Search Results

Search found 14282 results on 572 pages for 'performance counter'.

Page 112/572 | < Previous Page | 108 109 110 111 112 113 114 115 116 117 118 119  | Next Page >

• Performance when accessing class members

  - by Dr. Acula

  I'm writing something performance-critical and wanted to know if it could make a difference if I use: int test( int a, int b, int c ) { // Do millions of calculations with a, b, c } or class myStorage { public: int a, b, c; }; int test( myStorage values ) { // Do millions of calculations with values.a, values.b, values.c } Does this basically result in similar code? Is there an extra overhead of accessing the class members? I'm sure that this is clear to an expert in C++ so I won't try and write an unrealistic benchmark for it right now

  Read the article

• Performance effect of using print statements in Python script

  - by Sudar

  I have a Python script that process a huge text file (with around 4 millon lines) and writes the data into two separate files. I have added a print statement, which outputs a string for every line for debugging. I want to know how bad it could be from the performance perspective? If it is going to very bad, I can remove the debugging line. Edit It turns out that having a print statement for every line in a file with 4 million lines is increasing the time way too much.

  Read the article

• C++ STL: Array vs Vector: Raw element accessing performance

  - by oh boy

  I'm building an interpreter and as I'm aiming for raw speed this time, every clock cycle matters for me in this (raw) case. Do you have any experience or information what of the both is faster: Vector or Array? All what matters is the speed I can access an element (opcode receiving), I don't care about inserting, allocation, sorting, etc. I'm going to lean myself out of the window now and say: Arrays are at least a bit faster than vectors in terms of accessing an element i. It seems really logical for me. With vectors you have all those security and controlling overhead which doesn't exist for arrays. (Why) Am I wrong? No, I can't ignore the performance difference - even if it is so small - I have already optimized and minimized every other part of the VM which executes the opcodes :)

  Read the article

• Performance: Subquery or Joining

  - by Auro

  Hello I got a little question about performance of a subquery / joining another table INSERT INTO Original.Person ( PID, Name, Surname, SID ) ( SELECT ma.PID_new , TBL.Name , ma.Surname, TBL.SID FROM Copy.Person TBL , original.MATabelle MA WHERE TBL.PID = p_PID_old AND TBL.PID = MA.PID_old ); This is my SQL, now this thing runs around 1 million times or more. Now my question is what would be faster? if I change TBL.SID to (Select new from helptable where old = tbl.sid) or if I add helptable to the from and do the joining in the where? greets Auro

  Read the article

• Does async and await incease performance of an ASP.Net application

  - by Kerezo

  I recently read a article about c#-5 and new $ nice asynchronous programming. I see it works greate in windows application. The question came to me before is if this feature can increase ASP.Net performance? consider this code: public T GetData() { var d = GetSomeData(); return d; } and public async T GetData2() { var d = await GetSomeData(); return d; } Has in an ASP.Net appication that two codes difference? thanks

  Read the article

• Does 'throw' or 'try...catch' hinder performance?

  - by Richard

  I've been reading all over the place (including here) about when exception should / shouldn't be used. I now want to change my code that would throw to make the method return false and handle it like that, but my question is: Is it the throwing or try..catch-ing that can hinder performance...? What I mean is, would this be acceptable: bool method someMmethod() { try { // ...Do something catch (Exception ex) // Don't care too much what at the moment... { // Output error // Return false } return true // No errors Or would there be a better way to do it? (I'm bloody sick of seeing "Unhandled exception..." LOL!)

  Read the article

• Performance problem on a query.

  - by yapiskan

  Hi, I have a performance problem on a query. First table is a Customer table which has millions records in it. Customer table has a column of email address and some other information about customer. Second table is a CommunicationInfo table which contains just Email addresses. And What I want in here is; how many times the email address in CommunicationInfo table repeats in Customers table. What could be the the most performer query. The basic query that I can explain this situation is; Select ci.Email, count(*) from Customer c left join CommunicationInfo ci on c.Email1 = ci.Email or c.Email2 = ci.Email Group by ci.Email But sure, it takes about 5, 6 minutes in execution. Thanks in Advance.

