Search Results

Search found 1725 results on 69 pages for 'compute shader'.

Page 45/69 | < Previous Page | 41 42 43 44 45 46 47 48 49 50 51 52  | Next Page >

  • KVM guest storage difference with NBD and NFS

    - by WojonsTech
    I am setting up my own little private cloud for my own use maybe for a project or to. I am using linux kvm on debian 6. I have 3 servers 2 of them for compute nodes and 1 storage node. I would I have already installed kvm made a few test machines got my networking setup. I have 2 nics on each server 1 nic is for web traffic other nic is for network traffic. My first Idea was to use NFS for storing the guest machines which can range in size, maybe 8gb maybe 100gb, it just depends. I was doing have heard of nbd before seems like it could work but I dont know what the performance differences are and if it will effect my enviroment, nfs looks like it will be easier to use.

    Read the article

  • Analyse frequencies of date ranges in Google Drive

    - by wnstnsmth
    I have a Google Drive spreadsheet where I would like to compute occurrences of date ranges. As you can see in my sheet, there is a column date_utc+1 which contains almost random date data. https://docs.google.com/spreadsheet/ccc?key=0AhqMXeYxWMD_dGRkVGRqbkR3c05mWUdhYkJWcFo2Mmc What I would like to do is 1) put the date values into bins of 6 hours each, i.e. 12/5/2012 23:57:04 until 12/6/2012 0:03:17 would be in the first bin, 12/6/2012 11:20:53 until 12/6/2012 17:17:07 in the second bin, and so forth. Then, I would like to count the occurrence of those bins, such as bin_from bin_to freq ----------------------------------------------- 12/5/2012 23:57:04 12/6/2012 0:03:17 2 12/6/2012 11:20:53 12/6/2012 17:17:07 19 ... ... ... Hope it is clear what I mean. Partial hints are very welcome as well since I am pretty new to spreadsheeting.

    Read the article

  • Django - no module named app

    - by Koran
    Hi, I have been trying to get an application written in django working - but it is not working at all. I have been working on for some time too - and it is working on dev-server perfectly. But I am unable to put in the production env (apahce). My project name is apstat and the app name is basic. I try to access it as following Blockquote http://hostname/apstat But it shows the following error: MOD_PYTHON ERROR ProcessId: 6002 Interpreter: 'domU-12-31-39-06-DD-F4.compute-1.internal' ServerName: 'domU-12-31-39-06-DD-F4.compute-1.internal' DocumentRoot: '/home/ubuntu/server/' URI: '/apstat/' Location: '/apstat' Directory: None Filename: '/home/ubuntu/server/apstat/' PathInfo: '' Phase: 'PythonHandler' Handler: 'django.core.handlers.modpython' Traceback (most recent call last): File "/usr/lib/python2.6/dist-packages/mod_python/importer.py", line 1537, in HandlerDispatch default=default_handler, arg=req, silent=hlist.silent) File "/usr/lib/python2.6/dist-packages/mod_python/importer.py", line 1229, in _process_target result = _execute_target(config, req, object, arg) File "/usr/lib/python2.6/dist-packages/mod_python/importer.py", line 1128, in _execute_target result = object(arg) File "/usr/lib/pymodules/python2.6/django/core/handlers/modpython.py", line 228, in handler return ModPythonHandler()(req) File "/usr/lib/pymodules/python2.6/django/core/handlers/modpython.py", line 201, in __call__ response = self.get_response(request) File "/usr/lib/pymodules/python2.6/django/core/handlers/base.py", line 134, in get_response return self.handle_uncaught_exception(request, resolver, exc_info) File "/usr/lib/pymodules/python2.6/django/core/handlers/base.py", line 154, in handle_uncaught_exception return debug.technical_500_response(request, *exc_info) File "/usr/lib/pymodules/python2.6/django/views/debug.py", line 40, in technical_500_response html = reporter.get_traceback_html() File "/usr/lib/pymodules/python2.6/django/views/debug.py", line 114, in get_traceback_html return t.render(c) File "/usr/lib/pymodules/python2.6/django/template/__init__.py", line 178, in render return self.nodelist.render(context) File "/usr/lib/pymodules/python2.6/django/template/__init__.py", line 779, in render bits.append(self.render_node(node, context)) File "/usr/lib/pymodules/python2.6/django/template/debug.py", line 81, in render_node raise wrapped TemplateSyntaxError: Caught an exception while rendering: No module named basic Original Traceback (most recent call last): File "/usr/lib/pymodules/python2.6/django/template/debug.py", line 71, in render_node result = node.render(context) File "/usr/lib/pymodules/python2.6/django/template/debug.py", line 87, in render output = force_unicode(self.filter_expression.resolve(context)) File "/usr/lib/pymodules/python2.6/django/template/__init__.py", line 572, in resolve new_obj = func(obj, *arg_vals) File "/usr/lib/pymodules/python2.6/django/template/defaultfilters.py", line 687, in date return format(value, arg) File "/usr/lib/pymodules/python2.6/django/utils/dateformat.py", line 269, in format return df.format(format_string) File "/usr/lib/pymodules/python2.6/django/utils/dateformat.py", line 30, in format pieces.append(force_unicode(getattr(self, piece)())) File "/usr/lib/pymodules/python2.6/django/utils/dateformat.py", line 175, in r return self.format('D, j M Y H:i:s O') File "/usr/lib/pymodules/python2.6/django/utils/dateformat.py", line 30, in format pieces.append(force_unicode(getattr(self, piece)())) File "/usr/lib/pymodules/python2.6/django/utils/encoding.py", line 71, in force_unicode s = unicode(s) File "/usr/lib/pymodules/python2.6/django/utils/functional.py", line 201, in __unicode_cast return self.__func(*self.__args, **self.__kw) File "/usr/lib/pymodules/python2.6/django/utils/translation/__init__.py", line 62, in ugettext return real_ugettext(message) File "/usr/lib/pymodules/python2.6/django/utils/translation/trans_real.py", line 286, in ugettext return do_translate(message, 'ugettext') File "/usr/lib/pymodules/python2.6/django/utils/translation/trans_real.py", line 276, in do_translate _default = translation(settings.LANGUAGE_CODE) File "/usr/lib/pymodules/python2.6/django/utils/translation/trans_real.py", line 194, in translation default_translation = _fetch(settings.LANGUAGE_CODE) File "/usr/lib/pymodules/python2.6/django/utils/translation/trans_real.py", line 180, in _fetch app = import_module(appname) File "/usr/lib/pymodules/python2.6/django/utils/importlib.py", line 35, in import_module __import__(name) ImportError: No module named basic My settings.py is as follows: INSTALLED_APPS = ( 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'apstat.basic', 'django.contrib.admin', ) If I remove the apstat.basic, it goes through, but that is not a solution. Is it something I am doing in apache? My apache - settings are - <VirtualHost *:80> ServerAdmin webmaster@localhost DocumentRoot /home/ubuntu/server/ <Directory /> Options None AllowOverride None </Directory> <Directory /home/ubuntu/server/apstat> AllowOverride None Order allow,deny allow from all </Directory> <Location "/apstat"> SetHandler python-program PythonHandler django.core.handlers.modpython SetEnv DJANGO_SETTINGS_MODULE apstat.settings PythonOption django.root /home/ubuntu/server/ PythonDebug On PythonPath "['/home/ubuntu/server/'] + sys.path" </Location> </VirtualHost> I have now sat for more than a day on this. If someone can help me out, it would be very nice.

