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  • Command line CSV viewer?

    - by Benjamin Oakes
    Anyone know of a command-line CSV viewer for Linux/OS X? I'm thinking of something like less but that spaces out the columns in a more readable way. (I'd be fine with opening it with OpenOffice Calc or Excel, but that's way too overpowered for just looking at the data like I need to.) Having horizontal and vertical scrolling would be great.

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  • Customized command line parsing in Python

    - by Moshe
    I'm writing a shell for a project of mine, which by design parses commands that looks like this: COMMAND_NAME ARG1="Long Value" ARG2=123 [email protected] My problem is that Python's command line parsing libraries (getopt and optparse) forces me to use '-' or '--' in front of the arguments. This behavior doesn't match my requirements. Any ideas how can this be solved? Any existing library for this?

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  • php,unix command ,imagick help me

    - by jagadeeshbalu
    This command add the text "flower" to the image: convert flower.jpg -font courier -fill white -pointsize 20 -annotate +50+50 'Flower' flower_annotate1.jpg I'm using ImageMagick 2.2.0. I'm running it from PHP using: system('convert flower.jpg -font courier -fill white -pointsize 20 -annotate +50+50 'Flower' flower_annotate1.jpg'); but I'm not getting the result

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  • How to create a stand alone command line application with Node.js

    - by Fab
    I'm trying to find a way to use a command line nodejs application that I created on a computer without node.js installed. In other words how to package my application with node.js inside, in order to avoid the users to have node.js already installed. The tipical use case is: I run the application and the application works using the node core that is provide with the application (or the application checks if there is node.js installed, and if not it donwload and install it automatically). Do you have any idea?

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  • Command line semaphore utility

    - by compie
    I want to write a command line utility that can be used to synchronize the execution off programs in different consoles. Console A: $ first_program && semaphore -signal Console B: $ semaphore -wait && second_program The first program takes a long take to complete. The second program can only start when the first program has finished. Which synchronization object do I need to implement this?

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  • Command button requires two clicks (sometimes)

    - by Shelly
    i am using icefaces 1.8.2. For some command buttons, sometimes i need to click the button twice. previously whole applivcation was working fine with single click. after upgrade to 1.8.2,i am facing this problem......Any suggestion,what could be the reason??? Thanks

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  • compile a single ASP.NET (.aspx) page from the command line

    - by JoelFan
    I have a Perl script that calls aspnet_compiler.exe to compile a large ASP.NET website. The problem is that it takes very long to run. Is there any way to compile just a single .aspx file from the command line? (I think that would suit the needs of my script) It seems that all aspnet_compiler.exe can do is compile at a directory level.

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  • Easy command line Java compile

    - by Nash0
    So I have to send a java project to someone outside our company who has no experience with java and they need to compile it. Is there an easy way to do it on the windows command line that does not require writing out lists of the files? Personally I think javac should be smart enough to handle javac * when in the folder just under the root of the package hierarchy. Is there anything in this ballpark?

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  • Correct to check for a command line flag in bash

    - by BCS
    In the middle of a scrip, I want to check if a given flag was passed on the command line. The following does what I want but seems ugly: if echo $* | grep -e "--flag" -q then echo ">>>> Running with flag" else echo ">>>> Running without flag" fi Is there a better way? Note: I explicitly don't want to list all the flags in a switch/getopt. (And BTW the bodies of the if just set a set of vars)

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  • More CPU cores may not always lead to better performance – MAXDOP and query memory distribution in spotlight