  Read the article

• Slow performance of query

  - by user642378

  Hi, I have asked the performance of query and i tried to simplyfy it.but still it not works.I am adding my query below.Please can you simplify it more effectively select r.parent_itemid f_id, parent_item.name f_name, parent_item.typeid f_typeid, parent_item.ownerid f_ownerid, parent_item.created f_created, parent_item.modifiedby f_modifiedby, parent_item.modified f_modified, pt.name f_tname, child_item.id i_id, t.name i_tname, child_item.typeid i_typeid, child_item.name i_name, child_item.ownerid i_ownerid, child_item.created i_created, child_item.modifiedby i_modifiedby, child_item.modified i_modified, r.ordinal i_ordinal from item child_item, type t, relation r, item parent_item, type pt where r.child_itemid = child_item.id and t.id=child_item.typeid and parent_item.id = r.parent_itemid and pt.id = parent_item.typeid and parent_item.id in ( select itemid from permission where itemid=parent_item.id and (holder_itemid in (10,100) and level > 0) ) order by r.parent_itemid, r.relation_typeid, r.ordinal Thanks you regards jennie

  Read the article

• Investigation: Can different combinations of components effect Dataflow performance?

  - by jamiet

  Introduction The Dataflow task is one of the core components (if not the core component) of SQL Server Integration Services (SSIS) and often the most misunderstood. This is not surprising, its an incredibly complicated beast and we’re abstracted away from that complexity via some boxes that go yellow red or green and that have some lines drawn between them. Example dataflow In this blog post I intend to look under that facade and get into some of the nuts and bolts of the Dataflow Task by investigating how the decisions we make when building our packages can affect performance. I will do this by comparing the performance of three dataflows that all have the same input, all produce the same output, but which all operate slightly differently by way of having different transformation components. I also want to use this blog post to challenge a common held opinion that I see perpetuated over and over again on the SSIS forum. That is, that people assume adding components to a dataflow will be detrimental to overall performance. Its not surprising that people think this –it is intuitive to think that more components means more work- however this is not a view that I share. I have always been of the opinion that there are many factors affecting dataflow duration and the number of components is actually one of the less important ones; having said that I have never proven that assertion and that is one reason for this investigation. I have actually seen evidence that some people think dataflow duration is simply a function of number of rows and number of components. I’ll happily call that one out as a myth even without any investigation!  The Setup I have a 2GB datafile which is a list of 4731904 (~4.7million) customer records with various attributes against them and it contains 2 columns that I am going to use for categorisation: [YearlyIncome] [BirthDate] The data file is a SSIS raw format file which I chose to use because it is the quickest way of getting data into a dataflow and given that I am testing the transformations, not the source or destination adapters, I want to minimise external influences as much as possible. In the test I will split the customers according to month of birth (12 of those) and whether or not their yearly income is above or below 50000 (2 of those); in other words I will be splitting them into 24 discrete categories and in order to do it I shall be using different combinations of SSIS’ Conditional Split and Derived Column transformation components. The 24 datapaths that occur will each input to a rowcount component, again because this is the least resource intensive means of terminating a datapath. The test is being carried out on a Dell XPS Studio laptop with a quad core (8 logical Procs) Intel Core i7 at 1.73GHz and Samsung SSD hard drive. Its running SQL Server 2008 R2 on Windows 7. The Variables Here are the three combinations of components that I am going to test:     One Conditional Split - A single Conditional Split component CSPL Split by Month of Birth and income category that will use expressions on [YearlyIncome] & [BirthDate] to send each row to one of 24 outputs. This next screenshot displays the expression logic in use: Derived Column & Conditional Split - A Derived Column component DER Income Category that adds a new column [IncomeCategory] which will contain one of two possible text values {“LessThan50000”,”GreaterThan50000”} and uses [YearlyIncome] to determine which value each row should get. A Conditional Split component CSPL Split by Month of Birth and Income Category then uses that new column in conjunction with [BirthDate] to determine which of the same 24 outputs to send each row to. Put more simply, I am separating the Conditional Split of #1 into a Derived Column and a Conditional Split. The next screenshots display the expression logic in use: DER Income Category         CSPL Split by Month of Birth and Income Category       Three Conditional Splits - A Conditional Split component that produces two outputs based on [YearlyIncome], one for each Income Category. Each of those outputs will go to a further Conditional Split that splits the input into 12 outputs, one for each month of birth (identical logic in each). In this case then I am separating the single Conditional Split of #1 into three Conditional Split components. The next screenshots display the expression logic in use: CSPL Split by Income Category         CSPL Split by Month of Birth 1& 