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  • Hibernate Query - Get latest versions by timestamp?

    - by Slim
    I have a database that is being used as a sort of version control system. That is, instead of ever updating any rows, I add a new row with the same information. Each row also contains a version column that is a date timestamp, so the only difference is the new row will have a more recent timestamp. What I'm having trouble with is writing an efficient hibernate query to return the latest version of these rows. For the sake of example, these are rows in a table called Product, the timestamped column is version. There are multiple versions of multiple products in the table. So there may be multiple versions (rows) of ProductA, multiple versions of ProductB, etc. And I would like to grab the latest version of each. Can I do this in just a single hibernate query? session.createQuery("select product from Product product where...?"); Or would this require some intermediate steps?

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  • SQL query problem

    - by Brisonela
    Hi, I'm new to StackOverflow, and new to SQL Server, I'd like you to help me with some troublesome query. This is my database structure(It's half spanish, hope doesn't matter) Database My problem is that I don't now how to make a query that states which team is local and which is visitor(using table TMatch, knowing that the stadium belongs to only one team) This is as far as I can get Select P.NroMatch, (select * from fnTeam (P.TeamA)) as TeamA,(select * from fnTeam (P.TeamB)) as TeamB, (select * from fnEstadium (P.CodEstadium)) as Estadium, (cast(P.GolesTeamA as varchar)) + '-' + (cast(P.GolesTeamA as varchar)) as Score, P.Fecha from TMatch P Using this functions: If object_id ('fnTeam','fn')is not null drop function fnTeam go create function fnTeam(@CodTeam varchar(5)) returns table return(Select Name from TTeam where CodTeam = @CodTeam) go select * from fnTeam ('Eq001') go ----**** If object_id ('fnEstadium','fn')is not null drop function fnEstadium go create function fnEstadium(@CodEstadium varchar(5)) returns table return(Select Name from TEstadium where CodEstadium = @CodEstadium) go I hope I'd explained myself well, and I thank you help in advance

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  • SQL Query to select upcoming events with a start and end date

    - by Chris T
    I need to display upcoming events from a database. The problem is when I use the query I'm currently using any events with a start day that has passed will show up lower on the list of upcoming events regardless of the fact that they are current My table (yaml): columns: title: type: string(255) notnull: true default: Untitled Event start_time: type: time end_time: type: time start_day: type: date notnull: true end_day: type: date description: type: string(500) default: This event has no description category_id: integer My query (doctrine): $results = Doctrine_Query::create() ->from("sfEventItem e, e.Category c") ->select("e.title, e.start_day, e.description, e.category_id, e.slug") ->addSelect("c.title, c.slug") ->orderBy("e.start_day, e.start_time, e.title") ->limit(5) ->execute(array(), Doctrine_Core::HYDRATE_ARRAY); Basically I'd like any events that is currently going on (so if today is in between start_day and end_day) to be at the top of the list. How would I go about doing this if it's even possible? Raw sql queries are good answers too because they're pretty easy to turn into DQL.

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  • PHP mySQL query's and PHP Variables

    - by jon
    I'm trying to make an OO Login system for a project I'm working on, and am having trouble with inserting variables into the query strings. In the code below, if I replace "$TBL_NAME" with the actual table name it works. Why isn't $TBL_NAME translating to the value of $TBL_NAME? class UserDB { private $TBL_NAME = "users"; public static function CheckLogin($username, $password) { Database::Connect(); $username = stripslashes($username); $password = stripslashes($password); $username = mysql_real_escape_string($username); $password = mysql_real_escape_string($password); $sql="SELECT uid FROM $TBL_NAME WHERE username='$username' AND password='$password' "; $result =mysql_query($sql); $count=mysql_num_rows($result); if ($count==1) return true; else return false; } The Query is returning false.

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  • mysql query takes 145 seconds

    - by suomee
    I have a a mysql db with myisam engine. Along with many other table I have this table "et" which has around 8137037 records. I have created indexes (individual index of column hname and pnum, it did not help much later created joint index of hname and pnum and it help execute within a second)such that queries like "select st from et where hname='name' and pnum='1' limit 1;" execute fast (with in a second) but the problem is I must execute this query "select st from et where hname='name' and pnum='1' order by id limit 1" where id is the primary key of the table and this query sometimes take 145 seconds :( how can i resolve this issue?

