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  • A Knights Tale

    - by Phil Factor
    There are so many lessons to be learned from the story of Knight Capital losing nearly half a billion dollars as a result of a deployment gone wrong. The Knight Capital Group (KCG N) was an American global financial services firm engaging in market making, electronic execution, and institutional sales and trading. According to the recent order (File No.3.15570) against Knight Capital by U.S. Securities and Exchange Commission?, Knight had, for many years used some software which broke up incoming “parent” orders into smaller “child” orders that were then transmitted to various exchanges or trading venues for execution. A tracking ‘cumulative quantity’ function counted the number of ‘child’ orders and stopped the process once the total of child orders matched the ‘parent’ and so the parent order had been completed. Back in the mists of time, some code had been added to it  which was excuted if a particular flag was set. It was called ‘power peg’ and seems to have had a similar design and purpose, but, one guesses, would have shared the same tracking function. This code had been abandoned in 2003, but never deleted. In 2005, The tracking function was moved to an earlier point in the main process. It would seem from the account that, from that point, had that flag ever been set, the old ‘Power Peg’ would have been executed like Godzilla bursting from the ice, making child orders without limit without any tracking function. It wasn’t, presumably because the software that set the flag was removed. In 2012, nearly a decade after ‘Power Peg’ was abandoned, Knight prepared a new module to their software to cope with the imminent Retail Liquidity Program (RLP) for the New York Stock Exchange. By this time, the flag had remained unused and someone made the fateful decision to reuse it, and replace the old ‘power peg’ code with this new RLP code. Had the two actions been done together in a single automated deployment, and the new deployment tested, all would have been well. It wasn’t. To quote… “Beginning on July 27, 2012, Knight deployed the new RLP code in SMARS in stages by placing it on a limited number of servers in SMARS on successive days. During the deployment of the new code, however, one of Knight’s technicians did not copy the new code to one of the eight SMARS computer servers. Knight did not have a second technician review this deployment and no one at Knight realized that the Power Peg code had not been removed from the eighth server, nor the new RLP code added. Knight had no written procedures that required such a review.” (para 15) “On August 1, Knight received orders from broker-dealers whose customers were eligible to participate in the RLP. The seven servers that received the new code processed these orders correctly. However, orders sent with the repurposed flag to the eighth server triggered the defective Power Peg code still present on that server. As a result, this server began sending child orders to certain trading centers for execution. Because the cumulative quantity function had been moved, this server continuously sent child orders, in rapid sequence, for each incoming parent order without regard to the number of share executions Knight had already received from trading centers. Although one part of Knight’s order handling system recognized that the parent orders had been filled, this information was not communicated to SMARS.” (para 16) SMARS routed millions of orders into the market over a 45-minute period, and obtained over 4 million executions in 154 stocks for more than 397 million shares. By the time that Knight stopped sending the orders, Knight had assumed a net long position in 80 stocks of approximately $3.5 billion and a net short position in 74 stocks of approximately $3.15 billion. Knight’s shares dropped more than 20% after traders saw extreme volume spikes in a number of stocks, including preferred shares of Wells Fargo (JWF) and semiconductor company Spansion (CODE). Both stocks, which see roughly 100,000 trade per day, had changed hands more than 4 million times by late morning. Ultimately, Knight lost over $460 million from this wild 45 minutes of trading. Obviously, I’m interested in all this because, at one time, I used to write trading systems for the City of London. Obviously, the US SEC is in a far better position than any of us to work out the failings of Knight’s IT department, and the report makes for painful reading. I can’t help observing, though, that even with the breathtaking mistakes all along the way, that a robust automated deployment process that was ‘all-or-nothing’, and tested from soup to nuts would have prevented the disaster. The report reads like a Greek Tragedy. All the way along one wants to shout ‘No! not that way!’ and ‘Aargh! Don’t do it!’. As the tragedy unfolds, the audience weeps for the players, trapped by a cruel fate. All application development and deployment requires defense in depth. All IT goes wrong occasionally, but if there is a culture of defensive programming throughout, the consequences are usually containable. For financial systems, these defenses are required by statute, and ignored only by the foolish. Knight’s mistakes weren’t made by just one hapless sysadmin, but were progressive errors by an  IT culture spanning at least ten years.  One can spell these out, but I think they’re obvious. One can only hope that the industry studies what happened in detail, learns from the mistakes, and draws the right conclusions.

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  • Know your Data Lineage

    - by Simon Elliston Ball
    An academic paper without the footnotes isn’t an academic paper. Journalists wouldn’t base a news article on facts that they can’t verify. So why would anyone publish reports without being able to say where the data has come from and be confident of its quality, in other words, without knowing its lineage. (sometimes referred to as ‘provenance’ or ‘pedigree’) The number and variety of data sources, both traditional and new, increases inexorably. Data comes clean or dirty, processed or raw, unimpeachable or entirely fabricated. On its journey to our report, from its source, the data can travel through a network of interconnected pipes, passing through numerous distinct systems, each managed by different people. At each point along the pipeline, it can be changed, filtered, aggregated and combined. When the data finally emerges, how can we be sure that it is right? How can we be certain that no part of the data collection was based on incorrect assumptions, that key data points haven’t been left out, or that the sources are good? Even when we’re using data science to give us an approximate or probable answer, we cannot have any confidence in the results without confidence in the data from which it came. You need to know what has been done to your data, where it came from, and who is responsible for each stage of the analysis. This information represents your data lineage; it is your stack-trace. If you’re an analyst, suspicious of a number, it tells you why the number is there and how it got there. If you’re a developer, working on a pipeline, it provides the context you need to track down the bug. If you’re a manager, or an auditor, it lets you know the right things are being done. Lineage tracking is part of good data governance. Most audit and lineage systems require you to buy into their whole structure. If you are using Hadoop for your data storage and processing, then tools like Falcon allow you to track lineage, as long as you are using Falcon to write and run the pipeline. It can mean learning a new way of running your jobs (or using some sort of proxy), and even a distinct way of writing your queries. Other Hadoop tools provide a lot of operational and audit information, spread throughout the many logs produced by Hive, Sqoop, MapReduce and all the various moving parts that make up the eco-system. To get a full picture of what’s going on in your Hadoop system you need to capture both Falcon lineage and the data-exhaust of other tools that Falcon can’t orchestrate. However, the problem is bigger even that that. Often, Hadoop is just one piece in a larger processing workflow. The next step of the challenge is how you bind together the lineage metadata describing what happened before and after Hadoop, where ‘after’ could be  a data analysis environment like R, an application, or even directly into an end-user tool such as Tableau or Excel. One possibility is to push as much as you can of your key analytics into Hadoop, but would you give up the power, and familiarity of your existing tools in return for a reliable way of tracking lineage? Lineage and auditing should work consistently, automatically and quietly, allowing users to access their data with any tool they require to use. The real solution, therefore, is to create a consistent method by which to bring lineage data from these data various disparate sources into the data analysis platform that you use, rather than being forced to use the tool that manages the pipeline for the lineage and a different tool for the data analysis. The key is to keep your logs, keep your audit data, from every source, bring them together and use the data analysis tools to trace the paths from raw data to the answer that data analysis provides.