2       Each of these combinations will provide an input to one of the 24 rowcount components, just the same as before. For illustration here is a screenshot of the dataflow containing three Conditional Split components: As you can these dataflows have a fair bit of work to do and remember that they’re doing that work for 4.7million rows. I will execute each dataflow 10 times and use the average for comparison. I foresee three possible outcomes: The dataflow containing just one Conditional Split (i.e. #1) will be quicker There is no significant difference between any of them One of the two dataflows containing multiple transformation components will be quicker Regardless of which of those outcomes come to pass we will have learnt something and that makes this an interesting test to carry out. Note that I will be executing the dataflows using dtexec.exe rather than hitting F5 within BIDS. The Results and Analysis The table below shows all of the executions, 10 for each dataflow. It also shows the average for each along with a standard deviation. All durations are in seconds. I’m pasting a screenshot because I frankly can’t be bothered with the faffing about needed to make a presentable HTML table. It is plain to see from the average that the dataflow containing three conditional splits is significantly faster, the other two taking 43% and 52% longer respectively. This seems strange though, right? Why does the dataflow containing the most components outperform the other two by such a big margin? The answer is actually quite logical when you put some thought into it and I’ll explain that below. Before progressing, a side note. The standard deviation for the “Three Conditional Splits” dataflow is orders of magnitude smaller – indicating that performance for this dataflow can be predicted with much greater confidence too. The Explanation I refer you to the screenshot above that shows how CSPL Split by Month of Birth and salary category in the first dataflow is setup. Observe that there is a case for each combination of Month Of Date and Income Category – 24 in total. These expressions get evaluated in the order that they appear and hence if we assume that Month of Date and Income Category are uniformly distributed in the dataset we can deduce that the expected number of expression evaluations for each row is 12.5 i.e. 1 (the minimum) + 24 (the maximum) divided by 2 = 12.5. Now take a look at the screenshots for the second dataflow. We are doing one expression evaluation in DER Income Category and we have the same 24 cases in CSPL Split by Month of Birth and Income Category as we had before, only the expression differs slightly. In this case then we have 1 + 12.5 = 13.5 expected evaluations for each row – that would account for the slightly longer average execution time for this dataflow. Now onto the third dataflow, the quick one. CSPL Split by Income Category does a maximum of 2 expression evaluations thus the expected number of evaluations per row is 1.5. CSPL Split by Month of Birth 1 & CSPL Split by Month of Birth 2 both have less work to do than the previous Conditional Split components because they only have 12 cases to test for thus the expected number of expression evaluations is 6.5 There are two of them so total expected number of expression evaluations for this dataflow is 6.5 + 6.5 + 1.5 = 14.5. 14.5 is still more than 12.5 & 13.5 though so why is the third dataflow so much quicker? Simple, the conditional expressions in the first two dataflows have two boolean predicates to evaluate – one for Income Category and one for Month of Birth; the expressions in the Conditional Split in the third dataflow however only have one predicate thus they are doing a lot less work. To sum up, the difference in execution times can be attributed to the difference between: MONTH(BirthDate) == 1 && YearlyIncome <= 50000 and MONTH(BirthDate) == 1 In the first two dataflows YearlyIncome <= 50000 gets evaluated an average of 12.5 times for every row whereas in the third dataflow it is evaluated once and once only. Multiply those 11.5 extra operations by 4.7million rows and you get a significant amount of extra CPU cycles – that’s where our duration difference comes from. The Wrap-up The obvious point here is that adding new components to a dataflow isn’t necessarily going to make it go any slower, moreover you may be able to achieve significant improvements by splitting logic over multiple components rather than one. Performance tuning is all about reducing the amount of work that needs to be done and that doesn’t necessarily mean use less components, indeed sometimes you may be able to reduce workload in ways that aren’t immediately obvious as I think I have proven here. Of course there are many variables in play here and your mileage will most definitely vary. I encourage you to download the package and see if you get similar results – let me know in the comments. The package contains all three dataflows plus a fourth dataflow that will create the 2GB raw file for you (you will also need the [AdventureWorksDW2008] sample database from which to source the data); simply disable all dataflows except the one you want to test before executing the package and remember, execute using dtexec, not within BIDS. If you want to explore dataflow performance tuning in more detail then here are some links you might want to check out: Inequality joins, Asynchronous transformations and Lookups Destination Adapter Comparison Don’t turn the dataflow into a cursor SSIS Dataflow – Designing for performance (webinar) Any comments? Let me know! @Jamiet

  Read the article

• DataView.RowFilter Vs DataTable.Select() vs DataTable.Rows.Find()

