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  • How can I rank teams based off of head to head wins/losses

    - by TMP
    I'm trying to write an algorithm (specifically in Ruby) that will rank teams based on their record against each other. If a team A and team B have won the same amount of games against each other, then it goes down to point differentials. Here's an example: A beat B two times B beats C one time A beats D three times C bests D two times D beats C one time B beats A one time Which sort of reduces to A[B] = 2 B[C] = 1 A[D] = 3 C[D] = 2 D[C] = 1 B[A] = 1 Which sort of reduces to A[B] = 1 B[C] = 1 A[D] = 3 C[D] = 1 D[C] = -1 B[A] = -1 Which is about how far I've got I think the results of this specific algorithm would be: A, B, C, D But I'm stuck on how to transition from my nested hash-like structure to the results. My psuedo-code is as follows (I can post my ruby code too if someone wants): For each game(g): hash[g.winner][g.loser] += 1 That leaves hash as the first reduction above hash2 = clone of hash For each key(winner), value(losers hash) in hash: For each key(loser), value(losses against winner): hash2[loser][winner] -= losses Which leaves hash2 as the second reduction Feel free to as me question or edit this to be more clear, I'm not sure of how to put it in a very eloquent way. Thanks!

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  • Best Practice: What can be the hashCode() method implementation if custom field used in equals() method are null?

    - by goodspeed
    What is the best practice to return a value for hashCode() method if custom field used in equals are null ? I have a situation, where equals() override is implemented using custom fields. Usually it it is better to override hashCode() also using that custom fields used in equals(). But if all the custom fields used in equals() are null, then what would be the best implementation for hashCode()? Example: class Person { private String firstName; private String lastName; public String getFirstName() { return firstName; } public String getLastName() { return lastName; } @Override public boolean equals(Object object) { boolean result = false; if (object == null || object.getClass() != getClass()) { result = false; } else { Person person = (Person) object; if (this.firstName == person.getFirstName() && this.lastName == tiger.getLastName()) { result = true; } } return result; } @Override public int hashCode() { int hash = 3; if(this.firstName == null || this.lastName == null) { // <b>What is the best practice here, </b> // <b>is return super.hashCode() better ?</b> } hash = 7 * hash + this.firstName.hashCode(); hash = 7 * hash + this.lastName.hashCode(); return hash; } } is it required to check for null in hashCode() ? If yes, what should be returned if custom values are null ?

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  • Keyboard navigation for jQuery Tabs