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  • SQL Saturday and Exploring Data Privacy

    - by Johnm
    I have been highly impressed with the growth of the SQL Saturday phenomenon. It seems that an announcement for a new wonderful event finds its way to my inbox on a daily basis. I have had the opportunity to attend the first of the SQL Saturday's for Tampa, Chicago, Louisville and recently my home town of Indianapolis. It is my hope that there will be many more in my future. This past weekend I had the honor of being selected to speak amid a great line up of speakers at SQL Saturday #82 in Indianapolis. My session topic/title was "Exploring Data Privacy". Below is a brief synopsis of my session: Data Privacy in a Nutshell        - Definition of data privacy        - Examples of personally identifiable data        - Examples of Sensitive data Laws and Stuff        - Various examples of laws, regulations and policies that influence the definition of data privacy        - General rules of thumb that encompasses most laws Your Data Footprint        - Who has personal information about you?        - What are you exchanging data privacy for?        - The amazing resilience of data        - The cost of data loss Weapons of Mass Protection       - Data classification       - Extended properties       - Database Object Schemas       - An extraordinarily brief introduction of encryption       - The amazing data professional  <-the most important point of the entire session! The subject of data privacy is one that is quickly making its way to the forefront of the mind of many data professionals. Somewhere out there someone is storing personally identifiable and other sensitive data about you. In some cases it is kept reasonably secure. In other cases it is kept in total exposure without the consideration of its potential of damage to you. Who has access to it and how is it being used? Are we being unnecessarily required to supply sensitive data in exchange for products and services? These are just a few questions on everyone's mind. As data loss events of grand scale hit the headlines in a more frequent succession, the level of frustration and urgency for a solution increases. I assembled this session with the intent to raise awareness of sensitive data and remind us all that we, data professionals, are the ones who have the greatest impact and influence on how sensitive data is regarded and protected. Mahatma Gandhi once said "Be the change you want to see in the world." This is guidance that I keep near to my heart as I approached this topic of data privacy.

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  • How to make sure you see the truth with Management Studio

    - by fatherjack
    LiveJournal Tags: TSQL,How To,SSMS,Tips and Tricks Did you know that SQL Server Management Studio can mislead you with how your code is performing? I found a query that was using a scalar function to return a date and wanted to take the opportunity to remove it in favour of a table valued function that would be more efficient. The original function was simply returning the start date of the current financial year. The code we were using was: ALTER  FUNCTION...(read more)

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  • Another VSeWSS Error Resolved (List Template not installed on Farm)

    - by Damon
    Ran into a minor snag today trying to deploy a project with VSeWSS 1.3 - during the deployment it gave me the following error: Error    32    VSeWSS Service Error: Feature '2ade6552-200e-4425-8af5-f1f50c115b7e' for list template '10001' is not installed in this farm.  The operation could not be completed. At first glance, it looked my features were not installing in the correct order because the solution was installing a list that required a custom list definition before the custom list definition was being installed.  After switching the order in the WSP view (View -> Other Windows -> WSP View) -- you can use the up and down arrows on the view pane to switch feature installation order - I had the same error. I decided to try deleting the list, but upon visiting the list in the web interface I received a similar error about how the feature was not installed on the farm.  As such, I could not delete the list through the web interface.  Fortunately, the stsadm.exe tool worked just fine: stsadm.exe -o forcedeletelist -url <urltolisthere>

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  • News From EAP Testing

    - by Fatherjack
    There is a phrase that goes something like “Watch the pennies and the pounds/dollars will take care of themselves”, meaning that if you pay attention to the small things then the larger things are going to fare well too. I am lucky enough to be a Friend of Red Gate and once in a while I get told about new features in their tools and have a test copy of the software to trial. I got one of those emails a week or so ago and I have been exploring the SQL Prompt 6 EAP since then. One really useful feature of long standing in SQL Prompt is the idea of a code snippet that is automatically pasted into the SSMS editor when you type a few key letters. For example I can type “ssf” and then press the tab key and the text is expanded to SELECT * FROM. There are lots of these combinations and it is possible to create your own really easily. To create your own you use the Snippet Manager interface to define the shortcut letters and the code that you want to have put in their place. Let’s look at an example. Say I am writing a blog about something and want to have the demo code create a temporary table. It might looks like this; The first time you run the code everything is fine, a lovely set of dates fill the results grid but run it a second time and this happens.   Yep, we didn’t destroy the temporary table so the CREATE statement fails when it finds the table already exists. No matter, I have a snippet created that takes care of this.   Nothing too technical here but you will see that in the Code section there is $CURSOR$, this isn’t a TSQL keyword but a marker for SQL Prompt to place the cursor in that position when the Code is pasted into the SSMS Editor. I just place my cursor above the CREATE statement and type “ifobj” – the shortcut for my code to DROP the temporary table – which has been defined in the Snippet Manager as below. This means I am right-away ready to type the name of the offending table. Pretty neat and it’s been very useful in saving me lots of time over many years.   The news for SQL Prompt 6 is that Red Gate have added a new Snippet Command of $PASTE$. Let’s alter our snippet to the following and try it out   Once again, we will type type “ifobj” in the SSMS Editor but first of all, highlight the name of the table #TestTable and copy it to your clipboard. Now type “ifobj” and press Tab… Wherever the string $PASTE$ is placed in the snippet, the contents of your clipboard are merged into the pasted TSQL. This means I don’t need to type the table name into the code snippet, it’s already there and I am seeing a fully functioning piece of TSQL ready to run. This means it is it even easier to write TSQL quickly and consistently. Attention to detail like this from Red Gate means that their developer tools stay on track to keep winning awards year after year and help take the hard work out of writing neat, accurate TSQL. If you want to try out SQL Prompt all the details are at http://www.red-gate.com/products/sql-development/sql-prompt/.

