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• Azure, don't give me multiple VMs, give me one elastic VM

  - by FransBouma

  Yesterday, Microsoft revealed new major features for Windows Azure (see ScottGu's post). It all looks shiny and great, but after reading most of the material describing the new features, I still find the overall idea behind all of it flawed: why should I care on how much VMs my web app runs? Isn't that a problem to solve for the Windows Azure engineers / software? And what if I need the file system, why can't I simply get a virtual filesystem ? To illustrate my point, let's use a real example: a product website with a customer system/database and next to it a support site with accompanying database. Both are written in .NET, using ASP.NET and use a SQL Server database each. The product website offers files to download by customers, very simple. You have a couple of options to host these websites: Buy a server, place it in a rack at an ISP and run the sites on that server Use 'shared hosting' with an ISP, which means your sites' appdomains are running on the same machine, as well as the files stored, and the databases are hosted in the same server as the other shared databases. Hire a VM, install your OS of choice at an ISP, and host the sites on that VM, basically the same as the first option, except you don't have a physical server At some cloud-vendor, either host the sites 'shared' or in a VM. See above. With all of those options, scalability is a problem, even the cloud-based ones, though not due to the same reasons: The physical server solution has the obvious problem that if you need more power, you need to buy a bigger server or more servers which requires you to add replication and other overhead Shared hosting solutions are almost always capped on memory usage / traffic and database size: if your sites get too big, you have to move out of the shared hosting environment and start over with one of the other solutions The VM solution, be it a VM at an ISP or 'in the cloud' at e.g. Windows Azure or Amazon, in theory allows scaling out by simply instantiating more VMs, however that too introduces the same overhead problems as with the physical servers: suddenly more than 1 instance runs your sites. If a cloud vendor offers its services in the form of VMs, you won't gain much over having a VM at some ISP: the main problems you have to work around are still there: when you spin up more than one VM, your application must be completely stateless at any moment, including the DB sub system, because what's in memory in instance 1 might not be in memory in instance 2. This might sounds trivial but it's not. A lot of the websites out there started rather small: they were perfectly runnable on a single machine with normal memory and CPU power. After all, you don't need a big machine to run a website with even thousands of users a day. Moving these sites to a multi-VM environment will cause a problem: all the in-memory state they use, all the multi-page transitions they use while keeping state across the transition, they can't do that anymore like they did that on a single machine: state is something of the past, you have to store every byte of state in either a DB or in a viewstate or in a cookie somewhere so with the next request, all state information is available through the request, as nothing is kept in-memory. Our example uses a bunch of files in a file system. Using multiple VMs will require that these files move to a cloud storage system which is mounted in each VM so we don't have to store the files on each VM. This might require different file paths, but this change should be minor. What's perhaps less minor is the maintenance procedure in place on the new type of cloud storage used: instead of ftp-ing into a VM, you might have to update the files using different ways / tools. All in all this makes moving an existing website which was written for an environment that's based around a VM (namely .NET with its CLR) overly cumbersome and problematic: it forces you to refactor your website system to be able to be used 'in the cloud', which is caused by the limited way how e.g. Windows Azure offers its cloud services: in blocks of VMs. Offer a scalable, flexible VM which extends with my needs Instead, cloud vendors should offer simply one VM to me. On that VM I run the websites, store my DB and my files. As it's a virtual machine, how this machine is actually ran on physical hardware (e.g. partitioned), I don't care, as that's the problem for the cloud vendor to solve. If I need more resources, e.g. I have more traffic to my server, way more visitors per day, the VM stretches, like I bought a bigger box. This frees me from the problem which comes with multiple VMs: I don't have any refactoring to do at all: I can simply build my website as if it runs on my local hardware server, upload it to the VM offered by the cloud vendor, install it on the VM and I'm done. "But that might require changes to windows!" Yes, but Microsoft is Windows. Windows Azure is their service, they can make whatever change to what they offer to make it look like it's windows. Yet, they're stuck, like Amazon, in thinking in VMs, which forces developers to 'think ahead' and gamble whether they would need to migrate to a cloud with multiple VMs in the future or not. Which comes down to: gamble whether they should invest time in code / architecture which they might never need. (YAGNI anyone?) So the VM we're talking about, is that a low-level VM which runs a guest OS, or is that VM a different kind of VM? The flexible VM: .NET's CLR ? My example websites are ASP.NET based, which means they run inside a .NET appdomain, on the .NET CLR, which is a VM. The only physical OS resource the sites need is the file system, however this too is accessed through .NET. In short: all the websites see is what .NET allows the websites to see, the world as the websites know it is what .NET shows them and lets them access. How the .NET appdomain is run physically, that's the concern of .NET, not mine. This begs the question why Windows Azure doesn't offer virtual appdomains? Or better: .NET environments which look like one machine but could be physically multiple machines. In such an environment, no change has to be made to the websites to migrate them from a local machine or own server to the cloud to get proper scaling: the .NET VM will simply scale with the need: more memory needed, more CPU power needed, it stretches. What it offers to the application running inside the appdomain is simply increasing, but not fragmented: all resources are available to the application: this means that the problem of how to scale is back to where it should be: with the cloud vendor. "Yeah, great, but what about the databases?" The .NET application communicates with the database server through a .NET ADO.NET provider. Where the database is located is not a problem of the appdomain: the ADO.NET provider has to solve that. I.o.w.: we can host the databases in an environment which offers itself as a single resource and is accessible through one connection string without replication overhead on the outside, and use that environment inside the .NET VM as if it was a single DB. But what about memory replication and other problems? This environment isn't simple, at least not for the cloud vendor. But it is simple for the customer who wants to run his sites in that cloud: no work needed. No refactoring needed of existing code. Upload it, run it. Perhaps I'm dreaming and what I described above isn't possible. Yet, I think if cloud vendors don't move into that direction, what they're offering isn't interesting: it doesn't solve a problem at all, it simply offers a way to instantiate more VMs with the guest OS of choice at the cost of me needing to refactor my website code so it can run in the straight jacket form factor dictated by the cloud vendor. Let's not kid ourselves here: most of us developers will never build a website which needs a truck load of VMs to run it: almost all websites created by developers can run on just a few VMs at most. Yet, the most expensive change is right at the start: moving from one to two VMs. As soon as you have refactored your website code to run across multiple VMs, adding another one is just as easy as clicking a mouse button. But that first step, that's the problem here and as it's right there at the beginning of scaling the website, it's particularly strange that cloud vendors refuse to solve that problem and leave it to the developers to solve that. Which makes migrating 'to the cloud' particularly expensive.

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• Towards Database Continuous Delivery – What Next after Continuous Integration? A Checklist

  - by Ben Rees

  .dbd-banner p{ font-size:0.75em; padding:0 0 10px; margin:0 } .dbd-banner p span{ color:#675C6D; } .dbd-banner p:last-child{ padding:0; } @media ALL and (max-width:640px){ .dbd-banner{ background:#f0f0f0; padding:5px; color:#333; margin-top: 5px; } } -- Database delivery patterns & practices STAGE 4 AUTOMATED DEPLOYMENT If you’ve been fortunate enough to get to the stage where you’ve implemented some sort of continuous integration process for your database updates, then hopefully you’re seeing the benefits of that investment – constant feedback on changes your devs are making, advanced warning of data loss (prior to the production release on Saturday night!), a nice suite of automated tests to check business logic, so you know it’s going to work when it goes live, and so on. But what next? What can you do to improve your delivery process further, moving towards a full continuous delivery process for your database? In this article I describe some of the issues you might need to tackle on the next stage of this journey, and how to plan to overcome those obstacles before they appear. Our Database Delivery Learning Program consists of four stages, really three – source controlling a database, running continuous integration processes, then how to set up automated deployment (the middle stage is split in two – basic and advanced continuous integration, making four stages in total). If you’ve managed to work through the first three of these stages – source control, basic, then advanced CI, then you should have a solid change management process set up where, every time one of your team checks in a change to your database (whether schema or static reference data), this change gets fully tested automatically by your CI server. But this is only part of the story. Great, we know that our updates work, that the upgrade process works, that the upgrade isn’t going to wipe our 4Tb of production data with a single DROP TABLE. But – how do you get this (fully tested) release live? Continuous delivery means being always ready to release your software at any point in time. There’s a significant gap between your latest version being tested, and it being easily releasable. Just a quick note on terminology – there’s a nice piece here from Atlassian on the difference between continuous integration, continuous delivery and continuous deployment. This piece also gives a nice description of the benefits of continuous delivery. These benefits have been summed up by Jez Humble at Thoughtworks as: “Continuous delivery is a set of principles and practices to reduce the cost, time, and risk of delivering incremental changes to users” There’s another really useful piece here on Simple-Talk about the need for continuous delivery and how it applies to the database written by Phil Factor – specifically the extra needs and complexities of implementing a full CD solution for the database (compared to just implementing CD for, say, a web app). So, hopefully you’re convinced of moving on the the next stage! The next step after CI is to get some sort of automated deployment (or “release management”) process set up. But what should I do next? What do I need to plan and think about for getting my automated database deployment process set up? Can’t I just install one of the many release management tools available and hey presto, I’m ready! If only it were that simple. Below I list some of the areas that it’s worth spending a little time on, where a little planning and prep could go a long way. It’s also worth pointing out, that this should really be an evolving process. Depending on your starting point of course, it can be a long journey from your current setup to a full continuous delivery pipeline. If you’ve got a CI mechanism in place, you’re certainly a long way down that path. Nevertheless, we’d recommend evolving your process incrementally. Pages 157 and 129-141 of the book on Continuous Delivery (by Jez Humble and Dave Farley) have some great guidance on building up a pipeline incrementally: http://www.amazon.com/Continuous-Delivery-Deployment-Automation-Addison-Wesley/dp/0321601912 For now, in this post, we’ll look at the following areas for your checklist: You and Your Team Environments The Deployment Process Rollback and Recovery Development Practices You and Your Team It’s a cliché in the DevOps community that “It’s not all about processes and tools, really it’s all about a culture”. As stated in this DevOps report from Puppet Labs: “DevOps processes and tooling contribute to high performance, but these practices alone aren’t enough to achieve organizational success. The most common barriers to DevOps adoption are cultural: lack of manager or team buy-in, or the value of DevOps isn’t understood outside of a specific group”. Like most clichés, there’s truth in there – if you want to set up a database continuous delivery process, you need to get your boss, your department, your company (if relevant) onside. Why? Because it’s an investment with the benefits coming way down the line. But the benefits are huge – for HP, in the book A Practical Approach to Large-Scale Agile Development: How HP Transformed LaserJet FutureSmart Firmware, these are summarized as: -2008 to present: overall development costs reduced by 40% -Number of programs under development increased by 140% -Development costs per program down 78% -Firmware resources now driving innovation increased by a factor of 8 (from 5% working on new features to 40% But what does this mean? It means that, when moving to the next stage, to make that extra investment in automating your deployment process, it helps a lot if everyone is convinced that this is a good thing. That they understand the benefits of automated deployment and are willing to make the effort to transform to a new way of working. Incidentally, if you’re ever struggling to convince someone of the value I’d strongly recommend just buying them a copy of this book – a great read, and a very practical guide to how it can really work at a large org. I’ve spoken to many customers who have implemented database CI who describe their deployment process as “The point where automation breaks down. Up to that point, the CI process runs, untouched by human hand, but as soon as that’s finished we revert to manual.” This deployment process can involve, for example, a DBA manually comparing an environment (say, QA) to production, creating the upgrade scripts, reading through them, checking them against an Excel document emailed to him/her the night before, turning to page 29 in his/her notebook to double-check how replication is switched off and on for deployments, and so on and so on. Painful, error-prone and lengthy. But the point is, if this is something like your deployment process, telling your DBA “We’re changing everything you do and your toolset next week, to automate most of your role – that’s okay isn’t it?” isn’t likely to go down well. There’s some work here to bring him/her onside – to explain what you’re doing, why there will still be control of the deployment process and so on. Or of course, if you’re the DBA looking after this process, you have to do a similar job in reverse. You may have researched and worked out how you’d like to change your methodology to start automating your painful release process, but do the dev team know this? What if they have to start producing different artifacts for you? Will they be happy with this? Worth talking to them, to find out. As well as talking to your DBA/dev team, the other group to get involved before implementation is your manager. And possibly your manager’s manager too. As mentioned, unless there’s buy-in “from the top”, you’re going to hit problems when the implementation starts to get rocky (and what tool/process implementations don’t get rocky?!). You need to have support from someone senior in your organisation – someone you can turn to when you need help with a delayed implementation, lack of resources or lack of progress. Actions: Get your DBA involved (or whoever looks after live deployments) and discuss what you’re planning to do or, if you’re the DBA yourself, get the dev team up-to-speed with your plans, Get your boss involved too and make sure he/she is bought in to the investment. Environments Where are you going to deploy to? And really this question is – what environments do you want set up for your deployment pipeline? Assume everyone has “Production”, but do you have a QA environment? Dedicated development environments for each dev? Proper pre-production? I’ve seen every setup under the sun, and there is often a big difference between “What we want, to do continuous delivery properly” and “What we’re currently stuck with”. Some of these differences are: What we want What we’ve got Each developer with their own dedicated database environment A single shared “development” environment, used by everyone at once An Integration box used to test the integration of all check-ins via the CI process, along with a full suite of unit-tests running on that machine In fact if you have a CI process running, you’re likely to have some sort of integration server running (even if you don’t call it that!). Whether you have a full suite of unit tests running is a different question… Separate QA environment used explicitly for manual testing prior to release “We just test on the dev environments, or maybe pre-production” A proper pre-production (or “staging”) box that matches production as closely as possible Hopefully a pre-production box of some sort. But does it match production closely!? A production environment reproducible from source control A production box which has drifted significantly from anything in source control The big question is – how much time and effort are you going to invest in fixing these issues? In reality this just involves figuring out which new databases you’re going to create and where they’ll be hosted – VMs? Cloud-based? What about size/data issues – what data are you going to include on dev environments? Does it need to be masked to protect access to production data? And often the amount of work here really depends on whether you’re working on a new, greenfield project, or trying to update an existing, brownfield application. There’s a world if difference between starting from scratch with 4 or 5 clean environments (reproducible from source control of course!), and trying to re-purpose and tweak a set of existing databases, with all of their surrounding processes and quirks. But for a proper release management process, ideally you have: Dedicated development databases, An Integration server used for testing continuous integration and running unit tests. [NB: This is the point at which deployments are automatic, without human intervention. Each deployment after this point is a one-click (but human) action], QA – QA engineers use a one-click deployment process to automatically* deploy chosen releases to QA for testing, Pre-production. The environment you use to test the production release process, Production. * A note on the use of the word “automatic” – when carrying out automated deployments this does not mean that the deployment is happening without human intervention (i.e. that something is just deploying over and over again). It means that the process of carrying out the deployment is automatic in that it’s not a person manually running through a checklist or set of actions. The deployment still requires a single-click from a user. Actions: Get your environments set up and ready, Set access permissions appropriately, Make sure everyone understands what the environments will be used for (it’s not a “free-for-all” with all environments to be accessed, played with and changed by development). The Deployment Process As described earlier, most existing database deployment processes are pretty manual. The following is a description of a process we hear very often when we ask customers “How do your database changes get live? How does your manual process work?” Check pre-production matches production (use a schema compare tool, like SQL Compare). Sometimes done by taking a backup from production and restoring in to pre-prod, Again, use a schema compare tool to find the differences between the latest version of the database ready to go live (i.e. what the team have been developing). This generates a script, User (generally, the DBA), reviews the script. This often involves manually checking updates against a spreadsheet or similar, Run the script on pre-production, and check there are no errors (i.e. it upgrades pre-production to what you hoped), If all working, run the script on production.* * this assumes there’s no problem with production drifting away from pre-production in the interim time period (i.e. someone has hacked something in to the production box without going through the proper change management process). This difference could undermine the validity of your pre-production deployment test. Red Gate is currently working on a free tool to detect this problem – sign up here at www.sqllighthouse.com, if you’re interested in testing early versions. There are several variations on this process – some better, some much worse! How do you automate this? In particular, step 3 – surely you can’t automate a DBA checking through a script, that everything is in order!? The key point here is to plan what you want in your new deployment process. There are so many options. At one extreme, pure continuous deployment – whenever a dev checks something in to source control, the CI process runs (including extensive and thorough testing!), before the deployment process keys in and automatically deploys that change to the live box. Not for the faint hearted – and really not something we recommend. At the other extreme, you might be more comfortable with a semi-automated process – the pre-production/production matching process is automated (with an error thrown if these environments don’t match), followed by a manual intervention, allowing for script approval by the DBA. One he/she clicks “Okay, I’m happy for that to go live”, the latter stages automatically take the script through to live. And anything in between of course – and other variations. But we’d strongly recommended sitting down with a whiteboard and your team, and spending a couple of hours mapping out “What do we do now?”, “What do we actually want?”, “What will satisfy our needs for continuous delivery, but still maintaining some sort of continuous control over the process?” NB: Most of what we’re discussing here is about production deployments. It’s important to note that you will also need to map out a deployment process for earlier environments (for example QA). However, these are likely to be less onerous, and many customers opt for a much more automated process for these boxes. Actions: Sit down with your team and a whiteboard, and draw out the answers to the questions above for your production deployments – “What do we do now?”, “What do we actually want?”, “What will satisfy our needs for continuous delivery, but still maintaining some sort of continuous control over the process?” Repeat for earlier environments (QA and so on). Rollback and Recovery If only every deployment went according to plan! Unfortunately they don’t – and when things go wrong, you need a rollback or recovery plan for what you’re going to do in that situation. Once you move in to a more automated database deployment process, you’re far more likely to be deploying more frequently than before. No longer once every 6 months, maybe now once per week, or even daily. Hence the need for a quick rollback or recovery process becomes paramount, and should be planned for. NB: These are mainly scenarios for handling rollbacks after the transaction has been committed. If a failure is detected during the transaction, the whole transaction can just be rolled back, no problem. There are various options, which we’ll explore in subsequent articles, things like: Immediately restore from backup, Have a pre-tested rollback script (remembering that really this is a “roll-forward” script – there’s not really such a thing as a rollback script for a database!) Have fallback environments – for example, using a blue-green deployment pattern. Different options have pros and cons – some are easier to set up, some require more investment in infrastructure; and of course some work better than others (the key issue with using backups, is loss of the interim transaction data that has been added between the failed deployment and the restore). The best mechanism will be primarily dependent on how your application works and how much you need a cast-iron failsafe mechanism. Actions: Work out an appropriate rollback strategy based on how your application and business works, your appetite for investment and requirements for a completely failsafe process. Development Practices This is perhaps the more difficult area for people to tackle. The process by which you can deploy database updates is actually intrinsically linked with the patterns and practices used to develop that database and linked application. So you need to decide whether you want to implement some changes to the way your developers actually develop the database (particularly schema changes) to make the deployment process easier. A good example is the pattern “Branch by abstraction”. Explained nicely here, by Martin Fowler, this is a process that can be used to make significant database changes (e.g. splitting a table) in a step-wise manner so that you can always roll back, without data loss – by making incremental updates to the database backward compatible. Slides 103-108 of the following slidedeck, from Niek Bartholomeus explain the process: https://speakerdeck.com/niekbartho/orchestration-in-meatspace As these slides show, by making a significant schema change in multiple steps – where each step can be rolled back without any loss of new data – this affords the release team the opportunity to have zero-downtime deployments with considerably less stress (because if an increment goes wrong, they can roll back easily). There are plenty more great patterns that can be implemented – the book Refactoring Databases, by Scott Ambler and Pramod Sadalage is a great read, if this is a direction you want to go in: http://www.amazon.com/Refactoring-Databases-Evolutionary-paperback-Addison-Wesley/dp/0321774515 But the question is – how much of this investment are you willing to make? How often are you making significant schema changes that would require these best practices? Again, there’s a difference here between migrating old projects and starting afresh – with the latter it’s much easier to instigate best practice from the start. Actions: For your business, work out how far down the path you want to go, amending your database development patterns to “best practice”. It’s a trade-off between implementing quality processes, and the necessity to do so (depending on how often you make complex changes). Socialise these changes with your development group. No-one likes having “best practice” changes imposed on them, so good to introduce these ideas and the rationale behind them early.   Summary The next stages of implementing a continuous delivery pipeline for your database changes (once you have CI up and running) require a little pre-planning, if you want to get the most out of the work, and for the implementation to go smoothly. We’ve covered some of the checklist of areas to consider – mainly in the areas of “Getting the team ready for the changes that are coming” and “Planning our your pipeline, environments, patterns and practices for development”, though there will be more detail, depending on where you’re coming from – and where you want to get to. This article is part of our database delivery patterns & practices series on Simple Talk. Find more articles for version control, automated testing, continuous integration & deployment.