    - by Binyamin
    How to make Keyboard navigation left/up/right/down (like for photo gallery) feature for jQury Tabs with History? Demo without Keyboard feature in http://dl.dropbox.com/u/6594481/tabs/index.html Needed functions: 1. on keyboardtop/down make select and CSS showactivenested ajax tabs from 1-st to last level 2. on keyboardleft/right changeback/forwardcontent ofactivenested ajax tabs tab 3. an extra option, makeactivenested ajax tab on 'cursor-on' on concrete nested ajax tabs level Read more detailed question with example pictures in http://stackoverflow.com/questions/2975003/jquery-tools-to-make-keyboard-and-cookies-feature-for-ajaxed-tabs-with-history /** * @license * jQuery Tools @VERSION Tabs- The basics of UI design. * * NO COPYRIGHTS OR LICENSES. DO WHAT YOU LIKE. * * http://flowplayer.org/tools/tabs/ * * Since: November 2008 * Date: @DATE */ (function($) { // static constructs $.tools = $.tools || {version: '@VERSION'}; $.tools.tabs = { conf: { tabs: 'a', current: 'current', onBeforeClick: null, onClick: null, effect: 'default', initialIndex: 0, event: 'click', rotate: false, // 1.2 history: false }, addEffect: function(name, fn) { effects[name] = fn; } }; var effects = { // simple "toggle" effect 'default': function(i, done) { this.getPanes().hide().eq(i).show(); done.call(); }, /* configuration: - fadeOutSpeed (positive value does "crossfading") - fadeInSpeed */ fade: function(i, done) { var conf = this.getConf(), speed = conf.fadeOutSpeed, panes = this.getPanes(); if (speed) { panes.fadeOut(speed); } else { panes.hide(); } panes.eq(i).fadeIn(conf.fadeInSpeed, done); }, // for basic accordions slide: function(i, done) { this.getPanes().slideUp(200); this.getPanes().eq(i).slideDown(400, done); }, /** * AJAX effect */ ajax: function(i, done) { this.getPanes().eq(0).load(this.getTabs().eq(i).attr("href"), done); } }; var w; /** * Horizontal accordion * * @deprecated will be replaced with a more robust implementation */ $.tools.tabs.addEffect("horizontal", function(i, done) { // store original width of a pane into memory if (!w) { w = this.getPanes().eq(0).width(); } // set current pane's width to zero this.getCurrentPane().animate({width: 0}, function() { $(this).hide(); }); // grow opened pane to it's original width this.getPanes().eq(i).animate({width: w}, function() { $(this).show(); done.call(); }); }); function Tabs(root, paneSelector, conf) { var self = this, trigger = root.add(this), tabs = root.find(conf.tabs), panes = paneSelector.jquery ? paneSelector : root.children(paneSelector), current; // make sure tabs and panes are found if (!tabs.length) { tabs = root.children(); } if (!panes.length) { panes = root.parent().find(paneSelector); } if (!panes.length) { panes = $(paneSelector); } // public methods $.extend(this, { click: function(i, e) { var tab = tabs.eq(i); if (typeof i == 'string' && i.replace("#", "")) { tab = tabs.filter("[href*=" + i.replace("#", "") + "]"); i = Math.max(tabs.index(tab), 0); } if (conf.rotate) { var last = tabs.length -1; if (i < 0) { return self.click(last, e); } if (i > last) { return self.click(0, e); } } if (!tab.length) { if (current >= 0) { return self; } i = conf.initialIndex; tab = tabs.eq(i); } // current tab is being clicked if (i === current) { return self; } // possibility to cancel click action e = e || $.Event(); e.type = "onBeforeClick"; trigger.trigger(e, [i]); if (e.isDefaultPrevented()) { return; } // call the effect effects[conf.effect].call(self, i, function() { // onClick callback e.type = "onClick"; trigger.trigger(e, [i]); }); // default behaviour current = i; tabs.removeClass(conf.current); tab.addClass(conf.current); return self; }, getConf: function() { return conf; }, getTabs: function() { return tabs; }, getPanes: function() { return panes; }, getCurrentPane: function() { return panes.eq(current); }, getCurrentTab: function() { return tabs.eq(current); }, getIndex: function() { return current; }, next: function() { return self.click(current + 1); }, prev: function() { return self.click(current - 1); } }); // callbacks $.each("onBeforeClick,onClick".split(","), function(i, name) { // configuration if ($.isFunction(conf[name])) { $(self).bind(name, conf[name]); } // API self[name] = function(fn) { $(self).bind(name, fn); return self; }; }); if (conf.history && $.fn.history) { $.tools.history.init(tabs); conf.event = 'history'; } // setup click actions for each tab tabs.each(function(i) { $(this).bind(conf.event, function(e) { self.click(i, e); return e.preventDefault(); }); }); // cross tab anchor link panes.find("a[href^=#]").click(function(e) { self.click($(this).attr("href"), e); }); // open initial tab if (location.hash) { self.click(location.hash); } else { if (conf.initialIndex === 0 || conf.initialIndex > 0) { self.click(conf.initialIndex); } } } // jQuery plugin implementation $.fn.tabs = function(paneSelector, conf) { // return existing instance var el = this.data("tabs"); if (el) { return el; } if ($.isFunction(conf)) { conf = {onBeforeClick: conf}; } // setup conf conf = $.extend({}, $.tools.tabs.conf, conf); this.each(function() { el = new Tabs($(this), paneSelector, conf); $(this).data("tabs", el); }); return conf.api ? el: this; }; }) (jQuery); /** * @license * jQuery Tools @VERSION History "Back button for AJAX apps" * * NO COPYRIGHTS OR LICENSES. DO WHAT YOU LIKE. * * http://flowplayer.org/tools/toolbox/history.html * * Since: Mar 2010 * Date: @DATE */ (function($) { var hash, iframe, links, inited; $.tools = $.tools || {version: '@VERSION'}; $.tools.history = { init: function(els) { if (inited) { return; } // IE if ($.browser.msie && $.browser.version < '8') { // create iframe that is constantly checked for hash changes if (!iframe) { iframe = $("<iframe/>").attr("src", "javascript:false;").hide().get(0); $("body").append(iframe); setInterval(function() { var idoc = iframe.contentWindow.document, h = idoc.location.hash; if (hash !== h) { $.event.trigger("hash", h); } }, 100); setIframeLocation(location.hash || '#'); } // other browsers scans for location.hash changes directly without iframe hack } else { setInterval(function() { var h = location.hash; if (h !== hash) { $.event.trigger("hash", h); } }, 100); } links = !links ? els : links.add(els); els.click(function(e) { var href = $(this).attr("href"); if (iframe) { setIframeLocation(href); } // handle non-anchor links if (href.slice(0, 1) != "#") { location.href = "#" + href; return e.preventDefault(); } }); inited = true; } }; function setIframeLocation(h) { if (h) { var doc = iframe.contentWindow.document; doc.open().close(); doc.location.hash = h; } } // global histroy change listener $(window).bind("hash", function(e, h) { if (h) { links.filter(function() { var href = $(this).attr("href"); return href == h || href == h.replace("#", ""); }).trigger("history", [h]); } else { links.eq(0).trigger("history", [h]); } hash = h; window.location.hash = hash; }); // jQuery plugin implementation $.fn.history = function(fn) { $.tools.history.init(this); // return jQuery return this.bind("history", fn); }; })(jQuery); $(function() { $("#list").tabs("#content > div", {effect: 'ajax', history: true}); });

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  • Why is jQuery .load() firing twice?

