regex group - Page 462 - Developer IT

How to play an mp3 using fancybox

- by user2980783

I am adding fancybox to my page and to display different types of formats. I was able to implement video, text, and images seamlessly but when it doesn't load the audio. Once I click on the audio file on the gallery it opens on a blank page. I am trying to play the track as iframe if possible. Can anyone help with this. Thank you. <a class="hover-wrap" data-fancybox-type="iframe" data-fancybox-group="music" title="Breathin'" href="_include/download/Breathin'.mp3"> Song </a>

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Select products with users

- by Ploppe

I have not worked with SQL for quite a long time, and I need some help for a basic query. I have the three following tables: users (id, name) products (id, name) owners (userid, productid, date) One product can be sold by user A to user B and then back to A. Now, I want the list of all products currently owned by every single user with the date of transaction. Currently, my query is this one, but I'm stuck with old data (first association of one product to one user, and not the newest one): SELECT users.name, products.name, date FROM products JOIN owners ON products.id = owners.id JOIN users ON owners.id = user.id GROUP BY product.id Do you have some hints? Thanks

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Select multiple unique lines in MySQL

- by MartinW

Hi, I've got a table with the following columns: ID, sysid, x, y, z, timereceived ID is a unique number for each row. sysid is an ID number for a specific device (about 100 different of these) x, y and z is data received from the device. (totally random numbers) timereceived is a timestamp for when the data was received. I need a SQL query to show me the last inserted row for device a, device b, device c and so on. I've been playing around with a lot of different Select statements, but never got anything that works. I manage to get unique rows by using group by, but the rest of the information is random (or at least it feels very random). Anyone able to help me? There could be hundreds of thousands records in this table.

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return not breaking loop (c#)

- by David Wick

I'm trying to determine if a user is a member of a group or not in AD. However, the following doesn't seem to be working for some reason... public bool MemberOf(string sObjectName, string sGroup, bool bIsGroup) { DirectoryEntry dEntry = CreateDirectoryEntry(); DirectorySearcher dSearcher = new DirectorySearcher(dEntry); if (bIsGroup) dSearcher.Filter = "(distinguishedName=" + sObjectName + ")"; else dSearcher.Filter = "(&(sAMAccountName=" + sObjectName + ")(objectClass=user))"; SearchResult sResult = dSearcher.FindOne(); if (sResult != null) { foreach (object oGroup in sResult.Properties["MemberOf"]) { if (oGroup.ToString() == sGroup) return true; else this.MemberOf(oGroup.ToString(), sGroup, true); } } return false; } Another variation: http://users.business.uconn.edu/dwick/work/wtf/6-14-2010%201-15-15%20PM.png Doesn't work either. This seems like a really dumb question... but shouldn't it break the loop upon "return true;"

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Fill data gaps without UNION

- by Dave Jarvis

Problem There are data gaps that need to be filled, possibly using PARTITION BY. Query Statement The select statement reads as follows: SELECT count( r.incident_id ) AS incident_tally, r.severity_cd, r.incident_typ_cd FROM report_vw r GROUP BY r.severity_cd, r.incident_typ_cd ORDER BY r.severity_cd, r.incident_typ_cd Code Tables The severity codes and incident type codes are from: severity_vw incident_type_vw Actual Result Data 36 0 ENVIRONMENT 1 1 DISASTER 27 1 ENVIRONMENT 4 2 SAFETY 1 3 SAFETY Required Result Data 36 0 ENVIRONMENT 0 0 DISASTER 0 0 SAFETY 27 1 ENVIRONMENT 0 1 DISASTER 0 1 SAFETY 0 2 ENVIRONMENT 0 2 DISASTER 4 2 SAFETY 0 3 ENVIRONMENT 0 3 DISASTER 1 3 SAFETY Any ideas how to use PARTITION BY (or JOINs) to fill in the zero counts?

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How to make a SUM of Dictionary Value nested into a list with LINQ ?

- by user551108

Hi All, I have a product object declared as : Product { int ProductID; string ProductName; int ProductTypeID; string ProductTypeName; int UnitsSold Dictionary <string, int> UnitsSoldByYear; } I want to make a sum on UnitsSold and UnitsSoldByYear properties with a Linq query but I didn't know how to make this kind of sum on a dictionary ! Here is my begining linq query code : var ProductTypeSum = from i in ProductsList group i by new { i.ProductTypeID, i.ProductTypeName} into pt select new { ProductTypeID= pt.Key.ProductTypeID, ProductTypeName= pt.Key.ProductTypeName, UnitsSoldSum= pt.Sum(i => i.UnitsSold), // How to make a Dictionary sum here } Thank you for your help !

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Approach to Selecting top item matching a criteria

- by jkelley

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

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making mysql query using splite string?

- by Marco

lets say i have a group of number like (3,2,5) the normal way i use to split them and searching mysql to get value is to split them using explode in PHP EXAMPLE $string = '3,4,5'; $array = explode(',',$string); foreach($array as $value){ $query = 'SELECT ID FROM TABLE WHERE ID = "'.$value.'"'; } it work like this but it make the script extremely slow i need now if there is away to split this string into the query it self and return the result without looping with PHP ?

