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  • Would a PHP application benefit from being served from a RAM drive?

    - by Tom Marthenal
    I am in charge of hosting a PHP application that is large and slow, but easy to scale. The application is entirely static, with writable disk storage needed. We've profiled the application, and the main bottleneck appears to come from loading the application and not the work the application does. The application is not CPU-intensive, although it does use a fair amount of memory (think Magento). Currently we distribute it by having a series of servers with the same PHP files on their hard drive and a load balancer in front of them. Easy but expensive. I've been reading about RAM disks and the IO benefits they offer, and was wondering if they would be well-suited to PHP applications. Since PHP applications are loaded from disk for every request and often involve lots of different files (as opposed to being kept in memory like with a Java application), I would figure that disk performance can be a severe bottleneck. Would placing the PHP files on a RAM disk and using the mount point as Apache's document root offer performance benefits? A startup script could create the RAM drive and then copy the files (which are plain-text and small) from a permanent location to the temporary RAM drive. Does this make sense, or should I just trust the linux kernel to cache the appropriate files in memory by itself?

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  • i7 x980 at 70% speed

    - by Buxley
    Hi we bought a nice computer to use to solve optimization problems. intel i7 X980@3,33 Mhz with 12 Gb of Team Group 1600 MHz ddr3 Ram. When we use Gurobi The Computer uses all 12 cores at maximum in the beginning of the solve. However after a while (about 8 hrs) it all cores jump between 65 and 85% When I solve the same models on an I7 930 all cores are at a near 100% level even after longer solution times. we first thought that the Harddisk was the bottleneck since Gurobi writes out nodefiles after the memorylimit is used. However since the new computer have 12 GB of Ram we put the memorylimit to 7 GB so the solver only used the RAM and still with the same performance in the processor. Any Ideas about the bottleneck? As I said earlier it works at 100% for the first hours or so . Thanks very much for any answers! Our plan was to overclock it but we can't even get it to work at normal speed yet!

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  • Upgrade or replace?

    - by Felix
    My current PC is about four years old, although I have made upgrades to it throughout its existence. The current specs are: (old) Intel Pentium D 2.80Ghz (32K L1 / 2M L2), Gigabyte 945GCMX-S2 motherboard (old) 2.5GB DDR2 (slot0: 512MB @ 533Mhz; slot1: 2GB @ 667Mhz) (new) HIS Radeon HD 4670 - I think this is limited by the motherboard not supporting PCIe 2.0 (?) (old) WD Caviar 160GB - pretty slow (new) WD Caviar Black 640GB (if any more specs are relevant, let me know and I'll add them) Now, on to my question. I've been having performance issues lately, both in video games and in intensive applications. A couple of examples: Android application development (running Eclipse and the Android emulator) is painfully slow (on Linux). I only realized this when, at my new job as an Android dev, both tools are MUCH quicker. (I'm not sure what CPU I have there) The guys at my new job got me NFS Hot Pursuit, in which I barely get like 5-10FPS, even with graphics options turned all the way down My guess is that the bottleneck in my system is my CPU, so I'm thinking of upgrading to a Quad Core i5 + new motherboard + 4GB DDR3 (or more, 'cause I know you'll all jump and say 8GB minimum). Now: Is that a good idea? Is my CPU really a bottleneck, or is the whole system too old and I should replace it? I run Windows 7 on the old, 160GB HDD (which is on IDE, by the way). Could this slow down games as well? Should I get a new drive for Windows if I want to play new games? I know nothing about power supplies. Could that be a problem / will it be a problem if I upgrade to an i5? How come DiRT2 works on full graphics settings (pretty amazing graphics by the way) and NFS Hot Pursuit pulls only 5-10FPS?

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  • SQL SERVER – Guest Post – Jonathan Kehayias – Wait Type – Day 16 of 28

