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  • Algorithm for a lucky game [on hold]

    - by Ronnie
    Assume we have the following Keno(lottery type) game: From 80 numbers(from 1 to 80), 20 are being drawn. The players choose 1 or 2 or 3..... or 12 numbers to play(12 categories). If they choose for example 4 then they win if they predict correctly a certain amount of numbers(2,3 or 4) from the 4 they have played and lose if the predict only 1 or 0 numbers. They win X times their money accordingly to some predefined factor depending on how many numbers they predict from each category. The same with the other categories. And e.g 11 out of 11 gives 250000 times your money and 12 out of 12 gives 1000000 your money. So the company would want to avoid winnings so high. Every draw by the company is being made every 5 minutes and in each draw around 120000 (let's say) different predictions(Keno tickets) are being played. Let's assume 12000 are being played in category 10 and 12000 in category 11 and also 12000 in category 12. I'm wondering if there is an algorithm to allow the company that provides the game in the 5 minutes between the drawings, to find a 20 number set, in order to avoid any "12 out of 12" and "11 out of 11" and "11 out of 12" and "10 out of 11" and "10 out of 10" winning ticket. That means is there any algorithm, where in a time of less than 1 minute approximately(in todays hardware), to be able to find a 20 number set so that none of the 12000 12 and 11 and 10 number sets that the players played(in categories 10,11 and 12) contains any winning of "12 out of 12" and "11 out of 11" and "11 out of 12" and "10 out of 11" and "10 out of 10"? Or even better the generalization of the problem: What is the best algorithm(from a perspective of minimal time), to be able to find a Y number set from numbers 1 to Z(e.g Y=20, Z=80) so that none of the X sets of K-numbers that are being played(in category K) contains more than K-m numbers from the Y-set? (Note that for Y=K and m=1 there is a practical algorithm.)

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  • Rock Stars and now OPN All-Stars? Bring it.

    - by sandra.haan
    We are talking everything OPN All-Star - from home-court advantage to taking too many shots across a wide variety of industries, skill sets, focus areas, broad solution sets, applications and technologies. As a Platinum Partner, Intelenex levels of quality specialization range from ERP/EBS, CRM, AIA to Hyperion. Slam dunk! This is what gives Intelenex a well deserved star studded "baller" celebrity status like the LA Lakers very own Kobe Bryant. While Intelenex has been busy multi-specializing and taking names, Tyler Prince, group vp, North America Sales tells us a little bit about the value OPN's overall strategy brings to the table. This exclusive partnership allows OPN Specialized partners to provide customers with a solution that helps them adapt swiftly to new expansion conditions and changes. Namely, partners can pick an area to focus and can leverage that focus and competency to differentiate from the competition. You will be so HOT on the OPN court the Miami Heat will have nothing on you. Watch out, Lebron. Additionally, this specialization in products or set of products is recognized by the entire Oracle sales force, which is vital to all partners, but most importantly your end-customers. You will be so stylishly famous your cheerleader squad will not be able to steal the spotlight from you. Are you really All-Star worthy this season? Jump in and join Tyler's halftime report on OPN's All-Star program in this VAR Guy FastChat video to find out: Now that's what we call some March Madness - Good selling, The OPN Communications Team

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  • Patch Set Update: Hyperion Essbase 11.1.2.3.502