    Read the article

  • Performing Aggregate Functions on Multi-Million Row Tables

    - by Daniel Short
    I'm having some serious performance issues with a multi-million row table that I feel I should be able to get results from fairly quick. Here's a run down of what I have, how I'm querying it, and how long it's taking: I'm running SQL Server 2008 Standard, so Partitioning isn't currently an option I'm attempting to aggregate all views for all inventory for a specific account over the last 30 days. All views are stored in the following table: CREATE TABLE [dbo].[LogInvSearches_Daily]( [ID] [bigint] IDENTITY(1,1) NOT NULL, [Inv_ID] [int] NOT NULL, [Site_ID] [int] NOT NULL, [LogCount] [int] NOT NULL, [LogDay] [smalldatetime] NOT NULL, CONSTRAINT [PK_LogInvSearches_Daily] PRIMARY KEY CLUSTERED ( [ID] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON, FILLFACTOR = 90) ON [PRIMARY] ) ON [PRIMARY] This table has 132,000,000 records, and is over 4 gigs. A sample of 10 rows from the table: ID Inv_ID Site_ID LogCount LogDay -------------------- ----------- ----------- ----------- ----------------------- 1 486752 48 14 2009-07-21 00:00:00 2 119314 51 16 2009-07-21 00:00:00 3 313678 48 25 2009-07-21 00:00:00 4 298863 0 1 2009-07-21 00:00:00 5 119996 0 2 2009-07-21 00:00:00 6 463777 534 7 2009-07-21 00:00:00 7 339976 503 2 2009-07-21 00:00:00 8 333501 570 4 2009-07-21 00:00:00 9 453955 0 12 2009-07-21 00:00:00 10 443291 0 4 2009-07-21 00:00:00 (10 row(s) affected) I have the following index on LogInvSearches_Daily: /****** Object: Index [IX_LogInvSearches_Daily_LogDay] Script Date: 05/12/2010 11:08:22 ******/ CREATE NONCLUSTERED INDEX [IX_LogInvSearches_Daily_LogDay] ON [dbo].[LogInvSearches_Daily] ( [LogDay] ASC ) INCLUDE ( [Inv_ID], [LogCount]) WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, SORT_IN_TEMPDB = OFF, IGNORE_DUP_KEY = OFF, DROP_EXISTING = OFF, ONLINE = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] I need to pull inventory only from the Inventory for a specific account id. I have an index on the Inventory as well. I'm using the following query to aggregate the data and give me the top 5 records. This query is currently taking 24 seconds to return the 5 rows: StmtText ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- SELECT TOP 5 Sum(LogCount) AS Views , DENSE_RANK() OVER(ORDER BY Sum(LogCount) DESC, Inv_ID DESC) AS Rank , Inv_ID FROM LogInvSearches_Daily D (NOLOCK) WHERE LogDay DateAdd(d, -30, getdate()) AND EXISTS( SELECT NULL FROM propertyControlCenter.dbo.Inventory (NOLOCK) WHERE Acct_ID = 18731 AND Inv_ID = D.Inv_ID ) GROUP BY Inv_ID (1 row(s) affected) StmtText ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |--Top(TOP EXPRESSION:((5))) |--Sequence Project(DEFINE:([Expr1007]=dense_rank)) |--Segment |--Segment |--Sort(ORDER BY:([Expr1006] DESC, [D].[Inv_ID] DESC)) |--Stream Aggregate(GROUP BY:([D].[Inv_ID]) DEFINE:([Expr1006]=SUM([LOALogs].[dbo].[LogInvSearches_Daily].[LogCount] as [D].[LogCount]))) |--Sort(ORDER BY:([D].[Inv_ID] ASC)) |--Nested Loops(Inner Join, OUTER REFERENCES:([D].[Inv_ID])) |--Nested Loops(Inner Join, OUTER REFERENCES:([Expr1011], [Expr1012], [Expr1010])) | |--Compute Scalar(DEFINE:(([Expr1011],[Expr1012],[Expr1010])=GetRangeWithMismatchedTypes(dateadd(day,(-30),getdate()),NULL,(6)))) | | |--Constant Scan | |--Index Seek(OBJECT:([LOALogs].[dbo].[LogInvSearches_Daily].[IX_LogInvSearches_Daily_LogDay] AS [D]), SEEK:([D].[LogDay] > [Expr1011] AND [D].[LogDay] < [Expr1012]) ORDERED FORWARD) |--Index Seek(OBJECT:([propertyControlCenter].[dbo].[Inventory].[IX_Inventory_Acct_ID]), SEEK:([propertyControlCenter].[dbo].[Inventory].[Acct_ID]=(18731) AND [propertyControlCenter].[dbo].[Inventory].[Inv_ID]=[LOA (13 row(s) affected) I tried using a CTE to pick up the rows first and aggregate them, but that didn't run any faster, and gives me essentially the same execution plan. (1 row(s) affected) StmtText ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- --SET SHOWPLAN_TEXT ON; WITH getSearches AS ( SELECT LogCount -- , DENSE_RANK() OVER(ORDER BY Sum(LogCount) DESC, Inv_ID DESC) AS Rank , D.Inv_ID FROM LogInvSearches_Daily D (NOLOCK) INNER JOIN propertyControlCenter.dbo.Inventory I (NOLOCK) ON Acct_ID = 18731 AND I.Inv_ID = D.Inv_ID WHERE LogDay DateAdd(d, -30, getdate()) -- GROUP BY Inv_ID ) SELECT Sum(LogCount) AS Views, Inv_ID FROM getSearches GROUP BY Inv_ID (1 row(s) affected) StmtText ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |--Stream Aggregate(GROUP BY:([D].[Inv_ID]) DEFINE:([Expr1004]=SUM([LOALogs].[dbo].[LogInvSearches_Daily].[LogCount] as [D].[LogCount]))) |--Sort(ORDER BY:([D].[Inv_ID] ASC)) |--Nested Loops(Inner Join, OUTER REFERENCES:([D].[Inv_ID])) |--Nested Loops(Inner Join, OUTER REFERENCES:([Expr1008], [Expr1009], [Expr1007])) | |--Compute Scalar(DEFINE:(([Expr1008],[Expr1009],[Expr1007])=GetRangeWithMismatchedTypes(dateadd(day,(-30),getdate()),NULL,(6)))) | | |--Constant Scan | |--Index Seek(OBJECT:([LOALogs].[dbo].[LogInvSearches_Daily].[IX_LogInvSearches_Daily_LogDay] AS [D]), SEEK:([D].[LogDay] > [Expr1008] AND [D].[LogDay] < [Expr1009]) ORDERED FORWARD) |--Index Seek(OBJECT:([propertyControlCenter].[dbo].[Inventory].[IX_Inventory_Acct_ID] AS [I]), SEEK:([I].[Acct_ID]=(18731) AND [I].[Inv_ID]=[LOALogs].[dbo].[LogInvSearches_Daily].[Inv_ID] as [D].[Inv_ID]) ORDERED FORWARD) (8 row(s) affected) (1 row(s) affected) So given that I'm getting good Index Seeks in my execution plan, what can I do to get this running faster? Thanks, Dan

    Read the article

  • Make file Linking issue Undefined symbols for architecture x86_64

    - by user1035839
    I am working on getting a few files to link together using my make file and c++ and am getting the following error when running make. g++ -bind_at_load `pkg-config --cflags opencv` -c -o compute_gist.o compute_gist.cpp g++ -bind_at_load `pkg-config --cflags opencv` -c -o gist.o gist.cpp g++ -bind_at_load `pkg-config --cflags opencv` -c -o standalone_image.o standalone_image.cpp g++ -bind_at_load `pkg-config --cflags opencv` -c -o IplImageConverter.o IplImageConverter.cpp g++ -bind_at_load `pkg-config --cflags opencv` -c -o GistCalculator.o GistCalculator.cpp g++ -bind_at_load `pkg-config --cflags opencv` `pkg-config --libs opencv` compute_gist.o gist.o standalone_image.o IplImageConverter.o GistCalculator.o -o rungist Undefined symbols for architecture x86_64: "color_gist_scaletab(color_image_t*, int, int, int const*)", referenced from: _main in compute_gist.o ld: symbol(s) not found for architecture x86_64 collect2: ld returned 1 exit status make: *** [rungist] Error 1 My makefile is as follows (Note, I don't need opencv bindings yet, but will be coding in opencv later. CXX = g++ CXXFLAGS = -bind_at_load `pkg-config --cflags opencv` LFLAGS = `pkg-config --libs opencv` SRC = \ compute_gist.cpp \ gist.cpp \ standalone_image.cpp \ IplImageConverter.cpp \ GistCalculator.cpp OBJS = $(SRC:.cpp=.o) rungist: $(OBJS) $(CXX) $(CXXFLAGS) $(LFLAGS) $(OBJS) -o $@ all: rungist clean: rm -rf $(OBJS) rungist The method header is located in gist.h float *color_gist_scaletab(color_image_t *src, int nblocks, int n_scale, const int *n_orientations); And the method is defined in gist.cpp float *color_gist_scaletab(color_image_t *src, int w, int n_scale, const int *n_orientation) { And finally the compute_gist.cpp (main file) #include <stdio.h> #include <stdlib.h> #include <string.h> #include "gist.h" static color_image_t *load_ppm(const char *fname) { FILE *f=fopen(fname,"r"); if(!f) { perror("could not open infile"); exit(1); } int width,height,maxval; if(fscanf(f,"P6 %d %d %d",&width,&height,&maxval)!=3 || maxval!=255) { fprintf(stderr,"Error: input not a raw PPM with maxval 255\n"); exit(1); } fgetc(f); /* eat the newline */ color_image_t *im=color_image_new(width,height); int i; for(i=0;i<width*height;i++) { im->c1[i]=fgetc(f); im->c2[i]=fgetc(f); im->c3[i]=fgetc(f); } fclose(f); return im; } static void usage(void) { fprintf(stderr,"compute_gist options... [infilename]\n" "infile is a PPM raw file\n" "options:\n" "[-nblocks nb] use a grid of nb*nb cells (default 4)\n" "[-orientationsPerScale o_1,..,o_n] use n scales and compute o_i orientations for scale i\n" ); exit(1); } int main(int argc,char **args) { const char *infilename="/dev/stdin"; int nblocks=4; int n_scale=3; int orientations_per_scale[50]={8,8,4}; while(*++args) { const char *a=*args; if(!strcmp(a,"-h")) usage(); else if(!strcmp(a,"-nblocks")) { if(!sscanf(*++args,"%d",&nblocks)) { fprintf(stderr,"could not parse %s argument",a); usage(); } } else if(!strcmp(a,"-orientationsPerScale")) { char *c; n_scale=0; for(c=strtok(*++args,",");c;c=strtok(NULL,",")) { if(!sscanf(c,"%d",&orientations_per_scale[n_scale++])) { fprintf(stderr,"could not parse %s argument",a); usage(); } } } else { infilename=a; } } color_image_t *im=load_ppm(infilename); //Here's the method call -> :( float *desc=color_gist_scaletab(im,nblocks,n_scale,orientations_per_scale); int i; int descsize=0; //compute descriptor size for(i=0;i<n_scale;i++) descsize+=nblocks*nblocks*orientations_per_scale[i]; descsize*=3; // color //print descriptor for(i=0;i<descsize;i++) printf("%.4f ",desc[i]); printf("\n"); free(desc); color_image_delete(im); return 0; } Any help would be greatly appreciated. I hope this is enough info. Let me know if I need to add more.

