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  • How to specify character encoding for Ant Task parameters in Java

    - by räph
    I'm writing an ANT task in Java. In my build.xml I specify parameters, which should be read from my java class. Problems occur, when I use special characters, like german umlauts (Ö,Ä,Ü) in these parameters. In my java task they appear as ?-characters (using System.out.print). All my files are encoded as UTF-8. and my build.xml has the corresponding declaration: <?xml version="1.0" encoding="UTF-8" ?> For the details of writing the task: I do it according to http://ant.apache.org/manual/develop.html (especially Point 5 nested elements). I have nested elements in my task like: <parameter name="test" value="ÖÄÜtest"/> and a java method: public void addConfiguredParameter(Parameter prop) { System.out.println(prop.getValue()); //prints ???test } to read the parameter values.

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  • Count total children divs inside a container

    - by kuswantin
    I want to count the total divs inside a container and toggle their visibilities with structure like this. Please also note that the div.content may also reside inside another nested or even nested-nested containers. That's why I handle it with jquery to add div.topmost for each topmost parent container: <div id="parent"> <div class="counter">There are 3 div.contents inside the container below</div> <div class="container"> <div class="content"> 1 </div> <div class="container"> <!--container inside container --> <div class="content"> 2 </div> <div class="content"> 3 </div> </div> </div> <div class="counter">There are 5 div.contents inside the container below</div> <div class="container"> <div class="content"> 1 </div> <div class="content"> 2 </div> <div class="content"> 3 </div> <div class="content"> 4 </div> <div class="content"> 5 </div> </div> </div> And the jquery: // only grab the top most container $('#parent > .container').addClass('topmost'); var topMost = $(".topmost"); var totContent = topMost.children('.content').size(); if (topMost.length > 0) { topMost.before('<div class="toggle">There are ' + totContent + ' div.contents inside the container below</div>'); } topMost.hide(); $('#parent > .counter').click(function() { $(this).next('.topmost').toggle(); //alert(totContent); return false; }); But I can't make it work to loop for each div.counter. The counter always shows all div.content. So placing the each function is suspected to be the problem. Any hep would be very much appreciated. Thanks

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  • jQuery event.layerX/Y is not relative to the element that triggered the event, is that correct?

    - by michielvoo
    If I use for example a mousemove event handler on a div and check the layerX property of the event, it changes when my mouse enters a positioned element inside that div (like an image). According to the jQuery Event object documentation it should follow the W3C DOM Level 3 specifications. But there's no mention of the layerX/Y property for the MouseEvent interface, so I'm wondering what behavior is according to specification? To me it seems that you always want the layerX/Y to be relative to the layer that fires the event, if I wanted the layerX/Y of a nested element, I would check the event in a different phase (bubbling) or would attach my handler to that nested element. My question is (somewhat subjective): how should layerX/Y work?

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  • Actionscript base class in Flex AIR app

    - by Alan
    I'm trying to build a Flex AIR app using Flex Builder 3, which I'm just getting started with. In Flash CS4, there's a text field in the authoring environment where you can specify a class that will become the "base" class - your class inherits from Sprite and then "becomes" the Stage at runtime. Is there a a way to do the same thing with Flex/AIR? Failing that, can anyone explain how to create and use an external class? Originally I had this in TestApp.mxml: <?xml version="1.0" encoding="utf-8"?> <mx:WindowedApplication xmlns:mx="http://www.adobe.com/2006/mxml" layout="absolute"> <mx:Script source="TestApp.as"/> </mx:WindowedApplication> And this in TestApp.as: package { public class TestApp { public function TestApp() { trace('Hello World'); } } } That gives the error "packages cannot be nested", so I tried taking out the package statement: public class TestApp { public function TestApp() { trace('Hello World'); } } That gives an error "classes cannot be nested", so I finally gave up and tried to take out the class altogether, figuring I'd try to start with a bunch of functions instead: function init() { trace('Hello World'); } But that gives the error "A file found in a source-path must have an externally visible definition. If a definition in the file is meant to be externally visible, please put the definition in a package". I can't win! When I put my class in a package, it says I can't do that because it would be nested. When I don't, it says it needs to be in a package so it can be seen. Does anyone know how to fix this? If I can't do the custom-class-as-base-class thing, is there a way I could just have it like: <?xml version="1.0" encoding="utf-8"?> <mx:WindowedApplication xmlns:mx="http://www.adobe.com/2006/mxml" layout="absolute"> <mx:Script source="TestApp.as"/> <mx:Script> var app = new TestApp(); </mx:Script> </mx:WindowedApplication> At the moment I can't import the class definition at all, so even that won't work. Thanks in advance!