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  • MySql Query to return number of photos in each album

    - by GivenPie
    MY table is set up like this, all I need to do is call a query to my Photos table. I have PhotoID as the primary key and GalleryID as the foreign key to Gallery. How can I could the number of unique PhotoID's for each multiple GalleryIDs. So to speak there are may duplicate GalleryIDs because there are many photos in a gallery. So I just need to could the number of unique PhotoIDs associated with that GalleryID. Can it be done in one query?

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  • Big Data – Buzz Words: What is MapReduce – Day 7 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is Hadoop. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – MapReduce. What is MapReduce? MapReduce was designed by Google as a programming model for processing large data sets with a parallel, distributed algorithm on a cluster. Though, MapReduce was originally Google proprietary technology, it has been quite a generalized term in the recent time. MapReduce comprises a Map() and Reduce() procedures. Procedure Map() performance filtering and sorting operation on data where as procedure Reduce() performs a summary operation of the data. This model is based on modified concepts of the map and reduce functions commonly available in functional programing. The library where procedure Map() and Reduce() belongs is written in many different languages. The most popular free implementation of MapReduce is Apache Hadoop which we will explore tomorrow. Advantages of MapReduce Procedures The MapReduce Framework usually contains distributed servers and it runs various tasks in parallel to each other. There are various components which manages the communications between various nodes of the data and provides the high availability and fault tolerance. Programs written in MapReduce functional styles are automatically parallelized and executed on commodity machines. The MapReduce Framework takes care of the details of partitioning the data and executing the processes on distributed server on run time. During this process if there is any disaster the framework provides high availability and other available modes take care of the responsibility of the failed node. As you can clearly see more this entire MapReduce Frameworks provides much more than just Map() and Reduce() procedures; it provides scalability and fault tolerance as well. A typical implementation of the MapReduce Framework processes many petabytes of data and thousands of the processing machines. How do MapReduce Framework Works? A typical MapReduce Framework contains petabytes of the data and thousands of the nodes. Here is the basic explanation of the MapReduce Procedures which uses this massive commodity of the servers. Map() Procedure There is always a master node in this infrastructure which takes an input. Right after taking input master node divides it into smaller sub-inputs or sub-problems. These sub-problems are distributed to worker nodes. A worker node later processes them and does necessary analysis. Once the worker node completes the process with this sub-problem it returns it back to master node. Reduce() Procedure All the worker nodes return the answer to the sub-problem assigned to them to master node. The master node collects the answer and once again aggregate that in the form of the answer to the original big problem which was assigned master node. The MapReduce Framework does the above Map () and Reduce () procedure in the parallel and independent to each other. All the Map() procedures can run parallel to each other and once each worker node had completed their task they can send it back to master code to compile it with a single answer. This particular procedure can be very effective when it is implemented on a very large amount of data (Big Data). The MapReduce Framework has five different steps: Preparing Map() Input Executing User Provided Map() Code Shuffle Map Output to Reduce Processor Executing User Provided Reduce Code Producing the Final Output Here is the Dataflow of MapReduce Framework: Input Reader Map Function Partition Function Compare Function Reduce Function Output Writer In a future blog post of this 31 day series we will explore various components of MapReduce in Detail. MapReduce in a Single Statement MapReduce is equivalent to SELECT and GROUP BY of a relational database for a very large database. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – HDFS. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • SQL SERVER – Attach or Detach Database – SQL in Sixty Seconds #068