    - by Paul Anderson -Oracle
    A Patch Set Update (PSU) for Oracle Hyperion Essbase 11.1.2.3.x . The PSU downloads are from the My Oracle Support | Patches & Updates section. Hyperion Essbase Server 11.1.2.3.502 Patch 18950479: Essbase Server Hyperion Essbase Client 11.2.3.502 Patch 18950453: Essbase RTC Patch 18950474: Essbase Client Patch 18950482: Essbase MSI Hyperion Essbase Administration Services (EAS) 11.1.2.3.502 Patch 17767626: Essbase Server Patch 17767628: Essbase Console MSI Hyperion Analytic Provider Services (APS) 11.1.2.3.502 Patch 18907738: APS Services Hyperion Essbase Studio 11.1.2.3.502 Patch 18907980: Essbase Studio Server Patch 18907987: Essbase Studio Console MSI Refer to the Readme file prior to proceeding with this PSU implementation for important information that includes a full list of the defects fixed, along with additional support information, prerequisites, details for applying patch and troubleshooting FAQ's. It is important to ensure that the requirements and support paths to this patch are met as outlined within the Readme file. The Readme file is available from the Patches & Updates download screen. To locate the latest Essbase Patch Sets and Patch Set Updates at anytime visit the My Oracle Support (MOS) Knowledge Article: Available Patch Sets and Patch Set Updates for Oracle Hyperion Essbase Doc ID 1396084.1 Why not share your experience about installing this patch ... In the MOS | Patches & Updates screen simply click the "Start a Discussion" and submit your review. The patch install reviews and other patch related information is available within the My Oracle Support Communities. Visit the Oracle Hyperion EPM sub-space: Hyperion Patch Reviews

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  • OBIEE 11g 11.1.1.6.11 is Available For BI Enterprise and Exalytics

    - by p.anda
    (in via Ian & Martin) OBIEE 11g 11.1.1.6.11 is Available For BI Enterprise and Exalytics The Business Intelligence Enterprise Edition 11.1.1.6.11 patch set has been released and is available to download from My Oracle Support (https://support.oracle.com).Per the patch readme: This patch set is available for all customers who are using Oracle Business Intelligence Enterprise Edition 11.1.1.6.0, 11.1.1.6.1, 11.1.1.6.2, 11.1.1.6.2 BP1, 11.1.1.6.4, 11.1.1.6.5, 11.1.1.6.6, 11.1.1.6.7, 11.1.1.6.8, 11.1.1.6.9 and 11.1.1.6.10. Oracle Exalytics customers must only install this Oracle Business Intelligence patch set if it is certified for the specific Oracle Exalytics patch or patch set update that they are applying. For more information see Oracle Fusion Middleware Installation and Administration Guide for Oracle Exalytics In-Memory Machine and the Oracle Exalytics certification information. The Oracle Business Intelligence 11.1.1.6.11 patch set is comprised of the following patches: Patch 16747681 - 1 of 7 Oracle Business Intelligence Installer (BIINST)Patch 16747684 - 2 of 7 Oracle Real Time Decisions (RTD)Patch 16747692 - 3 of 7 Oracle Business Intelligence Publisher (BIP)Patch 16747699 - 4 of 7 Oracle Business Intelligence ADF Components (BIADFCOMPS)Patch 16747703 - 5 of 7 Enterprise Performance Management Components Installed from BI Installer 11.1.1.6.x (BIFNDNEPM)Patch 16717325 - 6 of 7 Oracle Business Intelligence: (OBIEE)Patch 16747708 - 7 of 7 Oracle Business Intelligence Platform Client Installers and MapViewer Note: - The Readme files for the above patches describe the bugs fixed in each patch, and any known bugs with the patch.- This patch is cumulative, and therefore, contains all of the fixes included in the earlier 11.1.1.6.2, 11.1.1.6.4, 11.1.1.6.5, 11.1.1.6.6, 11.1.1.6.7, 11.1.1.6.8, 11.1.1.6.9 and 11.1.1.6.10 patch sets.- However, lists of fixes from included patch sets need to be looked up in the respective patches' readme files, and are not included in the above patches' readme files.- The instructions to apply the above patches are identical, and are contained in the readme file for patch 16747681.- Please bear in mind, that the readme states to apply patch 13952743 for JDeveloper, too.

