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  • Optimizing sorting container of objects with heap-allocated buffers - how to avoid hard-copying buff

    - by Kache4
    I was making sure I knew how to do the op= and copy constructor correctly in order to sort() properly, so I wrote up a test case. After getting it to work, I realized that the op= was hard-copying all the data_. I figure if I wanted to sort a container with this structure (its elements have heap allocated char buffer arrays), it'd be faster to just swap the pointers around. Is there a way to do that? Would I have to write my own sort/swap function? #include <deque> //#include <string> //#include <utility> //#include <cstdlib> #include <cstring> #include <iostream> //#include <algorithm> // I use sort(), so why does this still compile when commented out? #include <boost/filesystem.hpp> #include <boost/foreach.hpp> using namespace std; namespace fs = boost::filesystem; class Page { public: // constructor Page(const char* path, const char* data, int size) : path_(fs::path(path)), size_(size), data_(new char[size]) { // cout << "Creating Page..." << endl; strncpy(data_, data, size); // cout << "done creating Page..." << endl; } // copy constructor Page(const Page& other) : path_(fs::path(other.path())), size_(other.size()), data_(new char[other.size()]) { // cout << "Copying Page..." << endl; strncpy(data_, other.data(), size_); // cout << "done copying Page..." << endl; } // destructor ~Page() { delete[] data_; } // accessors const fs::path& path() const { return path_; } const char* data() const { return data_; } int size() const { return size_; } // operators Page& operator = (const Page& other) { if (this == &other) return *this; char* newImage = new char[other.size()]; strncpy(newImage, other.data(), other.size()); delete[] data_; data_ = newImage; path_ = fs::path(other.path()); size_ = other.size(); return *this; } bool operator < (const Page& other) const { return path_ < other.path(); } private: fs::path path_; int size_; char* data_; }; class Book { public: Book(const char* path) : path_(fs::path(path)) { cout << "Creating Book..." << endl; cout << "pushing back #1" << endl; pages_.push_back(Page("image1.jpg", "firstImageData", 14)); cout << "pushing back #3" << endl; pages_.push_back(Page("image3.jpg", "thirdImageData", 14)); cout << "pushing back #2" << endl; pages_.push_back(Page("image2.jpg", "secondImageData", 15)); cout << "testing operator <" << endl; cout << pages_[0].path().string() << (pages_[0] < pages_[1]? " < " : " > ") << pages_[1].path().string() << endl; cout << pages_[1].path().string() << (pages_[1] < pages_[2]? " < " : " > ") << pages_[2].path().string() << endl; cout << pages_[0].path().string() << (pages_[0] < pages_[2]? " < " : " > ") << pages_[2].path().string() << endl; cout << "sorting" << endl; BOOST_FOREACH (Page p, pages_) cout << p.path().string() << endl; sort(pages_.begin(), pages_.end()); cout << "done sorting\n"; BOOST_FOREACH (Page p, pages_) cout << p.path().string() << endl; cout << "checking datas" << endl; BOOST_FOREACH (Page p, pages_) { char data[p.size() + 1]; strncpy((char*)&data, p.data(), p.size()); data[p.size()] = '\0'; cout << p.path().string() << " " << data << endl; } cout << "done Creating Book" << endl; } private: deque<Page> pages_; fs::path path_; }; int main() { Book* book = new Book("/some/path/"); }

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  • How can I optimize this subqueried and Joined MySQL Query?