  - by Aseem Gautam

  Considering the code below: Dataview someView = new DataView(sometable) someView.RowFilter = someFilter; if(someView.count > 0) { …. } Quite a number of articles which say Datatable.Select() is better than using DataViews, but these are prior to VS2008. Solved: The Mystery of DataView's Poor Performance with Large Recordsets Array of DataRecord vs. DataView: A Dramatic Difference in Performance Googling on this topic I found some articles/forum topics which mention Datatable.Select() itself is quite buggy(not sure on this) and underperforms in various scenarios. On this(Best Practices ADO.NET) topic on msdn it is suggested that if there is primary key defined on a datatable the findrows() or find() methods should be used insted of Datatable.Select(). This article here (.NET 1.1) benchmarks all the three approaches plus a couple more. But this is for version 1.1 so not sure if these are valid still now. Accroding to this DataRowCollection.Find() outperforms all approaches and Datatable.Select() outperforms DataView.RowFilter. So I am quite confused on what might be the best approach on finding rows in a datatable. Or there is no single good way to do this, multiple solutions exist depending upon the scenario?

  Read the article

• How to get decent MySQL driver perfomance in Ruby

  - by Zombies

  I notice that I am getting very poor performance for either or both inserts and queries. The queries themselves are basic and can execute with no delay directly from mysql. The ruby script that I wrote is only 1 thread, so only 1 connection is being used, and never closed unless the script is terminated. Pretty basic, I am just trying to insert a lot of rows. There is a look-up or two to get a surrogate key, or to check for duplicates, but the complexity is just O(n). Also, it isn't like there are millions of records, so again the queries themselves take no time to run. I am using: Ruby 1.9.1 Gem/driver:ruby-mysql 2.9.2 MySQL 5.1.37-1ubuntu5.1 ^ all 32 bit versions on a 32bit ubuntu distro I am getting about 1-2 inserts per second, pretty slow. I know a lot of people will suggest to change drivers, but that means I have some refactoring and resting to do. So I would really appreciate any help, but please if you do recomend that at least say why you do (eg: if you have used ruby-mysql x.x.x before and found another mysql driver to be better).ruby-mysql 2.9.2 What I would like to know: How can I improve performance with ruby-mysql 2.9.2 If and only if I cannot do this with ruby-mysql 2.9.2, what should I do?

  Read the article

• Will fixed-point arithmetic be worth my trouble?

  - by Thomas

  I'm working on a fluid dynamics Navier-Stokes solver that should run in real time. Hence, performance is important. Right now, I'm looking at a number of tight loops that each account for a significant fraction of the execution time: there is no single bottleneck. Most of these loops do some floating-point arithmetic, but there's a lot of branching in between. The floating-point operations are mostly limited to additions, subtractions, multiplications, divisions and comparisons. All this is done using 32-bit floats. My target platform is x86 with at least SSE1 instructions. (I've verified in the assembler output that the compiler indeed generates SSE instructions.) Most of the floating-point values that I'm working with have a reasonably small upper bound, and precision for near-zero values isn't very important. So the thought occurred to me: maybe switching to fixed-point arithmetic could speed things up? I know the only way to be really sure is to measure it, that might take days, so I'd like to know the odds of success beforehand. Fixed-point was all the rage back in the days of Doom, but I'm not sure where it stands anno 2010. Considering how much silicon is nowadays pumped into floating-point performance, is there a chance that fixed-point arithmetic will still give me a significant speed boost? Does anyone have any real-world experience that may apply to my situation?

  Read the article

• Using Oracle hint "FIRST_ROWS" to improve Oracle database performances

  - by bobetko

  I have a statement that runs on Oracle database server. The statement has about 5 joins and there is nothing unusual there. It looks pretty much like below: SELECT field1, field2, field3, ... FROM table1, table2, table3, table4, table5 WHERE table1.id = table2.id AND table2.id = table3.id AND ... table5.userid = 1 The problem (and what is interesting) is that statement for userid = 1 takes 1 second to return 590 records. Statement for userid = 2 takes around 30 seconds to return 70 records. I don't understand why is difference so big. It seems that different execution plan is chosen for statement with userid = 1 and different for userid = 2. After I implemented Oracle Hint FIRST_ROW, performance become significantly better. Both statements (for both ids 1 and 2) produce return in under 1 second. SELECT /*+ FIRST_ROWS */ field1, field2, field3, ... FROM table1, table2, table3, table4, table5 WHERE table1.id = table2.id AND table2.id = table3.id AND ... table5.userid = 1 Questions: 1) What are possible reasons for bad performance when userid = 2 (when hint is not used)? 2) Why would execution plan be different for one vs another statement (when hint is not used)? 3) Is there anything that I should be careful about when deciding to add this hint to my queries? Thanks