    - by LeslieOA
    Hello S-O. I'm using jQuery 1.4 with jQuery History and trying to figure out why Firebug/Web Inspector are showing 2 XHR GET requests on each page load (double that amount when visiting my sites homepage (/ or /#). e.g. Visit this (or any) page with Firebug enabled. Here's the edited/relevant code (see full source): - $(document).ready(function() { $('body').delegate('a', 'click', function(e) { var hash = this.href; if (hash.indexOf(window.location.hostname) > 0) { /* Internal */ hash = hash.substr((window.location.protocol+'//'+window.location.host+'/').length); $.historyLoad(hash); return false; } else if (hash.indexOf(window.location.hostname) == -1) { /* External */ window.open(hash); return false; } else { /* Nothing to do */ } }); $.historyInit(function(hash) { $('#loading').remove(); $('#container').append('<span id="loading">Loading...</span>'); $('#ajax').animate({height: 'hide'}, 'fast', 'swing', function() { $('#page').empty(); $('#loading').fadeIn('fast'); if (hash == '') { /* Index */ $('#ajax').load('/ #ajax','', function() { ajaxLoad(); }); } else { $('#ajax').load(hash + ' #ajax', '', function(responseText, textStatus, XMLHttpRequest) { switch (XMLHttpRequest.status) { case 200: ajaxLoad(); break; case 404: $('#ajax').load('/404 #ajax','', ajaxLoad); break; // Default 404 default: alert('We\'re experiencing technical difficulties. Try refreshing.'); break; } }); } }); // $('#ajax') }); // historyInit() function ajaxLoad() { $('#loading').fadeOut('fast', function() { $(this).remove(); $('#ajax').animate({height: 'show', opacity: '1'}, 'fast', 'swing'); }); } }); A few notes that may be helpful: - Using WordPress with default/standard .htaccess I'm redirecting /links-like/this to /#links-like/this via JavaScript only (PE) I'm achieving the above with window.location.replace(addr); and not window.location=addr; Feel free to visit my site if needed. Thanks in advanced.

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  • add a decorate function to a class

    - by wiso
    I have a decorated function (simplified version): class Memoize: def __init__(self, function): self.function = function self.memoized = {} def __call__(self, *args, **kwds): hash = args try: return self.memoized[hash] except KeyError: self.memoized[hash] = self.function(*args) return self.memoized[hash] @Memoize def _DrawPlot(self, options): do something... now I want to add this method to a pre-esisting class. ROOT.TChain.DrawPlot = _DrawPlot when I call this method: chain = TChain() chain.DrawPlot(opts) I got: self.memoized[hash] = self.function(*args) TypeError: _DrawPlot() takes exactly 2 arguments (1 given) why doesn't it propagate self?

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  • what data structure should I use for hash lookup as well as binary search?

    - by zebraman
    I am working on a school homework. I have a list of names. I want to be able to perform binary search on these names (find all names between a lower and upper bound) for first name as well as last name, and perform keyword searches as well (this will be accomplished using hashing. For example, if I have the names Garfield Cat Snoopy Dog Captain Crunch Fat Cat then a binary search of first names (C,H) will return Captain Crunch, Fat Cat, and Garfield Cat. A binary search of last names (Cr,D) will return Captain Crunch. A keyword search of 'cat' will return Fat Cat and Garfield Cat. I understand binary search will only work on a sorted list, but since I am planning on searching two different criteria, I will have to sort the list by last name or first name depending on what I'm searching for. I feel like it will be too inefficient to have to resort the list each time I want to perform a new binary search. Would it just be better for me to set up and maintain two sorted lists (one for sorted by first name, one for sorted by last name)? Also, for hashing, will I have to set up a different table of names for that as well? I understand each keyword will hash to some value determined by a hash function, and this value (or key) is a table address where the corresponding names are stored. So I just want to know what would be the best way to solve this problem? Maintaining separate structures, or is there a way to efficiently do everything I want with just one data structure?

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  • Do I need to Salt and Hash a randomly generated token?

    - by wag2639
    I'm using Adam Griffiths's Authentication Library for CodeIgniter and I'm tweaking the usermodel. I came across a generate function that he uses to generate tokens. His preferred approach is to reference a value from random.org but I considered that superfluous. I'm using his fall back approach of randomly generating a 20 character long string: $length = 20; $characters = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'; $token = ''; for ($i = 0; $i < $length; $i++) { $token .= $characters[mt_rand(0, strlen($characters)-1)]; } He then hashes this token using a salt (I'm combing code from different functions) sha1($this->CI->config->item('encryption_key').$str); I was wondering if theres any reason to to run the token through the salted hash? I've read that simply randomly generating strings was a naive way of making random passwords but is the sh1 hash and salt necessary? Note: I got my encryption_key from https://www.grc.com/passwords.htm (63 random alpha-numeric)

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  • IPSec VPN using ZyWALL IPSec VPN Client: unable to connect from some providers