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many-to-many relationship in CI (not using ORM)

- by Ross

I'm implementing a categories system in my CI app and trying to work out the best way of working with many to many relationships. I'm not using an ORM at this stage, but could use say Doctrine if necessary. Each entry may have multiple categories. I have three tables (simplified) Entries: entryID, entryName Categories: categoryID, categoryname Entry_Category: entryID, categoryID my CI code returns a record set like this: entryID, entryName, categoryID, categoryName but, as expected with Many-to-Many relationships, each "entry" is repeated for each "category". What would the best way to "group" the categories so that when I output the results, I am left with something like: Entry Name Appears in Category: Foo, Bar rather than: Entry Name Appears in Category: Foo Entry Name Appears in Category: Bar I believe the option is to track if the post ID matches a previous entry, and if so, store the respective category, and output it as one, rather than several, but am unsure of how to do this in CI. thanks for any pointers (I appreciate this is may be a vague/complex question without a better knowledge of the system).

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concatenation output problem (toString Array) - java

- by dowln

Hello, I am trying to display the output as "1(10) 2(23) 3(29)" but instead getting output as "1 2 3 (10)(23)(29)". I would be grateful if someone could have a look the code and possible help me. I don't want to use arraylist. the code this // int[] Groups = {10, 23, 29}; in the constructor public String toString() { String tempStringB = ""; String tempStringA = " "; String tempStringC = " "; for (int x = 1; x<=3; x+=1) { tempStringB = tempStringB + x + " "; } for(int i = 0; i < Group.length;i++) { tempStringA = tempStringA + "(" + Groups[i] + ")"; } tempStringC = tempStringB + tempStringA; return tempStringC; }

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mySQL : using BETWEEN in table ?

- by Meko

I have a table that includes somestudent group name ,lesson time,day names like Schedule. I am using C# whit MYSql and I want to find which lesson is when user press button from table. I can find it like entering exact value like in table 08:30 or 10:25 , it finds. But I want to make that getting system time and checking that is it between 08:30 and 10:25 or 10:25 and 12:30 . Then I can sythat it is first lesson or it is second lesson . I have also table includes Table_Time column has 5 record like 08:20 , 10:25 , 12:20 so on. Could I use like : select Lesson_Time from mydb.clock where Lesson_Time between (current time)-30 AND (current time)+30 Or can I use between operator between two columns ? Like creating Lesson_Time_Start and Lesson_Time_End and compairing current time like Lesson_Start_Time

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Put empty spaces in an SQL select

- by David Undy

I'm having difficulty creating a month-count select query in SQL. Basically, I have a list of entries, all of which have a date associated with them. What I want the end result to be, is a list containing 12 rows (one for each month), and each row would contain the month number (1 for January, 2 for February, etc), and a count of how many entries had that month set as it's date. Something like this: Month - Count 1 - 12 2 - 0 3 - 7 4 - 0 5 - 9 6 - 0 I can get an result containing months that have a count of higher than 0, but if the month contains no entries, the row isn't created. I get this result just by doing SELECT Month(goalDate) as monthNumber, count(*) as monthCount FROM goalsList WHERE Year(goalDate) = 2012 GROUP BY Month(goalDate) ORDER BY monthNumber Thanks in advance for the help!

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mysql query for change in values in a logging table

- by kiasectomondo

I have a table like this: Index , PersonID , ItemCount , UnixTimeStamp 1 , 1 , 1 , 1296000000 2 , 1 , 2 , 1296000100 3 , 2 , 4 , 1296003230 4 , 2 , 6 , 1296093949 5 , 1 , 0 , 1296093295 Time and index always go up. Its basically a logging table to log the itemcount each time it changes. I get the most recent ItemCount for each Person like this: SELECT * FROM table a INNER JOIN ( SELECT MAX(index) as i FROM table GROUP BY PersonID) b ON a.index = b.i; What I want to do is get get the most recent record for each PersonID that is at least 24 hours older than the most recent record for each Person ID. Then I want to take the difference in ItemCount between these two to get a change in itemcount for each person over the last 24 hours: personID ChangeInItemCountOverAtLeast24Hours 1 3 2 -11 3 6 Im sort of stuck with what to do next. How can I join another itemcount based on latest adjusted timestamp of individual rows?

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MySQL query to find the most popular value in a column joined by another value in a second table

- by Budove

I have two tables: users: user_id, user_zip settings: user_id, pref_ex_loc I need to find the single most popular 'pref_ex_loc' from the settings table based on a particular user_zip, which will be specified as the variable $userzip. Here is the query that I have now and obviously it doesn't work. $popularexloc = "SELECT pref_ex_loc, user_id COUNT(pref_ex_loc) AS countloc FROM settings FULL OUTER JOIN users ON settings.user_id = users.user_id WHERE users.user_zip='$userzip' GROUP BY settings.pref_ex_loc ORDER BY countloc LIMIT 1"; $popexloc = mysql_query($popularexloc) or die('SQL Error :: '.mysql_error()); $exlocrow = mysql_fetch_array($popexloc); $mostpopexloc=$exlocrow[0]; echo '<option value="'.$mostpopexloc.'">'.$mostpopexloc.'</option>'; What am I doing wrong here? I'm not getting any kind of error from this either.

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complex data requirement.

- by Abulalia

Here is my query: select Table1.a, Table1.b, Table1.c, Table1.d, Table2.e, Table3.f, Table4.g, Table5.h from Table1 left join Table6 on Table1.b=Table6.b left join Table3 on Table6.j=Table3.j left join Table7 on Table1.b=Table7.b left join Table5 on Table7.h=Table5.h inner join Table4 on Table1.k=Table4.k inner join Table2 on Table1.m=Table2.m where Table2.e <= x and Table2.n = y and Table3.f in (‘r’, ‘s’) and Table1.d = z group by Table1.a, Table1.b, Table1.c, Table1.d, Table2.e, Table3.f, Table4.g, Table5.h order by Table1.a, Table1.b, Table1.c I am looking for records (a,b,c,d,e,f,g,h) for every a when the very first record b (there are multiple records b for each a) is either 'r' or 's'. Can someone help?