  Read the article

• Optimizing code using PIL

  - by freakazo

  Firstly sorry for the long piece of code pasted below. This is my first time actually having to worry about performance of an application so I haven't really ever worried about performance. This piece of code pretty much searches for an image inside another image, it takes 30 seconds to run on my computer, converting the images to greyscale and other changes shaved of 15 seconds, I need another 15 shaved off. I did read a bunch of pages and looked at examples but I couldn't find the same problems in my code. So any help would be greatly appreciated. From the looks of it (cProfile) 25 seconds is spent within the Image module, and only 5 seconds in my code. from PIL import Image import os, ImageGrab, pdb, time, win32api, win32con import cProfile def GetImage(name): name = name + '.bmp' try: print(os.path.join(os.getcwd(),"Images",name)) image = Image.open(os.path.join(os.getcwd(),"Images",name)) except: print('error opening image;', name) return image def Find(name): image = GetImage(name) imagebbox = image.getbbox() screen = ImageGrab.grab() #screen = Image.open(os.path.join(os.getcwd(),"Images","Untitled.bmp")) YLimit = screen.getbbox()[3] - imagebbox[3] XLimit = screen.getbbox()[2] - imagebbox[2] image = image.convert("L") Screen = screen.convert("L") Screen.load() image.load() #print(XLimit, YLimit) Found = False image = image.getdata() for y in range(0,YLimit): for x in range(0,XLimit): BoxCoordinates = x, y, x+imagebbox[2], y+imagebbox[3] ScreenGrab = screen.crop(BoxCoordinates) ScreenGrab = ScreenGrab.getdata() if image == ScreenGrab: Found = True #print("woop") return x,y if Found == False: return "Not Found" cProfile.run('print(Find("Login"))')

  Read the article

• Java repaint is slow under certain conditions.

  - by Gabriel A. Zorrilla

  I'm doing a simple grid which each square is highlighted by the cursor: They are a couple of JPanels, mapgrid and overlay inside a JLayeredPane, with mapgrid on the bottom. Mapgrid just draws on initialization the grid, its paint metodh is: public void paintComponent(Graphics g) { super.paintComponent(g); Graphics2D g2d = (Graphics2D) g; g2d.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON); for (int i = 0; i < h; i++) { for (int j = 0; j < w; j++) { g2d.setColor(new Color(128, 128, 128, 255)); g2d.drawRect(tileSize * j, i * tileSize, tileSize, tileSize); } } In the overlay JPanel is where the highlighting occurs, this is what is repainted when the mouse is moved: public void paintComponent(Graphics g) { super.paintComponent(g); Graphics2D g2d = (Graphics2D) g; g2d.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON); g2d.setColor(new Color(255, 255, 128, 255)); g2d.drawRect((pointerX/tileSize)*tileSize,(pointerY/ tileSize)*tileSize, tileSize, tileSize); } I noticed that even though the base layer (mapgrid) is NOT repainted when the mouse moves, just the transparent overlay layer, the performance is lacking. If i give the overlay JPanel a background, its way faster. If i remove the mapgrid Antialiasing, its a bit faster too. I don't know why giving a background to the overlay layer (and thus, hiding the mapgrid) or disabling antialiasing in the mapgrid leads to much better performance. Is there a better way to do this? Why does this happen?

  Read the article

• Java Collections and Garbage Collector

  - by Anth0

  A little question regarding performance in a Java web app. Let's assume I have a List<Rubrique> listRubriques with ten Rubrique objects. A Rubrique contains one list of products (List<product> listProducts) and one list of clients (List<Client> listClients). What exactly happens in memory if I do this: listRubriques.clear(); listRubriques = null; My point of view would be that, since listRubriques is empty, all my objects previously referenced by this list (including listProducts and listClients) will be garbage collected pretty soon. But since Collection in Java are a little bit tricky and since I have quite performance issues with my app i'm asking the question :) edit : let's assume now that my Client object contains a List<Client>. Therefore, I have kind of a circular reference between my objects. What would happen then if my listRubrique is set to null? This time, my point of view would be that my Client objects will become "unreachable" and might create a memory leak?

  Read the article

• Sql serve Full Text Search with Containstable is very slow when Used in JOIN!