    - by kevzettler
    I'm pretty green on mysql and I need some tips on cleaning up a query. It is used in several variations through out a site. Its got some subquerys derived tables and fun going on. Heres the query: # Query_time: 2 Lock_time: 0 Rows_sent: 0 Rows_examined: 0 SELECT * FROM ( SELECT products . *, categories.category_name AS category, ( SELECT COUNT( * ) FROM distros WHERE distros.product_id = products.product_id) AS distro_count, (SELECT COUNT(*) FROM downloads WHERE downloads.product_id = products.product_id AND WEEK(downloads.date) = WEEK(curdate())) AS true_downloads, (SELECT COUNT(*) FROM views WHERE views.product_id = products.product_id AND WEEK(views.date) = WEEK(curdate())) AS true_views FROM products INNER JOIN categories ON products.category_id = categories.category_id ORDER BY created_date DESC, true_views DESC ) AS count_table WHERE count_table.distro_count > 0 AND count_table.status = 'published' AND count_table.active = 1 LIMIT 0, 8 Heres the explain: +----+--------------------+------------+-------+---------------+-------------+---------+------------------------------------+------+----------------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+--------------------+------------+-------+---------------+-------------+---------+------------------------------------+------+----------------------------------------------+ | 1 | PRIMARY | <derived2> | ALL | NULL | NULL | NULL | NULL | 232 | Using where | | 2 | DERIVED | categories | index | PRIMARY | idx_name | 47 | NULL | 13 | Using index; Using temporary; Using filesort | | 2 | DERIVED | products | ref | category_id | category_id | 4 | digizald_db.categories.category_id | 9 | | | 5 | DEPENDENT SUBQUERY | views | ref | product_id | product_id | 4 | digizald_db.products.product_id | 46 | Using where | | 4 | DEPENDENT SUBQUERY | downloads | ref | product_id | product_id | 4 | digizald_db.products.product_id | 14 | Using where | | 3 | DEPENDENT SUBQUERY | distros | ref | product_id | product_id | 4 | digizald_db.products.product_id | 1 | Using index | +----+--------------------+------------+-------+---------------+-------------+---------+------------------------------------+------+----------------------------------------------+ 6 rows in set (0.04 sec) And the Tables: mysql> describe products; +---------------+--------------------------------------------------+------+-----+-------------------+----------------+ | Field | Type | Null | Key | Default | Extra | +---------------+--------------------------------------------------+------+-----+-------------------+----------------+ | product_id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_key | char(32) | NO | | NULL | | | title | varchar(150) | NO | | NULL | | | company | varchar(150) | NO | | NULL | | | user_id | int(10) unsigned | NO | MUL | NULL | | | description | text | NO | | NULL | | | video_code | text | NO | | NULL | | | category_id | int(10) unsigned | NO | MUL | NULL | | | price | decimal(10,2) | NO | | NULL | | | quantity | int(10) unsigned | NO | | NULL | | | downloads | int(10) unsigned | NO | | NULL | | | views | int(10) unsigned | NO | | NULL | | | status | enum('pending','published','rejected','removed') | NO | | NULL | | | active | tinyint(1) | NO | | NULL | | | deleted | tinyint(1) | NO | | NULL | | | created_date | datetime | NO | | NULL | | | modified_date | timestamp | NO | | CURRENT_TIMESTAMP | | | scrape_source | varchar(215) | YES | | NULL | | +---------------+--------------------------------------------------+------+-----+-------------------+----------------+ 18 rows in set (0.00 sec) mysql> describe categories -> ; +------------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------------+------------------+------+-----+---------+----------------+ | category_id | int(10) unsigned | NO | PRI | NULL | auto_increment | | category_name | varchar(45) | NO | MUL | NULL | | | parent_id | int(10) unsigned | YES | MUL | NULL | | | category_type_id | int(10) unsigned | NO | | NULL | | +------------------+------------------+------+-----+---------+----------------+ 4 rows in set (0.00 sec) mysql> describe compatibilities -> ; +------------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------------+------------------+------+-----+---------+----------------+ | compatibility_id | int(10) unsigned | NO | PRI | NULL | auto_increment | | name | varchar(45) | NO | | NULL | | | code_name | varchar(45) | NO | | NULL | | | description | varchar(128) | NO | | NULL | | | position | int(10) unsigned | NO | | NULL | | +------------------+------------------+------+-----+---------+----------------+ 5 rows in set (0.01 sec) mysql> describe distros -> ; +------------------+--------------------------------------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------------+--------------------------------------------------+------+-----+---------+----------------+ | id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_id | int(10) unsigned | NO | MUL | NULL | | | compatibility_id | int(10) unsigned | NO | MUL | NULL | | | user_id | int(10) unsigned | NO | | NULL | | | status | enum('pending','published','rejected','removed') | NO | | NULL | | | distro_type | enum('file','url') | NO | | NULL | | | version | varchar(150) | NO | | NULL | | | filename | varchar(50) | YES | | NULL | | | url | varchar(250) | YES | | NULL | | | virus | enum('READY','PASS','FAIL') | YES | | NULL | | | downloads | int(10) unsigned | NO | | 0 | | +------------------+--------------------------------------------------+------+-----+---------+----------------+ 11 rows in set (0.01 sec) mysql> describe downloads; +------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+------------------+------+-----+---------+----------------+ | id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_id | int(10) unsigned | NO | MUL | NULL | | | distro_id | int(10) unsigned | NO | MUL | NULL | | | user_id | int(10) unsigned | NO | MUL | NULL | | | ip_address | varchar(15) | NO | | NULL | | | date | datetime | NO | | NULL | | +------------+------------------+------+-----+---------+----------------+ 6 rows in set (0.01 sec) mysql> describe views -> ; +------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+------------------+------+-----+---------+----------------+ | id | int(10) unsigned | NO | PRI | NULL | auto_increment | | product_id | int(10) unsigned | NO | MUL | NULL | | | user_id | int(10) unsigned | NO | MUL | NULL | | | ip_address | varchar(15) | NO | | NULL | | | date | datetime | NO | | NULL | | +------------+------------------+------+-----+---------+----------------+ 5 rows in set (0.00 sec)

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  • Wrappers of primitive types in arraylist vs arrays

    - by ismail marmoush
    Hi, In "Core java 1" I've read CAUTION: An ArrayList is far less efficient than an int[] array because each value is separately wrapped inside an object. You would only want to use this construct for small collections when programmer convenience is more important than efficiency. But in my software I've already used Arraylist instead of normal arrays due to some requirements, though "The software is supposed to have high performance and after I've read the quoted text I started to panic!" one thing I can change is changing double variables to Double so as to prevent auto boxing and I don't know if that is worth it or not, in next sample algorithm public void multiply(final double val) { final int rows = getSize1(); final int cols = getSize2(); for (int i = 0; i < rows; i++) { for (int j = 0; j < cols; j++) { this.get(i).set(j, this.get(i).get(j) * val); } } } My question is does changing double to Double makes a difference ? or that's a micro optimizing that won't affect anything ? keep in mind I might be using large matrices.2nd Should I consider redesigning the whole program again ?

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  • How can I optimize retrieving lowest edit distance from a large table in SQL?

    - by Matt
    Hey, I'm having troubles optimizing this Levenshtein Distance calculation I'm doing. I need to do the following: Get the record with the minimum distance for the source string as well as a trimmed version of the source string Pick the record with the minimum distance If the min distances are equal (original vs trimmed), choose the trimmed one with the lowest distance If there are still multiple records that fall under the above two categories, pick the one with the highest frequency Here's my working version: DECLARE @Results TABLE ( ID int, [Name] nvarchar(200), Distance int, Frequency int, Trimmed bit ) INSERT INTO @Results SELECT ID, [Name], (dbo.Levenshtein(@Source, [Name])) As Distance, Frequency, 'False' As Trimmed FROM MyTable INSERT INTO @Results SELECT ID, [Name], (dbo.Levenshtein(@SourceTrimmed, [Name])) As Distance, Frequency, 'True' As Trimmed FROM MyTable SET @ResultID = (SELECT TOP 1 ID FROM @Results ORDER BY Distance, Trimmed, Frequency) SET @Result = (SELECT TOP 1 [Name] FROM @Results ORDER BY Distance, Trimmed, Frequency) SET @ResultDist = (SELECT TOP 1 Distance FROM @Results ORDER BY Distance, Trimmed, Frequency) SET @ResultTrimmed = (SELECT TOP 1 Trimmed FROM @Results ORDER BY Distance, Trimmed, Frequency) I believe what I need to do here is to.. Not dumb the results to a temporary table Do only 1 select from `MyTable` Setting the results right in the select from the initial select statement. (Since select will set variables and you can set multiple variables in one select statement) I know there has to be a good implementation to this but I can't figure it out... this is as far as I got: SELECT top 1 @ResultID = ID, @Result = [Name], (dbo.Levenshtein(@Source, [Name])) As distOrig, (dbo.Levenshtein(@SourceTrimmed, [Name])) As distTrimmed, Frequency FROM MyTable WHERE /* ... yeah I'm lost */ ORDER BY distOrig, distTrimmed, Frequency Any ideas?