    - by pinaldave
    Jonathan Kehayias (Blog | Twitter) is a MCITP Database Administrator and Developer, who got started in SQL Server in 2004 as a database developer and report writer in the natural gas industry. After spending two and a half years working in TSQL, in late 2006, he transitioned to the role of SQL Database Administrator. His primary passion is performance tuning, where he frequently rewrites queries for better performance and performs in depth analysis of index implementation and usage. Jonathan blogs regularly on SQLBlog, and was a coauthor of Professional SQL Server 2008 Internals and Troubleshooting. On a personal note, I think Jonathan is extremely positive person. In every conversation with him I have found that he is always eager to help and encourage. Every time he finds something needs to be approved, he has contacted me without hesitation and guided me to improve, change and learn. During all the time, he has not lost his focus to help larger community. I am honored that he has accepted to provide his views on complex subject of Wait Types and Queues. Currently I am reading his series on Extended Events. Here is the guest blog post by Jonathan: SQL Server troubleshooting is all about correlating related pieces of information together to indentify where exactly the root cause of a problem lies. In my daily work as a DBA, I generally get phone calls like, “So and so application is slow, what’s wrong with the SQL Server.” One of the funny things about the letters DBA is that they go so well with Default Blame Acceptor, and I really wish that I knew exactly who the first person was that pointed that out to me, because it really fits at times. A lot of times when I get this call, the problem isn’t related to SQL Server at all, but every now and then in my initial quick checks, something pops up that makes me start looking at things further. The SQL Server is slow, we see a number of tasks waiting on ASYNC_IO_COMPLETION, IO_COMPLETION, or PAGEIOLATCH_* waits in sys.dm_exec_requests and sys.dm_exec_waiting_tasks. These are also some of the highest wait types in sys.dm_os_wait_stats for the server, so it would appear that we have a disk I/O bottleneck on the machine. A quick check of sys.dm_io_virtual_file_stats() and tempdb shows a high write stall rate, while our user databases show high read stall rates on the data files. A quick check of some performance counters and Page Life Expectancy on the server is bouncing up and down in the 50-150 range, the Free Page counter consistently hits zero, and the Free List Stalls/sec counter keeps jumping over 10, but Buffer Cache Hit Ratio is 98-99%. Where exactly is the problem? In this case, which happens to be based on a real scenario I faced a few years back, the problem may not be a disk bottleneck at all; it may very well be a memory pressure issue on the server. A quick check of the system spec’s and it is a dual duo core server with 8GB RAM running SQL Server 2005 SP1 x64 on Windows Server 2003 R2 x64. Max Server memory is configured at 6GB and we think that this should be enough to handle the workload; or is it? This is a unique scenario because there are a couple of things happening inside of this system, and they all relate to what the root cause of the performance problem is on the system. If we were to query sys.dm_exec_query_stats for the TOP 10 queries, by max_physical_reads, max_logical_reads, and max_worker_time, we may be able to find some queries that were using excessive I/O and possibly CPU against the system in their worst single execution. We can also CROSS APPLY to sys.dm_exec_sql_text() and see the statement text, and also CROSS APPLY sys.dm_exec_query_plan() to get the execution plan stored in cache. Ok, quick check, the plans are pretty big, I see some large index seeks, that estimate 2.8GB of data movement between operators, but everything looks like it is optimized the best it can be. Nothing really stands out in the code, and the indexing looks correct, and I should have enough memory to handle this in cache, so it must be a disk I/O problem right? Not exactly! If we were to look at how much memory the plan cache is taking by querying sys.dm_os_memory_clerks for the CACHESTORE_SQLCP and CACHESTORE_OBJCP clerks we might be surprised at what we find. In SQL Server 2005 RTM and SP1, the plan cache was allowed to take up to 75% of the memory under 8GB. I’ll give you a second to go back and read that again. Yes, you read it correctly, it says 75% of the memory under 8GB, but you don’t have to take my word for it, you can validate this by reading Changes in Caching Behavior between SQL Server 2000, SQL Server 2005 RTM and SQL Server 2005 SP2. In this scenario the application uses an entirely adhoc workload against SQL Server and this leads to plan cache bloat, and up to 4.5GB of our 6GB of memory for SQL can be consumed by the plan cache in SQL Server 2005 SP1. This in turn reduces the size of the buffer cache to just 1.5GB, causing our 2.8GB of data movement in this expensive plan to cause complete flushing of the buffer cache, not just once initially, but then another time during the queries execution, resulting in excessive physical I/O from disk. Keep in mind that this is not the only query executing at the time this occurs. Remember the output of sys.dm_io_virtual_file_stats() showed high read stalls on the data files for our user databases versus higher write stalls for tempdb? The memory pressure is also forcing heavier use of tempdb to handle sorting and hashing in the environment as well. The real clue here is the Memory counters for the instance; Page Life Expectancy, Free List Pages, and Free List Stalls/sec. The fact that Page Life Expectancy is fluctuating between 50 and 150 constantly is a sign that the buffer cache is experiencing constant churn of data, once every minute to two and a half minutes. If you add to the Page Life Expectancy counter, the consistent bottoming out of Free List Pages along with Free List Stalls/sec consistently spiking over 