#StackBounty: #mysql #performance #performance-tuning #explain A MySQL EXPLAIN number of rows discrepancy

Bounty: 50

MySQL 5.5.49-log

More questions on the query in Why does it use temporary? (MySQL) (the query is the same but the question is different):

I have the following table (filled with many rows):

CREATE TABLE `SectorGraphs2` (
  `Kind` tinyint(3) UNSIGNED NOT NULL COMMENT '1 - продюсер, 2 - жанр, 3 - регион',
  `Criterion` tinyint(3) UNSIGNED NOT NULL,
  `Period` tinyint(3) UNSIGNED NOT NULL,
  `PeriodStart` date NOT NULL,
  `SectorID` int(10) UNSIGNED NOT NULL,
  `Value` float NOT NULL

ALTER TABLE `SectorGraphs2`
  ADD UNIQUE KEY `Producer2` (`Kind`,`Criterion`,`Period`,`PeriodStart`,`SectorID`) USING BTREE,
  ADD KEY `SectorID` (`SectorID`);

then I run:

    SELECT SectorID, SUM(Value)
    FROM SectorGraphs2
    WHERE Kind = 1 AND Criterion = 7
      AND Period = 1
      AND PeriodStart >= ? AND PeriodStart < ? + INTERVAL 1 WEEK
    GROUP BY SectorID

and it produces:

| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
| 1 | SIMPLE | SectorGraphs2 | range | Producer2,SectorID | Producer2 | 6 | NULL | 1 | Using index condition; Using temporary; Using filesort |

See nicely formatted explain here.

My question: Why it is used a temporary table and filesort but it reports only 1 row examined? It seems that because of using a temporary table, it should process more than one row. How can I determine the real number of rows processed? How to solve this discrepancy about number of processed rows?

Note that the task I was assigned to do now is to eliminate heavy (involving too many rows) queries. And now I do not know how to do this.

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#StackBounty: #machine-learning #cross-validation #performance LFW face pair-matching performance evaluation, why retrain model on view2?

Bounty: 50

I am trying to understand how performance evaluation works in LFW(Labeled Faces in the Wild) dataset http://vis-www.cs.umass.edu/lfw/.

I am interested in task: pair-matching. However, as I dig deeper, I found myself confused.

Here is a brief summary on evaluating pair-matching performance in LFW dataset:

  1. LFW dataset is divided into View1 and View2. View1 is for development of algorithms, you can use it to select model, tune parameters and choose features. View2 is for reporting accuracy of your model produced by View1.

  2. View1 description:

    For development purposes, we recommend using the below training/testing split, which was generated randomly and independently of the splits for 10-fold cross validation, to avoid unfairly overfitting to the sets above during development. For instance, these sets may be viewed as a model selection set and a validation set. See the tech report below for more details.

    pairsDevTrain.txt, pairsDevTest.txt

  3. View2 description:

    As a benchmark for comparison, we suggest reporting performance as 10-fold cross validation using splits we have randomly generated.

I also found an example of carrying out the experiment with PCA for face pair-matching in the LFW 2008 paper.

Eigenfaces for pair matching. We computed eigenvectors from the training set of View 1 and determined the threshold value for classifying pairs as matched or mismatched that gave the best performance on the test set of View 1. For each run of View 2, the training set was used to compute the eigenvectors, and pairs were classified using the threshold on Euclidian distance from View 1.

State of the art pair matching. To determine the current best performance on pair matching, we ran an implementation of the current state of the art recognition system of Nowak and Jurie [14].11 The Nowak algorithm gives a similarity score to each pair, and View 1 was used to determine the threshold value for classifying pairs as matched or mismatched. For each of the 10 folds of View 2 of the database, we trained on 9 of the sets and computed similarity measures for the held out test set, and classified pairs using the threshold

My questions are:

  1. How to do training with View1 data using 10-fold cross validation?

    The data is already split into pairsDevTrain.txt and pairsDevTest.txt. Does it mean that I need to merge these two file and then do a standard 10-fold cross validation to train my model?

