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Wednesday, December 5, 2012

Solution: UNABLE TO READ DATA FROM THE TRANSPORT CONNECTION: NET_IO_CONNECTIONCLOSED.

Server Error in '/' Application.


Unable to read data from the transport connection: net_io_connectionclosed.


Description: An unhandled exception occurred during the execution of the current web request. Please review the stack trace for more information about the error and where it originated in the code. 

Exception Details: System.IO.IOException: Unable to read data from the transport connection: net_io_connectionclosed.

Source Error: 

 

Line 73:           //  client.Credentials = aCred;

Line 74:             MailMessage message = ComposeEmailMessage(fromName, fromEmail, toAddressList, subject, emailBody, isHTML);

Line 75:             client.Send(message);

Line 76:         }

Line 77:         catch (Exception e)


Source File: c:\inetpub\vhosts\aspdotnet-rajkumar.com\httpdocs\landing\Cont.aspx.cs    Line: 75 

Stack Trace: 

 

[IOException: Unable to read data from the transport connection: net_io_connectionclosed.]

   System.Net.Mail.SmtpReplyReaderFactory.ProcessRead(Byte[] buffer, Int32 offset, Int32 read, Boolean readLine) +1063455

   System.Net.Mail.SmtpReplyReaderFactory.ReadLines(SmtpReplyReader caller, Boolean oneLine) +248

   System.Net.Mail.SmtpReplyReaderFactory.ReadLine(SmtpReplyReader caller) +16

   System.Net.Mail.SmtpConnection.GetConnection(String host, Int32 port) +642

   System.Net.Mail.SmtpTransport.GetConnection(String host, Int32 port) +159

   System.Net.Mail.SmtpClient.GetConnection() +35

   System.Net.Mail.SmtpClient.Send(MailMessage message) +1213

 

[SmtpException: Failure sending mail.]

   System.Net.Mail.SmtpClient.Send(MailMessage message) +1531

   Contactus.SendSimpleEmail(String toAddressList, String subject, String emailBody, Boolean isHTML) in c:\inetpub\vhosts\aspdotnet-rajkumar.com\httpdocs\landing\Cont.aspx.cs:75

 

[Exception: Error sending email from info@aspdotnet-rajkumar.com]

   Contactus.SendSimpleEmail(String toAddressList, String subject, String emailBody, Boolean isHTML) in c:\inetpub\vhosts\aspdotnet-rajkumar.com\httpdocs\landing\Cont.aspx.cs:79

   Contactus.Sendadminmail(String name, String email, String Mobile) in c:\inetpub\vhosts\aspdotnet-rajkumar.com\httpdocs\landing\Cont.aspx.cs:34

   Contactus.Submit_Click1(Object sender, EventArgs e) in c:\inetpub\vhosts\aspdotnet-rajkumar.com\httpdocs\landing\Cont.aspx.cs:21

   System.Web.UI.WebControls.Button.OnClick(EventArgs e) +111

   System.Web.UI.WebControls.Button.RaisePostBackEvent(String eventArgument) +110

   System.Web.UI.WebControls.Button.System.Web.UI.IPostBackEventHandler.RaisePostBackEvent(String eventArgument) +10

   System.Web.UI.Page.RaisePostBackEvent(IPostBackEventHandler sourceControl, String eventArgument) +13

   System.Web.UI.Page.RaisePostBackEvent(NameValueCollection postData) +36

   System.Web.UI.Page.ProcessRequestMain(Boolean includeStagesBeforeAsyncPoint, Boolean includeStagesAfterAsyncPoint) +1565

 


Version Information: Microsoft .NET Framework Version:2.0.50727.5448; ASP.NET Version:2.0.50727.5456

Solve: WebException , the remote name could not be resolved

Server Error in '/' Application.


The remote name could not be resolved: 'mail.aspdotnet-rajkumar.com/'

Description: An unhandled exception occurred during the execution of the current web request. Please review the stack trace for more information about the error and where it originated in the code. 

Exception Details: System.Net.WebException: The remote name could not be resolved: 'mail.aspdotnet-rajkumar.com/'

Source Error: 

An unhandled exception was generated during the execution of the current web request. Information regarding the origin and location of the exception can be identified using the exception stack trace below.


Stack Trace: 

 

[WebException: The remote name could not be resolved: 'mail.aspdotnet-rajkumar.com/']

   System.Net.ServicePoint.GetConnection(PooledStream PooledStream, Object owner, Boolean async, IPAddress& address, Socket& abortSocket, Socket& abortSocket6, Int32 timeout) +5501831

   System.Net.PooledStream.Activate(Object owningObject, Boolean async, Int32 timeout, GeneralAsyncDelegate asyncCallback) +202

   System.Net.PooledStream.Activate(Object owningObject, GeneralAsyncDelegate asyncCallback) +21

   System.Net.ConnectionPool.GetConnection(Object owningObject, GeneralAsyncDelegate asyncCallback, Int32 creationTimeout) +332

   System.Net.Mail.SmtpConnection.GetConnection(String host, Int32 port) +160

   System.Net.Mail.SmtpTransport.GetConnection(String host, Int32 port) +159

   System.Net.Mail.SmtpClient.GetConnection() +35

   System.Net.Mail.SmtpClient.Send(MailMessage message) +1213

 

[SmtpException: Failure sending mail.]

   System.Net.Mail.SmtpClient.Send(MailMessage message) +1531

   Contactus.SendSimpleEmail(String toAddressList, String subject, String emailBody, Boolean isHTML) +103

 

[Exception: Error sending email from admin@aspdotnet-rajkumar.com/]

   Contactus.SendSimpleEmail(String toAddressList, String subject, String emailBody, Boolean isHTML) +161

   Contactus.Sendadminmail(String name, String email, String Mobile) +222

   Contactus.Submit_Click1(Object sender, EventArgs e) +83

   System.Web.UI.WebControls.Button.OnClick(EventArgs e) +111

   System.Web.UI.WebControls.Button.RaisePostBackEvent(String eventArgument) +110

   System.Web.UI.WebControls.Button.System.Web.UI.IPostBackEventHandler.RaisePostBackEvent(String eventArgument) +10

   System.Web.UI.Page.RaisePostBackEvent(IPostBackEventHandler sourceControl, String eventArgument) +13

   System.Web.UI.Page.RaisePostBackEvent(NameValueCollection postData) +36

   System.Web.UI.Page.ProcessRequestMain(Boolean includeStagesBeforeAsyncPoint, Boolean includeStagesAfterAsyncPoint) +1565

 


Version Information: Microsoft .NET Framework Version:2.0.50727.5448; ASP.NET Version:2.0.50727.5456

How to solve this error Bad sequence of commands. The server response was: This mail server requires authentication

Server Error in '/' Application.


Bad sequence of commands. The server response was: This mail server requires authentication when attempting to send to a non-local e-mail address. Please check your mail client settings or contact your administrator to verify that the domain or address is defined for this server.
Description: An unhandled exception occurred during the execution of the current web request. Please review the stack trace for more information about the error and where it originated in the code.

Exception Details: System.Net.Mail.SmtpException: Bad sequence of commands. The server response was: This mail server requires authentication when attempting to send to a non-local e-mail address. Please check your mail client settings or contact your administrator to verify that the domain or address is defined for this server.

Source Error: 
An unhandled exception was generated during the execution of the current web request. Information regarding the origin and location of the exception can be identified using the exception stack trace below.