10, and you have the perfect memory pressure scenario. All of sudden it may not be that our disk subsystem is the problem, but is instead an innocent bystander and victim. Side Note: The Page Life Expectancy counter dropping briefly and then returning to normal operating values intermittently is not necessarily a sign that the server is under memory pressure. The Books Online and a number of other references will tell you that this counter should remain on average above 300 which is the time in seconds a page will remain in cache before being flushed or aged out. This number, which equates to just five minutes, is incredibly low for modern systems and most published documents pre-date the predominance of 64 bit computing and easy availability to larger amounts of memory in SQL Servers. As food for thought, consider that my personal laptop has more memory in it than most SQL Servers did at the time those numbers were posted. I would argue that today, a system churning the buffer cache every five minutes is in need of some serious tuning or a hardware upgrade. Back to our problem and its investigation: There are two things really wrong with this server; first the plan cache is excessively consuming memory and bloated in size and we need to look at that and second we need to evaluate upgrading the memory to accommodate the workload being performed. In the case of the server I was working on there were a lot of single use plans found in sys.dm_exec_cached_plans (where usecounts=1). Single use plans waste space in the plan cache, especially when they are adhoc plans for statements that had concatenated filter criteria that is not likely to reoccur with any frequency.  SQL Server 2005 doesn’t natively have a way to evict a single plan from cache like SQL Server 2008 does, but MVP Kalen Delaney, showed a hack to evict a single plan by creating a plan guide for the statement and then dropping that plan guide in her blog post Geek City: Clearing a Single Plan from Cache. We could put that hack in place in a job to automate cleaning out all the single use plans periodically, minimizing the size of the plan cache, but a better solution would be to fix the application so that it uses proper parameterized calls to the database. You didn’t write the app, and you can’t change its design? Ok, well you could try to force parameterization to occur by creating and keeping plan guides in place, or we can try forcing parameterization at the database level by using ALTER DATABASE <dbname> SET PARAMETERIZATION FORCED and that might help. If neither of these help, we could periodically dump the plan cache for that database, as discussed as being a problem in Kalen’s blog post referenced above; not an ideal scenario. The other option is to increase the memory on the server to 16GB or 32GB, if the hardware allows it, which will increase the size of the plan cache as well as the buffer cache. In SQL Server 2005 SP1, on a system with 16GB of memory, if we set max server memory to 14GB the plan cache could use at most 9GB  [(8GB*.75)+(6GB*.5)=(6+3)=9GB], leaving 5GB for the buffer cache.  If we went to 32GB of memory and set max server memory to 28GB, the plan cache could use at most 16GB [(8*.75)+(20*.5)=(6+10)=16GB], leaving 12GB for the buffer cache. Thankfully we have SQL Server 2005 Service Pack 2, 3, and 4 these days which include the changes in plan cache sizing discussed in the Changes to Caching Behavior between SQL Server 2000, SQL Server 2005 RTM and SQL Server 2005 SP2 blog post. In real life, when I was troubleshooting this problem, I spent a week trying to chase down the cause of the disk I/O bottleneck with our Server Admin and SAN Admin, and there wasn’t much that could be done immediately there, so I finally asked if we could increase the memory on the server to 16GB, which did fix the problem. It wasn’t until I had this same problem occur on another system that I actually figured out how to really troubleshoot this down to the root cause.  I couldn’t believe the size of the plan cache on the server with 16GB of memory when I actually learned about this and went back to look at it. SQL Server is constantly telling a story to anyone that will listen. As the DBA, you have to sit back and listen to all that it’s telling you and then evaluate the big picture and how all the data you can gather from SQL about performance relate to each other. One of the greatest tools out there is actually a free in the form of Diagnostic Scripts for SQL Server 2005 and 2008, created by MVP Glenn Alan Berry. Glenn’s scripts collect a majority of the information that SQL has to offer for rapid troubleshooting of problems, and he includes a lot of notes about what the outputs of each individual query might be telling you. When I read Pinal’s blog post SQL SERVER – ASYNC_IO_COMPLETION – Wait Type – Day 11 of 28, I noticed that he referenced Checking Memory Related Performance Counters in his post, but there was no real explanation about why checking memory counters is so important when looking at an I/O related wait type. I thought I’d chat with him briefly on Google Talk/Twitter DM and point this out, and offer a couple of other points I noted, so that he could add the information to his blog post if he found it useful.  Instead he asked that I write a guest blog for this. I am honored to be a guest blogger, and to be able to share this kind of information with the community. The information contained in this blog post is a glimpse at how I do troubleshooting almost every day of the week in my own environment. SQL Server provides us with a lot of information about how it is running, and where it may be having problems, it is up to us to play detective and find out how all that information comes together to tell us what’s really the problem. This blog post is written by Jonathan Kehayias (Blog | Twitter). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: MVP, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Algorithm for querying linearly through a non-linear list of questions

