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  • Built in python hash() function

    - by sm1
    Windows XP, Python 2.5: hash('http://stackoverflow.com') Result: 1934711907 Google App Engine (http://shell.appspot.com/): hash('http://stackoverflow.com') Result: -5768830964305142685 Why is that? How can I have a hash function which will give me same results across different platforms (Windows, Linux, Mac)?

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  • GAE/J datastore backup

    - by jb
    What is the easiest way to do a GAE/J datastore backup? It looks like there is python bulkloader.py tool to do backup for Python apps, but what should I do to backup Java app? Is there any way to use python tool?

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  • Get node name with minidom

    - by Alex
    Is it possible to get the name of a node using minidom? for example i have a node: <heading><![CDATA[5 year]]></heading> what i'm trying to do is store the value heading so that i can use it as a key in a dictionary, the closest i can get is something like [<DOM Element: heading at 0x11e6d28>] i'm sure i'm overlooking something very simple here, thanks!

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  • Rules regarding iPhone apps

    - by iphone newbie
    iphone dev has this rule for iphone developers Be certain that the items you offer for purchase do not contain, or relate to, pornography, hate speech, defamation, or gambling (simulated gambling is acceptable). But how come there are apps in the app store such as iBetMate, which, in fact, allows users to gamble? Is there a clause or something that I missed in Apple's rules?

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  • GoogleAppEngine web proxy

    - by Cal S
    Does anyone know of a simple open source proxy capable of running on google app engine or where to start in making one? (preferably in python, I'm trying to bypass a site blocking system)

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  • How to display a page in my browser with python code that is run locally on my computer with "GAE" S

    - by brilliant
    When I run this code on my computer with the help of "Google App Engine SDK", it displays (in my browser) the HTML code of the Google home page: from google.appengine.api import urlfetch url = "http://www.google.com/" result = urlfetch.fetch(url) print result.content How can I make it display the page itself? I mean I want to see that page in my browser the way it would normally be seen by any user of the internet.

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  • How to do a back-reference on Google AppEngine?

    - by jCuga
    I'm trying to access an object that is linked to by a db.ReferenceProperty in Google app engine. Here's the model's code: class InquiryQuestion(db.Model): inquiry_ref = db.ReferenceProperty(reference_class=GiftInquiry, required=True, collection_name="inquiry_ref") And I am trying to access it in the following way: linkedObject = question.inquiry_ref and then linkedKey = linkedObject.key but it's not working. Can anyone please help?

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  • Get current URL in Python

    - by Alex
    How would i get the current URL with Python, I need to grab the current URL so i can check it for query strings e.g requested_url = "URL_HERE" url = urlparse(requested_url) if url[4]: params = dict([part.split('=') for part in url[4].split('&')]) also this is running in Google App Engine

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  • Users and roles in context

    - by Eric W.
    I'm trying to get a sense of how to implement the user/role relationships for an application I'm writing. The persistence layer is Google App Engine's datastore, which places some interesting (but generally beneficial) constraints on what can be done. Any thoughts are appreciated. It might be helpful to keep things very concrete. I would like there to be organizations, users, test content and test administrations (records of tests that have been taken). A user can have the role of participant (test-taker), contributor of test material or both. A user can also be a member of zero or more organizations. In the role of participant, the user can see the previous administrations of tests he or she has taken. The user can also see a test administration of another participant if that participant has given the user authorization. The user can see test material that has been made public, and he or she can see restricted content as a participant during a specific administration of a test for which that user has been authorized by an organization. As a member of an organization, the user can see restricted content in the role of contributor, and he or she might or might not also be able to edit the content. Each organization should have one or more administrators that can determine whether a member can see and edit content and determine who has admin privileges. There should also be one or more application-wide superusers that can troubleshoot and solve problems. Members of organizations can see the administrations of tests that the participants concerned have authorized them to see, and they can see anonymous data if no authorization has been given. A user cannot see the test results of another user in any other circumstances. Since there are no joins in the App Engine datastore, it might be necessary to have things less normalized than usual for the typical SQL database in order to ensure that queries that check permissions are fast (e.g., ones that determine whether a link is to be displayed). My questions are: How do I move forward on this? Should I spend a lot of time up front in order to get the model right, or can I iterate several times and gradually roll in additional complexity? Does anyone have some general ideas about how to break things up in this instance? Are there any GAE libraries that handle roles in a way that is compatible with this arrangement?

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  • Create top ten list

    - by newtech
    I'm trying to build an iPhone app where I have a list of names, and can search by year to find the top ten names for that year. How would I need to set that up in xcode?

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  • Mobile App Data Syncronization

    - by Matt Rogish
    Let's say I have a mobile app that uses HTML5 SQLite DB (and/or the HTML5 key-value store). Assets (media files, PDFs, etc.) are stored locally on the mobile device. Luckily enough, the mobile device is a read-only copy of the "centralized" storage, so the mobile device won't have to propagate changes upstream. However, as the server changes assets (creates new ones, modifies existing, deletes old ones) I need to propagate those changes back to the mobile app. Assume that server changes are grouped into changesets (version number n) that contain some information (added element XYZ, deleted id = 45, etc.) and that the mobile device has limited CPU/bandwidth, so most of the processing has to take place on the server. I can think of a couple of methods to do this. All have trade-offs and at this point, I'm unsure which is the right course of action... Method 1: For change set n, store the "diff" of the current n and previous n-1. When a client with version y asks if there have been any changes, send the change sets from version y up to the current version. e.g. added item 334, contents: xxx. Deleted picture 44. Deleted PDF 11. Changed 33. added picture 99. Characteristics: Diffs take up space, although in theory would be kept small. However, all diffs must be kept around indefinitely (should a v1 app have not been updated for a year, must apply v2..v100). High latency devices (mobile apps) will incur a penalty to send lots of small files (assume cannot be zipped or tarr'd up into one file) Very few server CPU resources required, as all it does is send the client a list of files "Dumb" - if I change an item in change set 3, and change it to something else in 4, the client is going to perform both actions, even though #3 is rendered moot by #4. Or, if an asset is added in #4 and removed in #5 - the client will download a file just to delete it later. Method 2: Very similar to method 1 except on the server, do some sort of a diff between the change sets represented by the app version and server version. Package that up and send that single change set to the client. Characteristics: Client-efficient: The client only has to process one file, duplicate or irrelevant changes are stripped out. Server CPU/space intensive. The change sets must be diff'd and then written out to a file that is then sent to the client. Makes diff server scalability an issue. Possibly ways to cache the results and re-use them, but in the wild there's likely to be a lot of different versions so the diff re-use has a limit Diff algorithm is complicated. The change sets must be structured in such a way that an efficient and effective diff can be performed. Method 3: Instead of keeping diffs, write out the entire versioned asset collection to a mobile-database import file. When client requests an update, send the entire database to client and have them update their assets appropriately. Characteristics: Conceptually simple -- easy to develop and deploy Very inefficient as the client database is restored every update. If only one new thing was added, the whole database is refreshed. Server space and CPU efficient. Only the latest version DB needs kept around and the server just throws the file to the client. Others?? Thoughts? Thanks!!