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  • Most Efficient Alternative Method of Storing Settings for iPhone Apps

    - by JPK
    I am not using the Settings bundle to store the settings for my app, as I prefer to allow the user to access the settings within the app (they may be changed fairly often). I do realize that there is the option to do both, but for now, I am trying to find the most optimal place to store the settings within the app. I have a good number of settings (from what I have read, probably too many for NSUserDefaults), and the two main options I am considering are: 1) storing the settings in a dictionary in the plist, loading the settings into a NSDictionary property in the app delegate and accessing them via the sharedDelegate 2) storing the settings in a Core Data entity (1 row on Settings entity), loading the settings into a Settings object in the app delegate and accessing them via the sharedDelegate Of these two, which would be the optimal method, performance wise?

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  • SQL Server indexed view matching of views with joins not working

    - by usr
    Does anyone have experience of when SQL Servr 2008 R2 is able to automatically match indexed view (also known as materialized views) that contain joins to a query? for example the view select dbo.Orders.Date, dbo.OrderDetails.ProductID from dbo.OrderDetails join dbo.Orders on dbo.OrderDetails.OrderID = dbo.Orders.ID cannot be automatically matched to the same exact query. When I select directly from this view ith (noexpand) I actually get a much faster query plan that does a scan on the clustered index of the indexed view. Can I get SQL Server to do this matching automatically? I have quite a few queries and views... I am on enterprise edition of SQL Server 2008 R2.

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  • Most flexible minimizer/compressor for ASP.NET MVC 2?

    - by AlexanderN
    From your experience, what's the most flexible minimizer/compressor (JS+CSS) for ASP.NET MVC you've dealt with? So far mbcompress doesn't seem to be too MVC friendly weboptimizer.codeplex.com lacks documentation clientdependency.codeplex.com is still in beta compress2 seems like a good candidate, but haven't tried it yet mvcscriptmanager only combines and compresses javascript but not CSS By flexible I mean Choose what should be compressed, minified, and combined Add exceptions. E.g. if debug don't compress XYZ.JS or don't minify ABC.CSS Caching In the end, it should help offer the best YSLOW score. If you know of any other assemblies out there, please list them also.

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  • Difference between Logarithmic and Uniform cost criteria

    - by Marthin
    I'v got some problem to understand the difference between Logarithmic(Lcc) and Uniform(Ucc) cost criteria and also how to use it in calculations. Could someone please explain the difference between the two and perhaps show how to calculate the complexity for a problem like A+B*C (Yes this is part of an assignment =) ) Thx for any help! /Marthin

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  • Single Large v/s Multiple Small MySQL tables for storing Options

    - by Prasad
    Hi there, I'm aware of several question on this forum relating to this. But I'm not talking about splitting tables for the same entity (like user for example) Suppose I have a huge options table that stores list options like Gender, Marital Status, and many more domain specific groups with same structure. I plan to capture in a OPTIONS table. Another simple option is to have the field set as ENUM, but there are disadvantages of that as well. http://www.brandonsavage.net/why-you-should-replace-enum-with-something-else/ OPTIONS Table: option_id <will be referred instead of the name> name value group Query: select .. from options where group = '15' - Since this table is expected to be multi-tenant, the no of rows could grow drastically. - I believe splitting the tables instead of finding by the group would be easier to write & faster to execute. - or perhaps partitioning by the group or tenant? Pl suggest. Thanks

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  • "Anagram solver" based on statistics rather than a dictionary/table?

    - by James M.
    My problem is conceptually similar to solving anagrams, except I can't just use a dictionary lookup. I am trying to find plausible words rather than real words. I have created an N-gram model (for now, N=2) based on the letters in a bunch of text. Now, given a random sequence of letters, I would like to permute them into the most likely sequence according to the transition probabilities. I thought I would need the Viterbi algorithm when I started this, but as I look deeper, the Viterbi algorithm optimizes a sequence of hidden random variables based on the observed output. I am trying to optimize the output sequence. Is there a well-known algorithm for this that I can read about? Or am I on the right track with Viterbi and I'm just not seeing how to apply it?

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  • Speeding up inner joins between a large table and a small table

    - by Zaid
    This may be a silly question, but it may shed some light on how joins work internally. Let's say I have a large table L and a small table S (100K rows vs. 100 rows). Would there be any difference in terms of speed between the following two options?: OPTION 1: OPTION 2: --------- --------- SELECT * SELECT * FROM L INNER JOIN S FROM S INNER JOIN L ON L.id = S.id; ON L.id = S.id; Notice that the only difference is the order in which the tables are joined. I realize performance may vary between different SQL languages. If so, how would MySQL compare to Access?

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  • Does the <script> tag position in HTML affects performance of the webpage?

    - by Rahul Joshi
    If the script tag is above or below the body in a HTML page, does it matter for the performance of a website? And what if used in between like this: <body> ..blah..blah.. <script language="JavaScript" src="JS_File_100_KiloBytes"> function f1() { .. some logic reqd. for manipulating contents in a webpage } </script> ... some text here too ... </body> Or is this better?: <script language="JavaScript" src="JS_File_100_KiloBytes"> function f1() { .. some logic reqd. for manipulating contents in a webpage } </script> <body> ..blah..blah.. ..call above functions on some events like onclick,onfocus,etc.. </body> Or this one?: <body> ..blah..blah.. ..call above functions on some events like onclick,onfocus,etc.. <script language="JavaScript" src="JS_File_100_KiloBytes"> function f1() { .. some logic reqd. for manipulating contents in a webpage } </script> </body> Need not tell everything is again in the <html> tag!! How does it affect performance of webpage while loading? Does it really? Which one is the best, either out of these 3 or some other which you know? And one more thing, I googled a bit on this, from which I went here: Best Practices for Speeding Up Your Web Site and it suggests put scripts at the bottom, but traditionally many people put it in <head> tag which is above the <body> tag. I know it's NOT a rule but many prefer it that way. If you don't believe it, just view source of this page! And tell me what's the better style for best performance.

