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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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• 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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• 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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• 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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• An image from byte to optimized web page presentation

  - by blgnklc

  I get the data of the stored image on database as byte[] array; then I convert it to System.Drawing.Image like the code shown below; public System.Drawing.Image CreateImage(byte[] bytes) { System.IO.MemoryStream memoryStream = new System.IO.MemoryStream(bytes); System.Drawing.Image image = System.Drawing.Image.FromStream(memoryStream); return image; } (*) On the other hand I am planning to show a list of images on asp.net pages as the client scrolls downs the page. The more user gets down and down on the page he/she does see the more photos. So it means fast page loads and rich user experience. (you may see what I mean on www.mashable.com, just take care the new loads of the photos as you scroll down.) Moreover, the returned imgae object from the method above, how can i show it in a loop dynamically using the (*) conditions above. Regards bk

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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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• Trying to reduce the speed overhead of an almost-but-not-quite-int number class

  - by Fumiyo Eda

  I have implemented a C++ class which behaves very similarly to the standard int type. The difference is that it has an additional concept of "epsilon" which represents some tiny value that is much less than 1, but greater than 0. One way to think of it is as a very wide fixed point number with 32 MSBs (the integer parts), 32 LSBs (the epsilon parts) and a huge sea of zeros in between. The following class works, but introduces a ~2x speed penalty in the overall program. (The program includes code that has nothing to do with this class, so the actual speed penalty of this class is probably much greater than 2x.) I can't paste the code that is using this class, but I can say the following: +, -, +=, <, > and >= are the only heavily used operators. Use of setEpsilon() and getInt() is extremely rare. * is also rare, and does not even need to consider the epsilon values at all. Here is the class: #include <limits> struct int32Uepsilon { typedef int32Uepsilon Self; int32Uepsilon () { _value = 0; _eps = 0; } int32Uepsilon (const int &i) { _value = i; _eps = 0; } void setEpsilon() { _eps = 1; } Self operator+(const Self &rhs) const { Self result = *this; result._value += rhs._value; result._eps += rhs._eps; return result; } Self operator-(const Self &rhs) const { Self result = *this; result._value -= rhs._value; result._eps -= rhs._eps; return result; } Self operator-( ) const { Self result = *this; result._value = -result._value; result._eps = -result._eps; return result; } Self operator*(const Self &rhs) const { return this->getInt() * rhs.getInt(); } // XXX: discards epsilon bool operator<(const Self &rhs) const { return (_value < rhs._value) || (_value == rhs._value && _eps < rhs._eps); } bool operator>(const Self &rhs) const { return (_value > rhs._value) || (_value == rhs._value && _eps > rhs._eps); } bool operator>=(const Self &rhs) const { return (_value >= rhs._value) || (_value == rhs._value && _eps >= rhs._eps); } Self &operator+=(const Self &rhs) { this->_value += rhs._value; this->_eps += rhs._eps; return *this; } Self &operator-=(const Self &rhs) { this->_value -= rhs._value; this->_eps -= rhs._eps; return *this; } int getInt() const { return(_value); } private: int _value; int _eps; }; namespace std { template<> struct numeric_limits<int32Uepsilon> { static const bool is_signed = true; static int max() { return 2147483647; } } }; The code above works, but it is quite slow. Does anyone have any ideas on how to improve performance? There are a few hints/details I can give that might be helpful: 32 bits are definitely insufficient to hold both _value and _eps. In practice, up to 24 ~ 28 bits of _value are used and up to 20 bits of _eps are used. I could not measure a significant performance difference between using int32_t and int64_t, so memory overhead itself is probably not the problem here. Saturating addition/subtraction on _eps would be cool, but isn't really necessary. Note that the signs of _value and _eps are not necessarily the same! This broke my first attempt at speeding this class up. Inline assembly is no problem, so long as it works with GCC on a Core i7 system running Linux!

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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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• Any difference between lazy loading Javascript files vs. placing just before </body>

  - by mhr

  Looked around, couldn't find this specific question discussed. Pretty sure the difference is negligible, just curious as to your thoughts. Scenario: All Javascript that doesn't need to be loaded before page render has been placed just before the closing </body> tag. Are there any benefits or detriments to lazy loading these instead through some Javascript code in the head that executes when the DOM load/ready event is fired? Let's say that this only concerns downloading one entire .js file full of functions and not lazy loading several individual files as needed upon usage. Hope that's clear, thanks.