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SharePoint 2010 – Central Admin tooling to create host header site collections

- by eJugnoo

Just like SharePoint 2007, you can create host-header based site collections in SharePoint 2010 as well. It means, that you do not necessarily need to create a site-collection under a managed path like /sites/, you can create multiple root-level site collections on same web-application/port by using host-header site collections. All you need to do is point your domain or sub-domain to your web-application and create a matching site-collection that you want. But, just like in 2007, it is something that you do by using STSADM, and is not available on Central Admin UI in 2010 as well. Yeah, though you can now also use PowerShell to create one: C:\PS>$w = Get-SPWebApplication http://sitename C:\PS>New-SPSite http://www.contoso.com -OwnerAlias "DOMAIN\jdoe" -HostHeaderWebApplication $w -Title "Contoso" -Template "STS#0" This example creates a host header site collection. Because the template is provided, the root Web of this site collection will be created. .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; } I’ve been playing with WCM in SharePoint 2010 more and more, and for that I preferred creating hosts file entries for desired domains and create site-collections by those headers – in my dev environment. I used PowerShell initially, but then got interested to build my own UI on Central Admin instead. Developed with Visual Studio 2010 So I used new Visual Studio 2010 tooling to create an empty SharePoint 2010 project. Added an application page (there is no option to add _Admin page item in VS 2010 RC), that got created in Layouts “mapped” folder. Created a new Admin mapped folder for 14-“hive”, and moved my new page there instead. Yes, I didn’t change the base class for page, its just that it runs under _admin, but it is indeed a LayoutsPageBase inherited page. To introduce a action-link in Central Admin console, I created following element: 1: <Elements xmlns="http://schemas.microsoft.com/sharepoint/"> 2: <CustomAction 3: Id="CreateSiteByHeader" 4: Location="Microsoft.SharePoint.Administration.Applications" 5: Title="Create site collections by host header" 6: GroupId="SiteCollections" 7: Sequence="15" 8: RequiredAdmin="Delegated" 9: Description="Create a new top-level web site, by host header" > 10: <UrlAction Url="/_admin/OfficeToolbox/CreateSiteByHeader.aspx" /> 11: </CustomAction> 12: </Elements> .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; } Used Reflector to understand any special code behind createpage.aspx, and created a new for our purpose – CreateSiteByHeader.aspx. From there I quickly created a similar code behind, without all the fancy of Farm Config Wizard handling and dealt with alternate implementations of sealed classes! Goal was to create a professional looking and OOB-type experience. I also added Regex validation to ensure user types a valid domain name as header value. Below is the result… Release @ Codeplex I’ve released to WSP on OfficeToolbox @ Codeplex, and you can download from here. Hope you find it useful… -- Sharad

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How do i return integers from a string ?

- by kannan.ambadi

Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Suppose you are passing a string(for e.g.: “My name has 1 K, 2 A and 3 N”) which may contain integers, letters or special characters. I want to retrieve only numbers from the input string. We can implement it in many ways such as splitting the string into an array or by using TryParse method. I would like to share another idea, that’s by using Regular expressions. All you have to do is, create an instance of Regular Expression with a specified pattern for integer. Regular expression class defines a method called Split, which splits the specified input string based on the pattern provided during object initialization. We can write the code as given below: public static int[] SplitIdSeqenceValues(object combinedArgs) { var _argsSeperator = new Regex(@"\D+", RegexOptions.Compiled); string[] splitedIntegers = _argsSeperator.Split(combinedArgs.ToString()); var args = new int[splitedIntegers.Length]; for (int i = 0; i < splitedIntegers.Length; i++) args[i] = MakeSafe.ToSafeInt32(splitedIntegers[i]); return args; } It would be better, if we set to RegexOptions.Compiled so that the regular expression will have performance boost by faster compilation. Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Happy Programming :))

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Windows Azure Learning Plan - Security

- by BuckWoody

This is one in a series of posts on a Windows Azure Learning Plan. You can find the main post here. This one deals with Security for Windows Azure. General Security Information Overview and general information about Windows Azure Security - what it is, how it works, and where you can learn more. General Security Whitepaper – answers most questions http://blogs.msdn.com/b/usisvde/archive/2010/08/10/security-white-paper-on-windows-azure-answers-many-faq.aspx Windows Azure Security Notes from the Patterns and Practices site http://blogs.msdn.com/b/jmeier/archive/2010/08/03/now-available-azure-security-notes-pdf.aspx Overview of Azure Security http://www.windowsecurity.com/articles/Microsoft-Azure-Security-Cloud.html Azure Security Resources http://reddevnews.com/articles/2010/08/19/microsoft-releases-windows-azure-security-resources.aspx Cloud Computing Security Considerations http://www.microsoft.com/downloads/en/details.aspx?FamilyID=68fedf9c-1c27-4642-aa5b-0a34472303ea&utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+MicrosoftDownloadCenter+%28Microsoft+Download+Center Security in Cloud Computing – a Microsoft Perspective http://www.microsoft.com/downloads/en/details.aspx?FamilyID=7c8507e8-50ca-4693-aa5a-34b7c24f4579&utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+MicrosoftDownloadCenter+%28Microsoft+Download+Center Physical Security for Microsoft’s Online Computing Information on the Infrastructure and Locations for Azure Physical Security. The Global Foundation Services Group at Microsoft handles physical security http://www.globalfoundationservices.com/security/index.html Microsoft’s Security Response Center http://www.microsoft.com/security/msrc/ Software Security for Microsoft’s Online Computing Steps we take as a company to develop secure software Windows Azure is developed using the Trustworthy Computing Initiative http://www.microsoft.com/about/twc/en/us/default.aspx and http://msdn.microsoft.com/en-us/library/ms995349.aspx Identity and Access in the Cloud http://blogs.msdn.com/b/technology_titbits_by_rajesh_makhija/archive/2010/10/29/identity-and-access-in-the-cloud.aspx Security Steps you should take While Microsoft takes great pains to secure the infrastructure, platform and code for Windows Azure, you have a responsibility to write secure code. These pointers can help you do that. Securing your cloud architecture, step-by-step http://technet.microsoft.com/en-us/magazine/gg296364.aspx Security Guidelines for Windows Azure http://redmondmag.com/articles/2010/06/15/microsoft-issues-security-guidelines-for-windows-azure.aspx Best Practices for Windows Azure Security http://blogs.msdn.com/b/vbertocci/archive/2010/06/14/security-best-practices-for-developing-windows-azure-applications.aspx Active Directory and Windows Azure http://blogs.msdn.com/b/plankytronixx/archive/2010/10/22/projecting-your-active-directory-identity-to-the-azure-cloud.aspx Understanding Encryption (great overview and tutorial) http://blogs.msdn.com/b/plankytronixx/archive/2010/10/23/crypto-primer-understanding-encryption-public-private-key-signatures-and-certificates.aspx Securing your Connection Strings (SQL Azure) http://blogs.msdn.com/b/sqlazure/archive/2010/09/07/10058942.aspx Getting started with Windows Identity Foundation (WIF) quickly http://blogs.msdn.com/b/alikl/archive/2010/10/26/windows-identity-foundation-wif-fast-track.aspx