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  • Optimize a MySQL count each duplicate Query

    - by Onema
    I have the following query That gets the city name, city id, the region name, and a count of duplicate names for that record: SELECT Country_CA.City AS currentCity, Country_CA.CityID, globe_region.region_name, ( SELECT count(Country_CA.City) FROM Country_CA WHERE City LIKE currentCity ) as counter FROM Country_CA LEFT JOIN globe_region ON globe_region.region_id = Country_CA.RegionID AND globe_region.country_code = Country_CA.CountryCode ORDER BY City This example is for Canada, and the cities will be displayed on a dropdown list. There are a few towns in Canada, and in other countries, that have the same names. Therefore I want to know if there is more than one town with the same name region name will be appended to the town name. Region names are found in the globe_region table. Country_CA and globe_region look similar to this (I have changed a few things for visualization purposes) CREATE TABLE IF NOT EXISTS `Country_CA` ( `City` varchar(75) NOT NULL DEFAULT '', `RegionID` varchar(10) NOT NULL DEFAULT '', `CountryCode` varchar(10) NOT NULL DEFAULT '', `CityID` int(11) NOT NULL DEFAULT '0', PRIMARY KEY (`City`,`RegionID`), KEY `CityID` (`CityID`) ) ENGINE=MyISAM DEFAULT CHARSET=utf8; AND CREATE TABLE IF NOT EXISTS `globe_region` ( `country_code` char(2) COLLATE utf8_unicode_ci NOT NULL, `region_code` char(2) COLLATE utf8_unicode_ci NOT NULL, `region_name` varchar(50) COLLATE utf8_unicode_ci NOT NULL, PRIMARY KEY (`country_code`,`region_code`) ) ENGINE=MyISAM DEFAULT CHARSET=utf8 COLLATE=utf8_unicode_ci; The query on the top does exactly what I want it to do, but It takes way too long to generate a list for 5000 records. I would like to know if there is a way to optimize the sub-query in order to obtain the same results faster. the results should look like this City CityID region_name counter sheraton 2349269 British Columbia 1 sherbrooke 2349270 Quebec 2 sherbrooke 2349271 Nova Scotia 2 shere 2349273 British Columbia 1 sherridon 2349274 Manitoba 1

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  • Can anyone recommend a decent tool for optimising images other than photoshop

    - by toomanyairmiles
    Can anyone recommend a decent tool for optimising images other than adobe photoshop, the gimp etc? I'm looking to optimise images for the web preferably online and free. Basically I have a client who can't install additional software on their work PC but needs to optimise photographs and other images for their website and is presently uploading 1 or 2 Mb files. On a personal level I'm interested to see what other people are using...

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  • c++ optimize array of ints