Stack Trace: 

[SmtpException: Bad sequence of commands. The server response was: This mail server requires authentication when attempting to send to a non-local e-mail address. Please check your mail client settings or contact your administrator to verify that the domain or address is defined for this server.]
   System.Net.Mail.RecipientCommand.CheckResponse(SmtpStatusCode statusCode, String response) +1066623
   System.Net.Mail.SmtpTransport.SendMail(MailAddress sender, MailAddressCollection recipients, String deliveryNotify, SmtpFailedRecipientException& exception) +241
   System.Net.Mail.SmtpClient.Send(MailMessage message) +1480
   Contactus.SendSimpleEmail(String toAddressList, String subject, String emailBody, Boolean isHTML) +103

[Exception: Error sending email from admin@aspdotnet-rajkumar.in]
   Contactus.SendSimpleEmail(String toAddressList, String subject, String emailBody, Boolean isHTML) +161
   Contactus.Sendadminmail(String name, String email, String Mobile) +222
   Contactus.Submit_Click1(Object sender, EventArgs e) +83
   System.Web.UI.WebControls.Button.OnClick(EventArgs e) +111
   System.Web.UI.WebControls.Button.RaisePostBackEvent(String eventArgument) +110
   System.Web.UI.WebControls.Button.System.Web.UI.IPostBackEventHandler.RaisePostBackEvent(String eventArgument) +10
   System.Web.UI.Page.RaisePostBackEvent(IPostBackEventHandler sourceControl, String eventArgument) +13
   System.Web.UI.Page.RaisePostBackEvent(NameValueCollection postData) +36
   System.Web.UI.Page.ProcessRequestMain(Boolean includeStagesBeforeAsyncPoint, Boolean includeStagesAfterAsyncPoint) +1565



Version Information: Microsoft .NET Framework Version:2.0.50727.5448; ASP.NET Version:2.0.50727.5456

Tuesday, December 4, 2012

Drop Box рокропрой்рокроЯுрод்родுро╡родு роОрок்рокроЯி ?

Drop Box рокропрой்рокроЯுрод்родுро╡родு роОрок்рокроЯி ?

Simplify Your Life

 

Download Dropbox

 

 

Dropbox роОрой்ро▒ாро▓் роОрой்рой ? роЗродройை роиாроо் роироо் роХроо்рок்ропூроЯ்роЯро░ிро▓் роЗрой்ро╕்роЯாро▓் роЪெроп்ро╡родாро▓் роироороХ்роХு роОрой்рой рокропрой் ? роЗродройை роиாроо் роироо் роХроо்рок்ропூроЯ்роЯро░ிро▓் роЗрой்ро╕்роЯாро▓் роЪெроп்родு рокропрой்рокроЯுрод்родுро╡родு роОрок்рокроЯி ? роОрой்рокродை рокро▒்ро▒ி роиாроо் роЗрои்род рокாроЯрод்родிро▓் родெро│ிро╡ாроХ рокாро░்рок்рокோроо்.....

 

роЖро░роо்рок роХாро▓род்родிро▓் роХроо்рок்ропூроЯ்роЯро░் рокропрой்рокроЯுрод்родுрокро╡ро░்роХро│் родроЩ்роХро│ுроЯைроп Personal File роХро│ை роХроо்рок்ропூроЯ்роЯро░ிро▓் роЪேрооிрод்родு ро╡ைрод்родு рокропрой்рокроЯுрод்родுроо்рокொро┤ுродு роЕрои்род рокைро▓்роХро│ை роЗрой்ройொро░ு роХாрок்рокி роОроЯுрод்родு ро╡ைрод்родுроХ்роХொрог்роЯு родேро╡ைропாрой роиேро░роЩ்роХро│ிро▓் родроо் роХроо்рок்ропூроЯ்роЯро░ிро▓ோ роЕро▓்ро▓родு  ро╡ேро▒ு роХроо்рок்ропூроЯ்роЯро░ிро▓ோ рокропрой்рокроЯுрод்родுро╡родро▒்роХு Floppy Disk роОрой்ро▒ роТрой்ро▒ை рокропрой்рокроЯுрод்родிройாро░்роХро│். 

 

                                                                                                                    Floppy Disk ( 1.44 MB Capacity )

                        

роЗрои்род Floppy Disk рой் рооொрод்род роЕро│ро╡ு роОро╡்ро╡ро│ро╡ு родெро░ிропுрооா ? 1.44 MB роороЯ்роЯுроо் родாрой். роЗрои்род 1.44 MB роЕро│ро╡ிро▓் родாрой் роиாроо் роироо் рокைро▓்роХро│ை роЪேрооிроХ்роХ рооுроЯிропுроо். 2 MB роЕро│ро╡ிро▓் роЙро│்ро│ роТро░ு рокைро▓ை роиாроо் роЗрои்род Floppy ро▓் роЪேрооிроХ்роХ рооுроЯிропாродு. роЕрок்рокроЯி роОрой்ройродாрой் роЗродிро▓் роиாроо் роЪேрооிрок்рокродு ? Windows 95 рооро▒்ро▒ுроо் 98 рокропрой்рокроЯுрод்родுроо் роХாро▓род்родிро▓் роиாроо் роЪேрооிроХ்роХ роиிройைрок்рокродு роЖроЯிропோ роЕро▓்ро▓родு ро╡ீроЯிропோ рокைро▓்роХро│ை роЕро▓்ро▓. Microsoft Excel рооро▒்ро▒ுроо் Word File роХро│ை роороЯ்роЯுроо்родாрой். роЗрои்род рооைроХ்ро░ோроЪாрок்роЯ் роЖрокீро╕் рокைро▓்роХро│் роТро╡்ро╡ொрой்ро▒ுроо் 50 KB, 200 KB, 300 KB роОрой்ро▒ роЕро│ро╡ிро▓்родாрой் роЗро░ுроХ்роХுроо். роЗрои்род рокைро▓்роХро│ிро▓் 10 роЕро▓்ро▓родு 15 рокைро▓்роХро│ை роиாроо் роЗрои்род роТро░ு Floppy Disk ро▓் рооொрод்родрооாроХ роЪேрооிрод்родு ро╡ைрод்родு Backup Disk роЖроХ роЗродройை рокропрой்рокроЯுрод்родிроХ்роХொро│்ро╡ோроо்.

 

роЕрок்рокроЯி роЗро░ுрои்род роХாро▓роо் рооாро▒ிрок்рокோроп் роЗрок்рокொро┤ுродு USB Pen Drive рокропрой்рокроЯுрод்родுроо் роХாро▓роо் ро╡рои்родுро╡ிроЯ்роЯродு.

 


 

роЗрои்род Pen Drive 256 MB, 512 MB, 1 GB роОрой்ро▒ு роЖро░роо்рокிрод்родு роЗрок்рокொро┤ுродு 8GB, 16 GB, 32 GB, 64 GB, 128 GB роОрой роЕроЪுро░ ро╡ேроХрод்родிро▓் роЕродрой் ро╡ро│ро░்роЪ்роЪி рооேро▓ே рокோроп்роХ்роХொрог்роЯிро░ுроХ்роХிро▒родு.

 

рооைроХ்ро░ோроЪாрок்роЯ் роЖрокீро╕் рокைро▓்роХро│ை роороЯ்роЯுроо் роХாрок்рокி роОроЯுрод்родு рокрод்родிро░рокроЯுрод்родி ро╡ைрод்родுроХ்роХொрог்роЯிро░ுрои்род роиாроо் роЗрок்рокொро┤ுродு Audio, Video, Digital Photos, Software рокோрой்ро▒ро╡ро▒்ро▒ைропுроо் роХாрок்рокி роОроЯுрод்родு рокрод்родிро░ рокроЯுрод்родி ро╡ைроХ்роХுроо் роХாро▓род்родிро▒்роХு ро╡рои்родுро╡ிроЯ்роЯோроо்.  роЕродройாро▓் родாрой் роироороХ்роХு роЗрок்рокொро┤ுродு 16 GB Pen Drive роХைропிро▓் роЗро░ுрои்родாро▓் роХூроЯ рокோродாродு роОрой்рокродுрокோро▓் роЖроХிро╡ிроЯ்роЯродு. роЪро░ி Drop Box роР рокро▒்ро▒ி роЪொро▓்ро▓ாрооро▓் ро╡ேро▒ு роОродைропோ роиாрой் роПрой் роЪொро▓்ро▓ிроХ்роХொрог்роЯு роЗро░ுроХ்роХிро▒ேрой் роОрой்ро▒ு роиீроЩ்роХро│் роиிройைрок்рокродு рокுро░ிроХிро▒родு. 