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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 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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• 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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• 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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• 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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• approximating log10[x^k0 + k1]

  - by Yale Zhang

  Greetings. I'm trying to approximate the function Log10[x^k0 + k1], where .21 < k0 < 21, 0 < k1 < ~2000, and x is integer < 2^14. k0 & k1 are constant. For practical purposes, you can assume k0 = 2.12, k1 = 2660. The desired accuracy is 5*10^-4 relative error. This function is virtually identical to Log[x], except near 0, where it differs a lot. I already have came up with a SIMD implementation that is ~1.15x faster than a simple lookup table, but would like to improve it if possible, which I think is very hard due to lack of efficient instructions. My SIMD implementation uses 16bit fixed point arithmetic to evaluate a 3rd degree polynomial (I use least squares fit). The polynomial uses different coefficients for different input ranges. There are 8 ranges, and range i spans (64)2^i to (64)2^(i + 1). The rational behind this is the derivatives of Log[x] drop rapidly with x, meaning a polynomial will fit it more accurately since polynomials are an exact fit for functions that have a derivative of 0 beyond a certain order. SIMD table lookups are done very efficiently with a single _mm_shuffle_epi8(). I use SSE's float to int conversion to get the exponent and significand used for the fixed point approximation. I also software pipelined the loop to get ~1.25x speedup, so further code optimizations are probably unlikely. What I'm asking is if there's a more efficient approximation at a higher level? For example: Can this function be decomposed into functions with a limited domain like log2((2^x) * significand) = x + log2(significand) hence eliminating the need to deal with different ranges (table lookups). The main problem I think is adding the k1 term kills all those nice log properties that we know and love, making it not possible. Or is it? Iterative method? don't think so because the Newton method for log[x] is already a complicated expression Exploiting locality of neighboring pixels? - if the range of the 8 inputs fall in the same approximation range, then I can look up a single coefficient, instead of looking up separate coefficients for each element. Thus, I can use this as a fast common case, and use a slower, general code path when it isn't. But for my data, the range needs to be ~2000 before this property hold 70% of the time, which doesn't seem to make this method competitive. Please, give me some opinion, especially if you're an applied mathematician, even if you say it can't be done. Thanks.

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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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• when is java faster than c++ (or when is JIT faster then precompiled)?

  - by kostja

  I have heard that under certain circumstances, Java programs or rather parts of java programs are able to be executed faster than the "same" code in C++ (or other precompiled code) due to JIT optimizations. This is due to the compiler being able to determine the scope of some variables, avoid some conditionals and pull similar tricks at runtime. Could you give an (or better - some) example, where this applies? And maybe outline the exact conditions under which the compiler is able to optimize the bytecode beyond what is possible with precompiled code? NOTE : This question is not about comparing Java to C++. Its about the possibilities of JIT compiling. Please no flaming. I am also not aware of any duplicates. Please point them out if you are.

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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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• 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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• Mysql - help me optimize this query (improved question)

  - by sandeepan-nath

  About the system: - There are tutors who create classes and packs - A tags based search approach is being followed.Tag relations are created when new tutors register and when tutors create packs (this makes tutors and packs searcheable). For details please check the section How tags work in this system? below. Following is the concerned query SELECT SUM(DISTINCT( t.tag LIKE "%Dictatorship%" )) AS key_1_total_matches, SUM(DISTINCT( t.tag LIKE "%democracy%" )) AS key_2_total_matches, COUNT(DISTINCT( od.id_od )) AS tutor_popularity, CASE WHEN ( IF(( wc.id_wc > 0 ), ( wc.wc_api_status = 1 AND wc.wc_type = 0 AND wc.class_date > '2010-06-01 22:00:56' AND wccp.status = 1 AND ( wccp.country_code = 'IE' OR wccp.country_code IN ( 'INT' ) ) ), 0) ) THEN 1 ELSE 0 END AS 'classes_published', CASE WHEN ( IF(( lp.id_lp > 0 ), ( lp.id_status = 1 AND lp.published = 1 AND lpcp.status = 1 AND ( lpcp.country_code = 'IE' OR lpcp.country_code IN ( 'INT' ) ) ), 0) ) THEN 1 ELSE 0 END AS 'packs_published', td . *, u . * FROM tutor_details AS td JOIN users AS u ON u.id_user = td.id_user LEFT JOIN learning_packs_tag_relations AS lptagrels ON td.id_tutor = lptagrels.id_tutor LEFT JOIN learning_packs AS lp ON lptagrels.id_lp = lp.id_lp LEFT JOIN learning_packs_categories AS lpc ON lpc.id_lp_cat = lp.id_lp_cat LEFT JOIN learning_packs_categories AS lpcp ON lpcp.id_lp_cat = lpc.id_parent LEFT JOIN learning_pack_content AS lpct ON ( lp.id_lp = lpct.id_lp ) LEFT JOIN webclasses_tag_relations AS wtagrels ON td.id_tutor = wtagrels.id_tutor LEFT JOIN webclasses AS wc ON wtagrels.id_wc = wc.id_wc LEFT JOIN learning_packs_categories AS wcc ON wcc.id_lp_cat = wc.id_wp_cat LEFT JOIN learning_packs_categories AS wccp ON wccp.id_lp_cat = wcc.id_parent LEFT JOIN order_details AS od ON td.id_tutor = od.id_author LEFT JOIN orders AS o ON od.id_order = o.id_order LEFT JOIN tutors_tag_relations AS ttagrels ON td.id_tutor = ttagrels.id_tutor JOIN tags AS t ON ( t.id_tag = ttagrels.id_tag ) OR ( t.id_tag = lptagrels.id_tag ) OR ( t.id_tag = wtagrels.id_tag ) WHERE ( u.country = 'IE' OR u.country IN ( 'INT' ) ) AND CASE WHEN ( ( t.id_tag = lptagrels.id_tag ) AND ( lp.id_lp 0 ) ) THEN lp.id_status = 1 AND lp.published = 1 AND lpcp.status = 1 AND ( lpcp.country_code = 'IE' OR lpcp.country_code IN ( 'INT' ) ) ELSE 1 END AND CASE WHEN ( ( t.id_tag = wtagrels.id_tag ) AND ( wc.id_wc 0 ) ) THEN wc.wc_api_status = 1 AND wc.wc_type = 0 AND wc.class_date '2010-06-01 22:00:56' AND wccp.status = 1 AND ( wccp.country_code = 'IE' OR wccp.country_code IN ( 'INT' ) ) ELSE 1 END AND CASE WHEN ( od.id_od 0 ) THEN od.id_author = td.id_tutor AND o.order_status = 'paid' AND CASE WHEN ( od.id_wc 0 ) THEN od.can_attend_class = 1 ELSE 1 END ELSE 1 END GROUP BY td.id_tutor HAVING key_1_total_matches = 1 AND key_2_total_matches = 1 ORDER BY tutor_popularity DESC, u.surname ASC, u.name ASC LIMIT 0, 20 The problem The results returned by the above query are correct (AND logic working as per expectation), but the time taken by the query rises alarmingly for heavier data and for the current data I have it is like 25 seconds as against normal query timings of the order of 0.005 - 0.0002 seconds, which makes it totally unusable. It is possible that some of the delay is being caused because all the possible fields have not yet been indexed. The tag field of tags table is indexed. Is there something faulty with the query? What can be the reason behind 20+ seconds of execution time? How tags work in this system? When a tutor registers, tags are entered and tag relations are created with respect to tutor's details like name, surname etc. When a Tutors create packs, again tags are entered and tag relations are created with respect to pack's details like pack name, description etc. tag relations for tutors stored in tutors_tag_relations and those for packs stored in learning_packs_tag_relations. All individual tags are stored in tags table. The explain query output:- Please see this screenshot - http://www.test.examvillage.com/Explain_query.jpg

