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  • Google I/O 2010 - Next gen queries

    Google I/O 2010 - Next gen queries Google I/O 2010 - Next gen queries App Engine 301 Alfred Fuller This session will discuss the design and implications of improvements to the Datastore query engine including support for AND, OR and NOT query operators, the solution to exploding indexes and paging backwards with Cursors. Specific technologies discussed will be an improved zigzag merge join algorithm, a new extensible multiquery framework (with geo-query support) and a smaller more versatile Cursor design. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 16 1 ratings Time: 50:17 More in Science & Technology

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  • How to get current gnome keyboad layout from terminal

    - by ftiaronsem
    For usage in a bash script, I need to get the gnome keyboard layout the user is currently using. For example if the user sets its keyboard layout to en-us , I need a bash command that prints me this. How can I get that information? Update: setxkbmap -query is unfortunatelly not working. Below is the ouput with the en (first command) and the de (second command) layout activated. Switching keyboard layout seems to be have some relation with gnome session configuration setxkbmap -query rules: evdev model: pc105 layout: us,de variant: , options: terminate:ctrl_alt_bksp,lv3:ralt_switch,grp:alts_toggle setxkbmap -query rules: evdev model: pc105 layout: us,de variant: , options: terminate:ctrl_alt_bksp,lv3:ralt_switch,grp:alts_toggle

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  • Part 1 - Load Testing In The Cloud

    - by Tarun Arora
    Azure is fascinating, but even more fascinating is the marriage of Azure and TFS! Introduction Recently a client I worked for had 2 major business critical applications being delivered, with very little time budgeted for Performance testing, we immediately hit a bottleneck when the performance testing phase started, the in house infrastructure team could not support the hardware requirements in the short notice. It was suggested that the performance testing be performed on one of the QA environments which was a fraction of the production environment. This didn’t seem right, the team decided to turn to the cloud. The team took advantage of the elasticity offered by Azure, starting with a single test agent which was provisioned and ready for use with in 30 minutes the team scaled up to 17 test agents to perform a very comprehensive performance testing cycle. Issues were identified and resolved but the highlight was that the cost of running the ‘test rig’ proved to be less than if hosted on premise by the infrastructure team. Thank you for taking the time out to read this blog post, in the series of posts, I’ll try and cover the start to end of everything you need to know to use Azure to build your Test Rig in the cloud. But Why Azure? I have my own Data Centre… If the environment is provisioned in your own datacentre, - No matter what level of service agreement you may have with your infrastructure team there will be down time when the environment is patched - How fast can you scale up or down the environments (keeping the enterprise processes in mind) Administration, Cost, Flexibility and Scalability are the areas you would want to think around when taking the decision between your own Data Centre and Azure! How is Microsoft's Public Cloud Offering different from Amazon’s Public Cloud Offering? Microsoft's offering of the Cloud is a hybrid of Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) which distinguishes Microsoft's offering from other providers such as Amazon (Amazon only offers IaaS). PaaS – Platform as a Service IaaS – Infrastructure as a Service Fills the needs of those who want to build and run custom applications as services. Similar to traditional hosting, where a business will use the hosted environment as a logical extension of the on-premises datacentre. A service provider offers a pre-configured, virtualized application server environment to which applications can be deployed by the development staff. Since the service providers manage the hardware (patching, upgrades and so forth), as well as application server uptime, the involvement of IT pros is minimized. On-demand scalability combined with hardware and application server management relieves developers from infrastructure concerns and allows them to focus on building applications. The servers (physical and virtual) are rented on an as-needed basis, and the IT professionals who manage the infrastructure have full control of the software configuration. This kind of flexibility increases the complexity of the IT environment, as customer IT professionals need to maintain the servers as though they are on-premises. The maintenance activities may include patching and upgrades of the OS and the application server, load balancing, failover clustering of database servers, backup and restoration, and any other activities that mitigate the risks of hardware and software failures.   The biggest advantage with PaaS is that you do not have to worry about maintaining the environment, you can focus all your time in solving the business problems with your solution rather than worrying about maintaining the environment. If you decide to use a VM Role on Azure, you are asking for IaaS, more on this later. A nice blog post here on the difference between Saas, PaaS and IaaS. Now that we are convinced why we should be turning to the cloud and why in specific Azure, let’s discuss about the Test Rig. The Load Test Rig – Topology Now the moment of truth, Of course a big part of getting value from cloud computing is identifying the most adequate workloads to take to the cloud, so I’ve decided to try to make a Load Testing rig where the Agents are running on Windows Azure.   I’ll talk you through the above Topology, - User: User kick starts the load test run from the developer workstation on premise. This passes the request to the Test Controller. - Test Controller: The Test Controller is on premise connected to the same domain as the developer workstation. As soon as the Test Controller receives the request it makes use of the Windows Azure Connect service to orchestrate the test responsibilities to all the Test Agents. The Windows Azure Connect endpoint software must be active on all Azure instances and on the Controller machine as well. This allows IP connectivity between them and, given that the firewall is properly configured, allows the Controller to send work loads to the agents. In parallel, the Controller will collect the performance data from the agents, using the traditional WMI mechanisms. - Test Agents: The Test Agents are on the Windows Azure Public Cloud, as soon as the test controller issues instructions to the test agents, the test agents start executing the load tests. The HTTP requests are issued against the web server on premise, the results are captured by the test agents. And finally the results are passed over to the controller. - Servers: The Web Server and DB Server are hosted on premise in the datacentre, this is usually the case with business critical applications, you probably want to manage them your self. Recap and What’s next? So, in the introduction in the series of blog posts on Load Testing in the cloud I highlighted why creating a test rig in the cloud is a good idea, what advantages does Windows Azure offer and the Test Rig topology that I will be using. I would also like to mention that i stumbled upon this [Video] on Azure in a nutshell, great watch if you are new to Windows Azure. In the next post I intend to start setting up the Load Test Environment and discuss pricing with respect to test agent machine types that will be used in the test rig. Hope you enjoyed this post, If you have any recommendations on things that I should consider or any questions or feedback, feel free to add to this blog post. Remember to subscribe to http://feeds.feedburner.com/TarunArora.  See you in Part II.   Share this post : CodeProject

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  • To My 24 Year Old Self, Wherever You Are&hellip;

    - by D'Arcy Lussier
    A decade is a milestone in one’s life, regardless of when it occurs. 2011 might seem like a weird year to mark a decade, but 2001 was a defining year for me. It marked my emergence into the technology industry, an unexpected loss of innocence, and triggered an ongoing struggle with faith and belief. Once you go through a valley, climbing the mountain and looking back over where you travelled, you can take in the entirety of the journey. Over the last 10 years I kept journals, and in this new year I took some time to review them. For those today that are me a decade ago, I share with you what I’ve gleamed from my experiences. Take it for what it’s worth, and safe travels on your own journeys through life. Life is a Performance-Based Sport Have confidence, believe you’re capable, but realize that life is a performance-based sport. Everything you get in life is based on whether you can show that you deserve it. Performance is also your best defense against personal attacks. Just make sure you know what standards you’re expected to hit and if people want to poke holes at you let them do the work of trying to find them. Sometimes performance won’t matter though. Good things will happen to bad people, and bad things to good people. What’s important is that you do the right things and ensure the good and bad even out in your own life. How you finish is just as important as how you start. Start strong, end strong. Respect is Your Most Prized Reward Respect is more important than status or ego. The formula is simple: Performing Well + Building Trust + Showing Dedication = Respect Focus on perfecting your craft and helping your team and respect will come. Life is a Team Sport Whatever aspect of your life, you can’t do it alone. You need to rely on the people around you and ensure you’re a positive aspect of their lives; even those that may be difficult or unpleasant. Avoid criticism and instead find ways to help colleagues and superiors better whatever environment you’re in (work, home, etc.). Don’t just highlight gaps and issues, but also come to the table with solutions. At the same time though, stand up for yourself and hold others accountable for the commitments they make to the team. A healthy team needs accountability. Give feedback early and often, and make it verbal. Issues should be dealt with immediately, and positives should be celebrated as they happen. Life is a Contact Sport Difficult moments will happen. Don’t run from them or shield yourself from experiencing them. Embrace them. They will further mold you and reveal who you will become. Find Your Tribe and Embrace Your Community We all need a tribe: a group of people that we gravitate to for support, guidance, wisdom, and friendship. Discover your tribe and immerse yourself in them. Don’t look for a non-existent tribe just to fill the need of belonging though that will leave you empty and bitter when they don’t meet your unrealistic expectations. Try to associate with people more experienced and more knowledgeable than you. You’ll always learn, and you’ll always remember you have much to learn. Put yourself out there, get involved with the community. Opportunities will present themselves. When we open ourselves up to be vulnerable, we also give others the chance to do the same. This helps us all to grow and help each other, it’s very important. And listen to your wife. (Easter *is* a romantic holiday btw, regardless of what you may think.) Don’t Believe Your Own Press Clippings (and by that I mean the ones you write) Until you have a track record of performance to refer to, any notions of grandeur are just that: notions. You lose your rookie status through trials and tribulations, not by the number of stamps in your passport. Be realistic about your own “experience and leadership” and be honest when you aren’t ready for something. And always remember: nobody really cares about you as much as you think they do. Don’t Let Assholes Get You Down The world isn’t evil, but there is evil in the world. Know the difference and don’t paint all people with the same brush. Do be wary of those that use personal beliefs to describe their business (i.e. “We’re a [religion] company”). What matters is the culture of the organization, and that will tell you the moral compass and what is truly valued. Don’t make someone or something a priority that only makes you an option. Life is unfair and enemies/opponents will succeed when you fail. Don’t waste your energy getting upset at this; the only one that will lose out is you. As mentioned earlier, nobody really cares about you as much as you think they do. Misc Ecclesiastes is bullshit. Everything is certainly *not* meaningless. Software development is about delivery, not the process. Having a great process means nothing if you don’t produce anything. Watch “The Weatherman” (“It’s not easy, but easy doesn’t enter into grownup life.”). Read Tony Dungee’s autobiography, even if you don’t like football, and even if you aren’t a Christian. Say no, don’t feel like you have to commit right away when someone asks you to.