    - by Pinal Dave
    When we have to move a database from one server to another server or when we have to move a database from one file to another file, we commonly use Database Attach or Detach process. I have been doing this for quite a while as well. Recently, when I was visiting an organization I found that in this organization lots of developers are still using an older version of the code to attach the database. I quickly pointed that out to them the new method to attach the database, however it was really interesting to find out that they really did not know that sp_attach_db is now a deprecated method to attach the database. This really made me to do today’s SQL in Sixty Seconds. I demonstrate in this SQL in Sixty Seconds how to attach or detach the database using a new method of attaching database. The code which I have used in this code is over here: -- Detach Database USE [master] GO EXEC MASTER.dbo.sp_detach_db @dbname = N'AdventureWorks2014_new' GO -- Deprecated Way to Attach Database USE [master] GO EXEC MASTER.dbo.sp_attach_db 'AdventureWorks2014_new', 'E:\AdventureWorks2012_Data_new.mdf', 'E:\AdventureWorks2012_log_new.ldf' GO -- Correct Way to Attach Database USE [master] GO CREATE DATABASE [AdventureWorks2014_new] ON ( FILENAME = 'E:\AdventureWorks2012_Data_new.mdf'), ( FILENAME = 'E:\AdventureWorks2012_log_new.ldf') FOR ATTACH GO Here is the question back to you – Do you still use old methods to attach database? If yes, I suggest that you start using the new method onwards. SQL in Sixty Seconds Video I have attempted to explain the same subject in simple words over in following video. Action Item Here are the blog posts I have previously written on the subject of SA password. You can read it over here: SQL SERVER – 2005 – T-SQL Script to Attach and Detach Database SQL SERVER – Move Database Files MDF and LDF to Another Location SQL SERVER – 2005 Take Off Line or Detach Database SQL SERVER – Attach mdf file without ldf file in Database SQL SERVER – Copy Database from Instance to Another Instance – Copy Paste in SQL Server You can subscribe to my YouTube Channel for frequent updates. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Book Review, SQLAuthority News, T SQL, Video

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  • SQL SERVER – Introduction to PERCENT_RANK() – Analytic Functions Introduced in SQL Server 2012

    - by pinaldave
    SQL Server 2012 introduces new analytical functions PERCENT_RANK(). This function returns relative standing of a value within a query result set or partition. It will be very difficult to explain this in words so I’d like to attempt to explain its function through a brief example. Instead of creating a new table, I will be using the AdventureWorks sample database as most developers use that for experiment purposes. Now let’s have fun following query: USE AdventureWorks GO SELECT SalesOrderID, OrderQty, RANK() OVER(ORDER BY SalesOrderID) Rnk, PERCENT_RANK() OVER(ORDER BY SalesOrderID) AS PctDist FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY PctDist DESC GO The above query will give us the following result: Now let us understand the resultset. You will notice that I have also included the RANK() function along with this query. The reason to include RANK() function was as this query is infect uses RANK function and find the relative standing of the query. The formula to find PERCENT_RANK() is as following: PERCENT_RANK() = (RANK() – 1) / (Total Rows – 1) If you want to read more about this function read here. Now let us attempt the same example with PARTITION BY clause USE AdventureWorks GO SELECT SalesOrderID, OrderQty, ProductID, RANK() OVER(PARTITION BY SalesOrderID ORDER BY ProductID ) Rnk, PERCENT_RANK() OVER(PARTITION BY SalesOrderID ORDER BY ProductID ) AS PctDist FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY PctDist DESC GO Now you will notice that the same logic is followed in follow result set. I have now quick question to you – how many of you know the logic/formula of PERCENT_RANK() before this blog post? Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Function, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Running & Managing Concurrent Queries in SQL Developer

    - by thatjeffsmith
    We’ve all been there – you’ve managed to write a query that takes longer than a few seconds to execute. Tuning aside, sometimes it takes longer than you want for a query to run. So what’s a SQL Developer user to do? I say, keep going! While you’re waiting for your query to finish, there’s no reason why you can’t continue on with your work. If you need to execute something else in a worksheet, there’s no reason to launch a 2nd or 3rd copy of SQL Developer. Just open an un-shared worksheet. Now while you’ve got 1 or more queries running, you can easily get yourself into a situation where you’re not sure what’s running where. Or maybe you want to cancel a query or just check how long something’s been running. Just open the Task Progress Panel If a query or task in SQL Developer takes more than 3-5 seconds, it will appear in the Task Progress panel. You can then watch the throbbers go back and forth while you sip your coffee/soda/Red Bull. Run a query, spawn a new worksheet, run another query, watch them in the Task Progress panel. Kudos and thanks to @leight0nn for helping me get the title of this post right If you’re looking for help in managing and monitoring sessions in general, check out this post.