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• Redering performance in FlasCC + UDK when compared to Stage3d and UDK on Windows?

  - by Arthur Wulf White

  http://gaming.adobe.com/technologies/flascc/ Developers can now access UDK for browser applications. Does this mean greater performance than using a Stage3D engine (Away3D 4) and how much of a noticeable difference in performance would it make in rendering speeds? Is there any benchmark you could propose that would allow to compare them fairly? I am asking this to help myself understand the consequences in performance for deciding to use UDK in a browser based game. I would also like to know how it compares with UDK running natively in Windows? I am not asking which technology to use or which is better. Only interested in the optimizing rendering speed in a 3d browser game with flash.

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• Refactoring a Single Rails Model with large methods & long join queries trying to do everything

  - by Kelseydh

  I have a working Ruby on Rails Model that I suspect is inefficient, hard to maintain, and full of unnecessary SQL join queries. I want to optimize and refactor this Model (Quiz.rb) to comply with Rails best practices, but I'm not sure how I should do it. The Rails app is a game that has Missions with many Stages. Users complete Stages by answering Questions that have correct or incorrect Answers. When a User tries to complete a stage by answering questions, the User gets a Quiz entry with many Attempts. Each Attempt records an Answer submitted for that Question within the Stage. A user completes a stage or mission by getting every Attempt correct, and their progress is tracked by adding a new entry to the UserMission & UserStage join tables. All of these features work, but unfortunately the Quiz.rb Model has been twisted to handle almost all of it exclusively. The callbacks began at 'Quiz.rb', and because I wasn't sure how to leave the Quiz Model during a multi-model update, I resorted to using Rails Console to have the @quiz instance variable via self.some_method do all the heavy lifting to retrieve every data value for the game's business logic; resulting in large extended join queries that "dance" all around the Database schema. The Quiz.rb Model that Smells: class Quiz < ActiveRecord::Base belongs_to :user has_many :attempts, dependent: :destroy before_save :check_answer before_save :update_user_mission_and_stage accepts_nested_attributes_for :attempts, :reject_if => lambda { |a| a[:answer_id].blank? }, :allow_destroy => true #Checks every answer within each quiz, adding +1 for each correct answer #within a stage quiz, and -1 for each incorrect answer def check_answer stage_score = 0 self.attempts.each do |attempt| if attempt.answer.correct? == true stage_score += 1 elsif attempt.answer.correct == false stage_score - 1 end end stage_score end def winner return true end def update_user_mission_and_stage ####### #Step 1: Checks if UserMission exists, finds or creates one. #if no UserMission for the current mission exists, creates a new UserMission if self.user_has_mission? == false @user_mission = UserMission.new(user_id: self.user.id, mission_id: self.current_stage.mission_id, available: true) @user_mission.save else @user_mission = self.find_user_mission end ####### #Step 2: Checks if current UserStage exists, stops if true to prevent duplicate entry if self.user_has_stage? @user_mission.save return true else ####### ##Step 3: if step 2 returns false: ##Initiates UserStage creation instructions #checks for winner (winner actions need to be defined) if they complete last stage of last mission for a given orientation if self.passed? && self.is_last_stage? && self.is_last_mission? create_user_stage_and_update_user_mission self.winner #NOTE: The rest are the same, but specify conditions that are available to add badges or other actions upon those conditions occurring: ##if user completes first stage of a mission elsif self.passed? && self.is_first_stage? && self.is_first_mission? create_user_stage_and_update_user_mission #creates user badge for finishing first stage of first mission self.user.add_badge(5) self.user.activity_logs.create(description: "granted first-stage badge", type_event: "badge", value: "first-stage") #If user completes last stage of a given mission, creates a new UserMission elsif self.passed? && self.is_last_stage? && self.is_first_mission? create_user_stage_and_update_user_mission #creates user badge for finishing first mission self.user.add_badge(6) self.user.activity_logs.create(description: "granted first-mission badge", type_event: "badge", value: "first-mission") elsif self.passed? create_user_stage_and_update_user_mission else self.passed? == false return true end end end #Creates a new UserStage record in the database for a successful Quiz question passing def create_user_stage_and_update_user_mission @nu_stage = @user_mission.user_stages.new(user_id: self.user.id, stage_id: self.current_stage.id) @nu_stage.save @user_mission.save self.user.add_points(50) end #Boolean that defines passing a stage as answering every question in that stage correct def passed? self.check_answer >= self.number_of_questions end #Returns the number of questions asked for that stage's quiz def number_of_questions self.attempts.first.answer.question.stage.questions.count end #Returns the current_stage for the Quiz, routing through 1st attempt in that Quiz def current_stage self.attempts.first.answer.question.stage end #Gives back the position of the stage relative to its mission. def stage_position self.attempts.first.answer.question.stage.position end #will find the user_mission for the current user and stage if it exists def find_user_mission self.user.user_missions.find_by_mission_id(self.current_stage.mission_id) end #Returns true if quiz was for the last stage within that mission #helpful for triggering actions related to a user completing a mission def is_last_stage? self.stage_position == self.current_stage.mission.stages.last.position end #Returns true if quiz was for the first stage within that mission #helpful for triggering actions related to a user completing a mission def is_first_stage? self.stage_position == self.current_stage.mission.stages_ordered.first.position end #Returns true if current user has a UserMission for the current stage def user_has_mission? self.user.missions.ids.include?(self.current_stage.mission.id) end #Returns true if current user has a UserStage for the current stage def user_has_stage? self.user.stages.include?(self.current_stage) end #Returns true if current user is on the last mission based on position within a given orientation def is_first_mission? self.user.missions.first.orientation.missions.by_position.first.position == self.current_stage.mission.position end #Returns true if current user is on the first stage & mission of a given orientation def is_last_mission? self.user.missions.first.orientation.missions.by_position.last.position == self.current_stage.mission.position end end My Question Currently my Rails server takes roughly 500ms to 1 sec to process single @quiz.save action. I am confident that the slowness here is due to sloppy code, not bad Database ERD design. What does a better solution look like? And specifically: Should I use join queries to retrieve values like I did here, or is it better to instantiate new objects within the model instead? Or am I missing a better solution? How should update_user_mission_and_stage be refactored to follow best practices? Relevant Code for Reference: quizzes_controller.rb w/ Controller Route Initiating Callback: class QuizzesController < ApplicationController before_action :find_stage_and_mission before_action :find_orientation before_action :find_question def show end def create @user = current_user @quiz = current_user.quizzes.new(quiz_params) if @quiz.save if @quiz.passed? if @mission.next_mission.nil? && @stage.next_stage.nil? redirect_to root_path, notice: "Congratulations, you have finished the last mission!" elsif @stage.next_stage.nil? redirect_to [@mission.next_mission, @mission.first_stage], notice: "Correct! Time for Mission #{@mission.next_mission.position}", info: "Starting next mission" else redirect_to [@mission, @stage.next_stage], notice: "Answer Correct! You passed the stage!" end else redirect_to [@mission, @stage], alert: "You didn't get every question right, please try again." end else redirect_to [@mission, @stage], alert: "Sorry. We were unable to save your answer. Please contact the admministrator." end @questions = @stage.questions.all end private def find_stage_and_mission @stage = Stage.find(params[:stage_id]) @mission = @stage.mission end def find_question @question = @stage.questions.find_by_id params[:id] end def quiz_params params.require(:quiz).permit(:user_id, :attempt_id, {attempts_attributes: [:id, :quiz_id, :answer_id]}) end def find_orientation @orientation = @mission.orientation @missions = @orientation.missions.by_position end end Overview of Relevant ERD Database Relationships: Mission - Stage - Question - Answer - Attempt <- Quiz <- User Mission - UserMission <- User Stage - UserStage <- User Other Models: Mission.rb class Mission < ActiveRecord::Base belongs_to :orientation has_many :stages has_many :user_missions, dependent: :destroy has_many :users, through: :user_missions #SCOPES scope :by_position, -> {order(position: :asc)} def stages_ordered stages.order(:position) end def next_mission self.orientation.missions.find_by_position(self.position.next) end def first_stage next_mission.stages_ordered.first end end Stage.rb: class Stage < ActiveRecord::Base belongs_to :mission has_many :questions, dependent: :destroy has_many :user_stages, dependent: :destroy has_many :users, through: :user_stages accepts_nested_attributes_for :questions, reject_if: :all_blank, allow_destroy: true def next_stage self.mission.stages.find_by_position(self.position.next) end end Question.rb class Question < ActiveRecord::Base belongs_to :stage has_many :answers, dependent: :destroy accepts_nested_attributes_for :answers, :reject_if => lambda { |a| a[:body].blank? }, :allow_destroy => true end Answer.rb: class Answer < ActiveRecord::Base belongs_to :question has_many :attempts, dependent: :destroy end Attempt.rb: class Attempt < ActiveRecord::Base belongs_to :answer belongs_to :quiz end User.rb: class User < ActiveRecord::Base belongs_to :school has_many :activity_logs has_many :user_missions, dependent: :destroy has_many :missions, through: :user_missions has_many :user_stages, dependent: :destroy has_many :stages, through: :user_stages has_many :orientations, through: :school has_many :quizzes, dependent: :destroy has_many :attempts, through: :quizzes def latest_stage_position self.user_missions.last.user_stages.last.stage.position end end UserMission.rb class UserMission < ActiveRecord::Base belongs_to :user belongs_to :mission has_many :user_stages, dependent: :destroy end UserStage.rb class UserStage < ActiveRecord::Base belongs_to :user belongs_to :stage belongs_to :user_mission end

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• Rendering performance in FlasCC + UDK when compared to Stage3d and UDK on Windows?