 

роЗройி роЗрои்род Drop Box роХродைроХ்роХு ро╡ро░ுро╡ோроо்....... роЗрои்род Dropbox рооெрой்рокொро░ுро│ைродропாро░ிрод்родро╡ро░்роХро│் роОрой்рой роЪொро▓்роХிро▒ாро░்роХро│் роОрой்ро▒ு родெро░ிропுрооா ? роиீроЩ்роХро│் роЙроЩ்роХро│் роХроо்рок்ропூроЯ்роЯро░ிро▓் рокропрой்рокроЯுрод்родுроо் рокைро▓்роХро│் роОродுро╡ாройாро▓ுроо் роЕродройை роиீроЩ்роХро│் рооро▒்ро▒ роЗроЯроЩ்роХро│ிро▓் рокропрой்рокроЯுрод்род Pen Drive ро╡ிро▓் роЕродройை роОроЯுрод்родு роЪெро▓்ро▓ ро╡ேрог்роЯிроп роЕро╡роЪிропроо் роЗро▓்ро▓ை. роЕродро▒்роХு рокродிро▓ாроХ роОроЩ்роХро│் Drop Box роР рокропрой்рокроЯுрод்родுроЩ்роХро│் роОрой்ро▒ு роЪொро▓்роХிро▒ாро░்роХро│். 

 

роЗродு роОрок்рокроЯி роЪாрод்родிропрооாроХுроо் роОрой்ро▒ு роХேроЯ்роХிро▒ீро░்роХро│ா ? роЗрой்ро▒ைроп роиро╡ீрой ропுроХрод்родிро▓் роЗрог்роЯெро░்роиெроЯ் роХройெроХ்роЪрой் роЗро▓்ро▓ாрод роХроо்рок்ропூроЯ்роЯро░் роОродுро╡ுроо் роЗро▓்ро▓ை. роХроо்рок்ропூроЯ்роЯро░் рокропрой்рокроЯுрод்родுроо் роТро╡்ро╡ொро░ுро╡ро░ுроо் роЗрог்роЯெро░் роиெроЯ் рокропрой்рокроЯுрод்родுрокро╡ро░ாроХро╡ே роЗро░ுроХ்роХிро▒ாро░்роХро│். роОройро╡ே роЗрог்роЯெро░் роиெроЯ் роЙроЩ்роХро│் роХроо்рок்ропூроЯ்роЯро░ிро▓் роЗро░ுрои்родாро▓் роороЯ்роЯுрооே роЗродு роЪாрод்родிропрооாроХுроо். роЗрог்роЯெро░் роиெроЯ் роЗро▓்ро▓ாродро╡ро░்роХро│் родроЩ்роХро│் рокைро▓்роХро│ை Pen Drive ро▓் роХாрок்рокி роЪெроп்родு ро╡ைрод்родு роороЯ்роЯுроо்родாрой் рокропрой்рокроЯுрод்род рооுроЯிропுроо். ро╡ேро▒ு ро╡ро┤ி роЗро▓்ро▓ை.

 

роиீроЩ்роХро│் роОрок்рокொро┤ுродுроо் роЗрогைроп роЗрогைрок்рокுроЯрой் роЙро│்ро│ роХроо்рок்ропூроЯ்роЯро░ை рокропрой்рокроЯுрод்родுрокро╡ро░ா ? роиீроЩ்роХро│் роороЯ்роЯுрооே роЗройி родொроЯрои்родு рокроЯிроХ்роХро▓ாроо்.....

 

роЙроЩ்роХро│ிроЯроо் Laptop,  Desktop, i phone, i pad роЕро▓்ро▓родு Samsung Galaxy phone, Galaxy Tab, Blackberry  роОрой்ро▒ு рокро▓ рокропрой்рокாроЯ்роЯு роОро▓ெроХ்роЯ்ро░ாройிроХ் роЪாродройроЩ்роХро│் роЗро░ுроХ்роХிро▒родு роОрой ро╡ைрод்родுроХ்роХொро│்ро╡ோроо். 

 

роЗрои்род Drop Box роР роиீроЩ்роХро│் рокропрой்рокроЯுрод்родிройாро▓் роиீроЩ்роХро│் роЙро░ுро╡ாроХ்роХி роЪேрооிроХ்роХுроо் рооைроХ்ро░ோроЪாрок் роЖрокீро╕் рокைро▓்роХро│் роЕро▓்ро▓родு роЗрогைропрод்родிро▓் роЗро░ுрои்родு роЯро╡ுрог்ро▓ோроЯு роЪெроп்родு роЪேрооிроХ்роХுроо் Audio, Video рооро▒்ро▒ுроо் Software рокோрой்ро▒ рокைро▓்роХро│் роТро░ே роиேро░род்родிро▓் роЗрои்род роЕройைрод்родு роОро▓ெроХ்роЯ்ро░ாройிроХ் роЪாродройроЩ்роХро│ிро▓ுроо் роЪேрооிроХ்роХрок்рокроЯுроо். роЕродு роОрок்рокроЯி

 

рооுродро▓ிро▓் роиீроЩ்роХро│் роЗрои்род Drop Box роР  www.dropbox.com роОрой்ро▒ роЗрогைроп родро│род்родிро▓் роЗро░ுрои்родு роЯро╡ுрог்ро▓ோроЯு роЪெроп்ропுроЩ்роХро│்.

 

рокிро▒роХு роЗродройை роиீроЩ்роХро│் роЙроЩ்роХро│் роХроо்ропூроЯ்роЯро░ிро▓் роЗрой்ро╕்роЯாро▓் роЪெроп்родுроХொро│்ро│ுроЩ்роХро│்.

 

 

 

роЗродройை роЗрой்ро╕்роЯாро▓் роЪெроп்ропுроо்рокொро┤ுродு роЗрои்род роЯிро░ாрок் рокாроХ்ро╕் рооூро▓роо்  роиீроЩ்роХро│் рокுродிродாроХ роТро░ு роХрогроХ்роХை роЙро░ுро╡ாроХ்роХ ро╡ேрог்роЯி роЗро░ுрок்рокродாро▓் I don't have a dropbox account роОрой்ро▒ роЖрок்роЪройை родேро░்рои்родெроЯுрод்родு Next роР роЕро┤ுрод்родுроЩ்роХро│்......... 

 

 

 

роЕроЯுрод்родு ро╡ро░ுроо் роЗрои்род рокроХுродிропிро▓் роЙроЩ்роХро│் рокெропро░் рооро▒்ро▒ுроо் роЙроЩ்роХро│் роИрооெропிро▓் рооுроХро╡ро░ிропை роЪро░ிропாроХ роЯைрок் роЪெроп்родுроХொрог்роЯு роХீро┤ே Terms of Service роР роЯிроХ் роЪெроп்родுроХொрог்роЯு Next роР роЕро┤ுрод்родுроЩ்роХро│்....

 

 

 

роЕроЯுрод்родு ро╡ро░ுроо் роЗрои்род рокроХுродிропிро▓் роиீроЩ்роХро│் роЗро▓ро╡роЪрооாроХ роЯிро░ாрок் рокாроХ்ро╕் роЕроХ்роХро╡ுрог்роЯை роУрок்рокрой் роЪெроп்ро╡родாро▓் 2 GB Free роЖрок்роЪройை родேро░்рои்родெроЯுрод்родு Next роР роЕро┤ுрод்родுроЩ்роХро│்......

 

 

 

роЕроЯுрод்родு роЗрои்род роЯிро░ாрок் рокாроХ்ро╕ை роиீроЩ்роХро│் роОрок்рокроЯி рокропрой்рокроЯுрод்родро╡ேрог்роЯுроо் роОрой்ро▒ роЪிро▓ роЯிрок்ро╕்роХро│் роХிроЯைроХ்роХுроо் рокроХுродி роЗродு... роЗрои்род роЯிрок்ро╕் родேро╡ை роЗро▓்ро▓ை роОройிро▓் Skip tour роОрой்ро▒ рокроЯ்роЯройை роХிро│ிроХ் роЪெроп்ропுроЩ்роХро│்.....

 

 

 

роЗро▒ுродிропாроХ роиீроЩ்роХро│் роЗрои்род рокроХுродிроХ்роХு ро╡рои்родродுроо் роЗрои்род Finish Button роР роХிро│ிроХ் роЪெроп்родு роЙроЩ்роХро│் Drop Box Installation роР рооுроЯிрод்родுроХ்роХொро│்ро│ுроЩ்роХро│்.........

