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  • Is a red-black tree my ideal data structure?

    - by Hugo van der Sanden
    I have a collection of items (big rationals) that I'll be processing. In each case, processing will consist of removing the smallest item in the collection, doing some work, and then adding 0-2 new items (which will always be larger than the removed item). The collection will be initialised with one item, and work will continue until it is empty. I'm not sure what size the collection is likely to reach, but I'd expect in the range 1M-100M items. I will not need to locate any item other than the smallest. I'm currently planning to use a red-black tree, possibly tweaked to keep a pointer to the smallest item. However I've never used one before, and I'm unsure whether my pattern of use fits its characteristics well. 1) Is there a danger the pattern of deletion from the left + random insertion will affect performance, eg by requiring a significantly higher number of rotations than random deletion would? Or will delete and insert operations still be O(log n) with this pattern of use? 2) Would some other data structure give me better performance, either because of the deletion pattern or taking advantage of the fact I only ever need to find the smallest item? Update: glad I asked, the binary heap is clearly a better solution for this case, and as promised turned out to be very easy to implement. Hugo

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  • Python performance improvement request for winkler

    - by Martlark
    I'm a python n00b and I'd like some suggestions on how to improve the algorithm to improve the performance of this method to compute the Jaro-Winkler distance of two names. def winklerCompareP(str1, str2): """Return approximate string comparator measure (between 0.0 and 1.0) USAGE: score = winkler(str1, str2) ARGUMENTS: str1 The first string str2 The second string DESCRIPTION: As described in 'An Application of the Fellegi-Sunter Model of Record Linkage to the 1990 U.S. Decennial Census' by William E. Winkler and Yves Thibaudeau. Based on the 'jaro' string comparator, but modifies it according to whether the first few characters are the same or not. """ # Quick check if the strings are the same - - - - - - - - - - - - - - - - - - # jaro_winkler_marker_char = chr(1) if (str1 == str2): return 1.0 len1 = len(str1) len2 = len(str2) halflen = max(len1,len2) / 2 - 1 ass1 = '' # Characters assigned in str1 ass2 = '' # Characters assigned in str2 #ass1 = '' #ass2 = '' workstr1 = str1 workstr2 = str2 common1 = 0 # Number of common characters common2 = 0 #print "'len1', str1[i], start, end, index, ass1, workstr2, common1" # Analyse the first string - - - - - - - - - - - - - - - - - - - - - - - - - # for i in range(len1): start = max(0,i-halflen) end = min(i+halflen+1,len2) index = workstr2.find(str1[i],start,end) #print 'len1', str1[i], start, end, index, ass1, workstr2, common1 if (index > -1): # Found common character common1 += 1 #ass1 += str1[i] ass1 = ass1 + str1[i] workstr2 = workstr2[:index]+jaro_winkler_marker_char+workstr2[index+1:] #print "str1 analyse result", ass1, common1 #print "str1 analyse result", ass1, common1 # Analyse the second string - - - - - - - - - - - - - - - - - - - - - - - - - # for i in range(len2): start = max(0,i-halflen) end = min(i+halflen+1,len1) index = workstr1.find(str2[i],start,end) #print 'len2', str2[i], start, end, index, ass1, workstr1, common2 if (index > -1): # Found common character common2 += 1 #ass2 += str2[i] ass2 = ass2 + str2[i] workstr1 = workstr1[:index]+jaro_winkler_marker_char+workstr1[index+1:] if (common1 != common2): print('Winkler: Wrong common values for strings "%s" and "%s"' % \ (str1, str2) + ', common1: %i, common2: %i' % (common1, common2) + \ ', common should be the same.') common1 = float(common1+common2) / 2.0 ##### This is just a fix ##### if (common1 == 0): return 0.0 # Compute number of transpositions - - - - - - - - - - - - - - - - - - - - - # transposition = 0 for i in range(len(ass1)): if (ass1[i] != ass2[i]): transposition += 1 transposition = transposition / 2.0 # Now compute how many characters are common at beginning - - - - - - - - - - # minlen = min(len1,len2) for same in range(minlen+1): if (str1[:same] != str2[:same]): break same -= 1 if (same > 4): same = 4 common1 = float(common1) w = 1./3.*(common1 / float(len1) + common1 / float(len2) + (common1-transposition) / common1) wn = w + same*0.1 * (1.0 - w) return wn

