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  • How do I calculate percentiles with python/numpy?

    - by Uri
    Is there a convenient way to calculate percentiles for a sequence or single-dimensional numpy array? I am looking for something similar to Excel's percentile function. I looked in NumPy's statistics reference, and couldn't find this. All I could find is the median (50th percentile), but not something more specific.

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  • C# Drawing Arc with 3 Points

    - by Keeper
    Hi, I need to draw an arc using GraphicsPath and having initial, median and final points. The arc has to pass on them. I tried .DrawCurve and .DrawBezier but the result isn't exactly an arc. What can I do?

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  • Definition of Connect, Processing, Waiting in apache bench.

    - by rpatel
    When I run apache bench I get results like: Command: abs.exe -v 3 -n 10 -c 1 https://mysite Connection Times (ms) min mean[+/-sd] median max Connect: 203 213 8.1 219 219 Processing: 78 177 88.1 172 359 Waiting: 78 169 84.6 156 344 Total: 281 389 86.7 391 563 I can't seem to find the definition of Connect, Processing and Waiting. What do those numbers mean?

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  • Standard library function in R for finding the mode?

    - by Nick
    In statistical language R, mean() and median() are standard functions which do what you'd expect. mode() tells you the internal storage mode of the R object, not the value that occurs the most in its argument. But surely there is a standard library function that implements mode for a vector (or list).

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  • Is there memory usage profiler available?

    - by prosseek
    For time profiler for XYZ, I can just run 'time XYZ', or if I have the source code in C/C++, I even can use gprof to get profiled results. Is there any similar tool for memory usage? Is there any tool I can use something like 'memory XYZ', to get info such as min/max/median memory usage? What tool do you use for memory profile with C++/Objective C/C#/Java?

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  • How do I analyze an Apache Bench result?

    - by Alan Hoffmeister
    I need some help with analyzing a log from Apache Bench: Benchmarking texteli.com (be patient) Completed 100 requests Completed 200 requests Completed 300 requests Completed 400 requests Completed 500 requests Completed 600 requests Completed 700 requests Completed 800 requests Completed 900 requests Completed 1000 requests Finished 1000 requests Server Software: Server Hostname: texteli.com Server Port: 80 Document Path: /4f84b59c557eb79321000dfa Document Length: 13400 bytes Concurrency Level: 200 Time taken for tests: 37.030 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 Total transferred: 13524000 bytes HTML transferred: 13400000 bytes Requests per second: 27.01 [#/sec] (mean) Time per request: 7406.024 [ms] (mean) Time per request: 37.030 [ms] (mean, across all concurrent requests) Transfer rate: 356.66 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 27 37 19.5 34 319 Processing: 80 6273 1673.7 6907 8987 Waiting: 47 3436 2085.2 3345 8856 Total: 115 6310 1675.8 6940 9022 Percentage of the requests served within a certain time (ms) 50% 6940 66% 6968 75% 6988 80% 7007 90% 7025 95% 7078 98% 8410 99% 8876 100% 9022 (longest request) What this results can tell me? Isn't 27 rps too slow?

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  • Lightweight alternative to R for RHEL?

    - by Eric Rath
    I want to use R for some statistical analysis of logfile information, but found that even the "limited" R-core RPM has a lot of dependencies not already installed. I don't want to install so many packages for a peripheral need. Are there lightweight alternatives for simple statistical analysis on RHEL 6? I have an R script that accepts on stdin a large set of values -- one value per line -- and prints out the min, max, mean, median, 95th percentile, and standard deviation. For more context, I'm using grep and awk to find GET requests for a particular path in our webserver log files, get the response times, and calculate the metrics listed above in order to measure the impact on performance of changes to a web application. I don't need any graphing capabilities, just simple computation. Is there something I've overlooked?

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  • Google Analytics: How long does it take users to trigger an event

    - by Stephen Ostermiller
    I implemented Google Analytics event tracking on my currency conversion website. The typical user flow is: User lands on a page about two currencies. User enters an amount to be converted. The site shows the user the value in the other currency. The JavaScript sends Google Analytics an "converted" event when the currency conversion is done. Because most of the sessions on my site are single page, the event tracking is very important to me to be able to know if users find my page useful. I'm looking for a way to be able to figure out how long it typically takes users to enter a value in the form. I expect that this data would form a bell curve with around a specific amount of time after page load. If I can't get a graph, I could make do with a median value. I would like to be able to use this as a core metric around usability testing. Is there a way to get this information out of Google Analytics?

