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  • Source-control 'wet-work'?

    - by Phil Factor
    When a design or creative work is flawed beyond remedy, it is often best to destroy it and start again. The other day, I lost the code to a long and intricate SQL batch I was working on. I’d thought it was impossible, but it happened. With all the technology around that is designed to prevent this occurring, this sort of accident has become a rare event.  If it weren’t for a deranged laptop, and my distraction, the code wouldn’t have been lost this time.  As always, I sighed, had a soothing cup of tea, and typed it all in again.  The new code I hastily tapped in  was much better: I’d held in my head the essence of how the code should work rather than the details: I now knew for certain  the start point, the end, and how it should be achieved. Instantly the detritus of half-baked thoughts fell away and I was able to write logical code that performed better.  Because I could work so quickly, I was able to hold the details of all the columns and variables in my head, and the dynamics of the flow of data. It was, in fact, easier and quicker to start from scratch rather than tidy up and refactor the existing code with its inevitable fumbling and half-baked ideas. What a shame that technology is now so good that developers rarely experience the cleansing shock of losing one’s code and having to rewrite it from scratch.  If you’ve never accidentally lost  your code, then it is worth doing it deliberately once for the experience. Creative people have, until Technology mistakenly prevented it, torn up their drafts or sketches, threw them in the bin, and started again from scratch.  Leonardo’s obsessive reworking of the Mona Lisa was renowned because it was so unusual:  Most artists have been utterly ruthless in destroying work that didn’t quite make it. Authors are particularly keen on writing afresh, and the results are generally positive. Lawrence of Arabia actually lost the entire 250,000 word manuscript of ‘The Seven Pillars of Wisdom’ by accidentally leaving it on a train at Reading station, before rewriting a much better version.  Now, any writer or artist is seduced by technology into altering or refining their work rather than casting it dramatically in the bin or setting a light to it on a bonfire, and rewriting it from the blank page.  It is easy to pick away at a flawed work, but the real creative process is far more brutal. Once, many years ago whilst running a software house that supplied commercial software to local businesses, I’d been supervising an accounting system for a farming cooperative. No packaged system met their needs, and it was all hand-cut code.  For us, it represented a breakthrough as it was for a government organisation, and success would guarantee more contracts. As you’ve probably guessed, the code got mangled in a disk crash just a week before the deadline for delivery, and the many backups all proved to be entirely corrupted by a faulty tape drive.  There were some fragments left on individual machines, but they were all of different versions.  The developers were in despair.  Strangely, I managed to re-write the bulk of a three-month project in a manic and caffeine-soaked weekend.  Sure, that elegant universally-applicable input-form routine was‘nt quite so elegant, but it didn’t really need to be as we knew what forms it needed to support.  Yes, the code lacked architectural elegance and reusability. By dawn on Monday, the application passed its integration tests. The developers rose to the occasion after I’d collapsed, and tidied up what I’d done, though they were reproachful that some of the style and elegance had gone out of the application. By the delivery date, we were able to install it. It was a smaller, faster application than the beta they’d seen and the user-interface had a new, rather Spartan, appearance that we swore was done to conform to the latest in user-interface guidelines. (we switched to Helvetica font to look more ‘Bauhaus’ ). The client was so delighted that he forgave the new bugs that had crept in. I still have the disk that crashed, up in the attic. In IT, we have had mixed experiences from complete re-writes. Lotus 123 never really recovered from a complete rewrite from assembler into C, Borland made the mistake with Arago and Quattro Pro  and Netscape’s complete rewrite of their Navigator 4 browser was a white-knuckle ride. In all cases, the decision to rewrite was a result of extreme circumstances where no other course of action seemed possible.   The rewrite didn’t come out of the blue. I prefer to remember the rewrite of Minix by young Linus Torvalds, or the rewrite of Bitkeeper by a slightly older Linus.  The rewrite of CP/M didn’t do too badly either, did it? Come to think of it, the guy who decided to rewrite the windowing system of the Xerox Star never regretted the decision. I’ll agree that one should often resist calls for a rewrite. One of the worst habits of the more inexperienced programmer is to denigrate whatever code he or she inherits, and then call loudly for a complete rewrite. They are buoyed up by the mistaken belief that they can do better. This, however, is a different psychological phenomenon, more related to the idea of some motorcyclists that they are operating on infinite lives, or the occasional squaddies that if they charge the machine-guns determinedly enough all will be well. Grim experience brings out the humility in any experienced programmer.  I’m referring to quite different circumstances here. Where a team knows the requirements perfectly, are of one mind on methodology and coding standards, and they already have a solution, then what is wrong with considering  a complete rewrite? Rewrites are so painful in the early stages, until that point where one realises the payoff, that even I quail at the thought. One needs a natural disaster to push one over the edge. The trouble is that source-control systems, and disaster recovery systems, are just too good nowadays.   If I were to lose this draft of this very blog post, I know I’d rewrite it much better. However, if you read this, you’ll know I didn’t have the nerve to delete it and start again.  There was a time that one prayed that unreliable hardware would deliver you from an unmaintainable mess of a codebase, but now technology has made us almost entirely immune to such a merciful act of God. An old friend of mine with long experience in the software industry has long had the idea of the ‘source-control wet-work’,  where one hires a malicious hacker in some wild eastern country to hack into one’s own  source control system to destroy all trace of the source to an application. Alas, backup systems are just too good to make this any more than a pipedream. Somehow, it would be difficult to promote the idea. As an alternative, could one construct a source control system that, on doing all the code-quality metrics, would systematically destroy all trace of source code that failed the quality test? Alas, I can’t see many managers buying into the idea. In reading the full story of the near-loss of Toy Story 2, it set me thinking. It turned out that the lucky restoration of the code wasn’t the happy ending one first imagined it to be, because they eventually came to the conclusion that the plot was fundamentally flawed and it all had to be rewritten anyway.  Was this an early  case of the ‘source-control wet-job’?’ It is very hard nowadays to do a rapid U-turn in a development project because we are far too prone to cling to our existing source-code.

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  • Book Review: Brownfield Application Development in .NET