    - by JoshLeaves
    For a multiplayers trivia game, I need to supply my users with a new quizz in a desired subject (Science, Maths, Litt. and such) at the start of every game. I've generated about 5K quizzes for each subject and filled my database with them. So my 'Quizzes' database looks like this: |ID |Subject |Question +-----+------------+---------------------------------- | 23 |Science | What's water? | 42 |Maths | What's 2+2? | 99 |Litt. | Who wrote "Pride and Prejudice"? | 123 |Litt. | Who wrote "On The Road"? | 146 |Maths | What's 2*2? | 599 |Science | You know what's cool? |1042 |Maths | What's the Fibonacci Sequence? |1056 |Maths | What's 42? And so on... (Much more detailed/complex but I'll keep the exemple simple) As you can see, due to technical constraints (MongoDB), my IDs are not linear but I can use them as an increasing suite. So far, my algorithm to ensure two users get a new quizz when they play together is the following: // Take the last played quizzes by P1 and P2 var q_one = player_one.getLastPlayedQuizz('Maths'); var q_two = player_two.getLastPlayedQuizz('Maths'); // If both of them never played in the subject, return first quizz in the list if ((q_one == NULL) && (q_two == NULL)) return QuizzDB.findOne({subject: 'Maths'}); // If one of them never played, play the next quizz for the other player // This quizz is found by asking for the first quizz in the desired subject where // the ID is greater than the last played quizz's ID (if the last played quizz ID // is 42, this will return 146 following the above example database) if (q_one == NULL) return QuizzDB.findOne({subject: 'Maths', ID > q_two}); if (q_two == NULL) return QuizzDB.findOne({subject: 'Maths', ID > q_one}); // And if both of them have a lastPlayedQuizz, we return the next quizz for the // player whose lastPlayedQuizz got the higher ID if (q_one > q_two) return QuizzDB.findOne({subject: 'Maths', ID > q_one}); else return QuizzDB.findOne({subject: 'Maths', ID > q_two}); Now here comes the real problem: Once I get to the end of my database (let's say, P1's last played quizz in 'Maths' is 1056, P2's is 146 and P3 is 1042), following my algorithm, P1's ID is the highest so I ask for the next question in 'Maths' where ID is superior to 1056. There is nothing, so I roll back to the beginning of my quizz list (with a random skipper to avoid having the first question always show up). P1 and P2's last played will then be 42 and they will start fresh from the beginning of the list. However, if P1 (42) plays against P3 (1042), the resulting ID will be 1056...which P1 already played two games ago. Basically, players who just "rolled back" to the beginning of the list will be brought back to the end of the list by players who still haven't rolled back. The rollback WILL happen in the end, but it'll take time and there'll be a "bottleneck" at the beginning and at the end. Thus my question: What would be the best algorith to avoid this bottleneck and ensure players don't get stuck endlessly on the same quizzes? Also bear in mind that I've got some technical constraints: I can't get a random question in a subject (ie: no "QuizzDB.findOne({subject: 'Maths'}).skip(random());"). It's cool to skip on one to twenty records, but the MongoDB documentation warns against skipping too many documents. I would like to avoid building an array of every quizz played by each player and find the next non-played in the database with a $nin. Thanks for your help