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  • NDepend Evaluation: Part 3

    - by Anthony Trudeau
    NDepend is a Visual Studio add-in designed for intense code analysis with the goal of high code quality. NDepend uses a number of metrics and aggregates the data in pleasing static and active visual reports. My evaluation of NDepend will be broken up into several different parts. In the first part of the evaluation I looked at installing the add-in.  And in the last part I went over my first impressions including an overview of the features.  In this installment I provide a little more detail on a few of the features that I really like. Dependency Matrix The dependency matrix is one of the rich visual components provided with NDepend.  At a glance it lets you know where you have coupling problems including cycles.  It does this with number indicating the weight of the dependency and a color-coding that indicates the nature of the dependency. Green and blue cells are direct dependencies (with the difference being whether the relationship is from row-to-column or column-to-row).  Black cells are the ones that you really want to know about.  These indicate that you have a cycle.  That is, type A refers to type B and type B also refers to Type A. But, that’s not the end of the story.  A handy pop-up appears when you hover over the cell in question.  It explains the color, the dependency, and provides several interesting links that will teach you more than you want to know about the dependency. You can double-click the problem cells to explode the dependency.  That will show the dependencies on a method-by-method basis allowing you to more easily target and fix the problem.  When you’re done you can click the back button on the toolbar. Dependency Graph The dependency graph is another component provided.  It’s complementary to the dependency matrix, but it isn’t as easy to identify dependency issues using the window. On a positive note, it does provide more information than the matrix. My biggest issue with the dependency graph is determining what is shown.  This was not readily obvious.  I ended up using the navigation buttons to get an acceptable view.  I would have liked to choose what I see. Once you see the types you want you can get a decent idea of coupling strength based on the width of the dependency lines.  Double-arrowed lines are problematic and are shown in red.  The size of the boxes will be related to the metric being displayed.  This is controlled using the Box Size drop-down in the toolbar.  Personally, I don’t find the size of the box to be helpful, so I change it to Constant Font. One nice thing about the display is that you can see the entire path of dependencies when you hover over a type.  This is done by color-coding the dependencies and dependants.  It would be nice if selecting the box for the type would lock the highlighting in place. I did find a perhaps unintended work-around to the color-coding.  You can lock the color-coding in by hovering over the type, right-clicking, and then clicking on the canvas area to clear the pop-up menu.  You can then do whatever with it including saving it to an image file with the color-coding. CQL NDepend uses a code query language (CQL) to work with your code just like it was a database.  CQL cannot be confused with the robustness of T-SQL or even LINQ, but it represents an impressive attempt at providing an expressive way to enumerate and interrogate your code. There are two main windows you’ll use when working with CQL.  The CQL Query Explorer allows you to define what queries (rules) are run as part of a report – I immediately unselected rules that I don’t want in my results.  The CQL Query Edit window is where you can view or author your own rules.  The explorer window is pretty self-explanatory, so I won’t mention it further other than to say that any queries you author will appear in the custom group. Authoring your own queries is really hard to screw-up.  The Intellisense-like pop-ups tell you what you can do while making composition easy.  I was able to create a query within two minutes of playing with the editor.  My query warns if any types that are interfaces don’t start with an “I”. WARN IF Count > 0 IN SELECT TYPES WHERE IsInterface AND !NameLike “I” The results from the CQL Query Edit window are immediate. That fact makes it useful for ad hoc querying.  It’s worth mentioning two things that could make the experience smoother.  First, out of habit from using Visual Studio I expect to be able to scroll and press Tab to select an item in the list (like Intellisense).  You have to press Enter when you scroll to the item you want.  Second, the commands are case-sensitive.  I don’t see a really good reason to enforce that. CQL has a lot of potential not just in enforcing code quality, but also enforcing architectural constraints that your enterprise has defined. Up Next My next update will be the final part of the evaluation.  I will summarize my experience and provide my conclusions on the NDepend add-in. ** View Part 1 of the Evaluation ** ** View Part 2 of the Evaluation ** Disclaimer: Patrick Smacchia contacted me about reviewing NDepend. I received a free license in return for sharing my experiences and talking about the capabilities of the add-in on this site. There is no expectation of a positive review elicited from the author of NDepend.

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  • What is the need of Odata when I have JSON ?

    - by punkouter
    I am trying to understand the point of Odata and when it would make sense. Right now how I work is I use ASP.NET and MVC/WebApi controller to serialize/deserialize objects into JSON and have javascript do something with it. From what I can tell the benefit of OData is being able to query directly from the URL ... But since I am writing the client and server code there is no need for that. Would anyone ever parse the results of a ODaya query in javascript?? Maybe OData is more about providing a generic endpoint for ALL clients to get detailed information from a query that JSON does not provide ? So if I was a provider of data then I suppose that is what odata is for ? Help me understand the purpose and use of REST/JSON/ODATA.

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  • Utility Queries–Database Files, (and Filegroups)

    - by drsql
    It has been a while since I last posted a utility query, and today, to avoid other work I am supposed to be doing, I decided to go ahead and work on another post.  Today, I went ahead and worked on a server configuration type query. One query I find I use pretty often is the following one that lists the files in the database. In this blog I will include 3 queries.  The first will deal with files and databases, and the second runs in a database to see the files and their filegroups (If there...(read more)

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  • What Counts for A DBA - Logic