 

 

 

роЙроЩ்роХро│் роХроо்рок்ропூроЯ்роЯро░ிро▓் роЗрои்род Drop Box роЗрой்ро╕்роЯாро▓் роЖроХி рооுроЯிрои்родродுроо் роЗродுрокோро▓் роТро░ு рокோро▓்роЯро░் роУрок்рокрой் роЖроХுроо். роЗродுродாрой் роЙроЩ்роХро│் роЯிро░ாрок் рокாроХ்ро╕் рокைро▓்роХро│ை роЪேрооிроХ்роХுроо் рокோро▓்роЯро░். роЗродிро▓் Drop Box рооூро▓роо் родாройாроХ роЪேрооிроХ்роХрок்рокроЯ்роЯ роЗро░рог்роЯு рокோро▓்роЯро░்роХро│் ро╡рои்родிро░ுрок்рокродை роиீроЩ்роХро│் рокாро░்роХ்роХро▓ாроо்...... роЗрои்род роЗро░рог்роЯு рокோро▓்роЯро░ிрой் роХீро┤்рокроХுродிропிро▓ுроо் роЗро░рог்роЯு роиீро▓ роХро▓ро░ிро▓் рокுро│்ро│ிроХро│் роЪроХ்роХро░роо்рокோро▓் роЪுро▒்ро▒ுро╡родை роиீроЩ்роХро│் роХாрогро▓ாроо். роЗрок்рокроЯி роЪроХ்ро░роо் рокோро▓் роЪுро▒்ро▒ுроо் роиேро░род்родிро▓் роЙроЩ்роХро│் роЯிро░ாрок் рокாроХ்ро╕ிро▓் роЗрои்род рокோро▓்роЯро░்роХро│் роЗрог்роЯெро░் роиெроЯ் рооூро▓роо் роЕродрой் роЙро│்ро│ே роЗрогைроХ்роХрок்рокроЯ்роЯ рокைро▓்роХро│ை роЯро╡ுрог்ро▓ோроЯு роЪெроп்родுроХொрог்роЯிро░ுроХ்роХிро▒родு роОрой роЕро░்род்родроо்......

 

 

рокோро▓்роЯро░ிрой் роХீро┤ே роЙро│்ро│ роЕрои்род роЪроХ்роХро░роо்рокோро▓் роЙро│்ро│ роРроХ்роХாрой் роЗроЩ்роХு роХாрог்рокродுрокோро▓் роЯிроХ் роЪெроп்родродுрокோро▓் рооாро▒ிро╡ிроЯ்роЯродு роОрой்ро▒ாро▓் рокைро▓்роХро│் роЪро░ிропாроХ роЯро╡ுрог்ро▓ோроЯு роЖроХிро╡ிроЯ்роЯродு роОрой்ро▒ு роЕро░்род்родроо். роЙроЯройே роЙроЩ்роХро│் роЯெроХ்ро╕்роЯாрок்рокிро▓் роЯைроо் рокроХ்роХрод்родிро▓் роОрод்родройை рокைро▓்роХро│் роЯро╡ுрог்ро▓ோроЯு роЖройродெрой்ро▒ роЪெроп்родி ро╡рои்родுро╡ிроЯுроо்.

 

 

 

роЗрои்род рокோро▓்роЯро░ிро▓் роиீроЩ்роХро│் роЙроЩ்роХро│் роХроо்рок்ропூроЯ்ро░ிро▓் ро╡ேро▒ு роЗроЯрод்родிро▓் роЙро│்ро│ роТро░ு рокோроЯ்роЯோро╡ைропோ роЕро▓்ро▓родு рокைро▓ைропோ роХாрок்рокி роЪெроп்родு роЗроЩ்роХு рокேро╕்роЯ் роЪெроп்роХிро▒ீро░்роХро│் роОрой ро╡ைрод்родுроХ்роХொро│்ро╡ோроо். роЕродுро╡ுроо் рооுрой்рокு роЪொрой்ройрооுро▒ைрок்рокроЯி роЗрог்роЯெро░் роиெроЯ் рооூро▓роо் роЕрок்роЯேроЯ் роЖроХ роЖро░роо்рокிроХ்роХுроо். ( роЗроЩ்роХு роХாрог்рокродுрокோро▓்)

 

 

 

 

 

роЗро▒ுродிропாроХ роЗроЩ்роХு роХாрог்рокродுрокோро▓் роЕродрой் роХீро┤ே роЯிроХ் ро╡рои்родுро╡ிроЯுроо். 

 

 

 

 

роЗрои்род рооுро▒ைрок்рокроЯி роиீроЩ்роХро│் роЙроЩ்роХро│் рокைро▓்роХро│ை (Audio, Video, Photo, Software рокோрой்ро▒ро╡ро▒்ро▒ை) 2 GB роЕро│ро╡ிро▓் роЗрои்род Drop Box роЕроХ்роХро╡ுрог்роЯ் рооூро▓рооாроХ роЪேрооிрод்родுроХ்роХொро│்ро│ро▓ாроо். 2 GB роХ்роХு рооேро▓் роЪேрооிроХ்роХ ро╡ேрог்роЯுрооெрой்ро▒ாро▓் роЗрои்род роЕроХ்роХро╡ுрог்роЯுроХ்роХு рокрогроо் роЪெро┤ுрод்родро╡ேрог்роЯுроо். рокрогроо் роЪெро┤ுрод்родாрооро▓் роЗрои்род роЕроХ்роХро╡ுрог்роЯிро▓் роиீроЩ்роХро│் роХூроЯுродро▓் GB роР рокெро▒ ро╡ேро▒ு роТро░ு ро╡ро┤ி роЙрог்роЯு. роЕродாро╡родு роиீроЩ்роХро│் роЗрои்род роЯிро░ாрок் рокாроХ்ро╕் рооூро▓роо் роЙроЩ்роХро│் роирог்рокро░்роХро│ுроХ்роХு рокைро▓்роХро│ை Sharing роЪெроп்ропро▓ாроо். роЕрок்рокроЯி Sharing роЪெроп்ропுроо்рокொро┤ுродு роЙроЩ்роХро│் роЯிро░ாрок் рокாроХ்ро╕் рооூро▓роо் роЪெро▓்ро▓ுроо் ро▓ிроЩ்роХ் рооூро▓рооாроХ роЙроЩ்роХро│் роирог்рокро░் роЗрои்род Drop Box роЕроХ்роХро╡ுрог்роЯ் роТрой்ро▒ை роЗро▓ро╡роЪрооாроХ роЙро░ுро╡ாроХ்роХிройாро░் роОрой்ро▒ாро▓் роЙроЩ்роХро│ுроХ்роХு 500 MB Space роЗро▓ро╡роЪрооாроХ роХிроЯைроХ்роХுроо். роЗрои்род рооுро▒ைрок்рокроЯி роиீроЩ்роХро│் 18 GB ро╡ро░ை роЙроЩ்роХро│் роЕроХ்роХро╡ுрог்роЯுроХ்роХு роЗроЯ ро╡роЪродிропை роХூроЯ்роЯро▓ாроо்.


роЪро░ி роЗрои்род Drop Box ро▓் роиாроо் роЪேрооிрод்род роироо் рокைро▓்роХро│ை роироо் рооொрокைро▓ிро▓் роОрок்рокроЯி рокропрой்рокроЯுрод்родுро╡родு ?

 

iPhone, iPad, Android mobiles and Blackberry Mobile рокோрой்ро▒ро╡ро▒்ро▒ிро▓ுроо் роиீроЩ்роХро│் роЗродுрокோро▓் Drop Box рооெрой்рокொро░ுро│ை роЗрой்ро╕்роЯாро▓் роЪெроп்родுроХொро│்ро│ро▓ாроо்...... роЕрок்рокроЯி роЗрой்ро╕்роЯாро▓் роЪெроп்ропுроо்рокொро┤ுродு роиீроЩ்роХро│் роПро▒்роХройро╡ே роЗродрой் роХрогроХ்роХை роЙро░ுро╡ாроХ்роХிро╡ிроЯ்роЯродாро▓் I already have a Drop box account роОрой்ро▒ роЖрок்роЪрой் рооூро▓рооாроХ роиீроЩ்роХро│் роЪெро▓்ро▓ுроЩ்роХро│்....