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Oracle Coherence, Split-Brain and Recovery Protocols In Detail

- by Ricardo Ferreira

This article provides a high level conceptual overview of Split-Brain scenarios in distributed systems. It will focus on a specific example of cluster communication failure and recovery in Oracle Coherence. This includes a discussion on the witness protocol (used to remove failed cluster members) and the panic protocol (used to resolve Split-Brain scenarios). Note that the removal of cluster members does not necessarily indicate a Split-Brain condition. Oracle Coherence does not (and cannot) detect a Split-Brain as it occurs, the condition is only detected when cluster members that previously lost contact with each other regain contact. Cluster Topology and Configuration In order to create an good didactic for the article, let's assume a cluster topology and configuration. In this example we have a six member cluster, consisting of one JVM on each physical machine. The member IDs are as follows: Member ID IP Address 1 10.149.155.76 2 10.149.155.77 3 10.149.155.236 4 10.149.155.75 5 10.149.155.79 6 10.149.155.78 Members 1, 2, and 3 are connected to a switch, and members 4, 5, and 6 are connected to a second switch. There is a link between the two switches, which provides network connectivity between all of the machines. Member 1 is the first member to join this cluster, thus making it the senior member. Member 6 is the last member to join this cluster. Here is a log snippet from Member 6 showing the complete member set: 2010-02-26 15:27:57.390/3.062 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=main, member=6): Started DefaultCacheServer... SafeCluster: Name=cluster:0xDDEB Group{Address=224.3.5.3, Port=35465, TTL=4} MasterMemberSet ( ThisMember=Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) OldestMember=Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) ActualMemberSet=MemberSet(Size=6, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) Member(Id=2, Timestamp=2010-02-26 15:27:17.847, Address=10.149.155.77:8088, MachineId=1101, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:296, Role=CoherenceServer) Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer) Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) Member(Id=5, Timestamp=2010-02-26 15:27:49.095, Address=10.149.155.79:8088, MachineId=1103, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:3229, Role=CoherenceServer) Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) ) RecycleMillis=120000 RecycleSet=MemberSet(Size=0, BitSetCount=0 ) ) At approximately 15:30, the connection between the two switches is severed: Thirty seconds later (the default packet timeout in development mode) the logs indicate communication failures across the cluster. In this example, the communication failure was caused by a network failure. In a production setting, this type of communication failure can have many root causes, including (but not limited to) network failures, excessive GC, high CPU utilization, swapping/virtual memory, and exceeding maximum network bandwidth. In addition, this type of failure is not necessarily indicative of a split brain. Any communication failure will be logged in this fashion. Member 2 logs a communication failure with Member 5: 2010-02-26 15:30:32.638/196.928 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=PacketPublisher, member=2): Timeout while delivering a packet; requesting the departure confirmation for Member(Id=5, Timestamp=2010-02-26 15:27:49.095, Address=10.149.155.79:8088, MachineId=1103, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:3229, Role=CoherenceServer) by MemberSet(Size=2, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) ) The Coherence clustering protocol (TCMP) is a reliable transport mechanism built on UDP. In order for the protocol to be reliable, it requires an acknowledgement (ACK) for each packet delivered. If a packet fails to be acknowledged within the configured timeout period, the Coherence cluster member will log a packet timeout (as seen in the log message above). When this occurs, the cluster member will consult with other members to determine who is at fault for the communication failure. If the witness members agree that the suspect member is at fault, the suspect is removed from the cluster. If the witnesses unanimously disagree, the accuser is removed. This process is known as the witness protocol. Since Member 2 cannot communicate with Member 5, it selects two witnesses (Members 1 and 4) to determine if the communication issue is with Member 5 or with itself (Member 2). However, Member 4 is on the switch that is no longer accessible by Members 1, 2 and 3; thus a packet timeout for member 4 is recorded as well: 2010-02-26 15:30:35.648/199.938 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=PacketPublisher, member=2): Timeout while delivering a packet; requesting the departure confirmation for Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) by MemberSet(Size=2, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) ) Member 1 has the ability to confirm the departure of member 4, however Member 6 cannot as it is also inaccessible. At the same time, Member 3 sends a request to remove Member 6, which is followed by a report from Member 3 indicating that Member 6 has departed the cluster: 2010-02-26 15:30:35.706/199.996 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=2): MemberLeft request for Member 6 received from Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer) 2010-02-26 15:30:35.709/199.999 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=2): MemberLeft notification for Member 6 received from Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer) The log for Member 3 determines how Member 6 departed the cluster: 2010-02-26 15:30:35.161/191.694 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=PacketPublisher, member=3): Timeout while delivering a packet; requesting the departure confirmation for Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) by MemberSet(Size=2, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) Member(Id=2, Timestamp=2010-02-26 15:27:17.847, Address=10.149.155.77:8088, MachineId=1101, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:296, Role=CoherenceServer) ) 2010-02-26 15:30:35.165/191.698 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=Cluster, member=3): Member departure confirmed by MemberSet(Size=2, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) Member(Id=2, Timestamp=2010-02-26 15:27:17.847, Address=10.149.155.77:8088, MachineId=1101, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:296, Role=CoherenceServer) ); removing Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) In this case, Member 3 happened to select two witnesses that it still had connectivity with (Members 1 and 2) thus resulting in a simple decision to remove Member 6. Given the departure of Member 6, Member 2 is left with a single witness to confirm the departure of Member 4: 2010-02-26 15:30:35.713/200.003 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=Cluster, member=2): Member departure confirmed by MemberSet(Size=1, BitSetCount=2 Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) ); removing Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) In the meantime, Member 4 logs a missing heartbeat from the senior member. This message is also logged on Members 5 and 6. 