  2. Why is 10-fold cross validation required in View2?

    Since model and parameter is all determined using data in View1, why not just use all View2 data to report performance.

  3. Since 10-fold cross validation is required in View2, there must be a training process. Why retrain another model?

    It is worth mentioning here, both in View1 and View2. train and test data don’t share common identity, i.e. person1 appear in train, will not appear in test.

  4. 10-fold cross validation is recommended for both View1 and View2. 10-fold splits are given for View2 but not View1. Is there a reason why?

Thank you beforehand for helping me understand the performance evaluation for LFW.

Get this bounty!!!

#StackBounty: #windows #ssd #performance #benchmarking #nvme SSD with write cache buffer flushing turned on is way slower in AS SSD but…

Bounty: 50

When running a benchmark using the program AS SSD on an NVMe drive, When the second checkbox seen in the image below is unchecked, the drive gets terrible write performance in AS SSD (see first benchmark screenshot below), but not in CrystalDiskMark (see last screenshot); however, if I check that box, then AS SSD performs well. Does anyone know what’s going on here? My concern is that according to that checkbox description, I should NOT have it checked since my drive doesn’t have its own power supply, but AS SSD is so slow, I’m concerned other programs may be affected.

In case it’s helpful, I learned about that checkbox in the first place from reading https://www.reddit.com/r/Dell/comments/628odr/toshiba_nvme_slow_write_speed_fix/, but this doesn’t explain to me why CDM would still be fast. See also Slow SSD performance (Toshiba 1GB NVMe). I updated my Intel RST drivers and that didn’t help.

I understand there could be a roundabout driver solution here, but I want to stay within warranty and try to find out what’s going on so I can get Dell to support this and issue an official solution.

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Get this bounty!!!

#StackBounty: #menus #navigation #performance #cache Nav and logo loading each time causing menu to move JointsWP – Foundation 6

Bounty: 50

Hi I was wondering if anyone could help.

I’m creating a site using JointsWP Foundation 6 theme and have created a new fixed side menu which includes the logo and social links. My problem is everytime a user click on the menu it reloads causing a shift – is there a way of stopping this – is it a page load issue or have i come about it the wrong way? I tried adding a caching plugin but it hasn’t seemed to help. Any suggestions appreciated.

Here is examples of my code:

<body <?php body_class(); ?>>


and the page.php

<?php get_header(); ?>
--> <!--
--> --> <main id="main" class="large-9 medium-9 columns contentSection" role="main"> <?php if (have_posts()) : while (have_posts()) : the_post(); ?> <?php get_template_part( 'parts/loop', 'page' ); ?> <?php endwhile; endif; ?> <!--</main> <!-- end #main --> <!--</div> <!-- end #inner-content --> <!--</div> <!-- end #content -->


i have added 2 test pages so that you can see – biggreenspace.com/test-page-1 and you will be able to navigate to test page 2 (the other menu will take you to the maintenance screen). This primarily happens in Chrome and Firefox – not in IE edge.

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#StackBounty: #python #performance #regex #natural-language-proc #cython Using lots of regex substitutions to tokenize text

Bounty: 50

I authored a piece of code that was merged into the nltk codebase. It is full of regex substitutions:

import re
from six import text_type

from nltk.tokenize.api import TokenizerI

class ToktokTokenizer(TokenizerI):
    This is a Python port of the tok-tok.pl from