 

 

 

  

роЙроЯройே роЕроЯுрод்родு ро╡ро░ுроо் рокроХுродிропிро▓் роЙроЩ்роХро│் роЬிрооெропிро▓் рооுроХро╡ро░ி рооро▒்ро▒ுроо் рокாро╕்ро╡ேро░்роЯை роЯைрок் роЪெроп்родு next рокроЯ்роЯройை роХிро│ிроХ் роЪெроп்родாро▓் рокோродுроо். роЙроЩ்роХро│் роЕроХ்роХро╡ுрог்роЯ் роУрок்рокрой் роЖроХிро╡ிроЯுроо். роЙроЯройே роиீроЩ்роХро│் роХроо்рок்ропூроЯ்роЯро░் рооூро▓рооாроХ роЪேрооிрод்род рокைро▓்роХро│் роЕройைрод்родுроо் роЙроЩ்роХро│் рооொрокைро▓ிро▓் роЯро╡ுрог்ро▓ோроЯு роЖроХிро╡ுроо்.

 

 

роЗрои்род рооுро▒ைрок்рокроЯி роиீроЩ்роХро│் роХроо்рок்ропூроЯ்роЯро░ிро▓் Drop Box рооூро▓роо் роЪேрооிрод்род рокைро▓்роХро│் роЕройைрод்родைропுроо் iPhone, iPad, Samsung Galaxy Tab рооро▒்ро▒ுроо் Android рооெрой்рокொро░ுро│் рокропрой்рокроЯுрод்родрок்рокроЯுроо் роЕройைрод்родு рооொрокைро▓்роХро│ிро▓ுроо் роЙроЯройுроХ்роХு роЙроЯрой் рокропрой்рокроЯுрод்родро▓ாроо்.

 

 

 

роЗрои்род Drop Box роР iTunes, iPhone App Stores рооூро▓роо் роиீроЩ்роХро│் роЙроЩ்роХро│் iPhone рооொрокைро▓்роХро│ுроХ்роХு рокропрой்рокроЯுрод்род роЯро╡ுрог்ро▓ோроЯு роЪெроп்родுроХொро│்ро│ро▓ாроо்....

 

 

 

роЕродே рокோро▓் роЗрои்род Drop Box роР Google Play Android Marker ро▓் роЗро░ுрои்родு роЙроЩ்роХро│் Android рооொрокைро▓்роХро│ுроХ்роХு рокропрой்рокроЯுрод்род  роиீроЩ்роХро│் роЯро╡ுрог்ро▓ோроЯு роЪெроп்родுроХொро│்ро│ро▓ாроо்....


   



роЗрои்род Drop Box рооூро▓роо் роиாроо் роЪேрооிроХ்роХுроо் рокைро▓்роХро│ை рооро▒்ро▒ роХроо்рок்ропூроЯ்роЯро░்роХро│ிро▓ுроо் ро▓ேрок்роЯாрок்рокிро▓ுроо் рокропрой்рокроЯுрод்родுро╡родு роОрок்рокроЯி ?

рооேро▓ே роЪொрой்рой рооுро▒ைрок்рокроЯி роиீроЩ்роХро│் роЙроЩ்роХро│் Drop Box Account User Name and Password рооூро▓роо் ро╡ேро▒ு роТро░ு роХроо்рок்ропூроЯ்роЯро░ிро▓ோ роЕро▓்ро▓родு роиீроЩ்роХро│் рокропрой்рокроЯுрод்родுроо் ро▓ேрок்роЯாрок்рокிро▓ோ роТро░ு Drop Box роР роЗрой்ро╕்роЯாро▓் роЪெроп்родு роЪெроЯ்роЯрок் роЪெроп்родுроХொрог்роЯாро▓் роТро░ே роиேро░род்родிро▓் роЙроЩ்роХро│் рокைро▓்роХро│ை роиீроЩ்роХро│் рокропрой்рокроЯுрод்родுроо் роЕройைрод்родு роХроо்рок்ропூроЯ்роЯро░்роХро│ிро▓ுроо் роЪேрооிрод்родு рокропрой்рокроЯுрод்родிроХ்роХொро│்ро│ро▓ாроо்.

рооேро▓ுроо் роиீроЩ்роХро│் роЙроЩ்роХро│் роХроо்рок்ропூроЯ்роЯро░ிро▓் роЙро│்ро│ Drop Box рокோро▓்роЯро░ிро▓் роЗрогைроХ்роХுроо் роТро╡்ро╡ொро░ு рокைро▓ுроо் роТро╡்ро╡ொро░ு рооுро▒ைропுроо் роЗрогைропрод்родிро▓் роЙроЩ்роХро│் Drop Box роЕроХ்роХро╡ுрог்роЯிро▓் роЪேрооிроХ்роХрок்рокроЯுро╡родாро▓் роЙроЩ்роХро│் роХроо்рок்ропூроЯ்роЯро░ிро▓் роПродேройுроо் рокிро░роЪ்роЪройை роПро▒்рокроЯ்роЯாро▓ுроо் роЙроЩ்роХро│ுроЯைроп рооுроХ்роХிропрооாрой рокைро▓்роХро│் роЙроЯройே роЕро┤ிрои்родுро╡ிроЯாродு. роХроо்рок்ропூроЯ்роЯро░ை рокாро░்рооெроЯ் роЪெроп்родு рооро▒ுрокроЯி роЗрог்ро╕்роЯாро▓் роЪெроп்род рокிро▒роХு родிро░ுроо்рокро╡ுроо் роЙроЩ்роХро│் Drop Box роЕроХ்роХро╡ுрог்роЯ் рооூро▓роо் роиீроЩ்роХро│் Drop Box роР роЗрой்ро╕்роЯாро▓் роЪெроп்родாро▓் рокோродுроо் роЕродிро▓் роиீроЩ்роХро│் роЪேрооிрод்род рокைро▓்роХро│் роЕройைрод்родுроо் рооро▒ுрокроЯிропுроо் роЙроЩ்роХро│் роХроо்рок்ропூроЯ்роЯро░ிро▓் роЪேрооிроХ்роХрок்рокроЯ்роЯுро╡ிроЯுроо்.

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Database Management System Chapter-12

Chapter 12:  Indexing and Hashing

 

n Basic Concepts

n Ordered Indices

n B+-Tree Index Files

n B-Tree Index Files

n Static Hashing

n Dynamic Hashing

n Comparison of Ordered Indexing and Hashing

n Index Definition in SQL

n Multiple-Key Access

 

Basic Concepts

 

n Indexing mechanisms used to speed up access to desired data.

├Ш E.g., author catalog in library

n Search Key - attribute to set of attributes used to look up records in a file.

n An index file consists of records (called index entries) of the form



n Index files are typically much smaller than the original file

n Two basic kinds of indices:

├Ш Ordered indices:  search keys are stored in sorted order

├Ш Hash indices:  search keys are distributed uniformly across “buckets” using a “hash function”.

 

Index Evaluation Metrics

 

n Access types supported efficiently.  E.g.,

├Ш records with a specified value in the attribute

├Ш or records with an attribute value falling in a specified range of values.

n Access time

n Insertion time

n Deletion time

n Space overhead

 

 

 

Ordered Indices

 

Indexing techniques evaluated on basis of:

 

n In an ordered index, index entries are stored sorted on the search key value.  E.g., author catalog in library.

n Primary index: in a sequentially ordered file, the index whose search key specifies the sequential order of the file.

├Ш Also called clustering index

├Ш The search key of a primary index is usually but not necessarily the primary key.

n Secondary index: an index whose search key specifies an order different from the sequential order of the file.  Also called
non-clustering index.

n Index-sequential file: ordered sequential file with a primary index.

 

Dense Index Files

 

n Dense index — Index record appears for every search-key value in the file.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Sparse Index Files

 

n Sparse Index:  contains index records for only some search-key values.

├Ш Applicable when records are sequentially ordered on search-key

n To locate a record with search-key value K we:

├Ш Find index record with largest search-key value < K

├Ш Search file sequentially starting at the record to which the index record points

n Less space and less maintenance overhead for insertions and deletions.

n Generally slower than dense index for locating records.

n Good tradeoff: sparse index with an index entry for every block in file, corresponding to least search-key value in the block.