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  • What is a good toy example to teach version control?

    - by janos
    I am looking for practical examples to use when teaching version control. Breaking down the material to basic concepts and providing examples is an obvious way to teach version control, but this can be very boring, unless the examples are really practical or interesting. One idea I have is customizing a wordpress theme. I use wordpress a lot and no theme is ever perfect, so I typically just put the theme directory in version control using any dvcs and start recording changes. The problem with this example is that not many people in the audience may be familiar with wordpress, let alone have shell access to a wordpress site to try out the commands. Preparing a mock site and giving access to everyone is also not an option for me. I need a "toy example" that can be interesting to a broad audience of software developers, and something they can try on their own computers. The tutorial will use a dvcs, but the practical example I'm looking for is only to teach the basic features of version control, ignoring the distributed features for the moment. (Now that I think of it, instead of a mock site, a customized live cd might do the trick...) Any better ideas?

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  • Google Analytics Social Tracking implementation. Is Google's example correct?

    - by s_a
    The current Google Analytics help page on Social tracking (developers.google.com/analytics/devguides/collection/gajs/gaTrackingSocial?hl=es-419) links to this page with an example of the implementation: http://analytics-api-samples.googlecode.com/svn/trunk/src/tracking/javascript/v5/social/facebook_js_async.html I've followed the example carefully yet social interactions are not registered. This is the webpage with the non-working setup: http://bit.ly/1dA00dY (obscured domain as per Google's Webmaster Central recommendations for their product forums) This is the structure of the page: In the : ga async code copied from the analytics' page a script tag linking to stored in the same domain. the twitter js loading tag In the the fb-root div the facebook async loading js including the _ga.trackFacebook(); call the social buttons afterwards, like so: (with the proper URL) Tweet (with the proper handle) That's it. As far as I can tell, I have implemented it exactly like in the example, but likes and twitts aren't registered. I have also altered the ga_social_tracking.js to register the social interactions as events, adding the code below. It doesn't work either. What could be wrong? Thanks! Code added to ga_social_tracking.js var url = document.URL; var category = 'Social Media'; /* Facebook */ FB.Event.subscribe('edge.create', function(href, widget) { _gaq.push(['_trackEvent', category, 'Facebook', url]); }); /* Twitter */ twttr.events.bind('tweet', function(event) { _gaq.push(['_trackEvent', category, 'Twitter', url]); });

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  • DNS works only with ip but does not work with NS CentOS + Bind9

    - by Borislav Yordanov
    I am having a headache with DNS. Lets say my public IP is 1.2.3.4, my local IP is 192.168.0.10 and my domain is example.com I am running CentOS on a virtual machine (Parallels Desktop for Mac) with a LAN card reserved for it, so it gets Ip directly from the router. I have ports 80,443,53 forwarded to 192.168.0.10. Both Mac OS and CentOs firewalls are Off. The strange is when I type dig @1.2.3.4 example.com from my other PC I get: ; <<>> DiG 9.8.3-P1 <<>> @1.2.3.4 example.com ; (1 server found) ;; global options: +cmd ;; Got answer: ;; ->>HEADER<<- opcode: QUERY, status: NOERROR, id: 16941 ;; flags: qr aa rd; QUERY: 1, ANSWER: 1, AUTHORITY: 2, ADDITIONAL: 2 ;; WARNING: recursion requested but not available ;; QUESTION SECTION: ;example.com. IN A ;; ANSWER SECTION: example.com. 86400 IN A 1.2.3.4 ;; AUTHORITY SECTION: example.com. 86400 IN NS ns2.example.com. example.com. 86400 IN NS ns1.example.com. ;; ADDITIONAL SECTION: ns1.example.com. 86400 IN A 1.2.3.4 ns2.example.com. 86400 IN A 1.2.3.4 ;; Query time: 8 msec ;; SERVER: 1.2.3.4#53(1.2.3.4) ;; WHEN: Sat Nov 2 09:37:36 2013 ;; MSG SIZE rcvd: 109 but when i type: dig @ns1.example.com example.com it waits a few seconds and returns dig: couldn't get address for 'ns1.dsht.in': not found This is my config file: /etc/named.conf options { listen-on-v6 { none; }; directory"/var/named"; dump-file"/var/named/data/cache_dump.db"; statistics-file"/var/named/data/named_stats.txt"; memstatistics-file"/var/named/data/named_mem_stats.txt"; allow-query{ localhost; 192.168.0.0/24; }; allow-transfer { localhost; 192.168.0.0/24; }; recursion yes; dnssec-enable yes; dnssec-validation yes; dnssec-lookaside auto; bindkeys-file "/etc/named.iscdlv.key"; managed-keys-directory "/var/named/dynamic"; }; logging { channel default_debug { file "data/named.run"; severity dynamic; }; }; # change all from here view "internal" { match-clients { localhost; 192.168.0.0/24; }; zone "." IN { type hint; file "named.ca"; }; zone "example.com" IN { type master; file "example.com.zone"; allow-update { none; }; }; zone "0.168.192.in-addr.arpa" IN { type master; file "0.168.192.in-addr.arpa"; allow-update { none; }; }; include "/etc/named.rfc1912.zones"; include "/etc/named.root.key"; }; view "external" { match-clients { any; }; allow-query { any; }; recursion no; zone "example.com" IN { type master; file "example.com.zone"; allow-update { none; }; }; zone "4.3.2.1.in-addr.arpa" IN { type master; file "4.3.2.1.in-addr.arpa"; allow-update { none; }; }; }; /var/named/exmaple.com.zone $TTL 86400 @ IN SOA ns1.example.com. host.example.com. ( 2013042201 ;Serial 3600 ;Refresh 1800 ;Retry 604800 ;Expire 86400 ;Minimum TTL ) ; Specify our two nameservers IN NS ns1.example.com. IN NS ns2.example.com. ; Resolve nameserver hostnames to IP, replace with your two droplet IP addresses. ns1 IN A 1.2.3.4 ns2 IN A 1.2.3.4 ; Define hostname -> IP pairs which you wish to resolve @ IN A 1.2.3.4 IN A 1.2.3.4 www IN A 1.2.3.4 server2 IN A 192.168.0.2 * IN A 1.2.3.4 /var/named/4.3.2.1.in-addr.arpa $TTL 2d ; 172800 seconds $ORIGIN 4.3.2.1.IN-ADDR.ARPA. @ IN SOA ns1.example.com. host.example.com. ( 2013010304 ; serial number 3h ; refresh 15m ; update retry 3w ; expiry 3h ; nx = nxdomain ttl ) IN NS ns1.example.com. IN NS ns2.example.com. IN PTR example.com. ; etc /var/named/0.168.192.in-addr.arpa $TTL 2d ; 172800 seconds $ORIGIN 0.168.192.IN-ADDR.ARPA. @ IN SOA ns1.example.com. host.example.com. ( 2013010304 ; serial number 3h ; refresh 15m ; update retry 3w ; expiry 3h ; nx = nxdomain ttl ) IN NS ns1.example.com. IN NS ns2.example.com. 10 IN PTR example.com. 2 IN PTR server2.example.com ; etc I will be very glad if someone can help me. Thank you in advance