    - by a432511
    I have a 2D lookup table of int16_t. int16_t my_array[37][73] = {{**DATA HERE**}} I have a mixture of values that range from just above the range of int8_t to just below the range of int8_t and some of the values repeat themselves. I am trying to reduce the size of this lookup table. What I have done so far is split each int16_t value into two int8_t values to visualize the wasted bytes. int8_t part_1 = original_value >> 4; int8_t part_2 = original_value & 0x0000FFFF; // If the upper 4 bits of the original_value were empty if(part_1 == 0) wasted_bytes_count++; I can easily remove the zero value int8_t that are wasting a byte of space and I can also remove the duplicate values, but my question is how do I do remove those values while retaining the ability to lookup based on the two indices? I contemplated translating this into a 1D array and adding a number following each duplicated value that would represent the number of duplicates that were removed, but I am struggling with how I would then identify what is a lookup value and what is a duplicate count. Also, it is further complicated by stripping out the zero int8_t values that were wasted bytes. EDIT: This array is stored in ROM already. RAM is even more limited than ROM so it is already stored in ROM. EDIT: I am going to post a bounty for this question as soon as I can. I need a complete answer of how to store the information AND retrieve it. It does not need to be a 2D array as long as I can get the same values. EDIT: Adding the actual array below: {150,145,140,135,130,125,120,115,110,105,100,95,90,85,80,75,70,65,60,55,50,45,40,35,30,25,20,15,10,5,0,-4,-9,-14,-19,-24,-29,-34,-39,-44,-49,-54,-59,-64,-69,-74,-79,-84,-89,-94,-99,104,109,114,119,124,129,134,139,144,149,154,159,164,169,174,179,175,170,165,160,155,150}, \ {143,137,131,126,120,115,110,105,100,95,90,85,80,75,71,66,62,57,53,48,44,39,35,31,27,22,18,14,9,5,1,-3,-7,-11,-16,-20,-25,-29,-34,-38,-43,-47,-52,-57,-61,-66,-71,-76,-81,-86,-91,-96,101,107,112,117,123,128,134,140,146,151,157,163,169,175,178,172,166,160,154,148,143}, \ {130,124,118,112,107,101,96,92,87,82,78,74,70,65,61,57,54,50,46,42,38,34,31,27,23,19,16,12,8,4,1,-2,-6,-10,-14,-18,-22,-26,-30,-34,-38,-43,-47,-51,-56,-61,-65,-70,-75,-79,-84,-89,-94,100,105,111,116,122,128,135,141,148,155,162,170,177,174,166,159,151,144,137,130}, \ {111,104,99,94,89,85,81,77,73,70,66,63,60,56,53,50,46,43,40,36,33,30,26,23,20,16,13,10,6,3,0,-3,-6,-9,-13,-16,-20,-24,-28,-32,-36,-40,-44,-48,-52,-57,-61,-65,-70,-74,-79,-84,-88,-93,-98,103,109,115,121,128,135,143,152,162,172,176,165,154,144,134,125,118,111}, \ {85,81,77,74,71,68,65,63,60,58,56,53,51,49,46,43,41,38,35,32,29,26,23,19,16,13,10,7,4,1,-1,-3,-6,-9,-13,-16,-19,-23,-26,-30,-34,-38,-42,-46,-50,-54,-58,-62,-66,-70,-74,-78,-83,-87,-91,-95,100,105,110,117,124,133,144,159,178,160,141,125,112,103,96,90,85}, \ {62,60,58,57,55,54,52,51,50,48,47,46,44,42,41,39,36,34,31,28,25,22,19,16,13,10,7,4,2,0,-3,-5,-8,-10,-13,-16,-19,-22,-26,-29,-33,-37,-41,-45,-49,-53,-56,-60,-64,-67,-70,-74,-77,-80,-83,-86,-89,-91,-94,-97,101,105,111,130,109,84,77,74,71,68,66,64,62}, \ {46,46,45,44,44,43,42,42,41,41,40,39,38,37,36,35,33,31,28,26,23,20,16,13,10,7,4,1,-1,-3,-5,-7,-9,-12,-14,-16,-19,-22,-26,-29,-33,-36,-40,-44,-48,-51,-55,-58,-61,-64,-66,-68,-71,-72,-74,-74,-75,-74,-72,-68,-61,-48,-25,2,22,33,40,43,45,46,47,46,46}, \ {36,36,36,36,36,35,35,35,35,34,34,34,34,33,32,31,30,28,26,23,20,17,14,10,6,3,0,-2,-4,-7,-9,-10,-12,-14,-15,-17,-20,-23,-26,-29,-32,-36,-40,-43,-47,-50,-53,-56,-58,-60,-62,-63,-64,-64,-63,-62,-59,-55,-49,-41,-30,-17,-4,6,15,22,27,31,33,34,35,36,36}, \ {30,30,30,30,30,30,30,29,29,29,29,29,29,29,29,28,27,26,24,21,18,15,11,7,3,0,-3,-6,-9,-11,-12,-14,-15,-16,-17,-19,-21,-23,-26,-29,-32,-35,-39,-42,-45,-48,-51,-53,-55,-56,-57,-57,-56,-55,-53,-49,-44,-38,-31,-23,-14,-6,0,7,13,17,21,24,26,27,29,29,30}, \ {25,25,26,26,26,25,25,25,25,25,25,25,25,26,25,25,24,23,21,19,16,12,8,4,0,-3,-7,-10,-13,-15,-16,-17,-18,-19,-20,-21,-22,-23,-25,-28,-31,-34,-37,-40,-43,-46,-48,-49,-50,-51,-51,-50,-48,-45,-42,-37,-32,-26,-19,-13,-7,-1,3,7,11,14,17,19,21,23,24,25,25}, \ {21,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,21,20,18,16,13,9,5,1,-3,-7,-11,-14,-17,-18,-20,-21,-21,-22,-22,-22,-23,-23,-25,-27,-29,-32,-35,-37,-40,-42,-44,-45,-45,-45,-44,-42,-40,-36,-32,-27,-22,-17,-12,-7,-3,0,3,7,9,12,14,16,18,19,20,21,21}, \ {18,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,18,17,16,14,10,7,2,-1,-6,-10,-14,-17,-19,-21,-22,-23,-24,-24,-24,-24,-23,-23,-23,-24,-26,-28,-30,-33,-35,-37,-38,-39,-39,-38,-36,-34,-31,-28,-24,-19,-15,-10,-6,-3,0,1,4,6,8,10,12,14,15,16,17,18,18}, \ {16,16,17,17,17,17,17,17,17,17,17,16,16,16,16,16,16,15,13,11,8,4,0,-4,-9,-13,-16,-19,-21,-23,-24,-25,-25,-25,-25,-24,-23,-21,-20,-20,-21,-22,-24,-26,-28,-30,-31,-32,-31,-30,-29,-27,-24,-21,-17,-13,-9,-6,-3,-1,0,2,4,5,7,9,10,12,13,14,15,16,16}, \ {14,14,14,15,15,15,15,15,15,15,14,14,14,14,14,14,13,12,11,9,5,2,-2,-6,-11,-15,-18,-21,-23,-24,-25,-25,-25,-25,-24,-22,-21,-18,-16,-15,-15,-15,-17,-19,-21,-22,-24,-24,-24,-23,-22,-20,-18,-15,-12,-9,-5,-3,-1,0,1,2,4,5,6,8,9,10,11,12,13,14,14}, \ {12,13,13,13,13,13,13,13,13,13,13,13,12,12,12,12,11,10,9,6,3,0,-4,-8,-12,-16,-19,-21,-23,-24,-24,-24,-24,-23,-22,-20,-17,-15,-12,-10,-9,-9,-10,-12,-13,-15,-17,-17,-18,-17,-16,-15,-13,-11,-8,-5,-3,-1,0,1,1,2,3,4,6,7,8,9,10,11,12,12,12}, \ {11,11,11,11,11,12,12,12,12,12,11,11,11,11,11,10,10,9,7,5,2,-1,-5,-9,-13,-17,-20,-22,-23,-23,-23,-23,-22,-20,-18,-16,-14,-11,-9,-6,-5,-4,-5,-6,-8,-9,-11,-12,-12,-12,-12,-11,-9,-8,-6,-3,-1,0,0,1,1,2,3,4,5,6,7,8,9,10,11,11,11}, \ {10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,7,6,3,0,-3,-6,-10,-14,-17,-20,-21,-22,-22,-22,-21,-19,-17,-15,-13,-10,-8,-6,-4,-2,-2,-2,-2,-4,-5,-7,-8,-8,-9,-8,-8,-7,-5,-4,-2,0,0,1,1,1,2,2,3,4,5,6,7,8,9,10,10,10}, \ {9,9,9,9,9,9,9,10,10,9,9,9,9,9,9,8,8,6,5,2,0,-4,-7,-11,-15,-17,-19,-21,-21,-21,-20,-18,-16,-14,-12,-10,-8,-6,-4,-2,-1,0,0,0,-1,-2,-4,-5,-5,-6,-6,-5,-5,-4,-3,-1,0,0,1,1,1,1,2,3,3,5,6,7,8,8,9,9,9}, \ {9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,7,5,4,1,-1,-5,-8,-12,-15,-17,-19,-20,-20,-19,-18,-16,-14,-11,-9,-7,-5,-4,-2,-1,0,0,1,1,0,0,-2,-3,-3,-4,-4,-4,-3,-3,-2,-1,0,0,0,0,0,1,1,2,3,4,5,6,7,8,8,9,9}, \ {9,9,9,8,8,8,9,9,9,9,9,8,8,8,8,7,6,5,3,0,-2,-5,-9,-12,-15,-17,-18,-19,-19,-18,-16,-14,-12,-9,-7,-5,-4,-2,-1,0,0,1,1,1,1,0,0,-1,-2,-2,-3,-3,-2,-2,-1,-1,0,0,0,0,0,0,0,1,2,3,4,5,6,7,8,8,9}, \ {8,8,8,8,8,8,9,9,9,9,9,9,8,8,8,7,6,4,2,0,-3,-6,-9,-12,-15,-17,-18,-18,-17,-16,-14,-12,-10,-8,-6,-4,-2,-1,0,0,1,2,2,2,2,1,0,0,-1,-1,-1,-2,-2,-1,-1,0,0,0,0,0,0,0,0,0,1,2,3,4,5,6,7,8,8}, \ {8,8,8,8,9,9,9,9,9,9,9,9,9,8,8,7,5,3,1,-1,-4,-7,-10,-13,-15,-16,-17,-17,-16,-15,-13,-11,-9,-6,-5,-3,-2,0,0,0,1,2,2,2,2,1,1,0,0,0,-1,-1,-1,-1,-1,0,0,0,0,-1,-1,-1,-1,-1,0,0,1,3,4,5,7,7,8}, \ {8,8,9,9,9,9,10,10,10,10,10,10,10,9,8,7,5,3,0,-2,-5,-8,-11,-13,-15,-16,-16,-16,-15,-13,-12,-10,-8,-6,-4,-2,-1,0,0,1,2,2,3,3,2,2,1,0,0,0,0,0,0,0,0,0,0,-1,-1,-2,-2,-2,-2,-2,-1,0,0,1,3,4,6,7,8}, \ {7,8,9,9,9,10,10,11,11,11,11,11,10,10,9,7,5,3,0,-2,-6,-9,-11,-13,-15,-16,-16,-15,-14,-13,-11,-9,-7,-5,-3,-2,0,0,1,1,2,3,3,3,3,2,2,1,1,0,0,0,0,0,0,0,-1,-1,-2,-3,-3,-4,-4,-4,-3,-2,-1,0,1,3,5,6,7}, \ {6,8,9,9,10,11,11,12,12,12,12,12,11,11,9,7,5,2,0,-3,-7,-10,-12,-14,-15,-16,-15,-15,-13,-12,-10,-8,-7,-5,-3,-1,0,0,1,2,2,3,3,4,3,3,3,2,2,1,1,1,0,0,0,0,-1,-2,-3,-4,-4,-5,-5,-5,-5,-4,-2,-1,0,2,3,5,6}, \ {6,7,8,10,11,12,12,13,13,14,14,13,13,11,10,8,5,2,0,-4,-8,-11,-13,-15,-16,-16,-16,-15,-13,-12,-10,-8,-6,-5,-3,-1,0,0,1,2,3,3,4,4,4,4,4,3,3,3,2,2,1,1,0,0,-1,-2,-3,-5,-6,-7,-7,-7,-6,-5,-4,-3,-1,0,2,4,6}, \ {5,7,8,10,11,12,13,14,15,15,15,14,14,12,11,8,5,2,-1,-5,-9,-12,-14,-16,-17,-17,-16,-15,-14,-12,-11,-9,-7,-5,-3,-1,0,0,1,2,3,4,4,5,5,5,5,5,5,4,4,3,3,2,1,0,-1,-2,-4,-6,-7,-8,-8,-8,-8,-7,-6,-4,-2,0,1,3,5}, \ {4,6,8,10,12,13,14,15,16,16,16,16,15,13,11,9,5,2,-2,-6,-10,-13,-16,-17,-18,-18,-17,-16,-15,-13,-11,-9,-7,-5,-4,-2,0,0,1,3,3,4,5,6,6,7,7,7,7,7,6,5,4,3,2,0,-1,-3,-5,-7,-8,-9,-10,-10,-10,-9,-7,-5,-4,-1,0,2,4}, \ {4,6,8,10,12,14,15,16,17,18,18,17,16,15,12,9,5,1,-3,-8,-12,-15,-18,-19,-20,-20,-19,-18,-16,-15,-13,-11,-8,-6,-4,-2,-1,0,1,3,4,5,6,7,8,9,9,9,9,9,9,8,7,5,3,1,-1,-3,-6,-8,-10,-11,-12,-12,-11,-10,-9,-7,-5,-2,0,1,4}, \ {4,6,8,11,13,15,16,18,19,19,19,19,18,16,13,10,5,0,-5,-10,-15,-18,-21,-22,-23,-22,-22,-20,-18,-17,-14,-12,-10,-8,-5,-3,-1,0,1,3,5,6,8,9,10,11,12,12,13,12,12,11,9,7,5,2,0,-3,-6,-9,-11,-12,-13,-13,-12,-11,-10,-8,-6,-3,-1,1,4}, \ {3,6,9,11,14,16,17,19,20,21,21,21,19,17,14,10,4,-1,-8,-14,-19,-22,-25,-26,-26,-26,-25,-23,-21,-19,-17,-14,-12,-9,-7,-4,-2,0,1,3,5,7,9,11,13,14,15,16,16,16,16,15,13,10,7,4,0,-3,-7,-10,-12,-14,-15,-14,-14,-12,-11,-9,-6,-4,-1,1,3}, \ {4,6,9,12,14,17,19,21,22,23,23,23,21,19,15,9,2,-5,-13,-20,-25,-28,-30,-31,-31,-30,-29,-27,-25,-22,-20,-17,-14,-11,-9,-6,-3,0,1,4,6,9,11,13,15,17,19,20,21,21,21,20,18,15,11,6,2,-2,-7,-11,-13,-15,-16,-16,-15,-13,-11,-9,-7,-4,-1,1,4}, \ {4,7,10,13,15,18,20,22,24,25,25,25,23,20,15,7,-2,-12,-22,-29,-34,-37,-38,-38,-37,-36,-34,-31,-29,-26,-23,-20,-17,-13,-10,-7,-4,-1,2,5,8,11,13,16,18,21,23,24,26,26,26,26,24,21,17,12,5,0,-6,-10,-14,-16,-16,-16,-15,-14,-12,-10,-7,-4,-1,1,4}, \ {4,7,10,13,16,19,22,24,26,27,27,26,24,19,11,-1,-15,-28,-37,-43,-46,-47,-47,-45,-44,-41,-39,-36,-32,-29,-26,-22,-19,-15,-11,-8,-4,-1,2,5,9,12,15,19,22,24,27,29,31,33,33,33,32,30,26,21,14,6,0,-6,-11,-14,-15,-16,-15,-14,-12,-9,-7,-4,-1,1,4}, \ {6,9,12,15,18,21,23,25,27,28,27,24,17,4,-14,-34,-49,-56,-60,-60,-60,-58,-56,-53,-50,-47,-43,-40,-36,-32,-28,-25,-21,-17,-13,-9,-5,-1,2,6,10,14,17,21,24,28,31,34,37,39,41,42,43,43,41,38,33,25,17,8,0,-4,-8,-10,-10,-10,-8,-7,-4,-2,0,3,6}, \ {22,24,26,28,30,32,33,31,23,-18,-81,-96,-99,-98,-95,-93,-89,-86,-82,-78,-74,-70,-66,-62,-57,-53,-49,-44,-40,-36,-32,-27,-23,-19,-14,-10,-6,-1,2,6,10,15,19,23,27,31,35,38,42,45,49,52,55,57,60,61,63,63,62,61,57,53,47,40,33,28,23,21,19,19,19,20,22}, \ {168,173,178,176,171,166,161,156,151,146,141,136,131,126,121,116,111,106,101,-96,-91,-86,-81,-76,-71,-66,-61,-56,-51,-46,-41,-36,-31,-26,-21,-16,-11,-6,-1,3,8,13,18,23,28,33,38,43,48,53,58,63,68,73,78,83,88,93,98,103,108,113,118,123,128,133,138,143,148,153,158,163,168}, \ Thanks for your time.