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  • PostgreSQL - fetch the row which has the Max value for a column

    - by Joshua Berry
    I'm dealing with a Postgres table (called "lives") that contains records with columns for time_stamp, usr_id, transaction_id, and lives_remaining. I need a query that will give me the most recent lives_remaining total for each usr_id There are multiple users (distinct usr_id's) time_stamp is not a unique identifier: sometimes user events (one by row in the table) will occur with the same time_stamp. trans_id is unique only for very small time ranges: over time it repeats remaining_lives (for a given user) can both increase and decrease over time example: time_stamp|lives_remaining|usr_id|trans_id ----------------------------------------- 07:00 | 1 | 1 | 1 09:00 | 4 | 2 | 2 10:00 | 2 | 3 | 3 10:00 | 1 | 2 | 4 11:00 | 4 | 1 | 5 11:00 | 3 | 1 | 6 13:00 | 3 | 3 | 1 As I will need to access other columns of the row with the latest data for each given usr_id, I need a query that gives a result like this: time_stamp|lives_remaining|usr_id|trans_id ----------------------------------------- 11:00 | 3 | 1 | 6 10:00 | 1 | 2 | 4 13:00 | 3 | 3 | 1 As mentioned, each usr_id can gain or lose lives, and sometimes these timestamped events occur so close together that they have the same timestamp! Therefore this query won't work: SELECT b.time_stamp,b.lives_remaining,b.usr_id,b.trans_id FROM (SELECT usr_id, max(time_stamp) AS max_timestamp FROM lives GROUP BY usr_id ORDER BY usr_id) a JOIN lives b ON a.max_timestamp = b.time_stamp Instead, I need to use both time_stamp (first) and trans_id (second) to identify the correct row. I also then need to pass that information from the subquery to the main query that will provide the data for the other columns of the appropriate rows. This is the hacked up query that I've gotten to work: SELECT b.time_stamp,b.lives_remaining,b.usr_id,b.trans_id FROM (SELECT usr_id, max(time_stamp || '*' || trans_id) AS max_timestamp_transid FROM lives GROUP BY usr_id ORDER BY usr_id) a JOIN lives b ON a.max_timestamp_transid = b.time_stamp || '*' || b.trans_id ORDER BY b.usr_id Okay, so this works, but I don't like it. It requires a query within a query, a self join, and it seems to me that it could be much simpler by grabbing the row that MAX found to have the largest timestamp and trans_id. The table "lives" has tens of millions of rows to parse, so I'd like this query to be as fast and efficient as possible. I'm new to RDBM and Postgres in particular, so I know that I need to make effective use of the proper indexes. I'm a bit lost on how to optimize. I found a similar discussion here. Can I perform some type of Postgres equivalent to an Oracle analytic function? Any advice on accessing related column information used by an aggregate function (like MAX), creating indexes, and creating better queries would be much appreciated! P.S. You can use the following to create my example case: create TABLE lives (time_stamp timestamp, lives_remaining integer, usr_id integer, trans_id integer); insert into lives values ('2000-01-01 07:00', 1, 1, 1); insert into lives values ('2000-01-01 09:00', 4, 2, 2); insert into lives values ('2000-01-01 10:00', 2, 3, 3); insert into lives values ('2000-01-01 10:00', 1, 2, 4); insert into lives values ('2000-01-01 11:00', 4, 1, 5); insert into lives values ('2000-01-01 11:00', 3, 1, 6); insert into lives values ('2000-01-01 13:00', 3, 3, 1);

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  • How's my pygame code?

    - by Isaiah
    I'm still getting the hang of lots of things and thought I should post some code I made with pygame and get some feedback^^. I posted code here: http://urlvars.com/code/snippet/39272/my-bouncing-program http://urlvars.com/code/snippet/39273/my-bouncing-program-classes There's tome things that I implemented that I'm not using yet I just realized like a timer at the bottom of the main while loop. If my code isn't readable, I'm sorry, I'm self taught and this is the first code I've ever posted anywhere. By the way I made some variables that take the screensize and half it to find a point to spit out the squares, but when I try to use it, it makes a weird effect :/ Try switching the list i have in the newbyte() function with the halfScreen variable and see it freak out o.O thank you

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  • Is there a way to optimize this mysql query...?