    Read the article

  • Oracle Linux Delivers Top CPU Benchmark Results on Sun Blades

    - by sergio.leunissen
    From the Performance and Best Practices blog: Fresh SPEC CPU2006 results for Sun Blade X6275 M2 Server Modules running Oracle Linux 5.5. The highlights: The dual-node Sun Blade X6275 M2 server module, equipped with two Intel Xeon X5670 2.93 GHz processors per node and running the Oracle Enterprise Linux 5.5 operating system delivered the best SPECint_rate2006 and SPECfp_rate2006 benchmark results for all systems with Intel Xeon processor 5000 sequence. With a SPECint_rate2006 benchmark result of 679, the Sun Blade X6275 M2 server module, with two compute nodes per blade, delivers maximum performance for space constrained environments. Comparing Oracle's dual-node blade to HP's dual-node blade server, based on their single node performance, the Sun Blade X6275 M2 server module SPECfp_rate2006 score of 241 outperforms the best published HP ProLiant BL2X220c G5 server score by 3.2x. A single node of a Sun Blade X6275 M2 server module using 2.93 GHz Intel Xeon X5670 processors delivered 37% improvement in SPECint_rate2006 benchmark results and 22% improvement in SPECfp_rate2006 benchmark results compared to the previous generation Sun Blade X6275 server module. Both nodes of a Sun Blade X6275 M2 server module using 2.93 GHz Intel Xeon X5670 processors delivered 59% improvement on the SPECint_rate2006 benchmark and 40% improvement on the SPECfp_rate2006 benchmark compared to the previous generation Sun Blade X6275 server module.

    Read the article

  • Oracle Announces Oracle Big Data Appliance X3-2 and Enhanced Oracle Big Data Connectors

    - by jgelhaus
    Enables Customers to Easily Harness the Business Value of Big Data at Lower Cost Engineered System Simplifies Big Data for the Enterprise Oracle Big Data Appliance X3-2 hardware features the latest 8-core Intel® Xeon E5-2600 series of processors, and compared with previous generation, the 18 compute and storage servers with 648 TB raw storage now offer: 33 percent more processing power with 288 CPU cores; 33 percent more memory per node with 1.1 TB of main memory; and up to a 30 percent reduction in power and cooling Oracle Big Data Appliance X3-2 further simplifies implementation and management of big data by integrating all the hardware and software required to acquire, organize and analyze big data. It includes: Support for CDH4.1 including software upgrades developed collaboratively with Cloudera to simplify NameNode High Availability in Hadoop, eliminating the single point of failure in a Hadoop cluster; Oracle NoSQL Database Community Edition 2.0, the latest version that brings better Hadoop integration, elastic scaling and new APIs, including JSON and C support; The Oracle Enterprise Manager plug-in for Big Data Appliance that complements Cloudera Manager to enable users to more easily manage a Hadoop cluster; Updated distributions of Oracle Linux and Oracle Java Development Kit; An updated distribution of open source R, optimized to work with high performance multi-threaded math libraries Read More   Data sheet: Oracle Big Data Appliance X3-2 Oracle Big Data Appliance: Datacenter Network Integration Big Data and Natural Language: Extracting Insight From Text Thomson Reuters Discusses Oracle's Big Data Platform Connectors Integrate Hadoop with Oracle Big Data Ecosystem Oracle Big Data Connectors is a suite of software built by Oracle to integrate Apache Hadoop with Oracle Database, Oracle Data Integrator, and Oracle R Distribution. Enhancements to Oracle Big Data Connectors extend these data integration capabilities. With updates to every connector, this release includes: Oracle SQL Connector for Hadoop Distributed File System, for high performance SQL queries on Hadoop data from Oracle Database, enhanced with increased automation and querying of Hive tables and now supported within the Oracle Data Integrator Application Adapter for Hadoop; Transparent access to the Hive Query language from R and introduction of new analytic techniques executing natively in Hadoop, enabling R developers to be more productive by increasing access to Hadoop in the R environment. Read More Data sheet: Oracle Big Data Connectors High Performance Connectors for Load and Access of Data from Hadoop to Oracle Database

    Read the article

  • CodePlex Daily Summary for Sunday, April 11, 2010

    CodePlex Daily Summary for Sunday, April 11, 2010New ProjectsArkzia: This Silverlight game.CodePlex Wiki Editor: CodePlex Wiki Editor makes it easier for CodePlex users to create their wiki documentations. This project offer a rich interface for the edition...Evaluate: Evaluate & Comet DWR like .NET library with powerfull Evaluate and Ajax Comet support. Also, you may use Evaluate library in your own .Net applicat...FamAccountor: 家庭记账薄Horadric: This is common tools freamwork!K8061.Managed: This is a solution to use the Velleman Extended K8061 USB Interface board with .net and to have a nice wrapper handling most of the overhead for us...Latent semantic analysis: all you need is to use!: Baggr is feed aggregator with web interface, user rating and LSA filter. Enjoy it!LIF7 ==> RISK : TOWER DEFENSE: Université Lyon 1, L2 MATH-INFO 2009-2010 Semestre de printemps Projet RISK : TOWER DEFENSE Membres : Jessica El Melhem, Vincent Sébille, et Jonat...Managed ESL Suite: Managed ESL Suite using C# for FreeSWITCH Omni-Tool - A program version concept of the tool used in Mass Effect.: A program version concept of the tool used in Mass Effect. It will support little apps (plugins) that run inside the UI. Its talor mainly at develo...PdxCodeCamp: Web application for Portland Code CampProjeto Vírus: Desenvolvimento do Jogo Virus em XNAsilverlight control - stars with rounded corners: Draw stars and cogs including rounded cornersSilverlight MathParser: Implementation of mathematical expressions parser to compute and functions.turing machine simulator: Project for JCE in course SW engeenering. Turing Machine simulator with GUI.WpD - Wallpapers Downloader: You can easy download wallpapers to your computer without any advertising or registration. On 5 minutes you can download so many wallpapers!New ReleasesAJAX Control Framework: v1.0.0.0: New AJAX project that helps you create AJAX enabled controls. Make use of control level AJAX methods, a Script Manager that works like you'd expect...AutoFixture: Version 1.1: This is version 1.1 of AutoFixture. This release contains no known bugs. Compared to Release Candidate 1 for version 1.1, there are no changes. Ho...AutoPoco: AutoPoco 0.3: Method Invocation in configuration Custom type providers during configuration Method invocation for generationBacicworx (Basic Services Framework): 3.0.10.410 (Beta): Major update, winnowing, and recode of the library. Removed redundant classses and methods which have similar functionality to those available in ...Bluetooth Radar: Version 1.5: Mostly UI and Animation Changes.BUtil: BUtil 4.9: 1. Icons of kneo are almost removed 2. Deployment was moved to codeplex.com 3. Adding of storages was unavailable when any of storages are used FIXEDcrudwork library: crudwork 2.2.0.3: few bug fixes new object viewer - allow the user to view and change an object through the property grid and/or the simple XML editor pivot table ...EnhSim: Release v1.9.8.5: Release v1.9.8.5Removed the debugging output from the Armor Penetration change.EnhSim: Release v1.9.8.6: Release v1.9.8.6Updated release to include the correct version of EnhSimGUIEvaluate: Evaluate Library: This file contains Evaluate library source code under Visual Studio project. Also, there is a sample project to see the use.ExcelDna: ExcelDna Version 0.25: This is an important bugfix release, with the following changes: Fix case where unpacked .config temp file might not be deleted. Fix compiler pro...FamAccountor: 家庭账薄 预览版v0.0.1: 家庭账薄 预览版v0.0.1 该版本提供基本功能,还有待扩展! Feature: 实现基本添加、编辑、删除功能。FamAccountor: 家庭账薄 预览版v0.0.2: 家庭账薄 预览版v0.0.2 该版本提供基本功能,还有待扩展! Feature: 添加账户管理功能。Folder Bookmarks: Folder Bookmarks 1.4.2: This is the latest version of Folder Bookmarks (1.4.2), with general improvements. It has an installer - it will create a directory 'CPascoe' in My...GKO Libraries: GKO Libraries 0.3 Beta: Added Silverlight support for Gko.Utils Added ExtensionsHash Calculator: HashCalculator 1.2: HashCalculator 1.2HD-Trailers.NET Downloader: Version: TrailersOnly if set to 'true' only titles with 'trailer' in the title will be download MinTrailerSize Added a minimum trailer size, this avoids t...Home Access Plus+: v3.2.6.0: v3.2.5.1 Release Change Log: Add lesson naming Fixed a bug in the help desk which was rendering the wrong URL for tickets Planning has started ...HTML Ruby: 6.20.0: All new concept, all new code. Because this release does not support complex ruby annotations, "Furigana Injector" is not supported by this release...HTML Ruby: 6.20.1: Fixed problem where ruby with closed tags but no rb tag will result in empty page Added support for complex ruby annotation (limited single ruby)...K8061.Managed: K8061.Managed: This is a pre-compilled K8061.Managed.DLL file release 1.0.Kooboo CMS: Kooboo CMS 2.1.0.0: Users of Kooboo CMS 2.0, please use the "Check updates" feature under System to upgrade New featuresWebDav support You can now use tools like w...Kooboo forum: Kooboo Forum Module for 2.1.0.0: Compatible with Kooboo cms 2.1.0.0 Upgrade to MVC 2Kooboo GoogleAnalytics: Kooboo GoogleAnalytics Module for 2.1.0.0: Compatible with Kooboo cms 2.1.0.0 Upgrade to MVC 2Kooboo wiki: Kooboo CMS Wiki module for 2.1.0.0: Compatible with Kooboo cms 2.1.0.0 Upgrade to MVC 2Mavention: Mavention Simple SiteMapPath: Mavention Simple SiteMapPath is a custom control that renders breadcrumbs as an unordered list what makes it a perfect solution for breadcrumbs on ...MetaSharp: MetaSharp v0.3: MetaSharp v0.3 Roadmap: Oslo Independence Custom Grammar library Improved build environment dogfooding Project structure simplificationsRoTwee: RoTwee (10.0.0.7): New feature of this version is support for mouse wheel. You can rotate tweets rotating mouse wheel.silverlight control - stars with rounded corners: first step: These are the first examples.Silverlight MathParser: Silverlight MathParser 1.0: Implementation of mathematical expressions parser to compute and functions.SimpleGeo.NET: SimpleGeo.NET example website project: ConfigurationYou must change these three configuration values in AppSettings.config: Google Maps API key: for the maps on the test site. Get one he...StickyTweets: 0.6.0: Version 0.6.0 Code - PERFORMANCE Hook into Async WinInet to perform async requests without adding an additional thread Code - Verify that async r...System.Html: Version 1.3; fixed bugs and improved performance: This release incorporates bug fixes, a new normalize method proposed by RudolfHenning of Codeplex.VCC: Latest build, v2.1.30410.0: Automatic drop of latest buildVFPX: FoxTabs 0.9.2: The following issues were addressed: 26744 24954 24767Visual Studio DSite: Advanced Guessing Number Game (Visual C++ 2008): A guessing number game made in visual c 2008.WpD - Wallpapers Downloader: WpD v0.1: My first release, I hope you enjoyMost Popular ProjectsWBFS ManagerRawrASP.NET Ajax LibraryMicrosoft SQL Server Product Samples: DatabaseAJAX Control ToolkitSilverlight ToolkitWindows Presentation Foundation (WPF)ASP.NETMicrosoft SQL Server Community & SamplesFacebook Developer ToolkitMost Active ProjectsRawrnopCommerce. Open Source online shop e-commerce solution.AutoPocopatterns & practices – Enterprise LibraryShweet: SharePoint 2010 Team Messaging built with PexFarseer Physics EngineNB_Store - Free DotNetNuke Ecommerce Catalog ModuleIonics Isapi Rewrite FilterBlogEngine.NETBeanProxy