    >>> toktok = ToktokTokenizer()
    >>> text = u'Is 9.5 or 525,600 my favorite number?'
    >>> print (toktok.tokenize(text, return_str=True))
    Is 9.5 or 525,600 my favorite number ?
    >>> text = u'The https://github.com/jonsafari/tok-tok/blob/master/tok-tok.pl is a website with/and/or slashes and sort of weird : things'
    >>> print (toktok.tokenize(text, return_str=True))
    The https://github.com/jonsafari/tok-tok/blob/master/tok-tok.pl is a website with/and/or slashes and sort of weird : things
    >>> text = u'xa1This, is a sentence with weirdxbb symbolsu2026 appearing everywherexbf'
    >>> expected = u'xa1 This , is a sentence with weird xbb symbols u2026 appearing everywhere xbf'
    >>> assert toktok.tokenize(text, return_str=True) == expected
    >>> toktok.tokenize(text) == [u'xa1', u'This', u',', u'is', u'a', u'sentence', u'with', u'weird', u'xbb', u'symbols', u'u2026', u'appearing', u'everywhere', u'xbf']
    # Replace non-breaking spaces with normal spaces.
    NON_BREAKING = re.compile(u"u00A0"), " "

    # Pad some funky punctuation.
    FUNKY_PUNCT_1 = re.compile(u'([،;؛¿!"])}»›”؟¡%٪°±©®।॥…])'), r" 1 "
    # Pad more funky punctuation.
    FUNKY_PUNCT_2 = re.compile(u'([({[“‘„‚«‹「『])'), r" 1 "
    # Pad En dash and em dash
    EN_EM_DASHES = re.compile(u'([–—])'), r" 1 "

    # Replace problematic character with numeric character reference.
    AMPERCENT = re.compile('& '), '&amp; '
    TAB = re.compile('t'), ' 	 '
    PIPE = re.compile('|'), ' | '

    # Pad numbers with commas to keep them from further tokenization. 
    COMMA_IN_NUM = re.compile(r'(?<!,)([,،])(?![,d])'), r' 1 '

    # Just pad problematic (often neurotic) hyphen/single quote, etc.
    PROB_SINGLE_QUOTES = re.compile(r"(['’`])"), r' 1 '
    # Group ` ` stupid quotes ' ' into a single token.
    STUPID_QUOTES_1 = re.compile(r" ` ` "), r" `` "
    STUPID_QUOTES_2 = re.compile(r" ' ' "), r" '' "

    # Don't tokenize period unless it ends the line and that it isn't 
    # preceded by another period, e.g.  
    # "something ..." -> "something ..." 
    # "something." -> "something ." 
    FINAL_PERIOD_1 = re.compile(r"(?<!.).$"), r" ."
    # Don't tokenize period unless it ends the line eg. 
    # " ... stuff." ->  "... stuff ."
    FINAL_PERIOD_2 = re.compile(r"""(?<!.).s*(["'’»›”]) *$"""), r" . 1"

    # Treat continuous commas as fake German,Czech, etc.: „
    MULTI_COMMAS = re.compile(r'(,{2,})'), r' 1 '
    # Treat continuous dashes as fake en-dash, etc.
    MULTI_DASHES = re.compile(r'(-{2,})'), r' 1 '
    # Treat multiple periods as a thing (eg. ellipsis)
    MULTI_DOTS = re.compile(r'(.{2,})'), r' 1 '

    # This is the p{Open_Punctuation} from Perl's perluniprops
    # see http://perldoc.perl.org/perluniprops.html
    OPEN_PUNCT = text_type(u'([{u0f3au0f3cu169bu201au201eu2045u207d'
    # This is the p{Close_Punctuation} from Perl's perluniprops
    CLOSE_PUNCT = text_type(u')]}u0f3bu0f3du169cu2046u207eu208eu232a'
    # This is the p{Close_Punctuation} from Perl's perluniprops
    CURRENCY_SYM = text_type(u'$xa2xa3xa4xa5u058fu060bu09f2u09f3u09fb'

    # Pad spaces after opening punctuations.
    OPEN_PUNCT_RE = re.compile(u'([{}])'.format(OPEN_PUNCT)), r'1 '
    # Pad spaces before closing punctuations.
    CLOSE_PUNCT_RE = re.compile(u'([{}])'.format(CLOSE_PUNCT)), r'1 '
    # Pad spaces after currency symbols.
    CURRENCY_SYM_RE = re.compile(u'([{}])'.format(CURRENCY_SYM)), r'1 '