    - by SpikETidE
    Hi Everyone... Say, I got these two tables.... Table 1 : Hotels hotel_id hotel_name 1 abc 2 xyz 3 efg Table 2 : Payments payment_id payment_date hotel_id total_amt comission p1 23-03-2010 1 100 10 p2 23-03-2010 2 50 5 p3 23-03-2010 2 200 25 p4 23-03-2010 1 40 2 Now, I need to get the following details from the two tables Given a particular date (say, 23-03-2010), the sum of the total_amt for each of the hotel for which a payment has been made on that particular date. All the rows that has the date 23-03-2010 ordered according to the hotel name A sample output is as follows... +------------+------------+------------+---------------+ | hotel_name | date | total_amt | commission | +------------+------------+------------+---------------+ | * abc | 23-03-2010 | 140 | 12 | +------------+------------+------------+---------------+ |+-----------+------------+------------+--------------+| || paymt_id | date | total_amt | commission || |+-----------+------------+------------+--------------+| || p1 | 23-03-2010 | 100 | 10 || |+-----------+------------+------------+--------------+| || p4 | 23-03-2010 | 40 | 2 || |+-----------+------------+------------+--------------+| +------------+------------+------------+---------------+ | * xyz | 23-03-2010 | 250 | 30 | +------------+------------+------------+---------------+ |+-----------+------------+------------+--------------+| || paymt_id | date | total_amt | commission || |+-----------+------------+------------+--------------+| || p2 | 23-03-2010 | 50 | 5 || |+-----------+------------+------------+--------------+| || p3 | 23-03-2010 | 200 | 25 || |+-----------+------------+------------+--------------+| +------------------------------------------------------+ Above the sample of the table that has to be printed... The idea is first to show the consolidated detail of each hotel, and when the '*' next to the hotel name is clicked the breakdown of the payment details will become visible... But that can be done by some jquery..!!! The table itself can be generated with php... Right now i am using two separate queries : One to get the sum of the amount and commission grouped by the hotel name. The next is to get the individual row for each entry having that date in the table. This is, of course, because grouping the records for calculating sum() returns only one row for each of the hotel with the sum of the amounts... Is there a way to combine these two queries into a single one and do the operation in a more optimized way...?? Hope i am being clear.. Thanks for your time and replies...

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  • MySQL efficiency as it relates to the database/table size

    - by mlissner
    I'm building a system using django, Sphinx and MySQL that's very quickly becoming quite large. The database currently has about 2000 rows, and I've written a program that's going to populate it with another 40,000 rows in a couple days. Since the database is live right now, and since I've never had a database with this much information in it, I'm worried about some things: Is adding all these rows going to seriously degrade the efficiency of my django app? Will I need to go back through it and optimize all my database calls so they're doing things more cleverly? Or will this make the database slow all around to the extent that I can't do anything about it at all? If you scoff at my 40k rows, then, my next question is, at what point SHOULD I be concerned? I will likely be adding another couple hundred thousand soon, so I worry, and I fret. How is sphinx going to feel about all this? Is it going to freak out when it realizes it has to index all this data? Or will it be fine? Is this normal for it? If it is, at what point should I be concerned that it's too much data for Sphinx? Thanks for any thoughts.

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  • Multiple ParticleSystems in cocos2d

    - by Mattias Akerman
    I wonder about what road I should go with ParticleSystem. In this particular case I want to create 1-20 small explosions at the same time but with different positions. Right now I'm creating a new ParticleSystem for each explosion and then release it, but of course this is very punishing to the performance. My question is: Is there a way to create one ParticleSystem with multiple emitting sources. If not should I create an array of ParticleSystem in init and then use a free one when an explosion is needed? Or is there another approach I haven't thought of?

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  • What is the best algorithm for this problem?