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  • An Introduction to Meteor

    - by Stephen.Walther
    The goal of this blog post is to give you a brief introduction to Meteor which is a framework for building Single Page Apps. In this blog entry, I provide a walkthrough of building a simple Movie database app. What is special about Meteor? Meteor has two jaw-dropping features: Live HTML – If you make any changes to the HTML, CSS, JavaScript, or data on the server then every client shows the changes automatically without a browser refresh. For example, if you change the background color of a page to yellow then every open browser will show the new yellow background color without a refresh. Or, if you add a new movie to a collection of movies, then every open browser will display the new movie automatically. With Live HTML, users no longer need a refresh button. Changes to an application happen everywhere automatically without any effort. The Meteor framework handles all of the messy details of keeping all of the clients in sync with the server for you. Latency Compensation – When you modify data on the client, these modifications appear as if they happened on the server without any delay. For example, if you create a new movie then the movie appears instantly. However, that is all an illusion. In the background, Meteor updates the database with the new movie. If, for whatever reason, the movie cannot be added to the database then Meteor removes the movie from the client automatically. Latency compensation is extremely important for creating a responsive web application. You want the user to be able to make instant modifications in the browser and the framework to handle the details of updating the database without slowing down the user. Installing Meteor Meteor is licensed under the open-source MIT license and you can start building production apps with the framework right now. Be warned that Meteor is still in the “early preview” stage. It has not reached a 1.0 release. According to the Meteor FAQ, Meteor will reach version 1.0 in “More than a month, less than a year.” Don’t be scared away by that. You should be aware that, unlike most open source projects, Meteor has financial backing. The Meteor project received an $11.2 million round of financing from Andreessen Horowitz. So, it would be a good bet that this project will reach the 1.0 mark. And, if it doesn’t, the framework as it exists right now is still very powerful. Meteor runs on top of Node.js. You write Meteor apps by writing JavaScript which runs both on the client and on the server. You can build Meteor apps on Windows, Mac, or Linux (Although the support for Windows is still officially unofficial). If you want to install Meteor on Windows then download the MSI from the following URL: http://win.meteor.com/ If you want to install Meteor on Mac/Linux then run the following CURL command from your terminal: curl https://install.meteor.com | /bin/sh Meteor will install all of its dependencies automatically including Node.js. However, I recommend that you install Node.js before installing Meteor by installing Node.js from the following address: http://nodejs.org/ If you let Meteor install Node.js then Meteor won’t install NPM which is the standard package manager for Node.js. If you install Node.js and then you install Meteor then you get NPM automatically. Creating a New Meteor App To get a sense of how Meteor works, I am going to walk through the steps required to create a simple Movie database app. Our app will display a list of movies and contain a form for creating a new movie. The first thing that we need to do is create our new Meteor app. Open a command prompt/terminal window and execute the following command: Meteor create MovieApp After you execute this command, you should see something like the following: Follow the instructions: execute cd MovieApp to change to your MovieApp directory, and run the meteor command. Executing the meteor command starts Meteor on port 3000. Open up your favorite web browser and navigate to http://localhost:3000 and you should see the default Meteor Hello World page: Open up your favorite development environment to see what the Meteor app looks like. Open the MovieApp folder which we just created. Here’s what the MovieApp looks like in Visual Studio 2012: Notice that our MovieApp contains three files named MovieApp.css, MovieApp.html, and MovieApp.js. In other words, it contains a Cascading Style Sheet file, an HTML file, and a JavaScript file. Just for fun, let’s see how the Live HTML feature works. Open up multiple browsers and point each browser at http://localhost:3000. Now, open the MovieApp.html page and modify the text “Hello World!” to “Hello Cruel World!” and save the change. The text in all of the browsers should update automatically without a browser refresh. Pretty amazing, right? Controlling Where JavaScript Executes You write a Meteor app using JavaScript. Some of the JavaScript executes on the client (the browser) and some of the JavaScript executes on the server and some of the JavaScript executes in both places. For a super simple app, you can use the Meteor.isServer and Meteor.isClient properties to control where your JavaScript code executes. For example, the following JavaScript contains a section of code which executes on the server and a section of code which executes in the browser: if (Meteor.isClient) { console.log("Hello Browser!"); } if (Meteor.isServer) { console.log("Hello Server!"); } console.log("Hello Browser and Server!"); When you run the app, the message “Hello Browser!” is written to the browser JavaScript console. The message “Hello Server!” is written to the command/terminal window where you ran Meteor. Finally, the message “Hello Browser and Server!” is execute on both the browser and server and the message appears in both places. For simple apps, using Meteor.isClient and Meteor.isServer to control where JavaScript executes is fine. For more complex apps, you should create separate folders for your server and client code. Here are the folders which you can use in a Meteor app: · client – This folder contains any JavaScript which executes only on the client. · server – This folder contains any JavaScript which executes only on the server. · common – This folder contains any JavaScript code which executes on both the client and server. · lib – This folder contains any JavaScript files which you want to execute before any other JavaScript files. · public – This folder contains static application assets such as images. For the Movie App, we need the client, server, and common folders. Delete the existing MovieApp.js, MovieApp.html, and MovieApp.css files. We will create new files in the right locations later in this walkthrough. Combining HTML, CSS, and JavaScript Files Meteor combines all of your JavaScript files, and all of your Cascading Style Sheet files, and all of your HTML files automatically. If you want to create one humongous JavaScript file which contains all of the code for your app then that is your business. However, if you want to build a more maintainable application, then you should break your JavaScript files into many separate JavaScript files and let Meteor combine them for you. Meteor also combines all of your HTML files into a single file. HTML files are allowed to have the following top-level elements: <head> — All <head> files are combined into a single <head> and served with the initial page load. <body> — All <body> files are combined into a single <body> and served with the initial page load. <template> — All <template> files are compiled into JavaScript templates. Because you are creating a single page app, a Meteor app typically will contain a single HTML file for the <head> and <body> content. However, a Meteor app typically will contain several template files. In other words, all of the interesting stuff happens within the <template> files. Displaying a List of Movies Let me start building the Movie App by displaying a list of movies. In order to display a list of movies, we need to create the following four files: · client\movies.html – Contains the HTML for the <head> and <body> of the page for the Movie app. · client\moviesTemplate.html – Contains the HTML template for displaying the list of movies. · client\movies.js – Contains the JavaScript for supplying data to the moviesTemplate. · server\movies.js – Contains the JavaScript for seeding the database with movies. After you create these files, your folder structure should looks like this: Here’s what the client\movies.html file looks like: <head> <title>My Movie App</title> </head> <body> <h1>Movies</h1> {{> moviesTemplate }} </body>   Notice that it contains <head> and <body> top-level elements. The <body> element includes the moviesTemplate with the syntax {{> moviesTemplate }}. The moviesTemplate is defined in the client/moviesTemplate.html file: <template name="moviesTemplate"> <ul> {{#each movies}} <li> {{title}} </li> {{/each}} </ul> </template> By default, Meteor uses the Handlebars templating library. In the moviesTemplate above, Handlebars is used to loop through each of the movies using {{#each}}…{{/each}} and display the title for each movie using {{title}}. The client\movies.js JavaScript file is used to bind the moviesTemplate to the Movies collection on the client. Here’s what this JavaScript