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  • Choosing Technology To Include In Software Design

    How many of us have been forced to select one technology over another when designing a new system? What factors do we and should we consider? How can we ensure the correct business decision is made? When faced with this type of decision it is important to gather as much information possible regarding each technology being considered as well as the project itself. Additionally, I tend to delay my decision about the technology until it is ultimately necessary to be made. The reason why I tend to delay such an important design decision is due to the fact that as the project progresses requirements and other factors can alter a decision for selecting the best technology for a project. Important factors to consider when making technology decisions: Time to Implement and Maintain Total Cost of Technology (including Implementation and maintenance) Adaptability of Technology Implementation Team’s Skill Sets Complexity of Technology (including Implementation and maintenance) orecasted Return On Investment (ROI) Forecasted Profit on Investment (POI) Of the factors to consider the ROI and POI weigh the heaviest because the take in to consideration the other factors when calculating the profitability and return on investments.For a real world example let us consider developing a web based lead management system for a new company. This system can either be hosted on Microsoft Windows based web server or on a Linux based web server. Important Factors for this Example Implementation Team’s Skill Sets Member 1  Skill Set: Classic ASP, ASP.Net, and MS SQL Server Experience: 10 years Member 2  Skill Set: PHP, MySQL, Photoshop and MS SQL Server Experience: 3 years Member 3  Skill Set: C++, VB6, ASP.Net, and MS SQL Server Experience: 12 years Total Cost of Technology (including Implementation and maintenance) Linux Initial Year: $5,000 (Random Value) Additional Years: $3,000 (Random Value) Windows Initial Year: $10,000 (Random Value) Additional Years: $3,000 (Random Value) Complexity of Technology Linux Large Learning Curve with user driven documentation Estimated learning cost: $30,000 Windows Minimal based on Teams skills with Microsoft based documentation Estimated learning cost: $5,000 ROI Linux Total Cost Initial Total Cost: $35,000 Additional Cost $3,000 per year Windows Total Cost Initial Total Cost: $15,000 Additional Cost $3,000 per year Based on the hypothetical numbers it would make more sense to select windows based web server because the initial investment of the technology is much lower initially compared to the Linux based web server.

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  • Find points whose pairwise distances approximate a given distance matrix

    - by Stephan Kolassa
    Problem. I have a symmetric distance matrix with entries between zero and one, like this one: D = ( 0.0 0.4 0.0 0.5 ) ( 0.4 0.0 0.2 1.0 ) ( 0.0 0.2 0.0 0.7 ) ( 0.5 1.0 0.7 0.0 ) I would like to find points in the plane that have (approximately) the pairwise distances given in D. I understand that this will usually not be possible with strictly correct distances, so I would be happy with a "good" approximation. My matrices are smallish, no more than 10x10, so performance is not an issue. Question. Does anyone know of an algorithm to do this? Background. I have sets of probability densities between which I calculate Hellinger distances, which I would like to visualize as above. Each set contains no more than 10 densities (see above), but I have a couple of hundred sets. What I did so far. I did consider posting at math.SE, but looking at what gets tagged as "geometry" there, it seems like this kind of computational geometry question would be more on-topic here. If the community thinks this should be migrated, please go ahead. This looks like a straightforward problem in computational geometry, and I would assume that anyone involved in clustering might be interested in such a visualization, but I haven't been able to google anything. One simple approach would be to randomly plonk down points and perturb them until the distance matrix is close to D, e.g., using Simulated Annealing, or run a Genetic Algorithm. I have to admit that I haven't tried that yet, hoping for a smarter way. One specific operationalization of a "good" approximation in the sense above is Problem 4 in the Open Problems section here, with k=2. Now, while finding an algorithm that is guaranteed to find the minimum l1-distance between D and the resulting distance matrix may be an open question, it still seems possible that there at least is some approximation to this optimal solution. If I don't get an answer here, I'll mail the gentleman who posed that problem and ask whether he knows of any approximation algorithm (and post any answer I get to that here).