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  • Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the Pig and Pig Latin in Big Data Story. In this article we will understand what is Sqoop and Zookeeper in Big Data Story. There are two most important components one should learn when learning about interacting with Hadoop – Sqoop and Zookper. What is Sqoop? Most of the business stores their data in RDBMS as well as other data warehouse solutions. They need a way to move data to the Hadoop system to do various processing and return it back to RDBMS from Hadoop system. The data movement can happen in real time or at various intervals in bulk. We need a tool which can help us move this data from SQL to Hadoop and from Hadoop to SQL. Sqoop (SQL to Hadoop) is such a tool which extract data from non-Hadoop data sources and transform them into the format which Hadoop can use it and later it loads them into HDFS. Essentially it is ETL tool where it Extracts, Transform and Load from SQL to Hadoop. The best part is that it also does extract data from Hadoop and loads them to Non-SQL (or RDBMS) data stores. Essentially, Sqoop is a command line tool which does SQL to Hadoop and Hadoop to SQL. It is a command line interpreter. It creates MapReduce job behinds the scene to import data from an external database to HDFS. It is very effective and easy to learn tool for nonprogrammers. What is Zookeeper? ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. In other words Zookeeper is a replicated synchronization service with eventual consistency. In simpler words – in Hadoop cluster there are many different nodes and one node is master. Let us assume that master node fails due to any reason. In this case, the role of the master node has to be transferred to a different node. The main role of the master node is managing the writers as that task requires persistence in order of writing. In this kind of scenario Zookeeper will assign new master node and make sure that Hadoop cluster performs without any glitch. Zookeeper is the Hadoop’s method of coordinating all the elements of these distributed systems. Here are few of the tasks which Zookeepr is responsible for. Zookeeper manages the entire workflow of starting and stopping various nodes in the Hadoop’s cluster. In Hadoop cluster when any processes need certain configuration to complete the task. Zookeeper makes sure that certain node gets necessary configuration consistently. In case of the master node fails, Zookeepr can assign new master node and make sure cluster works as expected. There many other tasks Zookeeper performance when it is about Hadoop cluster and communication. Basically without the help of Zookeeper it is not possible to design any new fault tolerant distributed application. Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Big Data Analytics. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • The Schema returned by the new query differ from the base query

    - by sochandara
    I am working on class project which required to work with Windows Application and this issue occurred to me that i don understand how to solved it can anybody help please? I want to show the NATIONALITYNAME instead of showing NATIONALITYID in the grid view SELECT COACH.COACHID, COACH.COACHFIRSTNAME, COACH.COACHLASTNAME, NATIONALITY.NATIONALITY FROM COACH INNER JOIN NATIONALITY ON COACH.NATIONALITYID = NATIONALITY.NATIONALITYID Error Message: "The Schema returned by the new query differ from the base query"![alt text][1]

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  • the shema return by the new query differ from the base query

    - by sochandara
    I am working on class project which required to work with Windows Application and this issue occurred to me that i don understand how to solved it can anybody help please? I want to show the NATIONALITYNAME instead of showing NATIONALITYID in the grid view SELECT COACH.COACHID , COACH.COACHFIRSTNAME , COACH.COACHLASTNAME , NATIONALITY.NATIONALITY FROM COACH INNER JOIN NATIONALITY ON COACH.NATIONALITYID = NATIONALITY.NATIONALITYID Error Message: "The Schema returned by the new query differ from the base query"

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  • SQL SERVER – Fundamentals of Columnstore Index