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  • Faster Memory Allocation Using vmtasks

    - by Steve Sistare
    You may have noticed a new system process called "vmtasks" on Solaris 11 systems: % pgrep vmtasks 8 % prstat -p 8 PID USERNAME SIZE RSS STATE PRI NICE TIME CPU PROCESS/NLWP 8 root 0K 0K sleep 99 -20 9:10:59 0.0% vmtasks/32 What is vmtasks, and why should you care? In a nutshell, vmtasks accelerates creation, locking, and destruction of pages in shared memory segments. This is particularly helpful for locked memory, as creating a page of physical memory is much more expensive than creating a page of virtual memory. For example, an ISM segment (shmflag & SHM_SHARE_MMU) is locked in memory on the first shmat() call, and a DISM segment (shmflg & SHM_PAGEABLE) is locked using mlock() or memcntl(). Segment operations such as creation and locking are typically single threaded, performed by the thread making the system call. In many applications, the size of a shared memory segment is a large fraction of total physical memory, and the single-threaded initialization is a scalability bottleneck which increases application startup time. To break the bottleneck, we apply parallel processing, harnessing the power of the additional CPUs that are always present on modern platforms. For sufficiently large segments, as many of 16 threads of vmtasks are employed to assist an application thread during creation, locking, and destruction operations. The segment is implicitly divided at page boundaries, and each thread is given a chunk of pages to process. The per-page processing time can vary, so for dynamic load balancing, the number of chunks is greater than the number of threads, and threads grab chunks dynamically as they finish their work. Because the threads modify a single application address space in compressed time interval, contention on locks protecting VM data structures locks was a problem, and we had to re-scale a number of VM locks to get good parallel efficiency. The vmtasks process has 1 thread per CPU and may accelerate multiple segment operations simultaneously, but each operation gets at most 16 helper threads to avoid monopolizing CPU resources. We may reconsider this limit in the future. Acceleration using vmtasks is enabled out of the box, with no tuning required, and works for all Solaris platform architectures (SPARC sun4u, SPARC sun4v, x86). The following tables show the time to create + lock + destroy a large segment, normalized as milliseconds per gigabyte, before and after the introduction of vmtasks: ISM system ncpu before after speedup ------ ---- ------ ----- ------- x4600 32 1386 245 6X X7560 64 1016 153 7X M9000 512 1196 206 6X T5240 128 2506 234 11X T4-2 128 1197 107 11x DISM system ncpu before after speedup ------ ---- ------ ----- ------- x4600 32 1582 265 6X X7560 64 1116 158 7X M9000 512 1165 152 8X T5240 128 2796 198 14X (I am missing the data for T4 DISM, for no good reason; it works fine). The following table separates the creation and destruction times: ISM, T4-2 before after ------ ----- create 702 64 destroy 495 43 To put this in perspective, consider creating a 512 GB ISM segment on T4-2. Creating the segment would take 6 minutes with the old code, and only 33 seconds with the new. If this is your Oracle SGA, you save over 5 minutes when starting the database, and you also save when shutting it down prior to a restart. Those minutes go directly to your bottom line for service availability.

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  • Copy Small Bitmaps on to Large Bitmap with Transparency Blend: What is faster than graphics.DrawImag

    - by Glenn
    I have identified this call as a bottleneck in a high pressure function. graphics.DrawImage(smallBitmap, x , y); Is there a faster way to blend small semi transparent bitmaps into a larger semi transparent one? Example Usage: XY[] locations = GetLocs(); Bitmap[] bitmaps = GetBmps(); //small images sizes vary approx 30px x 30px using (Bitmap large = new Bitmap(500, 500, PixelFormat.Format32bppPArgb)) using (Graphics largeGraphics = Graphics.FromImage(large)) { for(var i=0; i < largeNumber; i++) { //this is the bottleneck largeGraphics.DrawImage(bitmaps[i], locations[i].x , locations[i].y); } } var done = new MemoryStream(); large.Save(done, ImageFormat.Png); done.Position = 0; return (done); The DrawImage calls take a small 32bppPArgb bitmaps and copies them into a larger bitmap at locations that vary and the small bitmaps might only partially overlap the larger bitmaps visible area. Both images have semi transparent contents that get blended by DrawImage in a way that is important to the output. I've done some testing with BitBlt but not seen significant speed improvement and the alpha blending didn't come out the same in my tests. I'm open to just about any method including a better call to bitblt or unsafe c# code.

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  • Systems design question: DB connection management in load-balanced n-tier

    - by aoven
    I'm wondering about the best approach to designing a DB connection manager for a load-balanced n-tier system. Classic n-tier looks like this: Client -> BusinessServer -> DBServer A load-balancing solution as I see it would then look like this: +--> ... +--+ +--> BusinessServer +--+--> SessionServer --+ Client -> Gateway --+--> BusinessServer +--| +--> DBServer +--> BusinessServer +--+--------------------+ +--> ... +--+ As pictured, the business server component is being load-balanced via multiple instances, and a hardware gateway is distributing the load among them. Session server probably needs to be situated outside the load-balancing array, because it manages state, which mustn't be duplicated. Barring any major errors in design so far, what is the best way to implement DB connection management? I've come up with a couple of options, but there may be others I'm not aware of: Introduce a new Broker component between the DBServer and the other components and let it handle the DB connections. The upside is that all the connections can be managed from a single point, which is very convenient. The downside is that now there is an additional "single point of failure" in the system. Other components must go through it for every request that involves DB in some way, which also makes this a bottleneck. Move the DB connection management into BusinessServer and SessionServer components and let each handle its own DB connections. The upside is that there is no additional "single point of failure" or bottleneck components. The downside is that there is also no control over possible conflicts and deadlocks apart from what DBServer itself can provide. What else can be done? FWIW: Technology is .NET, but none of the vendor-specific stacks are used (e.g. no WCF, MSMQ or the like).

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  • Linux time sample based profiler.