    - by drsql
    "There are 10 kinds of people in the world. Those who will always wonder why there are only two items in my list and those who will figured it out the first time they saw this very old joke."  Those readers who will give up immediately and get frustrated with me for not explaining it to them are not likely going to be great technical professionals of any sort, much less a programmer or administrator who will be constantly dealing with the common failures that make up a DBA's day.  Many of these people will stare at this like a dog staring at a traffic signal and still have no more idea of how to decipher the riddle. Without explanation they will give up, call the joke "stupid" and, feeling quite superior, walk away indignantly to their job likely flipping patties of meat-by-product. As a data professional or any programmer who has strayed  to this very data-oriented blog, you would, if you are worth your weight in air, either have recognized immediately what was going on, or felt a bit ignorant.  Your friends are chuckling over the joke, but why is it funny? Unfortunately you left your smartphone at home on the dresser because you were up late last night programming and were running late to work (again), so you will either have to fake a laugh or figure it out.  Digging through the joke, you figure out that the word "two" is the most important part, since initially the joke mentioned 10. Hmm, why did they spell out two, but not ten? Maybe 10 could be interpreted a different way?  As a DBA, this sort of logic comes into play every day, and sometimes it doesn't involve nerdy riddles or Star Wars folklore.  When you turn on your computer and get the dreaded blue screen of death, you don't immediately cry to the help desk and sit on your thumbs and whine about not being able to work. Do that and your co-workers will question your nerd-hood; I know I certainly would. You figure out the problem, and when you have it narrowed down, you call the help desk and tell them what the problem is, usually having to explain that yes, you did in fact try to reboot before calling.  Of course, sometimes humility does come in to play when you reach the end of your abilities, but the ‘end of abilities’ is not something any of us recognize readily. It is handy to have the ability to use logic to solve uncommon problems: It becomes especially useful when you are trying to solve a data-related problem such as a query performance issue, and the way that you approach things will tell your coworkers a great deal about your abilities.  The novice is likely to immediately take the approach of  trying to add more indexes or blaming the hardware. As you become more and more experienced, it becomes increasingly obvious that performance issues are a very complex topic. A query may be slow for a myriad of reasons, from concurrency issues, a poor query plan because of a parameter value (like parameter sniffing,) poor coding standards, or just because it is a complex query that is going to be slow sometimes. Some queries that you will deal with may have twenty joins and hundreds of search criteria, and it can take a lot of thought to determine what is going on.  You can usually figure out the problem to almost any query by using basic knowledge of how joins and queries work, together with the help of such things as the query plan, profiler or monitoring tools.  It is not unlikely that it can take a full day’s work to understand some queries, breaking them down into smaller queries to find a very tiny problem. Not every time will you actually find the problem, and it is part of the process to occasionally admit that the problem is random, and everything works fine now.  Sometimes, it is necessary to realize that a problem is outside of your current knowledge, and admit temporary defeat: You can, at least, narrow down the source of the problem by looking logically at all of the possible solutions. By doing this, you can satisfy your curiosity and learn more about what the actual problem was. For example, in the joke, had you never been exposed to the concept of binary numbers, there is no way you could have known that binary - 10 = decimal - 2, but you could have logically come to the conclusion that 10 must not mean ten in the context of the joke, and at that point you are that much closer to getting the joke and at least won't feel so ignorant.

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  • Utility Queries–Database Files, (and Filegroups)

    - by drsql
    It has been a while since I last posted a utility query, and today, to avoid other work I am supposed to be doing, I decided to go ahead and work on another post.  Today, I went ahead and worked on a server configuration type query. One query I find I use pretty often is the following one that lists the files in the database. In this blog I will include 3 queries.  The first will deal with files and databases, and the second runs in a database to see the files and their filegroups (If there...(read more)

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  • Figuring our complex REST queries for SharePoint

    - by Sahil Malik
    SharePoint 2010 Training: more information A little while ago, I showed the REST query for a relatively complex query. Some readers have emailed me about how to figure out further queries, and especially for complex insert/delete/update scenarios. Well it is quite easy to figure out almost any query for SharePoint REST API. Okay, this is not just about SharePoint – you can apply what you read here for any REST API interface supported by Microsoft, like WCF data services. But, sometimes when you have many columns, or complex update operations, or are working with weird providers, it is tough to figure out the specific HTTP request you need to craft, error free, using REST. Well fear not, there is hope. As an example, what I did is, I created a SharePoint site at http://sp2010.winsmarts.internal/sampledata with 3 lists in it - 1. Artists (with one Column, Title) Read full article ....

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  • How to generate Visa checkout token? [on hold]

    - by Muhammad Junaid
    I am on process of creating a Visa checkout plugin but stuck in generating token Here are the token requirment: Format: Alphanumeric; maximum 100 characters in the form of token: x:UNIX_UTC_Timestamp:SHA256_hash, where UNIX_UTC_Timestamp is a UNIX Epoch timestamp SHA256_hash is an SHA256 hash of the following unseparated items: Your shared secret Timestamp from the transaction; exactly the same as UNIX_UTC_Timestamp Resource path (API name). This HTTPS request's query string Note: The query string includes one or more parameters in name-value pair format, whose names are separated from values by equal signs (=); an empty value may be omitted but the name and equal sign must be present. The initial question mark (?) is not included. Note: All parameters must be present. The parameters must be in lexicographic sort order (UTF-8, uppercase hex characters) with parameters separated from each other by an ampersand (&). Note: The query string must be URL encoded (excepting the following characters, per RFC 3986: hyp You can find on Google "visa checkout developer updating 1 px image"

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  • Easy QueryBuilder - A User-Friendly Ad-Hoc Advanced Search Solution

    Constructing an easy and powerful QueryBuilder interface becomes more important for complex data grid filtering and accurate reporting services. In this article, I'll discuss how to build a query search engine using ASP.NET AJAX and dynamic SQL. The main goal is to provide an interactive interface to allow users select query attributes, operators, attribute values, and T-SQL operators so that the data context query list can be easily composed and a search engine is invoked.Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Find out when a new domain appears in search results

    - by TerryB
    Does anyone know a way to perform the following: I want to know whenever a new domain starts appearing in the google search results for a particular query. For a given google search query, I'd like to receive an alert whenever a new domain pops up and starts appearing in the search results for that query. Alternatively, it would be great if you could just sort google search results by the age of the domain, making it easy to find new sites. As far as I can tell you can only sort by when the page was "last updated". Is something like this possible? EDIT: Following John's suggestion of Google Alerts. The problem with Google Alerts is that it sends you any new PAGES appearing in the search results, not just new DOMAINS.

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  • Sharing object between 2 classes

    - by Justin
    I am struggling to wrap my head around being able to share an object between two classes. I want to be able to create only one instance of the object, commonlib in my main class and then have the classes, foo1 and foo2, to be able to mutually share the properties of the commonlib. commonlib is a 3rd party class which has a property Queries that will be added to in each child class of bar. This is why it is vital that only one instance is created. I create two separate queries in foo1 and foo2. This is my setup: abstract class bar{ //common methods } class foo1 extends bar{ //add query to commonlib } class foo2 extends bar{ //add query to commonlib } class main { public $commonlib = new commonlib(); public function start(){ //goal is to share one instance of $this->commonlib between foo1 and foo2 //so that they can both add to the properites of $this->commonlib (global //between the two) //now execute all of the queries after foo1 and foo2 add their query $this->commonlib->RunQueries(); } }

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  • How does Google store search trends in backend?

    - by Achshar
    Google trends shows what query has been searched how many times and some other properties of the said query. But how is this data stored in a database? Storing a new row for every search does not seem right. They also tell the query on a time graph, so they must have some way to look for individual searches made by users, but the number of queries they get every day, it does not feel right that they would store every search in a database row along with a time-stamp. This does not apply to just Google trends or Google in general but any other big site that gets awful number of queries and then has tools to see them in depth. I am not an expert on this but I am interested to know some high level structure of how things work behind the scenes.

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  • Google Analytics Request URI to Event advanced filter

    - by confidentjohn
    I have a query string attached to a Request URI. Whilst I can see this data within the pages report and it works, I was thinking about setting up an advanced filter to convert the request URI to an Event, with the hope this would clean up my pages report and sit this query with related events in my data. I can see in advanced filters that this is possible, but seems limited to specifying a single event area, so Cat, action or Label, not all 3. Does any one know how I could set up an advanced filter to find any URIs that contain a specific query string, say example below. www.example.com?querystring=123 and convert this into an event, where I can set the Cat, action and label.

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  • Fixing Gatekeeper Row Cardinality Estimate Issues

    The Query Optimiser needs a good estimate of the number of rows likely to be returned by each physical operator in order to select the best query plan from the most likely alternatives. Sometimes these estimates can go so wildly wrong as to result in a very slow query. Joe Sack shows how it can happen with SQL Queries on a data warehouse with a star schema. Make working with SQL a breezeSQL Prompt 5.3 is the effortless way to write, edit, and explore SQL. It's packed with features such as code completion, script summaries, and SQL reformatting, that make working with SQL a breeze. Try it now.