    Read the article

  • Using a "white list" for extracting terms for Text Mining, Part 2

    - by [email protected]
    In my last post, we set the groundwork for extracting specific tokens from a white list using a CTXRULE index. In this post, we will populate a table with the extracted tokens and produce a case table suitable for clustering with Oracle Data Mining. Our corpus of documents will be stored in a database table that is defined as create table documents(id NUMBER, text VARCHAR2(4000)); However, any suitable Oracle Text-accepted data type can be used for the text. We then create a table to contain the extracted tokens. The id column contains the unique identifier (or case id) of the document. The token column contains the extracted token. Note that a given document many have many tokens, so there will be one row per token for a given document. create table extracted_tokens (id NUMBER, token VARCHAR2(4000)); The next step is to iterate over the documents and extract the matching tokens using the index and insert them into our token table. We use the MATCHES function for matching the query_string from my_thesaurus_rules with the text. DECLARE     cursor c2 is       select id, text       from documents; BEGIN     for r_c2 in c2 loop        insert into extracted_tokens          select r_c2.id id, main_term token          from my_thesaurus_rules          where matches(query_string,                        r_c2.text)>0;     end loop; END; Now that we have the tokens, we can compute the term frequency - inverse document frequency (TF-IDF) for each token of each document. create table extracted_tokens_tfidf as   with num_docs as (select count(distinct id) doc_cnt                     from extracted_tokens),        tf       as (select a.id, a.token,                            a.token_cnt/b.num_tokens token_freq                     from                        (select id, token, count(*) token_cnt                        from extracted_tokens                        group by id, token) a,                       (select id, count(*) num_tokens                        from extracted_tokens                        group by id) b                     where a.id=b.id),        doc_freq as (select token, count(*) overall_token_cnt                     from extracted_tokens                     group by token)   select tf.id, tf.token,          token_freq * ln(doc_cnt/df.overall_token_cnt) tf_idf   from num_docs,        tf,        doc_freq df   where df.token=tf.token; From the WITH clause, the num_docs query simply counts the number of documents in the corpus. The tf query computes the term (token) frequency by computing the number of times each token appears in a document and divides that by the number of tokens found in the document. The doc_req query counts the number of times the token appears overall in the corpus. In the SELECT clause, we compute the tf_idf. Next, we create the nested table required to produce one record per case, where a case corresponds to an individual document. Here, we COLLECT all the tokens for a given document into the nested column extracted_tokens_tfidf_1. CREATE TABLE extracted_tokens_tfidf_nt              NESTED TABLE extracted_tokens_tfidf_1                  STORE AS extracted_tokens_tfidf_tab AS              select id,                     cast(collect(DM_NESTED_NUMERICAL(token,tf_idf)) as DM_NESTED_NUMERICALS) extracted_tokens_tfidf_1              from extracted_tokens_tfidf              group by id;   To build the clustering model, we create a settings table and then insert the various settings. Most notable are the number of clusters (20), using cosine distance which is better for text, turning off auto data preparation since the values are ready for mining, the number of iterations (20) to get a better model, and the split criterion of size for clusters that are roughly balanced in number of cases assigned. CREATE TABLE km_settings (setting_name  VARCHAR2(30), setting_value VARCHAR2(30)); BEGIN  INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.clus_num_clusters, 20);  INSERT INTO km_settings (setting_name, setting_value)     VALUES (dbms_data_mining.kmns_distance, dbms_data_mining.kmns_cosine);   INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_off);   INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.kmns_iterations,20);   INSERT INTO km_settings (setting_name, setting_value) VALUES     VALUES (dbms_data_mining.kmns_split_criterion,dbms_data_mining.kmns_size);   COMMIT; END; With this in place, we can now build the clustering model. BEGIN     DBMS_DATA_MINING.CREATE_MODEL(     model_name          => 'TEXT_CLUSTERING_MODEL',     mining_function     => dbms_data_mining.clustering,     data_table_name     => 'extracted_tokens_tfidf_nt',     case_id_column_name => 'id',     settings_table_name => 'km_settings'); END;To generate cluster names from this model, check out my earlier post on that topic.

    Read the article

  • Not Playing Nice Together

    - by David Douglass
    One of the things I’ve noticed is that two industry trends are not playing nice together, those trends being multi-core CPUs and massive hard drives.  It’s not a problem if you keep your cores busy with compute intensive work, but for software developers the beauty of multi-core CPUs (along with gobs of RAM and a 64 bit OS) is virtualization.  But when you have only one hard drive (who needs another when it holds 2 TB of data?) you wind up with a serious hard drive bottleneck.  A solid state drive would definitely help, and might even be a complete solution, but the cost is ridiculous.  Two TB of solid state storage will set you back around $7,000!  A spinning 2 TB drive is only $150. I see a couple of solutions for this.  One is the mainframe concept of near and far storage: put the stuff that will be heavily access on a solid state drive and the rest on a spinning drive.  Another solution is multiple spinning drives.  Instead of a single 2 TB drive, get four 500 GB drives.  In total, the four 500 GB drives will cost about $100 more than the single 2 TB drive.  You’ll need to be smart about what drive you place things on so that the load is spread evenly.  Another option, for better performance, would be four 10,000 RPM 300 GB drives, but that would cost about $800 more than the singe 2 TB drive and would deliver only 1.2 TB of space. All pricing based on Microcenter as of March 14, 2010.

    Read the article

  • Attend my Fusion sessions

    - by Daniel Moth
    The inaugural Fusion conference was 1 year ago in June 2011 and I was there doing a demo in the keynote, and also presenting a breakout session. If you look at the abstract and title for that session you won't see the term "C++ AMP" in there because the technology wasn't announced and we didn't want to spill the beans ahead of the keynote, where the technology was announced. It was only an announcement, we did not give any bits out, and in fact the first bits came three months later in September 2011 with the Beta following in February 2012. So it really feels great 1 year later, to be back at Fusion presenting two sessions on C++ AMP, demonstrating our progress from that announcement, to the Visual Studio 2012 Release Candidate that came out last week. If you are attending Fusion (in person or virtually later), be sure to watch my two-part session. Part 1 is PT-3601 on Tuesday 4pm and part 2 is PT-3602 on Wednesday 4pm. Here is the shared abstract for both parts: Harnessing GPU Compute with C++ AMP C++ AMP is an open specification for taking advantage of accelerators like the GPU. In this session we will explore the C++ AMP implementation in Microsoft Visual Studio 2012. After a quick overview of the technology understanding its goals and its differentiation compared with other approaches, we will dive into the programming model and its modern C++ API. This is a code heavy, interactive, two-part session, where every part of the library will be explained. Demos will include showing off the richest parallel and GPU debugging story on the market, in the upcoming Visual Studio release. See you there! Comments about this post by Daniel Moth welcome at the original blog.