    - by Reshi
    I'm trying to configure an IPSec VPN to one company from my home. The company has SANET internet service provider. I was able to create a VPN connection from another company that has the same internet service provider. The problem begins when I'm trying to connect from another ISP like Orange or Telekom. Here is the log from ZyWall: 20120816 10:06:18:359 Default (SA Gateway-P1) SEND phase 1 Main Mode [SA] [VID] [VID] [VID] [VID] [VID] 20120816 10:06:18:375 Default (SA Gateway-P1) RECV phase 1 Main Mode [SA] [VID] [VID] [VID] [VID] [VID] [VID] [VID] [VID] 20120816 10:06:18:390 Default (SA Gateway-P1) SEND phase 1 Main Mode [KEY_EXCH] [NONCE] [NAT_D] [NAT_D] 20120816 10:06:18:718 Default (SA Gateway-P1) RECV phase 1 Main Mode [KEY_EXCH] [NONCE] [NAT_D] [NAT_D] 20120816 10:06:18:734 Default (SA Gateway-P1) SEND phase 1 Main Mode [HASH] [ID] 20120816 10:06:18:750 Default (SA Gateway-P1) RECV phase 1 Main Mode [HASH] [ID] 20120816 10:06:18:750 Default phase 1 done: initiator id [email protected], responder id 111.112.113.114 20120816 10:06:18:765 Default (SA Gateway-Tunnel-P2) SEND phase 2 Quick Mode [HASH] [SA] [KEY_EXCH] [NONCE] [ID] [ID] 20120816 10:06:18:953 Default (SA Gateway-Tunnel-P2) RECV phase 2 Quick Mode [HASH] [SA] [KEY_EXCH] [NONCE] [ID] [ID] 20120816 10:06:18:953 Default (SA Gateway-Tunnel-P2) SEND phase 2 Quick Mode [HASH] 20120816 10:06:48:968 Default (SA Gateway-P1) SEND Informational [HASH] [NOTIFY] type DPD_R_U_THERE 20120816 10:06:48:984 Default (SA Gateway-P1) RECV Informational [HASH] [NOTIFY] type DPD_R_U_THERE_ACK ZyWall informs me that the tunnel was opened. But I can't ping or access any computer in the network. My configuration at home: ISP: Orange Optical connection Terminal: GPON OPTICAL NETWORK TERMINAL G-25E Router: TPLink TL-WR941N --> SPI Firewall Enabled --> VPN - IPSEC Passthrough Enabled I was wondering if the problem could not be on ISP side (that he blocks somehow this connection because in SANET ISP it worked fine) or even in my terminal or router. What could I check? Where could be the problem ?

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  • Why is my code signing (MS authenticode) verification failing?

    - by Tim
    I posted this question and have a freshly minted code signing cert from Thawte. I followed the instructions (or so I thought) and the code signing claims to be done right, however when I try to verify the tool shows an error. I have no idea what it means and no idea how to fix this. Any comments would be appreciated. Command line to sign exe: signtool sign /f mdt.pfx /p password /t http://timestamp.verisign.com/scripts/timstamp.dll test.exe Results: The following certificate was selected: Issued to: [my company] Issued by: Thawte Code Signing CA Expires: 4/23/2011 7:59:59 PM SHA1 hash: 7D1A42364765F8969E83BC00AB77F901118F3601 Done Adding Additional Store Attempting to sign: test.exe Successfully signed and timestamped: test.exe Number of files successfully Signed: 1 Number of warnings: 0 Number of errors: 0 Note that there are no errors or warnings. Now, when I try to verify imagine my surprise: signtool verify /v test.exe results in: Verifying: test.exe SHA1 hash of file: 490BA0656517D3A322D19F432F1C6D40695CAD22 Signing Certificate Chain: Issued to: Thawte Premium Server CA Issued by: Thawte Premium Server CA Expires: 12/31/2020 7:59:59 PM SHA1 hash: 627F8D7827656399D27D7F9044C9FEB3F33EFA9A Issued to: Thawte Code Signing CA Issued by: Thawte Premium Server CA Expires: 8/5/2013 7:59:59 PM SHA1 hash: A706BA1ECAB6A2AB18699FC0D7DD8C7DE36F290F Issued to: [my company] Issued by: Thawte Code Signing CA Expires: 4/23/2011 7:59:59 PM SHA1 hash: 7D1A42364765F8969E83BC00AB77F901118F3601 The signature is timestamped: 4/27/2010 10:19:19 AM Timestamp Verified by: Issued to: Thawte Timestamping CA Issued by: Thawte Timestamping CA Expires: 12/31/2020 7:59:59 PM SHA1 hash: BE36A4562FB2EE05DBB3D32323ADF445084ED656 Issued to: VeriSign Time Stamping Services CA Issued by: Thawte Timestamping CA Expires: 12/3/2013 7:59:59 PM SHA1 hash: F46AC0C6EFBB8C6A14F55F09E2D37DF4C0DE012D Issued to: VeriSign Time Stamping Services Signer - G2 Issued by: VeriSign Time Stamping Services CA Expires: 6/14/2012 7:59:59 PM SHA1 hash: ADA8AAA643FF7DC38DD40FA4C97AD559FF4846DE Number of files successfully Verified: 0 Number of warnings: 0 Number of errors: 1

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  • Python metaclass for enforcing immutability of custom types