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  • Comparing Apples and Pairs

    - by Tony Davis
    A recent study, High Costs and Negative Value of Pair Programming, by Capers Jones, pulls no punches in its assessment of the costs-to- benefits ratio of pair programming, two programmers working together, at a single computer, rather than separately. He implies that pair programming is a method rushed into production on a wave of enthusiasm for Agile or Extreme Programming, without any real regard for its effectiveness. Despite admitting that his data represented a far from complete study of the economics of pair programming, his conclusions were stark: it was 2.5 times more expensive, resulted in a 15% drop in productivity, and offered no significant quality benefits. The author provides a more scientific analysis than Jon Evans’ Pair Programming Considered Harmful, but the theme is the same. In terms of upfront-coding costs, pair programming is surely more expensive. The claim of productivity loss is dubious and contested by other studies. The third claim, though, did surprise me. The author’s data suggests that if both the pair and the individual programmers employ static code analysis and testing, then there is no measurable difference in the resulting code quality, in terms of defects per function point. In other words, pair programming incurs a massive extra cost for no tangible return in investment. There were, inevitably, many criticisms of his data and his conclusions, a few of which are persuasive. Firstly, that the driver/observer model of pair programming, on which the study bases its findings, is far from the most effective. For example, many find Ping-Pong pairing, based on use of test-driven development, far more productive. Secondly, that it doesn’t distinguish between “expert” and “novice” pair programmers– that is, independently of other programming skills, how skilled was an individual at pair programming. Thirdly, that his measure of quality is too narrow. This point rings true, certainly at Red Gate, where developers don’t pair program all the time, but use the method in short bursts, while tackling a tricky problem and needing a fresh perspective on the best approach, or more in-depth knowledge in a particular domain. All of them argue that pair programming, and collective code ownership, offers significant rewards, if not in terms of immediate “bug reduction”, then in removing the likelihood of single points of failure, and improving the overall quality and longer-term adaptability/maintainability of the design. There is also a massive learning benefit for both participants. One developer told me how he once worked in the same team over consecutive summers, the first time with no pair programming and the second time pair-programming two-thirds of the time, and described the increased rate of learning the second time as “phenomenal”. There are a great many theories on how we should develop software (Scrum, XP, Lean, etc.), but woefully little scientific research in their effectiveness. For a group that spends so much time crunching other people’s data, I wonder if developers spend enough time crunching data about themselves. Capers Jones’ data may be incomplete, but should cause a pause for thought, especially for any large IT departments, supporting commerce and industry, who are considering pair programming. It certainly shouldn’t discourage teams from exploring new ways of developing software, as long as they also think about how to gather hard data to gauge their effectiveness.

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  • Data Model Dissonance

    - by Tony Davis
    So often at the start of the development of database applications, there is a premature rush to the keyboard. Unless, before we get there, we’ve mapped out and agreed the three data models, the Conceptual, the Logical and the Physical, then the inevitable refactoring will dog development work. It pays to get the data models sorted out up-front, however ‘agile’ you profess to be. The hardest model to get right, the most misunderstood, and the one most neglected by the various modeling tools, is the conceptual data model, and yet it is critical to all that follows. The conceptual model distils what the business understands about itself, and the way it operates. It represents the business rules that govern the required data, its constraints and its properties. The conceptual model uses the terminology of the business and defines the most important entities and their inter-relationships. Don’t assume that the organization’s understanding of these business rules is consistent or accurate. Too often, one department has a subtly different understanding of what an entity means and what it stores, from another. If our conceptual data model fails to resolve such inconsistencies, it will reduce data quality. If we don’t collect and measure the raw data in a consistent way across the whole business, how can we hope to perform meaningful aggregation? The conceptual data model has more to do with business than technology, and as such, developers often regard it as a worthy but rather arcane ceremony like saluting the flag or only eating fish on Friday. However, the consequences of getting it wrong have a direct and painful impact on many aspects of the project. If you adopt a silo-based (a.k.a. Domain driven) approach to development), you are still likely to suffer by starting with an incomplete knowledge of the domain. Even when you have surmounted these problems so that the data entities accurately reflect the business domain that the application represents, there are likely to be dire consequences from abandoning the goal of a shared, enterprise-wide understanding of the business. In reading this, you may recall experiences of the consequence of getting the conceptual data model wrong. I believe that Phil Factor, for example, witnessed the abandonment of a multi-million dollar banking project due to an inadequate conceptual analysis of how the bank defined a ‘customer’. We’d love to hear of any examples you know of development projects poleaxed by errors in the conceptual data model. Cheers, Tony

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  • 3 tips for SQL Azure connection perfection

    - by Richard Mitchell
    One of my main annoyances when dealing with SQL Azure is of course the occasional connection problems that communicating to a cloud database entails. If you're used to programming against a locally hosted SQL Server box this can be quite a change and annoying like you wouldn't believe. So after hitting the problem again in http://cloudservices.red-gate.com  I thought I'd write a little post to remind myself how I've got it working, I don't say it's right but at least "it works on my machine" Tip...(read more)

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  • Antenna Aligner part 2: Finding the right direction