 

Example of Sparse Index Files

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Multilevel Index

 

n If primary index does not fit in memory, access becomes expensive.

n To reduce number of disk accesses to index records, treat primary index kept on disk as a sequential file and construct a sparse index on it.

├Ш outer index – a sparse index of primary index

├Ш inner index – the primary index file

n If even outer index is too large to fit in main memory, yet another level of index can be created, and so on.

n Indices at all levels must be updated on insertion or deletion from the file.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Index Update:  Deletion

 

n If deleted record was the only record in the file with its particular search-key value, the search-key is deleted from the index also.

n Single-level index deletion:

├Ш Dense indices – deletion of search-key is similar to file record deletion.

├Ш Sparse indices – if an entry for the search key exists in the index, it is deleted by replacing the entry in the index with the next search-key value in the file (in search-key order).  If the next search-key value already has an index entry, the entry is deleted instead of being replaced.

Index Update:  Insertion

 

n Single-level index insertion:

├Ш Perform a lookup using the search-key value appearing in the record to be inserted.

├Ш Dense indices – if the search-key value does not appear in the index, insert it.

├Ш Sparse indices – if index stores an entry for each block of the file, no change needs to be made to the index unless a new block is created.  In this case, the first search-key value appearing in the new block is inserted into the index.

n Multilevel insertion (as well as deletion) algorithms are simple extensions of the single-level algorithms

 

Secondary Indices

 

n Frequently, one wants to find all the records whose values in a certain field (which is not the search-key of the primary index satisfy some condition.

├Ш Example 1: In the account database stored sequentially by account number, we may want to find all accounts in a particular branch

├Ш Example 2: as above, but where we want to find all accounts with a specified balance or range of balances

n We can have a secondary index with an index record for each search-key value; index record points to a bucket that contains pointers to all the actual records with that particular search-key value.

 

 

 

Secondary Index on balance field of account

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Primary and Secondary Indices

 

n Secondary indices have to be dense.

n Indices offer substantial benefits when searching for records.

n When a file is modified, every index on the file must be updated, Updating indices imposes overhead on database modification.

n Sequential scan using primary index is efficient, but a sequential scan using a secondary index is expensive

├Ш each record access may fetch a new block from disk

 

B+-Tree Index Files

 

B+-tree indices are an alternative to indexed-sequential files

 

n Disadvantage of indexed-sequential files: performance degrades as file grows, since many overflow blocks get created.  Periodic reorganization of entire file is required.

n Advantage of B+-tree index files:  automatically reorganizes itself with small, local, changes, in the face of insertions and deletions.  Reorganization of entire file is not required to maintain performance.

n Disadvantage of B+-trees: extra insertion and deletion overhead, space overhead.

n Advantages of B+-trees outweigh disadvantages, and they are used extensively.

 

 

A B+-tree is a rooted tree satisfying the following properties:

 

 

n All paths from root to leaf are of the same length

n Each node that is not a root or a leaf has between [n/2] and n children.

n A leaf node has between [(n–1)/2] and n–1 values

n Special cases:

├Ш If the root is not a leaf, it has at least 2 children.

├Ш If the root is a leaf (that is, there are no other nodes in the tree), it can have between 0 and (n–1) values.

 

B+-Tree Node Structure

n Typical node


 

 

 



├Ш Ki are the search-key values

├Ш Pi are pointers to children (for non-leaf nodes) or pointers to records or buckets of records (for leaf nodes).

n The search-keys in a node are ordered

           K1 < K2 < K3 < . . . < Kn–1

 

 

Leaf Nodes in B+-Trees

 

Properties of a leaf node:

 

 

n For i = 1, 2, . . ., n–1, pointer Pi either points to a file record with search-key value Ki, or to a bucket of pointers to file records, each record having search-key value KiOnly need bucket structure if search-key does not form a primary key.

n If Li, Lj are leaf nodes and i < j, Li’s search-key values are less than Lj’s search-key values

n Pn points to next leaf node in search-key order

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Non-Leaf Nodes in B+-Trees

 

n Non leaf nodes form a multi-level sparse index on the leaf nodes.  For a non-leaf node with m pointers:

├Ш All the search-keys in the subtree to which P1 points are less than K1

├Ш For 2 £ i £ n – 1, all the search-keys in the subtree to which Pi points have values greater than or equal to Ki–1 and less than Km–1

 

 

 

 

 

 

 

 

 

 


Example of a B+-tree

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


B+-tree for account file (n = 3)

 

 

Example of B+-tree

 

 

 

 

 

 

 

 

 

 

 

 


B+-tree for account file (n - 5)

 

 

 

 

 

 

 

 

 

 

n Leaf nodes must have between 2 and 4 values
(
├й(n–1)/2├╣ and n –1, with n = 5).

n Non-leaf nodes other than root must have between 3 and 5 children (├й(n/2├╣ and n with n =5).

n Root must have at least 2 children.

 

Observations about B+-trees

 

n Since the inter-node connections are done by pointers, “logically” close blocks need not be “physically” close.

n The non-leaf levels of the B+-tree form a hierarchy of sparse indices.

n The B+-tree contains a relatively small number of levels (logarithmic in the size of the main file), thus searches can be conducted efficiently.

n Insertions and deletions to the main file can be handled efficiently, as the index can be restructured in logarithmic time (as we shall see).

 

Queries on B+-Trees

 

n  Find all records with a search-key value of k.

H  Start with the root node

4 Examine the node for the smallest search-key value > k.

4 If such a value exists, assume it is KjThen follow Pi to the child node

4 Otherwise k ³ Km–1, where there are m pointers in the node.  Then follow Pm to the child node.

H  If the node reached by following the pointer above is not a leaf node, repeat the above procedure on the node, and follow the corresponding pointer.

H  Eventually reach a leaf node.  If for some i, key Ki = k  follow pointer Pi  to the desired record or bucket.  Else no record with search-key value k exists.

n In processing a query, a path is traversed in the tree from the root to some leaf node.

n If there are K search-key values in the file, the path is no longer than ├й log├йn/2├╣(K)├╣.

n A node is generally the same size as a disk block, typically 4 kilobytes, and n is typically around 100 (40 bytes per index entry).

n With 1 million search key values and n = 100, at most
log
50(1,000,000) = 4 nodes are accessed in a lookup.

n Contrast this with a balanced binary free with 1 million search key values — around 20 nodes are accessed in a lookup

├Ш above difference is significant since every node access may need a disk I/O, costing around 20 milliseconds!

 

Updates on B+-Trees:  Insertion

 

n Find the leaf node in which the search-key value would appear

n If the search-key value is already there in the leaf node, record is added to file and if necessary a pointer is inserted into the bucket.

n If the search-key value is not there, then add the record to the main file and create a bucket if necessary.  Then:

├Ш If there is room in the leaf node, insert (key-value, pointer) pair in the leaf node

├Ш Otherwise, split the node (along with the new (key-value, pointer) entry) as discussed in the next slide.

 

n Splitting a node:

├Ш take the n(search-key value, pointer) pairs (including the one being inserted) in sorted order.  Place the first ├й n/2 ├╣ in the original node, and the rest in a new node.

├Ш let the new node be p, and let k be the least key value in p.  Insert (k,p) in the parent of the node being split. If the parent is full, split it and propagate the split further up.

 

 

 

 

 

 

 

 

 


Result of splitting node containing Brighton and Downtown on
inserting Clearview

 

 

 

n The splitting of nodes proceeds upwards till a node that is not full is found.  In the worst case the root node may be split increasing the height of the tree by 1.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


B+-Tree before and after insertion of “Clearview”

 

 

 

Updates on B+-Trees: Deletion

 

n Find the record to be deleted, and remove it from the main file and from the bucket (if present)

n Remove (search-key value, pointer) from the leaf node if there is no bucket or if the bucket has become empty

n If the node has too few entries due to the removal, and the entries in the node and a sibling fit into a single node, then

├Ш Insert all the search-key values in the two nodes into a single node (the one on the left), and delete the other node.