    - by DotNetBlues
    I recently finished reading the book Brownfield Application Development in .NET by Kyle Baley and Donald Belcham.  The book is available from Manning.  First off, let me say that I'm a huge fan of Manning as a publisher.  I've found their books to be top-quality, over all.  As a Kindle owner, I also appreciate getting an ebook copy along with the dead tree copy.  I find ebooks to be much more convenient to read, but hard-copies are easier to reference. The book covers, surprisingly enough, working with brownfield applications.  Which is well and good, if that term has meaning to you.  It didn't for me.  Without retreading a chunk of the first chapter, the authors break code bases into three broad categories: greenfield, brownfield, and legacy.  Greenfield is, essentially, new development that hasn't had time to rust and is (hopefully) being approached with some discipline.  Legacy applications are those that are more or less stable and functional, that do not expect to see a lot of work done to them, and are more likely to be replaced than reworked. Brownfield code is the gray (brown?) area between the two and the authors argue, quite effectively, that it is the most likely state for an application to be in.  Brownfield code has, in some way, been allowed to tarnish around the edges and can be difficult to work with.  Although I hadn't realized it, most of the code I've worked on has been brownfield.  Sometimes, there's talk of scrapping and starting over.  Sometimes, the team dismisses increased discipline as ivory tower nonsense.  And, sometimes, I've been the ignorant culprit vexing my future self. The book is broken into two major sections, plus an introduction chapter and an appendix.  The first section covers what the authors refer to as "The Ecosystem" which consists of version control, build and integration, testing, metrics, and defect management.  The second section is on actually writing code for brownfield applications and discusses object-oriented principles, architecture, external dependencies, and, of course, how to deal with these when coming into an existing code base. The ecosystem section is just shy of 140 pages long and brings some real meat to the matter.  The focus on "pain points" immediately sets the tone as problem-solution, rather than academic.  The authors also approach some of the topics from a different angle than some essays I've read on similar topics.  For example, the chapter on automated testing is on just that -- automated testing.  It's all well and good to criticize a project as conflating integration tests with unit tests, but it really doesn't make anyone's life better.  The discussion on testing is more focused on the "right" level of testing for existing projects.  Sometimes, an integration test is the best you can do without gutting a section of functional code.  Even if you can sell other developers and/or management on doing so, it doesn't actually provide benefit to your customers to rewrite code that works.  This isn't to say the authors encourage sloppy coding.  Far from it.  Just that they point out the wisdom of ignoring the sleeping bear until after you deal with the snarling wolf. The other sections take a similarly real-world, workable approach to the pain points they address.  As the section moves from technical solutions like version control and continuous integration (CI) to the softer, process issues of metrics and defect tracking, the authors begin to gently suggest moving toward a zero defect count.  While that really sounds like an unreasonable goal for a lot of ongoing projects, it's quite apparent that the authors have first-hand experience with taming some gruesome projects.  The suggestions are grounded and workable, and the difficulty of some situations is explicitly acknowledged. I have to admit that I started getting bored by the end of the ecosystem section.  No matter how valuable I think a good project manager or business analyst is to a successful ALM, at the end of the day, I'm a gear-head.  Also, while I agreed with a lot of the ecosystem ideas, in theory, I didn't necessarily feel that a lot of the single-developer projects that I'm often involved in really needed that level of rigor.  It's only after reading the sidebars and commentary in the coding section that I had the context for the arguments made in favor of a strong ecosystem supporting the development process.  That isn't to say that I didn't support good product management -- indeed, I've probably pushed too hard, on occasion, for a strong ALM outside of just development.  This book gave me deeper insight into why some corners shouldn't be cut and how damaging certain sins of omission can be. The code section, though, kept me engaged for its entirety.  Many technical books can be used as reference material from day one.  The authors were clear, however, that this book is not one of these.  The first chapter of the section (chapter seven, over all) addresses object oriented (OO) practices.  I've read any number of definitions, discussions, and treatises on OO.  None of the chapter was new to me, but it was a good review, and I'm of the opinion that it's good to review the foundations of what you do, from time to time, so I didn't mind. The remainder of the book is really just about how to apply OOP to existing code -- and, just because all your code exists in classes does not mean that it's object oriented.  That topic has the potential to be extremely condescending, but the authors miraculously managed to never once make me feel like a dolt or that they were wagging their finger at me for my prior sins.  Instead, they continue the "pain points" and problem-solution presentation to give concrete examples of how to apply some pretty academic-sounding ideas.  That's a point worth emphasizing, as my experience with most OO discussions is that they stay in the academic realm.  This book gives some very, very good explanations of why things like the Liskov Substitution Principle exist and why a corporate programmer should even care.  Even if you know, with absolute certainty, that you'll never have to work on an existing code-base, I would recommend this book just for the clarity it provides on OOP. This book goes beyond just theory, or even real-world application.  It presents some methods for fixing problems that any developer can, and probably will, encounter in the wild.  First, the authors address refactoring application layers and internal dependencies.  Then, they take you through those layers from the UI to the data access layer and external dependencies.  Finally, they come full circle to tie it all back to the overall process.  By the time the book is done, you're left with a lot of ideas, but also a reasonable plan to begin to improve an existing project structure. Throughout the book, it's apparent that the authors have their own preferred methodology (TDD and domain-driven design), as well as some preferred tools.  The "Our .NET Toolbox" is something of a neon sign pointing to that latter point.  They do not beat the reader over the head with anything resembling a "One True Way" mentality.  Even for the most emphatic points, the tone is quite congenial and helpful.  With some of the near-theological divides that exist within the tech community, I found this to be one of the more remarkable characteristics of the book.  Although the authors favor tools that might be considered Alt.NET, there is no reason the advice and techniques given couldn't be quite successful in a pure Microsoft shop with Team Foundation Server.  For that matter, even though the book specifically addresses .NET, it could be applied to a Java and Oracle shop, as well.

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  • Nokia Windows Phone 8 App Collection

    - by Tim Murphy
    I recently upgraded to a Nokia Lumia 920.  Along with it came the availability of a number of Nokia developed apps or apps that Nokia has made available from other developers.  Below is a summary of some of the ones that I have used to this point.  There are quite a few of them so I won’t be covering everything that is available. Nokia Maps I am quite pleased with the accuracy of Nokia Maps and not having to tap the screen for each turn any more.  The information on the screen is quite good as well.  The couple of improvements I would like to see are for the voice directions to include which street or exit you need to use and improve the search accuracy.  Bing maps had much better search results in my opinion. Nokia Drive This one really had me confused when I first setup the phone.  I was driving down the road and suddenly I am getting notification tones, but there were no visual notifications on the phone.  It seems that in their infinite wisdom Nokia thinks I don’t know when I am going over the speed limit and need to be told. ESPN I really liked my ESPN app on Windows Phone 7.5, but I am not getting the type of experience I was looking for out of this app.  While it allows me to pick my favorite teams, but there isn’t a pivot page or panorama page that shows a summary of my favorite teams.  I have also found that the live tile don’t update very often.  Over all I am rather disappointed compared app produced by ESPN. Smart Shoot I really need to get the kids to let me use this on.  I like the concept, but I need to spend more time with it.  The idea how running the camera through a continuous shooting mode and then picking the best is something that I have done with my DSLR and am glad to see it available here. Cinemagraph Here is a fun filter.  It doesn’t have the most accurate editing features, but it is fun to stop certain parts of a scene and let other parts move.  As a test I stopped the traffic on the highway and let the traffic on the frontage road flow.  It makes for a fun effect.  If nothing else it could be great for sending prank animations to your friends. YouSendIt I have only briefly touched this application.  What I don’t understand is why it is needed.  Most of the functionality seems to be similar to SkyDrive and it gives you less storage.  They only feature that seems to differentiate the app is the signature capability. Creative Studio This app has some nice quick edits, but it is not very comprehensive.  I am also not to thrilled with the user experience.  It puts you though an initial color cast series that I’m not sure why it is there.  Discovery of the remaining adjustments isn’t that great.  In the end I found myself wanting Thumbia back. Panorama This is one of the apps that I like.  I found it easy to use as it guides you with a target circle that you center for it to take the next pictures.  It also stitches the images with amazing speed.  The one thing I wish it had was the capability to turn the phone into portrait orientation and do a taller panorama.  Perhaps we will see this in the future. Nokia Music After getting over the missing album art I found that there were a number of missing features with this app as well.  I have a Zune HD and I am used to being able to go through my collection and adding songs, albums or artists to my now playing.  There also doesn’t seem to be a way to manage playlists that I have seen yet.  Other than that the UI is familiar and it give Nokia City Lens Augmented reality is a cool concept, but I still haven’t seen it implemented in a compelling fashion beyond a demo at TED a couple of years ago.  The app still leaves me wanting as well.  It does give an interesting toy.  It gives you the ability to look for general categories and see general direction and clusters of locations.  I think as this concept is better thought out it will become more compelling. Nokia Trailers I don’t know how often I will use this app, but I do like being able to see what movies are being promoted.  I can’t wait for The Hobbit to come out and the trailer was just what the doctor ordered.  I can see coming back to this app from time to time. PhotoBeamer PhotoBeamer is a strange beast that needs a better instruction manual.  It seems a lot like magic but very confusing.  I need some more testing, but I don’t think this is something that most people are going to understand quickly and may give up before getting it to work.  I may put an update here after playing with it further. Ringtone Maker The app was just published and it didn’t work very well for me. It couldn’t find 95% of the songs that Nokia Music was playing for me and crashed several times.  It also had songs named wrong that when I checked them in Nokia Music they were fine.  This app looks like it has a long way to go. Summary In all I think that Nokia is offering a well rounded set of initial applications that can get any new owner started.  There is definitely room for improvement in all of these apps.  The main need is usability upgrades.  I would guess that with feedback from users they will come up to acceptable levels.  Try them out and see if you agree. del.icio.us Tags: Windows Phone,Nokia,Lumia,Nokia Apps,ESPN,PhotoBeamer,City Lens,YouSendIt,Drive,Maps

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  • Recover RAID 5 data after created new array instead of re-using