    - by Chris George
    Last time I managed to get "my first app(tm)" built, published and running on my iPhone. This was really cool, a piece of my code running on my very own device. Ok, so I'm easily pleased! The next challenge was actually trying to determine what it was I wanted this app to do, and how to do it. Reverting back to good old paper and pen, I started sketching out designs for the app. I knew I wanted it to get a list of transmitters, then clicking on a transmitter would display a compass type view, with an arrow pointing the right way. I figured there would not be much point in continuing until I know I could do the graphical part of the project, i.e. the rotating compass, so armed with that reasoning (plus the fact I just wanted to get on and code!), I once again dived into visual studio. Using my friend (google) I found some example code for getting the compass data from the phone using the PhoneGap framework. // onSuccess: Get the current heading // function onSuccess(heading) {    alert('Heading: ' + heading); } navigator.compass.getCurrentHeading(onSuccess, onError); Using the ripple mobile emulator this showed that it was successfully getting the compass heading. But it didn't work when uploaded to my phone. It turns out that the examples I had been looking at were for PhoneGap 1.0, and Nomad uses PhoneGap 1.4.1. In 1.4.1, getCurrentHeading provides a compass object to onSuccess, not just a numeric value, so the code now looks like // onSuccess: Get the current magnetic heading // function onSuccess(heading) {    alert('Heading: ' + heading.magneticHeading); }; navigator.compass.getCurrentHeading(onSuccess, onError); So the lesson learnt from this... read the documentation for the version you are actually using! This does, however, lead to compatibility problems with ripple as it only supports 1.0 which is a real pain. I hope that the ripple system is updated sometime soon.

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  • Taking our Friendships to the next level.

    - by RedAndTheCommunity
    Red Gate have been running the Friends of Red Gate program for years now, and over that time we've built some great relationships with some truly awesome members of the SQL and .NET communities. When I took over the running of the program from Annabel in 2011, I was overwhelmed by the enthusiasm and commitment of our Friends. There were just so many of them, however, that it was hard to make the most of the relationships we had with people, and I wanted to fix that. I decided to survey all our Friends, to find out what they wanted to get out of, and put into, being in the Friends of Red Gate (FoRG) program. From the results of that survey, I identified 30 FoRGs that were really willing and able to go that step further to help Red Gate improve their tools, improve their relationship with the community, and improve the Friends of Red Gate program. Those 30 Friends of Red Gate have been awarded 'FoRG+' status. That means they'll: Have a closer relationship with the product teams, by getting involved in projects Have even more access to the inside track about the tools they're interested in Get the opportunity to come visit us at the Red Gate office and really influence the development of the tools. Plus more, depending on how the individual FoRG+ wants to work with us. This doesn't mean I've forgotten our other Friends; I'm working on ways to improve their experience of the Friends of Red Gate program. I'll write about them in another post. If you're an existing Friend of Red Gate, and you're interested in finding out how to get involved in the FoRG+ program, then I'd love to chat to you. For anyone that's interested in joining the Friend of Red Gate program, take a look at the web page dedicated to the program, and get in touch at [email protected] to be put on the waiting list for our 2013 program.

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  • Antenna Aligner Part 6: Little Robots

    - by Chris George
    A week ago I took temporary ownership of a HTC Desire S so that I could start testing my app under Android. Support for Android was not in my original plan, but when Nomad added support for it recently, I starting thinking why not! So with some trepidation, I clicked the Build for Android button on the Nomad toolbar... nothing. Hmm... that's not right, I was expecting something to build. After a bit of faffing around I finally realised that I hadn't read the text on the Android setup page properly (yes that's right, RTFM!), and I needed a two-part application identifier, separated by a dot. I did this (not sure what the two part thing is all about, that one my list to investigate!) After making the change, the Android build worked and created the apk file. I uploaded this to the device and nervously ran it... it worked!!!  Well, more or less! So, there was not splash screen, but this was no surprise because I only have the iOS icons and splash screen in my project at the moment. What was more concerning was the compass update didn't seem to be working. I suspect this is a result of using an iOS specific option in the Phonegap compass watcher. Another thing to investigate. I've also just noticed that the css gradient background hasn't worked either... These issues aside, it was actually more successful than I was expecting, so happy days! Right, lets get Googling...   Next time: Preparing for submission to the App Store! :-)

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  • Learn Many Languages

    - by Jeff Foster
    My previous blog, Deliberate Practice, discussed the need for developers to “sharpen their pencil” continually, by setting aside time to learn how to tackle problems in different ways. However, the Sapir-Whorf hypothesis, a contested and somewhat-controversial concept from language theory, seems to hold reasonably true when applied to programming languages. It states that: “The structure of a language affects the ways in which its speakers conceptualize their world.” If you’re constrained by a single programming language, the one that dominates your day job, then you only have the tools of that language at your disposal to think about and solve a problem. For example, if you’ve only ever worked with Java, you would never think of passing a function to a method. A good developer needs to learn many languages. You may never deploy them in production, you may never ship code with them, but by learning a new language, you’ll have new ideas that will transfer to your current “day-job” language. With the abundant choices in programming languages, how does one choose which to learn? Alan Perlis sums it up best. “A language that doesn‘t affect the way you think about programming is not worth knowing“ With that in mind, here’s a selection of languages that I think are worth learning and that have certainly changed the way I think about tackling programming problems. Clojure Clojure is a Lisp-based language running on the Java Virtual Machine. The unique property of Lisp is homoiconicity, which means that a Lisp program is a Lisp data structure, and vice-versa. Since we can treat Lisp programs as Lisp data structures, we can write our code generation in the same style as our code. This gives Lisp a uniquely powerful macro system, and makes it ideal for implementing domain specific languages. Clojure also makes software transactional memory a first-class citizen, giving us a new approach to concurrency and dealing with the problems of shared state. Haskell Haskell is a strongly typed, functional programming language. Haskell’s type system is far richer than C# or Java, and allows us to push more of our application logic to compile-time safety. If it compiles, it usually works! Haskell is also a lazy language – we can work with infinite data structures. For example, in a board game we can generate the complete game tree, even if there are billions of possibilities, because the values are computed only as they are needed. Erlang Erlang is a functional language with a strong emphasis on reliability. Erlang’s approach to concurrency uses message passing instead of shared variables, with strong support from both the language itself and the virtual machine. Processes are extremely lightweight, and garbage collection doesn’t require all processes to be paused at the same time, making it feasible for a single program to use millions of processes at once, all without the mental overhead of managing shared state. The Benefits of Multilingualism By studying new languages, even if you won’t ever get the chance to use them in production, you will find yourself open to new ideas and ways of coding in your main language. For example, studying Haskell has taught me that you can do so much more with types and has changed my programming style in C#. A type represents some state a program should have, and a type should not be able to represent an invalid state. I often find myself refactoring methods like this… void SomeMethod(bool doThis, bool doThat) { if (!(doThis ^ doThat)) throw new ArgumentException(“At least one arg should be true”); if (doThis) DoThis(); if (doThat) DoThat(); } …into a type-based solution, like this: enum Action { DoThis, DoThat, Both }; void SomeMethod(Action action) { if (action == Action.DoThis || action == Action.Both) DoThis(); if (action == Action.DoThat || action == Action.Both) DoThat(); } At this point, I’ve removed the runtime exception in favor of a compile-time check. This is a trivial example, but is just one of many ideas that I’ve taken from one language and implemented in another.