    Read the article

  • Why is IaaS important in Azure&hellip;

    - by Steve Loethen
    Three weeks ago, Microsoft released the next phase of Azure.  I have had several clients waiting on this release.  The fact that they have been waiting and are now more receptive to looking at the cloud.  Customers expressed fear of the unknown.  And a fear of lack of control, even when that lack of control also means a huge degree of flexibility to innovate with concerns about the underlying infrastructure.  I think IaaS will be that “gateway drug” to get customers who have been hesitant to take another look at the cloud.  The dialog can change from the cloud being this big scary unknown to a resource for workloads.  The conversations should have always been, and can know be even stronger, geared toward the following points: 1) The cloud is not unicorns and glitter, the cloud is resources.  Compute, storage, db’s, services bus, cache…..  Like many of the resources we have on-premise.  Not magic, just another resource with advantages and obstacles like any other resource. 2) The cloud should be part of the conversation for any new project.  All of the same criteria should be applied, on-premise or off.  Cost, security, reliability, scalability, speed to deploy, cost of licenses, need to customize image, complex workloads.  We have been having these discussions for years when we talk about on-premise projects.  We make decisions on OS’s, Databases, ESB’s, configuration and products based on a myriad of factors.  We use the same factors but now we have a additional set of resources to consider in our process. 3) The cloud is a great solution looking for some interesting problems.  It is our job to recognize the right problems that fit into the cloud, weigh the factors and decide what to do. IaaS makes this discussion easier, offers more choices, and often choices that many enterprises will find more better than PaaS.  Looking forward to helping clients realize the power of the cloud.

    Read the article

  • Oracle VM Blade Cluster Reference Configuration

    - by Ferhat Hatay
    Today we are happy to announce the availability of the Oracle VM blade cluster reference configuration for Sun Blade 6000 modular systems.  The new Oracle VM blade cluster reference configuration can help reduce the time to deploy virtual infrastructure by up to 98 percent when compared to multi-vendor configurations. Oracle's virtualization strategy is to simplify the deployment, management, and support of the enterprise stack from application to disk. The Oracle VM blade cluster reference configuration is a single-vendor solution that addresses every layer of the virtualization stack with Oracle hardware and software components. It enables quick and easy deployment of the virtualized infrastructure using components that have been tested together and are all supported together by one vendor — Oracle. All components listed in the reference configuration have been tested together by Oracle, reducing the need for customer testing and the time-consuming and complex effort of designing and deploying a stable configuration. Benefitting from pre-installed Oracle VM Server for x86 software on Oracle’s highly scalable and reliable Sun Blade servers with built-in networking and Oracle’s Sun ZFS Storage Appliance product line, the configuration provides high availability via the blade cluster as well as a documented best practice guide that helps reduce deployment time and cost for customers implementing highly virtualized applications or private cloud Infrastructure as a Service (IaaS) architectures. To further support easier, faster and lower-cost deployments, Oracle Linux, Oracle Solaris and Oracle VM are available for pre-install on select Sun x86 systems, and Oracle VM Templates are available for download for Oracle Applications, Oracle Fusion Middleware, Oracle Database, Oracle Real Application Clusters, and many other Oracle products. Key benefits of the Oracle VM blade cluster reference configuration include: Faster time to value – Begin deploying applications immediately because the optimized software stack is pre-configured for best practices and is ready-to-run on the recommended hardware platforms. Reduced deployment cost and risk – The entire hardware and software stack has been tested and is supported together by Oracle. Elastic scalability – As capacity needs grow, the system can be easily scaled in multiple dimensions with the ability to add compute, storage, and networking resources independently. For more information, see: Oracle white paper: Accelerating deployment of virtualized infrastructures with the Oracle VM blade cluster reference configuration Oracle technical white paper: Best Practices and Guidelines for Deploying the Oracle VM Blade Cluster Reference Configuration

    Read the article

  • Optimize SUMMARIZE with ADDCOLUMNS in Dax #ssas #tabular #dax #powerpivot

    - by Marco Russo (SQLBI)
    If you started using DAX as a query language, you might have encountered some performance issues by using SUMMARIZE. The problem is related to the calculation you put in the SUMMARIZE, by adding what are called extension columns, which compute their value within a filter context defined by the rows considered in the group that the SUMMARIZE uses to produce each row in the output. Most of the time, for simple table expressions used in the first parameter of SUMMARIZE, you can optimize performance by removing the extended columns from the SUMMARIZE and adding them by using an ADDCOLUMNS function. In practice, instead of writing SUMMARIZE( <table>, <group_by_column>, <column_name>, <expression> ) you can write: ADDCOLUMNS(     SUMMARIZE( <table>, <group by column> ),     <column_name>, CALCULATE( <expression> ) ) The performance difference might be huge (orders of magnitude) but this optimization might produce a different semantic and in these cases it should not be used. A longer discussion of this topic is included in my Best Practices Using SUMMARIZE and ADDCOLUMNS article on SQLBI, which also include several details about the DAX syntax with extended columns. For example, did you know that you can create an extended column in SUMMARIZE and ADDCOLUMNS with the same name of existing measures? It is *not* a good thing to do, and by reading the article you will discover why. Enjoy DAX!