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  • Finding maximum number of congruent numbers

    - by Stefan Czarnecki
    Let's say we have a multiset (set with possible duplicates) of integers. We would like to find the size of the largest subset of the multiset such that all numbers in the subset are congruent to each other modulo some m 1. For example: 1 4 7 7 8 10 for m = 2 the subsets are: (1, 7, 7) and (4, 8, 10), both having size 3. for m = 3 the subsets are: (1, 4, 7, 7, 10) and (8), the larger set of size 5. for m = 4 the subsets are: (1), (4, 8), (7, 7), (10), the largest set of size 2. At this moment it is evident that the best answer is 5 for m = 3. Given m we can find the size of the largest subset in linear time. Because the answer is always equal or larger than half of the size of the set, it is enough to check for values of m upto median of the set. Also I noticed it is necessary to check for only prime values of m. However if values in the set are large the algorithm is still rather slow. Does anyone have any ideas how to improve it?

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  • Location Change Salary Differences [closed]

    - by GameDev
    DISCLAIMER: I know that this might be a "regional" question but I'm also asking for help as far as what resources to use to determine my decision. I'm currently talking to a recruiter for a game developer in the SF Bay area. I work in a relatively low-cost area in the south. I really want to get into game development but my current career is general web development. I'm very interested in taking the job, but my concern is that the amount they're willing to pay might be a relative pay cut. Here are some factors: It's not an entry-level position, the title is Senior Software Engineer. I have 5+ years of experience. The calculators online tell me that I should be expecting around 2x my current pay rate(http://www.bestplaces.net/col/). My current pay is in the mid $60k/yr, so that's like 120-130k. The recruiter told me at my experience level I can expect about $90-100/yr, and that those cost of living calculators were way off. The benefits will definitely be better, it's much larger company (help with commuting, catered meals, etc). But is the recruiter trying to give me a snow job on the pay scale, or is that a reasonable change from a smallish town in the south to somewhere in the SF bay area? How can I find this out? Glassdoor and Payscale seem to say "senior software developers" in that area make around 110 in median salary, but Payscale says it's closer to $135k, that range seems pretty large.

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  • Node.js vs PHP processing speed