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  • Developing Schema Compare for Oracle (Part 6): 9i Query Performance

    - by Simon Cooper
    All throughout the EAP and beta versions of Schema Compare for Oracle, our main request was support for Oracle 9i. After releasing version 1.0 with support for 10g and 11g, our next step was then to get version 1.1 of SCfO out with support for 9i. However, there were some significant problems that we had to overcome first. This post will concentrate on query execution time. When we first tested SCfO on a 9i server, after accounting for various changes to the data dictionary, we found that database registration was taking a long time. And I mean a looooooong time. The same database that on 10g or 11g would take a couple of minutes to register would be taking upwards of 30 mins on 9i. Obviously, this is not ideal, so a poke around the query execution plans was required. As an example, let's take the table population query - the one that reads ALL_TABLES and joins it with a few other dictionary views to get us back our list of tables. On 10g, this query takes 5.6 seconds. On 9i, it takes 89.47 seconds. The difference in execution plan is even more dramatic - here's the (edited) execution plan on 10g: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 108K| 939 || 1 | SORT ORDER BY | | 108K| 939 || 2 | NESTED LOOPS OUTER | | 108K| 938 ||* 3 | HASH JOIN RIGHT OUTER | | 103K| 762 || 4 | VIEW | ALL_EXTERNAL_LOCATIONS | 2058 | 3 ||* 20 | HASH JOIN RIGHT OUTER | | 73472 | 759 || 21 | VIEW | ALL_EXTERNAL_TABLES | 2097 | 3 ||* 34 | HASH JOIN RIGHT OUTER | | 39920 | 755 || 35 | VIEW | ALL_MVIEWS | 51 | 7 || 58 | NESTED LOOPS OUTER | | 39104 | 748 || 59 | VIEW | ALL_TABLES | 6704 | 668 || 89 | VIEW PUSHED PREDICATE | ALL_TAB_COMMENTS | 2025 | 5 || 106 | VIEW | ALL_PART_TABLES | 277 | 11 |------------------------------------------------------------------------------- And the same query on 9i: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 16P| 55G|| 1 | SORT ORDER BY | | 16P| 55G|| 2 | NESTED LOOPS OUTER | | 16P| 862M|| 3 | NESTED LOOPS OUTER | | 5251G| 992K|| 4 | NESTED LOOPS OUTER | | 4243M| 2578 || 5 | NESTED LOOPS OUTER | | 2669K| 1440 ||* 6 | HASH JOIN OUTER | | 398K| 302 || 7 | VIEW | ALL_TABLES | 342K| 276 || 29 | VIEW | ALL_MVIEWS | 51 | 20 ||* 50 | VIEW PUSHED PREDICATE | ALL_TAB_COMMENTS | 2043 | ||* 66 | VIEW PUSHED PREDICATE | ALL_EXTERNAL_TABLES | 1777K| ||* 80 | VIEW PUSHED PREDICATE | ALL_EXTERNAL_LOCATIONS | 1744K| ||* 96 | VIEW | ALL_PART_TABLES | 852K| |------------------------------------------------------------------------------- Have a look at the cost column. 10g's overall query cost is 939, and 9i is 55,000,000,000 (or more precisely, 55,496,472,769). It's also having to process far more data. What on earth could be causing this huge difference in query cost? After trawling through the '10g New Features' documentation, we found item 1.9.2.21. Before 10g, Oracle advised that you do not collect statistics on data dictionary objects. From 10g, it advised that you do collect statistics on the data dictionary; for our queries, Oracle therefore knows what sort of data is in the dictionary tables, and so can generate an efficient execution plan. On 9i, no statistics are present on the system tables, so Oracle has to use the Rule Based Optimizer, which turns most LEFT JOINs into nested loops. If we force 9i to use hash joins, like 10g, we get a much better plan: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 7587K| 3704 || 1 | SORT ORDER BY | | 7587K| 3704 ||* 2 | HASH JOIN OUTER | | 7587K| 822 ||* 3 | HASH JOIN OUTER | | 5262K| 616 ||* 4 | HASH JOIN OUTER | | 2980K| 465 ||* 5 | HASH JOIN OUTER | | 710K| 432 ||* 6 | HASH JOIN OUTER | | 398K| 302 || 7 | VIEW | ALL_TABLES | 342K| 276 || 29 | VIEW | ALL_MVIEWS | 51 | 20 || 50 | VIEW | ALL_PART_TABLES | 852K| 104 || 78 | VIEW | ALL_TAB_COMMENTS | 2043 | 14 || 93 | VIEW | ALL_EXTERNAL_LOCATIONS | 1744K| 31 || 106 | VIEW | ALL_EXTERNAL_TABLES | 1777K| 28 |------------------------------------------------------------------------------- That's much more like it. This drops the execution time down to 24 seconds. Not as good as 10g, but still an improvement. There are still several problems with this, however. 10g introduced a new join method - a right outer hash join (used in the first execution plan). The 9i query optimizer doesn't have this option available, so forcing a hash join means it has to hash the ALL_TABLES table, and furthermore re-hash it for every hash join in the execution plan; this could be thousands and thousands of rows. And although forcing hash joins somewhat alleviates this problem on our test systems, there's no guarantee that this will improve the execution time on customers' systems; it may even increase the time it takes (say, if all their tables are partitioned, or they've got a lot of materialized views). Ideally, we would want a solution that provides a speedup whatever the input. To try and get some ideas, we asked some oracle performance specialists to see if they had any ideas or tips. Their recommendation was to add a hidden hook into the product that allowed users to specify their own query hints, or even rewrite the queries entirely. However, we would prefer not to take that approach; as well as a lot of new infrastructure & a rewrite of the population code, it would have meant that any users of 9i would have to spend some time optimizing it to get it working on their system before they could use the product. Another approach was needed. All our population queries have a very specific pattern - a base table provides most of the information we need (ALL_TABLES for tables, or ALL_TAB_COLS for columns) and we do a left join to extra subsidiary tables that fill in gaps (for instance, ALL_PART_TABLES for partition information). All the left joins use the same set of columns to join on (typically the object owner & name), so we could re-use the hash information for each join, rather than re-hashing the same columns for every join. To allow us to do this, along with various other performance improvements that could be done for the specific query pattern we were using, we read all the tables individually and do a hash join on the client. Fortunately, this 'pure' algorithmic problem is the kind that can be very well optimized for expected real-world situations; as well as storing row data we're not using in the hash key on disk, we use very specific memory-efficient data structures to store all the information we need. This allows us to achieve a database population time that is as fast as on 10g, and even (in some situations) slightly faster, and a memory overhead of roughly 150 bytes per row of data in the result set (for schemas with 10,000 tables in that means an extra 1.4MB memory being used during population). Next: fun with the 9i dictionary views.