    Read the article

  • Hello Operator, My Switch Is Bored

    - by Paul White
    This is a post for T-SQL Tuesday #43 hosted by my good friend Rob Farley. The topic this month is Plan Operators. I haven’t taken part in T-SQL Tuesday before, but I do like to write about execution plans, so this seemed like a good time to start. This post is in two parts. The first part is primarily an excuse to use a pretty bad play on words in the title of this blog post (if you’re too young to know what a telephone operator or a switchboard is, I hate you). The second part of the post looks at an invisible query plan operator (so to speak). 1. My Switch Is Bored Allow me to present the rare and interesting execution plan operator, Switch: Books Online has this to say about Switch: Following that description, I had a go at producing a Fast Forward Cursor plan that used the TOP operator, but had no luck. That may be due to my lack of skill with cursors, I’m not too sure. The only application of Switch in SQL Server 2012 that I am familiar with requires a local partitioned view: CREATE TABLE dbo.T1 (c1 int NOT NULL CHECK (c1 BETWEEN 00 AND 24)); CREATE TABLE dbo.T2 (c1 int NOT NULL CHECK (c1 BETWEEN 25 AND 49)); CREATE TABLE dbo.T3 (c1 int NOT NULL CHECK (c1 BETWEEN 50 AND 74)); CREATE TABLE dbo.T4 (c1 int NOT NULL CHECK (c1 BETWEEN 75 AND 99)); GO CREATE VIEW V1 AS SELECT c1 FROM dbo.T1 UNION ALL SELECT c1 FROM dbo.T2 UNION ALL SELECT c1 FROM dbo.T3 UNION ALL SELECT c1 FROM dbo.T4; Not only that, but it needs an updatable local partitioned view. We’ll need some primary keys to meet that requirement: ALTER TABLE dbo.T1 ADD CONSTRAINT PK_T1 PRIMARY KEY (c1);   ALTER TABLE dbo.T2 ADD CONSTRAINT PK_T2 PRIMARY KEY (c1);   ALTER TABLE dbo.T3 ADD CONSTRAINT PK_T3 PRIMARY KEY (c1);   ALTER TABLE dbo.T4 ADD CONSTRAINT PK_T4 PRIMARY KEY (c1); We also need an INSERT statement that references the view. Even more specifically, to see a Switch operator, we need to perform a single-row insert (multi-row inserts use a different plan shape): INSERT dbo.V1 (c1) VALUES (1); And now…the execution plan: The Constant Scan manufactures a single row with no columns. The Compute Scalar works out which partition of the view the new value should go in. The Assert checks that the computed partition number is not null (if it is, an error is returned). The Nested Loops Join executes exactly once, with the partition id as an outer reference (correlated parameter). The Switch operator checks the value of the parameter and executes the corresponding input only. If the partition id is 0, the uppermost Clustered Index Insert is executed, adding a row to table T1. If the partition id is 1, the next lower Clustered Index Insert is executed, adding a row to table T2…and so on. In case you were wondering, here’s a query and execution plan for a multi-row insert to the view: INSERT dbo.V1 (c1) VALUES (1), (2); Yuck! An Eager Table Spool and four Filters! I prefer the Switch plan. My guess is that almost all the old strategies that used a Switch operator have been replaced over time, using things like a regular Concatenation Union All combined with Start-Up Filters on its inputs. Other new (relative to the Switch operator) features like table partitioning have specific execution plan support that doesn’t need the Switch operator either. This feels like a bit of a shame, but perhaps it is just nostalgia on my part, it’s hard to know. Please do let me know if you encounter a query that can still use the Switch operator in 2012 – it must be very bored if this is the only possible modern usage! 2. Invisible Plan Operators The second part of this post uses an example based on a question Dave Ballantyne asked using the SQL Sentry Plan Explorer plan upload facility. If you haven’t tried that yet, make sure you’re on the latest version of the (free) Plan Explorer software, and then click the Post to SQLPerformance.com button. That will create a site question with the query plan attached (which can be anonymized if the plan contains sensitive information). Aaron Bertrand and I keep a close eye on questions there, so if you have ever wanted to ask a query plan question of either of us, that’s a good way to do it. The problem The issue I want to talk about revolves around a query issued against a calendar table. The script below creates a simplified version and adds 100 years of per-day information to it: USE tempdb; GO CREATE TABLE dbo.Calendar ( dt date NOT NULL, isWeekday bit NOT NULL, theYear smallint NOT NULL,   CONSTRAINT PK__dbo_Calendar_dt PRIMARY KEY CLUSTERED (dt) ); GO -- Monday is the first day of the week for me SET DATEFIRST 1;   -- Add 100 years of data INSERT dbo.Calendar WITH (TABLOCKX) (dt, isWeekday, theYear) SELECT CA.dt, isWeekday = CASE WHEN DATEPART(WEEKDAY, CA.dt) IN (6, 7) THEN 0 ELSE 1 END, theYear = YEAR(CA.dt) FROM Sandpit.dbo.Numbers AS N CROSS APPLY ( VALUES (DATEADD(DAY, N.n - 1, CONVERT(date, '01 Jan 2000', 113))) ) AS CA (dt) WHERE N.n BETWEEN 1 AND 36525; The following query counts the number of weekend days in 2013: SELECT Days = COUNT_BIG(*) FROM dbo.Calendar AS C WHERE theYear = 2013 AND isWeekday = 0; It returns the correct result (104) using the following execution plan: The query optimizer has managed to estimate the number of rows returned from the table exactly, based purely on the default statistics created separately on the two columns referenced in the query’s WHERE clause. (Well, almost exactly, the unrounded estimate is 104.289 rows.) There is already an invisible operator in this query plan – a Filter operator used to apply the WHERE clause predicates. We can see it by re-running the query with the enormously useful (but undocumented) trace flag 9130 enabled: Now we can see the full picture. The whole table is scanned, returning all 36,525 rows, before the Filter narrows that down to just the 104 we want. Without the trace flag, the Filter is incorporated in the Clustered Index Scan as a residual predicate. It is a little bit more efficient than using a separate operator, but residual predicates are still something you will want to avoid where possible. The estimates are still spot on though: Anyway, looking to improve the performance of this query, Dave added the following filtered index to the Calendar table: CREATE NONCLUSTERED INDEX Weekends ON dbo.Calendar(theYear) WHERE isWeekday = 0; The original query now produces a much more efficient plan: Unfortunately, the estimated number of rows produced by the seek is now wrong (365 instead of 104): What’s going on? The estimate was spot on before we added the index! Explanation You might want to grab a coffee for this bit. Using another trace flag or two (8606 and 8612) we can see that the cardinality estimates were exactly right initially: The highlighted information shows the initial cardinality estimates for the base table (36,525 rows), the result of applying the two relational selects in our WHERE clause (104 rows), and after performing the COUNT_BIG(*) group by aggregate (1 row). All of these are correct, but that was before cost-based optimization got involved :) Cost-based optimization When cost-based optimization starts up, the logical tree above is copied into a structure (the ‘memo’) that has one group per logical operation (roughly speaking). The logical read of the base table (LogOp_Get) ends up in group 7; the two predicates (LogOp_Select) end up in group 8 (with the details of the selections in subgroups 0-6). These two groups still have the correct cardinalities as trace flag 8608 output (initial memo contents) shows: During cost-based optimization, a rule called SelToIdxStrategy runs on group 8. It’s job is to match logical selections to indexable expressions (SARGs). It successfully matches the selections (theYear = 2013, is Weekday = 0) to the filtered index, and writes a new alternative into the memo structure. The new alternative is entered into group 8 as option 1 (option 0 was the original LogOp_Select): The new alternative is to do nothing (PhyOp_NOP = no operation), but to instead follow the new logical instructions listed below the NOP. The LogOp_GetIdx (full read of an index) goes into group 21, and the LogOp_SelectIdx (selection on an index) is placed in group 22, operating on the result of group 21. The definition of the comparison ‘the Year = 2013’ (ScaOp_Comp downwards) was already present in the memo starting at group 2, so no new memo groups are created for that. New Cardinality Estimates The new memo groups require two new cardinality estimates to be derived. First, LogOp_Idx (full read of the index) gets a predicted cardinality of 10,436. This number comes from the filtered index statistics: DBCC SHOW_STATISTICS (Calendar, Weekends) WITH STAT_HEADER; The second new cardinality derivation is for the LogOp_SelectIdx applying the predicate (theYear = 2013). To get a number for this, the cardinality estimator uses statistics for the column ‘theYear’, producing an estimate of 365 rows (there are 365 days in 2013!): DBCC SHOW_STATISTICS (Calendar, theYear) WITH HISTOGRAM; This is where the mistake happens. Cardinality estimation should have used the filtered index statistics here, to get an estimate of 104 rows: DBCC SHOW_STATISTICS (Calendar, Weekends) WITH HISTOGRAM; Unfortunately, the logic has lost sight of the link between the read of the filtered index (LogOp_GetIdx) in group 22, and the selection on that index (LogOp_SelectIdx) that it is deriving a cardinality estimate for, in group 21. The correct cardinality estimate (104 rows) is still present in the memo, attached to group 8, but that group now has a PhyOp_NOP implementation. Skipping over the rest of cost-based optimization (in a belated attempt at brevity) we can see the optimizer’s final output using trace flag 8607: This output shows the (incorrect, but understandable) 365 row estimate for the index range operation, and the correct 104 estimate still attached to its PhyOp_NOP. This tree still has to go through a few post-optimizer rewrites and ‘copy out’ from the memo structure into a tree suitable for the execution engine. One step in this process removes PhyOp_NOP, discarding its 104-row cardinality estimate as it does so. To finish this section on a more positive note, consider what happens if we add an OVER clause to the query aggregate. This isn’t intended to be a ‘fix’ of any sort, I just want to show you that the 104 estimate can survive and be used if later cardinality estimation needs it: SELECT Days = COUNT_BIG(*) OVER () FROM dbo.Calendar AS C WHERE theYear = 2013 AND isWeekday = 0; The estimated execution plan is: Note the 365 estimate at the Index Seek, but the 104 lives again at the Segment! We can imagine the lost predicate ‘isWeekday = 0’ as sitting between the seek and the segment in an invisible Filter operator that drops the estimate from 365 to 104. Even though the NOP group is removed after optimization (so we don’t see it in the execution plan) bear in mind that all cost-based choices were made with the 104-row memo group present, so although things look a bit odd, it shouldn’t affect the optimizer’s plan selection. I should also mention that we can work around the estimation issue by including the index’s filtering columns in the index key: CREATE NONCLUSTERED INDEX Weekends ON dbo.Calendar(theYear, isWeekday) WHERE isWeekday = 0 WITH (DROP_EXISTING = ON); There are some downsides to doing this, including that changes to the isWeekday column may now require Halloween Protection, but that is unlikely to be a big problem for a static calendar table ;)  With the updated index in place, the original query produces an execution plan with the correct cardinality estimation showing at the Index Seek: That’s all for today, remember to let me know about any Switch plans you come across on a modern instance of SQL Server! Finally, here are some other posts of mine that cover other plan operators: Segment and Sequence Project Common Subexpression Spools Why Plan Operators Run Backwards Row Goals and the Top Operator Hash Match Flow Distinct Top N Sort Index Spools and Page Splits Singleton and Range Seeks Bitmaps Hash Join Performance Compute Scalar © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

    Read the article

  • glFramebufferTexture2D performance

    - by nornagon
    I'm doing heavy computation using the GPU, which involves a lot of render-to-texture operations. It's an iterative computation, so there's a lot of rendering to a texture, then rendering that texture to another texture, then rendering the second texture back to the first texture and so on, passing the texture through a shader each time. My question is: is it better to have a separate FBO for each texture I want to render into, or should I rather have one FBO and bind the target texture using glFramebufferTexture2D each time I want to change render target? My platform is OpenGL ES 2.0 on the iPhone.

    Read the article

  • ArchBeat Link-o-Rama for November 28, 2012

    - by Bob Rhubart
    Oracle BPM and Oracle Application Development Framework (ADF) | Dan Atwood Oracle ACE Dan Atwood shares an excerpt from "Oracle BPM and ADF (Part 1)," part of Avio Consulting's new self-paced online Oracle BPM Developer Workshop training. BPEL and Fire-and-Forget Web Services | Lonneke Dikmans Oracle ACE Director Lonneke Dikmans shares two use cases to illustrate the use of fire-and-forget web services. Backup and Recovery of an Exalogic vServer via rsync | Donald "On Exalogic a vServer will consist of a number of resources from the underlying machine," says the man known only as Donald. "These resources include compute power, networking and storage. In order to recover a vServer from a failure in the underlying rack all of these components have to be thoughts about. This article only discusses the backup and recovery strategies that apply to the storage system of a vServer." Making Architecture Matter | Harald Wesenberg and Einar Landre "As Architects, we want our architecture to matter. We want projects to implement our grand designs, one little step at a time, with each piece fitting perfectly into the big puzzle that is software architecture," say authors Harald Wesenberg and Einar Landre. "But reality is a bit trickier." Thought for the Day "A distributed system is one in which the failure of a computer you didn't even know existed can render your own computer unusable." — Leslie Lamport Source: SoftwareQuotes.com