    - by Cody Craven
    I've been looking into node.js recently and wanted to see a true comparison of processing speed for PHP vs Node.js. In most of the comparisons I had seen, Node trounced Apache/PHP set ups handily. However all of the tests were small 'hello worlds' that would not accurately reflect any webpage's markup. So I decided to create a basic HTML page with 10,000 hello world paragraph elements. In these tests Node with Cluster was beaten to a pulp by PHP on Nginx utilizing PHP-FPM. So I'm curious if I am misusing Node somehow or if Node is really just this bad at processing power. Note that my results were equivalent outputting "Hello world\n" with text/plain as the HTML, but I only included the HTML as it's closer to the use case I was investigating. My testing box: Core i7-2600 Intel CPU (has 8 threads with 4 cores) 8GB DDR3 RAM Fedora 16 64bit Node.js v0.6.13 Nginx v1.0.13 PHP v5.3.10 (with PHP-FPM) My test scripts: Node.js script var cluster = require('cluster'); var http = require('http'); var numCPUs = require('os').cpus().length; if (cluster.isMaster) { // Fork workers. for (var i = 0; i < numCPUs; i++) { cluster.fork(); } cluster.on('death', function (worker) { console.log('worker ' + worker.pid + ' died'); }); } else { // Worker processes have an HTTP server. http.Server(function (req, res) { res.writeHead(200, {'Content-Type': 'text/html'}); res.write('<html>\n<head>\n<title>Speed test</title>\n</head>\n<body>\n'); for (var i = 0; i < 10000; i++) { res.write('<p>Hello world</p>\n'); } res.end('</body>\n</html>'); }).listen(80); } This script is adapted from Node.js' documentation at http://nodejs.org/docs/latest/api/cluster.html PHP script <?php echo "<html>\n<head>\n<title>Speed test</title>\n</head>\n<body>\n"; for ($i = 0; $i < 10000; $i++) { echo "<p>Hello world</p>\n"; } echo "</body>\n</html>"; My results Node.js $ ab -n 500 -c 20 http://speedtest.dev/ This is ApacheBench, Version 2.3 <$Revision: 655654 $> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ Licensed to The Apache Software Foundation, http://www.apache.org/ Benchmarking speedtest.dev (be patient) Completed 100 requests Completed 200 requests Completed 300 requests Completed 400 requests Completed 500 requests Finished 500 requests Server Software: Server Hostname: speedtest.dev Server Port: 80 Document Path: / Document Length: 190070 bytes Concurrency Level: 20 Time taken for tests: 14.603 seconds Complete requests: 500 Failed requests: 0 Write errors: 0 Total transferred: 95066500 bytes HTML transferred: 95035000 bytes Requests per second: 34.24 [#/sec] (mean) Time per request: 584.123 [ms] (mean) Time per request: 29.206 [ms] (mean, across all concurrent requests) Transfer rate: 6357.45 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 0 0.2 0 2 Processing: 94 547 405.4 424 2516 Waiting: 0 331 399.3 216 2284 Total: 95 547 405.4 424 2516 Percentage of the requests served within a certain time (ms) 50% 424 66% 607 75% 733 80% 813 90% 1084 95% 1325 98% 1843 99% 2062 100% 2516 (longest request) PHP/Nginx $ ab -n 500 -c 20 http://speedtest.dev/test.php This is ApacheBench, Version 2.3 <$Revision: 655654 $> Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/ Licensed to The Apache Software Foundation, http://www.apache.org/ Benchmarking speedtest.dev (be patient) Completed 100 requests Completed 200 requests Completed 300 requests Completed 400 requests Completed 500 requests Finished 500 requests Server Software: nginx/1.0.13 Server Hostname: speedtest.dev Server Port: 80 Document Path: /test.php Document Length: 190070 bytes Concurrency Level: 20 Time taken for tests: 0.130 seconds Complete requests: 500 Failed requests: 0 Write errors: 0 Total transferred: 95109000 bytes HTML transferred: 95035000 bytes Requests per second: 3849.11 [#/sec] (mean) Time per request: 5.196 [ms] (mean) Time per request: 0.260 [ms] (mean, across all concurrent requests) Transfer rate: 715010.65 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 0 0.2 0 1 Processing: 3 5 0.7 5 7 Waiting: 1 4 0.7 4 7 Total: 3 5 0.7 5 7 Percentage of the requests served within a certain time (ms) 50% 5 66% 5 75% 5 80% 6 90% 6 95% 6 98% 6 99% 6 100% 7 (longest request) Additional details Again what I'm looking for is to find out if I'm doing something wrong with Node.js or if it is really just that slow compared to PHP on Nginx with FPM. I certainly think Node has a real niche that it could fit well, however with these test results (which I really hope I made a mistake with - as I like the idea of Node) lead me to believe that it is a horrible choice for even a modest processing load when compared to PHP (let alone JVM or various other fast solutions). As a final note, I also tried running an Apache Bench test against node with $ ab -n 20 -c 20 http://speedtest.dev/ and consistently received a total test time of greater than 0.900 seconds.

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  • What is the difference between Multiple R-squared and Adjusted R-squared in a single-variate least s

    - by fmark
    Could someone explain to the statistically naive what the difference between Multiple R-squared and Adjusted R-squared is? I am doing a single-variate regression analysis as follows: v.lm <- lm(epm ~ n_days, data=v) print(summary(v.lm)) Results: Call: lm(formula = epm ~ n_days, data = v) Residuals: Min 1Q Median 3Q Max -693.59 -325.79 53.34 302.46 964.95 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2550.39 92.15 27.677 <2e-16 *** n_days -13.12 5.39 -2.433 0.0216 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 410.1 on 28 degrees of freedom Multiple R-squared: 0.1746, Adjusted R-squared: 0.1451 F-statistic: 5.921 on 1 and 28 DF, p-value: 0.0216 Apologies for the newbiness of this question.

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  • A database of questions with unambiguous numeric answers.