    - by Mark Lehmacher
    Having searched for a way to enforce immutability of custom types and not having found a satisfactory answer I came up with my own shot at a solution in form of a metaclass: class ImmutableTypeException( Exception ): pass class Immutable( type ): ''' Enforce some aspects of the immutability contract for new-style classes: - attributes must not be created, modified or deleted after object construction - immutable types must implement __eq__ and __hash__ ''' def __new__( meta, classname, bases, classDict ): instance = type.__new__( meta, classname, bases, classDict ) # Make sure __eq__ and __hash__ have been implemented by the immutable type. # In the case of __hash__ also make sure the object default implementation has been overridden. # TODO: the check for eq and hash functions could probably be done more directly and thus more efficiently # (hasattr does not seem to traverse the type hierarchy) if not '__eq__' in dir( instance ): raise ImmutableTypeException( 'Immutable types must implement __eq__.' ) if not '__hash__' in dir( instance ): raise ImmutableTypeException( 'Immutable types must implement __hash__.' ) if _methodFromObjectType( instance.__hash__ ): raise ImmutableTypeException( 'Immutable types must override object.__hash__.' ) instance.__setattr__ = _setattr instance.__delattr__ = _delattr return instance def __call__( self, *args, **kwargs ): obj = type.__call__( self, *args, **kwargs ) obj.__immutable__ = True return obj def _setattr( self, attr, value ): if '__immutable__' in self.__dict__ and self.__immutable__: raise AttributeError( "'%s' must not be modified because '%s' is immutable" % ( attr, self ) ) object.__setattr__( self, attr, value ) def _delattr( self, attr ): raise AttributeError( "'%s' must not be deleted because '%s' is immutable" % ( attr, self ) ) def _methodFromObjectType( method ): ''' Return True if the given method has been defined by object, False otherwise. ''' try: # TODO: Are we exploiting an implementation detail here? Find better solution! return isinstance( method.__objclass__, object ) except: return False However, while the general approach seems to be working rather well there are still some iffy implementation details (also see TODO comments in code): How do I check if a particular method has been implemented anywhere in the type hierarchy? How do I check which type is the origin of a method declaration (i.e. as part of which type a method has been defined)?

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  • Deterministic/Consistent Unique Masking