    - by Caspin
    short version: Is there a good time based sampling profiler for Linux? long version: I generally use OProfile to optimize my applications. I recently found a shortcoming that has me wondering. The problem was a tight loop spawning c++filt to demangle a c++ name. I only stumbled upon the code by accident while chasing down another bottleneck. The OProfile didn't show anything unusual about the code so I almost ignored it but my code sense told me to optimize the call and see what happened. I changed the popen of c++filt to abi::__cxa_demangle. The runtime went from more than a minute to a little over a second. About a x60 speed up. Is there a way I could have configured OProfile to flag the popen call? As the profile data sits now OProfile thinks the bottle neck was the heap and std::string calls (which BTW once optimized dropped the runtime to less than a second, more than x2 speed up). Here is my OProfile configuration: $ sudo opcontrol --status Daemon not running Event 0: CPU_CLK_UNHALTED:90000:0:1:1 Separate options: library vmlinux file: none Image filter: /path/to/excutable Call-graph depth: 7 Buffer size: 65536 Is there another profiler for Linux that could have found the bottleneck? I suspect the issue is that OProfile only logs its samples to the currently running process. I'd like it to always log its samples to the process I'm profiling. So if the process is currently switched out (blocking on IO or a popen call) OProfile would just place its sample at the blocked call. If I can't fix this, OProfile will only be useful when the executable is pushing near 100% CPU. It can't help with executables that that have inefficient blocking calls.

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  • R optimization: How can I avoid a for loop in this situation?

    - by chrisamiller
    I'm trying to do a simple genomic track intersection in R, and running into major performance problems, probably related to my use of for loops. In this situation, I have pre-defined windows at intervals of 100bp and I'm trying to calculate how much of each window is covered by the annotations in mylist. Graphically, it looks something like this: 0 100 200 300 400 500 600 windows: |-----|-----|-----|-----|-----|-----| mylist: |-| |-----------| So I wrote some code to do just that, but it's fairly slow and has become a bottleneck in my code: ##window for each 100-bp segment windows <- numeric(6) ##second track mylist = vector("list") mylist[[1]] = c(1,20) mylist[[2]] = c(120,320) ##do the intersection for(i in 1:length(mylist)){ st <- floor(mylist[[i]][1]/100)+1 sp <- floor(mylist[[i]][2]/100)+1 for(j in st:sp){ b <- max((j-1)*100, mylist[[i]][1]) e <- min(j*100, mylist[[i]][2]) windows[j] <- windows[j] + e - b + 1 } } print(windows) [1] 20 81 101 21 0 0 Naturally, this is being used on data sets that are much larger than the example I provide here. Through some profiling, I can see that the bottleneck is in the for loops, but my clumsy attempt to vectorize it using *apply functions resulted in code that runs an order of magnitude more slowly. I suppose I could write something in C, but I'd like to avoid that if possible. Can anyone suggest another approach that will speed this calculation up?

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  • Why do I see a large performance hit with DRBD?

    - by BHS
    I see a much larger performance hit with DRBD than their user manual says I should get. I'm using DRBD 8.3.7 (Fedora 13 RPMs). I've setup a DRBD test and measured throughput of disk and network without DRBD: dd if=/dev/zero of=/data.tmp bs=512M count=1 oflag=direct 536870912 bytes (537 MB) copied, 4.62985 s, 116 MB/s / is a logical volume on the disk I'm testing with, mounted without DRBD iperf: [ 4] 0.0-10.0 sec 1.10 GBytes 941 Mbits/sec According to Throughput overhead expectations, the bottleneck would be whichever is slower, the network or the disk and DRBD should have an overhead of 3%. In my case network and I/O seem to be pretty evenly matched. It sounds like I should be able to get around 100 MB/s. So, with the raw drbd device, I get dd if=/dev/zero of=/dev/drbd2 bs=512M count=1 oflag=direct 536870912 bytes (537 MB) copied, 6.61362 s, 81.2 MB/s which is slower than I would expect. Then, once I format the device with ext4, I get dd if=/dev/zero of=/mnt/data.tmp bs=512M count=1 oflag=direct 536870912 bytes (537 MB) copied, 9.60918 s, 55.9 MB/s This doesn't seem right. There must be some other factor playing into this that I'm not aware of. global_common.conf global { usage-count yes; } common { protocol C; } syncer { al-extents 1801; rate 33M; } data_mirror.res resource data_mirror { device /dev/drbd1; disk /dev/sdb1; meta-disk internal; on cluster1 { address 192.168.33.10:7789; } on cluster2 { address 192.168.33.12:7789; } } For the hardware I have two identical machines: 6 GB RAM Quad core AMD Phenom 3.2Ghz Motherboard SATA controller 7200 RPM 64MB cache 1TB WD drive The network is 1Gb connected via a switch. I know that a direct connection is recommended, but could it make this much of a difference? Edited I just tried monitoring the bandwidth used to try to see what's happening. I used ibmonitor and measured average bandwidth while I ran the dd test 10 times. I got: avg ~450Mbits writing to ext4 avg ~800Mbits writing to raw device It looks like with ext4, drbd is using about half the bandwidth it uses with the raw device so there's a bottleneck that is not the network.