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  • Collision detection, stop gravity

    - by Scott Beeson
    I just started using Gamemaker Studio and so far it seems fairly intuitive. However, I set a room to "Room is Physics World" and set gravity to 10. I then enabled physics on my player object and created a block object to match a platform on my background sprite. I set up a Collision Detection event for the player and the block objects that sets the gravity to 0 (and even sets the vspeed to 0). I also put a notification in the collision event and I don't get that either. I have my key down and key up events working well, moving the player left and right and changing the sprites appropriately, so I think I understand the event system. I must just be missing something simple with the physics. I've tried making both and neither of the objects "solid". Pretty frustrating since it looks so easy. The player starting point is directly above the block object in the grid and the player does fall through the block. I even made the block sprite solid red so I could see it (initially it was invisible, obviously).

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  • Java Applet Tower Defence Game needs tweeking

    - by Ephiras
    Hello :) i have made a tower defence Game for my computer science class as one of my major projects, but have encountered some rather fatal roadblocks. here they are creating a menu screen (class Menu) that can set the total number of enimies, the max number of towers, starting money and the map. i tried creating a constructor in my Main class that sets all the values to whatever the Menu class passes in. I want the Menu screen to close after a difficulty has been selected and the main class to begin. Another problem i would really like some help with is instead of having to write entire arrays i would like to create a small segment of code that runs through an entire picture and sets up an array based on that pixels color.this way i can have multiple levels just dragged into a level folder and have the program read through them. users can even create their own. so a 1 if its yellow, a two if blue and a 3 if purple, then everything else = 0; you can download all the classes and code uif you'd like here sorry about having to redirect you but i wasn't sure how to efficently add a code spoiler. help is greatly appreciated

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  • Perfomance of 8 bit operations on 64 bit architechture

    - by wobbily_col
    I am usually a Python / Database programmer, and I am considering using C for a problem. I have a set of sequences, 8 characters long with 4 possible characters. My problem involves combining sets of these sequences and filtering which sets match a criteria. The combinations of 5 run into billions of rows and takes around an hour to run. So I can represent each sequence as 2 bytes. If I am working on a 64 bit architechture will I gain any advantage by keeping these data structures as 2 bytes when I generate the combinations, or will I be as well storing them as 8 bytes / double ? (64 bit = 8 x 8) If I am on a 64 bit architecture, all registers will be 64 bit, so in terms of operations that shouldn´t be any faster (please correct me if I am wrong). Will I gain anything from the smaller storage requirements - can I fit more combinations in memory, or will they all take up 64 bits anyway? And finally, am I likley to gain anything coding in C. I have a first version, which stores the sequence as a small int in a MySQL database. It then self joins the tabe to itself a number of times in order to generate all the possible combinations. The performance is acceptable, depending on how many combinations are generated. I assume the database must involve some overhead.

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  • Creating, using and managing XML component dictionaries quick tutorials

    - by drrwebber
    XML Component Dictionary capabilities are provided in conjunction with the CAM Editor toolset.  These dictionaries accelerate the development of consistent XML information exchanges using standard sets of dictionary components. The quick tutorials are aimed at showing the 'how to' of the basic capabilities to jump start use of XML dictionaries with the CAM Editor. The collection of dictionary tutorials videos run for a total of approximately 20 minutes.  Each video can be reviewed individually also. Learn how to use the dictionary functions to create dictionaries by harvesting data model components from existing XSD schema, SQL database table schema, or simple Excel / Open Office spreadsheets with tables of components listed.Also included are tips and functions relating to use of NIEM exchange development, IEPD and EIEM techniques.These videos should be viewed in conjunction with reviewing the overall concepts and techniques described in the companion video on the CAM Editor and Dictionaries overview.  The approach is aligned with OASIS and Core Components Technical Specification (CCTS) standards specifications for XML components and dictionaries.Dictionary collections can be stored locally on the file system, or local network, or collaboratively on the web or cloud deployment, or can be shared and managed securely using the Oracle Enterprise Repository (OER) tool. Also included are techniques relating to the use of the NIEM approach for developing XML exchange schema and IEPD packages.  This includes generating reuse scores, wantlist, and cross reference spreadsheets. Included in the latest release of the CAM Editor is the ability to use the analyse dictionary tool to determine duplicate components, conflicting component definitions, missing component descriptions and so on.  This ensures high quality dictionary component specifications.  Using the CAM Editor you can also create MindMap models and UML physical models of your dictionary components sets. For a complete guide to using the CAM Editor see the main YouTube video tutorials website and the CAM Editor website.