    # Use for tokenizing URL-unfriendly characters: [:/?#]
    URL_FOE_1 = re.compile(r':(?!//)'), r' : ' # in perl s{:(?!//)}{ : }g;
    URL_FOE_2 = re.compile(r'?(?!S)'), r' ? ' # in perl s{?(?!S)}{ ? }g;
    # in perl: m{://} or m{S+.S+/S+} or s{/}{ / }g;
    URL_FOE_3 = re.compile(r'(://)[S+.S+/S+][/]'), ' / '
    URL_FOE_4 = re.compile(r' /'), r' / ' # s{ /}{ / }g;

    # Left/Right strip, i.e. remove heading/trailing spaces.
    # These strip regexes should NOT be used,
    # instead use str.lstrip(), str.rstrip() or str.strip() 
    # (They are kept for reference purposes to the original toktok.pl code)  
    LSTRIP = re.compile(r'^ +'), ''
    RSTRIP = re.compile(r's+$'),'n' 
    # Merge multiple spaces.
    ONE_SPACE = re.compile(r' {2,}'), ' '

                      URL_FOE_1, URL_FOE_2, URL_FOE_3, URL_FOE_4,
                      AMPERCENT, TAB, PIPE,
                      OPEN_PUNCT_RE, CLOSE_PUNCT_RE, 
                      MULTI_COMMAS, COMMA_IN_NUM, FINAL_PERIOD_2,
                      FINAL_PERIOD_1, FINAL_PERIOD_2, ONE_SPACE]

    def tokenize(self, text, return_str=False):
        text = text_type(text) # Converts input string into unicode.
        for regexp, subsitution in self.TOKTOK_REGEXES:
            text = regexp.sub(subsitution, text)
        # Finally, strips heading and trailing spaces
        # and converts output string into unicode.
        text = text_type(text.strip()) 
        return text if return_str else text.split()

Is there a way to make the subtituition faster? E.g.

  • Combine the chain of regexes into one super regex.
  • Combine some of the regexes
  • Coding it in Cython (but Cython regexes are slow, no?)
  • Running the regex substitution in Julia and wrapping Julia code in Python

The use case for the tokenize() function usually takes a single input but if the same function is called 1,000,000,000 times, it’s rather slow and the GIL is going to lock up the core and process each sentence at a time.

The aim of the question is to ask for ways to speed up a Python code that’s made up of regex substitution, esp. when running the tokenize() function for 1,000,000,000+ times.

If Cython/Julia or any faster language + wrapper is suggested, it would be good if you give an one regex example of how the regex is written in Cython/Julia/Others and the suggestion on how the wrapper would look like.

Get this bounty!!!

#StackBounty: #ssd #performance SanDisk SSD Plus: Half the performance on Linux than on Windows?

Bounty: 50

I have two SSDs in my laptop:

  • Crucial MX300 725GB –> /dev/sda
  • SanDisk SSD Plus 240GB –> /dev/sdb

They’re performance reads on Linux and Windows like this:

Crucial MX300 –> same on both OSs

sudo hdparm -tT /dev/sda # Crucial
Timing cached reads:   13700 MB in  2.00 seconds = 6854.30 MB/sec
Timing buffered disk reads: 1440 MB in  3.00 seconds = 479.58 MB/sec

Crucial MX300 725GB

SanDisk Plus –> way faster on Windows!

sudo hdparm -tT /dev/sdb # SanDisk
Timing cached reads:   7668 MB in  2.00 seconds = 3834.92 MB/sec
Timing buffered disk reads: 798 MB in  3.00 seconds = 265.78 MB/sec # TOO LOW !!


The sequential read performance of the SanDisk on Linux is about half of its performance on Windows!