file looks like: // Declare client Movies collection Movies = new Meteor.Collection("movies"); // Bind moviesTemplate to Movies collection Template.moviesTemplate.movies = function () { return Movies.find(); }; The Movies collection is a client-side proxy for the server-side Movies database collection. Whenever you want to interact with the collection of Movies stored in the database, you use the Movies collection instead of communicating back to the server. The moviesTemplate is bound to the Movies collection by assigning a function to the Template.moviesTemplate.movies property. The function simply returns all of the movies from the Movies collection. The final file which we need is the server-side server\movies.js file: // Declare server Movies collection Movies = new Meteor.Collection("movies"); // Seed the movie database with a few movies Meteor.startup(function () { if (Movies.find().count() == 0) { Movies.insert({ title: "Star Wars", director: "Lucas" }); Movies.insert({ title: "Memento", director: "Nolan" }); Movies.insert({ title: "King Kong", director: "Jackson" }); } }); The server\movies.js file does two things. First, it declares the server-side Meteor Movies collection. When you declare a server-side Meteor collection, a collection is created in the MongoDB database associated with your Meteor app automatically (Meteor uses MongoDB as its database automatically). Second, the server\movies.js file seeds the Movies collection (MongoDB collection) with three movies. Seeding the database gives us some movies to look at when we open the Movies app in a browser. Creating New Movies Let me modify the Movies Database App so that we can add new movies to the database of movies. First, I need to create a new template file – named client\movieForm.html – which contains an HTML form for creating a new movie: <template name="movieForm"> <fieldset> <legend>Add New Movie</legend> <form> <div> <label> Title: <input id="title" /> </label> </div> <div> <label> Director: <input id="director" /> </label> </div> <div> <input type="submit" value="Add Movie" /> </div> </form> </fieldset> </template> In order for the new form to show up, I need to modify the client\movies.html file to include the movieForm.html template. Notice that I added {{> movieForm }} to the client\movies.html file: <head> <title>My Movie App</title> </head> <body> <h1>Movies</h1> {{> moviesTemplate }} {{> movieForm }} </body> After I make these modifications, our Movie app will display the form: The next step is to handle the submit event for the movie form. Below, I’ve modified the client\movies.js file so that it contains a handler for the submit event raised when you submit the form contained in the movieForm.html template: // Declare client Movies collection Movies = new Meteor.Collection("movies"); // Bind moviesTemplate to Movies collection Template.moviesTemplate.movies = function () { return Movies.find(); }; // Handle movieForm events Template.movieForm.events = { 'submit': function (e, tmpl) { // Don't postback e.preventDefault(); // create the new movie var newMovie = { title: tmpl.find("#title").value, director: tmpl.find("#director").value }; // add the movie to the db Movies.insert(newMovie); } }; The Template.movieForm.events property contains an event map which maps event names to handlers. In this case, I am mapping the form submit event to an anonymous function which handles the event. In the event handler, I am first preventing a postback by calling e.preventDefault(). This is a single page app, no postbacks are allowed! Next, I am grabbing the new movie from the HTML form. I’m taking advantage of the template find() method to retrieve the form field values. Finally, I am calling Movies.insert() to insert the new movie into the Movies collection. Here, I am explicitly inserting the new movie into the client-side Movies collection. Meteor inserts the new movie into the server-side Movies collection behind the scenes. When Meteor inserts the movie into the server-side collection, the new movie is added to the MongoDB database associated with the Movies app automatically. If server-side insertion fails for whatever reasons – for example, your internet connection is lost – then Meteor will remove the movie from the client-side Movies collection automatically. In other words, Meteor takes care of keeping the client Movies collection and the server Movies collection in sync. If you open multiple browsers, and add movies, then you should notice that all of the movies appear on all of the open browser automatically. You don’t need to refresh individual browsers to update the client-side Movies collection. Meteor keeps everything synchronized between the browsers and server for you. Removing the Insecure Module To make it easier to develop and debug a new Meteor app, by default, you can modify the database directly from the client. For example, you can delete all of the data in the database by opening up your browser console window and executing multiple Movies.remove() commands. Obviously, enabling anyone to modify your database from the browser is not a good idea in a production application. Before you make a Meteor app public, you should first run the meteor remove insecure command from a command/terminal window: Running meteor remove insecure removes the insecure package from the Movie app. Unfortunately, it also breaks our Movie app. We’ll get an “Access denied” error in our browser console whenever we try to insert a new movie. No worries. I’ll fix this issue in the next section. Creating Meteor Methods By taking advantage of Meteor Methods, you can create methods which can be invoked on both the client and the server. By taking advantage of Meteor Methods you can: 1. Perform form validation on both the client and the server. For example, even if an evil hacker bypasses your client code, you can still prevent the hacker from submitting an invalid value for a form field by enforcing validation on the server. 2. Simulate database operations on the client but actually perform the operations on the server. Let me show you how we can modify our Movie app so it uses Meteor Methods to insert a new movie. First, we need to create a new file named common\methods.js which contains the definition of our Meteor Methods: Meteor.methods({ addMovie: function (newMovie) { // Perform form validation if (newMovie.title == "") { throw new Meteor.Error(413, "Missing title!"); } if (newMovie.director == "") { throw new Meteor.Error(413, "Missing director!"); } // Insert movie (simulate on client, do it on server) return Movies.insert(newMovie); } }); The addMovie() method is called from both the client and the server. This method does two things. First, it performs some basic validation. If you don’t enter a title or you don’t enter a director then an error is thrown. Second, the addMovie() method inserts the new movie into the Movies collection. When called on the client, inserting the new movie into the Movies collection just updates the collection. When called on the server, inserting the new movie into the Movies collection causes the database (MongoDB) to be updated with the new movie. You must add the common\methods.js file to the common folder so it will get executed on both the client and the server. Our folder structure now looks like this: We actually call the addMovie() method within our client code in the client\movies.js file. Here’s what the updated file looks like: // Declare client Movies collection Movies = new Meteor.Collection("movies"); // Bind moviesTemplate to Movies collection Template.moviesTemplate.movies = function () { return Movies.find(); }; // Handle movieForm events Template.movieForm.events = { 'submit': function (e, tmpl) { // Don't postback e.preventDefault(); // create the new movie var newMovie = { title: tmpl.find("#title").value, director: tmpl.find("#director").value }; // add the movie to the db Meteor.call( "addMovie", newMovie, function (err, result) { if (err) { alert("Could not add movie " + err.reason); } } ); } }; The addMovie() method is called – on both the client and the server – by calling the Meteor.call() method. This method accepts the following parameters: · The string name of the method to call. · The data to pass to the method (You can actually pass multiple params for the data if you like). · A callback function to invoke after the method completes. In the JavaScript code above, the addMovie() method is called with the new movie retrieved from the HTML form. The callback checks for an error. If there is an error then the error reason is displayed in an alert (please don’t use alerts for validation errors in a production app because they are ugly!). Summary The goal of this blog post was to provide you with a brief walk through of a simple Meteor app. I showed you how you can create a simple Movie Database app which enables you to display a list of movies and create new movies. I also explained why it is important to remove the Meteor insecure package from a production app. I showed you how to use Meteor Methods to insert data into the database instead of doing it directly from the client. I’m very impressed with the Meteor framework. The support for Live HTML and Latency Compensation are required features for many real world Single Page Apps but implementing these features by hand is not easy. Meteor makes it easy.