    - by Brigadieren
    Folks please help - I am a newb with a major headache at hand (perfect storm situation). I have a 3 1tb hdd on my ubuntu 11.04 configured as software raid 5. The data had been copied weekly onto another separate off the computer hard drive until that completely failed and was thrown away. A few days back we had a power outage and after rebooting my box wouldn't mount the raid. In my infinite wisdom I entered mdadm --create -f... command instead of mdadm --assemble and didn't notice the travesty that I had done until after. It started the array degraded and proceeded with building and syncing it which took ~10 hours. After I was back I saw that that the array is successfully up and running but the raid is not I mean the individual drives are partitioned (partition type f8 ) but the md0 device is not. Realizing in horror what I have done I am trying to find some solutions. I just pray that --create didn't overwrite entire content of the hard driver. Could someone PLEASE help me out with this - the data that's on the drive is very important and unique ~10 years of photos, docs, etc. Is it possible that by specifying the participating hard drives in wrong order can make mdadm overwrite them? when I do mdadm --examine --scan I get something like ARRAY /dev/md/0 metadata=1.2 UUID=f1b4084a:720b5712:6d03b9e9:43afe51b name=<hostname>:0 Interestingly enough name used to be 'raid' and not the host hame with :0 appended. Here is the 'sanitized' config entries: DEVICE /dev/sdf1 /dev/sde1 /dev/sdd1 CREATE owner=root group=disk mode=0660 auto=yes HOMEHOST <system> MAILADDR root ARRAY /dev/md0 metadata=1.2 name=tanserv:0 UUID=f1b4084a:720b5712:6d03b9e9:43afe51b Here is the output from mdstat cat /proc/mdstat Personalities : [linear] [multipath] [raid0] [raid1] [raid6] [raid5] [raid4] [raid10] md0 : active raid5 sdd1[0] sdf1[3] sde1[1] 1953517568 blocks super 1.2 level 5, 512k chunk, algorithm 2 [3/3] [UUU] unused devices: <none> fdisk shows the following: fdisk -l Disk /dev/sda: 80.0 GB, 80026361856 bytes 255 heads, 63 sectors/track, 9729 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x000bf62e Device Boot Start End Blocks Id System /dev/sda1 * 1 9443 75846656 83 Linux /dev/sda2 9443 9730 2301953 5 Extended /dev/sda5 9443 9730 2301952 82 Linux swap / Solaris Disk /dev/sdb: 750.2 GB, 750156374016 bytes 255 heads, 63 sectors/track, 91201 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x000de8dd Device Boot Start End Blocks Id System /dev/sdb1 1 91201 732572001 8e Linux LVM Disk /dev/sdc: 500.1 GB, 500107862016 bytes 255 heads, 63 sectors/track, 60801 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x00056a17 Device Boot Start End Blocks Id System /dev/sdc1 1 60801 488384001 8e Linux LVM Disk /dev/sdd: 1000.2 GB, 1000204886016 bytes 255 heads, 63 sectors/track, 121601 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x000ca948 Device Boot Start End Blocks Id System /dev/sdd1 1 121601 976760001 fd Linux raid autodetect Disk /dev/dm-0: 1250.3 GB, 1250254913536 bytes 255 heads, 63 sectors/track, 152001 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x00000000 Disk /dev/dm-0 doesn't contain a valid partition table Disk /dev/sde: 1000.2 GB, 1000204886016 bytes 255 heads, 63 sectors/track, 121601 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0x93a66687 Device Boot Start End Blocks Id System /dev/sde1 1 121601 976760001 fd Linux raid autodetect Disk /dev/sdf: 1000.2 GB, 1000204886016 bytes 255 heads, 63 sectors/track, 121601 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0xe6edc059 Device Boot Start End Blocks Id System /dev/sdf1 1 121601 976760001 fd Linux raid autodetect Disk /dev/md0: 2000.4 GB, 2000401989632 bytes 2 heads, 4 sectors/track, 488379392 cylinders Units = cylinders of 8 * 512 = 4096 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 524288 bytes / 1048576 bytes Disk identifier: 0x00000000 Disk /dev/md0 doesn't contain a valid partition table Per suggestions I did clean up the superblocks and re-created the array with --assume-clean option but with no luck at all. Is there any tool that will help me to revive at least some of the data? Can someone tell me what and how the mdadm --create does when syncs to destroy the data so I can write a tool to un-do whatever was done? After the re-creating of the raid I run fsck.ext4 /dev/md0 and here is the output root@tanserv:/etc/mdadm# fsck.ext4 /dev/md0 e2fsck 1.41.14 (22-Dec-2010) fsck.ext4: Superblock invalid, trying backup blocks... fsck.ext4: Bad magic number in super-block while trying to open /dev/md0 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 Per Shanes' suggestion I tried root@tanserv:/home/mushegh# mkfs.ext4 -n /dev/md0 mke2fs 1.41.14 (22-Dec-2010) Filesystem label= OS type: Linux Block size=4096 (log=2) Fragment size=4096 (log=2) Stride=128 blocks, Stripe width=256 blocks 122101760 inodes, 488379392 blocks 24418969 blocks (5.00%) reserved for the super user First data block=0 Maximum filesystem blocks=0 14905 block groups 32768 blocks per group, 32768 fragments per group 8192 inodes per group Superblock backups stored on blocks: 32768, 98304, 163840, 229376, 294912, 819200, 884736, 1605632, 2654208, 4096000, 7962624, 11239424, 20480000, 23887872, 71663616, 78675968, 102400000, 214990848 and run fsck.ext4 with every backup block but all returned the following: root@tanserv:/home/mushegh# fsck.ext4 -b 214990848 /dev/md0 e2fsck 1.41.14 (22-Dec-2010) fsck.ext4: Invalid argument while trying to open /dev/md0 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> Any suggestions? Regards!

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  • (SQL) Selecting from a database based on multiple pairs of pairs

    - by Owen Allen
    The problem i've encountered is attempting to select rows from a database where 2 columns in that row align to specific pairs of data. IE selecting rows from data where id = 1 AND type = 'news'. Obviously, if it was 1 simple pair it would be easy, but the issue is we are selecting rows based on 100s of pair of data. I feel as if there must be some way to do this query without looping through the pairs and querying each individually. I'm hoping some SQL stackers can provide guidance. Here's a full code break down: Lets imagine that I have the following dataset where history_id is the primary key. I simplified the structure a bit regarding the dates for ease of reading. table: history history_id id type user_id date 1 1 news 1 5/1 2 1 news 1 5/1 3 1 photo 1 5/2 4 3 news 1 5/3 5 4 news 1 5/3 6 1 news 1 5/4 7 2 photo 1 5/4 8 2 photo 1 5/5 If the user wants to select rows from the database based on a date range we would take a subset of that data. SELECT history_id, id, type, user_id, date FROM history WHERE date BETWEEN '5/3' AND '5/5' Which returns the following dataset history_id id type user_id date 4 3 news 1 5/3 5 4 news 1 5/3 6 1 news 1 5/4 7 2 photo 1 5/4 8 2 photo 1 5/5 Now, using that subset of data I need to determine how many of those entries represent the first entry in the database for each type,id pairing. IE is row 4 the first time in the database that id: 3, type: news appears. So I use a with() min() query. In real code the two lists are programmatically generated from the result sets of our previous query, here I spelled them out for ease of reading. WITH previous AS ( SELECT history_id, id, type FROM history WHERE id IN (1,2,3,4) AND type IN ('news','photo') ) SELECT min(history_id) as history_id, id, type FROM previous GROUP BY id, type Which returns the following data set. history_id id type user_id date 1 1 news 1 5/1 2 1 news 1 5/1 3 1 photo 1 5/2 4 3 news 1 5/3 5 4 news 1 5/3 6 1 news 1 5/4 7 2 photo 1 5/4 8 2 photo 1 5/5 You'll notice it's the entire original dataset, because we are matching id and type individually in lists, rather than as a collective pairs. The result I desire is, but I can't figure out the SQL to get this result. history_id id type user_id date 1 1 news 1 5/1 4 3 news 1 5/3 5 4 news 1 5/3 7 2 photo 1 5/4 Obviously, I could go the route of looping through each pair and querying the database to determine it's first result, but that seems an inefficient solution. I figured one of the SQL gurus on this site might be able to spread some wisdom. In case I'm approaching this situation incorrectly, the gist of the whole routine is that the database stores all creations and edits in the same table. I need to track each users behavior and determine how many entries in the history table are edits or creations over a specific date range. Therefore I select all type:id pairs from the date range based on a user_id, and then for each pairing I determine if the user is responsible for the first that occurs in the database. If first, then creation else edit. Any assistance would be awesome.

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  • Effective optimization strategies on modern C++ compilers

    - by user168715
    I'm working on scientific code that is very performance-critical. An initial version of the code has been written and tested, and now, with profiler in hand, it's time to start shaving cycles from the hot spots. It's well-known that some optimizations, e.g. loop unrolling, are handled these days much more effectively by the compiler than by a programmer meddling by hand. Which techniques are still worthwhile? Obviously, I'll run everything I try through a profiler, but if there's conventional wisdom as to what tends to work and what doesn't, it would save me significant time. I know that optimization is very compiler- and architecture- dependent. I'm using Intel's C++ compiler targeting the Core 2 Duo, but I'm also interested in what works well for gcc, or for "any modern compiler." Here are some concrete ideas I'm considering: Is there any benefit to replacing STL containers/algorithms with hand-rolled ones? In particular, my program includes a very large priority queue (currently a std::priority_queue) whose manipulation is taking a lot of total time. Is this something worth looking into, or is the STL implementation already likely the fastest possible? Along similar lines, for std::vectors whose needed sizes are unknown but have a reasonably small upper bound, is it profitable to replace them with statically-allocated arrays? I've found that dynamic memory allocation is often a severe bottleneck, and that eliminating it can lead to significant speedups. As a consequence I'm interesting in the performance tradeoffs of returning large temporary data structures by value vs. returning by pointer vs. passing the result in by reference. Is there a way to reliably determine whether or not the compiler will use RVO for a given method (assuming the caller doesn't need to modify the result, of course)? How cache-aware do compilers tend to be? For example, is it worth looking into reordering nested loops? Given the scientific nature of the program, floating-point numbers are used everywhere. A significant bottleneck in my code used to be conversions from floating point to integers: the compiler would emit code to save the current rounding mode, change it, perform the conversion, then restore the old rounding mode --- even though nothing in the program ever changed the rounding mode! Disabling this behavior significantly sped up my code. Are there any similar floating-point-related gotchas I should be aware of? One consequence of C++ being compiled and linked separately is that the compiler is unable to do what would seem to be very simple optimizations, such as move method calls like strlen() out of the termination conditions of loop. Are there any optimization like this one that I should look out for because they can't be done by the compiler and must be done by hand? On the flip side, are there any techniques I should avoid because they are likely to interfere with the compiler's ability to automatically optimize code? Lastly, to nip certain kinds of answers in the bud: I understand that optimization has a cost in terms of complexity, reliability, and maintainability. For this particular application, increased performance is worth these costs. I understand that the best optimizations are often to improve the high-level algorithms, and this has already been done.