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• Will the compiler optimize escaping an inner loop?

  - by BCS

  The code I have looks like this (all uses of done shown): bool done = false; for(int i = 0; i < big; i++) { ... for(int j = 0; j < wow; j++) { ... if(foo(i,j)) { done = true; break; } ... } if(done) break; ... } will any compilers convert it to this: for(int i = 0; i < big; i++) { ... for(int j = 0; j < wow; j++) { ... if(foo(i,j)) goto __done; // same as a labeled break if we had it ... } ... } __done:;

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• how to speed up the code??

  - by kaushik

  in my program i have a method which requires about 4 files to be open each time it is called,as i require to take some data.all this data from the file i have been storing in list for manupalation. I approximatily need to call this method about 10,000 times.which is making my program very slow? any method for handling this files in a better ways and is storing the whole data in list time consuming what is better alternatives for list? I can give some code,but my previous question was closed as that only confused everyone as it is a part of big program and need to be explained completely to understand,so i am not giving any code,please suggest ways thinking this as a general question... thanks in advance

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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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• How can I strip Python logging calls without commenting them out?

  - by cdleary

  Today I was thinking about a Python project I wrote about a year back where I used logging pretty extensively. I remember having to comment out a lot of logging calls in inner-loop-like scenarios (the 90% code) because of the overhead (hotshot indicated it was one of my biggest bottlenecks). I wonder now if there's some canonical way to programmatically strip out logging calls in Python applications without commenting and uncommenting all the time. I'd think you could use inspection/recompilation or bytecode manipulation to do something like this and target only the code objects that are causing bottlenecks. This way, you could add a manipulator as a post-compilation step and use a centralized configuration file, like so: [Leave ERROR and above] my_module.SomeClass.method_with_lots_of_warn_calls [Leave WARN and above] my_module.SomeOtherClass.method_with_lots_of_info_calls [Leave INFO and above] my_module.SomeWeirdClass.method_with_lots_of_debug_calls Of course, you'd want to use it sparingly and probably with per-function granularity -- only for code objects that have shown logging to be a bottleneck. Anybody know of anything like this? Note: There are a few things that make this more difficult to do in a performant manner because of dynamic typing and late binding. For example, any calls to a method named debug may have to be wrapped with an if not isinstance(log, Logger). In any case, I'm assuming all of the minor details can be overcome, either by a gentleman's agreement or some run-time checking. :-)

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• Is there a faster TList implementation ?

  - by dmauric.mp

  My application makes heavy use of TList, so I was wondering if there are any alternative implementations that are faster or optimized for particular use case. I know of RtlVCLOptimize.pas 2.77, which has optimized implementations of several TList methods. But I'd like to know if there is anything else out there. I also don't require it to be a TList descendant, I just need the TList functionality regardless of how it's implemented. It's entirely possible, given the rather basic functionality TList provides, that there is not much room for improvement, but would still like to verify that, hence this question.

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