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  • Double pointer const-correctness warnings in C

    - by Michael Koval
    You can obviously cast a pointer to non-const data to a a pointer of the same type to const data: int *x = NULL; int const *y = x; Adding additional const qualifiers to match the additional indirection should logically work the same way: int * *x = NULL; int *const *y = x; /* okay */ int const *const *z = y; /* warning */ Compiling this with GCC or Clang with the -Wall flag, however, results in the following warning: test.c:4:23: warning: initializing 'int const *const *' with an expression of type 'int *const *' discards qualifiers in nested pointer types int const *const *z = y; /* warning */ ^ ~ Why does adding an additional const qualifier "discard qualifiers in nested pointer types"?

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  • How to connect to a network of activemq brokers from a client application?

    - by subh
    I have setup a network of brokers in activemq, how do i connect to that from my client application I tried with network:static:(tcp://master1.IP:61616,tcp://master2.IP:61617) and but I get the following exception javax.jms.JMSException: Uncategorized exception occured during JMS processing; nested exception is javax.jms.JMSException: Could not create Transport. Reason: java.io.IOException: Transport scheme NOT recognized: [network]; With static:(tcp://master1.IP:61616,tcp://master2.IP:61617) I get exception javax.jms.JMSException: Uncategorized exception occured during JMS processing; nested exception is javax.jms.JMSException: Could not create Transport. Reason: java.io.IOException: Transport scheme NOT recognized: [static]; Thanks

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  • Replace text in string with delimeters using Regex

    - by user1057735
    I have a string something like, string str = "(50%silicon +20%!(20%Gold + 80%Silver)| + 30%Alumnium)"; I need a Regular Expression which would Replace the contents in between ! and | with an empty string. The result should be (50%silicon +20% + 30%Alumnium). If the string contains something like (with nested delimiters): string str = "(50%silicon +20%!(80%Gold + 80%Silver + 20%!(20%Iron + 80%Silver)|)| + 30%Alumnium)"; The result should be (50%silicon +20% + 30%Alumnium) - ignoring the nested delimiters. I've tried the following Regex, but it doesn't ignore the nesting: Regex.Replace(str , @"!.+?\|", "", RegexOptions.IgnoreCase);

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  • Operate on pairs of rows of a data frame

    - by lorin
    I've got a data frame in R, and I'd like to perform a calculation on all pairs of rows. Is there a simpler way to do this than using a nested for loop? To make this concrete, consider a data frame with ten rows, and I want to calculate the difference of scores between all (45) possible pairs. > data.frame(ID=1:10,Score=4*10:1) ID Score 1 1 40 2 2 36 3 3 32 4 4 28 5 5 24 6 6 20 7 7 16 8 8 12 9 9 8 10 10 4 I know I could do this calculation with a nested for loop, but is there a better (more R-ish) way to do it?

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  • SQL query root parent child records

    - by Vish
    Hi, We have nested folders with parent-child relationship. We use MySQL MyISAM DB. The data is stored in the DB in the following manner. Every time a child folder is created in the nested structure, the previous parentID is added. I want to get the RootFolderID of a folder which is added in the hierarchy as tabulated below. FoldID ParentID |RootFolderID -----------------|------------------- 1 0 | 0 2 1 | 1 3 2 | 1 4 3 | 1 5 4 | 1 Please let me know how to get the root folderID and populate it in the RootFolderID column after a folder is created each time. Thanks.

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  • JDO in Google App Engine: order of keys in unowned one-to-many relationship

    - by Kel
    I'm implementing web application with JDO in Google App Engine. According to documentation, in owned one-to-many relationships, order of elements in "owner" object collection is determined either by automatically created index field, or by information given in explicit ordering clause. For example: @PersistenceCapable public class Person { // ... @Order(extensions = @Extension(vendorName="datanucleus", key="list-ordering", value="country asc, city asc")) private List<ContactInfo> contacts = new List<ContactInfo>(); In unowned relationships, "owner" object contains collection of keys of "nested" objects, for example: @PersistenceCapable public class Author { // ... @Persistent private List<Key> books; Is order of keys preserved, if I use List<Key> collection in "owner" object for storing keys of "nested" elements? I could not find answer neither in JDO relationships article, nor in Data Classes article :(

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  • Right floated div within a liquid-width div. How do I get this to work?