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  • SQLAuthority Book Review – DBA Survivor: Become a Rock Star DBA

    - by pinaldave
    DBA Survivor: Become a Rock Star DBA – Thomas LaRock Link to Amazon Link to Flipkart First of all, I thank all my readers when I wrote that I could not get this book in any local book stores, because they offered me to send a copy of this good book. A very special mention goes to Sripada and Jayesh for they gave so much effort in finding my home address and sending me the hard copy. Before, I did not have the copy of the book, but now I have two of it already! It surprises me how my readers were able to find my home address, which I have not publicly shared. Quick Review: This is indeed a one easy-to-read and fun book. We all work day and night with technology yet we should not forget to show our love and care for our family at home. For our souls that starve for peace and guidance, this one book is the “it” book for all the technology enthusiasts. Though this book was specifically written for DBAs, the reach is not limited to DBAs only because the lessons incorporated in it actually applies to all. This is one of the most motivating technical books I have read. Detailed Review: Let us go over a few questions first: Who wants to be as famous as rockstars in the field of Database Administration? How can one learn what it takes to become a top notch software developer? If you are a beginner in your field, how will you go to next level? Your boss may be very kind or like Dilbert’s Boss, what will you do? How do you keep growing when Eco-system around you does not support you? You are almost at top but there is someone else at the TOP, what do you do and how do you avoid office politics? As a database developer what should be your basic responsibility? and many more… I was able to completely read book in one sitting and I loved it. Before I continue with my opinion, I want to echo the opinion of Kevin Kline who has written the Forward of the book. He has truly suggested that “You hold in your hands a collection of insights and wisdom on the topic of database administration gained through many years of hard-won experience, long nights of study, and direct mentorship under some of the industry’s most talented database professionals and information technology (IT) experts.” Today, IT field is getting bigger and better, while talking about terabytes of the database becomes “more” normal every single day. The gods and demigods of database professionals are taking care of these large scale databases and are carefully maintaining them. In this world, there are only a few beginnings on the first step. There are many experts in different technology fields who are asked to address the issues with databases. There is YOU and ME, who is just new to this work. So we ask ourselves WHERE to begin and HOW to begin. We adore and follow the religion of our rockstars, but oftentimes we really have no idea about their background and their struggles. Every rockstar has his success story which needs to be digested before learning his tricks and tips. This book starts with the same note and teaches the two most important lessons for anybody who wants to be a DBA Rockstar –  to focus on their single goal of learning and to excel the technology. The story starts with three simple guidelines – Get Prepared, Get Trained, Get Certified. Once a person learns the skills, and then, it would be about time that he needs to enrich or to improve those skills you have learned. I am sure that the right opportunity will come finding themselves and they will not have to go run behind it. However, the real challenge for any person is the first day or first week. A new employee, no matter how much experienced he is, sometimes has no clue about what should one do at new job. Chapter 2 and chapter 3 precisely talk about what one should do as soon as the new job begins. It is also written with keeping the fact in focus that each job can be very much different but there are few infrastructure setups and programming concepts are the same. Learning basics of database was really interesting. I like to focus on the roots of any technology. It is important to understand the structure of the database before suggesting what indexes needs to be created, the same way this book covers the most essential knowledge one must learn by most database developers. I think the title of the fourth chapter is my favorite sentence in this book. I can see that I will be saying this again and again in the future – “A Development Server Is a Production Server to a Developer“. I have worked in the software industry for almost 8 years now and I have seen so many developers sitting on their chairs and waiting for instructions from their lead about how to improve the code or what to do the next. When I talk to them, I suggest that the experiment with their server and try various techniques. I think they all should understand that for them, a development server is their production server and needs to pay proper attention to the code from the beginning. There should be NO any inappropriate code from the beginning. One has to fully focus and give their best, if they are not sure they should ask but should do something and stay active. Chapter 5 and 6 talks about two essential skills for any developer and database administration – what are the ethics of developers when they are working with production server and how to support software which is running on the production server. I have met many people who know the theory by heart but when put in front of keyboard they do not know where to start. The first thing they do opening the browser and searching online, instead of opening SQL Server Management Studio. This can very well happen to anybody who is experienced as well. Chapter 5 and 6 addresses that situation as well includes the handy scripts which can solve almost all the basic trouble shooting issues. “Where’s the Buffet?” By far, this is the best chapter in this book. If you have ever met me, you would know that I love food. I think after reading this chapter, I felt Thomas has written this just keeping me in mind. I think there will be many other people who feel the same way, too. Even my wife who read this chapter thought this was specifically written for me. I will not talk any more about this chapter as this is one must read chapter. And of course this is about real ‘FOOD‘. I am an SQL Server Trainer and Consultant and I totally agree with the point made in the chapter 8 of this book. Yes, it says here that what is necessary to train employees and people. Millions of dollars worth the labor is continuously done in the world which has faults and incorrect. Once something goes wrong, very expensive consultant comes in and fixes the problem. This whole cycle which can be stopped and improved if proper training is done. There is plenty of free trainings available as well, if one cannot afford paid training. “Connect. Learn. Share” – I think this is a great summary and bird’s eye view of this book. Networking is the key. Everything which is discussed in this book can be taken to next level if one properly uses this tips and continuously grow with it. Connecting with others, helping learn each other and building the good knowledge sharing environment should be the goal of everyone. Before I end the review I want to share a real experience. I have personally met one DBA who has worked in a single department in a company for so long that when he was put in a different department in his company due to closing that department, he could not adjust and quit the job despite the same people and company around him. Adjusting in the new environment gets much tougher as one person gets more and more experienced. This book precisely addresses the same issue along with their solutions. I just cannot stop comparing the book with my personal journey. I found so many things which are coincidently in the book is written as how we developer and DBA think. I must express special thanks to Thomas for taking time in his personal life and write this book for us. This book is indeed a book for everybody who wants to grow healthy in the tough and competitive environment. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Book Review, SQLAuthority News, SQLServer, T SQL, Technology

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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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  • This is the End of Business as Usual...