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  • Consuming OData based Rest service in C# [en-US]

    - by ruimachado
    Nowadays comunication between applications is an active topic with daily usage and a large amount of pratical appliances. While developing an app in witch I had to consume an OData I found out that combining Linq with my code made this operation pretty easy.The algorithm to consume OData starts with adding a service reference to Visual Studio:After adding the service reference in wich you define the uri to the service, we start building our code.In your code the algorithm is the following:Define the Uri to your OData ServiceDefine the context of your odata, wich contains all entities exposed by the service.Query the context using LinqPrint the resultEasy and simple.Example code:01public static void Main(string[] args){02 03        Uri serviceUri= newUri("http://example.host.odataservice.net/service.svc", UriKind.Absolute);04        ODataService.ServiceEntities context = newODataService.ServiceEntities (serviceUri);05 06        context.Credentials = newSystem.Net.NetworkCredential(Username,Password);07 08         var query = from ServiceObject in context.YourEntity09                     select ServiceObject ;10 11        foreach (var myObject in query)12        {13            Console.WriteLine("\n Field1: {0} | Field2: {1}",14            myObject .Field1, myObject .Field2);15 16        }17}That’s it.Thank you,Rui Machadorpmachado.wordpress.com

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  • Where can I get the 10k common English dictionary words which Stack overflow uses in related question? [migrated]

    - by itpian.com
    Where can I get the 10k common English dictionary words which Stack overflow uses in related question? Here in SE podcast - http://blog.stackoverflow.com/2008/12/podcast-32/ One of our major performance optimizations for the “related questions” query is removing the top 10,000 most common English dictionary words (as determined by Google search) before submitting the query to the SQL Server 2008 full text engine. It’s shocking how little is left of most posts once you remove the top 10k English dictionary words. This helps limit and narrow the returned results, which makes the query dramatically faster.

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  • ??????????????????????????/????????????????????????·???????????|WebLogic Channel|??????

    - by ???02
    ??????????????????·???????Oracle Coherence??????Java???????????????????????????????????????????????????????????????????Java API??????Oracle Coherence???????????3????????????????????????Coherence???????????????????????????????3???????????(???)???????Coherence???????????????Java?????????? ???????????????????????·??????????/????·????Oracle Coherence???????????????????????Java API??????Oracle Coherence???????????????????????????????????????????????????????????????? ?????????????Coherence??????????????????????????????/???????????????????????????????????????????3???????(4)????????/???????????? ????????????????????????????????????·???/????????? Oracle Coherence????????????????1?????????????????????????????????????????????????????????????????????????????????????????????????????????????Oracle Coherence?????????????????????????·????????????????????????????????????????In-Place Processing????????Oracle Coherence???In-Place Processing????????????????????????????????????????? ????·???/???????????In-Place Processing??????????????????·?????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????? ???In-Place Processing????????·????????????Oracle Coherence 3.6????????????Coherence Query Language(CohQL)??????????????????????????????CohQL???????????????????SQL?????????????????SQL?WHERE?????????????????????????????(5)??????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????(?????)????????????????????????????????????????????????????????????????????????????? Oracle Coherence?????????????????????????????????????Oracle Coherence???????????????????????????????????????????????????????????????????????????????????????????????/???????????????????????????????????????????? ??????·????????????????????????????????????????????????????Oracle Coherence?????????????????????????????????????????????(??????)????????????????????????????????????????????????????????? ???????·????????????Continuous Query Cache??????????????????????????????????????????????????????????????????????????????????????????????????????????????Continuous Query Cache????????????????????????(6)??????????????????? ????Oracle Coherence????????????Invocation Service??????????????????(4)?????In-Place Processing?????????????·?????????????????????????????????????????????????????Java??????????????????????????????·???????????????????????????????????????????????? ??????????????????????Oracle Coherence ??????????????????????????????????????????CPU???????????????????????Oracle Coherence???????????????????????????????????Web???????????????????????????Oracle Coherence?????????????????????????????????????????*   *   * ?????????Java????????????????????Oracle Coherence?6???????????????????????????Oracle Coherence????????????????????????????????????????????????????????Oracle Coherence?????:Aleksandar Seovic?Mark Falco?Patrick Peralta??:????·??????????????·????Oracle Coherence: Share and Manage Data In Clusters?···3????????????·??????????????????????????

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  • Upload File to Windows Azure Blob in Chunks through ASP.NET MVC, JavaScript and HTML5