2010-02-26 15:30:07.906/150.453 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=PacketListenerN, member=4): Scheduled senior member heartbeat is overdue; rejoining multicast group. Next, Member 4 logs a TcpRing failure with Member 2, thus resulting in the termination of Member 2: 2010-02-26 15:30:21.421/163.968 Oracle Coherence GE 3.5.3/465p2 <D4> (thread=Cluster, member=4): TcpRing: Number of socket exceptions exceeded maximum; last was "java.net.SocketTimeoutException: connect timed out"; removing the member: 2 For quick process termination detection, Oracle Coherence utilizes a feature called TcpRing which is a sparse collection of TCP/IP-based connections between different members in the cluster. Each member in the cluster is connected to at least one other member, which (if at all possible) is running on a different physical box. This connection is not used for any data transfer, only heartbeat communications are sent once a second per each link. If a certain number of exceptions are thrown while trying to re-establish a connection, the member throwing the exceptions is removed from the cluster. Member 5 logs a packet timeout with Member 3 and cites witnesses Members 4 and 6: 2010-02-26 15:30:29.791/165.037 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=PacketPublisher, member=5): Timeout while delivering a packet; requesting the departure confirmation for Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer) by MemberSet(Size=2, BitSetCount=2 Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) ) 2010-02-26 15:30:29.798/165.044 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=Cluster, member=5): Member departure confirmed by MemberSet(Size=2, BitSetCount=2 Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) Member(Id=6, Timestamp=2010-02-26 15:27:58.635, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) ); removing Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer) Eventually we are left with two distinct clusters consisting of Members 1, 2, 3 and Members 4, 5, 6, respectively. In the latter cluster, Member 4 is promoted to senior member. The connection between the two switches is restored at 15:33. Upon the restoration of the connection, the cluster members immediately receive cluster heartbeats from the two senior members. In the case of Members 1, 2, and 3, the following is logged: 2010-02-26 15:33:14.970/369.066 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=Cluster, member=1): The member formerly known as Member(Id=4, Timestamp=2010-02-26 15:30:35.341, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) has been forcefully evicted from the cluster, but continues to emit a cluster heartbeat; henceforth, the member will be shunned and its messages will be ignored. Likewise for Members 4, 5, and 6: 2010-02-26 15:33:14.343/336.890 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=Cluster, member=4): The member formerly known as Member(Id=1, Timestamp=2010-02-26 15:30:31.64, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) has been forcefully evicted from the cluster, but continues to emit a cluster heartbeat; henceforth, the member will be shunned and its messages will be ignored. This message indicates that a senior heartbeat is being received from members that were previously removed from the cluster, in other words, something that should not be possible. For this reason, the recipients of these messages will initially ignore them. After several iterations of these messages, the existence of multiple clusters is acknowledged, thus triggering the panic protocol to reconcile this situation. When the presence of more than one cluster (i.e. Split-Brain) is detected by a Coherence member, the panic protocol is invoked in order to resolve the conflicting clusters and consolidate into a single cluster. The protocol consists of the removal of smaller clusters until there is one cluster remaining. In the case of equal size clusters, the one with the older Senior Member will survive. Member 1, being the oldest member, initiates the protocol: 2010-02-26 15:33:45.970/400.066 Oracle Coherence GE 3.5.3/465p2 <Warning> (thread=Cluster, member=1): An existence of a cluster island with senior Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) containing 3 nodes have been detected. Since this Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) is the senior of an older cluster island, the panic protocol is being activated to stop the other island's senior and all junior nodes that belong to it. Member 3 receives the panic: 2010-02-26 15:33:45.803/382.336 Oracle Coherence GE 3.5.3/465p2 <Error> (thread=Cluster, member=3): Received panic from senior Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer) caused by Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer) Member 4, the senior member of the younger cluster, receives the kill message from Member 3: 2010-02-26 15:33:44.921/367.468 Oracle Coherence GE 3.5.3/465p2 <Error> (thread=Cluster, member=4): Received a Kill message from a valid Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer); stopping cluster service. In turn, Member 4 requests the departure of its junior members 5 and 6: 2010-02-26 15:33:44.921/367.468 Oracle Coherence GE 3.5.3/465p2 <Error> (thread=Cluster, member=4): Received a Kill message from a valid Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer); stopping cluster service. 2010-02-26 15:33:43.343/349.015 Oracle Coherence GE 3.5.3/465p2 <Error> (thread=Cluster, member=6): Received a Kill message from a valid Member(Id=4, Timestamp=2010-02-26 15:27:39.574, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer); stopping cluster service. Once Members 4, 5, and 6 restart, they rejoin the original cluster with senior member 1. The log below is from Member 4. Note that it receives a different member id when it rejoins the cluster. 2010-02-26 15:33:44.921/367.468 Oracle Coherence GE 3.5.3/465p2 <Error> (thread=Cluster, member=4): Received a Kill message from a valid Member(Id=3, Timestamp=2010-02-26 15:27:24.892, Address=10.149.155.236:8088, MachineId=1260, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:32459, Role=CoherenceServer); stopping cluster service. 2010-02-26 15:33:46.921/369.468 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Service Cluster left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Invocation:InvocationService, member=4): Service InvocationService left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=OptimisticCache, member=4): Service OptimisticCache left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=ReplicatedCache, member=4): Service ReplicatedCache left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=DistributedCache, member=4): Service DistributedCache left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Invocation:Management, member=4): Service Management left the cluster 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member 6 left service Management with senior member 5 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member 6 left service DistributedCache with senior member 5 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member 6 left service ReplicatedCache with senior member 5 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member 6 left service OptimisticCache with senior member 5 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member 6 left service InvocationService with senior member 5 2010-02-26 15:33:47.046/369.593 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=4): Member(Id=6, Timestamp=2010-02-26 15:33:47.046, Address=10.149.155.78:8088, MachineId=1102, Location=process:228, Role=CoherenceServer) left Cluster with senior member 4 2010-02-26 15:33:49.218/371.765 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=main, member=n/a): Restarting cluster 2010-02-26 15:33:49.421/371.968 Oracle Coherence GE 3.5.3/465p2 <D5> (thread=Cluster, member=n/a): Service Cluster joined the cluster with senior service member n/a 2010-02-26 15:33:49.625/372.172 Oracle Coherence GE 3.5.3/465p2 <Info> (thread=Cluster, member=n/a): This Member(Id=5, Timestamp=2010-02-26 15:33:50.499, Address=10.149.155.75:8088, MachineId=1099, Location=process:800, Role=CoherenceServer, Edition=Grid Edition, Mode=Development, CpuCount=2, SocketCount=1) joined cluster "cluster:0xDDEB" with senior Member(Id=1, Timestamp=2010-02-26 15:27:06.931, Address=10.149.155.76:8088, MachineId=1100, Location=site:usdhcp.oraclecorp.com,machine:dhcp-burlington6-4fl-east-10-149,process:511, Role=CoherenceServer, Edition=Grid Edition, Mode=Development, CpuCount=2, SocketCount=2) Cool isn't it?