    - by Dinesh Rajasekharan-Oracle
    One of the key requirements while masking data in large databases or multi database environment is to consistently mask some columns, i.e. for a given input the output should always be the same. At the same time the masked output should not be predictable. Deterministic masking also eliminates the need to spend enormous amount of time spent in identifying data relationships, i.e. parent and child relationships among columns defined in the application tables. In this blog post I will explain different ways of consistently masking the data across databases using Oracle Data Masking and Subsetting The readers of post should have minimal knowledge on Oracle Enterprise Manager 12c, Application Data Modeling, Data Masking concepts. For more information on these concepts, please refer to Oracle Data Masking and Subsetting document Oracle Data Masking and Subsetting 12c provides four methods using which users can consistently yet irreversibly mask their inputs. 1. Substitute 2. SQL Expression 3. Encrypt 4. User Defined Function SUBSTITUTE The substitute masking format replaces the original value with a value from a pre-created database table. As the method uses a hash based algorithm in the back end the mappings are consistent. For example consider DEPARTMENT_ID in EMPLOYEES table is replaced with FAKE_DEPARTMENT_ID from FAKE_TABLE. The substitute masking transformation that all occurrences of DEPARTMENT_ID say ‘101’ will be replaced with ‘502’ provided same substitution table and column is used , i.e. FAKE_TABLE.FAKE_DEPARTMENT_ID. The following screen shot shows the usage of the Substitute masking format with in a masking definition: Note that the uniqueness of the masked value depends on the number of columns being used in the substitution table i.e. if the original table contains 50000 unique values, then for the masked output to be unique and deterministic the substitution column should also contain 50000 unique values without which only consistency is maintained but not uniqueness. SQL EXPRESSION SQL Expression replaces an existing value with the output of a specified SQL Expression. For example while masking an EMPLOYEES table the EMAIL_ID of an employee has to be in the format EMPLOYEE’s [email protected] while FIRST_NAME and LAST_NAME are the actual column names of the EMPLOYEES table then the corresponding SQL Expression will look like %FIRST_NAME%||’.’||%LAST_NAME%||’@COMPANY.COM’. The advantage of this technique is that if you are masking FIRST_NAME and LAST_NAME of the EMPLOYEES table than the corresponding EMAIL ID will be replaced accordingly by the masking scripts. One of the interesting aspect’s of a SQL Expressions is that you can use sub SQL expressions, which means that you can write a nested SQL and use it as SQL Expression to address a complex masking business use cases. SQL Expression can also be used to consistently replace value with hashed value using Oracle’s PL/SQL function ORA_HASH. The following SQL Expression will help in the previous example for replacing the DEPARTMENT_IDs with a hashed number ORA_HASH (%DEPARTMENT_ID%, 1000) The following screen shot shows the usage of encrypt masking format with in the masking definition: ORA_HASH takes three arguments: 1. Expression which can be of any data type except LONG, LOB, User Defined Type [nested table type is allowed]. In the above example I used the Original value as expression. 2. Number of hash buckets which can be number between 0 and 4294967295. The default value is 4294967295. You can also co-relate the number of hash buckets to a range of numbers. In the above example above the bucket value is specified as 1000, so the end result will be a hashed number in between 0 and 1000. 3. Seed, can be any number which decides the consistency, i.e. for a given seed value the output will always be same. The default seed is 0. In the above SQL Expression a seed in not specified, so it to 0. If you have to use a non default seed then the function will look like. ORA_HASH (%DEPARTMENT_ID%, 1000, 1234 The uniqueness depends on the input and the number of hash buckets used. However as ORA_HASH uses a 32 bit algorithm, considering birthday paradox or pigeonhole principle there is a 0.5 probability of collision after 232-1 unique values. ENCRYPT Encrypt masking format uses a blend of 3DES encryption algorithm, hashing, and regular expression to produce a deterministic and unique masked output. The format of the masked output corresponds to the specified regular expression. As this technique uses a key [string] to encrypt the data, the same string can be used to decrypt the data. The key also acts as seed to maintain consistent outputs for a given input. The following screen shot shows the usage of encrypt masking format with in the masking definition: Regular Expressions may look complex for the first time users but you will soon realize that it’s a simple language. There are many resources in internet, oracle documentation, oracle learning library, my oracle support on writing a Regular Expressions, out of all the following My Oracle Support document helped me to get started with Regular Expressions: Oracle SQL Support for Regular Expressions[Video](Doc ID 1369668.1) USER DEFINED FUNCTION [UDF] User Defined Function or UDF provides flexibility for the users to code their own masking logic in PL/SQL, which can be called from masking Defintion. The standard format of an UDF in Oracle Data Masking and Subsetting is: Function udf_func (rowid varchar2, column_name varchar2, original_value varchar2) returns varchar2; Where • rowid is the row identifier of the column that needs to be masked • column_name is the name of the column that needs to be masked • original_value is the column value that needs to be masked You can achieve deterministic masking by using Oracle’s built in hash functions like, ORA_HASH, DBMS_CRYPTO.MD4, DBMS_CRYPTO.MD5, DBMS_UTILITY. GET_HASH_VALUE.Please refers to the Oracle Database Documentation for more information on the Oracle Hash functions. For example the following masking UDF generate deterministic unique hexadecimal values for a given string input: CREATE OR REPLACE FUNCTION RD_DUX (rid varchar2, column_name varchar2, orig_val VARCHAR2) RETURN VARCHAR2 DETERMINISTIC PARALLEL_ENABLE IS stext varchar2 (26); no_of_characters number(2); BEGIN no_of_characters:=6; stext:=substr(RAWTOHEX(DBMS_CRYPTO.HASH(UTL_RAW.CAST_TO_RAW(text),1)),0,no_of_characters); RETURN stext; END; The uniqueness depends on the input and length of the string and number of bits used by hash algorithm. In the above function MD4 hash is used [denoted by argument 1 in the DBMS_CRYPTO.HASH function which is a 128 bit algorithm which produces 2^128-1 unique hashed values , however this is limited by the length of the input string which is 6, so only 6^6 unique values will be generated. Also do not forget about the birthday paradox/pigeonhole principle mentioned earlier in this post. An another example is to consistently replace characters or numbers preserving the length and special characters as shown below: CREATE OR REPLACE FUNCTION RD_DUS(rid varchar2,column_name varchar2,orig_val VARCHAR2) RETURN VARCHAR2 DETERMINISTIC PARALLEL_ENABLE IS stext varchar2(26); BEGIN DBMS_RANDOM.SEED(orig_val); stext:=TRANSLATE(orig_val,'ABCDEFGHILKLMNOPQRSTUVWXYZ',DBMS_RANDOM.STRING('U',26)); stext:=TRANSLATE(stext,'abcdefghijklmnopqrstuvwxyz',DBMS_RANDOM.STRING('L',26)); stext:=TRANSLATE(stext,'0123456789',to_char(DBMS_RANDOM.VALUE(1,9))); stext:=REPLACE(stext,'.','0'); RETURN stext; END; The following screen shot shows the usage of an UDF with in a masking definition: To summarize, Oracle Data Masking and Subsetting helps you to consistently mask data across databases using one or all of the methods described in this post. It saves the hassle of identifying the parent-child relationships defined in the application table. Happy Masking

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  • Is reading xml simple in rails or converting it to hash will be simpler?

    - by Salil
    Hi All, Sorry for this question but after spending 1-2 hours on how to read xml, i thought posting it on forum will be better. So i get a complex (very large)xml response from the plugin trackify. i want to read some values form it so i covert it into hash and then read it as follows For ex:- to read city @tracking_info['TrackResponse']['Shipment']['ShipTo']['Address']['City'] #>> "SEATTLE" my question is it proper way to getting xml response or there are some xml methods which is simple to use?

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  • What's the best library to do a URL hash/history in JQuery?

    - by alex
    I've been looking around JQuery libraries for the URL hash, but found none that were good. There is the "history plugin", but we all know it's buggy and isn't flexible. I am loading my pages inside a div. I'll need a way to do back/forward along with the url hashing. mydomain.com/#home mydomain.com/#aboutus mydomain.com/#register What's the best library that can handle all of this?