  - by Arthur Wulf White

  Adobe recently released the Flash C++ Compiler, which UDK uses to target Flash Player. Developers can now access UDK for browser applications. Does this mean greater performance than using a Stage3D engine (Away3D 4) and how much of a noticeable difference in performance would it make in rendering speeds? Is there any benchmark you could propose that would allow to compare them fairly? I am asking this to help myself understand the consequences in performance for deciding to use UDK in a browser based game. I would also like to know how it compares with UDK running natively in Windows? I am not asking which technology to use or which is better. Only interested in optimizing rendering speed in a 3d browser game with flash.

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• Network throughput issue (ARP-related)

  - by Joel Coel

  The small college where I work is having some very strange network issues. I'm looking for any advice or ideas here. We were fine over the summer, but the trouble began few days after students returned to campus in force for the fall term. Symptoms The main symptom is that internet access will work, but it's very slow... often to the point of timeouts. As an example, a typical result from Speedtest.net will return .4Mbps download, but allow 3 to 8 Mbps upload speed. Lesser symptoms may include severely limited performance transferring data to and from our file server, or even in some cases the inability to log in to the computer (cannot reach the domain controller). The issue crosses multiple vlans, and has effected devices on nearly every vlan we operate. The issue does not impact all machines on the network. An unaffected machine will typically see at least 11Mbps download from speedtest.net, and perhaps much more depending on larger campus traffic patterns at the time. There is one variation on the larger issue. We have one vlan where users were unable to log into nearly all of the machines at all. IT staff would log in using a local administrator account (or in some cases cached credentials), and from there a release/renew or pinging the gateway would allow the machine to work... for a while. Complicating this issue is that this vlan covers our computer labs, which use software called Deep Freeze to completely reset the hard drives after a reboot. It could just the same issue manifesting differently because of stale data on machines that have not permanently altered low-level info for weeks. We were able to solve this, however, by creating a new vlan and moving the labs over to the new vlan wholesale. Instigations Eventually we noticed that the effected machines all had recent dhcp leases. We can predict when a machine will become "slow" by watching when a dhcp lease comes up for renewal. We played with setting the lease time very short for a test vlan, but all that did was remove our ability to predict when the machine would become slow. Machines with static IPs have pretty much always worked normally. Manually releasing/renewing an address will never cause a machine to become slow. In fact, in some cases this process has fixed a machine in that state. Most of the time, though, it doesn't help. We also noticed that mobile machines like laptops are likely to become slow when they cross to new vlans. Wireless on campus is divided up into "zones", where each zone maps to a small set of buildings. Moving to a new building can place you in a zone, thereby causing you to get a new address. A machine resuming from sleep mode is also very likely to be slow. Mitigations Sometimes, but not always, clearing the arp cache on an effected machine will allow it to work normally again. As already mentioned, releasing/renewing a local machine's IP address can fix that machine, but it's not guaranteed. Pinging the default gateway can also sometimes help with a slow machine. What seems to help most to mitigate the issue is clearing the arp cache on our core layer-3 switch. This switch is used for our dhcp system as the default gateway on all vlans, and it handles inter-vlan routing. The model is a 3Com 4900SX. To try to mitigate the issue, we have the cache timeout set on the switch all the way down to the lowest possible time, but it hasn't helped. I also put together a script that runs every few minutes to automatically connect to the switch and reset the cache. Unfortunately, this does not always work, and can even cause some machines to end up in the slow state for a short time (though these seem to correct themselves after a few minutes). We currently have a scheduled job that runs every 10 minutes to force the core switch to clear it's ARP cache, but this is far from perfect or desirable. Reproduction We now have a test machine that we can force into the slow state at will. It is connected to a switch with ports set up for each of our vlans. We make the machine slow by connecting to different vlans, and after a new connection or two it will be slow. It's also worth noting in this section that this has happened before at the start of prior terms, but in the past the problem has gone away on it's own after a few days. It solved itself before we had a chance to do much diagnostic work... hence why we've allowed it to drag so long into the term this time 'round; the expectation was this would be a short-lived situation. Other Factors It's worth mentioning that we have had about half a dozen switches just outright fail over the last year. These are mainly 2003/2004-era 3Coms (mostly 4200's) that were all put in at about the same time. They should still be covered under warranty, buy HP has made getting service somewhat difficult. Mostly in power supplies that have failed, but in a couple cases we have used a power supply from a switch with a failed mainboard to bring a switch with a failed power supply back to life. We do have UPS devices on all but three of four switches now, but that was not the case when I started two and a half years ago. Severe budget constraints (we were on the Dept. of Ed's financially challenged institutions list a couple years back) have forced me to look to the likes of Netgear and TrendNet for replacements, but so far these low-end models seem to be holding their own. It's also worth mentioning that the big change on our network this summer was migrating from a single cross-campus wireless SSID to the zoned approach mentioned earlier. I don't think this is the source of the issue, as like I've said: we've seen this before. However, it's possible this is exacerbating the issue, and may be much of the reason it's been so hard to isolate. Diagnosis At first it seemed clear to us, given the timing and persistent nature of the problem, that the source of the issue was an infected (or malicious) student machine doing ARP cache poisoning. However, repeated attempts to isolate the source have failed. Those attempts include numerous wireshark packet traces, and even taking entire buildings offline for brief periods. We have not been able even to find a smoking gun bad ARP entry. My current best guess is an overloaded or failing core switch, but I'm not sure on how to test for this, and the cost of replacing it blindly is steep. Again, any ideas appreciated.

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• VS2012 Launch Event &ndash; Combating Bugs And Poor Performance In Production

  - by Tarun Arora

  I presented a session “A techies guide to combating bugs & poor performance in production” at the Microsoft IT Visual Studio Launch event.  The key message was to demonstrate what common production issues (non-reproducible bugs and poor performance) techie’s run into and how the tooling in Visual Studio can help you efficiently tackle these issues. Remember, a Techie without efficient tools is only half the good!                                                       A techies guide to combating bugs & poor performance in production from Avanade Enjoy!

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• systemstate dump ??

  - by JaneZhang(???)

          ???????????????hang????,????????systemstate dump?????????,?????,????????,???????????????,????systemstate dump?????????????       ??????,????????systemstate dump, ?????“WAITED TOO LONG FOR A ROW CACHE ENQUEUE LOCK”?        systemstate dump???????????,??????:??????,???????,????dump????????,???????M????)1. ?sysdba???????:$sqlplus / as sysdba??$sqlplus -prelim / as sysdba <==??????????hang?????SQL>oradebug setmypidSQL>oradebug unlimit;SQL>oradebug dump systemstate 266;?1~2??SQL>oradebug dump systemstate 266;?1~2??SQL>oradebug dump systemstate 266;SQL>oradebug tracefile_name;==>????????2. ????systemstate dump,??????hang analyze??????????????????$sqlplus / as sysdba??$sqlplus -prelim / as sysdba <==??????????hang?????SQL>oradebug setmypidSQL>oradebug unlimit;SQL>oradebug dump hanganalyze 3?1~2??SQL>oradebug dump hanganalyze 3?1~2??SQL>oradebug dump hanganalyze 3SQL>oradebug tracefile_name;==>??????????RAC???,????????????systemstate dump,???????????(?????????):$sqlplus / as sysdba??$sqlplus -prelim / as sysdba <==??????????hang?????SQL>oradebug setmypidSQL>oradebug unlimitSQL>oradebug -g all dump systemstate 266  <==-g all ??????????dump?1~2??SQL>oradebug -g all dump systemstate 266?1~2??SQL>oradebug -g all dump systemstate 266?RAC???hang analyze:SQL>oradebug setmypidSQL>oradebug unlimitSQL>oradebug -g all hanganalyze 3?1~2??SQL>oradebug -g all hanganalyze 3?1~2??SQL>oradebug -g all hanganalyze 3?????????????????systemstate dump,?????????????backgroud_dump_dest??diag trace???????????????????????????,?????hang?,?????systemstate dump?????:10:   dump11:   dump + global cache of RAC256: short stack (????)258: dump(???lock element) + short stack (????)266: 256+10 -->short stack+ dump267: 256+11 -->short stack+ dump + global cache of RAClevel 11? 267? dump global cache, ??????trace ??,??????????????,????????,???266,??????dump?????????,????????????????????short stack????,???????,??2000???,??????30??????????,????level 10 ?? level 258, level 258 ? level 10????short short stack, ??level 10?????lock element data.?????systemstate dump???,??????level?????:??????37???:-rw-r----- 1 oracle oinstall    72721 Aug 31 21:50 rac10g2_ora_31092.trc==>256 (short stack, ????2K)-rw-r----- 1 oracle oinstall  2724863 Aug 31 21:52 rac10g2_ora_31654.trc==>10    (dump,????72K )-rw-r----- 1 oracle oinstall  2731935 Aug 31 21:53 rac10g2_ora_32214.trc==>266 (dump + short stack ,????72K)RAC:-rw-r----- 1 oracle oinstall 55873057 Aug 31 21:49 rac10g2_ora_30658.trc ==>11   (dump+global cache,????1.4M)-rw-r----- 1 oracle oinstall 55879249 Aug 31 21:48 rac10g2_ora_28615.trc ==>267 (dump+global cache+short stack,????1.4M) ??,??????dump global cache(level 11?267,???????????????)??????????,?????????systemstate dump ??

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• IOUG Enterprise Manager SIG Webinar: WEBINAR: Performance Tuning your Database Cloud in Oracle Enterprise Manager 12c Cloud Control - 360 Degrees

  - by Patrick Rood

  October 25, 2013 EM 12c Sales Blast | IOUG Enterprise Manager SIG WEBINAR: Performance Tuning your Database Cloud in Oracle Enterprise Manager 12c Cloud Control - 360 Degrees Last year, the Independent Oracle User Group (IOUG) established a fast-growing Special Interest Group (SIG) devoted to Enterprise Manager, and has sponsored Quarterly Newsletters and Webinars about EM. To drive more interest in EM and the SIG, IOUG would like Oracle to invite customers to its latest techcast. Your customers will learn how to leverage Oracle Enterprise Manager 12c for tuning, trouble-shooting and monitoring their Oracle Database Cloud Ecosystem. The session covers lessons learned, tips/tricks, recommendations, best practices, "gotchas" and a whole lot more on how to effectively use Oracle Enterprise Manager 12c Cloud Control for quick, easy and intuitive performance tuning of an Oracle Database Cloud. Session Objectives: • Leveraging Enterprise Manager 12c Cloud Control for Oracle Database Tuning/Monitoring • Limited Deep-Dive on Automatic Workload Repository (AWR) • Oracle Database Cloud Performance Tuning • Best Practices for Database Cloud Maintenance and Monitoring Featured Speaker: Tariq Farooq, CEO, BrainSurface and Mike Ault Date & Time: Wednesday, October 30 12:00 PM- 1:00 PM Central Time (USA) Register Here 

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• Mybatis nested collection doesn't work correctly with column prefix

  - by Shikarn-O

  I need to set collection for object in another collection using mybatis mappings. It works for me w/o using columnPrefix, but I need it since there are a lot of repeteable columns. <collection property="childs" javaType="ArrayList" ofType="org.example.mybatis.Child" resultMap="ChildMap" columnPrefix="c_"/> </resultMap> <resultMap id="ChildMap" type="org.example.mybatis.Parent"> <id column="Id" jdbcType="VARCHAR" property="id" /> <id column="ParentId" jdbcType="VARCHAR" property="parentId" /> <id column="Name" jdbcType="VARCHAR" property="name" /> <id column="SurName" jdbcType="VARCHAR" property="surName" /> <id column="Age" jdbcType="INTEGER" property="age" /> <collection property="toys" javaType="ArrayList" ofType="org.example.mybatis.Toy" resultMap="ToyMap" columnPrefix="t_"/> </resultMap> <resultMap id="ToyMap" type="org.example.mybatis.Toy"> <id column="Id" jdbcType="VARCHAR" property="id" /> <id column="ChildId" jdbcType="VARCHAR" property="childId" /> <id column="Name" jdbcType="VARCHAR" property="name" /> <id column="Color" jdbcType="VARCHAR" property="color" /> </resultMap> <sql id="Parent_Column_List"> p.Id, p.Name, p.SurName, </sql> <sql id="Child_Column_List"> c.Id as c_Id, c.ParentId as c_ParentId, c.Name as c_Name, c.SurName as c_Surname, c.Age as c_Age, </sql> <sql id="Toy_Column_List"> t.Id as t_Id, t.Name as t_Name, t.Color as t_Color </sql> <select id="getParent" parameterType="java.lang.String" resultMap="ParentMap" > select <include refid="Parent_Column_List"/> <include refid="Child_Column_List" /> <include refid="Toy_Column_List" /> from Parent p left outer join Child c on p.Id = c.ParentId left outer join Toy t on c.Id = t.ChildId where p.id = #{id,jdbcType=VARCHAR} With columnPrefix all works fine, but nested toys collection is empty. Sql query on database works correctly and all toys are joined. May be i missed something or this is bug with mybatis?

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• Unable to execute stored Procedure using Java and JDBC

  - by jwmajors81

  I have been trying to execute a MS SQL Server stored procedure via JDBC today and have been unsuccessful thus far. The stored procedure has 1 input and 1 output parameter. With every combination I use when setting up the stored procedure call in code I get an error stating that the stored procedure couldn't be found. I have provided the stored procedure I'm executing below (NOTE: this is vendor code, so I cannot change it). set ANSI_NULLS ON set QUOTED_IDENTIFIER ON GO ALTER PROC [dbo].[spWCoTaskIdGen] @OutIdentifier int OUTPUT AS BEGIN DECLARE @HoldPolicyId int DECLARE @PolicyId char(14) IF NOT EXISTS ( SELECT * FROM UniqueIdentifierGen (UPDLOCK) ) INSERT INTO UniqueIdentifierGen VALUES (0) UPDATE UniqueIdentifierGen SET CurIdentifier = CurIdentifier + 1 SELECT @OutIdentifier = (SELECT CurIdentifier FROM UniqueIdentifierGen) END The code looks like: CallableStatement statement = connection .prepareCall("{call dbo.spWCoTaskIdGen(?)}"); statement.setInt(1, 0); ResultSet result = statement.executeQuery(); I get the following error: SEVERE: Could not find stored procedure 'dbo.spWCoTaskIdGen'. I have also tried CallableStatement statement = connection .prepareCall("{? = call dbo.spWCoTaskIdGen(?)}"); statement.registerOutParameter(1, java.sql.Types.INTEGER); statement.registerOutParameter(2, java.sql.Types.INTEGER); statement.executeQuery(); The above results in: SEVERE: Could not find stored procedure 'dbo.spWCoTaskIdGen'. I have also tried: CallableStatement statement = connection .prepareCall("{? = call spWCoTaskIdGen(?)}"); statement.registerOutParameter(1, java.sql.Types.INTEGER); statement.registerOutParameter(2, java.sql.Types.INTEGER); statement.executeQuery(); The code above resulted in the following error: Could not find stored procedure 'spWCoTaskIdGen'. Finally, I should also point out the following: I have used the MS SQL Server Management Studio tool and have been able to successfully run the stored procedure. The sql generated to execute the stored procedure is provided below: GO DECLARE @return_value int, @OutIdentifier int EXEC @return_value = [dbo].[spWCoTaskIdGen] @OutIdentifier = @OutIdentifier OUTPUT SELECT @OutIdentifier as N'@OutIdentifier ' SELECT 'Return Value' = @return_value GO The code being executed runs with the same user id that was used in point #1 above. In the code that creates the Connection object I log which database I'm connecting to and the code is connecting to the correct database. Any ideas? Thank you very much in advance.