    - by Michael Snow
    This week, we'll be hosting our last Social Business Thought Leader Series Webcast for 2012. Our featured guest this week will be Brian Solis of Altimeter Group. As we've been going through the preparations for Brian's webcast, it became very clear that an hour's time is barely scraping the surface of the depth of Brian's insights and analysis. Accordingly, in the spirit of sharing Brian's perspective for all of our readers, we'll be featuring guest posts all this week pulled from Brian's larger collection of blog postings on his own website. If you like what you've read here this week, we highly recommend digging deeper into his tome of wisdom. Guest Post by Brian Solis, Analyst, Altimeter Group as originally featured on his site with the minor change of the video addition at the beginning of the post. This is the End of Business as Usual and the Beginning of a New Era of Relevance - Brian Solis, Principal Analyst, Altimeter Group The Times They Are A-Changin’ Come gather ’round people Wherever you roam And admit that the waters Around you have grown And accept it that soon You’ll be drenched to the bone If your time to you Is worth savin’ Then you better start swimmin’ Or you’ll sink like a stone For the times they are a-changin’. - Bob Dylan I’m sure you are wondering why I chose lyrics to open this article. If you skimmed through them, stop here for a moment. Go back through the Dylan’s words and take your time. Carefully read, and feel, what it is he’s saying and savor the moment to connect the meaning of his words to the challenges you face today. His message is as important and true today as it was when they were first written in 1964. The tide is indeed once again turning. And even though the 60s now live in the history books, right here, right now, Dylan is telling us once again that this is our time to not only sink or swim, but to do something amazing. This is your time. This is our time. But, these times are different and what comes next is difficult to grasp. How people communicate. How people learn and share. How people make decisions. Everything is different now. Think about this…you’re reading this article because it was sent to you via email. Yet more people spend their online time in social networks than they do in email. Duh. According to Nielsen, of the total time spent online 22.5% are connecting and communicating in social networks. To put that in perspective, the time spent in the likes of Facebook, Twitter, and Youtube is greater than online gaming at 9.8%, email at 7.6% and search at 4%. Imagine for a moment if you and I were connected to one another in Facebook, which just so happens to be the largest social network in the world. How big? Well, Facebook is the size today of the entire Internet in 2004. There are over 1 billion people friending, Liking, commenting, sharing, and engaging in Facebook…that’s roughly 12% of the world’s population. Twitter has over 200 million users. Ever hear of tumblr? More time is spent on this popular microblogging community than Twitter. The point is that the landscape for communication and all that’s affected by human interaction is profoundly different than how you and I learned, shared or talked to one another yesterday. This transformation is only becoming more pervasive and, it’s not going back. Survival of the Fitting But social media is just one of the channels we can use to reach people. I must be honest. I’m as much a part of tomorrow as I am of yesteryear. It’s why I spend all of my time researching the evolution of media and its impact on business and culture. Because of you, I share everything I learn in newsletters, emails, blogs, Youtube videos, and also traditional books. I’m dedicated to helping everyone not only understand, but grasp the change that’s before you. Technologies such as social, mobile, virtual, augmented, et al compel us adapt our story and value proposition and extend our reach to be part of communities we don’t realize exist. The people who will keep you in business or running tomorrow are the very people you’re not reaching today. Before you continue to read on, allow me to clarify my point of view. My inspiration for writing this is to help you augment, not necessarily replace, the programs you’re running today. We must still reach those whom matter to us in the ways they prefer to be engaged. To reach what I call the connected consumer of Geneeration-C we must too reach them in the ways they wish to be engaged. And in all of my work, how they connect, talk to one another, influence others, and make decisions are not at all like the traditional consumers of the past. Nor are they merely the kids…the Millennial. Connected consumers are representative across every age group and demographic. As you can see, use of social networks, media sharing sites, microblogs, blogs, etc. equally span across Gen Y, Gen X, and Baby Boomers. The DNA of connected customers is indiscriminant of age or any other demographic for that matter. This is more about psychographics, the linkage of people through common interests (than it is their age, gender, education, nationality or level of income. Once someone is introduced to the marvels of connectedness, the sensation becomes a contagion. It touches and affects everyone. And, that’s why this isn’t going anywhere but normalcy. Social networking isn’t just about telling people what you’re doing. Nor is it just about generic, meaningless conversation. Today’s connected consumer is incredibly influential. They’re connected to hundreds and even thousands of other like-minded people. What they experiences, what they support, it’s shared throughout these networks and as information travels, it shapes and steers impressions, decisions, and experiences of others. For example, if we revisit the Nielsen research, we get an idea of just how big this is becoming. 75% spend heavily on music. How does that translate to the arts? I’d imagine the number is equally impressive. If 53% follow their favorite brand or organization, imagine what’s possible. Just like this email list that connects us, connections in social networks are powerful. The difference is however, that people spend more time in social networks than they do in email. Everything begins with an understanding of the “5 W’s and H.E.” – Who, What, When, Where, How, and to What Extent? The data that comes back tells you which networks are important to the people you’re trying to reach, how they connect, what they share, what they value, and how to connect with them. From there, your next steps are to create a community strategy that extends your mission, vision, and value and it align it with the interests, behavior, and values of those you wish to reach and galvanize. To help, I’ve prepared an action list for you, otherwise known as the 10 Steps Toward New Relevance: 1. Answer why you should engage in social networks and why anyone would want to engage with you 2. Observe what brings them together and define how you can add value to the conversation 3. Identify the influential voices that matter to your world, recognize what’s important to them, and find a way to start a dialogue that can foster a meaningful and mutually beneficial relationship 4. Study the best practices of not just organizations like yours, but also those who are successfully reaching the type of people you’re trying to reach – it’s benching marking against competitors and benchmarking against undefined opportunities 5. Translate all you’ve learned into a convincing presentation written to demonstrate tangible opportunity to your executive board, make the case through numbers, trends, data, insights – understanding they have no idea what’s going on out there and you are both the scout and the navigator (start with a recommended pilot so everyone can learn together) 6. Listen to what they’re saying and develop a process to learn from activity and adapt to interests and steer engagement based on insights 7. Recognize how they use social media and innovate based on what you observe to captivate their attention 8. Align your objectives with their objectives. If you’re unsure of what they’re looking for…ask 9. Invest in the development of content, engagement 10. Build a community, invest in values, spark meaningful dialogue, and offer tangible value…the kind of value they can’t get anywhere else. Take advantage of the medium and the opportunity! The reality is that we live and compete in a perpetual era of Digital Darwinism, the evolution of consumer behavior when society and technology evolve faster than our ability to adapt. This is why it’s our time to alter our course. We must connect with those who are defining the future of engagement, commerce, business, and how the arts are appreciated and supported. Even though it is the end of business as usual, it is the beginning of a new age of opportunity. The consumer revolution is already underway, and the question is: How do you better understand the role you play in this production as a connected or social consumer as well as business professional? Again, this is your time to define a new era of engagement and relevance. Originally written for The National Arts Marketing Project Connect with Brian via: Twitter | LinkedIn | Facebook | Google+ --- Note from Michael: If you really like this post above - check out Brian's TEDTalk and his thought process for preparing it in this post: 12.00 Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} http://www.briansolis.com/2012/10/tedtalk-reinventing-consumer-capitalism-screw-business-as-usual/

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  • Is your team is a high-performing team?