    - by sqlworkshops
    More hardware normally delivers better performance, but there are exceptions where it can hinder performance. Understanding these exceptions and working around it is a major part of SQL Server performance tuning.   When a memory allocating query executes in parallel, SQL Server distributes memory to each task that is executing part of the query in parallel. In our example the sort operator that executes in parallel divides the memory across all tasks assuming even distribution of rows. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union.   In reality, how often are column values evenly distributed, think about an example; are employees working for your company distributed evenly across all the Zip codes or mainly concentrated in the headquarters? What happens when you sort result set based on Zip codes? Do all products in the catalog sell equally or are few products hot selling items?   One of my customers tested the below example on a 24 core server with various MAXDOP settings and here are the results:MAXDOP 1: CPU time = 1185 ms, elapsed time = 1188 msMAXDOP 4: CPU time = 1981 ms, elapsed time = 1568 msMAXDOP 8: CPU time = 1918 ms, elapsed time = 1619 msMAXDOP 12: CPU time = 2367 ms, elapsed time = 2258 msMAXDOP 16: CPU time = 2540 ms, elapsed time = 2579 msMAXDOP 20: CPU time = 2470 ms, elapsed time = 2534 msMAXDOP 0: CPU time = 2809 ms, elapsed time = 2721 ms - all 24 cores.In the above test, when the data was evenly distributed, the elapsed time of parallel query was always lower than serial query.   Why does the query get slower and slower with more CPU cores / higher MAXDOP? Maybe you can answer this question after reading the article; let me know: [email protected].   Well you get the point, let’s see an example.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go   Let’s create the temporary table #FireDrill with all possible Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip from Employees update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --First serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) goThe query took 1011 ms to complete.   The execution plan shows the 77816 KB of memory was granted while the estimated rows were 799624.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1912 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 799624.  The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead. Sort properties shows the rows are unevenly distributed over the 4 threads.   Sort Warnings in SQL Server Profiler.   Intermediate Summary: The reason for the higher duration with parallel plan was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001. Now let’s update the Employees table and distribute employees evenly across all Zip codes.   update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go   The query took 751 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.   Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 661 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 784707.  Sort properties shows the rows are evenly distributed over the 4 threads. No Sort Warnings in SQL Server Profiler.    Intermediate Summary: When employees were distributed unevenly, concentrated on 1 Zip code, parallel sort spilled while serial sort performed well without spilling to tempdb. When the employees were distributed evenly across all Zip codes, parallel sort and serial sort did not spill to tempdb. This shows uneven data distribution may affect the performance of some parallel queries negatively. For detailed discussion of memory allocation, refer to webcasts available at www.sqlworkshops.com/webcasts.     Some of you might conclude from the above execution times that parallel query is not faster even when there is no spill. Below you can see when we are joining limited amount of Zip codes, parallel query will be fasted since it can use Bitmap Filtering.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go  Let’s create the temporary table #FireDrill with limited Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip       from Employees where Zip between 1800 and 2001 update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 989 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 785594. No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1799 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 785594.  Sort Warnings in SQL Server Profiler.    The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead.  Intermediate Summary: The reason for the higher duration with parallel plan even with limited amount of Zip codes was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001.   Now let’s update the Employees table and distribute employees evenly across all Zip codes. update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 250  ms to complete.  The execution plan shows the 9016 KB of memory was granted while the estimated rows were 79973.8.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0.  --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 85 ms to complete.  The execution plan shows the 13152 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.    Here you see, parallel query is much faster than serial query since SQL Server is using Bitmap Filtering to eliminate rows before the hash join.   Parallel queries are very good for performance, but in some cases it can hinder performance. If one identifies the reason for these hindrances, then it is possible to get the best out of parallelism. I covered many aspects of monitoring and tuning parallel queries in webcasts (www.sqlworkshops.com/webcasts) and articles (www.sqlworkshops.com/articles). I suggest you to watch the webcasts and read the articles to better understand how to identify and tune parallel query performance issues.   Summary: One has to avoid sort spill over tempdb and the chances of spills are higher when a query executes in parallel with uneven data distribution. Parallel query brings its own advantage, reduced elapsed time and reduced work with Bitmap Filtering. So it is important to understand how to avoid spills over tempdb and when to execute a query in parallel.   I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan  

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  • MySQL Query Select using sub-select takes too long

    - by True Soft
    I noticed something strange while executing a select from 2 tables: SELECT * FROM table_1 WHERE id IN ( SELECT id_element FROM table_2 WHERE column_2=3103); This query took approximatively 242 seconds. But when I executed the subquery SELECT id_element FROM table_2 WHERE column_2=3103 it took less than 0.002s (and resulted 2 rows). Then, when I did SELECT * FROM table_1 WHERE id IN (/* prev.result */) it was the same: 0.002s. I was wondering why MySQL is doing the first query like that, taking much more time than the last 2 queries separately? Is it an optimal solution for selecting something based from the results of a sub-query? Other details: table_1 has approx. 9000 rows, and table_2 has 90000 rows. After I added an index on column_2 from table_2, the first query took 0.15s.

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  • What's wrong with my MySql query ?!

    - by Anytime
    This is a query I am doing with mysql using PHP This is the query line <?php $query = "SELECT * FROM node WHERE type = 'student_report' AND uid = '{$uid}' LIMIT 1 ORDER BY created DESC"; ?> I get the following error You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'ORDER BY created DESC' at line 1

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  • complex sql which runs extremely slow when the query has order by clause

    - by basit.
    I have following complex query which I need to use. When I run it, it takes 30 to 40 seconds. But if I remove the order by clause, it takes 0.0317 sec to return the result, which is really fast compare to 30 sec or 40. select DISTINCT media.* , username from album as album , album_permission as permission , user as user, media as media where ((media.album_id = album.album_id and album.private = 'yes' and album.album_id = permission.album_id and (permission.email = '' or permission.user_id = '') ) or (media.album_id = album.album_id and album.private = 'no' ) or media.album_id = '0' ) and media.user_id = user.user_id and media.media_type = 'video' order by media.id DESC LIMIT 0,20 The id on order by is primary key which is indexed too. So I don't know what is the problem. I also have album and album permission table, just to check if media is public or private, if private then check if user has permission or not. I was thinking maybe that is causing the issue. What if I did this in sub query, would that work better? Also can someone help me write that sub query, if that is the solution? If you can't help write it, just at least tell me. I'm really going crazy with this issue.. SOLUTION MAYBE Yes, I think sub-query would be best solution for this, because the following query runs at 0.0022 seconds. But I'm not sure if validation of an album would be accurate or not, please check. select media.*, username from media as media , user as user where media.user_id = user.user_id and media.media_type = 'video' and media.id in (select media2.id from media as media2 , album as album , album_permission as permission where ((media2.album_id = album.album_id and album.private = 'yes' and album.album_id = permission.album_id and (permission.email = '' or permission.user_id = '')) or (media.album_id = album.album_id and album.private = 'no' ) or media.album_id = '0' ) and media.album_id = media2.album_id ) order by media.id DESC LIMIT 0,20

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