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  • GAE formpreview

    - by Niklas R
    I'm trying to enable form preview with Google App Engine. Getting the following error message I suspect being mistaken somewhere: ... handler = handler_class() TypeError: __call__() takes at least 2 arguments (1 given) Can you tell what's wrong with my attempt? Here is some of the code. from django.contrib.formtools.preview import FormPreview class AFormPreview(FormPreview): def done(self, request, cleaned_data): # Do something with the cleaned_data, then redirect # to a "success" page. self.response.out.write('Done!') class AForm(djangoforms.ModelForm): text = forms.CharField(widget=forms.Textarea(attrs={'rows':'11','cols':'70','class':'foo'}),label=_("content").capitalize()) def clean(self): cleaned_data = self.clean_data name = cleaned_data.get("name") if not name: raise forms.ValidationError("No name.") # Always return the full collection of cleaned data. return cleaned_data class Meta: model = A fields = ['category','currency','price','title','phonenumber','postaladress','name','text','email'] #change the order ... ('/aformpreview/([^/]*)', AFormPreview(AForm)), UPDATE: Here's a complete app where the preview is not working. Any ideas are most welcome: import cgi from google.appengine.api import users from google.appengine.ext import db from google.appengine.ext import webapp from google.appengine.ext.webapp import template from google.appengine.ext.webapp.util import run_wsgi_app from google.appengine.ext.db import djangoforms class Item(db.Model): name = db.StringProperty() quantity = db.IntegerProperty(default=1) target_price = db.FloatProperty() priority = db.StringProperty(default='Medium',choices=[ 'High', 'Medium', 'Low']) entry_time = db.DateTimeProperty(auto_now_add=True) added_by = db.UserProperty() class ItemForm(djangoforms.ModelForm): class Meta: model = Item exclude = ['added_by'] from django.contrib.formtools.preview import FormPreview class ItemFormPreview(FormPreview): def done(self, request, cleaned_data): # Do something with the cleaned_data, then redirect # to a "success" page. return HttpResponseRedirect('/') class MainPage(webapp.RequestHandler): def get(self): self.response.out.write('<html><body>' '<form method="POST" ' 'action="/">' '<table>') # This generates our shopping list form and writes it in the response self.response.out.write(ItemForm()) self.response.out.write('</table>' '<input type="submit">' '</form></body></html>') def post(self): data = ItemForm(data=self.request.POST) if data.is_valid(): # Save the data, and redirect to the view page entity = data.save(commit=False) entity.added_by = users.get_current_user() entity.put() self.redirect('/items.html') else: # Reprint the form self.response.out.write('<html><body>' '<form method="POST" ' 'action="/">' '<table>') self.response.out.write(data) self.response.out.write('</table>' '<input type="submit">' '</form></body></html>') class ItemPage(webapp.RequestHandler): def get(self): query = db.GqlQuery("SELECT * FROM Item ORDER BY name") for item in query: self.response.out.write('<a href="/edit?id=%d">Edit</a> - ' % item.key().id()) self.response.out.write("%s - Need to buy %d, cost $%0.2f each<br>" % (item.name, item.quantity, item.target_price)) class EditPage(webapp.RequestHandler): def get(self): id = int(self.request.get('id')) item = Item.get(db.Key.from_path('Item', id)) self.response.out.write('<html><body>' '<form method="POST" ' 'action="/edit">' '<table>') self.response.out.write(ItemForm(instance=item)) self.response.out.write('</table>' '<input type="hidden" name="_id" value="%s">' '<input type="submit">' '</form></body></html>' % id) def post(self): id = int(self.request.get('_id')) item = Item.get(db.Key.from_path('Item', id)) data = ItemForm(data=self.request.POST, instance=item) if data.is_valid(): # Save the data, and redirect to the view page entity = data.save(commit=False) entity.added_by = users.get_current_user() entity.put() self.redirect('/items.html') else: # Reprint the form self.response.out.write('<html><body>' '<form method="POST" ' 'action="/edit">' '<table>') self.response.out.write(data) self.response.out.write('</table>' '<input type="hidden" name="_id" value="%s">' '<input type="submit">' '</form></body></html>' % id) def main(): application = webapp.WSGIApplication( [('/', MainPage), ('/edit', EditPage), ('/items.html', ItemPage), ('/itemformpreview', ItemFormPreview(ItemForm)), ], debug=True) run_wsgi_app(application)