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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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[MINI HOW-TO] Change the Default Color Scheme in Office 2010

- by Mysticgeek

Like in Office 2007 the default color scheme for 2010 is blue. If you are not a fan of it, here we show you how to change it to silver or black. In this example we are using Microsoft Word, but it works the same way in Excel, Outlook, and PowerPoint as well. Once you change the color scheme in one Office application, it will change it for all of the other apps in the suite. Change Color Scheme To change the color scheme click on the File tab to access Backstage View and click on Options. In Word Options the General section should open by default…use the dropdown menu next to Color Scheme to change it to Silver, Blue, or Black then click OK. Here is what Black looks like…who knows why Microsoft decided to leave the blue around the edges. This is the default Blue color scheme… And finally we take a look at the Silver color scheme in Excel… That is all there is to it! It would be nice if they would incorporate other color schemes to Office 2010, as some of you may not be happy with only three choices. If you’re using Office 2007 check out our article on how to change the color scheme in it. Also, The Geek has a cool article on how to set the Color Scheme of Office 2007 with a quick registry hack. Similar Articles Productive Geek Tips Set the Office 2007 Color Scheme With a Quick Registry HackChange The Default Color Scheme In Office 2007Maximize Space by "Auto-Hiding" the Ribbon in Office 2007How To Personalize the Windows Command PromptOrganize & Group Your Tabs in Firefox the Easy Way TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Xobni Plus for Outlook All My Movies 5.9 CloudBerry Online Backup 1.5 for Windows Home Server Snagit 10 2010 World Cup Schedule Boot Snooze – Reboot and then Standby or Hibernate Customize Everything Related to Dates, Times, Currency and Measurement in Windows 7 Google Earth replacement Icon (Icons we like) Build Great Charts in Excel with Chart Advisor tinysong gives a shortened URL for you to post on Twitter (or anywhere)