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  • Virtual Pageview Goal Funnel Not Tracking Correctly

    - by cphill
    I have an AJAX form that has three stages: 1. The landing page where a user fills out a form and selects between three question sets and clicks begin assessment 2. The assessment page, where users fill out questions relating to the question set that they selected on the landing page. 3.The results page, which shows whether they are at High Risk or Low Risk. Since this is an AJAX form that does not open a new page for each step of the process, I implemented a virtual pageview that would fire on the pageload of each step of the form process. The following is my virtual pageview setup for each stage: /form/begin-assessment /form/assessment/* (* = Three different virtual pageviews depending on the users selection of the three sets of questions: /one, /two, /three) 3./form/finished-assessment I have set up three separate goals to track user progress through each step of the form assessment. Here is my Goal setup: Goal Description: -Goal Type: Destination Goal Details: -Destination: /form/finished-assessment -Funnel: On Step 1: /form/begin-assessment (Required: Yes) Step 2: /form/assessment/one (Step 2: replace /one with /two or /three and you have my two other goals setup) Now my goals are recording the correct data in the first step and show the completions in the destination, but the second step does not show any drop offs. They show the same data as the destination. Any ideas of how I set up the goals wrong?

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  • Now Released: "Oracle WebLogic Server 12c: Administration I" Certification Exam (1Z0-133)

    - by Brandye Barrington
    Oracle Certification is pleased to announce the production release of the Oracle WebLogic Server 12c: Administration I Certification Exam (1Z0-133). Passing this exam results in the "Oracle Certified Associate (OCA) - Oracle WebLogic Server 12c Administrator" certification. Oracle WebLogic Server sets the industry standard for Java application servers, and the "Oracle Certified Associate (OCA) - Oracle WebLogic Server 12c Administrator" certification sets the standard for WLS administrators. Obtaining this certification proves that you have the skills to set up server environments, tune performance and troubleshoot with confidence and raises the bar for your peers.  While training is not required for certification, the Oracle WebLogic Server 12c: Administration I course from Oracle University, can expedite you towards your certification - helping you gain the skills and knowledge to increase the performance and scalability of your organization’s applications and services with the #1 application server. Becoming certified gives you a competitive edge through proven expertise. The Oracle WebLogic Server 12c: Administration I exam (1Z0-133) is now available in production. Get all preparation details, including exam objectives, number of questions, time allotments, and pricing on the Oracle Certification website. Register now for exam 1Z0-133 at www.pearsonvue.com/oracle. QUICK LINKS: Certification Track: Oracle Certified Associate (OCA) - Oracle WebLogic Server 12c Administrator Certification Exam: Oracle WebLogic Server 12c: Administration I Certification Exam (1Z0-133) Recommended Training: Oracle WebLogic Server 12c: Administration I Register Now: Pearson VUE

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  • Opportunities in Cloud Computing