My Question is of course: Why and can that be fixed? Is this due to the SanDisk SSD Plus being handled as a SCSI drive?

From syslog:

~$ grep SDSSD /var/log/syslog
systemd[1]: Found device SanDisk_SDSSDA240G
kernel: [    2.152138] ata2.00: ATA-9: SanDisk SDSSDA240G, Z32070RL, max UDMA/133
kernel: [    2.174689] scsi 1:0:0:0: Direct-Access     ATA      SanDisk SDSSDA24 70RL PQ: 0 ANSI: 5
smartd[1035]: Device: /dev/sdb [SAT], SanDisk SDSSDA240G, S/N:162783441004, WWN:5-001b44-4a404e4f0, FW:Z32070RL, 240 GB
smartd[1035]: Device: /dev/sdb [SAT], state read from /var/lib/smartmontools/smartd.SanDisk_SDSSDA240G-162783441004.ata.state
smartd[1035]: Device: /dev/sdb [SAT], state written to /var/lib/smartmontools/smartd.SanDisk_SDSSDA240G-162783441004.ata.state

Compared to the Crucial MX300 which has on linux almost the same performance as on Windows:

~$ grep MX300 /var/log/syslog
systemd[1]: Found device Crucial_CT750MX300SSD1
kernel: [    1.775520] ata1.00: ATA-10: Crucial_CT750MX300SSD1,  M0CR050, max UDMA/133
smartd[1035]: Device: /dev/sda [SAT], Crucial_CT750MX300SSD1, S/N:16251486AC40, WWN:5-00a075-11486ac40, FW:M0CR050, 750 GB
smartd[1035]: Device: /dev/sda [SAT], state read from /var/lib/smartmontools/smartd.Crucial_CT750MX300SSD1-16251486AC40.ata.state
smartd[1035]: Device: /dev/sda [SAT], state written to /var/lib/smartmontools/smartd.Crucial_CT750MX300SSD1-16251486AC40.ata.state

Any help is very welcome!


The difference that hdparm is showing on Linux is very real. I created two identical directories, one in each of the two drives, each directory containing about 25Gb of files (36395 files), and ran the exact same hashdeep checksum creation script on both dirs (the script just creates a md5-checksum for every file in the test dirs and stores all the checksums in one single file). These are the results:

test-sandisk# time create-file-integrity-md5sums.sh .
real    1m49.000s
user    1m24.868s
sys 0m15.808s

test-mx300# time create-file-integrity-md5sums.sh .
real    0m54.180s
user    1m4.628s
sys 0m11.640s

Same test with a single 7Gb file:

test-sandisk# time create-file-integrity-md5sums.sh .
real    0m26.986s
user    0m19.168s
sys 0m3.232s

test-mx300# time create-file-integrity-md5sums.sh .
real    0m17.285s
user    0m16.248s
sys 0m1.368s

Get this bounty!!!

#StackBounty: #performance #python-3.x #iterator #yield #next Same iterator algorithm using function and yield two times faster compare…

Bounty: 50

In the below provided Python script code exactly the same algorithm for creating unique combinations from a non-unique list of elements was implemented using a Python function with yield and using a class with __next__. The code is ready to run after copy/paste, so you can see it for yourself what I am speaking about.

The same phenomenon observed for pure Python code propagates into C code of a Python extension module created out of the script code by Cython, so it is not limited to Python level code because it doesn’t vanish at the C code level.