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  • Does the java JFC hash table use seperate chaining resolution? Can I traverse each list in the table

    - by Matt
    I have written a program to store a bunch of strings in a JFC hash table. There are defiantly collisions going on, but I don't really know how it is handling them. My ultimate goal is to print the number of occurrences of each string in the table, and traversing the bucket or list would work nicely. Or maybe counting the collisions? Or do you have another idea of how I could get a count of the elements?

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  • Plan Caching and Query Memory Part I – When not to use stored procedure or other plan caching mechanisms like sp_executesql or prepared statement

    - by sqlworkshops
      The most common performance mistake SQL Server developers make: SQL Server estimates memory requirement for queries at compilation time. This mechanism is fine for dynamic queries that need memory, but not for queries that cache the plan. With dynamic queries the plan is not reused for different set of parameters values / predicates and hence different amount of memory can be estimated based on different set of parameter values / predicates. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union. This article covers Sort with examples. It is recommended to read Plan Caching and Query Memory Part II after this article which covers Hash Match operations.   When the plan is cached by using stored procedure or other plan caching mechanisms like sp_executesql or prepared statement, SQL Server estimates memory requirement based on first set of execution parameters. Later when the same stored procedure is called with different set of parameter values, the same amount of memory is used to execute the stored procedure. This might lead to underestimation / overestimation of memory on plan reuse, overestimation of memory might not be a noticeable issue for Sort operations, but underestimation of memory will lead to spill over tempdb resulting in poor performance.   This article covers underestimation / overestimation of memory for Sort. Plan Caching and Query Memory Part II covers underestimation / overestimation for Hash Match operation. It is important to note that underestimation of memory for Sort and Hash Match operations lead to spill over tempdb and hence negatively impact performance. Overestimation of memory affects the memory needs of other concurrently executing queries. In addition, it is important to note, with Hash Match operations, overestimation of memory can actually lead to poor performance.   To read additional articles I wrote click here.   In most cases it is cheaper to pay for the compilation cost of dynamic queries than huge cost for spill over tempdb, unless memory requirement for a stored procedure does not change significantly based on predicates.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script. Most of these concepts are also covered in our webcasts: www.sqlworkshops.com/webcasts   Enough theory, let’s see an example where we sort initially 1 month of data and then use the stored procedure to sort 6 months of data.   Let’s create a stored procedure that sorts customers by name within certain date range.   --Example provided by www.sqlworkshops.com create proc CustomersByCreationDate @CreationDateFrom datetime, @CreationDateTo datetime as begin       declare @CustomerID int, @CustomerName varchar(48), @CreationDate datetime       select @CustomerName = c.CustomerName, @CreationDate = c.CreationDate from Customers c             where c.CreationDate between @CreationDateFrom and @CreationDateTo             order by c.CustomerName       option (maxdop 1)       end go Let’s execute the stored procedure initially with 1 month date range.   set statistics time on go --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-31' go The stored procedure took 48 ms to complete.     The stored procedure was granted 6656 KB based on 43199.9 rows being estimated.       The estimated number of rows, 43199.9 is similar to actual number of rows 43200 and hence the memory estimation should be ok.       There was no Sort Warnings in SQL Profiler.      Now let’s execute the stored procedure with 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-06-30' go The stored procedure took 679 ms to complete.      The stored procedure was granted 6656 KB based on 43199.9 rows being estimated.      The estimated number of rows, 43199.9 is way different from the actual number of rows 259200 because the estimation is based on the first set of parameter value supplied to the stored procedure which is 1 month in our case. This underestimation will lead to sort spill over tempdb, resulting in poor performance.      There was Sort Warnings in SQL Profiler.    To monitor the amount of data written and read from tempdb, one can execute select num_of_bytes_written, num_of_bytes_read from sys.dm_io_virtual_file_stats(2, NULL) before and after the stored procedure execution, for additional information refer to the webcast: www.sqlworkshops.com/webcasts.     Let’s recompile the stored procedure and then let’s first execute the stored procedure with 6 month date range.  In a production instance it is not advisable to use sp_recompile instead one should use DBCC FREEPROCCACHE (plan_handle). This is due to locking issues involved with sp_recompile, refer to our webcasts for further details.   exec sp_recompile CustomersByCreationDate go --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-06-30' go Now the stored procedure took only 294 ms instead of 679 ms.    The stored procedure was granted 26832 KB of memory.      The estimated number of rows, 259200 is similar to actual number of rows of 259200. Better performance of this stored procedure is due to better estimation of memory and avoiding sort spill over tempdb.      There was no Sort Warnings in SQL Profiler.       Now let’s execute the stored procedure with 1 month date range.   --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-31' go The stored procedure took 49 ms to complete, similar to our very first stored procedure execution.     This stored procedure was granted more memory (26832 KB) than necessary memory (6656 KB) based on 6 months of data estimation (259200 rows) instead of 1 month of data estimation (43199.9 rows). This is because the estimation is based on the first set of parameter value supplied to the stored procedure which is 6 months in this case. This overestimation did not affect performance, but it might affect performance of other concurrent queries requiring memory and hence overestimation is not recommended. This overestimation might affect performance Hash Match operations, refer to article Plan Caching and Query Memory Part II for further details.    