  - by Bob

  Hello, I am using sql 2008 full text search and I am having serious issues with performance depending on how I use Contains or ContainsTable. Here are sample: (table one has about 5000 records and there is a covered index on table1 which has all the fields in the where clause. I tried to simplify the statements so forgive me if there is syntax issues.) Scenario 1: select * from table1 as t1 where t1.field1=90 and t1.field2='something' and Exists(select top 1 * from containstable(table1,*, 'something') as t2 where t2.[key]=t1.id) results: 10 second (very slow) Scenario 2: select * from table1 as t1 join containstable(table1,*, 'something') as t2 on t2.[key] = t1.id where t1.field1=90 and t1.field2='something' results: 10 second (very slow) Scenario 3: Declare @tbl Table(id uniqueidentifier primary key) insert into @tbl select {key] from containstable(table1,*, 'something') select * from table1 as t1 where t1.field1=90 and t1.field2='something' and Exists(select id from @tbl as tbl where id=req1.id) results: fraction of a second (super fast) Bottom line, it seems if I use Containstable in any kind of join or where clause condition of a select statement that also has other conditions, the performance is really bad. In addition if you look at profiler, the number of reads from the database goes to the roof. But if I first do the full text search and put results in a table variable and use that variable everything goes super fast. The number of reads are also much lower. It seems in "bad" scenarios, somehow it gets stuck in a loop which causes it to read many times from teh database but of course I don't understant why. Now the question is first of all whyis that happening? and question two is that how scalable table variables are? what if it results to 10s of thousands of records? is it still going to be fast. Any ideas? Thanks

  Read the article

• Why is the Clojure Hello World program so slow compared to Java and Python?

  - by viksit

  Hi all, I'm reading "Programming Clojure" and I was comparing some languages I use for some simple code. I noticed that the clojure implementations were the slowest in each case. For instance, Python - hello.py def hello_world(name): print "Hello, %s" % name hello_world("world") and result, $ time python hello.py Hello, world real 0m0.027s user 0m0.013s sys 0m0.014s Java - hello.java import java.io.*; public class hello { public static void hello_world(String name) { System.out.println("Hello, " + name); } public static void main(String[] args) { hello_world("world"); } } and result, $ time java hello Hello, world real 0m0.324s user 0m0.296s sys 0m0.065s and finally, Clojure - hellofun.clj (defn hello-world [username] (println (format "Hello, %s" username))) (hello-world "world") and results, $ time clj hellofun.clj Hello, world real 0m1.418s user 0m1.649s sys 0m0.154s Thats a whole, garangutan 1.4 seconds! Does anyone have pointers on what the cause of this could be? Is Clojure really that slow, or are there JVM tricks et al that need to be used in order to speed up execution? More importantly - isn't this huge difference in performance going to be an issue at some point? (I mean, lets say I was using Clojure for a production system - the gain I get in using lisp seems completely offset by the performance issues I can see here). The machine used here is a 2007 Macbook Pro running Snow Leopard, a 2.16Ghz Intel C2D and 2G DDR2 SDRAM. BTW, the clj script I'm using is from here and looks like, #!/bin/bash JAVA=/System/Library/Frameworks/JavaVM.framework/Versions/1.6/Home/bin/java CLJ_DIR=/opt/jars CLOJURE=$CLJ_DIR/clojure.jar CONTRIB=$CLJ_DIR/clojure-contrib.jar JLINE=$CLJ_DIR/jline-0.9.94.jar CP=$PWD:$CLOJURE:$JLINE:$CONTRIB # Add extra jars as specified by `.clojure` file if [ -f .clojure ] then CP=$CP:`cat .clojure` fi if [ -z "$1" ]; then $JAVA -server -cp $CP \ jline.ConsoleRunner clojure.lang.Repl else scriptname=$1 $JAVA -server -cp $CP clojure.main $scriptname -- $* fi

  Read the article

• List of objects or parallel arrays of properties?

  - by Headcrab

  The question is, basically: what would be more preferable, both performance-wise and design-wise - to have a list of objects of a Python class or to have several lists of numerical properties? I am writing some sort of a scientific simulation which involves a rather large system of interacting particles. For simplicity, let's say we have a set of balls bouncing inside a box so each ball has a number of numerical properties, like x-y-z-coordinates, diameter, mass, velocity vector and so on. How to store the system better? Two major options I can think of are: to make a class "Ball" with those properties and some methods, then store a list of objects of the class, e. g. [b1, b2, b3, ...bn, ...], where for each bn we can access bn.x, bn.y, bn.mass and so on; to make an array of numbers for each property, then for each i-th "ball" we can access it's 'x' coordinate as xs[i], 'y' coordinate as ys[i], 'mass' as masses[i] and so on; To me it seems that the first option represents a better design. The second option looks somewhat uglier, but might be better in terms of performance, and it could be easier to use it with numpy and scipy, which I try to use as much as I can. I am still not sure if Python will be fast enough, so it may be necessary to rewrite it in C++ or something, after initial prototyping in Python. Would the choice of data representation be different for C/C++? What about a hybrid approach, e.g. Python with C++ extension?