    - by mark
    What is the most efficient algorithm to solve the following problem? Given 6 arrays, D1,D2,D3,D4,D5 and D6 each containing 6 numbers like: D1[0] = number D2[0] = number ...... D6[0] = number D1[1] = another number D2[1] = another number .... ..... .... ...... .... D1[5] = yet another number .... ...... .... Given a second array ST1, containing 1 number: ST1[0] = 6 Given a third array ans, containing 6 numbers: ans[0] = 3, ans[1] = 4, ans[2] = 5, ......ans[5] = 8 Using as index for the arrays D1,D2,D3,D4,D5 and D6, the number that goes from 0, to the number stored in ST1[0] minus one, in this example 6, so from 0 to 6-1, compare each res array against each D array My algorithm so far is: I tried to keep everything unlooped as much as possible. EML := ST1[0] //number contained in ST1[0] EML1 := 0 //start index for the arrays D While EML1 < EML if D1[ELM1] = ans[0] goto two if D2[ELM1] = ans[0] goto two if D3[ELM1] = ans[0] goto two if D4[ELM1] = ans[0] goto two if D5[ELM1] = ans[0] goto two if D6[ELM1] = ans[0] goto two ELM1 = ELM1 + 1 return 0 //bad row of numbers, if while ends two: EML1 := 0 start index for arrays Ds While EML1 < EML if D1[ELM1] = ans[1] goto two if D2[ELM1] = ans[1] goto two if D3[ELM1] = ans[1] goto two if D4[ELM1] = ans[1] goto two if D5[ELM1] = ans[1] goto two if D6[ELM1] = ans[1] goto two ELM1 = ELM1 + 1 return 0 three: EML1 := 0 start index for arrays Ds While EML1 < EML if D1[ELM1] = ans[2] goto two if D2[ELM1] = ans[2] goto two if D3[ELM1] = ans[2] goto two if D4[ELM1] = ans[2] goto two if D5[ELM1] = ans[2] goto two if D6[ELM1] = ans[2] goto two ELM1 = ELM1 + 1 return 0 four: EML1 := 0 start index for arrays Ds While EML1 < EML if D1[ELM1] = ans[3] goto two if D2[ELM1] = ans[3] goto two if D3[ELM1] = ans[3] goto two if D4[ELM1] = ans[3] goto two if D5[ELM1] = ans[3] goto two if D6[ELM1] = ans[3] goto two ELM1 = ELM1 + 1 return 0 five: EML1 := 0 start index for arrays Ds While EML1 < EML if D1[ELM1] = ans[4] goto two if D2[ELM1] = ans[4] goto two if D3[ELM1] = ans[4] goto two if D4[ELM1] = ans[4] goto two if D5[ELM1] = ans[4] goto two if D6[ELM1] = ans[4] goto two ELM1 = ELM1 + 1 return 0 six: EML1 := 0 start index for arrays Ds While EML1 < EML if D1[ELM1] = ans[0] return 1 //good row of numbers if D2[ELM1] = ans[0] return 1 if D3[ELM1] = ans[0] return 1 if D4[ELM1] = ans[0] return 1 if D5[ELM1] = ans[0] return 1 if D6[ELM1] = ans[0] return 1 ELM1 = ELM1 + 1 return 0 As language of choice, it would be pure c

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  • Access cost of dynamically created objects with dynamically allocated members

    - by user343547
    I'm building an application which will have dynamic allocated objects of type A each with a dynamically allocated member (v) similar to the below class class A { int a; int b; int* v; }; where: The memory for v will be allocated in the constructor. v will be allocated once when an object of type A is created and will never need to be resized. The size of v will vary across all instances of A. The application will potentially have a huge number of such objects and mostly need to stream a large number of these objects through the CPU but only need to perform very simple computations on the members variables. Could having v dynamically allocated could mean that an instance of A and its member v are not located together in memory? What tools and techniques can be used to test if this fragmentation is a performance bottleneck? If such fragmentation is a performance issue, are there any techniques that could allow A and v to allocated in a continuous region of memory? Or are there any techniques to aid memory access such as pre-fetching scheme? for example get an object of type A operate on the other member variables whilst pre-fetching v. If the size of v or an acceptable maximum size could be known at compile time would replacing v with a fixed sized array like int v[max_length] lead to better performance? The target platforms are standard desktop machines with x86/AMD64 processors, Windows or Linux OSes and compiled using either GCC or MSVC compilers.

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  • optimal memory layout for read-only/write memory segments.

    - by aaa
    hello. Suppose I have two memory segments (equal size each, approximately 1kb in size) , one is read-only (after initialization), and other is read/write. what is the best layout in memory for such segments in terms of memory performance? one allocation, contiguous segments or two allocations (in general not contiguous). my primary architecture is linux Intel 64-bit. my feeling is former (cache friendlier) case is better. is there circumstances, where second layout is preferred? Thanks

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  • Position of least significant bit that is set

    - by peterchen
    I am looking for an efficient way to determine the position of the least significant bit that is set in an integer, e.g. for 0x0FF0 it would be 4. A trivial implementation is this: unsigned GetLowestBitPos(unsigned value) { assert(value != 0); // handled separately unsigned pos = 0; while (!(value & 1)) { value >>= 1; ++pos; } return pos; } Any ideas how to squeeze some cycles out of it? (Note: this question is for people that enjoy such things, not for people to tell me xyzoptimization is evil.) [edit] Thanks everyone for the ideas! I've learnt a few other things, too. Cool!