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• Symantec Protection Suite and System Recovery 2011 Desktop Edition

  - by rihatum

  I am re-posting this as my previous question was being treated as if I am "Shopping or seeking Product Recommendations" even though I was NOT - BTW they have deleted my comments too which were not offensive in nature. anyway - I have re-phrased some parts of my question and I hope SF Admins "Do Not Modify / Edit" this one - will be most grateful for that. I have a lot of respect for the People who visit this SITE and help others ! Just To clarify : Just to go by SF rules - I am not seeking someone to Design this solution, I am simply seeking real world examples, experiences, technical expert opinions / suggestions, any tips or tricks they may have or any problems they may have faced while doing something similar above with these products. I am also not asking for Capacity Planning for Storage, We have done some research and I am seeking Expert Assurance / Suggestions. We (our company) are planning to deploy Symantec Endpoint Protection and Symantec Desktop Recovery 2011 Desktop Edition to our 3000 - 4000 workstations (Windows7 32 and 64) with a few 100s with Windows XP 32/64 Bit. I have read the implementation guide for SEP and have read tech-notes for Desktop Recovery 2011. Our team have planned to deploy this as follows : 1 x dedicated SQL 2008R2 for Symantec Endpoint Protection (Instead of using the Embedded Database) 1 x Dedicated SQL 2008R2 for Symantec Desktop Recovery 2011 (Instead of using the Embedded Database) 1 x Dedicated W2K8 R2 Box for the SEPM (Symantec Endpoint Protection Manager - Mgmt. APP) 1 x Dedicated W2K8 R2 Box for the Symantec Desktop Recovery 2011 Management Application Agent Deployment : As per Symantec Documentation for both of the above, an agent can be pushed via the Mgmt. Application (provided no firewalls are blocking ports required etc. - we have Windows firewall disabled already). Server Hardware : Per SQL Server : 16GB RAM + SAS DISKS + Dual XEON, RAID-10 for the SQL DB or I can always mount a LUN from our existing Hitachi or EMC SAN. SEPM Server : 16GB RAM + SAS DISKS + DUAL XEON System Recovery MGMT SERVER : 16GB RAM + SAS DISKS + DUAL XEON Above is the initial plan we have for 3000 - 4000 client workstation (Windows) Now my Questions :-) a) If we had these users distributed amongst two sites with AD DC / GC in each site, How would I restrict SEPM and Desktop Mgmt. solution to only check for users in their respective site ? b) At present all users are under one building but we are going to move some dept. to a new location (with dedicated connectivity), How would we control which SEPM / MGMT Server is responsible for which site ? c) We have netbackup in our environment backing up other servers, I am planning to protect these 4 (2 x SQL, 1 x SEPM, 1 x System Recovery Mgmt. Server) via netbackup or I can use System recovery 2011 server edition on all 4 of these boxes as well. (License is not an issue as we have the complete symantec portfolio included in our license). d) Now - Saving Desktop backups - What strategies have you implemented ? Any best practice recommendation for a large user base ? I was thinking to either mount a LUN from our Hitachi SAN on the Symantec Recovery Server itself or backup to the users hard drive locally and then copy it over to a network location ? Suggestions welcome :-) If you have anything to add / correct - that will be really helpful before diving into the actual implementation phase. Will be most grateful with your suggestions, recommendations and corrections with above - Many Thanks !

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• Distributed and/or Parallel SSIS processing

  - by Jeff

  Background: Our company hosts SaaS DSS applications, where clients provide us data Daily and/or Weekly, which we process & merge into their existing database. During business hours, load in the servers are pretty minimal as it's mostly users running simple pre-defined queries via the website, or running drill-through reports that mostly hit the SSAS OLAP cube. I manage the IT Operations Team, and so far this has presented an interesting "scaling" issue for us. For our daily-refreshed clients, the server is only "busy" for about 4-6 hrs at night. For our weekly-refresh clients, the server is only "busy" for maybe 8-10 hrs per week! We've done our best to use some simple methods of distributing the load by spreading the daily clients evenly among the servers such that we're not trying to process daily clients back-to-back over night. But long-term this scaling strategy creates two notable issues. First, it's going to consume a pretty immense amount of hardware that sits idle for large periods of time. Second, it takes significant Production Support over-head to basically "schedule" the ETL such that they don't over-lap, and move clients/schedules around if they out-grow the resources on a particular server or allocated time-slot. As the title would imply, one option we've tried is running multiple SSIS packages in parallel, but in most cases this has yielded VERY inconsistent results. The most common failures are DTExec, SQL, and SSAS fighting for physical memory and throwing out-of-memory errors, and ETLs running 3,4,5x longer than expected. So from my practical experience thus far, it seems like running multiple ETL packages on the same hardware isn't a good idea, but I can't be the first person that doesn't want to scale multiple ETLs around manual scheduling, and sequential processing. One option we've considered is virtualizing the servers, which obviously doesn't give you any additional resources, but moves the resource contention onto the hypervisor, which (from my experience) seems to manage simultaneous CPU/RAM/Disk I/O a little more gracefully than letting DTExec, SQL, and SSAS battle it out within Windows. Question to the forum: So my question to the forum is, are we missing something obvious here? Are there tools out there that can help manage running multiple SSIS packages on the same hardware? Would it be more "efficient" in terms of parallel execution if instead of running DTExec, SQL, and SSAS same machine (with every machine running that configuration), we run in pairs of three machines with SSIS running on one machine, SQL on another, and SSAS on a third? Obviously that would only make sense if we could process more than the three ETL we were able to process on the machine independently. Another option we've considered is completely re-architecting our SSIS package to have one "master" package for all clients that attempts to intelligently chose a server based off how "busy" it already is in terms of CPU/Memory/Disk utilization, but that would be a herculean effort, and seems like we're trying to reinvent something that you would think someone would sell (although I haven't had any luck finding it). So in summary, are we missing an obvious solution for this, and does anyone know if any tools (for free or for purchase, doesn't matter) that facilitate running multiple SSIS ETL packages in parallel and on multiple servers? (What I would call a "queue & node based" system, but that's not an official term). Ultimately VMWare's Distributed Resource Scheduler addresses this as you simply run a consistent number of clients per VM that you know will never conflict scheduleing-wise, then leave it up to VMWare to move the VMs around to balance out hardware usage. I'm definitely not against using VMWare to do this, but since we're a 100% Microsoft app stack, it seems like -someone- out there would have solved this problem at the application layer instead of the hypervisor layer by checking on resource utilization at the OS, SQL, SSAS levels. I'm open to ANY discussion on this, and remember no suggestion is too crazy or radical! :-) Right now, VMWare is the only option we've found to get away from "manually" balancing our resources, so any suggestions that leave us on a pure Microsoft stack would be great. Thanks guys, Jeff

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• Beware Sneaky Reads with Unique Indexes

  - by Paul White NZ

  A few days ago, Sandra Mueller (twitter | blog) asked a question using twitter’s #sqlhelp hash tag: “Might SQL Server retrieve (out-of-row) LOB data from a table, even if the column isn’t referenced in the query?” Leaving aside trivial cases (like selecting a computed column that does reference the LOB data), one might be tempted to say that no, SQL Server does not read data you haven’t asked for.  In general, that’s quite correct; however there are cases where SQL Server might sneakily retrieve a LOB column… Example Table Here’s a T-SQL script to create that table and populate it with 1,000 rows: CREATE TABLE dbo.LOBtest ( pk INTEGER IDENTITY NOT NULL, some_value INTEGER NULL, lob_data VARCHAR(MAX) NULL, another_column CHAR(5) NULL, CONSTRAINT [PK dbo.LOBtest pk] PRIMARY KEY CLUSTERED (pk ASC) ); GO DECLARE @Data VARCHAR(MAX); SET @Data = REPLICATE(CONVERT(VARCHAR(MAX), 'x'), 65540);   WITH Numbers (n) AS ( SELECT ROW_NUMBER() OVER (ORDER BY (SELECT 0)) FROM master.sys.columns C1, master.sys.columns C2 ) INSERT LOBtest WITH (TABLOCKX) ( some_value, lob_data ) SELECT TOP (1000) N.n, @Data FROM Numbers N WHERE N.n <= 1000; Test 1: A Simple Update Let’s run a query to subtract one from every value in the some_value column: UPDATE dbo.LOBtest WITH (TABLOCKX) SET some_value = some_value - 1; As you might expect, modifying this integer column in 1,000 rows doesn’t take very long, or use many resources.  The STATITICS IO and TIME output shows a total of 9 logical reads, and 25ms elapsed time.  The query plan is also very simple: Looking at the Clustered Index Scan, we can see that SQL Server only retrieves the pk and some_value columns during the scan: The pk column is needed by the Clustered Index Update operator to uniquely identify the row that is being changed.  The some_value column is used by the Compute Scalar to calculate the new value.  (In case you are wondering what the Top operator is for, it is used to enforce SET ROWCOUNT). Test 2: Simple Update with an Index Now let’s create a nonclustered index keyed on the some_value column, with lob_data as an included column: CREATE NONCLUSTERED INDEX [IX dbo.LOBtest some_value (lob_data)] ON dbo.LOBtest (some_value) INCLUDE ( lob_data ) WITH ( FILLFACTOR = 100, MAXDOP = 1, SORT_IN_TEMPDB = ON ); This is not a useful index for our simple update query; imagine that someone else created it for a different purpose.  Let’s run our update query again: UPDATE dbo.LOBtest WITH (TABLOCKX) SET some_value = some_value - 1; We find that it now requires 4,014 logical reads and the elapsed query time has increased to around 100ms.  The extra logical reads (4 per row) are an expected consequence of maintaining the nonclustered index. The query plan is very similar to before (click to enlarge): The Clustered Index Update operator picks up the extra work of maintaining the nonclustered index. The new Compute Scalar operators detect whether the value in the some_value column has actually been changed by the update.  SQL Server may be able to skip maintaining the nonclustered index if the value hasn’t changed (see my previous post on non-updating updates for details).  Our simple query does change the value of some_data in every row, so this optimization doesn’t add any value in this specific case. The output list of columns from the Clustered Index Scan hasn’t changed from the one shown previously: SQL Server still just reads the pk and some_data columns.  Cool. Overall then, adding the nonclustered index hasn’t had any startling effects, and the LOB column data still isn’t being read from the table.  Let’s see what happens if we make the nonclustered index unique. Test 3: Simple Update with a Unique Index Here’s the script to create a new unique index, and drop the old one: CREATE UNIQUE NONCLUSTERED INDEX [UQ dbo.LOBtest some_value (lob_data)] ON dbo.LOBtest (some_value) INCLUDE ( lob_data ) WITH ( FILLFACTOR = 100, MAXDOP = 1, SORT_IN_TEMPDB = ON ); GO DROP INDEX [IX dbo.LOBtest some_value (lob_data)] ON dbo.LOBtest; Remember that SQL Server only enforces uniqueness on index keys (the some_data column).  The lob_data column is simply stored at the leaf-level of the non-clustered index.  With that in mind, we might expect this change to make very little difference.  Let’s see: UPDATE dbo.LOBtest WITH (TABLOCKX) SET some_value = some_value - 1; Whoa!  Now look at the elapsed time and logical reads: Scan count 1, logical reads 2016, physical reads 0, read-ahead reads 0, lob logical reads 36015, lob physical reads 0, lob read-ahead reads 15992.   CPU time = 172 ms, elapsed time = 16172 ms. Even with all the data and index pages in memory, the query took over 16 seconds to update just 1,000 rows, performing over 52,000 LOB logical reads (nearly 16,000 of those using read-ahead). Why on earth is SQL Server reading LOB data in a query that only updates a single integer column? The Query Plan The query plan for test 3 looks a bit more complex than before: In fact, the bottom level is exactly the same as we saw with the non-unique index.  The top level has heaps of new stuff though, which I’ll come to in a moment. You might be expecting to find that the Clustered Index Scan is now reading the lob_data column (for some reason).  After all, we need to explain where all the LOB logical reads are coming from.  Sadly, when we look at the properties of the Clustered Index Scan, we see exactly the same as before: SQL Server is still only reading the pk and some_value columns – so what’s doing the LOB reads? Updates that Sneakily Read Data We have to go as far as the Clustered Index Update operator before we see LOB data in the output list: [Expr1020] is a bit flag added by an earlier Compute Scalar.  It is set true if the some_value column has not been changed (part of the non-updating updates optimization I mentioned earlier). The Clustered Index Update operator adds two new columns: the lob_data column, and some_value_OLD.  The some_value_OLD column, as the name suggests, is the pre-update value of the some_value column.  At this point, the clustered index has already been updated with the new value, but we haven’t touched the nonclustered index yet. An interesting observation here is that the Clustered Index Update operator can read a column into the data flow as part of its update operation.  SQL Server could have read the LOB data as part of the initial Clustered Index Scan, but that would mean carrying the data through all the operations that occur prior to the Clustered Index Update.  The server knows it will have to go back to the clustered index row to update it, so it delays reading the LOB data until then.  Sneaky! Why the LOB Data Is Needed This is all very interesting (I hope), but why is SQL Server reading the LOB data?  For that matter, why does it need to pass the pre-update value of the some_value column out of the Clustered Index Update? The answer relates to the top row of the query plan for test 3.  I’ll reproduce it here for convenience: Notice that this is a wide (per-index) update plan.  SQL Server used a narrow (per-row) update plan in test 2, where the Clustered Index Update took care of maintaining the nonclustered index too.  I’ll talk more about this difference shortly. The Split/Sort/Collapse combination is an optimization, which aims to make per-index update plans more efficient.  It does this by breaking each update into a delete/insert pair, reordering the operations, removing any redundant operations, and finally applying the net effect of all the changes to the nonclustered index. Imagine we had a unique index which currently holds three rows with the values 1, 2, and 3.  If we run a query that adds 1 to each row value, we would end up with values 2, 3, and 4.  The net effect of all the changes is the same as if we simply deleted the value 1, and added a new value 4. By applying net changes, SQL Server can also avoid false unique-key violations.  If we tried to immediately update the value 1 to a 2, it would conflict with the existing value 2 (which would soon be updated to 3 of course) and the query would fail.  You might argue that SQL Server could avoid the uniqueness violation by starting with the highest value (3) and working down.  That’s fine, but it’s not possible to generalize this logic to work with every possible update query. SQL Server has to use a wide update plan if it sees any risk of false uniqueness violations.  It’s worth noting that the logic SQL Server uses to detect whether these violations are possible has definite limits.  As a result, you will often receive a wide update plan, even when you can see that no violations are possible. Another benefit of this optimization is that it includes a sort on the index key as part of its work.  Processing the index changes in index key order promotes sequential I/O against the nonclustered index. A side-effect of all this is that the net changes might include one or more inserts.  In order to insert a new row in the index, SQL Server obviously needs all the columns – the key column and the included LOB column.  This is the reason SQL Server reads the LOB data as part of the Clustered Index Update. In addition, the some_value_OLD column is required by the Split operator (it turns updates into delete/insert pairs).  In order to generate the correct index key delete operation, it needs the old key value. The irony is that in this case the Split/Sort/Collapse optimization is anything but.  Reading all that LOB data is extremely expensive, so it is sad that the current version of SQL Server has no way to avoid it. Finally, for completeness, I should mention that the Filter operator is there to filter out the non-updating updates. Beating the Set-Based Update with a Cursor One situation where SQL Server can see that false unique-key violations aren’t possible is where it can guarantee that only one row is being updated.  Armed with this knowledge, we can write a cursor (or the WHILE-loop equivalent) that updates one row at a time, and so avoids reading the LOB data: SET NOCOUNT ON; SET STATISTICS XML, IO, TIME OFF;   DECLARE @PK INTEGER, @StartTime DATETIME; SET @StartTime = GETUTCDATE();   DECLARE curUpdate CURSOR LOCAL FORWARD_ONLY KEYSET SCROLL_LOCKS FOR SELECT L.pk FROM LOBtest L ORDER BY L.pk ASC;   OPEN curUpdate;   WHILE (1 = 1) BEGIN FETCH NEXT FROM curUpdate INTO @PK;   IF @@FETCH_STATUS = -1 BREAK; IF @@FETCH_STATUS = -2 CONTINUE;   UPDATE dbo.LOBtest SET some_value = some_value - 1 WHERE CURRENT OF curUpdate; END;   CLOSE curUpdate; DEALLOCATE curUpdate;   SELECT DATEDIFF(MILLISECOND, @StartTime, GETUTCDATE()); That completes the update in 1280 milliseconds (remember test 3 took over 16 seconds!) I used the WHERE CURRENT OF syntax there and a KEYSET cursor, just for the fun of it.  One could just as well use a WHERE clause that specified the primary key value instead. Clustered Indexes A clustered index is the ultimate index with included columns: all non-key columns are included columns in a clustered index.  Let’s re-create the test table and data with an updatable primary key, and without any non-clustered indexes: IF OBJECT_ID(N'dbo.LOBtest', N'U') IS NOT NULL DROP TABLE dbo.LOBtest; GO CREATE TABLE dbo.LOBtest ( pk INTEGER NOT NULL, some_value INTEGER NULL, lob_data VARCHAR(MAX) NULL, another_column CHAR(5) NULL, CONSTRAINT [PK dbo.LOBtest pk] PRIMARY KEY CLUSTERED (pk ASC) ); GO DECLARE @Data VARCHAR(MAX); SET @Data = REPLICATE(CONVERT(VARCHAR(MAX), 'x'), 65540);   WITH Numbers (n) AS ( SELECT ROW_NUMBER() OVER (ORDER BY (SELECT 0)) FROM master.sys.columns C1, master.sys.columns C2 ) INSERT LOBtest WITH (TABLOCKX) ( pk, some_value, lob_data ) SELECT TOP (1000) N.n, N.n, @Data FROM Numbers N WHERE N.n <= 1000; Now here’s a query to modify the cluster keys: UPDATE dbo.LOBtest SET pk = pk + 1; The query plan is: As you can see, the Split/Sort/Collapse optimization is present, and we also gain an Eager Table Spool, for Halloween protection.  In addition, SQL Server now has no choice but to read the LOB data in the Clustered Index Scan: The performance is not great, as you might expect (even though there is no non-clustered index to maintain): Table 'LOBtest'. Scan count 1, logical reads 2011, physical reads 0, read-ahead reads 0, lob logical reads 36015, lob physical reads 0, lob read-ahead reads 15992.   Table 'Worktable'. Scan count 1, logical reads 2040, physical reads 0, read-ahead reads 0, lob logical reads 34000, lob physical reads 0, lob read-ahead reads 8000.   SQL Server Execution Times: CPU time = 483 ms, elapsed time = 17884 ms. Notice how the LOB data is read twice: once from the Clustered Index Scan, and again from the work table in tempdb used by the Eager Spool. If you try the same test with a non-unique clustered index (rather than a primary key), you’ll get a much more efficient plan that just passes the cluster key (including uniqueifier) around (no LOB data or other non-key columns): A unique non-clustered index (on a heap) works well too: Both those queries complete in a few tens of milliseconds, with no LOB reads, and just a few thousand logical reads.  (In fact the heap is rather more efficient). There are lots more fun combinations to try that I don’t have space for here. Final Thoughts The behaviour shown in this post is not limited to LOB data by any means.  If the conditions are met, any unique index that has included columns can produce similar behaviour – something to bear in mind when adding large INCLUDE columns to achieve covering queries, perhaps. Paul White Email: [email protected] Twitter: @PaulWhiteNZ

