169 0000:02:00.0: eth0: link up [ 2029.347649] r8169 0000:02:00.0: eth0: link up [ 2030.775556] r8169 0000:02:00.0: eth0: link up [ 2038.242203] r8169 0000:02:00.0: eth0: link up [ 2057.267801] r8169 0000:02:00.0: eth0: link up [ 2062.871770] r8169 0000:02:00.0: eth0: link up [ 2082.479712] r8169 0000:02:00.0: eth0: link up [ 2285.630797] r8169 0000:02:00.0: eth0: link up [ 2308.417640] r8169 0000:02:00.0: eth0: link up [ 2480.948290] r8169 0000:02:00.0: eth0: link up [ 2824.884798] r8169 0000:02:00.0: eth0: link up [ 3030.022183] r8169 0000:02:00.0: eth0: link up [ 3306.587353] r8169 0000:02:00.0: eth0: link up [ 3523.566881] r8169 0000:02:00.0: eth0: link up [ 3619.839585] r8169 0000:02:00.0: eth0: link up [ 3682.154393] nf_conntrack version 0.5.0 (16384 buckets, 65536 max) [ 3899.866854] r8169 0000:02:00.0: eth0: link up [ 4723.978269] r8169 0000:02:00.0: eth0: link up [ 4807.415682] r8169 0000:02:00.0: eth0: link up [ 5101.865686] r8169 0000:02:00.0: eth0: link up How do i fix it? -- http://ubuntuforums.org/showthread.php?t=1959794 $ apt-get install openipml openhpi-plugin-ipml $ openipmish > help redisp_cmd on|off > redisp_cmd on redisp set Final follow up: Step 1: BUG for network card driver r8169 Step 2: get the latest build version http://www.realtek.com/downloads/downloadsView.aspx?Langid=1&PNid=4&PFid=4&Level=5&Conn=4&DownTypeID=3&GetDown=false&Downloads=true#RTL8110SC(L) Step 3: build / make $ cd /var/tmp/driver $ tar xvfj r8169.tar.bz2 $ make clean modules && make install $ rmmod r8169 $ depmod $ cp src/r8169.ko /lib/modules/3.xxxx/kernel/drivers/net/r8169.ko $ modprobe r8169 $ update-initramfs -u $ init 6 Voila!!

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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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• CodePlex Daily Summary for Wednesday, October 17, 2012