The question is:

Where does the huge difference in speed of execution come from?
Is there anything that can be done to get both code variants to run at comparable speed? Is there something went wrong with the class/next implementation compared to the function/yield variant? Both are to my knowledge exactly the same code …

Here the code (tweaking the number in the highlighted line changes the level of uniqueness of elements in the list the combinations are generated from what has a huge impact on the running time):

def uniqCmboYieldIter(lstItems, lenCmbo):
    dctCounter = {}
    lenLstItems = len(lstItems)
    for idx in range(lenLstItems):
        item = lstItems[idx]
        if item in dctCounter.keys(): 
            dctCounter[item] += 1
            dctCounter[item]  = 1
    lstUniqs   = sorted(dctCounter.keys())
    lstCntRpts = [dctCounter[item] for item in lstUniqs]
    lenUniqs   = len(lstUniqs)
    cmboAsIdxUniqs = [None] * lenCmbo
    multiplicities = [0] * lenUniqs
    idxIntoCmbo, idxIntoUniqs = 0, 0

    while idxIntoCmbo != lenCmbo and idxIntoUniqs != lenUniqs:
        count = min(lstCntRpts[idxIntoUniqs], lenCmbo-idxIntoCmbo)
        cmboAsIdxUniqs[idxIntoCmbo : idxIntoCmbo + count] = [idxIntoUniqs] * count
        multiplicities[idxIntoUniqs] = count
        idxIntoCmbo  += count
        idxIntoUniqs += 1

    if idxIntoCmbo != lenCmbo:

    while True:
        yield tuple(lstUniqs[idxUniqs] for idxUniqs in cmboAsIdxUniqs)

        for idxIntoCmbo in reversed(range(lenCmbo)):
            x = cmboAsIdxUniqs[idxIntoCmbo]
            y = x + 1

            if y < lenUniqs and multiplicities[y] < lstCntRpts[y]:

        for idxIntoCmbo in range(idxIntoCmbo, lenCmbo):
            x = cmboAsIdxUniqs[idxIntoCmbo]
            cmboAsIdxUniqs[idxIntoCmbo] = y
            multiplicities[x] -= 1
            multiplicities[y] += 1
            # print("# multiplicities:", multiplicities)

            while y != lenUniqs and multiplicities[y] == lstCntRpts[y]:
                y += 1

            if y == lenUniqs:

class uniqCmboClassIter:
    # ----------------------------------------------------------------------------------------------
    def __iter__(self):
       return self

    # ----------------------------------------------------------------------------------------------
    def __init__(self, lstItems, lenCmbo):
        dctCounter = {}
        lenLstItems = len(lstItems)
        for idx in range(lenLstItems):
            item = lstItems[idx]
            if item in dctCounter.keys(): 
                dctCounter[item] += 1
                dctCounter[item]  = 1

        self.lstUniqs   = sorted(dctCounter.keys())
        self.lenUniqs   = len(self.lstUniqs)
        self.lstCntRpts = [dctCounter[item] for item in self.lstUniqs]

        self.lenCmbo        = lenCmbo
        self.cmboAsIdxUniqs = [None] * lenCmbo
        self.multiplicities = [0] * self.lenUniqs
        self.idxIntoCmbo, self.idxIntoUniqs = 0, 0

        while self.idxIntoCmbo != self.lenCmbo and self.idxIntoUniqs != self.lenUniqs:
            count = min(self.lstCntRpts[self.idxIntoUniqs], self.lenCmbo-self.idxIntoCmbo)
            self.cmboAsIdxUniqs[self.idxIntoCmbo : self.idxIntoCmbo + count] = [self.idxIntoUniqs] * count
            self.multiplicities[self.idxIntoUniqs] = count
            self.idxIntoCmbo  += count
            self.idxIntoUniqs += 1
            # print("self.multiplicities:", self.multiplicities)
            # print("self.cmboAsIdxUniqs:", self.cmboAsIdxUniqs)

        if self.idxIntoCmbo != self.lenCmbo:

        self.stopIteration = False
        self.x = None
        self.y = None


    # ----------------------------------------------------------------------------------------------
    def __next__(self):

        if self.stopIteration is True:
            raise StopIteration

        nextCmbo = tuple(self.lstUniqs[idxUniqs] for idxUniqs in self.cmboAsIdxUniqs)