    - by DavidR
    I have a div, within the div is a name in an <h4> tag (it's semantically correct with the layout) and a div with some values describing that <h4> value. I want the nested div to be on the right side, and the only way I can get this to work is a fixed-width container and float: right;. As you can guess, the object breaks when the value of the <h4> causes the nested div to overflow. I've tried min-width, but it ends up stretching to the maximum size of the div containing the container div.

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  • <jaxrs:client> not getting autowired

    - by himangshu
    I am trying to build a restful client using jaxrs:client as defined in http://svn.apache.org/repos/asf/cxf/trunk/systests/jaxrs/src/test/resources/jaxrs_soap_rest/WEB-INF/beans.xml In my test class I am getting org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'com.abc.service.ExportServiceTest': Injection of autowired dependencies failed; nested exception is org.springframework.beans.factory.BeanCreationException: Could not autowire field: private com.bankbazaar.service.ExportService com.abc.service.ExportServiceTest.exportClient; nested exception is org.springframework.beans.factory.NoSuchBeanDefinitionException: No matching bean of type [com.abc.service.ExportService] found for dependency: expected at least 1 bean which qualifies as autowire candidate for this dependency. Dependency annotations: {@org.springframework.beans.factory.annotation.Autowired(required=true), @org.springframework.beans.factory.annotation.Qualifier(value=exportClient)} this is my spring config However exportClient=(ExportService)applicationContext.getBean("exportClient"); this works. Thanks Himangshu

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  • Which jQuery/css menu library for working with ASP.NET TreeView or Menu controls.

    - by ProfK
    I'm looking for a good jQuery or CSS, or combo, library to enance my left side menu in an an intranet application. I don't like the 'hover only' expand/collapse style of the ASP.NET Menu control on its own, and I don't like the 'icon-click only' expand/collapse style of the TreeView control on its own. I plan on trying the CSS Control Adapters, to render the menu with some self-respect, i.e. as nested <ul> or <ol> elements instead of the usual orgy of tables. Beyond that, I need something to give a bit of style and menulike behaviour to these nested lists, and I would prefer a jQuery plugin for this. Which should I use?

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  • What is the pythonic way to add type information to an object's attributes?

    - by Tikitu
    I'm building classes where I know the types of the attributes, but Python of course doesn't. While it's un-pythonic to want to tell it, supposing I do want to, is there an idiomatic way to do so? Why: I'm reading in serialised data (without type information) involving objects-nested-inside-objects. It's easy to put it into nested dictionaries, but I want it in objects of my class-types, to get the right behaviours as well as the data. For instance: suppose my class Book has an attribute isbn which I will fill with an ISBNumber object. My serialised data gives me the isbn as a string; I would like to be able to look at Book and say "That field should be filled by ISBNumber(theString)." Bonus glee for me if the solution can be applied to classes I get from someone else without editing their code.

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  • chosen add multiple row dinamical

    - by Mario Jose Mixco
    I have a question with the plugin ajax-chosen, I need to add multiple dynamically on a form on page load the first no problem but when I try to dynamically add a new element does not work, I hope you can help me and again sorry for my English $ ("a.add-nested-field"). each (function (index, element) { return $ (element). on ("click", function () { var association, new_id, regexp, template; association = $ (element). attr ("data-association"); template = $ ("#" + association + "_fields_template"). html (); regexp = new RegExp ("new_" + association, "g"); new_id = new Date (). getTime (); $ (element). closest ("form"). FIND (". nested-field: visible: last"). after (template.replace (regexp, new_id)); return false; }); });

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  • Fastest method in merging of the two: dicts vs lists

    - by tipu
    I'm doing some indexing and memory is sufficient but CPU isn't. So I have one huge dictionary and then a smaller dictionary I'm merging into the bigger one: big_dict = {"the" : {"1" : 1, "2" : 1, "3" : 1, "4" : 1, "5" : 1}} smaller_dict = {"the" : {"6" : 1, "7" : 1}} #after merging resulting_dict = {"the" : {"1" : 1, "2" : 1, "3" : 1, "4" : 1, "5" : 1, "6" : 1, "7" : 1}} My question is for the values in both dicts, should I use a dict (as displayed above) or list (as displayed below) when my priority is to use as much memory as possible to gain the most out of my CPU? For clarification, using a list would look like: big_dict = {"the" : [1, 2, 3, 4, 5]} smaller_dict = {"the" : [6,7]} #after merging resulting_dict = {"the" : [1, 2, 3, 4, 5, 6, 7]} Side note: The reason I'm using a dict nested into a dict rather than a set nested in a dict is because JSON won't let me do json.dumps because a set isn't key/value pairs, it's (as far as the JSON library is concerned) {"a", "series", "of", "keys"} Also, after choosing between using dict to a list, how would I go about implementing the most efficient, in terms of CPU, method of merging them? I appreciate the help.