    - by dreeves
    I (and co-hackers) are building a sort of trivia game inspired by this blog post: http://messymatters.com/calibration. The idea is to give confidence intervals and learn how to be calibrated (when you're "90% sure" you should be right 90% of the time). We're thus looking for, ideally, thousands of questions with unambiguous numerical answers. Also, they shouldn't be too boring. There are a lot of random statistics out there -- eg, enclosed water area in different countries -- that would make the game mind-numbing. Things like release dates of classic movies are more interesting (to most people). Other interesting ones we've found include Olympic records, median incomes for different professions, dates of famous inventions, and celebrity ages. Scraping things like above, by the way, was my reason for asking this question: http://stackoverflow.com/questions/2611418/scrape-html-tables So, if you know of other sources of interesting numerical facts (in a parsable form) I'm eager for pointers to them. Thanks!

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  • filter that uses elements from two arrays at the same time

    - by Gacek
    Let's assume we have two arrays of the same size - A and B. Now, we need a filter that, for a given mask size, selects elements from A, but removes the central element of the mask, and inserts there corresponding element from B. So the 3x3 "pseudo mask" will look similar to this: A A A A B A A A A Doing something like this for averaging filter is quite simple. We can compute the mean value for elements from A without the central element, and then combine it with a proper proportion with elements from B: h = ones(3,3); h(2,2) =0; h = h/sum(h(:)); A_ave = filter2(h, A); C = (8/9) * A_ave + (1/9) * B; But how to do something similar for median filter (medfilt2 or even better for ordfilt2)

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  • R Question. Numeric variable vs. Non-numeric and "names" function

    - by Michael
    > scores=cbind(UNCA.score, A.score, B.score, U.m.A, U.m.B) > names(scores)=c('UNCA.scores', 'A.scores', 'B.scores','UNCA.minus.A', 'UNCA.minus.B') > names(scores) [1] "UNCA.scores" "A.scores" "B.scores" "UNCA.minus.A" "UNCA.minus.B" > summary(UNCA.scores) X6.69230769230769 Min. : 4.154 1st Qu.: 7.333 Median : 8.308 Mean : 8.451 3rd Qu.: 9.538 Max. :12.000 > is.numeric(UNCA.scores) [1] FALSE > is.numeric(scores[,1]) [1] TRUE My question is, what is the difference between UNCA.scores and scores[,1]? UNCA.scores is the first column in the data.frame 'scores', but they are not the same thing, since one is numeric and the other isn't. If UNCA.scores is just a label here how can I make it be equivalent to 'scores[,1]? Thanks!

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  • Methodologies or algorithms for filling in missing data

    - by tbone
    I am dealing with datasets with missing data and need to be able to fill forward, backward, and gaps. So, for example, if I have data from Jan 1, 2000 to Dec 31, 2010, and some days are missing, when a user requests a timespan that begins before, ends after, or encompasses the missing data points, I need to "fill in" these missing values. Is there a proper term to refer to this concept of filling in data? Imputation is one term, don't know if it is "the" term for it though. I presume there are multiple algorithms & methodologies for filling in missing data (use last measured, using median/average/moving average, etc between 2 known numbers, etc. Anyone know the proper term for this problem, any online resources on this topic, or ideally links to open source implementations of some algorithms (C# preferably, but any language would be useful)

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  • small string optimization for vector?

    - by BuschnicK
    I know several (all?) STL implementations implement a "small string" optimization where instead of storing the usual 3 pointers for begin, end and capacity a string will store the actual character data in the memory used for the pointers if sizeof(characters) <= sizeof(pointers). I am in a situation where I have lots of small vectors with an element size <= sizeof(pointer). I cannot use fixed size arrays, since the vectors need to be able to resize dynamically and may potentially grow quite large. However, the median (not mean) size of the vectors will only be 4-12 bytes. So a "small string" optimization adapted to vectors would be quite useful to me. Does such a thing exist? I'm thinking about rolling my own by simply brute force converting a vector to a string, i.e. providing a vector interface to a string. Good idea?