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  • What can I do to speed up createrepo?

    - by jsd
    We are using a yum repository to distribute our software to our production instances. Unfortunately, createrepo is becoming a bottleneck, and we only have 469 packages in the repository. $ time createrepo /opt/tm-yum-repo Spawning worker 0 with 469 pkgs Workers Finished Gathering worker results Saving Primary metadata Saving file lists metadata Saving other metadata Generating sqlite DBs Sqlite DBs complete real 0m43.188s user 0m37.798s sys 0m1.296s What can I do to make it faster?

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  • VMWare Server - Writing files to virtual hard drive performance

    - by Ardman
    We have just moved our infrastructure from physical servers to virtual machines. Everything is running great and we are happy with the result of the move. We have identified one problem, and that is reading/writing performance. We have an application that compiles files and writes to disk. This is considerably slower on the new virtual machines compared to the physical machines. Is there a performance bottleneck when writing to a virtual hard drive compared to a physical hard drive?

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  • VMWare - Writing files to virtual hard drive performance

    - by Ardman
    We have just moved our infrastructure from physical servers to virtual machines. Everything is running great and we are happy with the result of the move. We have identified one problem, and that is reading/writing performance. We have an application that compiles files and writes to disk. This is considerably slower on the new virtual machines compared to the physical machines. Is there a performance bottleneck when writing to a virtual hard drive compared to a physical hard drive?

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  • How stable is zfs-fuse 0.6.9 on Linux?

    - by Mavrik
    I'm thinking of using ZFS for my home-made NAS array. I would have 4 HDDs in raidz on a Ubuntu Server 10.04 machine. I'd like to use the snapshot capability and dedup when storing data. I'm not so much concerned about the speed, since the machine is accessed via N wireless network and that is probably going to be the bottleneck. So does anyone have any practical experience with zfs-fuse 0.6.9 on such (or simillar) configuration?

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  • Hardware profiling [closed]

    - by mgroves
    I'd like to upgrade my computer so that it's faster when editing/rendering video. I'm thinking of first getting a faster hard drive, but I'd like to be able to run some sort of profiling software to tell me what the bottleneck is when rendering video. Any suggestions about software that can do this for preferably Windows XP and preferably for free?

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  • HP Smart Array p400i with Intel X25-M 160 SSD

    - by user67304
    I have a pair of x25-M 160 Intel SSD's in an HP DL360 G5 with a p400i Smart Array running 512 BBWC. The disk performance I am getting on this box and another identical one does not come close to matching the same two drives running through a cheap 3ware RAID card. Any idea? I have played with the cache settings, but nothing allows me to get the same results. It seems like the Smart Array controller is the bottleneck.

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  • HP Smart Array p400i with Intel X25-M 160 SSD

    - by user67304
    I have a pair of x25-M 160 Intel SSD's in an HP DL360 G5 with a p400i Smart Array running 512 BBWC. The disk performance I am getting on this box and another identical one does not come close to matching the same two drives running through a cheap 3ware RAID card. Any idea? I have played with the cache settings, but nothing allows me to get the same results. It seems like the Smart Array controller is the bottleneck.

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  • Ngingx max worker_connections and access log

    - by MotoTribe
    I'm troubleshoot an issue with my site. I'm seeing in the ngingx-error.log that the max worker_connection limit has been reached when the site went down. I'm not seeing an increase of requests during that time in the ngingx-access.log. Does that mean the mysql database had a bottleneck at that time that caused the requests to queue up? Or would it not log any requests that where made after the max worker_connection limit has been reached?

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  • Current trends in Random Access Memory speed [closed]

    - by Vetal
    As I know for now because of laws of Physics there will be not any tangible improvements in CPU cycles per second for the nearest future. However because of Von Neumann bottleneck it seems to not be an issue for non-server applications. So what about RAM, is there any upcoming technologies that promise to improve memory speed or we are stack with the current situation till quantum computers will come out from labs?

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  • Firewall/Router upgrade

    - by Atlas
    We've been using a SonicWall TZ170 for several years, it's been working fine with occasional glitches. Now we switched to a 100Mpbs broadband, and the firewall has become the bottleneck for internet access because its max throughput is around 20-30Mpbs. Any ideas for a replacement? Brand/Model?