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  • Monitoring Baseline

    - by Grant Fritchey
    Knowing what's happening on your servers is important, that's monitoring. Knowing what happened on your server is establishing a baseline. You need to do both. I really enjoyed this blog post by Ted Krueger (blog|twitter). It's not enough to know what happened in the last hour or yesterday, you need to compare today to last week, especially if you released software this weekend. You need to compare today to 30 days ago in order to begin to establish future projections. How your data has changed over 30 days is a great indicator how it's going to change for the next 30. No, it's not perfect, but predicting the future is not exactly a science, just ask your local weatherman. Red Gate's SQL Monitor can show you the last week, the last 30 days, the last year, or all data you've collected (if you choose to keep a year's worth of data or more, please have PLENTY of storage standing by). You have a lot of choice and control here over how much data you store. Here's the configuration window showing how you can set this up: This is for version 2.3 of SQL Monitor, so if you're running an older version, you might want to update. The key point is, a baseline simply represents a moment in time in your server. The ability to compare now to then is what you're looking for in order to really have a useful baseline as Ted lays out so well in his post.

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  • Azure

    - by Grant Fritchey
    I've been tasked to learn SQL Azure, as well as test all the Red Gate products on it. My one, BIG, fear has been that I'll receive some mongo bill in the mail because I've exceeded the MSDN testing limit. I know people that have had that problem. I've been trying to keep an eye on my usage, but, let's face it, it's not something I think about every day. But now I don't have to. Red Gate has been working with Azure, long before I showed up. They already released a little piece of software that I just found out about, it's called CloudTally. It gathers your usage and sends you a daily email so you can know if you're starting to approach that limit. Check it out, it's free.

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  • Sharing My Thoughts on Space Flight

    - by Grant Fritchey
    This went out in the DBA newsletter from Red Gate, but I enjoyed writing it so much, I thought I'd share it to a wider audience: I grew up watching the US space program. I watched men walk on the moon for the first time in 1969, when I was only six years old. From that moment on, I dreamed of going into space. I studied aeronautics and tried to get into the Air Force Academy, all in preparation for my long career as an astronaut. Clearly, that didn't quite work out for me. But it sure could for you. At Red Gate, we're running a new contest: DBA in Space. The prize is a sub-orbital flight. When I first got word of this contest, my immediate response was, "And you need me to go right away and do a test flight? Excellent!" No, no test flight needed, plus I was pretty low on the list of volunteers. "That's OK, I'll just enter." Then I was told that, as a Red Gate employee, I couldn't win. My next response was, "I quit".eventually, I was talked down off the ledge, and agreed to help make this special for some other DBA. Many (most?) of us are science fiction fans, either the soft science of Star Trek and Star Wars, or the hard science of Niven and Pournelle, or Allen Steele. We watched the Shuttles go up and land. We've been dreaming of our own trips into orbit and our vacation-home on the Moon for a long, long time. All that might not arrive on schedule, but you've got a shot at breaking clear of the atmosphere. The first stage is a video quiz, starring Brad McGehee, and it's live at www.DBAinSpace.com now. Go for it. Good luck and God speed!

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  • Documentation and Test Assertions in Databases