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  • Calculating percentiles in Excel with "buckets" data instead of the data list itself

    - by G B
    I have a bunch of data in Excel that I need to get certain percentile information from. The problem is that instead of having the data set made up of each value, I instead have info on the number of or "bucket" data. For example, imagine that my actual data set looks like this: 1,1,2,2,2,2,3,3,4,4,4 The data set that I have is this: Value No. of occurrences 1 2 2 4 3 2 4 3 Is there an easy way for me to calculate percentile information (as well as the median) without having to explode the summary data out to full data set? (Once I did that, I know that I could just use the Percentile(A1:A5, p) function) This is important because my data set is very large. If I exploded the data out, I would have hundreds of thousands of rows and I would have to do it for a couple of hundred data sets. Help!

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  • How do you calculate expanding mean on time series using pandas?

    - by mlo
    How would you create a column(s) in the below pandas DataFrame where the new columns are the expanding mean/median of 'val' for each 'Mod_ID_x'. Imagine this as if were time series data and 'ID' 1-2 was on Day 1 and 'ID' 3-4 was on Day 2. I have tried every way I could think of but just can't seem to get it right. left4 = pd.DataFrame({'ID': [1,2,3,4],'val': [10000, 25000, 20000, 40000],'Mod_ID': [15, 35, 15, 42], 'car': ['ford','honda', 'ford', 'lexus']}) right4 = pd.DataFrame({'ID': [3,1,2,4],'color': ['red', 'green', 'blue', 'grey'], 'wheel': ['4wheel','4wheel', '2wheel', '2wheel'], 'Mod_ID': [15, 15, 35, 42]}) df1 = pd.merge(left4, right4, on='ID').drop('Mod_ID_y', axis=1)

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  • SQL SERVER – Introduction to PERCENTILE_DISC() – Analytic Functions Introduced in SQL Server 2012

    - by pinaldave
    SQL Server 2012 introduces new analytical function PERCENTILE_DISC(). The book online gives following definition of this function: Computes a specific percentile for sorted values in an entire rowset or within distinct partitions of a rowset in Microsoft SQL Server 2012 Release Candidate 0 (RC 0). For a given percentile value P, PERCENTILE_DISC sorts the values of the expression in the ORDER BY clause and returns the value with the smallest CUME_DIST value (with respect to the same sort specification) that is greater than or equal to P. If you are clear with understanding of the function – no need to read further. If you got lost here is the same in simple words – find value of the column which is equal or more than CUME_DIST. Before you continue reading this blog I strongly suggest you read about CUME_DIST function over here Introduction to CUME_DIST – Analytic Functions Introduced in SQL Server 2012. Now let’s have fun following query: USE AdventureWorks GO SELECT SalesOrderID, OrderQty, ProductID, CUME_DIST() OVER(PARTITION BY SalesOrderID ORDER BY ProductID ) AS CDist, PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY ProductID) OVER (PARTITION BY SalesOrderID) AS PercentileDisc FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY SalesOrderID DESC GO The above query will give us the following result: You can see that I have used PERCENTILE_DISC(0.5) in query, which is similar to finding median but not exactly. PERCENTILE_DISC() function takes a percentile as a passing parameters. It returns the value as answer which value is equal or great to the percentile value which is passed into the example. For example in above example we are passing 0.5 into the PERCENTILE_DISC() function. It will go through the resultset and identify which rows has values which are equal to or great than 0.5. In first example it found two rows which are equal to 0.5 and the value of ProductID of that row is the answer of PERCENTILE_DISC(). In some third windowed resultset there is only single row with the CUME_DIST() value as 1 and that is for sure higher than 0.5 making it as a answer. To make sure that we are clear with this example properly. Here is one more example where I am passing 0.6 as a percentile. Now let’s have fun following query: USE AdventureWorks GO SELECT SalesOrderID, OrderQty, ProductID, CUME_DIST() OVER(PARTITION BY SalesOrderID ORDER BY ProductID ) AS CDist, PERCENTILE_DISC(0.6) WITHIN GROUP (ORDER BY ProductID) OVER (PARTITION BY SalesOrderID) AS PercentileDisc FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY SalesOrderID DESC GO The above query will give us the following result: The result of the PERCENTILE_DISC(0.6) is ProductID of which CUME_DIST() is more than 0.6. This means for SalesOrderID 43670 has row with CUME_DIST() 0.75 is the qualified row, resulting answer 773 for ProductID. I hope this explanation makes it further clear. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Function, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Apache https is slsow