        for self.idxIntoCmbo in reversed(range(self.lenCmbo)):
            self.x = self.cmboAsIdxUniqs[self.idxIntoCmbo]
            self.y = self.x + 1

            if self.y < self.lenUniqs and self.multiplicities[self.y] < self.lstCntRpts[self.y]:
            self.stopIteration = True
            return nextCmbo

        for self.idxIntoCmbo in range(self.idxIntoCmbo, self.lenCmbo):
            self.x = self.cmboAsIdxUniqs[self.idxIntoCmbo]
            self.cmboAsIdxUniqs[self.idxIntoCmbo] = self.y
            self.multiplicities[self.x] -= 1
            self.multiplicities[self.y] += 1
            # print("# multiplicities:", multiplicities)

            while self.y != self.lenUniqs and self.multiplicities[self.y] == self.lstCntRpts[self.y]:
                self.y += 1

            if self.y == self.lenUniqs:

        return nextCmbo

# ============================================================================================================================================
lstSize   = 48 # 48
uniqLevel =  12 # (7 ~60% unique) higher level => more unique items in the generated list 
aList = []
from random import randint
for _ in range(lstSize):
    aList.append( ( randint(1,uniqLevel), randint(1,uniqLevel) ) )
lenCmbo = 6
percUnique = 100.0 - 100.0*(lstSize-len(set(aList)))/lstSize
print("========================  lenCmbo:", lenCmbo, 
      "   sizeOfList:", len(aList), 
      "   noOfUniqueInList", len(set(aList)), 
      "   percUnique",  int(percUnique) ) 

import time
from itertools import combinations
# itertools.combinations
# ---
# def   uniqCmboYieldIter(lstItems, lenCmbo):
# class uniqCmboClassIter: def __init__(self, lstItems, lenCmbo):
# ---
start_time = time.time()
print("Combos:%9i"%len(list(combinations(aList, lenCmbo))), " ", end='')
duration = time.time() - start_time
print("print(len(list(     combinations(aList, lenCmbo)))):",  "{:9.5f}".format(duration), "seconds.")

start_time = time.time()
print("Combos:%9i"%len(list(uniqCmboYieldIter(aList, lenCmbo))), " ", end='')
duration = time.time() - start_time
print("print(len(list(uniqCmboYieldIter(aList, lenCmbo)))):",  "{:9.5f}".format(duration), "seconds.")

start_time = time.time()
print("Combos:%9i"%len(list(uniqCmboClassIter(aList, lenCmbo))), " ", end='')
duration = time.time() - start_time
print("print(len(list(uniqCmboClassIter(aList, lenCmbo)))):", "{:9.5f}".format(duration), "seconds.")

and the timings on my box:

>python3.6 -u "nonRecursiveUniqueCombos_Cg.py"
========================  lenCmbo: 6    sizeOfList: 48    noOfUniqueInList 32    percUnique 66
Combos: 12271512  print(len(list(     combinations(aList, lenCmbo)))):   2.04635 seconds.
Combos:  1296058  print(len(list(uniqCmboYieldIter(aList, lenCmbo)))):   3.25447 seconds.
Combos:  1296058  print(len(list(uniqCmboClassIter(aList, lenCmbo)))):   5.97371 seconds.
>Exit code: 0
  [2017-05-02_03:23]  207474 <-Chrs,Keys-> 1277194 OnSave(): '/home/claudio/CgMint18/_Cg.DIR/ClaudioOnline/at-stackoverflow/bySubject/uniqueCombinations/nonRecursiveUniqueCombos_Cg.py'
>python3.6 -u "nonRecursiveUniqueCombos_Cg.py"
========================  lenCmbo: 6    sizeOfList: 48    noOfUniqueInList 22    percUnique 45
Combos: 12271512  print(len(list(     combinations(aList, lenCmbo)))):   2.05199 seconds.
Combos:   191072  print(len(list(uniqCmboYieldIter(aList, lenCmbo)))):   0.47343 seconds.
Combos:   191072  print(len(list(uniqCmboClassIter(aList, lenCmbo)))):   0.89860 seconds.
>Exit code: 0
  [2017-05-02_03:23]  207476 <-Chrs,Keys-> 1277202 OnSave(): '/home/claudio/CgMint18/_Cg.DIR/ClaudioOnline/at-stackoverflow/bySubject/uniqueCombinations/nonRecursiveUniqueCombos_Cg.py'
>python3.6 -u "nonRecursiveUniqueCombos_Cg.py"
========================  lenCmbo: 6    sizeOfList: 48    noOfUniqueInList 43    percUnique 89
Combos: 12271512  print(len(list(     combinations(aList, lenCmbo)))):   2.17285 seconds.
Combos:  6560701  print(len(list(uniqCmboYieldIter(aList, lenCmbo)))):  16.72573 seconds.
Combos:  6560701  print(len(list(uniqCmboClassIter(aList, lenCmbo)))):  31.17714 seconds.
>Exit code: 0