    - by Phil Factor
    When I first worked with Sybase/SQL Server, we thought our databases were impressively large but they were, by today’s standards, pathetically small. We had one script to build the whole database. Every script I ever read was richly annotated; it was more like reading a document. Every table had a comment block, and every line would be commented too. At the end of each routine (e.g. procedure) was a quick integration test, or series of test assertions, to check that nothing in the build was broken. We simply ran the build script, stored in the Version Control System, and it pulled everything together in a logical sequence that not only created the database objects but pulled in the static data. This worked fine at the scale we had. The advantage was that one could, by reading the source code, reach a rapid understanding of how the database worked and how one could interface with it. The problem was that it was a system that meant that only one developer at the time could work on the database. It was very easy for a developer to execute accidentally the entire build script rather than the selected section on which he or she was working, thereby cleansing the database of everyone else’s work-in-progress and data. It soon became the fashion to work at the object level, so that programmers could check out individual views, tables, functions, constraints and rules and work on them independently. It was then that I noticed the trend to generate the source for the VCS retrospectively from the development server. Tables were worst affected. You can, of course, add or delete a table’s columns and constraints retrospectively, which means that the existing source no longer represents the current object. If, after your development work, you generate the source from the live table, then you get no block or line comments, and the source script is sprinkled with silly square-brackets and other confetti, thereby rendering it visually indigestible. Routines, too, were affected. In our system, every routine had a directly attached string of unit-tests. A retro-generated routine has no unit-tests or test assertions. Yes, one can still commit our test code to the VCS but it’s a separate module and teams end up running the whole suite of tests for every individual change, rather than just the tests for that routine, which doesn’t scale for database testing. With Extended properties, one can get the best of both worlds, and even use them to put blame, praise or annotations into your VCS. It requires a lot of work, though, particularly the script to generate the table. The problem is that there are no conventional names beyond ‘MS_Description’ for the special use of extended properties. This makes it difficult to do splendid things such ensuring the integrity of the build by running a suite of tests that are actually stored in extended properties within the database and therefore the VCS. We have lost the readability of database source code over the years, and largely jettisoned the use of test assertions as part of the database build. This is not unexpected in view of the increasing complexity of the structure of databases and number of programmers working on them. There must, surely, be a way of getting them back, but I sometimes wonder if I’m one of very few who miss them.

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  • Last Chance At Space

    - by Grant Fritchey
    All entries for the DBA In Space contest have to be in by this Friday, the 18th. I’m so jealous of all of you who can enter this contest. Just think about it. You’re getting a chance to take a sub-orbital rocket ride. But, here’s the kicker, the chances are limited to data professionals. That’s a pretty small sub-set when you think about it. Further, you have to gotten the answers to the quiz questions correct, which only takes a little bit of honest research, but come on. That further limits the result set. You’ve really got an excellent shot at this (and the jealousy rears it’s ugly head again). If you haven’t finished your entry, go on over to the link and get it taken care of. There’s really no reason to not do it. Oh, and by the way, if you’re one of those (I’d say crazy) people who don’t want to ride the rocket, you can take the prize in cash. Although I’d be mighty disappointed in you if you did.

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  • Developing Schema Compare for Oracle (Part 4): Script Configuration

    - by Simon Cooper
    If you've had a chance to play around with the Schema Compare for Oracle beta, you may have come across this screen in the synchronization wizard: This screen is one of the few screens that, along with the project configuration form, doesn't come from SQL Compare. This screen was designed to solve a couple of issues that, although aren't specific to Oracle, are much more of a problem than on SQL Server: Datatype conversions and NOT NULL columns. 1. Datatype conversions SQL Server is generally quite forgiving when it comes to datatype conversions using ALTER TABLE. For example, you can convert from a VARCHAR to INT using ALTER TABLE as long as all the character values are parsable as integers. Oracle, on the other hand, only allows ALTER TABLE conversions that don't change the internal data format. Essentially, every change that requires an actual datatype conversion has to be done using a rebuild with a conversion function. That's OK, as we can simply hard-code the various conversion functions for the valid datatype conversions and insert those into the rebuild SELECT list. However, as there always is with Oracle, there's a catch. Have a look at the NUMTODSINTERVAL function. As well as specifying the value (or column) to convert, you have to specify an interval_unit, which tells oracle how to interpret the input number. We can't hardcode a default for this parameter, as it is entirely dependent on the user's data context! So, in order to convert NUMBER to INTERVAL DAY TO SECOND/INTERVAL YEAR TO MONTH, we need to have feedback from the user as to what to put in this parameter while we're generating the sync script - this requires a new step in the engine action/script generation to insert these values into the script, as well as new UI to allow the user to specify these values in a sensible fashion. In implementing the engine and UI infrastructure to allow this it made much more sense to implement it for any rebuild datatype conversion, not just NUMBER to INTERVALs. For conversions which we can do, we pre-fill the 'value' box with the appropriate function from the documentation. The user can also type in arbitary SQL expressions, which allows the user to specify optional format parameters for the relevant conversion functions, or indeed call their own functions to convert between values that don't have a built-in conversion defined. As the value gets inserted as-is into the rebuild SELECT list, any expression that is valid in that context can be specified as the conversion value. 2. NOT NULL columns Another problem that is solved by the new step in the sync wizard is adding a NOT NULL column to a table. If the table contains data (as most database tables do), you can't just add a NOT NULL column, as Oracle doesn't know what value to put in the new column for existing rows - the DDL statement will fail. There are actually 3 separate scenarios for this problem that have separate solutions within the engine: Adding a NOT NULL column to a table without a rebuild Here, the workaround is to add a column default with an appropriate value to the column you're adding: ALTER TABLE tbl1 ADD newcol NUMBER DEFAULT <value> NOT NULL; Note, however, there is something to bear in mind about this solution; once specified on a column, a default cannot be removed. To 'remove' a default from a column you change it to have a default of NULL, hence there's code in the engine to treat a NULL default the same as no default at all. Adding a NOT NULL column to a table, where a separate change forced a table rebuild Fortunately, in this case, a column default is not required - we can simply insert the default value into the rebuild SELECT clause. Changing an existing NULL to a NOT NULL column To implement this, we run an UPDATE command before the ALTER TABLE to change all the NULLs in the column to the required default value. For all three, we need some way of allowing the user to specify a default value to use instead of NULL; as this is essentially the same problem as datatype conversion (inserting values into the sync script), we can re-use the UI and engine implementation of datatype conversion values. We also provide the option to alter the new column to allow NULLs, or to ignore the problem completely. Note that there is the same (long-running) problem in SQL Compare, but it is much more of an issue in Oracle as you cannot easily roll back executed DDL statements if the script fails at some point during execution. Furthermore, the engine of SQL Compare is far less conducive to inserting user-supplied values into the generated script. As we're writing the Schema Compare engine from scratch, we used what we learnt from the SQL Compare engine and designed it to be far more modular, which makes inserting procedures like this much easier.