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• Advantage Database Server: slow stored procedure performance.

  - by ie

  I have a question about a performance of stored procedures in the ADS. I created a simple database with the following structure: CREATE TABLE MainTable ( Id INTEGER PRIMARY KEY, Name VARCHAR(50), Value INTEGER ); CREATE UNIQUE INDEX MainTableName_UIX ON MainTable ( Name ); CREATE TABLE SubTable ( Id INTEGER PRIMARY KEY, MainId INTEGER, Name VARCHAR(50), Value INTEGER ); CREATE INDEX SubTableMainId_UIX ON SubTable ( MainId ); CREATE UNIQUE INDEX SubTableName_UIX ON SubTable ( Name ); CREATE PROCEDURE CreateItems ( MainName VARCHAR ( 20 ), SubName VARCHAR ( 20 ), MainValue INTEGER, SubValue INTEGER, MainId INTEGER OUTPUT, SubId INTEGER OUTPUT ) BEGIN DECLARE @MainName VARCHAR ( 20 ); DECLARE @SubName VARCHAR ( 20 ); DECLARE @MainValue INTEGER; DECLARE @SubValue INTEGER; DECLARE @MainId INTEGER; DECLARE @SubId INTEGER; @MainName = (SELECT MainName FROM __input); @SubName = (SELECT SubName FROM __input); @MainValue = (SELECT MainValue FROM __input); @SubValue = (SELECT SubValue FROM __input); @MainId = (SELECT MAX(Id)+1 FROM MainTable); @SubId = (SELECT MAX(Id)+1 FROM SubTable ); INSERT INTO MainTable (Id, Name, Value) VALUES (@MainId, @MainName, @MainValue); INSERT INTO SubTable (Id, Name, MainId, Value) VALUES (@SubId, @SubName, @MainId, @SubValue); INSERT INTO __output SELECT @MainId, @SubId FROM system.iota; END; CREATE PROCEDURE UpdateItems ( MainName VARCHAR ( 20 ), MainValue INTEGER, SubValue INTEGER ) BEGIN DECLARE @MainName VARCHAR ( 20 ); DECLARE @MainValue INTEGER; DECLARE @SubValue INTEGER; DECLARE @MainId INTEGER; @MainName = (SELECT MainName FROM __input); @MainValue = (SELECT MainValue FROM __input); @SubValue = (SELECT SubValue FROM __input); @MainId = (SELECT TOP 1 Id FROM MainTable WHERE Name = @MainName); UPDATE MainTable SET Value = @MainValue WHERE Id = @MainId; UPDATE SubTable SET Value = @SubValue WHERE MainId = @MainId; END; CREATE PROCEDURE SelectItems ( MainName VARCHAR ( 20 ), CalculatedValue INTEGER OUTPUT ) BEGIN DECLARE @MainName VARCHAR ( 20 ); @MainName = (SELECT MainName FROM __input); INSERT INTO __output SELECT m.Value * s.Value FROM MainTable m INNER JOIN SubTable s ON m.Id = s.MainId WHERE m.Name = @MainName; END; CREATE PROCEDURE DeleteItems ( MainName VARCHAR ( 20 ) ) BEGIN DECLARE @MainName VARCHAR ( 20 ); DECLARE @MainId INTEGER; @MainName = (SELECT MainName FROM __input); @MainId = (SELECT TOP 1 Id FROM MainTable WHERE Name = @MainName); DELETE FROM SubTable WHERE MainId = @MainId; DELETE FROM MainTable WHERE Id = @MainId; END; Actually, the problem I had - even so light stored procedures work very-very slow (about 50-150 ms) relatively to plain queries (0-5ms). To test the performance, I created a simple test (in F# using ADS ADO.NET provider): open System; open System.Data; open System.Diagnostics; open Advantage.Data.Provider; let mainName = "main name #"; let subName = "sub name #"; // INSERT let cmdTextScriptInsert = " DECLARE @MainId INTEGER; DECLARE @SubId INTEGER; @MainId = (SELECT MAX(Id)+1 FROM MainTable); @SubId = (SELECT MAX(Id)+1 FROM SubTable ); INSERT INTO MainTable (Id, Name, Value) VALUES (@MainId, :MainName, :MainValue); INSERT INTO SubTable (Id, Name, MainId, Value) VALUES (@SubId, :SubName, @MainId, :SubValue); SELECT @MainId, @SubId FROM system.iota;"; let cmdTextProcedureInsert = "CreateItems"; // UPDATE let cmdTextScriptUpdate = " DECLARE @MainId INTEGER; @MainId = (SELECT TOP 1 Id FROM MainTable WHERE Name = :MainName); UPDATE MainTable SET Value = :MainValue WHERE Id = @MainId; UPDATE SubTable SET Value = :SubValue WHERE MainId = @MainId;"; let cmdTextProcedureUpdate = "UpdateItems"; // SELECT let cmdTextScriptSelect = " SELECT m.Value * s.Value FROM MainTable m INNER JOIN SubTable s ON m.Id = s.MainId WHERE m.Name = :MainName;"; let cmdTextProcedureSelect = "SelectItems"; // DELETE let cmdTextScriptDelete = " DECLARE @MainId INTEGER; @MainId = (SELECT TOP 1 Id FROM MainTable WHERE Name = :MainName); DELETE FROM SubTable WHERE MainId = @MainId; DELETE FROM MainTable WHERE Id = @MainId;"; let cmdTextProcedureDelete = "DeleteItems"; let cnnStr = @"data source=D:\DB\test.add; ServerType=local; user id=adssys; password=***;"; let cnn = new AdsConnection(cnnStr); try cnn.Open(); let cmd = cnn.CreateCommand(); let parametrize ix prms = cmd.Parameters.Clear(); let addParam = function | "MainName" -> cmd.Parameters.Add(":MainName" , mainName + ix.ToString()) |> ignore; | "SubName" -> cmd.Parameters.Add(":SubName" , subName + ix.ToString() ) |> ignore; | "MainValue" -> cmd.Parameters.Add(":MainValue", ix * 3 ) |> ignore; | "SubValue" -> cmd.Parameters.Add(":SubValue" , ix * 7 ) |> ignore; | _ -> () prms |> List.iter addParam; let runTest testData = let (cmdType, cmdName, cmdText, cmdParams) = testData; let toPrefix cmdType cmdName = let prefix = match cmdType with | CommandType.StoredProcedure -> "Procedure-" | CommandType.Text -> "Script -" | _ -> "Unknown -" in prefix + cmdName; let stopWatch = new Stopwatch(); let runStep ix prms = parametrize ix prms; stopWatch.Start(); cmd.ExecuteNonQuery() |> ignore; stopWatch.Stop(); cmd.CommandText <- cmdText; cmd.CommandType <- cmdType; let startId = 1500; let count = 10; for id in startId .. startId+count do runStep id cmdParams; let elapsed = stopWatch.Elapsed; Console.WriteLine("Test '{0}' - total: {1}; per call: {2}ms", toPrefix cmdType cmdName, elapsed, Convert.ToInt32(elapsed.TotalMilliseconds)/count); let lst = [ (CommandType.Text, "Insert", cmdTextScriptInsert, ["MainName"; "SubName"; "MainValue"; "SubValue"]); (CommandType.Text, "Update", cmdTextScriptUpdate, ["MainName"; "MainValue"; "SubValue"]); (CommandType.Text, "Select", cmdTextScriptSelect, ["MainName"]); (CommandType.Text, "Delete", cmdTextScriptDelete, ["MainName"]) (CommandType.StoredProcedure, "Insert", cmdTextProcedureInsert, ["MainName"; "SubName"; "MainValue"; "SubValue"]); (CommandType.StoredProcedure, "Update", cmdTextProcedureUpdate, ["MainName"; "MainValue"; "SubValue"]); (CommandType.StoredProcedure, "Select", cmdTextProcedureSelect, ["MainName"]); (CommandType.StoredProcedure, "Delete", cmdTextProcedureDelete, ["MainName"])]; lst |> List.iter runTest; finally cnn.Close(); And I'm getting the following results: Test 'Script -Insert' - total: 00:00:00.0292841; per call: 2ms Test 'Script -Update' - total: 00:00:00.0056296; per call: 0ms Test 'Script -Select' - total: 00:00:00.0051738; per call: 0ms Test 'Script -Delete' - total: 00:00:00.0059258; per call: 0ms Test 'Procedure-Insert' - total: 00:00:01.2567146; per call: 125ms Test 'Procedure-Update' - total: 00:00:00.7442440; per call: 74ms Test 'Procedure-Select' - total: 00:00:00.5120446; per call: 51ms Test 'Procedure-Delete' - total: 00:00:01.0619165; per call: 106ms The situation with the remote server is much better, but still a great gap between plaqin queries and stored procedures: Test 'Script -Insert' - total: 00:00:00.0709299; per call: 7ms Test 'Script -Update' - total: 00:00:00.0161777; per call: 1ms Test 'Script -Select' - total: 00:00:00.0258113; per call: 2ms Test 'Script -Delete' - total: 00:00:00.0166242; per call: 1ms Test 'Procedure-Insert' - total: 00:00:00.5116138; per call: 51ms Test 'Procedure-Update' - total: 00:00:00.3802251; per call: 38ms Test 'Procedure-Select' - total: 00:00:00.1241245; per call: 12ms Test 'Procedure-Delete' - total: 00:00:00.4336334; per call: 43ms Is it any chance to improve the SP performance? Please advice. ADO.NET driver version - 9.10.2.9 Server version - 9.10.0.9 (ANSI - GERMAN, OEM - GERMAN) Thanks!