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  • Are python list comprehensions always a good programming practice?

    - by dln385
    To make the question clear, I'll use a specific example. I have a list of college courses, and each course has a few fields (all of which are strings). The user gives me a string of search terms, and I return a list of courses that match all of the search terms. This can be done in a single list comprehension or a few nested for loops. Here's the implementation. First, the Course class: class Course: def __init__(self, date, title, instructor, ID, description, instructorDescription, *args): self.date = date self.title = title self.instructor = instructor self.ID = ID self.description = description self.instructorDescription = instructorDescription self.misc = args Every field is a string, except misc, which is a list of strings. Here's the search as a single list comprehension. courses is the list of courses, and query is the string of search terms, for example "history project". def searchCourses(courses, query): terms = query.lower().strip().split() return tuple(course for course in courses if all( term in course.date.lower() or term in course.title.lower() or term in course.instructor.lower() or term in course.ID.lower() or term in course.description.lower() or term in course.instructorDescription.lower() or any(term in item.lower() for item in course.misc) for term in terms)) You'll notice that a complex list comprehension is difficult to read. I implemented the same logic as nested for loops, and created this alternative: def searchCourses2(courses, query): terms = query.lower().strip().split() results = [] for course in courses: for term in terms: if (term in course.date.lower() or term in course.title.lower() or term in course.instructor.lower() or term in course.ID.lower() or term in course.description.lower() or term in course.instructorDescription.lower()): break for item in course.misc: if term in item.lower(): break else: continue break else: continue results.append(course) return tuple(results) That logic can be hard to follow too. I have verified that both methods return the correct results. Both methods are nearly equivalent in speed, except in some cases. I ran some tests with timeit, and found that the former is three times faster when the user searches for multiple uncommon terms, while the latter is three times faster when the user searches for multiple common terms. Still, this is not a big enough difference to make me worry. So my question is this: which is better? Are list comprehensions always the way to go, or should complicated statements be handled with nested for loops? Or is there a better solution altogether?

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  • Silently binding a variable instance to a class in C++?

    - by gct
    So I've got a plugin-based system I'm writing. Users can create a child class of a Plugin class and then it will be loaded at runtime and integrated with the rest of the system. When a Plugin is run from the system, it's run in the context of a group of plugins, which I call a Session. My problem is that inside the user plugins, two streaming classes called pf_ostream and pf_istream can be used to read/write data to the system. I'd like to bind the plugin instance's session variable to pf_ostream and pf_istream somehow so that when the user instantiates those classes, it's already bound to the session for them (basically I don't want them to see the session internals) I could just do this with a macro, wrapping a call to the constructor like: #define MAKE_OSTREAM = pf_ostream_int(this->session) But I thought there might be a better way. I looked at using a nested class inside Plugin wrapping pf_ostream but it appears nested classes don't get access to the enclosing classes variables in a closure sort of way. Does anyone know of a neat way to do this?

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  • Approach to Selecting top item matching a criteria

    - by jkelley
    I have a SQL problem that I've come up against routinely, and normally just solved w/ a nested query. I'm hoping someone can suggest a more elegant solution. It often happens that I need to select a result set for a user, conditioned upon it being the most recent, or the most sizeable or whatever. For example: Their complete list of pages created, but I only want the most recent name they applied to a page. It so happens that the database contains many entries for each page, and only the most recent one is desired. I've been using a nested select like: SELECT pg.customName, pg.id FROM ( select id, max(createdAt) as mostRecent from pages where userId = @UserId GROUP BY id ) as MostRecentPages JOIN pages pg ON pg.id = MostRecentPages.id AND pg.createdAt = MostRecentPages.mostRecent Is there a better syntax to perform this selection?