    - by Paul Sorensen
    A recent article from CIO Journal indicates that there is an extreme labor shortage (in certain technology areas) that is is leading to upward pressure on wages for IT Workers. This represents a great opportunity for those with certain skill-sets, among which include Java (Oracle certification is mentioned specifically). The article points out that a key driver of the labor shortage is the expansion of cloud computing. Cloud computing is set up to make life extremely simple for end-users, but the model pushes the complexity to back-end systems which are sophisticated, enterprise-level computing stacks (Oracle has an extensive set of cloud computing solutions). These complex systems require very highly-skilled IT professionals (the best-of-the-best) to successfully develop, implement, administer and maintain them. What this mean for you is that there is opportunity for those who have the appropriate skills at the appropriate levels. If you want to be a part of this opportunity you should do a self-assessment of your own skill-sets and experience. Based upon your results you can decide where it would be most appropriate to spend your time and resources for the highest return on your investment. By expanding and sharpening your skills and by gaining greater experience you will be better prepared to take advantage of career opportunities (like this) that come along periodically. As you evaluate your needs remember that Oracle University has a tremendous selection of high-quality eduction offerings (including training and certification) that can you help move your career forward. Thanks and best of luck!

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  • Please recommend the best tools to build a test plan management tool

    - by fzkl
    I have mostly worked on hardware testing in my professional career and would like to get onto the software development side. I thought working on a practically usable project will help motivate me and help acquire some skills. I have decided to build a test plan management tool for the QA team I work in (We use excel sheets!). The test plan management tool should be browser based and should support this: There would be many test plans, each test plan having test sets, test sets having test cases and test cases having instructions, attachments and Pass/fail status marking and bug info in case of failure. It should also have an export to excel option. I have a visual picture of the tool I am looking to build but I don't have enough experience to figure our where to start. My current programming skills are limited to C and shell programming and I want to pick up python. What tools (programming language, database and anything else?) would you recommend for me to get this done? Also what are the key concepts in the recommended programming language that I should focus on to build a browser based tool like this?

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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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  • Improving CSS With .LESS

    Cascading Style Sheets, or CSS, is a syntax used to describe the look and feel of the elements in a web page. CSS allows a web developer to separate the document content - the HTML, text, and images - from the presentation of that content. Such separation makes the markup in a page easier to read, understand, and update; it can result in reduced bandwidth as the style information can be specified in a separate file and cached by the browser; and makes site-wide changes easier to apply. For a great example of the flexibility and power of CSS, check out CSS Zen Garden. This website has a single page with fixed markup, but allows web developers from around the world to submit CSS rules to define alternate presentation information. Unfortunately, certain aspects of CSS's syntax leave a bit to be desired. Many style sheets include repeated styling information because CSS does not allow the use of variables. Such repetition makes the resulting style sheet lengthier and harder to read; it results in more rules that need to be changed when the website is redesigned to use a new primary color. Specifying inherited CSS rules, such as indicating that a elements (i.e., hyperlinks) in h1 elements should not be underlined, requires creating a single selector name, like h1 a. Ideally, CSS would allow for nested rules, enabling you to define the a rules directly within the h1 rules. .LESS is 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, mixins, and nested rules. Behind the scenes, .LESS converts the enhanced CSS rules into standard CSS rules. This conversion can happen automatically and on-demand through the use of an HTTP Handler, or done manually as part of the build process. Moreover, .LESS can be configured to automatically minify the resulting CSS, saving bandwidth and making the end user's experience a snappier one. This article shows how to get started using .LESS in your ASP.NET websites. Read on to learn more! Read More >

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  • Improving CSS With .LESS