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  • Google Web Optimizer -- How long until winning combination?

    - by Django Reinhardt
    I've had an A/B Test running in Google Web Optimizer for six weeks now, and there's still no end in sight. Google is still saying: "We have not gathered enough data yet to show any significant results. When we collect more data we should be able to show you a winning combination." Is there any way of telling how close Google is to making up its mind? (Does anyone know what algorithm does it use to decide if there's been any "high confidence winners"?) According to the Google help documentation: Sometimes we simply need more data to be able to reach a level of high confidence. A tested combination typically needs around 200 conversions for us to judge its performance with certainty. But all of our conversions have over 200 conversations at the moment: 230 / 4061 (Original) 223 / 3937 (Variation 1) 205 / 3984 (Variation 2) 205 / 4007 (Variation 3) How much longer is it going to have to run?? Thanks for any help.

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  • Is opening too many datacontexts bad?

    - by ryudice
    I've been checking my application with linq 2 sql profiler, and I noticed that it opens a lot of datacontexts, most of them are opened by the linq datasource I used, since my repositories use only the instance stored in Request.Items, is it bad to open too many datacontext? and how can I make my linqdatasource to use the datacontext that I store in Request.Items for the duration of the request? thanks for any help!

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  • Find point which sum of distances to set of other points is minimal

    - by Pawel Markowski
    I have one set (X) of points (not very big let's say 1-20 points) and the second (Y), much larger set of points. I need to choose some point from Y which sum of distances to all points from X is minimal. I came up with an idea that I would treat X as a vertices of a polygon and find centroid of this polygon, and then I will choose a point from Y nearest to the centroid. But I'm not sure whether centroid minimizes sum of its distances to the vertices of polygon, so I'm not sure whether this is a good way? Is there any algorithm for solving this problem? Points are defined by geographical coordinates.

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  • Is SQL DATEDIFF(year, ..., ...) an Expensive Computation?

    - by rlb.usa
    I'm trying to optimize up some horrendously complicated SQL queries because it takes too long to finish. In my queries, I have dynamically created SQL statements with lots of the same functions, so I created a temporary table where each function is only called once instead of many, many times - this cut my execution time by 3/4. So my question is, can I expect to see much of a difference if say, 1,000 datediff computations are narrowed to 100?

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  • Rewriting a for loop in pure NumPy to decrease execution time

    - by Statto
    I recently asked about trying to optimise a Python loop for a scientific application, and received an excellent, smart way of recoding it within NumPy which reduced execution time by a factor of around 100 for me! However, calculation of the B value is actually nested within a few other loops, because it is evaluated at a regular grid of positions. Is there a similarly smart NumPy rewrite to shave time off this procedure? I suspect the performance gain for this part would be less marked, and the disadvantages would presumably be that it would not be possible to report back to the user on the progress of the calculation, that the results could not be written to the output file until the end of the calculation, and possibly that doing this in one enormous step would have memory implications? Is it possible to circumvent any of these? import numpy as np import time def reshape_vector(v): b = np.empty((3,1)) for i in range(3): b[i][0] = v[i] return b def unit_vectors(r): return r / np.sqrt((r*r).sum(0)) def calculate_dipole(mu, r_i, mom_i): relative = mu - r_i r_unit = unit_vectors(relative) A = 1e-7 num = A*(3*np.sum(mom_i*r_unit, 0)*r_unit - mom_i) den = np.sqrt(np.sum(relative*relative, 0))**3 B = np.sum(num/den, 1) return B N = 20000 # number of dipoles r_i = np.random.random((3,N)) # positions of dipoles mom_i = np.random.random((3,N)) # moments of dipoles a = np.random.random((3,3)) # three basis vectors for this crystal n = [10,10,10] # points at which to evaluate sum gamma_mu = 135.5 # a constant t_start = time.clock() for i in range(n[0]): r_frac_x = np.float(i)/np.float(n[0]) r_test_x = r_frac_x * a[0] for j in range(n[1]): r_frac_y = np.float(j)/np.float(n[1]) r_test_y = r_frac_y * a[1] for k in range(n[2]): r_frac_z = np.float(k)/np.float(n[2]) r_test = r_test_x +r_test_y + r_frac_z * a[2] r_test_fast = reshape_vector(r_test) B = calculate_dipole(r_test_fast, r_i, mom_i) omega = gamma_mu*np.sqrt(np.dot(B,B)) # write r_test, B and omega to a file frac_done = np.float(i+1)/(n[0]+1) t_elapsed = (time.clock()-t_start) t_remain = (1-frac_done)*t_elapsed/frac_done print frac_done*100,'% done in',t_elapsed/60.,'minutes...approximately',t_remain/60.,'minutes remaining'

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  • Long-running Database Query