    As a child I can remember looking out of the car window as my father drove along the Interstate in Florida while seeing prisoners wearing bright orange jump suits and prison guards keeping a watchful eye on them. The prisoners were taking part in a prison road gang. These road gangs were formed to help the state maintain the state highway infrastructure. The prisoner’s primary responsibilities are to pick up trash and debris from the roadway. This is a prime example of a work group or working group used by most prison systems in the United States. Work groups or working groups can be defined as a collection of individuals or entities working together to achieve a specific goal or accomplish a specific set of tasks. Typically these groups are only established for a short period of time and are dissolved once the desired outcome has been achieved. More often than not group members usually feel as though they are expendable to the group and some even dread that they are even in the group. "A team is a small number of people with complementary skills who are committed to a common purpose, performance goals, and approach for which they are mutually accountable." (Katzenbach and Smith, 1993) So how do you determine that a team is a high-performing team?  This can be determined by three base line criteria that include: consistently high quality output, the promotion of personal growth and well being of all team members, and most importantly the ability to learn and grow as a unit. Initially, a team can successfully create high-performing output without meeting all three criteria, however this will erode over time because team members will feel detached from the group or that they are not growing then the quality of the output will decline. High performing teams are similar to work groups because they both utilize a collection of individuals or entities to accomplish tasks. What distinguish a high-performing team from a work group are its characteristics. High-performing teams contain five core characteristics. These characteristics are what separate a group from a team. The five characteristics of a high-performing team include: Purpose, Performance Measures, People with Tasks and Relationship Skills, Process, and Preparation and Practice. A high-performing team is much more than a work group, and typically has a life cycle that can vary from team to team. The standard team lifecycle consists of five states and is comparable to a human life cycle. The five states of a high-performing team lifecycle include: Formulating, Storming, Normalizing, Performing, and Adjourning. The Formulating State of a team is first realized when the team members are first defined and roles are assigned to all members. This initial stage is very important because it can set the tone for the team and can ultimately determine its success or failure. In addition, this stage requires the team to have a strong leader because team members are normally unclear about specific roles, specific obstacles and goals that my lay ahead of them.  Finally, this stage is where most team members initially meet one another prior to working as a team unless the team members already know each other. The Storming State normally arrives directly after the formulation of a new team because there are still a lot of unknowns amongst the newly formed assembly. As a general rule most of the parties involved in the team are still getting used to the workload, pace of work, deadlines and the validity of various tasks that need to be performed by the group.  In this state everything is questioned because there are so many unknowns. Items commonly questioned include the credentials of others on the team, the actual validity of a project, and the leadership abilities of the team leader.  This can be exemplified by looking at the interactions between animals when they first meet.  If we look at a scenario where two people are walking directly toward each other with their dogs. The dogs will automatically enter the Storming State because they do not know the other dog. Typically in this situation, they attempt to define which is more dominating via play or fighting depending on how the dogs interact with each other. Once dominance has been defined and accepted by both dogs then they will either want to play or leave depending on how the dogs interacted and other environmental variables. Once the Storming State has been realized then the Normalizing State takes over. This state is entered by a team once all the questions of the Storming State have been answered and the team has been tested by a few tasks or projects.  Typically, participants in the team are filled with energy, and comradery, and a strong alliance with team goals and objectives.  A high school football team is a perfect example of the Normalizing State when they start their season.  The player positions have been assigned, the depth chart has been filled and everyone is focused on winning each game. All of the players encourage and expect each other to perform at the best of their abilities and are united by competition from other teams. The Performing State is achieved by a team when its history, working habits, and culture solidify the team as one working unit. In this state team members can anticipate specific behaviors, attitudes, reactions, and challenges are seen as opportunities and not problems. Additionally, each team member knows their role in the team’s success, and the roles of others. This is the most productive state of a group and is where all the time invested working together really pays off. If you look at an Olympic figure skating team skate you can easily see how the time spent working together benefits their performance. They skate as one unit even though it is comprised of two skaters. Each skater has their routine completely memorized as well as their partners. This allows them to anticipate each other’s moves on the ice makes their skating look effortless. The final state of a team is the Adjourning State. This state is where accomplishments by the team and each individual team member are recognized. Additionally, this state also allows for reflection of the interactions between team members, work accomplished and challenges that were faced. Finally, the team celebrates the challenges they have faced and overcome as a unit. Currently in the workplace teams are divided into two different types: Co-located and Distributed Teams. Co-located teams defined as the traditional group of people working together in an office, according to Andy Singleton of Assembla. This traditional type of a team has dominated business in the past due to inadequate technology, which forced workers to primarily interact with one another via face to face meetings.  Team meetings are primarily lead by the person with the highest status in the company. Having personally, participated in meetings of this type, usually a select few of the team members dominate the flow of communication which reduces the input of others in group discussions. Since discussions are dominated by a select few individuals the discussions and group discussion are skewed in favor of the individuals who communicate the most in meetings. In addition, Team members might not give their full opinions on a topic of discussion in part not to offend or create controversy amongst the team and can alter decision made in meetings towards those of the opinions of the dominating team members. Distributed teams are by definition spread across an area or subdivided into separate sections. That is exactly what distributed teams when compared to a more traditional team. It is common place for distributed teams to have team members across town, in the next state, across the country and even with the advances in technology over the last 20 year across the world. These teams allow for more diversity compared to the other type of teams because they allow for more flexibility regarding location. A team could consist of a 30 year old male Italian project manager from New York, a 50 year old female Hispanic from California and a collection of programmers from India because technology allows them to communicate as if they were standing next to one another.  In addition, distributed team members consult with more team members prior to making decisions compared to traditional teams, and take longer to come to decisions due to the changes in time zones and cultural events. However, team members feel more empowered to speak out when they do not agree with the team and to notify others of potential issues regarding the work that the team is doing. Virtual teams which are a subset of the distributed team type is changing organizational strategies due to the fact that a team can now in essence be working 24 hrs a day because of utilizing employees in various time zones and locations.  A primary example of this is with customer services departments, a company can have multiple call centers spread across multiple time zones allowing them to appear to be open 24 hours a day while all a employees work from 9AM to 5 PM every day. Virtual teams also allow human resources departments to go after the best talent for the company regardless of where the potential employee works because they will be a part of a virtual team all that is need is the proper technology to be setup to allow everyone to communicate. In addition to allowing employees to work from home, the company can save space and resources by not having to provide a desk for every team member. In fact, those team members that randomly come into the office can actually share one desk amongst multiple people. This is definitely a cost cutting plus given the current state of the economy. One thing that can turn a team into a high-performing team is leadership. High-performing team leaders need to focus on investing in ongoing personal development, provide team members with direction, structure, and resources needed to accomplish their work, make the right interventions at the right time, and help the team manage boundaries between the team and various external parties involved in the teams work. A team leader needs to invest in ongoing personal development in order to effectively manage their team. People have said that attitude is everything; this is very true about leaders and leadership. A team takes on the attitudes and behaviors of its leaders. This can potentially harm the team and the team’s output. Leaders must concentrate on self-awareness, and understanding their team’s group dynamics to fully understand how to lead them. In addition, always learning new leadership techniques from other effective leaders is also very beneficial. Providing team members with direction, structure, and resources that they need to accomplish their work collectively sounds easy, but it is not.  Leaders need to be able to effectively communicate with their team on how their work helps the company reach for its organizational vision. Conversely, the leader needs to allow his team to work autonomously within specific guidelines to turn the company’s vision into a reality.  This being said the team must be appropriately staffed according to the size of the team’s tasks and their complexity. These tasks should be clear, and be meaningful to the company’s objectives and allow for feedback to be exchanged with the leader and the team member and the leader and upper management. Now if the team is properly staffed, and has a clear and full understanding of what is to be done; the company also must supply the workers with the proper tools to achieve the tasks that they are asked to do. No one should be asked to dig a hole without being given a shovel.  Finally, leaders must reward their team members for accomplishments that they achieve. Awards could range from just a simple congratulatory email, a party to close the completion of a large project, or other monetary rewards. Managing boundaries is very important for team leaders because it can alter attitudes of team members and can add undue stress to the team which will force them to loose focus on the tasks at hand for the group. Team leaders should promote communication between team members so that burdens are shared amongst the team and solutions can be derived from hearing the opinions of multiple sources. This also reinforces team camaraderie and working as a unit. Team leaders must manage the type and timing of interventions as to not create an even bigger mess within the team. Poorly timed interventions can really deflate team members and make them question themselves. This could really increase further and undue interventions by the team leader. Typically, the best time for interventions is when the team is just starting to form so that all unproductive behaviors are removed from the team and that it can retain focus on its agenda. If an intervention is effectively executed the team will feel energized about the work that they are doing, promote communication and interaction amongst the group and improve moral overall. High-performing teams are very import to organizations because they consistently produce high quality output and develop a collective purpose for their work. This drive to succeed allows team members to utilize specific talents allowing for growth in these areas.  In addition, these team members usually take on a sense of ownership with their projects and feel that the other team members are irreplaceable. References: http://blog.assembla.com/assemblablog/tabid/12618/bid/3127/Three-ways-to-organize-your-team-co-located-outsourced-or-global.aspx Katzenbach, J.R. & Smith, D.K. (1993). The Wisdom of Teams: Creating the High-performance Organization. Boston: Harvard Business School.

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  • How to model a relationship that NHibernate (or Hibernate) doesn’t easily support

    - by MylesRip
    I have a situation in which the ideal relationship, I believe, would involve Value Object Inheritance. This is unfortunately not supported in NHibernate so any solution I come up with will be less than perfect. Let’s say that: “Item” entities have a “Location” that can be in one of multiple different formats. These formats are completely different with no overlapping fields. We will deal with each Location in the format that is provided in the data with no attempt to convert from one format to another. Each Item has exactly one Location. “SpecialItem” is a subtype of Item, however, that is unique in that it has exactly two Locations. “Group” entities aggregate Items. “LocationGroup” is as subtype of Group. LocationGroup also has a single Location that can be in any of the formats as described above. Although I’m interested in Items by Group, I’m also interested in being able to find all items with the same Location, regardless of which group they are in. I apologize for the number of stipulations listed above, but I’m afraid that simplifying it any further wouldn’t really reflect the difficulties of the situation. Here is how the above could be diagrammed: Mapping Dilemma Diagram: (http://www.freeimagehosting.net/uploads/592ad48b1a.jpg) (I tried placing the diagram inline, but Stack Overflow won't allow that until I have accumulated more points. I understand the reasoning behind it, but it is a bit inconvenient for now.) Hmmm... Apparently I can't have multiple links either. :-( Analyzing the above, I make the following observations: I treat Locations polymorphically, referring to the supertype rather than the subtype. Logically, Locations should be “Value Objects” rather than entities since it is meaningless to differentiate between two Location objects that have all the same values. Thus equality between Locations should be based on field comparisons, not identifiers. Also, value objects should be immutable and shared references should not be allowed. Using NHibernate (or Hibernate) one would typically map value objects using the “component” keyword which would cause the fields of the class to be mapped directly into the database table that represents the containing class. Put another way, there would not be a separate “Locations” table in the database (and Locations would therefore have no identifiers). NHibernate (or Hibernate) do not currently support inheritance for value objects. My choices as I see them are: Ignore the fact that Locations should be value objects and map them as entities. This would take care of the inheritance mapping issues since NHibernate supports entity inheritance. The downside is that I then have to deal with aliasing issues. (Meaning that if multiple objects share a reference to the same Location, then changing values for one object’s Location would cause the location to change for other objects that share the reference the same Location record.) I want to avoid this if possible. Another downside is that entities are typically compared by their IDs. This would mean that two Location objects would be considered not equal even if the values of all their fields are the same. This would be invalid and unacceptable from the business perspective. Flatten Locations into a single class so that there are no longer inheritance relationships for Locations. This would allow Locations to be treated as value objects which could easily be handled by using “component” mapping in NHibernate. The downside in this case would be that the domain model becomes weaker, more fragile and less maintainable. Do some “creative” mapping in the hbm files in order to force Location fields to be mapped into the containing entities’ tables without using the “component” keyword. This approach is described by Colin Jack here. My situation is more complicated than the one he describes due to the fact that SpecialItem has a second Location and the fact that a different entity, LocatedGroup, also has Locations. I could probably get it to work, but the mappings would be non-intuitive and therefore hard to understand and maintain by other developers in the future. Also, I suspect that these tricky mappings would likely not be possible using Fluent NHibernate so I would use the advantages of using that tool, at least in that situation. Surely others out there have run into similar situations. I’m hoping someone who has “been there, done that” can share some wisdom. :-) So here’s the question… Which approach should be preferred in this situation? Why?