    Read the article

  • Problem with SLATEC routine usage with gfortran

    - by user39461
    I am trying to compute the Bessel function of the second kind (Bessel_y) using the SLATEC's Amos library available on Netlib. Here is the SLATEC code I use. Below I have pasted my test program that calls SLATEC routine CBESY. PROGRAM BESSELTEST IMPLICIT NONE REAL:: FNU INTEGER, PARAMETER :: N = 2, KODE = 1 COMPLEX,ALLOCATABLE :: CWRK (:), CY (:) COMPLEX:: Z, ci INTEGER :: NZ, IERR ALLOCATE(CWRK(N), CY(N)) ci = cmplx (0.0, 1.0) FNU = 0.0e0 Z = CMPLX(0.3e0, 0.4e0) CALL CBESY(Z, FNU, KODE, N, CY, NZ, CWRK, IERR) WRITE(*,*) 'CY: ', CY WRITE(*,*) 'IERR: ', IERR STOP END PROGRAM And here is the output of the above program: CY: ( 5.78591091E-39, 5.80327020E-39) ( 0.0000000 , 0.0000000 ) IERR: 4 Ierr = 4 meaning there is some problem with the input itself. To be precise, the IERR = 4 means the following as per the header info in CBESY.f file: ! IERR=4, CABS(Z) OR FNU+N-1 TOO LARGE - NO COMPUTA- ! TION BECAUSE OF COMPLETE LOSSES OF SIGNIFI- ! CANCE BY ARGUMENT REDUCTION Clearly, CABS(Z) (which is 0.50) or FNU + N - 1 (which is 1.0) are not too large but still the routine CBESY throws the error message number 4 as above. The CY array should have following values for the argument given in above code: CY(1) = -0.4983 + 0.6700i CY(2) = -1.0149 + 0.9485i These values are computed using Matlab. I can't figure out what's the problem when I call CBESY from SLATEC library. Any clues? Much thanks for the suggestions/help. PS: if it is of any help, I used gfortran to compile, link and then create the SLATEC library file ( the .a file ) which I keep in the same directory as my test program above. shell command to execute above code: gfortran -c BesselTest.f95 gfortran -o a *.o libslatec.a a GD.

    Read the article

  • Pivotal Announces JSR-352 Compliance for Spring Batch

    - by reza_rahman
    Pivotal, the company currently funding development of the popular Spring Framework, recently announced JSR 352 (aka Batch Applications for the Java Platform) compliance for the Spring Batch project. More specifically, Spring Batch targets JSR-352 Java SE runtime compatibility rather than Java EE runtime compatibility. If you are surprised that APIs included in Java EE can pass TCKs targeted for Java SE, you should not be. Many other Java EE APIs target compatibility in Java SE environments such as JMS and JPA. You can read about Spring Batch's support for JSR-352 here as well as the Spring configuration to get JSR-352 working in Spring (typically a very low level implementation concern intended to be completely transparent to most JSR-352 users). JSR 352 is one of the few very encouraging cases of major active contribution to the Java EE standard from the Spring development team (the other major effort being Rod Johnson's co-leadership of JSR 330 along with Bob Lee). While IBM's Christopher Vignola led the spec and contributed IBM's years of highly mission critical batch processing experience from products like WebSphere Compute Grid and z/OS batch, the Spring team provided major influences to the API in particular for the chunk processing, listeners, splits and operational interfaces. The GlassFish team's own Mahesh Kannan also contributed, in particular by implementing much of the Java EE integration work for the reference implementation. This was an excellent example of multilateral engineering collaboration through the standards process. For many complex reasons it is not too hard to find evidence of less than amicable interaction between the Spring ecosystem and the Java EE standard over the years if one cares to dig deep enough. In reality most developers see Spring and Java EE as two sides of the same server-side Java coin. At the core Spring and Java EE ecosystems have always shared deep undercurrents of common user bases, bi-directional flows of ideas and perhaps genuine if not begrudging mutual respect. We can all hope for continued strength for both ecosystems and graceful high notes of collaboration via efforts like JSR 352.

    Read the article

  • Basic OpenGL ES2 (iPhone Simulator) question...

    - by David
    Hi! I'm trying to modify the fragment shader which is part of the standard iPhone/XCode OpenGL ES template. I want to make it so that every other row of pixels is transparent. I have this code so far: varying lowp vec4 colorVarying; void main() { gl_FragColor = vec4(colorVarying.x, colorVarying.y, colorVarying.z, floor(mod(gl_FragCoord.y, 2.0))); } But when I compile and run I still get the same square moving up and down with no other effects. What am I doing wrong here? I'm a complete n00b at Glsl - I'm trying to teach myself the very basics. (starting with this tutorial - http://www.mobileorchard.com/getting-started-with-opengl-es-20-on-the-iphone-3gs/) Please help! Thanks! David :)

    Read the article

  • Gain Total Control of Systems running Oracle Linux

    - by Anand Akela
    Oracle Linux is the best Linux for enterprise computing needs and Oracle Enterprise Manager enables enterprises to gain total control over systems running Oracle Linux. Linux Management functionality is available as part of Oracle Enterprise Manager 12c and is available to Oracle Linux Basic and Premier Support customers at no cost. The solution provides an integrated and cost-effective solution for complete Linux systems lifecycle management and delivers comprehensive provisioning, patching, monitoring, and administration capabilities via a single, web-based user interface thus significantly reducing the complexity and cost associated with managing Linux operating system environments. Many enterprises are transforming their IT infrastructure from multiple independent datacenters to an Infrastructure-as-a-Service (IaaS) model, in which shared pools of compute and storage are made available to end-users on a self-service basis. While providing significant improvements when implemented properly, this strategy introduces change and complexity at a time when datacenters are already understaffed and overburdened. To aid in this transformation, IT managers need the proper tools to help them provide the array of IT capabilities required throughout the organization without stretching their staff and budget to the limit. Oracle Enterprise Manager 12c offers  the advanced capabilities to enable IT departments and end-users to take advantage of many benefits and cost savings of IaaS. Oracle Enterprise Manager Ops Center 12c addresses this challenge with a converged approach that integrates systems management across the infrastructure stack, helping organizations to streamline operations, increase productivity, and reduce system downtime.  You can see the Linux management functionality in action by watching the latest integrated Linux management demo . Stay Connected with Oracle Enterprise Manager: Twitter |  Face book |  You Tube |  Linked in |  Newsletter