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  • LiveMeeting VC PowerShell PASS – Troubleshooting SQL Server with PowerShell

    - by Laerte Junior
    Guys, join me on Wednesday July 18th 12 noon EDT (GMT -4) for a presentation called Troubleshooting SQL Server With PowerShell. It will be in English, so please make allowances for this. I’m sure that you’re aware that my English is not perfect, but it is not so bad. I will do my best, you can be sure. The registration link will be available soon from PowerShell.sqlpass.org, so I hope to see you there. It will be a session without slides. Just code; pure PowerShell code. Trust me, We will see a lot of COOL stuff.Big thanks to Aaron Nelson (@sqlvariant) for the opportunity! Here are some more details about the presentation: “Troubleshooting SQL Server with PowerShell – The Next Level’ It is normal for us to have to face poorly performing queries or even complete failure in our SQL server environments. This can happen for a variety of reasons including poor Database Designs, hardware failure, improperly-configured systems and OS Updates applied without testing. As Database Administrators, we need to take precaution to minimize the impact of these problems when they occur, and so we need the tools and methodology required to identify and solve issues quickly. In this Session we will use PowerShell to explore some common troubleshooting techniques used in our day-to-day work as s DBA. This will include a variety of such activities including Gathering Performance Counters in several servers at the same time using background jobs, identifying Blocked Sessions and Reading & filtering the SQL Error Log even if the Instance is offline The approach will be using some advanced PowerShell techniques that allow us to scale the code for multiple servers and run the data collection in asynchronous mode.

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  • Optimizing Transaction Log Throughput

    As a DBA, it is vital to manage transaction log growth explicitly, rather than let SQL Server auto-growth events "manage" it for you. If you undersize the log, and then let SQL Server auto-grow it in small increments, you'll end up with a very fragmented log. Examples in the article, extracted from SQL Server Transaction Log Management by Tony Davis and Gail Shaw, demonstrate how this can have a significant impact on the performance of any SQL Server operations that need to read the log.

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  • Software Tuned to Humanity

    - by Phil Factor
    I learned a great deal from a cynical old programmer who once told me that the ideal length of time for a compiler to do its work was the same time it took to roll a cigarette. For development work, this is oh so true. After intently looking at the editing window for an hour or so, it was a relief to look up, stretch, focus the eyes on something else, and roll the possibly-metaphorical cigarette. This was software tuned to humanity. Likewise, a user’s perception of the “ideal” time that an application will take to move from frame to frame, to retrieve information, or to process their input has remained remarkably static for about thirty years, at around 200 ms. Anything else appears, and always has, to be either fast or slow. This could explain why commercial applications, unlike games, simulations and communications, aren’t noticeably faster now than they were when I started programming in the Seventies. Sure, they do a great deal more, but the SLAs that I negotiated in the 1980s for application performance are very similar to what they are nowadays. To prove to myself that this wasn’t just some rose-tinted misperception on my part, I cranked up a Z80-based Jonos CP/M machine (1985) in the roof-space. Within 20 seconds from cold, it had loaded Wordstar and I was ready to write. OK, I got it wrong: some things were faster 30 years ago. Sure, I’d now have had all sorts of animations, wizzy graphics, and other comforting features, but it seems a pity that we have used all that extra CPU and memory to increase the scope of what we develop, and the graphical prettiness, but not to speed the processes needed to complete a business procedure. Never mind the weight, the response time’s great! To achieve 200 ms response times on a Z80, or similar, performance considerations influenced everything one did as a developer. If it meant writing an entire application in assembly code, applying every smart algorithm, and shortcut imaginable to get the application to perform to spec, then so be it. As a result, I’m a dyed-in-the-wool performance freak and find it difficult to change my habits. Conversely, many developers now seem to feel quite differently. While all will acknowledge that performance is important, it’s no longer the virtue is once was, and other factors such as user-experience now take precedence. Am I wrong? If not, then perhaps we need a new school of development technique to rival Agile, dedicated once again to producing applications that smoke the rear wheels rather than pootle elegantly to the shops; that forgo skeuomorphism, cute animation, or architectural elegance in favor of the smell of hot rubber. I struggle to name an application I use that is truly notable for its blistering performance, and would dearly love one to do my everyday work – just as long as it doesn’t go faster than my brain.

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  • New features in SQL Prompt 6.4

    - by Tom Crossman
    We’re pleased to announce a new beta version of SQL Prompt. We’ve been trying out a few new core technologies, and used them to add features and bug fixes suggested by users on the SQL Prompt forum and suggestions forum. You can download the SQL Prompt 6.4 beta here (zip file). Let us know what you think! New features Execute current statement In a query window, you can now execute the SQL statement under your cursor by pressing Shift + F5. For example, if you have a query containing two statements and your cursor is placed on the second statement: When you press Shift + F5, only the second statement is executed:   Insert semicolons You can now use SQL Prompt to automatically insert missing semicolons after each statement in a query. To insert semicolons, go to the SQL Prompt menu and click Insert Semicolons. Alternatively, hold Ctrl and press B then C. BEGIN…END block highlighting When you place your cursor over a BEGIN or END keyword, SQL Prompt now automatically highlights the matching keyword: Rename variables and aliases You can now use SQL Prompt to rename all occurrences of a variable or alias in a query. To rename a variable or alias, place your cursor over an instance of the variable or alias you want to rename and press F2: Improved loading dialog box The database loading dialog box now shows actual progress, and you can cancel loading databases:   Single suggestion improvement SQL Prompt no longer suggests keywords if the keyword has been typed and no other suggestions exist. Performance improvement SQL Prompt now has less impact on Management Studio start up time. What do you think? We want to hear your feedback about the beta. If you have any suggestions, or bugs to report, tell us on the SQL Prompt forum or our suggestions forum.

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  • Getting the URL to the Content Type Hub Programmatically in SharePoint 2010

    - by Damon
    Many organizations use the content-type hub to manage content-types in their SharePoint 2010 environment.  As a developer in these types of organizations, you may one day find yourself in need of getting the URL of the content type hub programmatically.  Here is a quick snippet that demonstrates how to do it fairly painlessly: public static Uri GetContentTypeHubUri(SPSite site) {     TaxonomySession session = new TaxonomySession(site);     return Session.DefaultSiteCollectionTermStore         .ContentTypePublishingHub; }

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