  CodePlex Daily Summary for Wednesday, October 17, 2012Popular ReleasesD3 Loot Tracker: 1.5.5: Compatible with 1.05.Test Management eXtensions PowerShell module: TMX 0.4.5: Bugfix in BackUp-TMXTestResults: 1. adding escape characters to sring data (at least, part) 2. ErrorRecord is now supported as a string Known issue: only one screenshot per test result.Write Once, Play Everywhere: MonoGame 3.0 (BETA): This is a beta release of the up coming MonoGame 3.0. It contains an Installer which will install a binary release of MonoGame on windows boxes with the following platforms. Windows, Linux, Android and Windows 8. If you need to build for iOS or Mac you will need to get the source code at this time as the installers for those platforms are not available yet. The installer will also install a bunch of Project templates for Visual Studio 2010 , 2012 and MonoDevleop. For those of you wish...Windawesome: Windawesome v1.4.1 x64: Fixed switching of applications across monitors Changed window flashing API (fix your config files) Added NetworkMonitorWidget (thanks to weiwen) Any issues/recommendations/requests for future versions? This is the 64-bit version of the release. Be sure to use that if you are on a 64-bit Windows. Works with "Required DLLs v3".CODE Framework: 4.0.21017.0: See change log in the Documentation section for details.Global Stock Exchange (Hobby Project): Global Stock Exchange - Invst Banking (Hobby Proj): Initial VersionMagelia WebStore Open-source Ecommerce software: Magelia WebStore 2.1: Add support for .net 4.0 to Magelia.Webstore.Client and StarterSite version 2.1.254.3 Scheduler Import & Export feature UTC datetime and timezone support .net 4.5 and Visual Studio 2012 migration client magelia global refactoring nugget package http://nuget.org/packages/Magelia.Webstore.Client burst optimisation burst time improvment (multithreading, index, ...) current burst is still active when a new burst is generating bugfixes version 2.1.254.1RazorSourceGenerator: RazorSourceGenerator v1.0 Installer: RazorSourceGenerator v1.0 InstallerJayData - The cross-platform HTML5 data-management library for JavaScript: JayData 1.2.2: JayData is a unified data access library for JavaScript to CRUD + Query data from different sources like OData, MongoDB, WebSQL, SqLite, HTML5 localStorage, Facebook or YQL. The library can be integrated with Knockout.js or Sencha Touch 2 and can be used on Node.js as well. See it in action in this 6 minutes video Sencha Touch 2 example app using JayData: Netflix browser. What's new in JayData 1.2.2 For detailed release notes check the release notes. Revitalized IndexedDB providerNow you c...VFPX: FoxcodePlus: FoxcodePlus - Visual Studio like extensions to Visual FoxPro IntelliSense.Droid Explorer: Droid Explorer 0.8.8.8 Beta: fixed the icon for packages on the desktop fixed the install dialog closing right when it starts removed the link to "set up the sdk for me" as this is no longer supported. fixed bug where the device selection dialog would show, even if there was only one device connected. fixed toolbar from having "gap" between other toolbar removed main menu items that do not have any menus Fiskalizacija za developere: FiskalizacijaDev 1.0: Prva verzija ovog projekta, još je uvijek oznacena kao BETA - ovo znaci da su naša testiranja prošla uspješno :) No, kako mi ne proizvodimo neki software za blagajne, tako sve ovo nije niti isprobano u "realnim" uvjetima - svaka je sugestija, primjedba ili prijava bug-a je dobrodošla. Za sve ovo koristite, molimo, Discussions ili Issue Tracker. U ovom trenutku runtime binary je raspoloživ kao Any CPU za .NET verzije 2.0. Javite ukoliko trebaju i verzije buildane za 32-bit/64-bit kao i za .N...Squiggle - A free open source LAN Messenger: Squiggle 3.2 (Development): NOTE: This is development release and not recommended for production use. This release is mainly for enabling extensibility and interoperability with other platforms. Support for plugins Support for extensions Communication layer and protocol is platform independent (ZeroMQ, ProtocolBuffers) Bug fixes New /invite command Edit the sent message Disable update check NOTE: This is development release and not recommended for production use.AcDown????? - AcDown Downloader Framework: AcDown????? v4.2: ??