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  • Much Ado About Nothing: Stub Objects

    - by user9154181
    The Solaris 11 link-editor (ld) contains support for a new type of object that we call a stub object. A stub object is a shared object, built entirely from mapfiles, that supplies the same linking interface as the real object, while containing no code or data. Stub objects cannot be executed — the runtime linker will kill any process that attempts to load one. However, you can link to a stub object as a dependency, allowing the stub to act as a proxy for the real version of the object. You may well wonder if there is a point to producing an object that contains nothing but linking interface. As it turns out, stub objects are very useful for building large bodies of code such as Solaris. In the last year, we've had considerable success in applying them to one of our oldest and thorniest build problems. In this discussion, I will describe how we came to invent these objects, and how we apply them to building Solaris. This posting explains where the idea for stub objects came from, and details our long and twisty journey from hallway idea to standard link-editor feature. I expect that these details are mainly of interest to those who work on Solaris and its makefiles, those who have done so in the past, and those who work with other similar bodies of code. A subsequent posting will omit the history and background details, and instead discuss how to build and use stub objects. If you are mainly interested in what stub objects are, and don't care about the underlying software war stories, I encourage you to skip ahead. The Long Road To Stubs This all started for me with an email discussion in May of 2008, regarding a change request that was filed in 2002, entitled: 4631488 lib/Makefile is too patient: .WAITs should be reduced This CR encapsulates a number of cronic issues with Solaris builds: We build Solaris with a parallel make (dmake) that tries to build as much of the code base in parallel as possible. There is a lot of code to build, and we've long made use of parallelized builds to get the job done quicker. This is even more important in today's world of massively multicore hardware. Solaris contains a large number of executables and shared objects. Executables depend on shared objects, and shared objects can depend on each other. Before you can build an object, you need to ensure that the objects it needs have been built. This implies a need for serialization, which is in direct opposition to the desire to build everying in parallel. To accurately build objects in the right order requires an accurate set of make rules defining the things that depend on each other. This sounds simple, but the reality is quite complex. In practice, having programmers explicitly specify these dependencies is a losing strategy: It's really hard to get right. It's really easy to get it wrong and never know it because things build anyway. Even if you get it right, it won't stay that way, because dependencies between objects can change over time, and make cannot help you detect such drifing. You won't know that you got it wrong until the builds break. That can be a long time after the change that triggered the breakage happened, making it hard to connect the cause and the effect. Usually this happens just before a release, when the pressure is on, its hard to think calmly, and there is no time for deep fixes. As a poor compromise, the libraries in core Solaris were built using a set of grossly incomplete hand written rules, supplemented with a number of dmake .WAIT directives used to group the libraries into sets of non-interacting groups that can be built in parallel because we think they don't depend on each other. From time to time, someone will suggest that we could analyze the built objects themselves to determine their dependencies and then generate make rules based on those relationships. This is possible, but but there are complications that limit the usefulness of that approach: To analyze an object, you have to build it first. This is a classic chicken and egg scenario. You could analyze the results of a previous build, but then you're not necessarily going to get accurate rules for the current code. It should be possible to build the code without having a built workspace available. The analysis will take time, and remember that we're constantly trying to make builds faster, not slower. By definition, such an approach will always be approximate, and therefore only incremantally more accurate than the hand written rules described above. The hand written rules are fast and cheap, while this idea is slow and complex, so we stayed with the hand written approach. Solaris was built that way, essentially forever, because these are genuinely difficult problems that had no easy answer. The makefiles were full of build races in which the right outcomes happened reliably for years until a new machine or a change in build server workload upset the accidental balance of things. After figuring out what had happened, you'd mutter "How did that ever work?", add another incomplete and soon to be inaccurate make dependency rule to the system, and move on. This was not a satisfying solution, as we tend to be perfectionists in the Solaris group, but we didn't have a better answer. It worked well enough, approximately. And so it went for years. We needed a different approach — a new idea to cut the Gordian Knot. In that discussion from May 2008, my fellow linker-alien Rod Evans had the initial spark that lead us to a game changing series of realizations: The link-editor is used to link objects together, but it only uses the ELF metadata in the object, consisting of symbol tables, ELF versioning sections, and similar data. Notably, it does not look at, or understand, the machine code that makes an object useful at runtime. If you had an object that only contained the ELF metadata for a dependency, but not the code or data, the link-editor would find it equally useful for linking, and would never know the difference. Call it a stub object. In the core Solaris OS, we require all objects to be built with a link-editor mapfile that describes all of its publically available functions and data. Could we build a stub object using the mapfile for the real object? It ought to be very fast to build stub objects, as there are no input objects to process. Unlike the real object, stub objects would not actually require any dependencies, and so, all of the stubs for the entire system could be built in parallel. When building the real objects, one could link against the stub objects instead of the real dependencies. This means that all the real objects can be built built in parallel too, without any serialization. We could replace a system that requires perfect makefile rules with a system that requires no ordering rules whatsoever. The results would be considerably more robust. We immediately realized that this idea had potential, but also that there were many details to sort out, lots of work to do, and that perhaps it wouldn't really pan out. As is often the case, it would be necessary to do the work and see how it turned out. Following that conversation, I set about trying to build a stub object. We determined that a faithful stub has to do the following: Present the same set of global symbols, with the same ELF versioning, as the real object. Functions are simple — it suffices to have a symbol of the right type, possibly, but not necessarily, referencing a null function in its text segment. Copy relocations make data more complicated to stub. The possibility of a copy relocation means that when you create a stub, the data symbols must have the actual size of the real data. Any error in this will go uncaught at link time, and will cause tragic failures at runtime that are very hard to diagnose. For reasons too obscure to go into here, involving tentative symbols, it is also important that the data reside in bss, or not, matching its placement in the real object. If the real object has more than one symbol pointing at the same data item, we call these aliased symbols. All data symbols in the stub object must exhibit the same aliasing as the real object. We imagined the stub library feature working as follows: A command line option to ld tells it to produce a stub rather than a real object. In this mode, only mapfiles are examined, and any object or shared libraries on the command line are are ignored. The extra information needed (function or data, size, and bss details) would be added to the mapfile. When building the real object instead of the stub, the extra information for building stubs would be validated against the resulting object to ensure that they match. In exploring these ideas, I immediately run headfirst into the reality of the original mapfile syntax, a subject that I would later write about as The Problem(s) With Solaris SVR4 Link-Editor Mapfiles. The idea of extending that poor language was a non-starter. Until a better mapfile syntax became available, which seemed unlikely in 2008, the solution could not involve extentions to the mapfile syntax. Instead, we cooked up the idea (hack) of augmenting mapfiles with stylized comments that would carry the necessary information. A typical definition might look like: # DATA(i386) __iob 0x3c0 # DATA(amd64,sparcv9) __iob 0xa00 # DATA(sparc) __iob 0x140 iob; A further problem then became clear: If we can't extend the mapfile syntax, then there's no good way to extend ld with an option to produce stub objects, and to validate them against the real objects. The idea of having ld read comments in a mapfile and parse them for content is an unacceptable hack. The entire point of comments is that they are strictly for the human reader, and explicitly ignored by the tool. Taking all of these speed bumps into account, I made a new plan: A perl script reads the mapfiles, generates some small C glue code to produce empty functions and data definitions, compiles and links the stub object from the generated glue code, and then deletes the generated glue code. Another perl script used after both objects have been built, to compare the real and stub objects, using data from elfdump, and validate that they present the same linking interface. By June 2008, I had written the above, and generated a stub object for libc. It was a useful prototype process to go through, and it allowed me to explore the ideas at a deep level. Ultimately though, the result was unsatisfactory as a basis for real product. There were so many issues: The use of stylized comments were fine for a prototype, but not close to professional enough for shipping product. The idea of having to document and support it was a large concern. The ideal solution for stub objects really does involve having the link-editor accept the same arguments used to build the real object, augmented with a single extra command line option. Any other solution, such as our prototype script, will require makefiles to be modified in deeper ways to support building stubs, and so, will raise barriers to converting existing code. A validation script that rederives what the linker knew when it built an object will always be at a disadvantage relative to the actual linker that did the work. A stub object should be identifyable as such. In the prototype, there was no tag or other metadata that would let you know that they weren't real objects. Being able to identify a stub object in this way means that the file command can tell you what it is, and that the runtime linker can refuse to try and run a program that loads one. At that point, we needed to apply this prototype to building Solaris. As you might imagine, the task of modifying all the makefiles in the core Solaris code base in order to do this is a massive task, and not something you'd enter into lightly. The quality of the prototype just wasn't good enough to justify that sort of time commitment, so I tabled the project, putting it on my list of long term things to think about, and moved on to other work. It would sit there for a couple of years. Semi-coincidentally, one