    - by raucous12
    Hey, I've set apache up to use SSL with a self signed certificate. With http (KeepAlive off), I can get over 5000 requests per second. However, with https, I can only get 13 requests per second. I know there is supposed to be a bit of an overhead, but this seems abnormal. Can anyone suggest how I might go about debugging this. Here is the ab log for https: Server Software: Apache/2.2.3 Server Hostname: 127.0.0.1 Server Port: 443 SSL/TLS Protocol: TLSv1/SSLv3,DHE-RSA-AES256-SHA,4096,256 Document Path: /hello.html Document Length: 29 bytes Concurrency Level: 5 Time taken for tests: 30.49425 seconds Complete requests: 411 Failed requests: 0 Write errors: 0 Total transferred: 119601 bytes HTML transferred: 11919 bytes Requests per second: 13.68 [#/sec] (mean) Time per request: 365.565 [ms] (mean) Time per request: 73.113 [ms] (mean, across all concurrent requests) Transfer rate: 3.86 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 190 347 74.3 333 716 Processing: 0 14 24.0 1 166 Waiting: 0 11 21.6 0 165 Total: 191 361 80.8 345 716 Percentage of the requests served within a certain time (ms) 50% 345 66% 377 75% 408 80% 421 90% 468 95% 521 98% 578 99% 596 100% 716 (longest request)

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  • Apache https is slow

    - by raucous12
    Hey, I've set apache up to use SSL with a self signed certificate. With https (KeepAlive on), I can get over 3000 requests per second. However, with https (KeepAlive off), I can only get 13 requests per second. I know there is supposed to be a bit of an overhead, but this seems abnormal. Can anyone suggest how I might go about debugging this. Here is the ab log for https: Server Software: Apache/2.2.3 Server Hostname: 127.0.0.1 Server Port: 443 SSL/TLS Protocol: TLSv1/SSLv3,DHE-RSA-AES256-SHA,4096,256 Document Path: /hello.html Document Length: 29 bytes Concurrency Level: 5 Time taken for tests: 30.49425 seconds Complete requests: 411 Failed requests: 0 Write errors: 0 Total transferred: 119601 bytes HTML transferred: 11919 bytes Requests per second: 13.68 [#/sec] (mean) Time per request: 365.565 [ms] (mean) Time per request: 73.113 [ms] (mean, across all concurrent requests) Transfer rate: 3.86 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 190 347 74.3 333 716 Processing: 0 14 24.0 1 166 Waiting: 0 11 21.6 0 165 Total: 191 361 80.8 345 716 Percentage of the requests served within a certain time (ms) 50% 345 66% 377 75% 408 80% 421 90% 468 95% 521 98% 578 99% 596 100% 716 (longest request)

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  • amazon ec2-medium apache requests per second terrible

    - by TheDayIsDone
    EDITED -- test running from localhost now to rule out network... i have a c1.medium using EBS. when i do an apache benchmark and i'm just printing a "hello" for the test from localhost - no database hits, it's very slow. i can repeat this test many times with the same results. any thoughts? thanks in advance. ab -n 1000 -c 100 http://localhost/home/test/ Benchmarking localhost (be patient) Completed 100 requests Completed 200 requests Completed 300 requests Completed 400 requests Completed 500 requests Completed 600 requests Completed 700 requests Completed 800 requests Completed 900 requests Completed 1000 requests Finished 1000 requests Server Software: Apache/2.2.23 Server Hostname: localhost Server Port: 80 Document Path: /home/test/ Document Length: 5 bytes Concurrency Level: 100 Time taken for tests: 25.300 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 Total transferred: 816000 bytes HTML transferred: 5000 bytes Requests per second: 39.53 [#/sec] (mean) Time per request: 2530.037 [ms] (mean) Time per request: 25.300 [ms] (mean, across all concurrent requests) Transfer rate: 31.50 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 0 7 21.0 0 73 Processing: 81 2489 665.7 2500 4057 Waiting: 80 2443 654.0 2445 4057 Total: 85 2496 653.5 2500 4057 Percentage of the requests served within a certain time (ms) 50% 2500 66% 2651 75% 2842 80% 2932 90% 3301 95% 3506 98% 3762 99% 3838 100% 4057 (longest request)

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