●AcDown??????????、??、??、???????。????，????，?????????????????????????。???????????Acfun、????(Bilibili)、??、??、YouTube、??、???、??????、SF????、????????????。 ●??????AcPlay?????，??????、????????????????。 ● AcDown??????????????????，????????????????????????????。 ● AcDown???????C#??，????.NET Framework 2.0??。?????"Acfun?????"。 ????32??64? Windows XP/Vista/7/8 ???? 32??64? ???Linux ????(1)????????Windows XP???，????????.NET Framework 2.0???(x86)，?????"?????????"??? (2)???????????Linux???，????????Mono?? ??2...PHPExcel: PHPExcel 1.7.8: See Change Log for details of the new features and bugfixes included in this release, and methods that are now deprecated. Note changes to the PDF Writer: tcPDF is no longer bundled with PHPExcel, but should be installed separately if you wish to use that 3rd-Party library with PHPExcel. Alternatively, you can choose to use mPDF or DomPDF as PDF Rendering libraries instead: PHPExcel now provides a configurable wrapper allowing you a choice of PDF renderer. See the documentation, or the PDF s...ALM Assessment Guidance: Community Value-Adds: Important: This download has been created using ALM Ranger bits by the community, for the community. Although ALM Rangers were involved in the process, the content has not been through their quality review. Please post your candid feedback and improvement suggestions to the Community tab of this Codeplex project. 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Use Live SDK with REST API in any flavor of windows.MacSonuclari: project to show soccer match result to for windows phone devices mozcms: mozcms for .NET 4.0 MVC 4.0!MS_Descriptions_Changer: This is utility change MS_Descriptions attribute on MS SQL Server 2005-2008 for Tables and Columns.Onestop.Contrib.DistributedEvict: Onestop.Contrib.DistributedEvict is an advanced module that has 2 primary features for managing cache in a web farm: Output Cache Evict & Remote SignalsOrchard Scripting Extensions: Core module for running scripts inside Orchard.Orchard Scripting Extensions: PHP: A child module for Orchard Scripting Extensions for running PHP code inside Orchard.Page Generated Skin Object for DotNetNuke: The Page Generated Skin Object for DotNetNuke displays the time taken to generate the current page in your custom DNN skin. ProductStore: this project is demo for mvc4, ef codefirst...Remote Controlled Switch for all RC Receivers.: AVR Tiny based simple switch for any rc receiver. Allows to turn off / on lights, sound effect, retracts chassis, using free servo channel. Report Generator in C#: This is a library used to generate reports.Resharper text localization add-in: Text localization plugin for Resharper 7.0 and some plugins development documentationRoad Addicts: Road Addicts is a mix between a strategy and a traffic simulation game.Sistema Distribuido de Pedidos de Insumos Médicos: This is school projectSplitOS: SplitOS - The user-friendly Text OSSQL Server Connection Auditor (SSCA): SSCA helps you test your database audit solution's effectiveness at auditing Microsoft SQL Server connections by automating DB connections, results, and logs.TCP Cellular Radio Driver: Addresses TCP mode shortcomings in the CellularRadio driver provided by GHI Electronics for the Seeed module. Allows transparent data connection over TCP.test1325on17: hellotesttom10162012git01: fds fdsUMK Game: Game Edukasi 3D Tatatertib LalulintasWebMatrix Extension Documentation - Staging: WebMatrix Extensión Development ServiceWhipstaff: Whipstaff is a PoC library for designing a common UI library leveraging WPF, ReactiveUI and DHGMS Data Manager. It is written in C#Windows 8 Store Apps - Tutoriales Paso a Paso: No hay mejor forma de aprender a escribir código, que leyendo código de otros. Conocé aquí tutoriales completos para crear tu primer app para Windows 8 Store. Yet Another Expression Parser - Reverse Polish Notation - C#: Following project contains a class library with simple Reverse Polish Notation implementation.