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• How to tune down the Hyperic built-in postgresql database for a small setup

  - by Svish

  We are testing out Hyperic 4.5.1 in a quite small environment for now. Currently there are just 1-5 agents and there probably won't be any more than 10-15. When I run ps ax there are 20(!) postgres processes running. For a small setup like this, that can't be necessary, can it? I'm a software developer and don't have much experience with setting up servers and such though, so don't really know. Either way, what settings are appropriate for a small Hyperic setup like this? Current, default and untouched configuration file, hqdb/data/postgresql.conf: # ----------------------------- # PostgreSQL configuration file # ----------------------------- # # This file consists of lines of the form: # # name = value # # (The '=' is optional.) White space may be used. Comments are introduced # with '#' anywhere on a line. The complete list of option names and # allowed values can be found in the PostgreSQL documentation. The # commented-out settings shown in this file represent the default values. # # Please note that re-commenting a setting is NOT sufficient to revert it # to the default value, unless you restart the server. # # Any option can also be given as a command line switch to the server, # e.g., 'postgres -c log_connections=on'. Some options can be changed at # run-time with the 'SET' SQL command. # # This file is read on server startup and when the server receives a # SIGHUP. If you edit the file on a running system, you have to SIGHUP the # server for the changes to take effect, or use "pg_ctl reload". Some # settings, which are marked below, require a server shutdown and restart # to take effect. # # Memory units: kB = kilobytes MB = megabytes GB = gigabytes # Time units: ms = milliseconds s = seconds min = minutes h = hours d = days #--------------------------------------------------------------------------- # FILE LOCATIONS #--------------------------------------------------------------------------- # The default values of these variables are driven from the -D command line # switch or PGDATA environment variable, represented here as ConfigDir. #data_directory = 'ConfigDir' # use data in another directory # (change requires restart) #hba_file = 'ConfigDir/pg_hba.conf' # host-based authentication file # (change requires restart) #ident_file = 'ConfigDir/pg_ident.conf' # ident configuration file # (change requires restart) # If external_pid_file is not explicitly set, no extra PID file is written. #external_pid_file = '(none)' # write an extra PID file # (change requires restart) #--------------------------------------------------------------------------- # CONNECTIONS AND AUTHENTICATION #--------------------------------------------------------------------------- # - Connection Settings - #listen_addresses = 'localhost' # what IP address(es) to listen on; # comma-separated list of addresses; # defaults to 'localhost', '*' = all # (change requires restart) port = 9432 # (change requires restart) max_connections = 100 # (change requires restart) # Note: increasing max_connections costs ~400 bytes of shared memory per # connection slot, plus lock space (see max_locks_per_transaction). You # might also need to raise shared_buffers to support more connections. #superuser_reserved_connections = 3 # (change requires restart) #unix_socket_directory = '' # (change requires restart) #unix_socket_group = '' # (change requires restart) #unix_socket_permissions = 0777 # octal # (change requires restart) #bonjour_name = '' # defaults to the computer name # (change requires restart) # - Security & Authentication - #authentication_timeout = 1min # 1s-600s #ssl = off # (change requires restart) #password_encryption = on #db_user_namespace = off # Kerberos #krb_server_keyfile = '' # (change requires restart) #krb_srvname = 'postgres' # (change requires restart) #krb_server_hostname = '' # empty string matches any keytab entry # (change requires restart) #krb_caseins_users = off # (change requires restart) # - TCP Keepalives - # see 'man 7 tcp' for details #tcp_keepalives_idle = 0 # TCP_KEEPIDLE, in seconds; # 0 selects the system default #tcp_keepalives_interval = 0 # TCP_KEEPINTVL, in seconds; # 0 selects the system default #tcp_keepalives_count = 0 # TCP_KEEPCNT; # 0 selects the system default #--------------------------------------------------------------------------- # RESOURCE USAGE (except WAL) #--------------------------------------------------------------------------- # - Memory - shared_buffers = 64MB # min 128kB or max_connections*16kB # (change requires restart) #temp_buffers = 8MB # min 800kB #max_prepared_transactions = 5 # can be 0 or more # (change requires restart) # Note: increasing max_prepared_transactions costs ~600 bytes of shared memory # per transaction slot, plus lock space (see max_locks_per_transaction). work_mem = 2MB # min 64kB maintenance_work_mem = 32MB # min 1MB #max_stack_depth = 2MB # min 100kB # - Free Space Map - max_fsm_pages = 204800 # min max_fsm_relations*16, 6 bytes each # (change requires restart) #max_fsm_relations = 1000 # min 100, ~70 bytes each # (change requires restart) # - Kernel Resource Usage - #max_files_per_process = 1000 # min 25 # (change requires restart) #shared_preload_libraries = '' # (change requires restart) # - Cost-Based Vacuum Delay - #vacuum_cost_delay = 0 # 0-1000 milliseconds #vacuum_cost_page_hit = 1 # 0-10000 credits #vacuum_cost_page_miss = 10 # 0-10000 credits #vacuum_cost_page_dirty = 20 # 0-10000 credits #vacuum_cost_limit = 200 # 0-10000 credits # - Background writer - #bgwriter_delay = 200ms # 10-10000ms between rounds #bgwriter_lru_percent = 1.0 # 0-100% of LRU buffers scanned/round #bgwriter_lru_maxpages = 5 # 0-1000 buffers max written/round #bgwriter_all_percent = 0.333 # 0-100% of all buffers scanned/round #bgwriter_all_maxpages = 5 # 0-1000 buffers max written/round #--------------------------------------------------------------------------- # WRITE AHEAD LOG #--------------------------------------------------------------------------- # - Settings - fsync = on # turns forced synchronization on or off #wal_sync_method = fsync # the default is the first option # supported by the operating system: # open_datasync # fdatasync # fsync # fsync_writethrough # open_sync #full_page_writes = on # recover from partial page writes #wal_buffers = 64kB # min 32kB # (change requires restart) commit_delay = 100000 # range 0-100000, in microseconds #commit_siblings = 5 # range 1-1000 # - Checkpoints - checkpoint_segments = 10 # in logfile segments, min 1, 16MB each #checkpoint_timeout = 5min # range 30s-1h #checkpoint_warning = 30s # 0 is off # - Archiving - #archive_command = '' # command to use to archive a logfile segment #archive_timeout = 0 # force a logfile segment switch after this # many seconds; 0 is off #--------------------------------------------------------------------------- # QUERY TUNING #--------------------------------------------------------------------------- # - Planner Method Configuration - #enable_bitmapscan = on #enable_hashagg = on #enable_hashjoin = on #enable_indexscan = on #enable_mergejoin = on #enable_nestloop = on #enable_seqscan = on #enable_sort = on #enable_tidscan = on # - Planner Cost Constants - #seq_page_cost = 1.0 # measured on an arbitrary scale #random_page_cost = 4.0 # same scale as above #cpu_tuple_cost = 0.01 # same scale as above #cpu_index_tuple_cost = 0.005 # same scale as above #cpu_operator_cost = 0.0025 # same scale as above #effective_cache_size = 128MB # - Genetic Query Optimizer - #geqo = on #geqo_threshold = 12 #geqo_effort = 5 # range 1-10 #geqo_pool_size = 0 # selects default based on effort #geqo_generations = 0 # selects default based on effort #geqo_selection_bias = 2.0 # range 1.5-2.0 # - Other Planner Options - #default_statistics_target = 10 # range 1-1000 #constraint_exclusion = off #from_collapse_limit = 8 #join_collapse_limit = 8 # 1 disables collapsing of explicit # JOINs #--------------------------------------------------------------------------- # ERROR REPORTING AND LOGGING #--------------------------------------------------------------------------- # - Where to Log - log_destination = 'stderr' # Valid values are combinations of # stderr, syslog and eventlog, # depending on platform. # This is used when logging to stderr: redirect_stderr = on # Enable capturing of stderr into log # files # (change requires restart) # These are only used if redirect_stderr is on: log_directory = '../../logs' # Directory where log files are written # Can be absolute or relative to PGDATA log_filename = 'hqdb-%Y-%m-%d.log' # Log file name pattern. # Can include strftime() escapes #log_truncate_on_rotation = off # If on, any existing log file of the same # name as the new log file will be # truncated rather than appended to. But # such truncation only occurs on # time-driven rotation, not on restarts # or size-driven rotation. Default is # off, meaning append to existing files # in all cases. log_rotation_age = 1d # Automatic rotation of logfiles will # happen after that time. 0 to # disable. #log_rotation_size = 10MB # Automatic rotation of logfiles will # happen after that much log # output. 0 to disable. # These are relevant when logging to syslog: #syslog_facility = 'LOCAL0' #syslog_ident = 'postgres' # - When to Log - #client_min_messages = notice # Values, in order of decreasing detail: # debug5 # debug4 # debug3 # debug2 # debug1 # log # notice # warning # error #log_min_messages = notice # Values, in order of decreasing detail: # debug5 # debug4 # debug3 # debug2 # debug1 # info # notice # warning # error # log # fatal # panic #log_error_verbosity = default # terse, default, or verbose messages #log_min_error_statement = error # Values in order of increasing severity: # debug5 # debug4 # debug3 # debug2 # debug1 # info # notice # warning # error # fatal # panic (effectively off) log_min_duration_statement = 10000 # -1 is disabled, 0 logs all statements # and their durations. #silent_mode = off # DO NOT USE without syslog or # redirect_stderr # (change requires restart) # - What to Log - #debug_print_parse = off #debug_print_rewritten = off #debug_print_plan = off #debug_pretty_print = off #log_connections = off #log_disconnections = off #log_duration = off #log_line_prefix = '' # Special values: # %u = user name # %d = database name # %r = remote host and port # %h = remote host # %p = PID # %t = timestamp (no milliseconds) # %m = timestamp with milliseconds # %i = command tag # %c = session id # %l = session line number # %s = session start timestamp # %x = transaction id # %q = stop here in non-session # processes # %% = '%' # e.g. '<%u%%%d> ' #log_statement = 'none' # none, ddl, mod, all #log_hostname = off #--------------------------------------------------------------------------- # RUNTIME STATISTICS #--------------------------------------------------------------------------- # - Query/Index Statistics Collector - #stats_command_string = on #update_process_title = on stats_start_collector = on # needed for block or row stats # (change requires restart) stats_block_level = on stats_row_level = on stats_reset_on_server_start = off # (change requires restart) # - Statistics Monitoring - #log_parser_stats = off #log_planner_stats = off #log_executor_stats = off #log_statement_stats = off #--------------------------------------------------------------------------- # AUTOVACUUM PARAMETERS #--------------------------------------------------------------------------- #autovacuum = off # enable autovacuum subprocess? # 'on' requires stats_start_collector # and stats_row_level to also be on #autovacuum_naptime = 1min # time between autovacuum runs #autovacuum_vacuum_threshold = 500 # min # of tuple updates before # vacuum #autovacuum_analyze_threshold = 250 # min # of tuple updates before # analyze #autovacuum_vacuum_scale_factor = 0.2 # fraction of rel size before # vacuum #autovacuum_analyze_scale_factor = 0.1 # fraction of rel size before # analyze #autovacuum_freeze_max_age = 200000000 # maximum XID age before forced vacuum # (change requires restart) #autovacuum_vacuum_cost_delay = -1 # default vacuum cost delay for # autovacuum, -1 means use # vacuum_cost_delay #autovacuum_vacuum_cost_limit = -1 # default vacuum cost limit for # autovacuum, -1 means use # vacuum_cost_limit #--------------------------------------------------------------------------- # CLIENT CONNECTION DEFAULTS #--------------------------------------------------------------------------- # - Statement Behavior - #search_path = '"$user",public' # schema names #default_tablespace = '' # a tablespace name, '' uses # the default #check_function_bodies = on #default_transaction_isolation = 'read committed' #default_transaction_read_only = off #statement_timeout = 0 # 0 is disabled #vacuum_freeze_min_age = 100000000 # - Locale and Formatting - datestyle = 'iso, mdy' #timezone = unknown # actually, defaults to TZ # environment setting #timezone_abbreviations = 'Default' # select the set of available timezone # abbreviations. Currently, there are # Default # Australia # India # However you can also create your own # file in share/timezonesets/. #extra_float_digits = 0 # min -15, max 2 #client_encoding = sql_ascii # actually, defaults to database # encoding # These settings are initialized by initdb -- they might be changed lc_messages = 'C' # locale for system error message # strings lc_monetary = 'C' # locale for monetary formatting lc_numeric = 'C' # locale for number formatting lc_time = 'C' # locale for time formatting # - Other Defaults - #explain_pretty_print = on #dynamic_library_path = '$libdir' #local_preload_libraries = '' #--------------------------------------------------------------------------- # LOCK MANAGEMENT #--------------------------------------------------------------------------- #deadlock_timeout = 1s #max_locks_per_transaction = 64 # min 10 # (change requires restart) # Note: each lock table slot uses ~270 bytes of shared memory, and there are # max_locks_per_transaction * (max_connections + max_prepared_transactions) # lock table slots. #--------------------------------------------------------------------------- # VERSION/PLATFORM COMPATIBILITY #--------------------------------------------------------------------------- # - Previous Postgres Versions - #add_missing_from = off #array_nulls = on #backslash_quote = safe_encoding # on, off, or safe_encoding #default_with_oids = off #escape_string_warning = on #standard_conforming_strings = off #regex_flavor = advanced # advanced, extended, or basic #sql_inheritance = on # - Other Platforms & Clients - #transform_null_equals = off #--------------------------------------------------------------------------- # CUSTOMIZED OPTIONS #--------------------------------------------------------------------------- #custom_variable_classes = '' # list of custom variable class names SELECT * FROM pg_stat_activity; datid | datname | procpid | usesysid | usename | current_query | waiting | query_start | backend_start | client_addr | client_port -------+---------+---------+----------+---------+---------------------------------+---------+-------------------------------+-------------------------------+-------------+------------- 16384 | hqdb | 3267 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.036781+01 | 2011-02-08 15:51:20.02413+01 | 127.0.0.1 | 47892 16384 | hqdb | 3268 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.050994+01 | 2011-02-08 15:51:20.047393+01 | 127.0.0.1 | 47893 16384 | hqdb | 3269 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.056661+01 | 2011-02-08 15:51:20.053201+01 | 127.0.0.1 | 47894 16384 | hqdb | 3271 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.062351+01 | 2011-02-08 15:51:20.058822+01 | 127.0.0.1 | 47895 16384 | hqdb | 3272 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.068328+01 | 2011-02-08 15:51:20.064517+01 | 127.0.0.1 | 47896 16384 | hqdb | 3273 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.07444+01 | 2011-02-08 15:51:20.070755+01 | 127.0.0.1 | 47897 16384 | hqdb | 3274 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.080941+01 | 2011-02-08 15:51:20.076983+01 | 127.0.0.1 | 47898 16384 | hqdb | 3275 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.08741+01 | 2011-02-08 15:51:20.083697+01 | 127.0.0.1 | 47899 16384 | hqdb | 3276 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:20.093597+01 | 2011-02-08 15:51:20.089977+01 | 127.0.0.1 | 47900 16384 | hqdb | 3277 | 10 | hqadmin | <IDLE> in transaction | f | 2011-02-08 15:51:20.133974+01 | 2011-02-08 15:51:20.096149+01 | 127.0.0.1 | 47901 16384 | hqdb | 3308 | 10 | hqadmin | <IDLE> | f | 2011-02-09 10:49:27.402197+01 | 2011-02-08 15:51:29.826321+01 | 127.0.0.1 | 47902 16384 | hqdb | 3309 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.572395+01 | 2011-02-08 15:51:29.865243+01 | 127.0.0.1 | 47903 16384 | hqdb | 3310 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.586273+01 | 2011-02-08 15:51:29.874346+01 | 127.0.0.1 | 47904 16384 | hqdb | 3311 | 10 | hqadmin | <IDLE> | f | 2011-02-09 10:10:03.024088+01 | 2011-02-08 15:51:29.883598+01 | 127.0.0.1 | 47905 16384 | hqdb | 3312 | 10 | hqadmin | <IDLE> in transaction | f | 2011-02-08 15:51:35.804457+01 | 2011-02-08 15:51:29.892925+01 | 127.0.0.1 | 47906 16384 | hqdb | 3418 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.580207+01 | 2011-02-08 15:51:55.56911+01 | 127.0.0.1 | 47910 16384 | hqdb | 3419 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.59781+01 | 2011-02-08 15:51:55.588609+01 | 127.0.0.1 | 47911 16384 | hqdb | 3422 | 10 | hqadmin | <IDLE> | f | 2011-02-09 10:10:02.668836+01 | 2011-02-08 15:51:55.603076+01 | 127.0.0.1 | 47914 16384 | hqdb | 3421 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.770427+01 | 2011-02-08 15:51:55.603086+01 | 127.0.0.1 | 47913 16384 | hqdb | 3420 | 10 | hqadmin | <IDLE> | f | 2011-02-08 15:51:55.680785+01 | 2011-02-08 15:51:55.637058+01 | 127.0.0.1 | 47912 16384 | hqdb | 18233 | 10 | hqadmin | SELECT * FROM pg_stat_activity; | f | 2011-02-09 10:49:29.688949+01 | 2011-02-09 10:48:13.031475+01 | | -1 (21 rows)