  Read the article

• HttpURLConnection inside a loop

  - by Carlos Garces

  Hi! I'm trying to connect to one URL that I know that exist but I don't know when. I don't have access to this server so I can't change anything to receive a event. The actual code is this. URL url = new URL(urlName); for(int j = 0 ; j< POOLING && disconnected; j++){ HttpURLConnection connection = (HttpURLConnection) url.openConnection(); int status = connection.getResponseCode(); if(status == HttpURLConnection.HTTP_OK || status == HttpURLConnection.HTTP_NOT_MODIFIED){ //Some work }else{ //wait 3s Thread.sleep(3000); } } Java not is my best skill and I'm not sure if this code is good from the point of view of performance. I'm opening a new connection every 3 seconds? or the connection is reused? If I call to disconnect() I ensure that no new connections are open in the loop, but... it will impact in performance?. Suggestions? What is the fast/best ways to know it a URL exist?

  Read the article

• How I shoud use BIT in MS SQL 2005

  - by adopilot

  Regarding to SQL performance. I have Scalar-Valued function for checking some specific condition in base, It returns BIT value True or False, I now do not know how I should fill @BIT parameter If I write. set @bit = convert(bit,1) or set @bit = 1 or set @bit='true' Function will work anyway but I do not know which method is recommended for daily use. Another Question, I have table in my base with around 4 million records, Daily insert is about 4K records in that table. Now I want to add CONSTRAINT on that table whit scalar valued function that I mentioned already Something like this ALTER TABLE fin_stavke ADD CONSTRAINT fin_stavke_knjizenje CHECK ( dbo.fn_ado_chk_fin(id)=convert(bit,1)) Where is filed "id" primary key of table fin_stavke and dbo.fn_ado_chk_fin looks like create FUNCTION fn_ado_chk_fin ( @stavka_id int ) RETURNS bit AS BEGIN declare @bit bit if exists (select * from fin_stavke where id=@stavka_id and doc_id is null and protocol_id is null) begin set @bit=0 end else begin set @bit=1 end return @bit; END GO Will this type and method of cheeking constraint will affect badly performance on my table and SQL at all ? If there is also better way to add control on this table please let me know.

  Read the article

• Measuring execution time of selected loops

  - by user95281

  I want to measure the running times of selected loops in a C program so as to see what percentage of the total time for executing the program (on linux) is spent in these loops. I should be able to specify the loops for which the performance should be measured. I have tried out several tools (vtune, hpctoolkit, oprofile) in the last few days and none of them seem to do this. They all find the performance bottlenecks and just show the time for those. Thats because these tools only store the time taken that is above a threshold (~1ms). So if one loop takes lesser time than that then its execution time won't be reported. The basic block counting feature of gprof depends on a feature in older compilers thats not supported now. I could manually write a simple timer using gettimeofday or something like that but for some cases it won't give accurate results. For ex: for (i = 0; i < 1000; ++i) { for (j = 0; j < N; ++j) { //do some work here } } Now here I want to measure the total time spent in the inner loop and I will have to put a call to gettimeofday inside the first loop. So gettimeofday itself will get called a 1000 times which introduces its own overhead and the result will be inaccurate.

  Read the article

• Oracle: Insertion on an indexed table, avoiding duplicates. Looking for tips and advice.

  - by Tom

  Hi everyone, Im looking for the best solution (performance wise) to achieve this. I have to insert records into a table, avoiding duplicates. For example, take table A Insert into A ( Select DISTINCT [FIELDS] from B,C,D.. WHERE (JOIN CONDITIONS ON B,C,D..) AND NOT EXISTS ( SELECT * FROM A ATMP WHERE ATMP.SOMEKEY = A.SOMEKEY ) ); I have an index over A.SOMEKEY, just to optimize the NOT EXISTS query, but i realize that inserting on an indexed table will be a performance hit. So I was thinking of duplicating Table A in a Global Temporary Table, where I would keep the index. Then, removing the index from Table A and executing the query, but modified Insert into A ( Select DISTINCT [FIELDS] from B,C,D.. WHERE (JOIN CONDITIONS ON B,C,D..) AND NOT EXISTS ( SELECT * FROM GLOBAL_TEMPORARY_TABLE_A ATMP WHERE ATMP.SOMEKEY = A.SOMEKEY ) ); This would solve the "inserting on an index table", but I would have to update the Global Temporary A with each insertion I make. I'm kind of lost here, Is there a better way to achieve this? Thanks in advance,