    Cascading Style Sheets, or CSS, is a syntax used to describe the look and feel of the elements in a web page. CSS allows a web developer to separate the document content - the HTML, text, and images - from the presentation of that content. Such separation makes the markup in a page easier to read, understand, and update; it can result in reduced bandwidth as the style information can be specified in a separate file and cached by the browser; and makes site-wide changes easier to apply. For a great example of the flexibility and power of CSS, check out CSS Zen Garden. This website has a single page with fixed markup, but allows web developers from around the world to submit CSS rules to define alternate presentation information. Unfortunately, certain aspects of CSS's syntax leave a bit to be desired. Many style sheets include repeated styling information because CSS does not allow the use of variables. Such repetition makes the resulting style sheet lengthier and harder to read; it results in more rules that need to be changed when the website is redesigned to use a new primary color. Specifying inherited CSS rules, such as indicating that a elements (i.e., hyperlinks) in h1 elements should not be underlined, requires creating a single selector name, like h1 a. Ideally, CSS would allow for nested rules, enabling you to define the a rules directly within the h1 rules. .LESS is 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, mixins, and nested rules. Behind the scenes, .LESS converts the enhanced CSS rules into standard CSS rules. This conversion can happen automatically and on-demand through the use of an HTTP Handler, or done manually as part of the build process. Moreover, .LESS can be configured to automatically minify the resulting CSS, saving bandwidth and making the end user's experience a snappier one. This article shows how to get started using .LESS in your ASP.NET websites. Read on to learn more! Read More >

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  • New Features in ASP.NET Web API 2 - Part I

    - by dwahlin
    I’m a big fan of ASP.NET Web API. It provides a quick yet powerful way to build RESTful HTTP services that can easily be consumed by a variety of clients. While it’s simple to get started using, it has a wealth of features such as filters, formatters, and message handlers that can be used to extend it when needed. In this post I’m going to provide a quick walk-through of some of the key new features in version 2. I’ll focus on some two of my favorite features that are related to routing and HTTP responses and cover additional features in a future post.   Attribute Routing Routing has been a core feature of Web API since it’s initial release and something that’s built into new Web API projects out-of-the-box. However, there are a few scenarios where defining routes can be challenging such as nested routes (more on that in a moment) and any situation where a lot of custom routes have to be defined. For this example, let’s assume that you’d like to define the following nested route:   /customers/1/orders   This type of route would select a customer with an Id of 1 and then return all of their orders. Defining this type of route in the standard WebApiConfig class is certainly possible, but it isn’t the easiest thing to do for people who don’t understand routing well. Here’s an example of how the route shown above could be defined:   public static class WebApiConfig { public static void Register(HttpConfiguration config) { config.Routes.MapHttpRoute( name: "CustomerOrdersApiGet", routeTemplate: "api/customers/{custID}/orders", defaults: new { custID = 0, controller = "Customers", action = "Orders" } ); config.Routes.MapHttpRoute( name: "DefaultApi", routeTemplate: "api/{controller}/{id}", defaults: new { id = RouteParameter.Optional } ); GlobalConfiguration.Configuration.Formatters.Insert(0, new JsonpFormatter()); } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; }   With attribute based routing, defining these types of nested routes is greatly simplified. To get started you first need to make a call to the new MapHttpAttributeRoutes() method in the standard WebApiConfig class (or a custom class that you may have created that defines your routes) as shown next:   public static class WebApiConfig { public static void Register(HttpConfiguration config) { // Allow for attribute based routes config.MapHttpAttributeRoutes(); config.Routes.MapHttpRoute( name: "DefaultApi", routeTemplate: "api/{controller}/{id}", defaults: new { id = RouteParameter.Optional } ); } } Once attribute based routes are configured, you can apply the Route attribute to one or more controller actions. Here’s an example:   [HttpGet] [Route("customers/{custId:int}/orders")] public List<Order> Orders(int custId) { var orders = _Repository.GetOrders(custId); if (orders == null) { throw new HttpResponseException(new HttpResponseMessage(HttpStatusCode.NotFound)); } return orders; }   This example maps