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• C++ custom exceptions: run time performance and passing exceptions from C++ to C

  - by skyeagle

  I am writing a custom C++ exception class (so I can pass exceptions occuring in C++ to another language via a C API). My initial plan of attack was to proceed as follows: //C++ myClass { public: myClass(); ~myClass(); void foo() // throws myException int foo(const int i, const bool b) // throws myException } * myClassPtr; // C API #ifdef __cplusplus extern "C" { #endif myClassPtr MyClass_New(); void MyClass_Destroy(myClassPtr p); void MyClass_Foo(myClassPtr p); int MyClass_FooBar(myClassPtr p, int i, bool b); #ifdef __cplusplus }; #endif I need a way to be able to pass exceptions thrown in the C++ code to the C side. The information I want to pass to the C side is the following: (a). What (b). Where (c). Simple Stack Trace (just the sequence of error messages in order they occured, no debugging info etc) I want to modify my C API, so that the API functions take a pointer to a struct ExceptionInfo, which will contain any exception info (if an exception occured) before consuming the results of the invocation. This raises two questions: Question 1 1. Implementation of each of the C++ methods exposed in the C API needs to be enclosed in a try/catch statement. The performance implications for this seem quite serious (according to this article): "It is a mistake (with high runtime cost) to use C++ exception handling for events that occur frequently, or for events that are handled near the point of detection." At the same time, I remember reading somewhere in my C++ days, that all though exception handling is expensive, it only becmes expensive when an exception actually occurs. So, which is correct?. what to do?. Is there an alternative way that I can trap errors safely and pass the resulting error info to the C API?. Or is this a minor consideration (the article after all, is quite old, and hardware have improved a bit since then). Question 2 I wuld like to modify the exception class given in that article, so that it contains a simple stack trace, and I need some help doing that. Again, in order to make the exception class 'lightweight', I think its a good idea not to include any STL classes, like string or vector (good idea/bad idea?). Which potentially leaves me with a fixed length C string (char*) which will be stack allocated. So I can maybe just keep appending messages (delimted by a unique separator [up to maximum length of buffer])... Its been a while since I did any serious C++ coding, and I will be grateful for the help. BTW, this is what I have come up with so far (I am intentionally, not deriving from std::exception because of the performance reasons mentioned in the article, and I am instead, throwing an integral exception (based on an exception enumeration): class fast_exception { public: fast_exception(int what, char const* file=0, int line=0) : what_(what), line_(line), file_(file) {/*empty*/} int what() const { return what_; } int line() const { return line_; } char const* file() const { return file_; } private: int what_; int line_; char const[MAX_BUFFER_SIZE] file_; }

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• Opening an SQL CE file at runtime with Entity Framework 4

  - by David Veeneman

  I am getting started with Entity Framework 4, and I an creating a demo app as a learning exercise. The app is a simple documentation builder, and it uses a SQL CE store. Each documentation project has its own SQL CE data file, and the user opens one of these files to work on a project. The EDM is very simple. A documentation project is comprised of a list of subjects, each of which has a title, a description, and zero or more notes. So, my entities are Subject, which contains Title and Text properties, and Note, which has Title and Text properties. There is a one-to-many association from Subject to Note. I am trying to figure out how to open an SQL CE data file. A data file must match the schema of the SQL CE database created by EF4's Create Database Wizard, and I will implement a New File use case elsewhere in the app to implement that requirement. Right now, I am just trying to get an existing data file open in the app. I have reproduced my existing 'Open File' code below. I have set it up as a static service class called File Services. The code isn't working quite yet, but there is enough to show what I am trying to do. I am trying to hold the ObjectContext open for entity object updates, disposing it when the file is closed. So, here is my question: Am I on the right track? What do I need to change to make this code work with EF4? Is there an example of how to do this properly? Thanks for your help. My existing code: public static class FileServices { #region Private Fields // Member variables private static EntityConnection m_EntityConnection; private static ObjectContext m_ObjectContext; #endregion #region Service Methods /// <summary> /// Opens an SQL CE database file. /// </summary> /// <param name="filePath">The path to the SQL CE file to open.</param> /// <param name="viewModel">The main window view model.</param> public static void OpenSqlCeFile(string filePath, MainWindowViewModel viewModel) { // Configure an SQL CE connection string var sqlCeConnectionString = string.Format("Data Source={0}", filePath); // Configure an EDM connection string var builder = new EntityConnectionStringBuilder(); builder.Metadata = "res://*/EF4Model.csdl|res://*/EF4Model.ssdl|res://*/EF4Model.msl"; builder.Provider = "System.Data.SqlServerCe"; builder.ProviderConnectionString = sqlCeConnectionString; var entityConnectionString = builder.ToString(); // Connect to the model m_EntityConnection = new EntityConnection(entityConnectionString); m_EntityConnection.Open(); // Create an object context m_ObjectContext = new Model1Container(); // Get all Subject data IQueryable<Subject> subjects = from s in Subjects orderby s.Title select s; // Set view model data property viewModel.Subjects = new ObservableCollection<Subject>(subjects); } /// <summary> /// Closes an SQL CE database file. /// </summary> public static void CloseSqlCeFile() { m_EntityConnection.Close(); m_ObjectContext.Dispose(); } #endregion }

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• push_back of STL list got bad performance?

  - by Leon Zhang

  I wrote a simple program to test STL list performance against a simple C list-like data structure. It shows bad performance at "push_back()" line. Any comments on it? $ ./test2 Build the type list : time consumed -> 0.311465 Iterate over all items: time consumed -> 0.00898 Build the simple C List: time consumed -> 0.020275 Iterate over all items: time consumed -> 0.008755 The source code is: #include <stdexcept> #include "high_resolution_timer.hpp" #include <list> #include <algorithm> #include <iostream> #define TESTNUM 1000000 /* The test struct */ struct MyType { int num; }; /* * C++ STL::list Test */ typedef struct MyType* mytype_t; void myfunction(mytype_t t) { } int test_stl_list() { std::list<mytype_t> mylist; util::high_resolution_timer t; /* * Build the type list */ t.restart(); for(int i = 0; i < TESTNUM; i++) { mytype_t aItem = (mytype_t) malloc(sizeof(struct MyType)); if(aItem == NULL) { printf("Error: while malloc\n"); return -1; } aItem->num = i; mylist.push_back(aItem); } std::cout << " Build the type list : time consumed -> " << t.elapsed() << std::endl; /* * Iterate over all item */ t.restart(); std::for_each(mylist.begin(), mylist.end(), myfunction); std::cout << " Iterate over all items: time consumed -> " << t.elapsed() << std::endl; return 0; } /* * a simple C list */ struct MyCList; struct MyCList{ struct MyType m; struct MyCList* p_next; }; int test_simple_c_list() { struct MyCList* p_list_head = NULL; util::high_resolution_timer t; /* * Build it */ t.restart(); struct MyCList* p_new_item = NULL; for(int i = 0; i < TESTNUM; i++) { p_new_item = (struct MyCList*) malloc(sizeof(struct MyCList)); if(p_new_item == NULL) { printf("ERROR : while malloc\n"); return -1; } p_new_item->m.num = i; p_new_item->p_next = p_list_head; p_list_head = p_new_item; } std::cout << " Build the simple C List: time consumed -> " << t.elapsed() << std::endl; /* * Iterate all items */ t.restart(); p_new_item = p_list_head; while(p_new_item->p_next != NULL) { p_new_item = p_new_item->p_next; } std::cout << " Iterate over all items: time consumed -> " << t.elapsed() << std::endl; return 0; } int main(int argc, char** argv) { if(test_stl_list() != 0) { printf("ERROR: error at testcase1\n"); return -1; } if(test_simple_c_list() != 0) { printf("ERROR: error at testcase2\n"); return -1; } return 0; }

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• UIButton performance in UITableViewCell vs UIView

  - by marcel salathe

  I'd like to add a UIButton to a custom UITableViewCell (programmatically). This is easy to do, but I'm finding that the "performance" of the button in the cell is slow - that is, when I touch the button, there is quite a bit of delay until the button visually goes into the highlighted state. The same type of button on a regular UIView is very responsive in comparison. In order to isolate the problem, I've created two views - one is a simple UIView, the other is a UITableView with only one UITableViewCell. I've added buttons to both views (the UIView and the UITableViewCell), and the performance difference is quite striking. I've searched the web and read the Apple docs but haven't really found the cause of the problem. My guess is that it somehow has to do with the responder chain, but I can't quite put my finger on it. I must be doing something wrong, and I'd appreciate any help. Thanks. Demo code: ViewController.h #import <UIKit/UIKit.h> @interface ViewController : UIViewController <UITableViewDelegate, UITableViewDataSource> @property UITableView* myTableView; @property UIView* myView; ViewController.m #import "ViewController.h" #import "CustomCell.h" @implementation ViewController @synthesize myTableView, myView; - (id)initWithNibName:(NSString *)nibNameOrNil bundle:(NSBundle *)nibBundleOrNil { self = [super initWithNibName:nibNameOrNil bundle:nibBundleOrNil]; if (self) { [self initMyView]; [self initMyTableView]; } return self; } - (void) initMyView { UIView* newView = [[UIView alloc] initWithFrame:CGRectMake(0,0,[[UIScreen mainScreen] bounds].size.width,100)]; self.myView = newView; // button on regularView UIButton* myButton = [UIButton buttonWithType:UIButtonTypeRoundedRect]; [myButton addTarget:self action:@selector(pressedMyButton) forControlEvents:UIControlEventTouchUpInside]; [myButton setTitle:@"I'm fast" forState:UIControlStateNormal]; [myButton setFrame:CGRectMake(20.0, 10.0, 160.0, 30.0)]; [[self myView] addSubview:myButton]; } - (void) initMyTableView { UITableView *newTableView = [[UITableView alloc] initWithFrame:CGRectMake(0,100,[[UIScreen mainScreen] bounds].size.width,[[UIScreen mainScreen] bounds].size.height-100) style:UITableViewStyleGrouped]; self.myTableView = newTableView; self.myTableView.delegate = self; self.myTableView.dataSource = self; } -(void) pressedMyButton { NSLog(@"pressedMyButton"); } - (void)viewDidLoad { [super viewDidLoad]; [[self view] addSubview:self.myView]; [[self view] addSubview:self.myTableView]; } - (NSInteger)numberOfSectionsInTableView:(UITableView *)tableView { return 1; } - (NSInteger)tableView:(UITableView *)tableView numberOfRowsInSection:(NSInteger)section { return 1; } - (UITableViewCell *)tableView:(UITableView *)tableView cellForRowAtIndexPath:(NSIndexPath *)indexPath { CustomCell *customCell = [tableView dequeueReusableCellWithIdentifier:@"CustomCell"]; if (customCell == nil) { customCell = [[CustomCell alloc] initWithStyle:UITableViewCellStyleSubtitle reuseIdentifier:@"CustomCell"]; } return customCell; } @end CustomCell.h #import <UIKit/UIKit.h> @interface CustomCell : UITableViewCell @property (retain, nonatomic) UIButton* cellButton; @end CustomCell.m #import "CustomCell.h" @implementation CustomCell @synthesize cellButton; - (id)initWithStyle:(UITableViewCellStyle)style reuseIdentifier:(NSString *)reuseIdentifier { self = [super initWithStyle:style reuseIdentifier:reuseIdentifier]; if (self) { // button within cell cellButton = [UIButton buttonWithType:UIButtonTypeRoundedRect]; [cellButton addTarget:self action:@selector(pressedCellButton) forControlEvents:UIControlEventTouchUpInside]; [cellButton setTitle:@"I'm sluggish" forState:UIControlStateNormal]; [cellButton setFrame:CGRectMake(20.0, 10.0, 160.0, 30.0)]; [self addSubview:cellButton]; } return self; } - (void) pressedCellButton { NSLog(@"pressedCellButton"); } @end

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• What is the fastest cyclic synchronization in Java (ExecutorService vs. CyclicBarrier vs. X)?