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• Android: OutOfMemoryError while uploading video...

  - by AP257

  Hi all, I have the same problem as described here, but I will supply a few more details. While trying to upload a video in Android, I'm reading it into memory, and if the video is large I get an OutOfMemoryError. Here's my code: // get bytestream to upload videoByteArray = getBytesFromFile(cR, fileUriString); public static byte[] getBytesFromFile(ContentResolver cR, String fileUriString) throws IOException { Uri tempuri = Uri.parse(fileUriString); InputStream is = cR.openInputStream(tempuri); byte[] b3 = readBytes(is); is.close(); return b3; } public static byte[] readBytes(InputStream inputStream) throws IOException { ByteArrayOutputStream byteBuffer = new ByteArrayOutputStream(); // this is storage overwritten on each iteration with bytes int bufferSize = 1024; byte[] buffer = new byte[bufferSize]; int len = 0; while ((len = inputStream.read(buffer)) != -1) { byteBuffer.write(buffer, 0, len); } return byteBuffer.toByteArray(); } And here's the traceback (the error is thrown on the byteBuffer.write(buffer, 0, len) line): 04-08 11:56:20.456: ERROR/dalvikvm-heap(6088): Out of memory on a 16775184-byte allocation. 04-08 11:56:20.456: INFO/dalvikvm(6088): "IntentService[UploadService]" prio=5 tid=17 RUNNABLE 04-08 11:56:20.456: INFO/dalvikvm(6088): | group="main" sCount=0 dsCount=0 s=N obj=0x449a3cf0 self=0x38d410 04-08 11:56:20.456: INFO/dalvikvm(6088): | sysTid=6119 nice=0 sched=0/0 cgrp=default handle=4010416 04-08 11:56:20.456: INFO/dalvikvm(6088): at java.io.ByteArrayOutputStream.expand(ByteArrayOutputStream.java:~93) 04-08 11:56:20.456: INFO/dalvikvm(6088): at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:218) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.readBytes(UploadService.java:199) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.getBytesFromFile(UploadService.java:182) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.doUploadinBackground(UploadService.java:118) 04-08 11:56:20.456: INFO/dalvikvm(6088): at com.android.election2010.UploadService.onHandleIntent(UploadService.java:85) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.app.IntentService$ServiceHandler.handleMessage(IntentService.java:30) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.os.Handler.dispatchMessage(Handler.java:99) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.os.Looper.loop(Looper.java:123) 04-08 11:56:20.456: INFO/dalvikvm(6088): at android.os.HandlerThread.run(HandlerThread.java:60) 04-08 11:56:20.467: WARN/dalvikvm(6088): threadid=17: thread exiting with uncaught exception (group=0x4001b180) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): Uncaught handler: thread IntentService[UploadService] exiting due to uncaught exception 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): java.lang.OutOfMemoryError 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at java.io.ByteArrayOutputStream.expand(ByteArrayOutputStream.java:93) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:218) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.readBytes(UploadService.java:199) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.getBytesFromFile(UploadService.java:182) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.doUploadinBackground(UploadService.java:118) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at com.android.election2010.UploadService.onHandleIntent(UploadService.java:85) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.app.IntentService$ServiceHandler.handleMessage(IntentService.java:30) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.os.Handler.dispatchMessage(Handler.java:99) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.os.Looper.loop(Looper.java:123) 04-08 11:56:20.467: ERROR/AndroidRuntime(6088): at android.os.HandlerThread.run(HandlerThread.java:60) 04-08 11:56:20.496: INFO/Process(4657): Sending signal. PID: 6088 SIG: 3 I guess that as @DroidIn suggests, I need to upload it in chunks. But (newbie question alert) does that mean that I should make multiple PostMethod requests, and glue the file together at the server end? Or can I load the bytestream into memory in chunks, and glue it together in the Android code? If anyone could give me a clue as to the best approach, I would be very grateful.

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• Understanding the memory consumption on iPhone