the custId route parameter to the custId parameter in the Orders() method and also ensures that the route parameter is typed as an integer. The Orders() method can be called using the following route: /customers/2/orders   While this is extremely easy to use and gets the job done, it doesn’t include the default “api” string on the front of the route that you might be used to seeing. You could add “api” in front of the route and make it “api/customers/{custId:int}/orders” but then you’d have to repeat that across other attribute-based routes as well. To simply this type of task you can add the RoutePrefix attribute above the controller class as shown next so that “api” (or whatever the custom starting point of your route is) is applied to all attribute routes: [RoutePrefix("api")] public class CustomersController : ApiController { [HttpGet] [Route("customers/{custId:int}/orders")] public List<Order> Orders(int custId) { var orders = _Repository.GetOrders(custId); if (orders == null) { throw new HttpResponseException(new HttpResponseMessage(HttpStatusCode.NotFound)); } return orders; } }   There’s much more that you can do with attribute-based routing in ASP.NET. Check out the following post by Mike Wasson for more details.   Returning Responses with IHttpActionResult The first version of Web API provided a way to return custom HttpResponseMessage objects which were pretty easy to use overall. However, Web API 2 now wraps some of the functionality available in version 1 to simplify the process even more. A new interface named IHttpActionResult (similar to ActionResult in ASP.NET MVC) has been introduced which can be used as the return type for Web API controller actions. To return a custom response you can use new helper methods exposed through ApiController such as: Ok NotFound Exception Unauthorized BadRequest Conflict Redirect InvalidModelState Here’s an example of how IHttpActionResult and the helper methods can be used to cleanup code. This is the typical way to return a custom HTTP response in version 1:   public HttpResponseMessage Delete(int id) { var status = _Repository.DeleteCustomer(id); if (status) { return new HttpResponseMessage(HttpStatusCode.OK); } else { throw new HttpResponseException(HttpStatusCode.NotFound); } } With version 2 we can replace HttpResponseMessage with IHttpActionResult and simplify the code quite a bit:   public IHttpActionResult Delete(int id) { var status = _Repository.DeleteCustomer(id); if (status) { //return new HttpResponseMessage(HttpStatusCode.OK); return Ok(); } else { //throw new HttpResponseException(HttpStatusCode.NotFound); return NotFound(); } } You can also cleanup post (insert) operations as well using the helper methods. Here’s a version 1 post action:   public HttpResponseMessage Post([FromBody]Customer cust) { var newCust = _Repository.InsertCustomer(cust); if (newCust != null) { var msg = new HttpResponseMessage(HttpStatusCode.Created); msg.Headers.Location = new Uri(Request.RequestUri + newCust.ID.ToString()); return msg; } else { throw new HttpResponseException(HttpStatusCode.Conflict); } } This is what the code looks like in version 2:   public IHttpActionResult Post([FromBody]Customer cust) { var newCust = _Repository.InsertCustomer(cust); if (newCust != null) { return Created<Customer>(Request.RequestUri + newCust.ID.ToString(), newCust); } else { return Conflict(); } } More details on IHttpActionResult and the different helper methods provided by the ApiController base class can be found here. Conclusion Although there are several additional features available in Web API 2 that I could cover (CORS support for example), this post focused on two of my favorites features. If you have .NET 4.5.1 available then I definitely recommend checking the new features out. Additional articles that cover features in ASP.NET Web API 2 can be found here.

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  • Back to Basics: Structuring a Web Page with CSS and ASP.NET

    Nick Harrison explains why such habits as using nested HTML Tables to position content in the right place on the browser page is bad practice and, nowadays, avoidable. This is just one 'Markup smell' that he discusses on the way to demonstrating the benefits of CSS Style-sheets and ASP.NET Master Pages. span.fullpost {display:none;}

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  • To access parentAM instance from within nestedAM JUnit test class

    - by Abhishek Dwivedi
    In normal model project, the way to access parent AM from within nested AM is simple - ParentAMImpl parentAM =  (ParentAMImpl)this.getRootApplicationModule(); However, the same approach doesn't help in JUnit model project. Use the following approach -  Inside setUp() method --  ParentAM parentAM =  (ParentAM)Configuration.createRootApplicationModule(ROOT_AM, ROOT_AM_CONFIG); Inside tearDown() method -- Configuration.releaseRootApplicationModule(parentAM, true);

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