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  • Persistance JDO - How to query a property of a collection with JDOQL?

    - by Sergio del Amo
    I want to build an application where a user identified by an email address can have several application accounts. Each account can have one o more users. I am trying to use the JDO Storage capabilities with Google App Engine Java. Here is my attempt: @PersistenceCapable @Inheritance(strategy = InheritanceStrategy.NEW_TABLE) public class AppAccount { @PrimaryKey @Persistent(valueStrategy = IdGeneratorStrategy.IDENTITY) private Long id; @Persistent private String companyName; @Persistent List<Invoices> invoices = new ArrayList<Invoices>(); @Persistent List<AppUser> users = new ArrayList<AppUser>(); // Getter Setters and Other Fields } @PersistenceCapable @EmbeddedOnly public class AppUser { @Persistent private String username; @Persistent private String firstName; @Persistent private String lastName; // Getter Setters and Other Fields } When a user logs in, I want to check how many accounts does he belongs to. If he belongs to more than one he will be presented with a dashboard where he can click which account he wants to load. This is my code to retrieve a list of app accounts where he is registered. public static List<AppAccount> getUserAppAccounts(String username) { PersistenceManager pm = JdoUtil.getPm(); Query q = pm.newQuery(AppAccount.class); q.setFilter("users.username == usernameParam"); q.declareParameters("String usernameParam"); return (List<AppAccount>) q.execute(username); } But I get the next error: SELECT FROM invoices.server.AppAccount WHERE users.username == usernameParam PARAMETERS String usernameParam: Encountered a variable expression that isn't part of a join. Maybe you're referencing a non-existent field of an embedded class. org.datanucleus.store.appengine.FatalNucleusUserException: SELECT FROM com.softamo.pelicamo.invoices.server.AppAccount WHERE users.username == usernameParam PARAMETERS String usernameParam: Encountered a variable expression that isn't part of a join. Maybe you're referencing a non-existent field of an embedded class. at org.datanucleus.store.appengine.query.DatastoreQuery.getJoinClassMetaData(DatastoreQuery.java:1154) at org.datanucleus.store.appengine.query.DatastoreQuery.addLeftPrimaryExpression(DatastoreQuery.java:1066) at org.datanucleus.store.appengine.query.DatastoreQuery.addExpression(DatastoreQuery.java:846) at org.datanucleus.store.appengine.query.DatastoreQuery.addFilters(DatastoreQuery.java:807) at org.datanucleus.store.appengine.query.DatastoreQuery.performExecute(DatastoreQuery.java:226) at org.datanucleus.store.appengine.query.JDOQLQuery.performExecute(JDOQLQuery.java:85) at org.datanucleus.store.query.Query.executeQuery(Query.java:1489) at org.datanucleus.store.query.Query.executeWithArray(Query.java:1371) at org.datanucleus.jdo.JDOQuery.execute(JDOQuery.java:243) at com.softamo.pelicamo.invoices.server.Store.getUserAppAccounts(Store.java:82) at com.softamo.pelicamo.invoices.test.server.StoreTest.testgetUserAppAccounts(StoreTest.java:39) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:44) at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:15) at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:41) at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:20) at org.junit.internal.runners.statements.RunBefores.evaluate(RunBefores.java:28) at org.junit.internal.runners.statements.RunAfters.evaluate(RunAfters.java:31) at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:76) at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:50) at org.junit.runners.ParentRunner$3.run(ParentRunner.java:193) at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:52) at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:191) at org.junit.runners.ParentRunner.access$000(ParentRunner.java:42) at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:184) at org.junit.runners.ParentRunner.run(ParentRunner.java:236) at org.eclipse.jdt.internal.junit4.runner.JUnit4TestReference.run(JUnit4TestReference.java:46) at org.eclipse.jdt.internal.junit.runner.TestExecution.run(TestExecution.java:38) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.runTests(RemoteTestRunner.java:467) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.runTests(RemoteTestRunner.java:683) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.run(RemoteTestRunner.java:390) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.main(RemoteTestRunner.java:197) Any idea? I am getting JDO persistance totally wrong?