    - by JamesMLV
    I have a long-running SQL Server 2005 query that I have been hoping to optimize. When I look at the actual execution plan, it says a Clustered Index Seek has 66% of the cost. Execuation Plan Snippit: <RelOp AvgRowSize="31" EstimateCPU="0.0113754" EstimateIO="0.0609028" EstimateRebinds="0" EstimateRewinds="0" EstimateRows="10198.5" LogicalOp="Clustered Index Seek" NodeId="16" Parallel="false" PhysicalOp="Clustered Index Seek" EstimatedTotalSubtreeCost="0.0722782"> <OutputList> <ColumnReference Database="[wf_1]" Schema="[dbo]" Table="[Indices]" Alias="[I]" Column="quoteDate" /> <ColumnReference Database="[wf_1]" Schema="[dbo]" Table="[Indices]" Alias="[I]" Column="price" /> <ColumnReference Database="[wf_1]" Schema="[dbo]" Table="[Indices]" Alias="[I]" Column="tenure" /> </OutputList> <RunTimeInformation> <RunTimeCountersPerThread Thread="0" ActualRows="1067" ActualEndOfScans="1" ActualExecutions="1" /> </RunTimeInformation> <IndexScan Ordered="true" ScanDirection="FORWARD" ForcedIndex="false" NoExpandHint="false"> <DefinedValues> <DefinedValue> <ColumnReference Database="[wf_1]" Schema="[dbo]" Table="[Indices]" Alias="[I]" Column="quoteDate" /> </DefinedValue> <DefinedValue> <ColumnReference Database="[wf_1]" Schema="[dbo]" Table="[Indices]" Alias="[I]" Column="price" /> </DefinedValue> <DefinedValue> <ColumnReference Database="[wf_1]" Schema="[dbo]" Table="[Indices]" Alias="[I]" Column="tenure" /> </DefinedValue> </DefinedValues> <Object Database="[wf_1]" Schema="[dbo]" Table="[Indices]" Index="[_dta_index_Indices_14_320720195__K5_K2_K1_3]" Alias="[I]" /> <SeekPredicates> <SeekPredicate> <Prefix ScanType="EQ"> <RangeColumns> <ColumnReference Database="[wf_1]" Schema="[dbo]" Table="[Indices]" Alias="[I]" Column="HedgeProduct" ComputedColumn="true" /> </RangeColumns> <RangeExpressions> <ScalarOperator ScalarString="(1)"> <Const ConstValue="(1)" /> </ScalarOperator> </RangeExpressions> </Prefix> <StartRange ScanType="GE"> <RangeColumns> <ColumnReference Database="[wf_1]" Schema="[dbo]" Table="[Indices]" Alias="[I]" Column="tenure" /> </RangeColumns> <RangeExpressions> <ScalarOperator ScalarString="[@StartMonth]"> <Identifier> <ColumnReference Column="@StartMonth" /> </Identifier> </ScalarOperator> </RangeExpressions> </StartRange> <EndRange ScanType="LE"> <RangeColumns> <ColumnReference Database="[wf_1]" Schema="[dbo]" Table="[Indices]" Alias="[I]" Column="tenure" /> </RangeColumns> <RangeExpressions> <ScalarOperator ScalarString="[@EndMonth]"> <Identifier> <ColumnReference Column="@EndMonth" /> </Identifier> </ScalarOperator> </RangeExpressions> </EndRange> </SeekPredicate> </SeekPredicates> </IndexScan> </RelOp> From this, does anyone see an obvious problem that would be causing this to take so long? Here is the query: (SELECT quotedate, tenure, price, ActualVolume, HedgePortfolioValue, Price AS UnhedgedPrice, ((ActualVolume*Price - HedgePortfolioValue)/ActualVolume) AS HedgedPrice FROM ( SELECT [quoteDate] ,[price] , tenure ,isnull(wf_1.[Risks].[HedgePortValueAsOfDate2](1,tenureMonth,quotedate,price),0) as HedgePortfolioValue ,[TotalOperatingGasVolume] as ActualVolume FROM [wf_1].[dbo].[Indices] I inner join ( SELECT DISTINCT tenureMonth FROM [wf_1].[Risks].[KnowRiskTrades] WHERE HedgeProduct = 1 AND portfolio <> 'Natural Gas Hedge Transactions' ) B ON I.tenure=B.tenureMonth inner join ( SELECT [Month],[TotalOperatingGasVolume] FROM [wf_1].[Risks].[ActualGasVolumes] ) C ON C.[Month]=B.tenureMonth WHERE HedgeProduct = 1 AND quoteDate>=dateadd(day, -3*365, tenureMonth) AND quoteDate<=dateadd(day,-3,tenureMonth) )A )

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  • Can I use Duff's Device on an array in C?

    - by Ben Fossen
    I have a loop here and I want to make it run faster. I am passing in a large array. I recently heard of Duff's Device can it be applied to this for loop? any ideas? for (i = 0; i < dim; i++) { for (j = 0; j < dim; j++) { dst[RIDX(dim-1-j, i, dim)] = src[RIDX(i, j, dim)]; } }

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  • Quickest way to write to file in java

    - by user1097772
    I'm writing an application which compares directory structure. First I wrote an application which writes gets info about files - one line about each file or directory. My soulution is: calling method toFile Static PrintWriter pw = new PrintWriter(new BufferedWriter( new FileWriter("DirStructure.dlis")), true); String line; // info about file or directory public void toFile(String line) { pw.println(line); } and of course pw.close(), at the end. My question is, can I do it quicker? What is the quickest way? Edit: quickest way = quickest writing in the file

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  • Data Access from single table in sql server 2005 is too slow

    - by Muhammad Kashif Nadeem
    Following is the script of table. Accessing data from this table is too slow. SET ANSI_NULLS ON GO SET QUOTED_IDENTIFIER ON GO CREATE TABLE [dbo].[Emails]( [id] [int] IDENTITY(1,1) NOT NULL, [datecreated] [datetime] NULL CONSTRAINT [DF_Emails_datecreated] DEFAULT (getdate()), [UID] [nvarchar](250) COLLATE Latin1_General_CI_AS NULL, [From] [nvarchar](100) COLLATE Latin1_General_CI_AS NULL, [To] [nvarchar](100) COLLATE Latin1_General_CI_AS NULL, [Subject] [nvarchar](max) COLLATE Latin1_General_CI_AS NULL, [Body] [nvarchar](max) COLLATE Latin1_General_CI_AS NULL, [HTML] [nvarchar](max) COLLATE Latin1_General_CI_AS NULL, [AttachmentCount] [int] NULL, [Dated] [datetime] NULL ) ON [PRIMARY] Following query takes 50 seconds to fetch data. select id, datecreated, UID, [From], [To], Subject, AttachmentCount, Dated from emails If I include Body and Html in select then time is event worse. indexes are on: id unique clustered From Non unique non clustered To Non unique non clustered Tabls has currently 180000+ records. There might be 100,000 records each month so this will become more slow as time will pass. Does splitting data into two table will solve the problem? What other indexes should be there?

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