    - by pinaldave
    There are two kind of storage in database. Row Store and Column Store. Row store does exactly as the name suggests – stores rows of data on a page – and column store stores all the data in a column on the same page. These columns are much easier to search – instead of a query searching all the data in an entire row whether the data is relevant or not, column store queries need only to search much lesser number of the columns. This means major increases in search speed and hard drive use. Additionally, the column store indexes are heavily compressed, which translates to even greater memory and faster searches. I am sure this looks very exciting and it does not mean that you convert every single index from row store to column store index. One has to understand the proper places where to use row store or column store indexes. Let us understand in this article what is the difference in Columnstore type of index. Column store indexes are run by Microsoft’s VertiPaq technology. However, all you really need to know is that this method of storing data is columns on a single page is much faster and more efficient. Creating a column store index is very easy, and you don’t have to learn new syntax to create them. You just need to specify the keyword “COLUMNSTORE” and enter the data as you normally would. Keep in mind that once you add a column store to a table, though, you cannot delete, insert or update the data – it is READ ONLY. However, since column store will be mainly used for data warehousing, this should not be a big problem. You can always use partitioning to avoid rebuilding the index. A columnstore index stores each column in a separate set of disk pages, rather than storing multiple rows per page as data traditionally has been stored. The difference between column store and row store approaches is illustrated below: In case of the row store indexes multiple pages will contain multiple rows of the columns spanning across multiple pages. In case of column store indexes multiple pages will contain multiple single columns. This will lead only the columns needed to solve a query will be fetched from disk. Additionally there is good chance that there will be redundant data in a single column which will further help to compress the data, this will have positive effect on buffer hit rate as most of the data will be in memory and due to same it will not need to be retrieved. Let us see small example of how columnstore index improves the performance of the query on a large table. As a first step let us create databaseset which is large enough to show performance impact of columnstore index. The time taken to create sample database may vary on different computer based on the resources. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 Now let us do quick performance test. I have kept STATISTICS IO ON for measuring how much IO following queries take. In my test first I will run query which will use regular index. We will note the IO usage of the query. After that we will create columnstore index and will measure the IO of the same. -- Performance Test -- Comparing Regular Index with ColumnStore Index USE AdventureWorks GO SET STATISTICS IO ON GO -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO -- Table 'MySalesOrderDetail'. Scan count 1, logical reads 342261, physical reads 0, read-ahead reads 0. -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO -- Select Table with Columnstore Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO It is very clear from the results that query is performance extremely fast after creating ColumnStore Index. The amount of the pages it has to read to run query is drastically reduced as the column which are needed in the query are stored in the same page and query does not have to go through every single page to read those columns. If we enable execution plan and compare we can see that column store index performance way better than regular index in this case. Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO In future posts we will see cases where Columnstore index is not appropriate solution as well few other tricks and tips of the columnstore index. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Kendo UI Mobile with Knockout for Master-Detail Views