    - by Shaun
    Originally posted on: http://geekswithblogs.net/shaunxu/archive/2013/07/01/upload-file-to-windows-azure-blob-in-chunks-through-asp.net.aspxMany people are using Windows Azure Blob Storage to store their data in the cloud. Blob storage provides 99.9% availability with easy-to-use API through .NET SDK and HTTP REST. For example, we can store JavaScript files, images, documents in blob storage when we are building an ASP.NET web application on a Web Role in Windows Azure. Or we can store our VHD files in blob and mount it as a hard drive in our cloud service. If you are familiar with Windows Azure, you should know that there are two kinds of blob: page blob and block blob. The page blob is optimized for random read and write, which is very useful when you need to store VHD files. The block blob is optimized for sequential/chunk read and write, which has more common usage. Since we can upload block blob in blocks through BlockBlob.PutBlock, and them commit them as a whole blob with invoking the BlockBlob.PutBlockList, it is very powerful to upload large files, as we can upload blocks in parallel, and provide pause-resume feature. There are many documents, articles and blog posts described on how to upload a block blob. Most of them are focus on the server side, which means when you had received a big file, stream or binaries, how to upload them into blob storage in blocks through .NET SDK.  But the problem is, how can we upload these large files from client side, for example, a browser. This questioned to me when I was working with a Chinese customer to help them build a network disk production on top of azure. The end users upload their files from the web portal, and then the files will be stored in blob storage from the Web Role. My goal is to find the best way to transform the file from client (end user’s machine) to the server (Web Role) through browser. In this post I will demonstrate and describe what I had done, to upload large file in chunks with high speed, and save them as blocks into Windows Azure Blob Storage.   Traditional Upload, Works with Limitation The simplest way to implement this requirement is to create a web page with a form that contains a file input element and a submit button. 1: @using (Html.BeginForm("About", "Index", FormMethod.Post, new { enctype = "multipart/form-data" })) 2: { 3: <input type="file" name="file" /> 4: <input type="submit" value="upload" /> 5: } And then in the backend controller, we retrieve the whole content of this file and upload it in to the blob storage through .NET SDK. We can split the file in blocks and upload them in parallel and commit. The code had been well blogged in the community. 1: [HttpPost] 2: public ActionResult About(HttpPostedFileBase file) 3: { 4: var container = _client.GetContainerReference("test"); 5: container.CreateIfNotExists(); 6: var blob = container.GetBlockBlobReference(file.FileName); 7: var blockDataList = new Dictionary<string, byte[]>(); 8: using (var stream = file.InputStream) 9: { 10: var blockSizeInKB = 1024; 11: var offset = 0; 12: var index = 0; 13: while (offset < stream.Length) 14: { 15: var readLength = Math.Min(1024 * blockSizeInKB, (int)stream.Length - offset); 16: var blockData = new byte[readLength]; 17: offset += stream.Read(blockData, 0, readLength); 18: blockDataList.Add(Convert.ToBase64String(BitConverter.GetBytes(index)), blockData); 19:  20: index++; 21: } 22: } 23:  24: Parallel.ForEach(blockDataList, (bi) => 25: { 26: blob.PutBlock(bi.Key, new MemoryStream(bi.Value), null); 27: }); 28: blob.PutBlockList(blockDataList.Select(b => b.Key).ToArray()); 29:  30: return RedirectToAction("About"); 31: } This works perfect if we selected an image, a music or a small video to upload. But if I selected a large file, let’s say a 6GB HD-movie, after upload for about few minutes the page will be shown as below and the upload will be terminated. In ASP.NET there is a limitation of request length and the maximized request length is defined in the web.config file. It’s a number which less than about 4GB. So if we want to upload a really big file, we cannot simply implement in this way. Also, in Windows Azure, a cloud service network load balancer will terminate the connection if exceed the timeout period. From my test the timeout looks like 2 - 3 minutes. Hence, when we need to upload a large file we cannot just use the basic HTML elements. Besides the limitation mentioned above, the simple HTML file upload cannot provide rich upload experience such as chunk upload, pause and pause-resume. So we need to find a better way to upload large file from the client to the server.   Upload in Chunks through HTML5 and JavaScript In order to break those limitation mentioned above we will try to upload the large file in chunks. This takes some benefit to us such as - No request size limitation: Since we upload in chunks, we can define the request size for each chunks regardless how big the entire file is. - No timeout problem: The size of chunks are controlled by us, which means we should be able to make sure request for each chunk upload will not exceed the timeout period of both ASP.NET and Windows Azure load balancer. It was a big challenge to upload big file in chunks until we have HTML5. There are some new features and improvements introduced in HTML5 and we will use them to implement our solution.   In HTML5, the File interface had been improved with a new method called “slice”. It can be used to read part of the file by specifying the start byte index and the end byte index. For example if the entire file was 1024 bytes, file.slice(512, 768) will read the part of this file from the 512nd byte to 768th byte, and return a new object of interface called "Blob”, which you can treat as an array of bytes. In fact,  a Blob object represents a file-like object of immutable, raw data. The File interface is based on Blob, inheriting blob functionality and expanding it to support files on the user's system. For more information about the Blob please refer here. File and Blob is very useful to implement the chunk upload. We will use File interface to represent the file the user selected from the browser and then use File.slice to read the file in chunks in the size we wanted. For example, if we wanted to upload a 10MB file with 512KB chunks, then we can read it in 512KB blobs by using File.slice in a loop.   Assuming we have a web page as below. User can select a file, an input box to specify the block size in KB and a button to start upload. 1: <div> 2: <input type="file" id="upload_files" name="files[]" /><br /> 3: Block Size: <input type="number" id="block_size" value="512" name="block_size" />KB<br /> 4: <input type="button" id="upload_button_blob" name="upload" value="upload (blob)" /> 5: </div> Then we can have the JavaScript function to upload the file in chunks when user clicked the button. 1: <script type="text/javascript"> 1: 2: $(function () { 3: $("#upload_button_blob").click(function () { 4: }); 5: });</script> Firstly we need to ensure the client browser supports the interfaces we are going to use. Just try to invoke the File, Blob and FormData from the “window” object. If any of them is “undefined” the condition result will be “false” which means your browser doesn’t support these premium feature and it’s time for you to get your browser updated. FormData is another new feature we are going to use in the future. It could generate a temporary form for us. We will use this interface to create a form with chunk and associated metadata when invoked the service through ajax. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: if (window.File && window.Blob && window.FormData) { 4: alert("Your brwoser is awesome, let's rock!"); 5: } 6: else { 7: alert("Oh man plz update to a modern browser before try is cool stuff out."); 8: return; 9: } 10: }); Each browser supports these interfaces by their own implementation and currently the Blob, File and File.slice are supported by Chrome 21, FireFox 13, IE 10, Opera 12 and Safari 5.1 or higher. After that we worked on the files the user selected one by one since in HTML5, user can select multiple files in one file input box. 1: var files = $("#upload_files")[0].files; 2: for (var i = 0; i < files.length; i++) { 3: var file = files[i]; 4: var fileSize = file.size; 5: var fileName = file.name; 6: } Next, we calculated the start index and end index for each chunks based on the size the user specified from the browser. We put them into an array with the file name and the index, which will be used when we upload chunks into Windows Azure Blob Storage as blocks since we need to specify the target blob name and the block index. At the same time we will store the list of all indexes into another variant which will be used to commit blocks into blob in Azure Storage once all chunks had been uploaded successfully. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10:  11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: var blockSizeInKB = $("#block_size").val(); 14: var blockSize = blockSizeInKB * 1024; 15: var blocks = []; 16: var offset = 0; 17: var index = 0; 18: var list = ""; 19: while (offset < fileSize) { 20: var start = offset; 21: var end = Math.min(offset + blockSize, fileSize); 22:  23: blocks.push({ 24: name: fileName, 25: index: index, 26: start: start, 27: end: end 28: }); 29: list += index + ","; 30:  31: offset = end; 32: index++; 33: } 34: } 35: }); Now we have all chunks’ information ready. The next step should be upload them one by one to the server side, and at the server side when received a chunk it will upload as a block into Blob Storage, and finally commit them with the index list through BlockBlobClient.PutBlockList. But since all these invokes are ajax calling, which means not synchronized call. So we need to introduce a new JavaScript library to help us coordinate the asynchronize operation, which named “async.js”. You can download this JavaScript library here, and you can find the document here. I will not explain this library too much in this post. We will put all procedures we want to execute as a function array, and pass into the proper function defined in async.js to let it help us to control the execution sequence, in series or in parallel. Hence we will define an array and put the function for chunk upload into this array. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4:  5: // start to upload each files in chunks 6: var files = $("#upload_files")[0].files; 7: for (var i = 0; i < files.length; i++) { 8: var file = files[i]; 9: var fileSize = file.size; 10: var fileName = file.name; 11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: ... ... 14:  15: // define the function array and push all chunk upload operation into this array 16: blocks.forEach(function (block) { 17: putBlocks.push(function (callback) { 18: }); 19: }); 20: } 21: }); 22: }); As you can see, I used File.slice method to read each chunks based on the start and end byte index we calculated previously, and constructed a temporary HTML form with the file name, chunk index and chunk data through another new feature in HTML5 named FormData. Then post this form to the backend server through jQuery.ajax. This is the key part of our solution. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: blocks.forEach(function (block) { 15: putBlocks.push(function (callback) { 16: // load blob based on the start and end index for each chunks 17: var blob = file.slice(block.start, block.end); 18: // put the file name, index and blob into a temporary from 19: var fd = new FormData(); 20: fd.append("name", block.name); 21: fd.append("index", block.index); 22: fd.append("file", blob); 23: // post the form to backend service (asp.net mvc controller action) 24: $.ajax({ 25: url: "/Home/UploadInFormData", 26: data: fd, 27: processData: false, 28: contentType: "multipart/form-data", 29: type: "POST", 30: success: function (result) { 31: if (!result.success) { 32: alert(result.error); 33: } 34: callback(null, block.index); 35: } 36: }); 37: }); 38: }); 39: } 40: }); Then we will invoke these functions one by one by using the async.js. And once all functions had been executed successfully I invoked another ajax call to the backend service to commit all these chunks (blocks) as the blob in Windows Azure Storage. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.series(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); That’s all in the client side. The outline of our logic would be - Calculate the start and end byte index for each chunks based on the block size. - Defined the functions of reading the chunk form file and upload the content to the backend service through ajax. - Execute the functions defined in previous step with “async.js”. - Commit the chunks by invoking the backend service in Windows Azure Storage finally.   Save Chunks as Blocks into Blob Storage In above we finished the client size JavaScript code. It uploaded the file in chunks to the backend service which we are going to implement in this step. We will use ASP.NET MVC as our backend service, and it will receive the chunks, upload into Windows Azure Bob Storage in blocks, then finally commit as one blob. As in the client side we uploaded chunks by invoking the ajax call to the URL "/Home/UploadInFormData", I created a new action under the Index controller and it only accepts HTTP POST request. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: } 8: catch (Exception e) 9: { 10: error = e.ToString(); 11: } 12:  13: return new JsonResult() 14: { 15: Data = new 16: { 17: success = string.IsNullOrWhiteSpace(error), 18: error = error 19: } 20: }; 21: } Then I retrieved the file name, index and the chunk content from the Request.Form object, which was passed from our client side. And then, used the Windows Azure SDK to create a blob container (in this case we will use the container named “test”.) and create a blob reference with the blob name (same as the file name). Then uploaded the chunk as a block of this blob with the index, since in Blob Storage each block must have an index (ID) associated with so that finally we can put all blocks as one blob by specifying their block ID list. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var index = int.Parse(Request.Form["index"]); 9: var file = Request.Files[0]; 10: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 11:  12: var container = _client.GetContainerReference("test"); 13: container.CreateIfNotExists(); 14: var blob = container.GetBlockBlobReference(name); 15: blob.PutBlock(id, file.InputStream, null); 16: } 17: catch (Exception e) 18: { 19: error = e.ToString(); 20: } 21:  22: return new JsonResult() 23: { 24: Data = new 25: { 26: success = string.IsNullOrWhiteSpace(error), 27: error = error 28: } 29: }; 30: } Next, I created another action to commit the blocks into blob once all chunks had been uploaded. Similarly, I retrieved the blob name from the Request.Form. I also retrieved the chunks ID list, which is the block ID list from the Request.Form in a string format, split them as a list, then invoked the BlockBlob.PutBlockList method. After that our blob will be shown in the container and ready to be download. 1: [HttpPost] 2: public JsonResult Commit() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var list = Request.Form["list"]; 9: var ids = list 10: .Split(',') 11: .Where(id => !string.IsNullOrWhiteSpace(id)) 12: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 13: .ToArray(); 14:  15: var container = _client.GetContainerReference("test"); 16: container.CreateIfNotExists(); 17: var blob = container.GetBlockBlobReference(name); 18: blob.PutBlockList(ids); 19: } 20: catch (Exception e) 21: { 22: error = e.ToString(); 23: } 24:  25: return new JsonResult() 26: { 27: Data = new 28: { 29: success = string.IsNullOrWhiteSpace(error), 30: error = error 31: } 32: }; 33: } Now we finished all code we need. The whole process of uploading would be like this below. Below is the full client side JavaScript code. 1: <script type="text/javascript" src="~/Scripts/async.js"></script> 2: <script type="text/javascript"> 3: $(function () { 4: $("#upload_button_blob").click(function () { 5: // assert the browser support html5 6: if (window.File && window.Blob && window.FormData) { 7: alert("Your brwoser is awesome, let's rock!"); 8: } 9: else { 10: alert("Oh man plz update to a modern browser before try is cool stuff out."); 11: return; 12: } 13:  14: // start to upload each files in chunks 15: var files = $("#upload_files")[0].files; 16: for (var i = 0; i < files.length; i++) { 17: var file = files[i]; 18: var fileSize = file.size; 19: var fileName = file.name; 20:  21: // calculate the start and end byte index for each blocks(chunks) 22: // with the index, file name and index list for future using 23: var blockSizeInKB = $("#block_size").val(); 24: var blockSize = blockSizeInKB * 1024; 25: var blocks = []; 26: var offset = 0; 27: var index = 0; 28: var list = ""; 29: while (offset < fileSize) { 30: var start = offset; 31: var end = Math.min(offset + blockSize, fileSize); 32:  33: blocks.push({ 34: name: fileName, 35: index: index, 36: start: start, 37: end: end 38: }); 39: list += index + ","; 40:  41: offset = end; 42: index++; 43: } 44:  45: // define the function array and push all chunk upload operation into this array 46: var putBlocks = []; 47: blocks.forEach(function (block) { 48: putBlocks.push(function (callback) { 49: // load blob based on the start and end index for each chunks 50: var blob = file.slice(block.start, block.end); 51: // put the file name, index and blob into a temporary from 52: var fd = new FormData(); 53: fd.append("name", block.name); 54: fd.append("index", block.index); 55: fd.append("file", blob); 56: // post the form to backend service (asp.net mvc controller action) 57: $.ajax({ 58: url: "/Home/UploadInFormData", 59: data: fd, 60: processData: false, 61: contentType: "multipart/form-data", 62: type: "POST", 63: success: function (result) { 64: if (!result.success) { 65: alert(result.error); 66: } 67: callback(null, block.index); 68: } 69: }); 70: }); 71: }); 72:  73: // invoke the functions one by one 74: // then invoke the commit ajax call to put blocks into blob in azure storage 75: async.series(putBlocks, function (error, result) { 76: var data = { 77: name: fileName, 78: list: list 79: }; 80: $.post("/Home/Commit", data, function (result) { 81: if (!result.success) { 82: alert(result.error); 83: } 84: else { 85: alert("done!"); 86: } 87: }); 88: }); 89: } 90: }); 91: }); 92: </script> And below is the full ASP.NET MVC controller code. 1: public class HomeController : Controller 2: { 3: private CloudStorageAccount _account; 4: private CloudBlobClient _client; 5:  6: public HomeController() 7: : base() 8: { 9: _account = CloudStorageAccount.Parse(CloudConfigurationManager.GetSetting("DataConnectionString")); 10: _client = _account.CreateCloudBlobClient(); 11: } 12:  13: public ActionResult Index() 14: { 15: ViewBag.Message = "Modify this template to jump-start your ASP.NET MVC application."; 16:  17: return View(); 18: } 19:  20: [HttpPost] 21: public JsonResult UploadInFormData() 22: { 23: var error = string.Empty; 24: try 25: { 26: var name = Request.Form["name"]; 27: var index = int.Parse(Request.Form["index"]); 28: var file = Request.Files[0]; 29: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 30:  31: var container = _client.GetContainerReference("test"); 32: container.CreateIfNotExists(); 33: var blob = container.GetBlockBlobReference(name); 34: blob.PutBlock(id, file.InputStream, null); 35: } 36: catch (Exception e) 37: { 38: error = e.ToString(); 39: } 40:  41: return new JsonResult() 42: { 43: Data = new 44: { 45: success = string.IsNullOrWhiteSpace(error), 46: error = error 47: } 48: }; 49: } 50:  51: [HttpPost] 52: public JsonResult Commit() 53: { 54: var error = string.Empty; 55: try 56: { 57: var name = Request.Form["name"]; 58: var list = Request.Form["list"]; 59: var ids = list 60: .Split(',') 61: .Where(id => !string.IsNullOrWhiteSpace(id)) 62: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 63: .ToArray(); 64:  65: var container = _client.GetContainerReference("test"); 66: container.CreateIfNotExists(); 67: var blob = container.GetBlockBlobReference(name); 68: blob.PutBlockList(ids); 69: } 70: catch (Exception e) 71: { 72: error = e.ToString(); 73: } 74:  75: return new JsonResult() 76: { 77: Data = new 78: { 79: success = string.IsNullOrWhiteSpace(error), 80: error = error 81: } 82: }; 83: } 84: } And if we selected a file from the browser we will see our application will upload chunks in the size we specified to the server through ajax call in background, and then commit all chunks in one blob. Then we can find the blob in our Windows Azure Blob Storage.   Optimized by Parallel Upload In previous example we just uploaded our file in chunks. This solved the problem that ASP.NET MVC request content size limitation as well as the Windows Azure load balancer timeout. But it might introduce the performance problem since we uploaded chunks in sequence. In order to improve the upload performance we could modify our client side code a bit to make the upload operation invoked in parallel. The good news is that, “async.js” library provides the parallel execution function. If you remembered the code we invoke the service to upload chunks, it utilized “async.series” which means all functions will be executed in sequence. Now we will change this code to “async.parallel”. This will invoke all functions in parallel. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallel(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); In this way all chunks will be uploaded to the server side at the same time to maximize the bandwidth usage. This should work if the file was not very large and the chunk size was not very small. But for large file this might introduce another problem that too many ajax calls are sent to the server at the same time. So the best solution should be, upload the chunks in parallel with maximum concurrency limitation. The code below specified the concurrency limitation to 4, which means at the most only 4 ajax calls could be invoked at the same time. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallelLimit(putBlocks, 4, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: });   Summary In this post we discussed how to upload files in chunks to the backend service and then upload them into Windows Azure Blob Storage in blocks. We focused on the frontend side and leverage three new feature introduced in HTML 5 which are - File.slice: Read part of the file by specifying the start and end byte index. - Blob: File-like interface which contains the part of the file content. - FormData: Temporary form element that we can pass the chunk alone with some metadata to the backend service. Then we discussed the performance consideration of chunk uploading. Sequence upload cannot provide maximized upload speed, but the unlimited parallel upload might crash the browser and server if too many chunks. So we finally came up with the solution to upload chunks in parallel with the concurrency limitation. We also demonstrated how to utilize “async.js” JavaScript library to help us control the asynchronize call and the parallel limitation.   Regarding the chunk size and the parallel limitation value there is no “best” value. You need to test vary composition and find out the best one for your particular scenario. It depends on the local bandwidth, client machine cores and the server side (Windows Azure Cloud Service Virtual Machine) cores, memory and bandwidth. Below is one of my performance test result. The client machine was Windows 8 IE 10 with 4 cores. I was using Microsoft Cooperation Network. The web site was hosted on Windows Azure China North data center (in Beijing) with one small web role (1.7GB 1 core CPU, 1.75GB memory with 100Mbps bandwidth). The test cases were - Chunk size: 512KB, 1MB, 2MB, 4MB. - Upload Mode: Sequence, parallel (unlimited), parallel with limit (4 threads, 8 threads). - Chunk Format: base64 string, binaries. - Target file: 100MB. - Each case was tested 3 times. Below is the test result chart. Some thoughts, but not guidance or best practice: - Parallel gets better performance than series. - No significant performance improvement between parallel 4 threads and 8 threads. - Transform with binaries provides better performance than base64. - In all cases, chunk size in 1MB - 2MB gets better performance.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • value types in the vm