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  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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  • Tales from the Trenches – Building a Real-World Silverlight Line of Business Application

    - by dwahlin
    There's rarely a boring day working in the world of software development. Part of the fun associated with being a developer is that change is guaranteed and the more you learn about a particular technology the more you realize there's always a different or better way to perform a task. I've had the opportunity to work on several different real-world Silverlight Line of Business (LOB) applications over the past few years and wanted to put together a list of some of the key things I've learned as well as key problems I've encountered and resolved. There are several different topics I could cover related to "lessons learned" (some of them were more painful than others) but I'll keep it to 5 items for this post and cover additional lessons learned in the future. The topics discussed were put together for a TechEd talk: Pick a Pattern and Stick To It Data Binding and Nested Controls Notify Users of Successes (and failures) Get an Agent – A Service Agent Extend Existing Controls The first topic covered relates to architecture best practices and how the MVVM pattern can save you time in the long run. When I was first introduced to MVVM I thought it was a lot of work for very little payoff. I've since learned (the hard way in some cases) that my initial impressions were dead wrong and that my criticisms of the pattern were generally caused by doing things the wrong way. In addition to MVVM pros the slides and sample app below also jump into data binding tricks in nested control scenarios and discuss how animations and media can be used to enhance LOB applications in subtle ways. Finally, a discussion of creating a re-usable service agent to interact with backend services is discussed as well as how existing controls make good candidates for customization. I tried to keep the samples simple while still covering the topics as much as possible so if you’re new to Silverlight you should definitely be able to follow along with a little study and practice. I’d recommend starting with the SilverlightDemos.View project, moving to the SilverlightDemos.ViewModels project and then going to the SilverlightDemos.ServiceAgents project. All of the backend “Model” code can be found in the SilverlightDemos.Web project. Custom controls used in the app can be found in the SivlerlightDemos.Controls project.   Sample Code and Slides

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  • 2D Array of 2D Arrays (C# / XNA) [on hold]

    - by Lemoncreme
    I want to create a 2D array that contains many other 2D arrays. The problem is I'm not quite sure what I'm doing but this is the initialization code I have: int[,][,] chunk = new int[64, 64][32, 32]; For some reason Visual Studio doesn't like this and says that it's and 'invalid rank specifier'. Also, I'm not sure how to use the nested arrays once I've declared them... Some help and some insight, please?

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  • Listing common SQL Code Smells.