    - by Steve Michelotti
    Lately I’ve been playing with Kendo UI Mobile to build iPhone apps. It’s similar to jQuery Mobile in that they are both HTML5/JavaScript based frameworks for buildings mobile apps. The primary thing that drew me to investigate Kendo UI was its innate ability to adaptively render a native looking app based on detecting the device it’s currently running on. In other words, it will render to look like a native iPhone app if it’s running on an iPhone and it will render to look like a native Droid app if it’s running on a Droid. This is in contrast to jQuery Mobile which looks the same on all devices and, therefore, it can never quite look native for whatever device it’s running on. My first impressions of Kendo UI were great. Using HTML5 data-* attributes to define “roles” for UI elements is easy, the rendering looked great, and the basic navigation was simple and intuitive. However, I ran into major confusion when trying to figure out how to “correctly” build master-detail views. Since I was already very family with KnockoutJS, I set out to use that framework in conjunction with Kendo UI Mobile to build the following simple scenario: I wanted to have a simple “Task Manager” application where my first screen just showed a list of tasks like this:   Then clicking on a specific task would navigate to a detail screen that would show all details of the specific task that was selected:   Basic navigation between views in Kendo UI is simple. The href of an <a> tag just needs to specify a hash tag followed by the ID of the view to navigate to as shown in this jsFiddle (notice the href of the <a> tag matches the id of the second view):   Direct link to jsFiddle: here. That is all well and good but the problem I encountered was: how to pass data between the views? Specifically, I need the detail view to display all the details of whichever task was selected. If I was doing this with my typical technique with KnockoutJS, I know exactly what I would do. First I would create a view model that had my collection of tasks and a property for the currently selected task like this: 1: function ViewModel() { 2: var self = this; 3: self.tasks = ko.observableArray(data); 4: self.selectedTask = ko.observable(null); 5: } Then I would bind my list of tasks to the unordered list - I would attach a “click” handler to each item (each <li> in the unordered list) so that it would select the “selectedTask” for the view model. The problem I found is this approach simply wouldn’t work for Kendo UI Mobile. It completely ignored the click handlers that I was trying to attach to the <a> tags – it just wanted to look at the href (at least that’s what I observed). But if I can’t intercept this, then *how* can I pass data or any context to the next view? The only thing I was able to find in the Kendo documentation is that you can pass query string arguments on the view name you’re specifying in the href. This enabled me to do the following: Specify the task ID in each href – something like this: <a href=”#taskDetail?id=3></a> Attach an “init method” (via the “data-show” attribute on the details view) that runs whenever the view is activated Inside this “init method”, grab the task ID passed from the query string to look up the item from my view model’s list of tasks in order to set the selected task I was able to get all that working with about 20 lines of JavaScript as shown in this jsFiddle. If you click on the Results tab, you can navigate between views and see the the detail screen is correctly binding to the selected item:   Direct link to jsFiddle: here.   With all that being done, I was very happy to get it working with the behavior I wanted. However, I have no idea if that is the “correct” way to do it or if there is a “better” way to do it. I know that Kendo UI comes with its own data binding framework but my preference is to be able to use (the well-documented) KnockoutJS since I’m already familiar with that framework rather than having to learn yet another new framework. While I think my solution above is probably “acceptable”, there are still a couple of things that bug me about it. First, it seems odd that I have to loop through my items to *find* my selected item based on the ID that was passed on the query string - normally, with Knockout I can just refer directly to my selected item from where it was used. Second, it didn’t feel exactly right that I had to rely on the “data-show” method of the details view to set my context – normally with Knockout, I could just attach a click handler to the <a> tag that was actually clicked by the user in order to set the “selected item.” I’m not sure if I’m being too picky. I know there are many people that have *way* more expertise in Kendo UI compared to me – I’d be curious to know if there are better ways to achieve the same results.

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  • MySQL enters another value that the one given by PHP

    - by Tristan
    Hello, The big problem : mysql does not stores the information i told him to via PHP Example (this req is an echo just before the query) : INSERT INTO serveur (GSP_nom , IPserv, port, tickrate, membre, nomPays, finContrat, type, jeux, slot, ipClient, email) VALUES ( 'ckras', '88.191.88.57', '37060', '100' , '', 'Allemagne','20110519', '2', '4','99' ,'82.220.201.183','[email protected]'); But on the MySQL i have : 403 ckras 88.191.88.57 32767 100 Allemagne 20110519 1 2010-04-25 00:51:47 2 4 99 82.220.201.183 [email protected] port : 37060 (right value) //// 32767 (MySQL's drug?) Any help would be appreciated, i'm worse than stuck and i'm ** off PS: *There is no trigger on the mysql as far as i know / there is no controll on the port which means that nowhere i modify the "port" value and this script works for 80% of the time ( it seems that as soon as the users enters a port = 30000 it causes that bug), an user first reported to me this error today and the script was running since 3 months* Thanks