Let’s recompile the stored procedure and then let’s first execute the stored procedure with 2 day date range. exec sp_recompile CustomersByCreationDate go --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-02' go The stored procedure took 1 ms.      The stored procedure was granted 1024 KB based on 1440 rows being estimated.      There was no Sort Warnings in SQL Profiler.      Now let’s execute the stored procedure with 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-06-30' go   The stored procedure took 955 ms to complete, way higher than 679 ms or 294ms we noticed before.      The stored procedure was granted 1024 KB based on 1440 rows being estimated. But we noticed in the past this stored procedure with 6 month date range needed 26832 KB of memory to execute optimally without spill over tempdb. This is clear underestimation of memory and the reason for the very poor performance.      There was Sort Warnings in SQL Profiler. Unlike before this was a Multiple pass sort instead of Single pass sort. This occurs when granted memory is too low.      Intermediate Summary: This issue can be avoided by not caching the plan for memory allocating queries. Other possibility is to use recompile hint or optimize for hint to allocate memory for predefined date range.   Let’s recreate the stored procedure with recompile hint. --Example provided by www.sqlworkshops.com drop proc CustomersByCreationDate go create proc CustomersByCreationDate @CreationDateFrom datetime, @CreationDateTo datetime as begin       declare @CustomerID int, @CustomerName varchar(48), @CreationDate datetime       select @CustomerName = c.CustomerName, @CreationDate = c.CreationDate from Customers c             where c.CreationDate between @CreationDateFrom and @CreationDateTo             order by c.CustomerName       option (maxdop 1, recompile)       end go Let’s execute the stored procedure initially with 1 month date range and then with 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-30' exec CustomersByCreationDate '2001-01-01', '2001-06-30' go The stored procedure took 48ms and 291 ms in line with previous optimal execution times.      The stored procedure with 1 month date range has good estimation like before.      The stored procedure with 6 month date range also has good estimation and memory grant like before because the query was recompiled with current set of parameter values.      The compilation time and compilation CPU of 1 ms is not expensive in this case compared to the performance benefit.     Let’s recreate the stored procedure with optimize for hint of 6 month date range.   --Example provided by www.sqlworkshops.com drop proc CustomersByCreationDate go create proc CustomersByCreationDate @CreationDateFrom datetime, @CreationDateTo datetime as begin       declare @CustomerID int, @CustomerName varchar(48), @CreationDate datetime       select @CustomerName = c.CustomerName, @CreationDate = c.CreationDate from Customers c             where c.CreationDate between @CreationDateFrom and @CreationDateTo             order by c.CustomerName       option (maxdop 1, optimize for (@CreationDateFrom = '2001-01-01', @CreationDateTo ='2001-06-30'))       end go Let’s execute the stored procedure initially with 1 month date range and then with 6 month date range.   --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-30' exec CustomersByCreationDate '2001-01-01', '2001-06-30' go The stored procedure took 48ms and 291 ms in line with previous optimal execution times.    The stored procedure with 1 month date range has overestimation of rows and memory. This is because we provided hint to optimize for 6 months of data.      The stored procedure with 6 month date range has good estimation and memory grant because we provided hint to optimize for 6 months of data.       Let’s execute the stored procedure with 12 month date range using the currently cashed plan for 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-12-31' go The stored procedure took 1138 ms to complete.      2592000 rows were estimated based on optimize for hint value for 6 month date range. Actual number of rows is 524160 due to 12 month date range.      The stored procedure was granted enough memory to sort 6 month date range and not 12 month date range, so there will be spill over tempdb.      There was Sort Warnings in SQL Profiler.      As we see above, optimize for hint cannot guarantee enough memory and optimal performance compared to recompile hint.   This article covers underestimation / overestimation of memory for Sort. Plan Caching and Query Memory Part II covers underestimation / overestimation for Hash Match operation. It is important to note that underestimation of memory for Sort and Hash Match operations lead to spill over tempdb and hence negatively impact performance. Overestimation of memory affects the memory needs of other concurrently executing queries. In addition, it is important to note, with Hash Match operations, overestimation of memory can actually lead to poor performance.   Summary: Cached plan might lead to underestimation or overestimation of memory because the memory is estimated based on first set of execution parameters. It is recommended not to cache the plan if the amount of memory required to execute the stored procedure has a wide range of possibilities. One can mitigate this by using recompile hint, but that will lead to compilation overhead. However, in most cases it might be ok to pay for compilation rather than spilling sort over tempdb which could be very expensive compared to compilation cost. The other possibility is to use optimize for hint, but in case one sorts more data than hinted by optimize for hint, this will still lead to spill. On the other side there is also the possibility of overestimation leading to unnecessary memory issues for other concurrently executing queries. In case of Hash Match operations, this overestimation of memory might lead to poor performance. When the values used in optimize for hint are archived from the database, the estimation will be wrong leading to worst performance, so one has to exercise caution before using optimize for hint, recompile hint is better in this case. I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.     Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.     Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan

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