    - by Phil Factor
    Once you’ve done a number of SQL Code-reviews, you’ll know those signs in the code that all might not be well. These ’Code Smells’ are coding styles that don’t directly cause a bug, but are indicators that all is not well with the code. . Kent Beck and Massimo Arnoldi seem to have coined the phrase in the "OnceAndOnlyOnce" page of www.C2.com, where Kent also said that code "wants to be simple". Bad Smells in Code was an essay by Kent Beck and Martin Fowler, published as Chapter 3 of the book ‘Refactoring: Improving the Design of Existing Code’ (ISBN 978-0201485677) Although there are generic code-smells, SQL has its own particular coding habits that will alert the programmer to the need to re-factor what has been written. See Exploring Smelly Code   and Code Deodorants for Code Smells by Nick Harrison for a grounding in Code Smells in C# I’ve always been tempted by the idea of automating a preliminary code-review for SQL. It would be so useful to trawl through code and pick up the various problems, much like the classic ‘Lint’ did for C, and how the Code Metrics plug-in for .NET Reflector by Jonathan 'Peli' de Halleux is used for finding Code Smells in .NET code. The problem is that few of the standard procedural code smells are relevant to SQL, and we need an agreed list of code smells. Merrilll Aldrich made a grand start last year in his blog Top 10 T-SQL Code Smells.However, I'd like to make a start by discovering if there is a general opinion amongst Database developers what the most important SQL Smells are. One can be a bit defensive about code smells. I will cheerfully write very long stored procedures, even though they are frowned on. I’ll use dynamic SQL occasionally. You can only use them as an aid for your own judgment and it is fine to ‘sign them off’ as being appropriate in particular circumstances. Also, whole classes of ‘code smells’ may be irrelevant for a particular database. The use of proprietary SQL, for example, is only a ‘code smell’ if there is a chance that the database will have to be ported to another RDBMS. The use of dynamic SQL is a risk only with certain security models. As the saying goes,  a CodeSmell is a hint of possible bad practice to a pragmatist, but a sure sign of bad practice to a purist. Plamen Ratchev’s wonderful article Ten Common SQL Programming Mistakes lists some of these ‘code smells’ along with out-and-out mistakes, but there are more. The use of nested transactions, for example, isn’t entirely incorrect, even though the database engine ignores all but the outermost: but it does flag up the possibility that the programmer thinks that nested transactions are supported. If anything requires some sort of general agreement, the definition of code smells is one. I’m therefore going to make this Blog ‘dynamic, in that, if anyone twitters a suggestion with a #SQLCodeSmells tag (or sends me a twitter) I’ll update the list here. If you add a comment to the blog with a suggestion of what should be added or removed, I’ll do my best to oblige. In other words, I’ll try to keep this blog up to date. The name against each 'smell' is the name of the person who Twittered me, commented about or who has written about the 'smell'. it does not imply that they were the first ever to think of the smell! Use of deprecated syntax such as *= (Dave Howard) Denormalisation that requires the shredding of the contents of columns. (Merrill Aldrich) Contrived interfaces Use of deprecated datatypes such as TEXT/NTEXT (Dave Howard) Datatype mis-matches in predicates that rely on implicit conversion.(Plamen Ratchev) Using Correlated subqueries instead of a join   (Dave_Levy/ Plamen Ratchev) The use of Hints in queries, especially NOLOCK (Dave Howard /Mike Reigler) Few or No comments. Use of functions in a WHERE clause. (Anil Das) Overuse of scalar UDFs (Dave Howard, Plamen Ratchev) Excessive ‘overloading’ of routines. The use of Exec xp_cmdShell (Merrill Aldrich) Excessive use of brackets. (Dave Levy) Lack of the use of a semicolon to terminate statements Use of non-SARGable functions on indexed columns in predicates (Plamen Ratchev) Duplicated code, or strikingly similar code. Misuse of SELECT * (Plamen Ratchev) Overuse of Cursors (Everyone. Special mention to Dave Levy & Adrian Hills) Overuse of CLR routines when not necessary (Sam Stange) Same column name in different tables with different datatypes. (Ian Stirk) Use of ‘broken’ functions such as ‘ISNUMERIC’ without additional checks. Excessive use of the WHILE loop (Merrill Aldrich) INSERT ... EXEC (Merrill Aldrich) The use of stored procedures where a view is sufficient (Merrill Aldrich) Not using two-part object names (Merrill Aldrich) Using INSERT INTO without specifying the columns and their order (Merrill Aldrich) Full outer joins even when they are not needed. (Plamen Ratchev) Huge stored procedures (hundreds/thousands of lines). Stored procedures that can produce different columns, or order of columns in their results, depending on the inputs. Code that is never used. Complex and nested conditionals WHILE (not done) loops without an error exit. Variable name same as the Datatype Vague identifiers. Storing complex data  or list in a character map, bitmap or XML field User procedures with sp_ prefix (Aaron Bertrand)Views that reference views that reference views that reference views (Aaron Bertrand) Inappropriate use of sql_variant (Neil Hambly) Errors with identity scope using SCOPE_IDENTITY @@IDENTITY or IDENT_CURRENT (Neil Hambly, Aaron Bertrand) Schemas that involve multiple dated copies of the same table instead of partitions (Matt Whitfield-Atlantis UK) Scalar UDFs that do data lookups (poor man's join) (Matt Whitfield-Atlantis UK) Code that allows SQL Injection (Mladen Prajdic) Tables without clustered indexes (Matt Whitfield-Atlantis UK) Use of "SELECT DISTINCT" to mask a join problem (Nick Harrison) Multiple stored procedures with nearly identical implementation. (Nick Harrison) Excessive column aliasing may point to a problem or it could be a mapping implementation. (Nick Harrison) Joining "too many" tables in a query. (Nick Harrison) Stored procedure returning more than one record set. (Nick Harrison) A NOT LIKE condition (Nick Harrison) excessive "OR" conditions. (Nick Harrison) User procedures with sp_ prefix (Aaron Bertrand) Views that reference views that reference views that reference views (Aaron Bertrand) sp_OACreate or anything related to it (Bill Fellows) Prefixing names with tbl_, vw_, fn_, and usp_ ('tibbling') (Jeremiah Peschka) Aliases that go a,b,c,d,e... (Dave Levy/Diane McNurlan) Overweight Queries (e.g. 4 inner joins, 8 left joins, 4 derived tables, 10 subqueries, 8 clustered GUIDs, 2 UDFs, 6 case statements = 1 query) (Robert L Davis) Order by 3,2 (Dave Levy) MultiStatement Table functions which are then filtered 'Sel * from Udf() where Udf.Col = Something' (Dave Ballantyne) running a SQL 2008 system in SQL 2000 compatibility mode(John Stafford)

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  • Improving CSS With .LESS

    Improve your CSS skills using .LESS, a free, open-source port of Ruby's LESS library. LESS (and .LESS, by extension) is a parser that allows web developers to create style sheets using new and improved language features, including variables, operations, mix-ins, and nested rules.

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