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  • Seeking on a Heap, and Two Useful DMVs

    - by Paul White
    So far in this mini-series on seeks and scans, we have seen that a simple ‘seek’ operation can be much more complex than it first appears.  A seek can contain one or more seek predicates – each of which can either identify at most one row in a unique index (a singleton lookup) or a range of values (a range scan).  When looking at a query plan, we will often need to look at the details of the seek operator in the Properties window to see how many operations it is performing, and what type of operation each one is.  As you saw in the first post in this series, the number of hidden seeking operations can have an appreciable impact on performance. Measuring Seeks and Scans I mentioned in my last post that there is no way to tell from a graphical query plan whether you are seeing a singleton lookup or a range scan.  You can work it out – if you happen to know that the index is defined as unique and the seek predicate is an equality comparison, but there’s no separate property that says ‘singleton lookup’ or ‘range scan’.  This is a shame, and if I had my way, the query plan would show different icons for range scans and singleton lookups – perhaps also indicating whether the operation was one or more of those operations underneath the covers. In light of all that, you might be wondering if there is another way to measure how many seeks of either type are occurring in your system, or for a particular query.  As is often the case, the answer is yes – we can use a couple of dynamic management views (DMVs): sys.dm_db_index_usage_stats and sys.dm_db_index_operational_stats. Index Usage Stats The index usage stats DMV contains counts of index operations from the perspective of the Query Executor (QE) – the SQL Server component that is responsible for executing the query plan.  It has three columns that are of particular interest to us: user_seeks – the number of times an Index Seek operator appears in an executed plan user_scans – the number of times a Table Scan or Index Scan operator appears in an executed plan user_lookups – the number of times an RID or Key Lookup operator appears in an executed plan An operator is counted once per execution (generating an estimated plan does not affect the totals), so an Index Seek that executes 10,000 times in a single plan execution adds 1 to the count of user seeks.  Even less intuitively, an operator is also counted once per execution even if it is not executed at all.  I will show you a demonstration of each of these things later in this post. Index Operational Stats The index operational stats DMV contains counts of index and table operations from the perspective of the Storage Engine (SE).  It contains a wealth of interesting information, but the two columns of interest to us right now are: range_scan_count – the number of range scans (including unrestricted full scans) on a heap or index structure singleton_lookup_count – the number of singleton lookups in a heap or index structure This DMV counts each SE operation, so 10,000 singleton lookups will add 10,000 to the singleton lookup count column, and a table scan that is executed 5 times will add 5 to the range scan count. The Test Rig To explore the behaviour of seeks and scans in detail, we will need to create a test environment.  The scripts presented here are best run on SQL Server 2008 Developer Edition, but the majority of the tests will work just fine on SQL Server 2005.  A couple of tests use partitioning, but these will be skipped if you are not running an Enterprise-equivalent SKU.  Ok, first up we need a database: USE master; GO IF DB_ID('ScansAndSeeks') IS NOT NULL DROP DATABASE ScansAndSeeks; GO CREATE DATABASE ScansAndSeeks; GO USE ScansAndSeeks; GO ALTER DATABASE ScansAndSeeks SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE ScansAndSeeks SET AUTO_CLOSE OFF, AUTO_SHRINK OFF, AUTO_CREATE_STATISTICS OFF, AUTO_UPDATE_STATISTICS OFF, PARAMETERIZATION SIMPLE, READ_COMMITTED_SNAPSHOT OFF, RESTRICTED_USER ; Notice that several database options are set in particular ways to ensure we get meaningful and reproducible results from the DMVs.  In particular, the options to auto-create and update statistics are disabled.  There are also three stored procedures, the first of which creates a test table (which may or may not be partitioned).  The table is pretty much the same one we used yesterday: The table has 100 rows, and both the key_col and data columns contain the same values – the integers from 1 to 100 inclusive.  The table is a heap, with a non-clustered primary key on key_col, and a non-clustered non-unique index on the data column.  The only reason I have used a heap here, rather than a clustered table, is so I can demonstrate a seek on a heap later on.  The table has an extra column (not shown because I am too lazy to update the diagram from yesterday) called padding – a CHAR(100) column that just contains 100 spaces in every row.  It’s just there to discourage SQL Server from choosing table scan over an index + RID lookup in one of the tests. The first stored procedure is called ResetTest: CREATE PROCEDURE dbo.ResetTest @Partitioned BIT = 'false' AS BEGIN SET NOCOUNT ON ; IF OBJECT_ID(N'dbo.Example', N'U') IS NOT NULL BEGIN DROP TABLE dbo.Example; END ; -- Test table is a heap -- Non-clustered primary key on 'key_col' CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, padding CHAR(100) NOT NULL DEFAULT SPACE(100), CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col) ) ; IF @Partitioned = 'true' BEGIN -- Enterprise, Trial, or Developer -- required for partitioning tests IF SERVERPROPERTY('EngineEdition') = 3 BEGIN EXECUTE (' DROP TABLE dbo.Example ; IF EXISTS ( SELECT 1 FROM sys.partition_schemes WHERE name = N''PS'' ) DROP PARTITION SCHEME PS ; IF EXISTS ( SELECT 1 FROM sys.partition_functions WHERE name = N''PF'' ) DROP PARTITION FUNCTION PF ; CREATE PARTITION FUNCTION PF (INTEGER) AS RANGE RIGHT FOR VALUES (20, 40, 60, 80, 100) ; CREATE PARTITION SCHEME PS AS PARTITION PF ALL TO ([PRIMARY]) ; CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, padding CHAR(100) NOT NULL DEFAULT SPACE(100), CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col) ) ON PS (key_col); '); END ELSE BEGIN RAISERROR('Invalid SKU for partition test', 16, 1); RETURN; END; END ; -- Non-unique non-clustered index on the 'data' column CREATE NONCLUSTERED INDEX [IX dbo.Example data] ON dbo.Example (data) ; -- Add 100 rows INSERT dbo.Example WITH (TABLOCKX) ( key_col, data ) SELECT key_col = V.number, data = V.number FROM master.dbo.spt_values AS V WHERE V.