├Ш Delete the pair (Ki–1, Pi), where Pi is the pointer to the deleted node, from its parent, recursively using the above procedure.

 

Updates on B+-Trees:  Deletion

n Otherwise, if the node has too few entries due to the removal, and the entries in the node and a sibling fit into a single node, then

├Ш Redistribute the pointers between the node and a sibling such that both have more than the minimum number of entries.

├Ш Update the corresponding search-key value in the parent of the node.

n The node deletions may cascade upwards till a node which has  ├йn/2 ├╣ or more pointers is found.  If the root node has only one pointer after deletion, it is deleted and the sole child becomes the root.

Examples of B+-Tree Deletion

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Before and after deleting “Downtown”

 

 

n  The removal of the leaf node containing “Downtown” did not result in its parent having too little pointers.  So the cascaded deletions stopped with the deleted leaf node’s parent.

Examples of B+-Tree Deletion (Cont.)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Deletion of “Perryridge” from result of previous example

 

 

n  Node with “Perryridge” becomes underfull (actually empty, in this special case) and merged with its sibling.

n  As a result “Perryridge” node’s parent became underfull, and was merged with its sibling (and an entry was deleted from their parent)

n  Root node then had only one child, and was deleted and its child became the new root node

Example of B+-tree Deletion (Cont.)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Before and after deletion of “Perryridge” from earlier example

 

 

n  Parent  of leaf containing Perryridge became underfull, and borrowed a pointer from its left sibling

n  Search-key value in the parent’s parent changes as a result

 

 

B+-Tree File Organization

 

n Index file degradation problem is solved by using B+-Tree indices.  Data file degradation problem is solved by using B+-Tree File Organization.

n The leaf nodes in a B+-tree file organization store records, instead of pointers.

n Since records are larger than pointers, the maximum number of records that can be stored in a leaf node is less than the number of pointers in a nonleaf node.

n Leaf nodes are still required to be half full.

n Insertion and deletion are handled in the same way as insertion and deletion of entries in a B+-tree index.

 

B+-Tree File Organization (Cont.)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Example of B+-tree File Organization

 

 

 

n  Good space utilization important since records use more space than pointers. 

n  To improve space utilization, involve more sibling nodes in redistribution during splits and merges

├Ш Involving 2 siblings in redistribution (to avoid split / merge where possible) results in each node having at least              entries

 

B-Tree Index Files

 

nSimilar to B+-tree, but B-tree allows search-key values to appear only once; eliminates redundant storage of search keys.

nSearch keys in nonleaf nodes appear nowhere else in the B-tree; an additional pointer field for each search key in a nonleaf node must be included.

nGeneralized B-tree leaf node


 

 

 

 

 

 

 

 

 

 

 

 

n Nonleaf node – pointers Bi are the bucket or file record pointers.

B-Tree Index File Example

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


B-tree (above) and B+-tree (below) on same data

 

 

 

 

 

 

 

 

 

 

 

 


n Advantages of B-Tree indices:

├Ш May use less tree nodes than a corresponding B+-Tree.

├Ш Sometimes possible to find search-key value before reaching leaf node.

n Disadvantages of B-Tree indices:

├Ш Only small fraction of all search-key values are found early

├Ш Non-leaf nodes are larger, so fan-out is reduced.  Thus B-Trees typically have greater depth than corresponding
B+-Tree

├Ш Insertion and deletion more complicated than in B+-Trees

├Ш Implementation is harder than B+-Trees.

n Typically, advantages of B-Trees do not out weigh disadvantages.

 

Static Hashing

 

n A bucket is a unit of storage containing one or more records (a bucket is typically a disk block).

n In a hash file organization we obtain the bucket of a record directly from its search-key value using a hash function.

n Hash function h is a function from the set of all search-key values K to the set of all bucket addresses B.

n Hash function is used to locate records for access, insertion as well as deletion.

n Records with different search-key values may be mapped to the same bucket; thus entire bucket has to be searched sequentially to locate a record.

 

Example of Hash File Organization (Cont.)

Hash file organization of account file, using branch-name as key
(See figure in next slide.)

 

 

n There are 10 buckets,

n The binary representation of the ith character is assumed to be the integer i.

n The hash function returns the sum of the binary representations of the characters modulo 10

├Ш E.g. h(Perryridge) = 5    h(Round Hill) = 3   h(Brighton) = 3

 

Example of Hash File Organization

Hash file organization of account file, using branch-name as key
                           (see previous slide for details).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Hash Functions

 

n Worst has function maps all search-key values to the same bucket; this makes access time proportional to the number of search-key values in the file.

n An ideal hash function is uniform, i.e., each bucket is assigned the same number of search-key values from the set of all possible values.

n Ideal hash function is random, so each bucket will have the same number of records assigned to it irrespective of the actual distribution of search-key values in the file.

n Typical hash functions perform computation on the internal binary representation of the search-key.

├Ш  For example, for a string search-key, the binary representations of all the characters in the string could be added and the sum modulo the number of buckets could be returned. .

 

Handling of Bucket Overflows

 

n Bucket overflow can occur because of

├Ш Insufficient buckets

├Ш Skew in distribution of records.  This can occur due to two reasons:

├к multiple records have same search-key value

├к chosen hash function produces non-uniform distribution of key values

n Although the probability of bucket overflow can be reduced, it cannot be eliminated; it is handled by using overflow buckets.

 

n Overflow chaining – the overflow buckets of a given bucket are chained together in a linked list.

n Above scheme is called closed hashing

├Ш An alternative, called open hashing, which does not use overflow buckets,  is not suitable for database applications.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Hash Indices

 

n Hashing can be used not only for file organization, but also for index-structure creation. 

n A hash index organizes the search keys, with their associated record pointers, into a hash file structure.

n Strictly speaking, hash indices are always secondary indices

├Ш if the file itself is organized using hashing, a separate primary hash index on it using the same search-key is unnecessary. 

├Ш However, we use the term hash index to refer to both secondary index structures and hash organized files.

 

 

 

 

 

 

 

 

 

 

Example of Hash Index

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Deficiencies of Static Hashing

 

n In static hashing, function h maps search-key values to a fixed set of B of bucket addresses.

├Ш Databases grow with time.  If initial number of buckets is too small, performance will degrade due to too much overflows.

├Ш If file size at some point in the future is anticipated and number of buckets allocated accordingly, significant amount of space will be wasted initially.

├Ш If database shrinks, again space will be wasted.

├Ш One option is periodic re-organization of the file with a new hash function, but it is very expensive.

n These problems can be avoided by using techniques that allow the number of buckets to be modified dynamically.

 

Dynamic Hashing

 

n Good for database that grows and shrinks in size

n Allows the hash function to be modified dynamically

n Extendable hashing – one form of dynamic hashing

├Ш Hash function generates values over a large range — typically b-bit integers, with b = 32.

├Ш At any time use only a prefix of the hash function to index into a table of bucket addresses.  

├Ш Let the length of the prefix be i bits,  0 £ i £ 32. 

├Ш Bucket address table size = 2i.  Initially i = 0

├Ш Value of i grows and shrinks as the size of the database grows and shrinks.

├Ш Multiple entries in the bucket address table may point to a bucket.

├Ш Thus, actual number of buckets is < 2i

├к The number of buckets also changes dynamically due to coalescing and splitting of buckets.

 

General Extendable Hash Structure

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


In this structure, i2 = i3 = i, whereas i1 = i – 1 (see next slide for details)

 

 

Use of Extendable Hash Structure

 

n Each bucket j stores a value ij; all the entries that point to the same bucket have the same values on the first ij bits.

n To locate the bucket containing search-key Kj:

1.  Compute h(Kj) = X

2.  Use the first i high order bits of X as a displacement into bucket address table, and follow the pointer to appropriate bucket

n To insert a record with search-key value Kj

├Ш follow same procedure as look-up and locate the bucket, say j

├Ш If there is room in the bucket j insert record in the bucket. 

├Ш Else the bucket must be split and insertion re-attempted (next slide.)

├к Overflow buckets used instead in some cases (will see shortly)

         

Updates in Extendable Hash Structure

 

To split a bucket j when inserting record with search-key value Kj:

 

n If i > ij (more than one pointer to bucket j)

├Ш allocate a new bucket z, and set ij and iz to the old ij -+ 1.