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  • Custom View - Avoid redrawing when non-interactive

    - by MasterGaurav
    I have a complex custom view - photo collage. What is observed is whenever any UI interaction happens, the view is redrawn. How can I avoid complete redrawing (for example, use a cached UI) of the view specially when I click the "back" button to go back to previous activity because that also causes redrawing of the view. While exploring the API and web, I found a method - getDrawingCache() - but don't know how to use it effectively. How do I use it effectively? I've had other issues with Custom Views that I outline here.

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  • Grand Central Strategy for Opening Multiple Files

    - by user276632
    I have a working implementation using Grand Central dispatch queues that (1) opens a file and computes an OpenSSL DSA hash on "queue1", (2) writing out the hash to a new "side car" file for later verification on "queue2". I would like to open multiple files at the same time, but based on some logic that doesn't "choke" the OS by having 100s of files open and exceeding the hard drive's sustainable output. Photo browsing applications such as iPhoto or Aperture seem to open multiple files and display them, so I'm assuming this can be done. I'm assuming the biggest limitation will be disk I/O, as the application can (in theory) read and write multiple files simultaneously. Any suggestions? TIA

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  • Strange: Planner takes decision with lower cost, but (very) query long runtime

    - by S38
    Facts: PGSQL 8.4.2, Linux I make use of table inheritance Each Table contains 3 million rows Indexes on joining columns are set Table statistics (analyze, vacuum analyze) are up-to-date Only used table is "node" with varios partitioned sub-tables Recursive query (pg = 8.4) Now here is the explained query: WITH RECURSIVE rows AS ( SELECT * FROM ( SELECT r.id, r.set, r.parent, r.masterid FROM d_storage.node_dataset r WHERE masterid = 3533933 ) q UNION ALL SELECT * FROM ( SELECT c.id, c.set, c.parent, r.masterid FROM rows r JOIN a_storage.node c ON c.parent = r.id ) q ) SELECT r.masterid, r.id AS nodeid FROM rows r QUERY PLAN ----------------------------------------------------------------------------------------------------------------------------------------------------------------- CTE Scan on rows r (cost=2742105.92..2862119.94 rows=6000701 width=16) (actual time=0.033..172111.204 rows=4 loops=1) CTE rows -> Recursive Union (cost=0.00..2742105.92 rows=6000701 width=28) (actual time=0.029..172111.183 rows=4 loops=1) -> Index Scan using node_dataset_masterid on node_dataset r (cost=0.00..8.60 rows=1 width=28) (actual time=0.025..0.027 rows=1 loops=1) Index Cond: (masterid = 3533933) -> Hash Join (cost=0.33..262208.33 rows=600070 width=28) (actual time=40628.371..57370.361 rows=1 loops=3) Hash Cond: (c.parent = r.id) -> Append (cost=0.00..211202.04 rows=12001404 width=20) (actual time=0.011..46365.669 rows=12000004 loops=3) -> Seq Scan on node c (cost=0.00..24.00 rows=1400 width=20) (actual time=0.002..0.002 rows=0 loops=3) -> Seq Scan on node_dataset c (cost=0.00..55001.01 rows=3000001 width=20) (actual time=0.007..3426.593 rows=3000001 loops=3) -> Seq Scan on node_stammdaten c (cost=0.00..52059.01 rows=3000001 width=20) (actual time=0.008..9049.189 rows=3000001 loops=3) -> Seq Scan on node_stammdaten_adresse c (cost=0.00..52059.01 rows=3000001 width=20) (actual time=3.455..8381.725 rows=3000001 loops=3) -> Seq Scan on node_testdaten c (cost=0.00..52059.01 rows=3000001 width=20) (actual time=1.810..5259.178 rows=3000001 loops=3) -> Hash (cost=0.20..0.20 rows=10 width=16) (actual time=0.010..0.010 rows=1 loops=3) -> WorkTable Scan on rows r (cost=0.00..0.20 rows=10 width=16) (actual time=0.002..0.004 rows=1 loops=3) Total runtime: 172111.371 ms (16 rows) (END) So far so bad, the planner decides to choose hash joins (good) but no indexes (bad). Now after doing the following: SET enable_hashjoins TO false; The explained query looks like that: QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- CTE Scan on rows r (cost=15198247.00..15318261.02 rows=6000701 width=16) (actual time=0.038..49.221 rows=4 loops=1) CTE rows -> Recursive Union (cost=0.00..15198247.00 rows=6000701 width=28) (actual time=0.032..49.201 rows=4 loops=1) -> Index Scan using node_dataset_masterid on node_dataset r (cost=0.00..8.60 rows=1 width=28) (actual time=0.028..0.031 rows=1 loops=1) Index Cond: (masterid = 3533933) -> Nested Loop (cost=0.00..1507822.44 rows=600070 width=28) (actual time=10.384..16.382 rows=1 loops=3) Join Filter: (r.id = c.parent) -> WorkTable Scan on rows r (cost=0.00..0.20 rows=10 width=16) (actual time=0.001..0.003 rows=1 loops=3) -> Append (cost=0.00..113264.67 rows=3001404 width=20) (actual time=8.546..12.268 rows=1 loops=4) -> Seq Scan on node c (cost=0.00..24.00 rows=1400 width=20) (actual time=0.001..0.001 rows=0 loops=4) -> Bitmap Heap Scan on node_dataset c (cost=58213.87..113214.88 rows=3000001 width=20) (actual time=1.906..1.906 rows=0 loops=4) Recheck Cond: (c.parent = r.id) -> Bitmap Index Scan on node_dataset_parent (cost=0.00..57463.87 rows=3000001 width=0) (actual time=1.903..1.903 rows=0 loops=4) Index Cond: (c.parent = r.id) -> Index Scan using node_stammdaten_parent on node_stammdaten c (cost=0.00..8.60 rows=1 width=20) (actual time=3.272..3.273 rows=0 loops=4) Index Cond: (c.parent = r.id) -> Index Scan using node_stammdaten_adresse_parent on node_stammdaten_adresse c (cost=0.00..8.60 rows=1 width=20) (actual time=4.333..4.333 rows=0 loops=4) Index Cond: (c.parent = r.id) -> Index Scan using node_testdaten_parent on node_testdaten c (cost=0.00..8.60 rows=1 width=20) (actual time=2.745..2.746 rows=0 loops=4) Index Cond: (c.parent = r.id) Total runtime: 49.349 ms (21 rows) (END) - incredibly faster, because indexes were used. Notice: Cost of the second query ist somewhat higher than for the first query. So the main question is: Why does the planner make the first decision, instead of the second? Also interesing: Via SET enable_seqscan TO false; i temp. disabled seq scans. Than the planner used indexes and hash joins, and the query still was slow. So the problem seems to be the hash join. Maybe someone can help in this confusing situation? thx, R.

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  • Whats faster in Javascript a bunch of small setInterval loops, or one big one?

    - by RobertWHurst
    Just wondering if its worth it to make a monolithic loop function or just add loops were they're needed. The big loop option would just be a loop of callbacks that are added dynamically with an add function. adding a function would look like this setLoop(function(){ alert('hahaha! I\'m a really annoying loop that bugs you every tenth of a second'); }); setLoop would add the function to the monolithic loop. so is the is worth anything in performance or should I just stick to lots of little loops using setInterval?

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  • Improving the speed of php

    - by cast01
    I'm currently working on a website in PHP, and I'm wondering what the best practices/methods are to reduce the time requests take. I've build the site in a modular way, so a page would consist of a number of modules, and each of these would need to request information. For example, I have a cart module, that (if a cart is set) will fetch the cart with the id (stored in a session variable) from the database and return its contents. I have another module that lists categories and this needs to fetch the categories from the database. My system is built with models, and each model might also make a request, for example a category model will make a request to get products in that category.

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  • How to optimize neural network by using genetic algorithm?

    - by Billy Coen
    I'm quite new with this topic so any help would be great. What i need is to optimize a neural network in MATLAB by using GA. My network has [2x98] input and [1x98] target, i've tried consulting matlab help but im still kind of clueless about what to do :( so, any help would be appreciated. Thanks in advance. edit: i guess i didn't say what is there to be optimized as Dan said in the 1st answer. I guess most important thing is number of hidden neurons. And maybe number of hidden layers and training parameters like number of epochs or so. Sorry for not providing enough info, i'm still learning about this.