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Chicago SQL Saturday

- by Johnm

This past Saturday, April 17, 2010, I journeyed North to the great city of Chicago for some SQL Server fun, learning and fellowship. The Chicago edition of this grassroots phenomenon was the 31st scheduled SQL Saturday since the program's birth in late 2007. The Chicago SQL Saturday consisted of four tracks with eight sessions each and was a very energetic and fast paced day for the 300+/- SQL Server enthusiasts in attendance. The speaker line up included national notables such as Kevin Kline, Brent Ozar, and Brad McGehee. My hometown of Indianapolis was well represented in the speaker line up with Arie Jones, Aaron King and Derek Comingore. The day began with a very humorous keynote by Kevin Kline and Brent Ozar who emphasized the importance of community events such as SQL Saturday and the monthly user group meetings. They also brilliantly included the impact that getting involved in the SQL community through social media can have on your professional career. My approach to the day was to try to experience as much of the event as I could, so there were very few sessions that I attended for their full duration. I leaped from session to session like a bumble bee, gleaning bits of nectar from each session. Amid these leaps I took the opportunity to briefly chat with some of the in-the-queue speakers as well as other attendees that wondered the hallways. I especially enjoyed a great discussion with Devin Knight about his plans regarding the upcoming Jacksonville SQL Saturday as well as an interesting SQL interpretation of the Iron Chef, which I think would catch on like wild-fire. There were two sessions that stood out as exceptional. So much so that I could not pull myself away: Kevin Kline presented on "SQL Server Internals and Architecture". This session could have been classified as one that is intended for the beginner. Kevin even personally warned me of such as I entered the room. I am a believer in revisiting the basics regardless of the level of your mastery, so I entered into this session in that spirit. It was a very clear and precise presentation. Masterfully illustrated and demonstrated. Brad McGehee presented on "How and When to Use Indexed Views". This was a topic that I was recently exploring and was considering to for use in an integration project. Brad effectively communicated the complexity of this feature and what is involved to gain their full benefit. It was clear at the conclusion of this session that it was not the right feature for my specific needs. Overall, the event was a great success. The use of volunteers, from an attendee's perspective was masterful. The only recommendation that I would have for the next Chicago SQL Saturday would be to include more time in between sessions to permit some level of networking among the attendees, one-on-one questions for speakers and visits to the sponsor booths. Congratulations to Wendy Pastrick, Ted Krueger, and Aaron Lowe for their efforts and a very successful SQL Saturday!

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Need help fixing a strange path error in bash

- by Evan

UPDATE Ok, I found some errors in the path which I think I fixed, but now it's not running in any case - which for some reason I think is a step forward. Thanks for suggesting the following steps, here is their output: user@computer:~$ echo $PATH /usr/share/fsl/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/bin:/usr/bin:/bin:/usr/bin/X11:/usr/games:/usr/local/matlab/bin:/usr/local/VoxBo/bin:/usr/local/itt/idl64/bin:/usr/local/afni/bin/:/usr/local/mricron:/usr/lib/voxbo/bin:/home/user/folder:/usr/local/bin:/usr/bin:/bin:/usr/bin/X11/:/usr/games/:/usr/local/matlab/bin:/usr/local/VoxBo/bin/:/usr/local/itt/idl64/bin:/usr/local/afni/bin/:/usr/local/mricron/ user@computer:~$ typeset -p PATH declare -x PATH="/usr/share/fsl/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/bin:/usr/bin:/bin:/usr/bin/X11:/usr/games:/usr/local/matlab/bin:/usr/local/VoxBo/bin:/usr/local/itt/idl64/bin:/usr/local/afni/bin/:/usr/local/mricron:/usr/lib/voxbo/bin:/home/user/folder:/usr/local/bin:/usr/bin:/bin:/usr/bin/X11/:/usr/games/:/usr/local/matlab/bin:/usr/local/VoxBo/bin/:/usr/local/itt/idl64/bin:/usr/local/afni/bin/:/usr/local/mricron/" user@computer:~$ type app1 app1 is /home/user/folder/app1 user@computer:~$ type app2 app2 is /home/user/folder/app2 user@computer:~$ app1 bash: /home/user/folder/app1: No such file or directory user@computer:~$ app2 bash: /home/user/folder/app2: No such file or directory user@computer:~$ /home/user/folder/app1 bash: /home/user/folder/app1: No such file or directory user@computer:~$ /home/user/folder/app2 bash: /home/user/folder/app2: No such file or directory user@computer:~$ cd /home/user/folder user@computer:~/folder$ app1 bash: /home/user/folder/app1: No such file or directory user@computer:~/folder$ ./app1 bash: ./app1: No such file or directory user@computer:~/folder$ ./app2 bash: ./app2: No such file or directory user@computer:~/folder$ ls -l total 29384 -rwxr-xr-x 1 user user 14949776 2011-02-03 11:09 app1 -rwxr-xr-x 1 user user 15137300 2011-02-03 11:10 app2 user@computer:~/folder$ Thanks for everyone's input! ORIGINAL QUESTION I have two executable files I downloaded and am trying to add to the path. They are located in /home/user/folder and the specific files are /home/user/folder/app1 /home/user/folder/app2 Both app1 and app2 have the executable flag set to all (user, group, other). I can execute the files if I am in /home/user/folder and I execute these commands ./app1 ./app2 However I can't run them from elsewhere. I added this line to my .profile PATH="$PATH:/home/user/folder" and then sourced the path with . /home/user/.profile and I can see app1 and app2 when I use command completion (pressing tab). However here is what happens when I try to run app1 or app2 with the following commands (the following only shows 'app1' but the same is true of 'app2') user@comp:~$ app1 -bash: app1: command not found user@comp:~$ /home/user/folder/app1 -bash: app1: command not found user@comp:~/folder$ ./app1 (program runs) I'm stumped :), I must have missed something simple. Thanks for your help!!