  Read the article

• Index Tuning for SSIS tasks

  - by Raj More

  I am loading tables in my warehouse using SSIS. Since my SSIS is slow, it seemed like a great idea to build indexes on the tables. There are no primary keys (and therefore, foreign keys), indexes (clustered or otherwise), constraints, on this warehouse. In other words, it is 100% efficiency free. We are going to put indexes based on usage - by analyzing new queries and current query performance. So, instead of doing it our old fashioned sweat and grunt way of actually reading the SQL statements and execution plans, I thought I'd put the shiny new Database Engine Tuning Advisor to use. I turned SQL logging off in my SSIS package and ran a "Tuning" trace, saved it to a table and analyzed the output in the Tuning Advisor. Most of the lookups are done as: exec sp_executesql N'SELECT [Active], [CompanyID], [CompanyName], [CompanyShortName], [CompanyTypeID], [HierarchyNodeID] FROM [dbo].[Company] WHERE ([CompanyID]=@P1) AND ([StartDateTime] IS NOT NULL AND [EndDateTime] IS NULL)',N'@P1 int',1 exec sp_executesql N'SELECT [Active], [CompanyID], [CompanyName], [CompanyShortName], [CompanyTypeID], [HierarchyNodeID] FROM [dbo].[Company] WHERE ([CompanyID]=@P1) AND ([StartDateTime] IS NOT NULL AND [EndDateTime] IS NULL)',N'@P1 int',2 exec sp_executesql N'SELECT [Active], [CompanyID], [CompanyName], [CompanyShortName], [CompanyTypeID], [HierarchyNodeID] FROM [dbo].[Company] WHERE ([CompanyID]=@P1) AND ([StartDateTime] IS NOT NULL AND [EndDateTime] IS NULL)',N'@P1 int',3 exec sp_executesql N'SELECT [Active], [CompanyID], [CompanyName], [CompanyShortName], [CompanyTypeID], [HierarchyNodeID] FROM [dbo].[Company] WHERE ([CompanyID]=@P1) AND ([StartDateTime] IS NOT NULL AND [EndDateTime] IS NULL)',N'@P1 int',4 and when analyzed, these statements have the reason "Event does not reference any tables". Huh? Does it not see the FROM dbo.Company??!! What is going on here? So, I have multiple questions: How do I get it to capture the actual statement executing in my trace, not what was submitted in a batch? Are there any best practices to follow for tuning performance related to SSIS packages running against SQL Server 2008?

  Read the article

• Downloading javascript Without Blocking

  - by doug

  The context: My question relates to improving web-page loading performance, and in particular the effect that javascript has on page-loading (resources/elements below the script are blocked from downloading/rendering). This problem is usually avoided/mitigated by placing the scripts at the bottom (eg, just before the tag). The code i am looking at is for web analytics. Placing it at the bottom reduces its accuracy; and because this script has no effect on the page's content, ie, it's not re-writing any part of the page--i want to move it inside the head. Just how to do that without ruining page-loading performance is the crux. From my research, i've found six techniques (w/ support among all or most of the major browsers) for downloading scripts so that they don't block down-page content from loading/rendering: (i) XHR + eval(); (ii) XHR + 'inject'; (iii) download the HTML-wrapped script as in iFrame; (iv) setting the script tag's 'async' flag to 'TRUE' (HTML 5 only); (v) setting the script tag's 'defer' attribute; and (vi) 'Script DOM Element'. It's the last of these i don't understand. The javascript to implement the pattern (vi) is: (function() { var q1 = document.createElement('script'); q1.src = 'http://www.my_site.com/q1.js' document.documentElement.firstChild.appendChild(q1) })(); Seems simple enough: inside this anonymous function, a script element is created, its 'src' element is set to it's location, then the script element is added to the DOM. But while each line is clear, it's still not clear to me how exactly this pattern allows script loading without blocking down-page elements/resources from rendering/loading?

  Read the article

< Previous Page | 108 109 110 111 112 113 114 115 116 117 118 119  | Next Page >