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#StackBounty: #7 #theming #database #performance Is putting content in the codebase or the database better for performance?

Bounty: 50

I recently created a website footer using a block template. Some of the footer’s content was entered through the block’s wysiwyg (stored in the database), but other code-heavy elements were placed in the block template file (stored in code). I’ve noticed that the footer block loads slow at times. Other times, the content inserted through the wysiwyg loads a few seconds before the content that was inserted in the the block template.

I’m curious, is storing html content in the database better for performance than storing html content in template files?

EDIT: After I posted this, I also realized that the footer is being rendered with Blocks whereas the rest of the page is being render with Panels. I’m not sure if that would have any effects of its performance.

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#StackBounty: #sql-server #performance #performance-tuning #multi-thread Identify threads with maximum difference in time

Bounty: 50

I have many parallel executing queries in my SQL Server database. I need to find out queries where the difference in time for the longest thread and the shortest thread is high (excluding co-ordinator thread). In fact, I need to find top 10 such queries with highest difference (from the queries that are getting exeuted in the span of 1 hour). How to track this information in SQL Server?

Note: I am using SQL Server 2012.

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#StackBounty: #python #performance #algorithm #strings #search Naive implementation of KMP algorithm

Bounty: 50

After reading this answer to the question “High execution time to count overlapping substrings”, I decided to implement the suggested Knuth-Morris-Pratt (KMP) algorithm. I used the pseudo-code listed on Wikipedia for the functions kmp_table and kmp_search.

However, when running it on some corner-cases, I have observed that it is a lot slower than the standard str.find, which apparently uses a modified Boyer-Moore-Horspool algorithm and should thus have worse worst-case performance.

The specific case I looked at is:

$ ipython -i kmp.py
In [1]: text = "A"*1000000 + "B"
In [2]: word = "A"*100 + "B"
In [3]: %timeit kmp_search(text, word)
1 loop, best of 3: 410 ms per loop
In [4}: %timeit text.find(word)
1000 loops, best of 3: 703 µs per loop

So the difference is about a factor 1000 for this input. This is probably due to the fact that the native one is written in C and this is written in Python, but I still wanted to see if I did anything stupid here or missed any obvious optimization.

def kmp_table(word):
    table = [0] * len(word)
    position, candidate = 2, 0
    table[0] = -1

    while position < len(word):
        if word[position - 1] == word[candidate]:
            table[position] = candidate + 1
            candidate += 1
            position += 1
        elif candidate > 0:
            candidate = table[candidate]
            table[position] = 0
            position += 1
    return table

def kmp_search(text, word):
    m, i = 0, 0
    table = kmp_table(word)
    while m + i < len(text):
        if word[i] == text[m + i]:
            if i == len(word) - 1:
                return m
            i += 1
            if table[i] > -1:
                m += i - table[i]
                i = table[i]
                m += 1
                i = 0
    return len(text)

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