    - by john.rose
    value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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  • Is there a work around for slow performance of do.call(cbind.xts,...) in R 2.15.2?

    - by Petr Matousu
    I would expect cbind.xts and do.call(cbind.xts) to perform with similar elapsed time. That was true for R2.11, R2.14. For R2.15.2 and xts 0.8-8, the do.call(cbind.xts,...) variant performs drastically slower, which effectively breaks my previous codes. As Josh Ulrich notes in a comment below, the xts package maintainers are aware of this problem. In the meantime, is there a convenient work around?

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  • AIX Checklist for stable obiee deployment

    - by user554629
    Common AIX configuration issues     ( last updated 27 Aug 2012 ) OBIEE is a complicated system with many moving parts and connection points.The purpose of this article is to provide a checklist to discuss OBIEE deployment with your systems administrators. The information in this article is time sensitive, and updated as I discover new  issues or details. What makes OBIEE different? When Tech Support suggests AIX component upgrades to a stable, locked-down production AIX environment, it is common to get "push back".  "Why is this necessary?  We aren't we seeing issues with other software?"It's a fair question that I have often struggled to answer; here are the talking points: OBIEE is memory intensive.  It is the entire purpose of the software to trade memory for repetitive, more expensive database requests across a network. OBIEE is implemented in C++ and is very dependent on the C++ runtime to behave correctly. OBIEE is aggressively thread efficient;  if atomic operations on a particular architecture do not work correctly, the software crashes. OBIEE dynamically loads third-party database client libraries directly into the nqsserver process.  If the library is not thread-safe, or corrupts process memory the OBIEE crash happens in an unrelated part of the code.  These are extremely difficult bugs to find. OBIEE software uses 99% common source across multiple platforms:  Windows, Linux, AIX, Solaris and HPUX.  If a crash happens on only one platform, we begin to suspect other factors.  load intensity, system differences, configuration choices, hardware failures.  It is rare to have a single product require so many diverse technical skills.   My role in support is to understand system configurations, performance issues, and crashes.   An analyst trained in Business Analytics can't be expected to know AIX internals in the depth required to make configuration choices.  Here are some guidelines. AIX C++ Runtime must be at  version 11.1.0.4$ lslpp -L | grep xlC.aixobiee software will crash if xlC.aix.rte is downlevel;  this is not a "try it" suggestion.Nov 2011 11.1.0.4 version  is appropriate for all AIX versions ( 5, 6, 7 )Download from here:https://www-304.ibm.com/support/docview.wss?uid=swg24031426 No reboot is necessary to install, it can even be installed while applications are using the current version.Restart the apps, and they will pick up the latest version. AIX 5.3 Technology Level 12 is required when running on Power5,6,7 processorsAIX 6.1 was introduced with the newer Power chips, and we have seen no issues with 6.1 or 7.1 versions.Customers with an unstable deployment, dozens of unexplained crashes, became stable after the upgrade.If your AIX system is 5.3, the minimum TL level should be at or higher than this:$ oslevel -s  5300-12-03-1107IBM typically supports only the two latest versions of AIX ( 6.1 and 7.1, for example).  AIX 5.3 is still supported and popular running in an LPAR. obiee userid limits$ ulimit -Ha  ( hard limits )$ ulimit -a   ( default limits )core file size (blocks)     unlimiteddata seg size (kbytes)      unlimitedfile size (blocks)          unlimitedmax memory size (kbytes)    unlimitedopen files                  10240 cpu time (seconds)          unlimitedvirtual memory (kbytes)     unlimitedIt is best to establish the values in /etc/security/limitsroot user is needed to observe and modify this file.If you modify a limit, you will need to relog in to change it again.  For example,$ ulimit -c 0$ ulimit -c 2097151cannot modify limit: Operation not permitted$ ulimit -c unlimited$ ulimit -c0There are only two meaningful values for ulimit -c ; zero or unlimited.Anything else is likely to produce a truncated core file that cannot be analyzed. Deploy 32-bit or 64-bit ?Early versions of OBIEE offered 32-bit or 64-bit choice to AIX customers.The 32-bit choice was needed if a database vendor did not supply a 64-bit client library.That's no longer an issue and beginning with OBIEE 11, 32-bit code is no longer shipped.A common error that leads to "out of memory" conditions to to accept the 32-bit memory configuration choices on 64-bit deployments.  The significant configuration choices are: Maximum process data (heap) size is in an AIX environment variableLDR_CNTRL=IGNOREUNLOAD@LOADPUBLIC@PREREAD_SHLIB@MAXDATA=0x... Two thread stack sizes are made in obiee NQSConfig.INI[ SERVER ]SERVER_THREAD_STACK_SIZE = 0;DB_GATEWAY_THREAD_STACK_SIZE = 0; Sort memory in NQSConfig.INI[ GENERAL ]SORT_MEMORY_SIZE = 4 MB ;SORT_BUFFER_INCREMENT_SIZE = 256 KB ; Choosing a value for MAXDATA:0x080000000  2GB Default maximum 32-bit heap size ( 8 with 7 zeros )0x100000000  4GB 64-bit breaking even with 32-bit ( 1 with 8 zeros )0x200000000  8GB 64-bit double 32-bit max0x400000000 16GB 64-bit safetyUsing 2GB heap size for a 64-bit process will almost certainly lead to an out-of-memory situation.Registers are twice as big ... consume twice as much memory in the heap.Upgrading to a 4GB heap for a 64-bit process is just "breaking even" with 32-bit.A 32-bit process is constrained by the 32-bit virtual addressing limits.  Heap memory is used for dynamic requirements of obiee software, thread stacks for each of the configured threads, and sometimes for shared libraries. 64-bit processes are not constrained in this way;  extra heap space can be configured for safety against a query that might create a sudden requirement for excessive storage.  If the storage is not available, this query might crash the whole server and disrupt existing users.There is no performance penalty on AIX for configuring more memory than required;  extra memory can be configured for safety.  If there are no other considerations, start with 8GB.Choosing a value for Thread Stack size:zero is the value documented to select an appropriate default for thread stack size.  My preference is to change this to an absolute value, even if you intend to use the documented default;  it provides better documentation and removes the "surprise" factor.There are two thread types that can be configured. GATEWAY is used by a thread pool to call a database client library to establish a DB connection.The default size is 256KB;  many customers raise this to 512KB ( no performance penalty for over-configuring ). This value must be set to 1 MB if Teradata connections are used. SERVER threads are used to run queries.  OBIEE uses recursive algorithms during the analysis of query structures which can consume significant thread stack storage.  It's difficult to provide guidance on a value that depends on data and complexity.  The general notion is to provide more space than you think you need,  "double down" and increase the value if you run out, otherwise inspect the query to understand why it is too complex for the thread stack.  There are protections built into the software to abort a single user query that is too complex, but the algorithms don't cover all situations.256 KB  The default 32-bit stack size.  Many customers increased this to 512KB on 32-bit.  A 64-bit server is very likely to crash with this value;  the stack contains mostly register values, which are twice as big.512 KB  The documented 64-bit default.  Some early releases of obiee didn't set this correctly, resulting in 256KB stacks.1 MB  The recommended 64-bit setting.  If your system only ever uses 512KB of stack space, there is no performance penalty for using 1MB stack size.2 MB  Many large customers use this value for safety.  No performance penalty.nqscheduler does not use the NQSConfig.INI file to set thread stack size.If this process crashes because the thread stack is too small, use this to set 2MB:export OBI_BACKGROUND_STACK_SIZE=2048 Shared libraries are not (shared) When application libraries are loaded at run-time, AIX makes a decision on whether to load the libraries in a "public" memory segment.  If the filesystem library permissions do not have the "Read-Other" permission bit, AIX loads the library into private process memory with two significant side-effects:* The libraries reduce the heap storage available.      Might be significant in 32-bit processes;  irrelevant in 64-bit processes.* Library code is loaded into multiple real pages for execution;  one copy for each process.Multiple execution images is a significant issue for both 32- and 64-bit processes.The "real memory pages" saved by using public memory segments is a minor concern.  Today's machines typically have plenty of real memory.The real problem with private copies of libraries is that they consume processor cache blocks, which are limited.   The same library instructions executing in different real pages will cause memory delays as the i-cache ( instruction cache 128KB blocks) are refreshed from real memory.   Performance loss because instructions are delayed is something that is difficult to measure without access to low-level cache fault data.   The machine just appears to be running slowly for no observable reason.This is an easy problem to detect, and an easy problem to correct.Detection:  "genld -l" AIX command produces a list of the libraries used by each process and the AIX memory address where they are loaded.32-bit public segment is 13 ( "dxxxxxxx" ).   private segments are 2-a.64-bit public segment is 9 ( "9xxxxxxxxxxxxxxx") ; private segment is 8.genld -l | grep -v ' d| 9' | sort +2provides a list of privately loaded libraries. Repair: chmod o+r <libname>AIX shared libraries will have a suffix of ".so" or ".a".Another technique is to change all libraries in a selected directory to repair those that might not be currently loaded.   The usual directories that need repair are obiee code, httpd code and plugins, database client libraries and java.chmod o+r /shr/dir/*.a /shr/dir/*.so Configure your system for diagnosticsProduction systems shouldn't crash, and yet bad things happen to good software.If obiee software crashes and produces a core, you should configure your system for reliable transfer of the failing conditions to Oracle Tech Support.  Here's what we need to be able to diagnose a core file from your system.* fullcore enabled. chdev -lsys0 -a fullcore=true* core naming enabled. chcore -n on -d* ulimit must not truncate core. see item 3.* pstack.sh is used to capture core documentation.* obidoc is used to capture current AIX configuration.* snapcore  AIX utility captures core and libraries. Use the proper syntax. $ snapcore -r corename executable-fullpath   /tmp/snapcore will contain the .pax.Z output file.  It is compressed.* If cores are directed to a common directory, ensure obiee userid can write to the directory.  ( chcore -p /cores -d ; chmod 777 /cores )The filesystem must have sufficient space to hold a crashing obiee application.Use:  df -k  Check the "Free" column ( not "% Used" )  8388608 is 8GB. Disable Oracle Client Library signal handlingThe Oracle DB Client Library is frequently distributed with the sqlplus development kit.By default, the library enables a signal handler, which will document a call stack if the application crashes.   The signal handler is not needed, and definitely disruptive to obiee diagnostics.   It needs to be disabled.   sqlnet.ora is typically located at:   $ORACLE_HOME/network/admin/sqlnet.oraAdd this line at the top of the file:   DIAG_SIGHANDLER_ENABLED=FALSE Disable async query in the RPD connection pool.This might be an obiee 10.1.3.4 issue only ( still checking  )."async query" must be disabled in the connection pools.It was designed to enable query cancellation to a database, and turned out to have too many edge conditions in normal communication that produced random corruption of data and crashes.  Please ensure it is turned off in the RPD. Check AIX error report (errpt).Errors external to obiee applications can trigger crashes.  $ /bin/errpt -aHardware errors ( firmware, adapters, disks ) should be reported to IBM support.All application core files are recorded by AIX;  the most recent ones are listed first. Reserved for something important to say.