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  • Is it possible to run multiple mongod instances on a single set of database files

    - by 9point6
    We have large multi-gigabyte data sets on which we run very complex queries, for example { $or: [ { id: 30000001, ... }, { id: 30000005, ... }, ..., { id: 30001005, ... } ] } It seems that CPU is actually a bottleneck at this point, so I'd be advantageous to be able to run multiple mongod instances on the same set of database files. We've considered using replica sets to this end, but would prefer to not require the extra disk space simply for CPU reasons.

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  • Troubleshooting Application Timeouts in SQL Server

    - by Tara Kizer
    I recently received the following email from a blog reader: "We are having an OLTP database instance, using SQL Server 2005 with little to moderate traffic (10-20 requests/min). There are also bulk imports that occur at regular intervals in this DB and the import duration ranges between 10secs to 1 min, depending on the data size. Intermittently (2-3 times in a week), we face an issue, where queries get timed out (default of 30 secs set in application). On analyzing, we found two stored procedures, having queries with multiple table joins inside them of taking a long time (5-10 mins) in getting executed, when ideally the execution duration ranges between 5-10 secs. Execution plan of the same displayed Clustered Index Scan happening instead of Clustered Index Seek. All required Indexes are found to be present and Index fragmentation is also minimal as we Rebuild Indexes regularly alongwith Updating Statistics. With no other alternate options occuring to us, we restarted SQL server and thereafter the performance was back on track. But sometimes it was still giving timeout errors for some hits and so we also restarted IIS and that stopped the problem as of now." Rather than respond directly to the blog reader, I thought it would be more interesting to share my thoughts on this issue in a blog. There are a few things that I can think of that could cause abnormal timeouts: Blocking Bad plan in cache Outdated statistics Hardware bottleneck To determine if blocking is the issue, we can easily run sp_who/sp_who2 or a query directly on sysprocesses (select * from master..sysprocesses where blocking <> 0).  If blocking is present and consistent, then you'll need to determine whether or not to kill the parent blocking process.  Killing a process will cause the transaction to rollback, so you need to proceed with caution.  Killing the parent blocking process is only a temporary solution, so you'll need to do more thorough analysis to figure out why the blocking was present.  You should look into missing indexes and perhaps consider changing the database's isolation level to READ_COMMITTED_SNAPSHOT. The blog reader mentions that the execution plan shows a clustered index scan when a clustered index seek is normal for the stored procedure.  A clustered index scan might have been chosen either because that is what is in cache already or because of out of date statistics.  The blog reader mentions that bulk imports occur at regular intervals, so outdated statistics is definitely something that could cause this issue.  The blog reader may need to update statistics after imports are done if the imports are changing a lot of data (greater than 10%).  If the statistics are good, then the query optimizer might have chosen to scan rather than seek in a previous execution because the scan was determined to be less costly due to the value of an input parameter.  If this parameter value is rare, then its execution plan in cache is what we call a bad plan.  You want the best plan in cache for the most frequent parameter values.  If a bad plan is a recurring problem on your system, then you should consider rewriting the stored procedure.  You might want to break up the code into multiple stored procedures so that each can have a different execution plan in cache. To remove a bad plan from cache, you can recompile the stored procedure.  An alternative method is to run DBCC FREEPROCACHE which drops the procedure cache.  It is better to recompile stored procedures rather than dropping the procedure cache as dropping the procedure cache affects all plans in cache rather than just the ones that were bad, so there will be a temporary performance penalty until the plans are loaded into cache again. To determine if there is a hardware bottleneck occurring such as slow I/O or high CPU utilization, you will need to run Performance Monitor on the database server.  Hopefully you already have a baseline of the server so you know what is normal and what is not.  Be on the lookout for I/O requests taking longer than 12 milliseconds and CPU utilization over 90%.  The servers that I support typically are under 30% CPU utilization, but your baseline could be higher and be within a normal range. If restarting the SQL Server service fixes the problem, then the problem was most likely due to blocking or a bad plan in the procedure cache.  Rather than restarting the SQL Server service, which causes downtime, the blog reader should instead analyze the above mentioned things.  Proceed with caution when restarting the SQL Server service as all transactions that have not completed will be rolled back at startup.  This crash recovery process could take longer than normal if there was a long-running transaction running when the service was stopped.  Until the crash recovery process is completed on the database, it is unavailable to your applications. If restarting IIS fixes the problem, then the problem might not have been inside SQL Server.  Prior to taking this step, you should do analysis of the above mentioned things. If you can think of other reasons why the blog reader is facing this issue a few times a week, I'd love to hear your thoughts via a blog comment.

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