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  • explicit copy constructor or implicit parameter by value

    - by R Samuel Klatchko
    I recently read (and unfortunately forgot where), that the best way to write operator= is like this: foo &operator=(foo other) { swap(*this, other); return *this; } instead of this: foo &operator=(const foo &other) { foo copy(other); swap(*this, copy); return *this; } The idea is that if operator= is called with an rvalue, the first version can optimize away construction of a copy. So when called with a rvalue, the first version is faster and when called with an lvalue the two are equivalent. I'm curious as to what other people think about this? Would people avoid the first version because of lack of explicitness? Am I correct that the first version can be better and can never be worse?

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  • set difference in SQL query

    - by TheObserver
    I'm trying to select records with a statement SELECT * FROM A WHERE LEFT(B, 5) IN (SELECT * FROM (SELECT LEFT(A.B,5), COUNT(DISTINCT A.C) c_count FROM A GROUP BY LEFT(B,5) ) p1 WHERE p1.c_count = 1 ) AND C IN (SELECT * FROM (SELECT A.C , COUNT(DISTINCT LEFT(A.B,5)) b_count FROM A GROUP BY C ) p2 WHERE p2.b_count = 1) which takes a long time to run ~15 sec. Is there a better way of writing this SQL?

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  • Jruby rspec to be run parallely

    - by Priyank
    Hi. Is there something like Spork for Jruby too? We want to parallelize our specs to run faster and pre-load the classes while running the rake task; however we have not been able to do so. Since our project is considerable in size, specs take about 15 minutes to complete and this poses a serious challenge to quick turnaround. Any ideas are more than welcome. Cheers

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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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  • cheapest way to draw a fullscreen quad

    - by Soubok
    I wondering if there is a faster way to draw a full-screen quad in OpenGL: NewList(); PushMatrix(); LoadIdentity(); MatrixMode(PROJECTION); PushMatrix(); LoadIdentity(); Begin(QUADS); Vertex(-1,-1,0); Vertex(1,-1,0); Vertex(1,1,0); Vertex(-1,1,0); End(); PopMatrix(); MatrixMode(MODELVIEW); PopMatrix(); EndList();

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  • Very slow Eclipse 4.2, how to make it more responsive?

    - by Laurent
    I'm using Eclipse PDT on a rather large PHP project and the IDE is almost unusable. It takes nearly 30 seconds to open a file, and other actions, like selecting a folder in the file explorer, editing some text, etc. are equally slow. I followed various instructions to speed it up but nothing seems to work. This is my current eclipse.ini file. Any idea how I can improve it? -startup plugins/org.eclipse.equinox.launcher_1.3.0.v20120522-1813.jar --launcher.library plugins/org.eclipse.equinox.launcher.win32.win32.x86_1.1.200.v20120522-1813 -showsplash org.eclipse.platform --launcher.XXMaxPermSize 256m --launcher.defaultAction openFile -vmargs -server -Dosgi.requiredJavaVersion=1.7 -Xmn128m -Xms1024m -Xmx1024m -Xss2m -XX:PermSize=128m -XX:MaxPermSize=128m -XX:+UseParallelGC System: Eclipse 4.2.0, Windows 7, 4 GB RAM

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  • Why better isolation level means better performance in SQL Server

    - by Oleg Zhylin
    When measuring performance on my query I came up with a dependency between isolation level and elapsed time that was surprising to me READUNCOMMITTED - 409024 READCOMMITTED - 368021 REPEATABLEREAD - 358019 SERIALIZABLE - 348019 Left column is table hint, and the right column is elapsed time in microseconds (sys.dm_exec_query_stats.total_elapsed_time). Why better isolation level gives better performance? This is a development machine and no concurrency whatsoever happens. I would expect READUNCOMMITTED to be the fasted due to less locking overhead. Update: I did measure this with DBCC DROPCLEANBUFFERS DBCC FREEPROCCACHE issued and Profiler confirms there're no cache hits happening. Update2: The query in question is an OLAP one and we need to run it as fast as possible. Closing the production server from outside world to get the computation done is not out of question if this gives performance benefits.

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