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Evolution Of High Definition TV Viewing

- by Gopinath

The following guest post is written by Rob, who is also blogging on entertainment technology topics on iwantsky.com Gone are the days when you need to squint to be able to see the emotions on the faces of Humphrey Bogart and Ingrid Bergman as the lovers bid each other adieu in the classic film Casablanca. These days, watching an ordinary ant painstakingly carry a leaf in Animal Planet can be an exhilarating experience as you get to see not only the slightest movement but also the demarcation line between the insect’s head, thorax and abdomen. The crystal clear imagery was made possible by the sharp minds and the tinkering hands of the scientists that have designed the modern world’s HDTV. What is HDTV and what makes people so agog to have this new innovation in TV watching? HDTV stands for High Definition TV. Television viewing has indeed made a big leap. From the grainy black and whites, TV viewing had moved to colored TVs, progressed to SD TVs and now to HDTV. HDTV is the emerging trend in TV viewing as it delivers bigger and clearer pictures and better audio. Viewers can have a cinema-like TV viewing experience right in the comforts of their own home. With HDTV the viewer is allowed to have a better viewing range. With Standard (SD) TV, the viewer has to be at a distance that is from 3 to 6 times the size of the screen. HDTV allows the viewer to enjoy sharper and clearer images as it is possible to sit at a distance that is 1.5 or 3 times the size of the screen without noticing any image pixilation. Although HDTV appears to be a fairly new innovation, this system has actually existed in various forms years ago. Development of the HDTV was started in Europe as early as 1940s. However, the NTSC and the PAL/SECAM, the two analog TV standards became dominant and became popular worldwide. The analog TV was replaced by the digital TV platform in the 1990s. Even during the analog era, attempts have been made to develop HDTV. Japan has come out with MUSE system. However, due to channel bandwidth requirement concerns, the program was shelved. The entry of four organizations into the HDTV market spurred the development of a beneficial coalition. The AT&T, ATRC, MIT and Zenith HDTV combined forces. In 1993, a Grand Alliance was formed. This group is composed of researchers and HDTV manufacturers. A common standard for the broadcast system of HDTV was developed. In 1995, the system was tested and found successful. With the higher screen resolution of HDTV, viewing has never been more enjoyable. [Image courtesy: samsung] This article titled,Evolution Of High Definition TV Viewing, was originally published at Tech Dreams. Grab our rss feed or fan us on Facebook to get updates from us.

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It’s time that you ought to know what you don’t know

- by fatherjack

There is a famous quote about unknown unknowns and known knowns and so on but I’ll let you review that if you are interested. What I am worried about is that there are things going on in your environment that you ought to know about, indeed you have asked to be told about but you are not getting the information. When you schedule a SQL Agent job you can set it to send an email to an inbox monitored by someone who needs to know and indeed can do something about it. However, what happens if the email process isnt successful? Check your servers with this: USE [msdb] GO /* This code selects the top 10 most recent SQLAgent jobs that failed to complete successfully and where the email notification failed too. Jonathan Allen Jul 2012 */ DECLARE @Date DATETIME SELECT @Date = DATEADD(d, DATEDIFF(d, '19000101', GETDATE()) - 1, '19000101') SELECT TOP 10 [s].[name] , [sjh].[step_name] , [sjh].[sql_message_id] , [sjh].[sql_severity] , [sjh].[message] , [sjh].[run_date] , [sjh].[run_time] , [sjh].[run_duration] , [sjh].[operator_id_emailed] , [sjh].[operator_id_netsent] , [sjh].[operator_id_paged] , [sjh].[retries_attempted] FROM [dbo].[sysjobhistory] AS sjh INNER JOIN [dbo].[sysjobs] AS s ON [sjh].[job_id] = [s].[job_id] WHERE EXISTS ( SELECT * FROM [dbo].[sysjobs] AS s INNER JOIN [dbo].[sysjobhistory] AS s2 ON [s].[job_id] = [s2].[job_id] WHERE [sjh].[job_id] = [s2].[job_id] AND [s2].[message] LIKE '%failed to notify%' AND CONVERT(DATETIME, CONVERT(VARCHAR(15), [s2].[run_date])) >= @date AND [s2].[run_status] = 0 ) AND sjh.[run_status] = 0 AND sjh.[step_id] != 0 AND CONVERT(DATETIME, CONVERT(VARCHAR(15), [run_date])) >= @date ORDER BY [sjh].[run_date] DESC , [sjh].[run_time] DESC go USE [msdb] go /* This code summarises details of SQLAgent jobs that failed to complete successfully and where the email notification failed too. Jonathan Allen Jul 2012 */ DECLARE @Date DATETIME SELECT @Date = DATEADD(d, DATEDIFF(d, '19000101', GETDATE()) - 1, '19000101') SELECT [s].name , [s2].[step_id] , CONVERT(DATETIME, CONVERT(VARCHAR(15), [s2].[run_date])) AS [rundate] , COUNT(*) AS [execution count] FROM [dbo].[sysjobs] AS s INNER JOIN [dbo].[sysjobhistory] AS s2 ON [s].[job_id] = [s2].[job_id] WHERE [s2].[message] LIKE '%failed to notify%' AND CONVERT(DATETIME, CONVERT(VARCHAR(15), [s2].[run_date])) >= @date AND [s2].[run_status] = 0 GROUP BY name , [s2].[step_id] , [s2].[run_date] ORDER BY [s2].[run_dateDESC] These two result sets will show if there are any SQL Agent jobs that have run on your servers that failed and failed to successfully email about the failure. I hope it’s of use to you. Disclaimer – Jonathan is a Friend of Red Gate and as such, whenever they are discussed, will have a generally positive disposition towards Red Gate tools. Other tools are often available and you should always try others before you come back and buy the Red Gate ones. All code in this blog is provided “as is” and no guarantee, warranty or accuracy is applicable or inferred, run the code on a test server and be sure to understand it before you run it on a server that means a lot to you or your manager.

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