  - by zoul

  Hello! I am working on a 2D iPhone game using OpenGL ES and I keep hitting the 24 MB memory limit – my application keeps crashing with the error code 101. I tried real hard to find where the memory goes, but the numbers in Instruments are still much bigger than what I would expect. I ran the application with the Memory Monitor, Object Alloc, Leaks and OpenGL ES instruments. When the application gets loaded, free physical memory drops from 37 MB to 23 MB, the Object Alloc settles around 7 MB, Leaks show two or three leaks a few bytes in size, the Gart Object Size is about 5 MB and Memory Monitor says the application takes up about 14 MB of real memory. I am perplexed as where did the memory go – when I dig into the Object Allocations, most of the memory is in the textures, exactly as I would expect. But both my own texture allocation counter and the Gart Object Size agree that the textures should take up somewhere around 5 MB. I am not aware of allocating anything else that would be worth mentioning, and the Object Alloc agrees. Where does the memory go? (I would be glad to supply more details if this is not enough.) Update: I really tried to find where I could allocate so much memory, but with no results. What drives me wild is the difference between the Object Allocations (~7 MB) and real memory usage as shown by Memory Monitor (~14 MB). Even if there were huge leaks or huge chunks of memory I forget about, the should still show up in the Object Allocations, shouldn’t they? I’ve already tried the usual suspects, ie. the UIImage with its caching, but that did not help. Is there a way to track memory usage “debugger-style”, line by line, watching each statement’s impact on memory usage? What I have found so far: I really am using that much memory. It is not easy to measure the real memory consumption, but after a lot of counting I think the memory consumption is really that high. My fault. I found no easy way to measure the memory used. The Memory Monitor numbers are accurate (these are the numbers that really matter), but the Memory Monitor can’t tell you where exactly the memory goes. The Object Alloc tool is almost useless for tracking the real memory usage. When I create a texture, the allocated memory counter goes up for a while (reading the texture into the memory), then drops (passing the texture data to OpenGL, freeing). This is OK, but does not always happen – sometimes the memory usage stays high even after the texture has been passed on to OpenGL and freed from “my” memory. This means that the total amount of memory allocated as shown by the Object Alloc tool is smaller than the real total memory consumption, but bigger than the real consumption minus textures (real – textures < object alloc < real). Go figure. I misread the Programming Guide. The memory limit of 24 MB applies to textures and surfaces, not the whole application. The actual red line lies a bit further, but I could not find any hard numbers. The consensus is that 25–30 MB is the ceiling. When the system gets short on memory, it starts sending the memory warning. I have almost nothing to free, but other applications do release some memory back to the system, especially Safari (which seems to be caching the websites). When the free memory as shown in the Memory Monitor goes zero, the system starts killing. I had to bite the bullet and rewrite some parts of the code to be more efficient on memory, but I am probably still pushing it. I

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• How do I use connect to DB2 with DBI and mod_perl?

  - by Matthew

  I'm having issues with getting DBI's IBM DB2 driver to work with mod_perl. My test script is: #!/usr/bin/perl use strict; use CGI; use Data::Dumper; use DBI; { my $q; my $dsn; my $username; my $password; my $sth; my $dbc; my $row; $q = CGI->new; print $q->header; print $q->start_html(); $dsn = "DBI:DB2:SAMPLE"; $username = "username"; $password = "password"; print "<pre>".$q->escapeHTML(Dumper(\%ENV))."</pre>"; $dbc = DBI->connect($dsn, $username, $password); $sth = $dbc->prepare("SELECT * FROM SOME_TABLE WHERE FIELD='SOMETHING'"); $sth->execute(); $row = $sth->fetchrow_hashref(); print "<pre>".$q->escapeHTML(Dumper($row))."</pre>"; print $q->end_html; } This script works as CGI but not under mod_perl. I get this error in apache's error log: DBD::DB2::dr connect warning: [unixODBC][Driver Manager]Data source name not found, and no default driver specified at /usr/lib/perl5/site_perl/5.8.8/Apache/DBI.pm line 190. DBI connect('SAMPLE','username',...) failed: [unixODBC][Driver Manager]Data source name not found, and no default driver specified at /data/www/perl/test.pl line 15 First of all, why is it using ODBC? The native DB2 driver is installed (hence it works as CGI). Running Apache 2.2.3, mod_perl 2.0.4 under RHEL5. This guy had the same problem as me: http://www.mail-archive.com/[email protected]/msg22909.html But I have no idea how he fixed it. What does mod_php4 have to do with mod_perl? Any help would be greatly appreciated, I'm having no luck with google. Update: As james2vegas pointed out, the problem has something to do with PHP: I disable PHP all together I get the a different error: Total Environment allocation failure! Did you set up your DB2 client environment? I believe this error is to do with environment variables not being set up correctly, namely DB2INSTANCE. However, I'm not able to turn off PHP to resolve this problem (I need it for some legacy applications). So I now have 2 questions: How can I fix the original issue without disabling PHP all together? How can I fix the environment issue? I've set DB2INSTANCE, DB2_PATH and SQLLIB variables correctly using SetEnv and PerlSetEnv in httpd.conf, but with no luck. Note: I've edited the code to determine if the problem was to do with Global Variable Persistence.

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