    Read the article

  • OpenGL basics: calling glDrawElements once per object

    - by Bethor
    Hi all, continuing on from my explorations of the basics of OpenGL (see this question), I'm trying to figure out the basic principles of drawing a scene with OpenGL. I am trying to render a simple cube repeated n times in every direction. My method appears to yield terrible performance : 1000 cubes brings performance below 50fps (on a QuadroFX 1800, roughly a GeForce 9600GT). My method for drawing these cubes is as follows: done once: set up a vertex buffer and array buffer containing my cube vertices in model space set up an array buffer indexing the cube for drawing as 12 triangles done for each frame: update uniform values used by the vertex shader to move all cubes at once done for each cube, for each frame: update uniform values used by the vertex shader to move each cube to its position call glDrawElements to draw the positioned cube Is this a sane method ? If not, how does one go about something like this ? I'm guessing I need to minimize calls to glUniform, glDrawElements, or both, but I'm not sure how to do that. Full code for my little test : (depends on gletools and pyglet) I'm aware that my init code (at least) is really ugly; I'm concerned with the rendering code for each frame right now, I'll move to something a little less insane for the creation of the vertex buffers and such later on. import pyglet from pyglet.gl import * from pyglet.window import key from numpy import deg2rad, tan from gletools import ShaderProgram, FragmentShader, VertexShader, GeometryShader vertexData = [-0.5, -0.5, -0.5, 1.0, -0.5, 0.5, -0.5, 1.0, 0.5, -0.5, -0.5, 1.0, 0.5, 0.5, -0.5, 1.0, -0.5, -0.5, 0.5, 1.0, -0.5, 0.5, 0.5, 1.0, 0.5, -0.5, 0.5, 1.0, 0.5, 0.5, 0.5, 1.0] elementArray = [2, 1, 0, 1, 2, 3,## back face 4, 7, 6, 4, 5, 7,## front face 1, 3, 5, 3, 7, 5,## top face 2, 0, 4, 2, 4, 6,## bottom face 1, 5, 4, 0, 1, 4,## left face 6, 7, 3, 6, 3, 2]## right face def toGLArray(input): return (GLfloat*len(input))(*input) def toGLushortArray(input): return (GLushort*len(input))(*input) def initPerspectiveMatrix(aspectRatio = 1.0, fov = 45): frustumScale = 1.0 / tan(deg2rad(fov) / 2.0) fzNear = 0.5 fzFar = 300.0 perspectiveMatrix = [frustumScale*aspectRatio, 0.0 , 0.0 , 0.0 , 0.0 , frustumScale, 0.0 , 0.0 , 0.0 , 0.0 , (fzFar+fzNear)/(fzNear-fzFar) , -1.0, 0.0 , 0.0 , (2*fzFar*fzNear)/(fzNear-fzFar), 0.0 ] return perspectiveMatrix class ModelObject(object): vbo = GLuint() vao = GLuint() eao = GLuint() initDone = False verticesPool = [] indexPool = [] def __init__(self, vertices, indexing): super(ModelObject, self).__init__() if not ModelObject.initDone: glGenVertexArrays(1, ModelObject.vao) glGenBuffers(1, ModelObject.vbo) glGenBuffers(1, ModelObject.eao) glBindVertexArray(ModelObject.vao) initDone = True self.numIndices = len(indexing) self.offsetIntoVerticesPool = len(ModelObject.verticesPool) ModelObject.verticesPool.extend(vertices) self.offsetIntoElementArray = len(ModelObject.indexPool) ModelObject.indexPool.extend(indexing) glBindBuffer(GL_ARRAY_BUFFER, ModelObject.vbo) glEnableVertexAttribArray(0) #position glVertexAttribPointer(0, 4, GL_FLOAT, GL_FALSE, 0, 0) glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, ModelObject.eao) glBufferData(GL_ARRAY_BUFFER, len(ModelObject.verticesPool)*4, toGLArray(ModelObject.verticesPool), GL_STREAM_DRAW) glBufferData(GL_ELEMENT_ARRAY_BUFFER, len(ModelObject.indexPool)*2, toGLushortArray(ModelObject.indexPool), GL_STREAM_DRAW) def draw(self): glDrawElements(GL_TRIANGLES, self.numIndices, GL_UNSIGNED_SHORT, self.offsetIntoElementArray) class PositionedObject(object): def __init__(self, mesh, pos, objOffsetUf): super(PositionedObject, self).__init__() self.mesh = mesh self.pos = pos self.objOffsetUf = objOffsetUf def draw(self): glUniform3f(self.objOffsetUf, self.pos[0], self.pos[1], self.pos[2]) self.mesh.draw() w = 800 h = 600 AR = float(h)/float(w) window = pyglet.window.Window(width=w, height=h, vsync=False) window.set_exclusive_mouse(True) pyglet.clock.set_fps_limit(None) ## input forward = [False] left = [False] back = [False] right = [False] up = [False] down = [False] inputs = {key.Z: forward, key.Q: left, key.S: back, key.D: right, key.UP: forward, key.LEFT: left, key.DOWN: back, key.RIGHT: right, key.PAGEUP: up, key.PAGEDOWN: down} ## camera camX = 0.0 camY = 0.0 camZ = -1.0 def simulate(delta): global camZ, camX, camY scale = 10.0 move = scale*delta if forward[0]: camZ += move if back[0]: camZ += -move if left[0]: camX += move if right[0]: camX += -move if up[0]: camY += move if down[0]: camY += -move pyglet.clock.schedule(simulate) @window.event def on_key_press(symbol, modifiers): global forward, back, left, right, up, down if symbol in inputs.keys(): inputs[symbol][0] = True @window.event def on_key_release(symbol, modifiers): global forward, back, left, right, up, down if symbol in inputs.keys(): inputs[symbol][0] = False ## uniforms for shaders camOffsetUf = GLuint() objOffsetUf = GLuint() perspectiveMatrixUf = GLuint() camRotationUf = GLuint() program = ShaderProgram( VertexShader(''' #version 330 layout(location = 0) in vec4 objCoord; uniform vec3 objOffset; uniform vec3 cameraOffset; uniform mat4 perspMx; void main() { mat4 translateCamera = mat4(1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, cameraOffset.x, cameraOffset.y, cameraOffset.z, 1.0f); mat4 translateObject = mat4(1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, objOffset.x, objOffset.y, objOffset.z, 1.0f); vec4 modelCoord = objCoord; vec4 positionedModel = translateObject*modelCoord; vec4 cameraPos = translateCamera*positionedModel; gl_Position = perspMx * cameraPos; }'''), FragmentShader(''' #version 330 out vec4 outputColor; const vec4 fillColor = vec4(1.0f, 1.0f, 1.0f, 1.0f); void main() { outputColor = fillColor; }''') ) shapes = [] def init(): global camOffsetUf, objOffsetUf with program: camOffsetUf = glGetUniformLocation(program.id, "cameraOffset") objOffsetUf = glGetUniformLocation(program.id, "objOffset") perspectiveMatrixUf = glGetUniformLocation(program.id, "perspMx") glUniformMatrix4fv(perspectiveMatrixUf, 1, GL_FALSE, toGLArray(initPerspectiveMatrix(AR))) obj = ModelObject(vertexData, elementArray) nb = 20 for i in range(nb): for j in range(nb): for k in range(nb): shapes.append(PositionedObject(obj, (float(i*2), float(j*2), float(k*2)), objOffsetUf)) glEnable(GL_CULL_FACE) glCullFace(GL_BACK) glFrontFace(GL_CW) glEnable(GL_DEPTH_TEST) glDepthMask(GL_TRUE) glDepthFunc(GL_LEQUAL) glDepthRange(0.0, 1.0) glClearDepth(1.0) def update(dt): print pyglet.clock.get_fps() pyglet.clock.schedule_interval(update, 1.0) @window.event def on_draw(): with program: pyglet.clock.tick() glClear(GL_COLOR_BUFFER_BIT|GL_DEPTH_BUFFER_BIT) glUniform3f(camOffsetUf, camX, camY, camZ) for shape in shapes: shape.draw() init() pyglet.app.run()

    Read the article

  • Where is the SQL Azure Development Environment

    - by BuckWoody
    Recently I posted an entry explaining that you can develop in Windows Azure without having to connect to the main service on the Internet, using the Software Development Kit (SDK) which installs two emulators - one for compute and the other for storage. That brought up the question of the same kind of thing for SQL Azure. The short answer is that there isn’t one. While we’ll make the development experience for all versions of SQL Server, including SQL Azure more easy to write against, you can simply treat it as another edition of SQL Server. For instance, many of us use the SQL Server Developer Edition - which in versions up to 2008 is actually the Enterprise Edition - to develop our code. We might write that code against all kinds of environments, from SQL Express through Enterprise Edition. We know which features work on a certain edition, what T-SQL it supports and so on, and develop accordingly. We then test on the actual platform to ensure the code runs as expected. You can simply fold SQL Azure into that same development process. When you’re ready to deploy, if you’re using SQL Server Management Studio 2008 R2 or higher, you can script out the database when you’re done as a SQL Azure script (with change notifications where needed) by selecting the right “Engine Type” on the scripting panel: (Thanks to David Robinson for pointing this out and my co-worker Rick Shahid for the screen-shot - saved me firing up a VM this morning!) Will all this change? Will SSMS, “Data Dude” and other tools change to include SQL Azure? Well, I don’t have a specific roadmap for those tools, but we’re making big investments on Windows Azure and SQL Azure, so I can say that as time goes on, it will get easier. For now, make sure you know what features are and are not included in SQL Azure, and what T-SQL is supported. Here are a couple of references to help: General Guidelines and Limitations: http://msdn.microsoft.com/en-us/library/ee336245.aspx Transact-SQL Supported by SQL Azure: http://msdn.microsoft.com/en-us/library/ee336250.aspx SQL Azure Learning Plan: http://blogs.msdn.com/b/buckwoody/archive/2010/12/13/windows-azure-learning-plan-sql-azure.aspx

    Read the article

  • Attend my GTC sessions

    - by Daniel Moth
    The last GTC conference in the US was 2 years ago and I was there as an attendee. You may recall from that blog post that we were running UX studies at the time. It really feels great 2 years later, to be back at GTC presenting two sessions on C++ AMP, demonstrating our progress that includes input from those early studies. If you are attending GTC (in person or virtually later), be sure to watch my two-part session. Part 1 is S0242 on Wednesday 5pm and part 2 is S0244 on Thursday 10am. Here is the shared abstract for both parts: Harnessing GPU Compute with C++ AMP C++ AMP is an open specification for taking advantage of accelerators like the GPU. In this session we will explore the C++ AMP implementation in Microsoft Visual Studio 11 Beta. After a quick overview of the technology understanding its goals and its differentiation compared with other approaches, we will dive into the programming model and its modern C++ API. This is a code heavy, interactive, two-part session, where every part of the library will be explained. Demos will include showing off the richest parallel and GPU debugging story on the market, in the upcoming Visual Studio release. See you there! Comments about this post by Daniel Moth welcome at the original blog.

    Read the article

  • Where is the SQL Azure Development Environment

    - by BuckWoody
    Recently I posted an entry explaining that you can develop in Windows Azure without having to connect to the main service on the Internet, using the Software Development Kit (SDK) which installs two emulators - one for compute and the other for storage. That brought up the question of the same kind of thing for SQL Azure. The short answer is that there isn’t one. While we’ll make the development experience for all versions of SQL Server, including SQL Azure more easy to write against, you can simply treat it as another edition of SQL Server. For instance, many of us use the SQL Server Developer Edition - which in versions up to 2008 is actually the Enterprise Edition - to develop our code. We might write that code against all kinds of environments, from SQL Express through Enterprise Edition. We know which features work on a certain edition, what T-SQL it supports and so on, and develop accordingly. We then test on the actual platform to ensure the code runs as expected. You can simply fold SQL Azure into that same development process. When you’re ready to deploy, if you’re using SQL Server Management Studio 2008 R2 or higher, you can script out the database when you’re done as a SQL Azure script (with change notifications where needed) by selecting the right “Engine Type” on the scripting panel: (Thanks to David Robinson for pointing this out and my co-worker Rick Shahid for the screen-shot - saved me firing up a VM this morning!) Will all this change? Will SSMS, “Data Dude” and other tools change to include SQL Azure? Well, I don’t have a specific roadmap for those tools, but we’re making big investments on Windows Azure and SQL Azure, so I can say that as time goes on, it will get easier. For now, make sure you know what features are and are not included in SQL Azure, and what T-SQL is supported. Here are a couple of references to help: General Guidelines and Limitations: http://msdn.microsoft.com/en-us/library/ee336245.aspx Transact-SQL Supported by SQL Azure: http://msdn.microsoft.com/en-us/library/ee336250.aspx SQL Azure Learning Plan: http://blogs.msdn.com/b/buckwoody/archive/2010/12/13/windows-azure-learning-plan-sql-azure.aspx

    Read the article

< Previous Page | 41 42 43 44 45 46 47 48 49 50 51 52  | Next Page >