├Ш make the second half of the bucket address table entries pointing to j to point to z

├Ш remove and reinsert each record in bucket j.

├Ш recompute new bucket for Kj and insert record in the bucket (further splitting is required if the bucket is still full)

n If i = ij (only one pointer to bucket j)

├Ш increment i and double the size of the bucket address table.

├Ш replace each entry in the table by two entries that point to the same bucket.

├Ш recompute new bucket address table entry for Kj
Now i > ij  so use the first case above.  

 

n When inserting a value, if the bucket is full after several splits (that is, i reaches some limit b) create an overflow bucket instead of splitting bucket entry table further.

n To delete a key value,

├Ш locate it in its bucket and remove it.

├Ш The bucket itself can be removed if it becomes empty (with appropriate updates to the bucket address table).

├Ш Coalescing of buckets can be done (can coalesce only with a “buddy” bucket having same value of ij and same ij –1 prefix, if it is present)

├Ш Decreasing bucket address table size is also possible

├к Note: decreasing bucket address table size is an expensive operation and should be done only if number of buckets becomes much smaller than the size of the table

 

Use of Extendable Hash Structure:  Example

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Initial Hash structure, bucket size = 2

 

 

 

n Hash structure after  insertion of one Brighton and two Downtown records

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Example (Cont.)

 

 


Hash structure after insertion of Mianus record

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Example (Cont.)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Hash structure after insertion of  three Perryridge records

 

Example (Cont.)

n Hash structure after insertion of Redwood and Round Hill records

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Extendable Hashing vs. Other Schemes

 

n Benefits of extendable hashing: 

├Ш Hash performance does not degrade with growth of file

├Ш Minimal space overhead

n Disadvantages of extendable hashing

├Ш Extra level of indirection to find desired record

├Ш Bucket address table may itself become very big (larger than memory)

├к Need a tree structure to locate desired record in the structure!

├Ш Changing size of bucket address table is an expensive operation

n Linear hashing is an alternative mechanism which avoids these disadvantages at the possible cost of more bucket overflows

 

Comparison of Ordered Indexing and Hashing

 

n Cost of periodic re-organization

n Relative frequency of insertions and deletions

n Is it desirable to optimize average access time at the expense of worst-case access time?

n Expected type of queries:

├Ш Hashing is generally better at retrieving records having a specified value of the key.

├Ш If range queries are common, ordered indices are to be preferred

 

Index Definition in SQL

 

n Create an index

     create index <index-name> or <relation-name>
                     <attribute-list>)

E.g.:  create index  b-index on branch(branch-name)

n Use create unique index to indirectly specify and enforce the condition that the search key is a candidate key is a candidate key.

├Ш Not really required if SQL unique integrity constraint is supported

n To drop an index

              drop index <index-name>

 

Multiple-Key Access

 

n Use multiple indices for certain types of queries.

n Example:

select account-number

from account

where branch-name = “Perryridge” and  balance - 1000

n Possible strategies for processing query using indices on single attributes:

1.  Use index on branch-name to find accounts with balances of $1000; test branch-name = “Perryridge”.

2.  Use index on balance to find accounts with balances of $1000; test branch-name = “Perryridge”.

3.     Use branch-name index to find pointers to all records pertaining to the Perryridge branch.  Similarly use index on balance.  Take intersection of both sets of pointers obtained.

 

Indices on Multiple Attributes

 

Suppose we have an index on combined search-key

                                             (branch-name, balance).

 

 

 

n With the where clause
where branch-name = “Perryridge” and balance = 1000
the index on the combined search-key will fetch only records that satisfy both conditions.
Using separate indices in less efficient — we may fetch many records (or pointers) that satisfy only one of the conditions.

n Can also efficiently handle
where branch-name - “Perryridge” and balance < 1000

n But cannot efficiently handle
where branch-name < “Perryridge” and balance = 1000
May fetch many records that satisfy the first but not the second condition.

 

Grid Files

 

n Structure used to speed the processing of general multiple search-key queries involving one or more comparison operators.

n The grid file has a single grid array and one linear scale for each search-key attribute.  The grid array has number of dimensions equal to number of search-key attributes.

n Multiple cells of grid array can point to same bucket

n To find the bucket for a search-key value, locate the row and column of its cell using the linear scales and follow pointer

Example Grid File for account

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Queries on a Grid File

 

n A grid file on two attributes A and B can handle queries of all following forms with reasonable efficiency

├Ш (a1 £ A £ a2)

├Ш (b1 £ B £ b2)

├Ш (a1 £ A £ a2  ├Щ  b1 £ B £ b2),.

n E.g., to answer (a1 £ A £ a2  ├Щ  b1 £ B £ b2), use linear scales to find corresponding candidate grid array cells, and look up all the buckets pointed to from those cells.

 

n During insertion, if a bucket becomes full, new bucket can be created if more than one cell points to it.

├Ш Idea similar to extendable hashing, but on multiple dimensions

├Ш  If only one cell points to it, either an overflow bucket must be created or the grid size must be increased

n Linear scales must be chosen to uniformly distribute records across cells.

├Ш  Otherwise there will be too many overflow buckets.

n Periodic re-organization to increase grid size will help.

├Ш But reorganization can be very expensive.

n Space overhead of grid array can be high.

n R-trees (Chapter 23) are an alternative

 

Bitmap Indices

 

n Bitmap indices are a special type of index designed for efficient querying on multiple keys

n Records in a relation are assumed to be numbered sequentially from, say, 0

├Ш Given a number n it must be easy to retrieve record n

├к Particularly easy if records are of fixed size

n Applicable on attributes that take on a relatively small number of distinct values

├Ш E.g. gender, country, state, …

├Ш E.g. income-level (income broken up into a small number of  levels such as 0-9999, 10000-19999, 20000-50000, 50000- infinity)

n A bitmap is simply an array of bits

 

n In its simplest form a bitmap index on an attribute has a bitmap for each value of the attribute

├Ш Bitmap has as many bits as records

├Ш In a bitmap for value v, the bit for a record is 1 if the record has the value v for the attribute, and is 0 otherwise

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


n  Bitmap indices are useful for queries on multiple attributes

├Ш not particularly useful for single attribute queries

n  Queries are answered using bitmap operations

├Ш Intersection (and)

├Ш Union (or)

├Ш Complementation (not)

n  Each operation takes two bitmaps of the same size and applies the operation on corresponding bits to get the result bitmap

├Ш E.g.   100110  AND 110011 = 100010

               100110  OR  110011 = 110111
                       NOT 100110  = 011001

├Ш Males with income level L1:   10010 AND 10100 = 10000

├к Can then retrieve required tuples.

├к Counting number of matching tuples is even faster

 

n Bitmap indices generally very small compared with relation size

├Ш E.g. if record is 100 bytes, space for a single bitmap is 1/800 of space used by relation. 

├к If number of distinct attribute values is 8, bitmap is only 1% of relation size

n Deletion needs to be handled properly

├Ш Existence bitmap to note if there is a valid record at a record location

├Ш Needed for complementation

├к not(A=v):      (NOT bitmap-A-v) AND ExistenceBitmap

n Should keep bitmaps for all values, even null value

├Ш To correctly handle SQL null semantics for  NOT(A=v):

├к  intersect above result with  (NOT bitmap-A-Null)

 

Efficient Implementation of Bitmap Operations

 

n Bitmaps are packed into words;  a single word and (a basic CPU instruction) computes and of 32 or 64 bits at once

├Ш E.g. 1-million-bit maps can be anded with just 31,250 instruction

n Counting number of 1s can be done fast by a trick:

├Ш Use each byte to index into a precomputed array of 256 elements each storing the count of 1s in the binary representation

├к Can use pairs of bytes to speed up further at a higher memory cost

├Ш Add up the retrieved counts

n Bitmaps can be used instead of Tuple-ID lists at leaf levels of
B
+-trees, for values that have a large number of matching records

├Ш Worthwhile if > 1/64 of the records have that value, assuming a tuple-id is 64 bits

├Ш Above technique merges benefits of bitmap and B+-tree indices

 

 

 

 

 

 

 

End of Chapter