of the projects I tacked after that was to create a new mapfile syntax for the Solaris link-editor. We had wanted to do something about the old mapfile syntax for many years. Others before me had done some paper designs, and a great deal of thought had already gone into the features it should, and should not have, but for various reasons things had never moved beyond the idea stage. When I joined Sun in late 2005, I got involved in reviewing those things and thinking about the problem. Now in 2008, fresh from relearning for the Nth time why the old mapfile syntax was a huge impediment to linker progress, it seemed like the right time to tackle the mapfile issue. Paving the way for proper stub object support was not the driving force behind that effort, but I certainly had them in mind as I moved forward. The new mapfile syntax, which we call version 2, integrated into Nevada build snv_135 in in February 2010: 6916788 ld version 2 mapfile syntax PSARC/2009/688 Human readable and extensible ld mapfile syntax In order to prove that the new mapfile syntax was adequate for general purpose use, I had also done an overhaul of the ON consolidation to convert all mapfiles to use the new syntax, and put checks in place that would ensure that no use of the old syntax would creep back in. That work went back into snv_144 in June 2010: 6916796 OSnet mapfiles should use version 2 link-editor syntax That was a big putback, modifying 517 files, adding 18 new files, and removing 110 old ones. I would have done this putback anyway, as the work was already done, and the benefits of human readable syntax are obvious. However, among the justifications listed in CR 6916796 was this We anticipate adding additional features to the new mapfile language that will be applicable to ON, and which will require all sharable object mapfiles to use the new syntax. I never explained what those additional features were, and no one asked. It was premature to say so, but this was a reference to stub objects. By that point, I had already put together a working prototype link-editor with the necessary support for stub objects. I was pleased to find that building stubs was indeed very fast. On my desktop system (Ultra 24), an amd64 stub for libc can can be built in a fraction of a second: % ptime ld -64 -z stub -o stubs/libc.so.1 -G -hlibc.so.1 \ -ztext -zdefs -Bdirect ... real 0.019708910 user 0.010101680 sys 0.008528431 In order to go from prototype to integrated link-editor feature, I knew that I would need to prove that stub objects were valuable. And to do that, I knew that I'd have to switch the Solaris ON consolidation to use stub objects and evaluate the outcome. And in order to do that experiment, ON would first need to be converted to version 2 mapfiles. Sub-mission accomplished. Normally when you design a new feature, you can devise reasonably small tests to show it works, and then deploy it incrementally, letting it prove its value as it goes. The entire point of stub objects however was to demonstrate that they could be successfully applied to an extremely large and complex code base, and specifically to solve the Solaris build issues detailed above. There was no way to finesse the matter — in order to move ahead, I would have to successfully use stub objects to build the entire ON consolidation and demonstrate their value. In software, the need to boil the ocean can often be a warning sign that things are trending in the wrong direction. Conversely, sometimes progress demands that you build something large and new all at once. A big win, or a big loss — sometimes all you can do is try it and see what happens. And so, I spent some time staring at ON makefiles trying to get a handle on how things work, and how they'd have to change. It's a big and messy world, full of complex interactions, unspecified dependencies, special cases, and knowledge of arcane makefile features... ...and so, I backed away, put it down for a few months and did other work... ...until the fall, when I felt like it was time to stop thinking and pondering (some would say stalling) and get on with it. Without stubs, the following gives a simplified high level view of how Solaris is built: An initially empty directory known as the proto, and referenced via the ROOT makefile macro is established to receive the files that make up the Solaris distribution. A top level setup rule creates the proto area, and performs operations needed to initialize the workspace so that the main build operations can be launched, such as copying needed header files into the proto area. Parallel builds are launched to build the kernel (usr/src/uts), libraries (usr/src/lib), and commands. The install makefile target builds each item and delivers a copy to the proto area. All libraries and executables link against the objects previously installed in the proto, implying the need to synchronize the order in which things are built. Subsequent passes run lint, and do packaging. Given this structure, the additions to use stub objects are: A new second proto area is established, known as the stub proto and referenced via the STUBROOT makefile macro. The stub proto has the same structure as the real proto, but is used to hold stub objects. All files in the real proto are delivered as part of the Solaris product. In contrast, the stub proto is used to build the product, and then thrown away. A new target is added to library Makefiles called stub. This rule builds the stub objects. The ld command is designed so that you can build a stub object using the same ld command line you'd use to build the real object, with the addition of a single -z stub option. This means that the makefile rules for building the stub objects are very similar to those used to build the real objects, and many existing makefile definitions can be shared between them. A new target is added to the Makefiles called stubinstall which delivers the stub objects built by the stub rule into the stub proto. These rules reuse much of existing plumbing used by the existing install rule. The setup rule runs stubinstall over the entire lib subtree as part of its initialization. All libraries and executables link against the objects in the stub proto rather than the main proto, and can therefore be built in parallel without any synchronization. There was no small way to try this that would yield meaningful results. I would have to take a leap of faith and edit approximately 1850 makefiles and 300 mapfiles first, trusting that it would all work out. Once the editing was done, I'd type make and see what happened. This took about 6 weeks to do, and there were many dark days when I'd question the entire project, or struggle to understand some of the many twisted and complex situations I'd uncover in the makefiles. I even found a couple of new issues that required changes to the new stub object related code I'd added to ld. With a substantial amount of encouragement and help from some key people in the Solaris group, I eventually got the editing done and stub objects for the entire workspace built. I found that my desktop system could build all the stub objects in the workspace in roughly a minute. This was great news, as it meant that use of the feature is effectively free — no one was likely to notice or care about the cost of building them. After another week of typing make, fixing whatever failed, and doing it again, I succeeded in getting a complete build! The next step was to remove all of the make rules and .WAIT statements dedicated to controlling the order in which libraries under usr/src/lib are built. This came together pretty quickly, and after a few more speed bumps, I had a workspace that built cleanly and looked like something you might actually be able to integrate someday. This was a significant milestone, but there was still much left to do. I turned to doing full nightly builds. Every type of build (open, closed, OpenSolaris, export, domestic) had to be tried. Each type failed in a new and unique way, requiring some thinking and rework. As things came together, I became aware of things that could have been done better, simpler, or cleaner, and those things also required some rethinking, the seeking of wisdom from others, and some rework. After another couple of weeks, it was in close to final form. My focus turned towards the end game and integration. This was a huge workspace, and needed to go back soon, before changes in the gate would made merging increasingly difficult. At this point, I knew that the stub objects had greatly simplified the makefile logic and uncovered a number of race conditions, some of which had been there for years. I assumed that the builds were faster too, so I did some builds intended to quantify the speedup in build time that resulted from this approach. It had never occurred to me that there might not be one. And so, I was very surprised to find that the wall clock build times for a stock ON workspace were essentially identical to the times for my stub library enabled version! This is why it is important to always measure, and not just to assume. One can tell from first principles, based on all those removed dependency rules in the library makefile, that the stub object version of ON gives dmake considerably more opportunities to overlap library construction. Some hypothesis were proposed, and shot down: Could we have disabled dmakes parallel feature? No, a quick check showed things being build in parallel. It was suggested that we might be I/O bound, and so, the threads would be mostly idle. That's a plausible explanation, but system stats didn't really support it. Plus, the timing between the stub and non-stub cases were just too suspiciously identical. Are our machines already handling as much parallelism as they are capable of, and unable to exploit these additional opportunities? Once again, we didn't see the evidence to back this up. Eventually, a more plausible and obvious reason emerged: We build the libraries and commands (usr/src/lib, usr/src/cmd) in parallel with the kernel (usr/src/uts). The kernel is the long leg in that race, and so, wall clock measurements of build time are essentially showing how long it takes to build uts. Although it would have been nice to post a huge speedup immediately, we can take solace in knowing that stub objects simplify the makefiles and reduce the possibility of race conditions. The next step in reducing build time should be to find ways to reduce or overlap the uts part of the builds. When that leg of the build becomes shorter, then the increased parallelism in the libs and commands will pay additional dividends. Until then, we'll just have to settle for simpler and more robust. And so, I integrated the link-editor support for creating stub objects into snv_153 (November 2010) with 6993877 ld should produce stub objects PSARC/2010/397 ELF Stub Objects followed by the work to convert the ON consolidation in snv_161 (February 2011) with 7009826 OSnet should use stub objects 4631488 lib/Makefile is too patient: .WAITs should be reduced This was a huge putback, with 2108 modified files, 8 new files, and 2 removed files. Due to the size, I was allowed a window after snv_160 closed in which to do the putback. It went pretty smoothly for something this big, a few more preexisting race conditions would be discovered and addressed over the next few weeks, and things have been quiet since then. Conclusions and Looking Forward Solaris has been built with stub objects since February. The fact that developers no longer specify the order in which libraries are built has been a big success, and we've eliminated an entire class of build error. That's not to say that there are no build races left in the ON makefiles, but we've taken a substantial bite out of the problem while generally simplifying and improving things. The introduction of a stub proto area has also opened some interesting new possibilities for other build improvements. As this article has become quite long, and as those uses do not involve stub objects, I will defer that discussion to a future article.

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