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  • Trouble with a query

    - by Mark Allison
    Hi there, I'm having trouble with a query in SQL Server 2008 on some forex trading data. I have a trades table and an orders table. A trade needs to comprise of 2 or more orders. DDL schema and sample data below. What I want to do is write a query that shows the profit/loss in pips for each trade. A pip is 1/1000th of a currency. So the difference between USD 1.3441 and 1.3442 is 1 pip in forex-speak. A trade usually has one entry order and multiple exit orders. So for example if I buy 3 lots of the currency pair GBP/USD at the exchange rate of 1.6100 and then sell 1 lot at 1.6150, 1 lot at 1.6200 and 1 lot at 1.6250 then the profit is (1.6150 - 1.6100) + (1.6200 - 1.6100) + (1.6250 - 1.6100), or 50 + 100 + 150 = 300 pips profit. The trade could also go the other way (Shorting). For example the currency pair can be sold first before it's bought back later at a cheaper price. I would like a query that returns the following: tradeId, currencyPair, profitInPips It seems like a pretty straightforward query, but it's eluding me right now. Here's my DDL and sample data: CREATE TABLE [dbo].[trades]( [tradeId] [int] IDENTITY(1,1) NOT NULL, [currencyPair] [char](6) NOT NULL, CONSTRAINT [PK_trades] PRIMARY KEY CLUSTERED ( [tradeId] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] GO SET ANSI_PADDING OFF GO SET IDENTITY_INSERT [dbo].[trades] ON INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (1, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (2, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (3, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (4, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (5, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (6, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (7, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (8, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (9, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (10, N'GBPUSD') SET IDENTITY_INSERT [dbo].[trades] OFF GO CREATE TABLE [dbo].[orders]( [orderId] [int] IDENTITY(1,1) NOT NULL, [tradeId] [int] NOT NULL, [amount] [decimal](18, 1) NOT NULL, [buySell] [char](1) NOT NULL, [rate] [decimal](18, 6) NOT NULL, [orderDateTime] [datetime] NOT NULL, CONSTRAINT [PK_orders] PRIMARY KEY CLUSTERED ( [orderId] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] GO SET ANSI_PADDING OFF GO SET IDENTITY_INSERT [dbo].[orders] ON INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (1, 1, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.606500 AS Decimal(18, 6)), CAST(0x00009CF40083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (2, 1, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.615500 AS Decimal(18, 6)), CAST(0x00009CF400A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (3, 2, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.608000 AS Decimal(18, 6)), CAST(0x00009CF500000000 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (4, 2, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.603000 AS Decimal(18, 6)), CAST(0x00009CF50083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (5, 2, CAST(2.0 AS Decimal(18, 1)), N'B', CAST(1.605500 AS Decimal(18, 6)), CAST(0x00009CF50107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (6, 3, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.595500 AS Decimal(18, 6)), CAST(0x00009CF70083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (7, 3, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.590500 AS Decimal(18, 6)), CAST(0x00009CF700C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (8, 3, CAST(2.0 AS Decimal(18, 1)), N'B', CAST(1.594500 AS Decimal(18, 6)), CAST(0x00009CF701499700 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (9, 4, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.611000 AS Decimal(18, 6)), CAST(0x00009CFB0083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (10, 4, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.616000 AS Decimal(18, 6)), CAST(0x00009CFB00A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (11, 4, CAST(2.0 AS Decimal(18, 1)), N'S', CAST(1.611500 AS Decimal(18, 6)), CAST(0x00009CFB0107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (12, 5, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.613000 AS Decimal(18, 6)), CAST(0x00009CFC0083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (13, 5, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.618000 AS Decimal(18, 6)), CAST(0x00009CFC0107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (14, 5, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.623000 AS Decimal(18, 6)), CAST(0x00009CFC0083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (15, 5, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.628000 AS Decimal(18, 6)), CAST(0x00009CFD00C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (16, 6, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.632000 AS Decimal(18, 6)), CAST(0x00009D020083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (17, 6, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.637000 AS Decimal(18, 6)), CAST(0x00009D0200A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (18, 6, CAST(2.0 AS Decimal(18, 1)), N'S', CAST(1.630000 AS Decimal(18, 6)), CAST(0x00009D0200C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (19, 7, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.634500 AS Decimal(18, 6)), CAST(0x00009D0201499700 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (20, 7, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.639500 AS Decimal(18, 6)), CAST(0x00009D0300000000 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (21, 7, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.644500 AS Decimal(18, 6)), CAST(0x00009D030083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (22, 7, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.637500 AS Decimal(18, 6)), CAST(0x00009D0300C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (23, 8, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.625000 AS Decimal(18, 6)), CAST(0x00009D0400C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (24, 8, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.620000 AS Decimal(18, 6)), CAST(0x00009D050083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (25, 8, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.615000 AS Decimal(18, 6)), CAST(0x00009D0500A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (26, 8, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.623000 AS Decimal(18, 6)), CAST(0x00009D050107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (27, 9, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.618000 AS Decimal(18, 6)), CAST(0x00009D0600C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (28, 9, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.613000 AS Decimal(18, 6)), CAST(0x00009D0600D63BC0 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (29, 9, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.608000 AS Decimal(18, 6)), CAST(0x00009D0600E6B680 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (30, 9, 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