  - by Alex Dunlop

  Which Java synchronization construct is likely to provide the best performance for a concurrent, iterative processing scenario with a fixed number of threads like the one outlined below? After experimenting on my own for a while (using ExecutorService and CyclicBarrier) and being somewhat surprised by the results, I would be grateful for some expert advice and maybe some new ideas. Existing questions here do not seem to focus primarily on performance, hence this new one. Thanks in advance! The core of the app is a simple iterative data processing algorithm, parallelized to the spread the computational load across 8 cores on a Mac Pro, running OS X 10.6 and Java 1.6.0_07. The data to be processed is split into 8 blocks and each block is fed to a Runnable to be executed by one of a fixed number of threads. Parallelizing the algorithm was fairly straightforward, and it functionally works as desired, but its performance is not yet what I think it could be. The app seems to spend a lot of time in system calls synchronizing, so after some profiling I wonder whether I selected the most appropriate synchronization mechanism(s). A key requirement of the algorithm is that it needs to proceed in stages, so the threads need to sync up at the end of each stage. The main thread prepares the work (very low overhead), passes it to the threads, lets them work on it, then proceeds when all threads are done, rearranges the work (again very low overhead) and repeats the cycle. The machine is dedicated to this task, Garbage Collection is minimized by using per-thread pools of pre-allocated items, and the number of threads can be fixed (no incoming requests or the like, just one thread per CPU core). V1 - ExecutorService My first implementation used an ExecutorService with 8 worker threads. The program creates 8 tasks holding the work and then lets them work on it, roughly like this: // create one thread per CPU executorService = Executors.newFixedThreadPool( 8 ); ... // now process data in cycles while( ...) { // package data into 8 work items ... // create one Callable task per work item ... // submit the Callables to the worker threads executorService.invokeAll( taskList ); } This works well functionally (it does what it should), and for very large work items indeed all 8 CPUs become highly loaded, as much as the processing algorithm would be expected to allow (some work items will finish faster than others, then idle). However, as the work items become smaller (and this is not really under the program's control), the user CPU load shrinks dramatically: blocksize | system | user | cycles/sec 256k 1.8% 85% 1.30 64k 2.5% 77% 5.6 16k 4% 64% 22.5 4096 8% 56% 86 1024 13% 38% 227 256 17% 19% 420 64 19% 17% 948 16 19% 13% 1626 Legend: - block size = size of the work item (= computational steps) - system = system load, as shown in OS X Activity Monitor (red bar) - user = user load, as shown in OS X Activity Monitor (green bar) - cycles/sec = iterations through the main while loop, more is better The primary area of concern here is the high percentage of time spent in the system, which appears to be driven by thread synchronization calls. As expected, for smaller work items, ExecutorService.invokeAll() will require relatively more effort to sync up the threads versus the amount of work being performed in each thread. But since ExecutorService is more generic than it would need to be for this use case (it can queue tasks for threads if there are more tasks than cores), I though maybe there would be a leaner synchronization construct. V2 - CyclicBarrier The next implementation used a CyclicBarrier to sync up the threads before receiving work and after completing it, roughly as follows: main() { // create the barrier barrier = new CyclicBarrier( 8 + 1 ); // create Runable for thread, tell it about the barrier Runnable task = new WorkerThreadRunnable( barrier ); // start the threads for( int i = 0; i < 8; i++ ) { // create one thread per core new Thread( task ).start(); } while( ... ) { // tell threads about the work ... // N threads + this will call await(), then system proceeds barrier.await(); // ... now worker threads work on the work... // wait for worker threads to finish barrier.await(); } } class WorkerThreadRunnable implements Runnable { CyclicBarrier barrier; WorkerThreadRunnable( CyclicBarrier barrier ) { this.barrier = barrier; } public void run() { while( true ) { // wait for work barrier.await(); // do the work ... // wait for everyone else to finish barrier.await(); } } } Again, this works well functionally (it does what it should), and for very large work items indeed all 8 CPUs become highly loaded, as before. However, as the work items become smaller, the load still shrinks dramatically: blocksize | system | user | cycles/sec 256k 1.9% 85% 1.30 64k 2.7% 78% 6.1 16k 5.5% 52% 25 4096 9% 29% 64 1024 11% 15% 117 256 12% 8% 169 64 12% 6.5% 285 16 12% 6% 377 For large work items, synchronization is negligible and the performance is identical to V1. But unexpectedly, the results of the (highly specialized) CyclicBarrier seem MUCH WORSE than those for the (generic) ExecutorService: throughput (cycles/sec) is only about 1/4th of V1. A preliminary conclusion would be that even though this seems to be the advertised ideal use case for CyclicBarrier, it performs much worse than the generic ExecutorService. V3 - Wait/Notify + CyclicBarrier It seemed worth a try to replace the first cyclic barrier await() with a simple wait/notify mechanism: main() { // create the barrier // create Runable for thread, tell it about the barrier // start the threads while( ... ) { // tell threads about the work // for each: workerThreadRunnable.setWorkItem( ... ); // ... now worker threads work on the work... // wait for worker threads to finish barrier.await(); } } class WorkerThreadRunnable implements Runnable { CyclicBarrier barrier; @NotNull volatile private Callable<Integer> workItem; WorkerThreadRunnable( CyclicBarrier barrier ) { this.barrier = barrier; this.workItem = NO_WORK; } final protected void setWorkItem( @NotNull final Callable<Integer> callable ) { synchronized( this ) { workItem = callable; notify(); } } public void run() { while( true ) { // wait for work while( true ) { synchronized( this ) { if( workItem != NO_WORK ) break; try { wait(); } catch( InterruptedException e ) { e.printStackTrace(); } } } // do the work ... // wait for everyone else to finish barrier.await(); } } } Again, this works well functionally (it does what it should). blocksize | system | user | cycles/sec 256k 1.9% 85% 1.30 64k 2.4% 80% 6.3 16k 4.6% 60% 30.1 4096 8.6% 41% 98.5 1024 12% 23% 202 256 14% 11.6% 299 64 14% 10.0% 518 16 14.8% 8.7% 679 The throughput for small work items is still much worse than that of the ExecutorService, but about 2x that of the CyclicBarrier. Eliminating one CyclicBarrier eliminates half of the gap. V4 - Busy wait instead of wait/notify Since this app is the primary one running on the system and the cores idle anyway if they're not busy with a work item, why not try a busy wait for work items in each thread, even if that spins the CPU needlessly. The worker thread code changes as follows: class WorkerThreadRunnable implements Runnable { // as before final protected void setWorkItem( @NotNull final Callable<Integer> callable ) { workItem = callable; } public void run() { while( true ) { // busy-wait for work while( true ) { if( workItem != NO_WORK ) break; } // do the work ... // wait for everyone else to finish barrier.await(); } } } Also works well functionally (it does what it should). blocksize | system | user | cycles/sec 256k 1.9% 85% 1.30 64k 2.2% 81% 6.3 16k 4.2% 62% 33 4096 7.5% 40% 107 1024 10.4% 23% 210 256 12.0% 12.0% 310 64 11.9% 10.2% 550 16 12.2% 8.6% 741 For small work items, this increases throughput by a further 10% over the CyclicBarrier + wait/notify variant, which is not insignificant. But it is still much lower-throughput than V1 with the ExecutorService. V5 - ? So what is the best synchronization mechanism for such a (presumably not uncommon) problem? I am weary of writing my own sync mechanism to completely replace ExecutorService (assuming that it is too generic and there has to be something that can still be taken out to make it more efficient). It is not my area of expertise and I'm concerned that I'd spend a lot of time debugging it (since I'm not even sure my wait/notify and busy wait variants are correct) for uncertain gain. Any advice would be greatly appreciated.

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• Null-free "maps": Is a callback solution slower than tryGet()?

  - by David Moles

  In comments to "How to implement List, Set, and Map in null free design?", Steven Sudit and I got into a discussion about using a callback, with handlers for "found" and "not found" situations, vs. a tryGet() method, taking an out parameter and returning a boolean indicating whether the out parameter had been populated. Steven maintained that the callback approach was more complex and almost certain to be slower; I maintained that the complexity was no greater and the performance at worst the same. But code speaks louder than words, so I thought I'd implement both and see what I got. The original question was fairly theoretical with regard to language ("And for argument sake, let's say this language don't even have null") -- I've used Java here because that's what I've got handy. Java doesn't have out parameters, but it doesn't have first-class functions either, so style-wise, it should suck equally for both approaches. (Digression: As far as complexity goes: I like the callback design because it inherently forces the user of the API to handle both cases, whereas the tryGet() design requires callers to perform their own boilerplate conditional check, which they could forget or get wrong. But having now implemented both, I can see why the tryGet() design looks simpler, at least in the short term.) First, the callback example: class CallbackMap<K, V> { private final Map<K, V> backingMap; public CallbackMap(Map<K, V> backingMap) { this.backingMap = backingMap; } void lookup(K key, Callback<K, V> handler) { V val = backingMap.get(key); if (val == null) { handler.handleMissing(key); } else { handler.handleFound(key, val); } } } interface Callback<K, V> { void handleFound(K key, V value); void handleMissing(K key); } class CallbackExample { private final Map<String, String> map; private final List<String> found; private final List<String> missing; private Callback<String, String> handler; public CallbackExample(Map<String, String> map) { this.map = map; found = new ArrayList<String>(map.size()); missing = new ArrayList<String>(map.size()); handler = new Callback<String, String>() { public void handleFound(String key, String value) { found.add(key + ": " + value); } public void handleMissing(String key) { missing.add(key); } }; } void test() { CallbackMap<String, String> cbMap = new CallbackMap<String, String>(map); for (int i = 0, count = map.size(); i < count; i++) { String key = "key" + i; cbMap.lookup(key, handler); } System.out.println(found.size() + " found"); System.out.println(missing.size() + " missing"); } } Now, the tryGet() example -- as best I understand the pattern (and I might well be wrong): class TryGetMap<K, V> { private final Map<K, V> backingMap; public TryGetMap(Map<K, V> backingMap) { this.backingMap = backingMap; } boolean tryGet(K key, OutParameter<V> valueParam) { V val = backingMap.get(key); if (val == null) { return false; } valueParam.value = val; return true; } } class OutParameter<V> { V value; } class TryGetExample { private final Map<String, String> map; private final List<String> found; private final List<String> missing; public TryGetExample(Map<String, String> map) { this.map = map; found = new ArrayList<String>(map.size()); missing = new ArrayList<String>(map.size()); } void test() { TryGetMap<String, String> tgMap = new TryGetMap<String, String>(map); for (int i = 0, count = map.size(); i < count; i++) { String key = "key" + i; OutParameter<String> out = new OutParameter<String>(); if (tgMap.tryGet(key, out)) { found.add(key + ": " + out.value); } else { missing.add(key); } } System.out.println(found.size() + " found"); System.out.println(missing.size() + " missing"); } } And finally, the performance test code: public static void main(String[] args) { int size = 200000; Map<String, String> map = new HashMap<String, String>(); for (int i = 0; i < size; i++) { String val = (i % 5 == 0) ? null : "value" + i; map.put("key" + i, val); } long totalCallback = 0; long totalTryGet = 0; int iterations = 20; for (int i = 0; i < iterations; i++) { { TryGetExample tryGet = new TryGetExample(map); long tryGetStart = System.currentTimeMillis(); tryGet.test(); totalTryGet += (System.currentTimeMillis() - tryGetStart); } System.gc(); { CallbackExample callback = new CallbackExample(map); long callbackStart = System.currentTimeMillis(); callback.test(); totalCallback += (System.currentTimeMillis() - callbackStart); } System.gc(); } System.out.println("Avg. callback: " + (totalCallback / iterations)); System.out.println("Avg. tryGet(): " + (totalTryGet / iterations)); } On my first attempt, I got 50% worse performance for callback than for tryGet(), which really surprised me. But, on a hunch, I added some garbage collection, and the performance penalty vanished. This fits with my instinct, which is that we're basically talking about taking the same number of method calls, conditional checks, etc. and rearranging them. But then, I wrote the code, so I might well have written a suboptimal or subconsicously penalized tryGet() implementation. Thoughts?

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• Oracle performance report

  - by John

  Hi, Is there any way of running the $ORACLE_HOME/rdbms/admin/awrrpt.sql so that it doesn't require any input parameters, as in automatically collects a report for the previous hour? /j

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• Preloading Winforms using a Stack and Hidden Form

  - by msarchet

  I am currently working on a project where we have a couple very control heavy user controls that are being used inside a MDI Controller. This is a Line of Business app and it is very data driven. The problem that we were facing was the aforementioned controls would load very very slowly, we dipped our toes into the waters of multi-threading for the control loading but that was not a solution for a plethora of reasons. Our solution to increasing the performance of the controls ended up being to 'pre-load' the forms onto a hidden window, create a stack of the existing forms, and pop off of the stack as the user requested a form. Now the current issue that I'm seeing that will arise as we push this 'fix' out to our testers, and the ultimately our users is this: Currently the 'hidden' window that contains the preloaded forms is visible in task manager, and can be shut down thus causing all of the controls to be lost. Then you have to create them on the fly losing the performance increase. Secondly, when the user uses up the stack we lose the performance increase (current solution to this is discussed below). For the first problem, is there a way to hide this window from task manager, perhaps by creating a parent form that encapsulates both the main form for the program and the hidden form? Our current solution to the second problem is to have an inactivity timer that when it fires checks the stacks for the forms, and loads a new form onto the stack if it isn't full. However this still has the potential of causing a hang in the UI while it creates the forms. A possible solutions for this would be to put 'used' forms back onto the stack, but I feel like there may be a better way. EDIT: For control design clarification From the comments I have realized there is a lack of clarity on what exactly the control is doing. Here is a detailed explanation of one of the controls. I have defined for this control loading time as the time it takes from when a user performs an action that would open a control, until the time a control is accessible to be edited. The control is for entering Prescriptions for a patient in the system, it has about 5 tabbed groups with a total of about 180 controls. The user selects to open a new Prescription control from inside the main program, this control is loaded into the MDI Child area of the Main Form (which is a DevExpress Ribbon Control). From the time the user clicks New (or loads an existing record) until the control is visible. The list of actions that happens in the program is this: The stack is checked for the existence of a control. If the control exists it is popped off of the stack. The control is rendered on screen. This is what takes 2 seconds The control then is populated with a blank object, or with existing data. The control is ready to use. The average percentage of loading time, across about 10 different machines, with different hardware the control rendering takes about 85 - 95 percent of the control loading time. Without using the stack the control takes about 2 seconds to load, with the stack it takes about .8 seconds, this second time is acceptable. I have looked at Henry's link and I had previously already implemented the applicable suggestions. Again I re-iterate my question as What is the best method to move controls to and from the stack with as little UI interruption as possible?

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• Random Complete System Unresponsiveness Running Mathematical Functions

  - by Computer Guru

  I have a program that loads a file (anywhere from 10MB to 5GB) a chunk at a time (ReadFile), and for each chunk performs a set of mathematical operations (basically calculates the hash). After calculating the hash, it stores info about the chunk in an STL map (basically <chunkID, hash>) and then writes the chunk itself to another file (WriteFile). That's all it does. This program will cause certain PCs to choke and die. The mouse begins to stutter, the task manager takes 2 min to show, ctrl+alt+del is unresponsive, running programs are slow.... the works. I've done literally everything I can think of to optimize the program, and have triple-checked all objects. What I've done: Tried different (less intensive) hashing algorithms. Switched all allocations to nedmalloc instead of the default new operator Switched from stl::map to unordered_set, found the performance to still be abysmal, so I switched again to Google's dense_hash_map. Converted all objects to store pointers to objects instead of the objects themselves. Caching all Read and Write operations. Instead of reading a 16k chunk of the file and performing the math on it, I read 4MB into a buffer and read 16k chunks from there instead. Same for all write operations - they are coalesced into 4MB blocks before being written to disk. Run extensive profiling with Visual Studio 2010, AMD Code Analyst, and perfmon. Set the thread priority to THREAD_MODE_BACKGROUND_BEGIN Set the thread priority to THREAD_PRIORITY_IDLE Added a Sleep(100) call after every loop. Even after all this, the application still results in a system-wide hang on certain machines under certain circumstances. Perfmon and Process Explorer show minimal CPU usage (with the sleep), no constant reads/writes from disk, few hard pagefaults (and only ~30k pagefaults in the lifetime of the application on a 5GB input file), little virtual memory (never more than 150MB), no leaked handles, no memory leaks. The machines I've tested it on run Windows XP - Windows 7, x86 and x64 versions included. None have less than 2GB RAM, though the problem is always exacerbated under lower memory conditions. I'm at a loss as to what to do next. I don't know what's causing it - I'm torn between CPU or Memory as the culprit. CPU because without the sleep and under different thread priorities the system performances changes noticeably. Memory because there's a huge difference in how often the issue occurs when using unordered_set vs Google's dense_hash_map. What's really weird? Obviously, the NT kernel design is supposed to prevent this sort of behavior from ever occurring (a user-mode application driving the system to this sort of extreme poor performance!?)..... but when I compile the code and run it on OS X or Linux (it's fairly standard C++ throughout) it performs excellently even on poor machines with little RAM and weaker CPUs. What am I supposed to do next? How do I know what the hell it is that Windows is doing behind the scenes that's killing system performance, when all the indicators are that the application itself isn't doing anything extreme? Any advice would be most welcome.

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