[type] = N'P' AND V.number BETWEEN 1 AND 100 ; END; GO The second stored procedure, ShowStats, displays information from the Index Usage Stats and Index Operational Stats DMVs: CREATE PROCEDURE dbo.ShowStats @Partitioned BIT = 'false' AS BEGIN -- Index Usage Stats DMV (QE) SELECT index_name = ISNULL(I.name, I.type_desc), scans = IUS.user_scans, seeks = IUS.user_seeks, lookups = IUS.user_lookups FROM sys.dm_db_index_usage_stats AS IUS JOIN sys.indexes AS I ON I.object_id = IUS.object_id AND I.index_id = IUS.index_id WHERE IUS.database_id = DB_ID(N'ScansAndSeeks') AND IUS.object_id = OBJECT_ID(N'dbo.Example', N'U') ORDER BY I.index_id ; -- Index Operational Stats DMV (SE) IF @Partitioned = 'true' SELECT index_name = ISNULL(I.name, I.type_desc), partitions = COUNT(IOS.partition_number), range_scans = SUM(IOS.range_scan_count), single_lookups = SUM(IOS.singleton_lookup_count) FROM sys.dm_db_index_operational_stats ( DB_ID(N'ScansAndSeeks'), OBJECT_ID(N'dbo.Example', N'U'), NULL, NULL ) AS IOS JOIN sys.indexes AS I ON I.object_id = IOS.object_id AND I.index_id = IOS.index_id GROUP BY I.index_id, -- Key I.name, I.type_desc ORDER BY I.index_id; ELSE SELECT index_name = ISNULL(I.name, I.type_desc), range_scans = SUM(IOS.range_scan_count), single_lookups = SUM(IOS.singleton_lookup_count) FROM sys.dm_db_index_operational_stats ( DB_ID(N'ScansAndSeeks'), OBJECT_ID(N'dbo.Example', N'U'), NULL, NULL ) AS IOS JOIN sys.indexes AS I ON I.object_id = IOS.object_id AND I.index_id = IOS.index_id GROUP BY I.index_id, -- Key I.name, I.type_desc ORDER BY I.index_id; END; The final stored procedure, RunTest, executes a query written against the example table: CREATE PROCEDURE dbo.RunTest @SQL VARCHAR(8000), @Partitioned BIT = 'false' AS BEGIN -- No execution plan yet SET STATISTICS XML OFF ; -- Reset the test environment EXECUTE dbo.ResetTest @Partitioned ; -- Previous call will throw an error if a partitioned -- test was requested, but SKU does not support it IF @@ERROR = 0 BEGIN -- IO statistics and plan on SET STATISTICS XML, IO ON ; -- Test statement EXECUTE (@SQL) ; -- Plan and IO statistics off SET STATISTICS XML, IO OFF ; EXECUTE dbo.ShowStats @Partitioned; END; END; The Tests The first test is a simple scan of the heap table: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example'; The top result set comes from the Index Usage Stats DMV, so it is the Query Executor’s (QE) view.  The lower result is from Index Operational Stats, which shows statistics derived from the actions taken by the Storage Engine (SE).  We see that QE performed 1 scan operation on the heap, and SE performed a single range scan.  Let’s try a single-value equality seek on a unique index next: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col = 32'; This time we see a single seek on the non-clustered primary key from QE, and one singleton lookup on the same index by the SE.  Now for a single-value seek on the non-unique non-clustered index: EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data = 32'; QE shows a single seek on the non-clustered non-unique index, but SE shows a single range scan on that index – not the singleton lookup we saw in the previous test.  That makes sense because we know that only a single-value seek into a unique index is a singleton seek.  A single-value seek into a non-unique index might retrieve any number of rows, if you think about it.  The next query is equivalent to the IN list example seen in the first post in this series, but it is written using OR (just for variety, you understand): EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data = 32 OR data = 33'; The plan looks the same, and there’s no difference in the stats recorded by QE, but the SE shows two range scans.  Again, these are range scans because we are looking for two values in the data column, which is covered by a non-unique index.  I’ve added a snippet from the Properties window to show that the query plan does show two seek predicates, not just one.  Now let’s rewrite the query using BETWEEN: EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data BETWEEN 32 AND 33'; Notice the seek operator only has one predicate now – it’s just a single range scan from 32 to 33 in the index – as the SE output shows.  For the next test, we will look up four values in the key_col column: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col IN (2,4,6,8)'; Just a single seek on the PK from the Query Executor, but four singleton lookups reported by the Storage Engine – and four seek predicates in the Properties window.  On to a more complex example: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example WITH (INDEX([PK dbo.Example key_col])) WHERE key_col BETWEEN 1 AND 8'; This time we are forcing use of the non-clustered primary key to return eight rows.  The index is not covering for this query, so the query plan includes an RID lookup into the heap to fetch the data and padding columns.  The QE reports a seek on the PK and a lookup on the heap.  The SE reports a single range scan on the PK (to find key_col values between 1 and 8), and eight singleton lookups on the heap.  Remember that a bookmark lookup (RID or Key) is a seek to a single value in a ‘unique index’ – it finds a row in the heap or cluster from a unique RID or clustering key – so that’s why lookups are always singleton lookups, not range scans. Our next example shows what happens when a query plan operator is not executed at all: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col = 8 AND @@TRANCOUNT < 0'; The Filter has a start-up predicate which is always false (if your @@TRANCOUNT is less than zero, call CSS immediately).  The index seek is never executed, but QE still records a single seek against the PK because the operator appears once in an executed plan.  The SE output shows no activity at all.  This next example is 2008 and above only, I’m afraid: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example WHERE key_col BETWEEN 1 AND 30', @Partitioned = 'true'; This is the first example to use a partitioned table.  QE reports a single seek on the heap (yes – a seek on a heap), and the SE reports two range scans on the heap.  SQL Server knows (from the partitioning definition) that it only needs to look at partitions 1 and 2 to find all the rows where key_col is between 1 and 30 – the engine seeks to find the two partitions, and performs a range scan seek on each partition. The final example for today is another seek on a heap – try to work out the output of the query before running it! EXECUTE dbo.RunTest @SQL = 'SELECT TOP (2) WITH TIES * FROM Example WHERE key_col BETWEEN 1 AND 50 ORDER BY $PARTITION.PF(key_col) DESC', @Partitioned = 'true'; Notice the lack of an explicit Sort operator in the query plan to enforce the ORDER BY clause, and the backward range scan. © 2011 Paul White email: [email protected] twitter: @SQL_Kiwi

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  • iPhone web app from dashcode rss feed template works deployed on simulator but not on iphone

    - by Arge Anton
    iPhone web app from dashcode rss feed template works deployed on simulator but not on iphone. The web app is deployed at; http://www.alila.se/wordpress/index.html If i run the simulator and enter that adress, it fetches the rss feed and displays it. Everything fine. When i enter the adress into my iphone, same os 3.1.2 as the simulator, i just get the header part of the page. No rss feed. The rss feed is; alila.se/wordpress/?feed=rss2 and the source can be downloaded from: alila.se/rss_feed.zip

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