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  • Nashorn in the Twitterverse, Continued

    - by jlaskey
    After doing the Twitter example, it seemed reasonable to try graphing the result with JavaFX.  At this time the Nashorn project doesn't have an JavaFX shell, so we have to go through some hoops to create an JavaFX application.  I thought showing you some of those hoops might give you some idea about what you can do mixing Nashorn and Java (we'll add a JavaFX shell to the todo list.) First, let's look at the meat of the application.  Here is the repackaged version of the original twitter example. var twitter4j      = Packages.twitter4j; var TwitterFactory = twitter4j.TwitterFactory; var Query          = twitter4j.Query; function getTrendingData() {     var twitter = new TwitterFactory().instance;     var query   = new Query("nashorn OR nashornjs");     query.since("2012-11-21");     query.count = 100;     var data = {};     do {         var result = twitter.search(query);         var tweets = result.tweets;         for each (tweet in tweets) {             var date = tweet.createdAt;             var key = (1900 + date.year) + "/" +                       (1 + date.month) + "/" +                       date.date;             data[key] = (data[key] || 0) + 1;         }     } while (query = result.nextQuery());     return data; } Instead of just printing out tweets, getTrendingData tallies "tweets per date" during the sample period (since "2012-11-21", the date "New Project: Nashorn" was posted.)   getTrendingData then returns the resulting tally object. Next, use JavaFX BarChart to display that data. var javafx         = Packages.javafx; var Stage          = javafx.stage.Stage var Scene          = javafx.scene.Scene; var Group          = javafx.scene.Group; var Chart          = javafx.scene.chart.Chart; var FXCollections  = javafx.collections.FXCollections; var ObservableList = javafx.collections.ObservableList; var CategoryAxis   = javafx.scene.chart.CategoryAxis; var NumberAxis     = javafx.scene.chart.NumberAxis; var BarChart       = javafx.scene.chart.BarChart; var XYChart        = javafx.scene.chart.XYChart; var Series         = XYChart.Series; var Data           = XYChart.Data; function graph(stage, data) {     var root = new Group();     stage.scene = new Scene(root);     var dates = Object.keys(data);     var xAxis = new CategoryAxis();     xAxis.categories = FXCollections.observableArrayList(dates);     var yAxis = new NumberAxis("Tweets", 0.0, 200.0, 50.0);     var series = FXCollections.observableArrayList();     for (var date in data) {         series.add(new Data(date, data[date]));     }     var tweets = new Series("Tweets", series);     var barChartData = FXCollections.observableArrayList(tweets);     var chart = new BarChart(xAxis, yAxis, barChartData, 25.0);     root.children.add(chart); } I should point out that there is a lot of subtlety going on in the background.  For example; stage.scene = new Scene(root) is equivalent to stage.setScene(new Scene(root)). If Nashorn can't find a property (scene), then it searches (via Dynalink) for the Java Beans equivalent (setScene.)  Also note, that Nashorn is magically handling the generic class FXCollections.  Finally,  with the call to observableArrayList(dates), Nashorn is automatically converting the JavaScript array dates to a Java collection.  It really is hard to identify which objects are JavaScript and which are Java.  Does it really matter? Okay, with the meat out of the way, let's talk about the hoops. When working with JavaFX, you start with a main subclass of javafx.application.Application.  This class handles the initialization of the JavaFX libraries and the event processing.  This is what I used for this example; import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; import javafx.application.Application; import javafx.stage.Stage; import javax.script.ScriptEngine; import javax.script.ScriptEngineManager; import javax.script.ScriptException; public class TrendingMain extends Application { private static final ScriptEngineManager MANAGER = new ScriptEngineManager(); private final ScriptEngine engine = MANAGER.getEngineByName("nashorn"); private Trending trending; public static void main(String[] args) { launch(args); } @Override public void start(Stage stage) throws Exception { trending = (Trending) load("Trending.js"); trending.start(stage); } @Override public void stop() throws Exception { trending.stop(); } private Object load(String script) throws IOException, ScriptException { try (final InputStream is = TrendingMain.class.getResourceAsStream(script)) { return engine.eval(new InputStreamReader(is, "utf-8")); } } } To initialize Nashorn, we use JSR-223's javax.script.  private static final ScriptEngineManager MANAGER = new ScriptEngineManager(); private final ScriptEngine engine = MANAGER.getEngineByName("nashorn"); This code sets up an instance of the Nashorn engine for evaluating scripts. The  load method reads a script into memory and then gets engine to eval that script.  Note, that load also returns the result of the eval. Now for the fun part.  There are several different approaches we could use to communicate between the Java main and the script.  In this example we'll use a Java interface.  The JavaFX main needs to do at least start and stop, so the following will suffice as an interface; public interface Trending {     public void start(Stage stage) throws Exception;     public void stop() throws Exception; } At the end of the example's script we add; (function newTrending() {     return new Packages.Trending() {         start: function(stage) {             var data = getTrendingData();             graph(stage, data);             stage.show();         },         stop: function() {         }     } })(); which instantiates a new subclass instance of Trending and overrides the start and stop methods.  The result of this function call is what is returned to main via the eval. trending = (Trending) load("Trending.js"); To recap, the script Trending.js contains functions getTrendingData, graph and newTrending, plus the call at the end to newTrending.  Back in the Java code, we cast the result of the eval (call to newTrending) to Trending, thus, we end up with an object that we can then use to call back into the script.  trending.start(stage); Voila. ?

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