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  • I need a fast runtime expression parser

    - by Chris Lively
    I need to locate a fast, lightweight expression parser. Ideally I want to pass it a list of name/value pairs (e.g. variables) and a string containing the expression to evaluate. All I need back from it is a true/false value. The types of expressions should be along the lines of: varA == "xyz" and varB==123 Basically, just a simple logic engine whose expression is provided at runtime. UPDATE At minimum it needs to support ==, !=, , =, <, <= Regarding speed, I expect roughly 5 expressions to be executed per request. We'll see somewhere in the vicinity of 100/requests a second. Our current pages tend to execute in under 50ms. Usually there will only be 2 or 3 variables involved in any expression. However, I'll need to load approximately 30 into the parser prior to execution. UPDATE 2012/11/5 Update about performance. We implemented nCalc nearly 2 years ago. Since then we've expanded it's use such that we average 40+ expressions covering 300+ variables on post backs. There are now thousands of post backs occurring per second with absolutely zero performance degradation. We've also extended it to include a handful of additional functions, again with no performance loss. In short, nCalc met all of our needs and exceeded our expectations.

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  • Web Shop Schema - Document Db

    - by Maxem
    I'd like to evaluate a document db, probably mongo db in an ASP.Net MVC web shop. A little reasoning at the beginning: There are about 2 million products. The product model would be pretty bad for rdbms as there'd be many different kinds of products with unique attributes. For example, there'd be books which have isbn, authors, title, pages etc as well as dvds with play time, directors, artists etc and quite a few more types. In the end, I'd have about 9 different products with a combined column count (counting common columns like title only once) of about 70 to 100 whereas each individual product has 15 columns at most. The three commonly used ways in RDBMS would be: EAV model which would have pretty bad performance characteristics and would make it either impractical or perform even worse if I'd like to display the author of a book in a list of different products (think start page, recommended products etc.). Ignore the column count and put it all in the product table: Although I deal with somewhat bigger databases (row wise), I don't have any experience with tables with more than 20 columns as far as performance is concered but I guess 100 columns would have some implications. Create a table for each product type: I personally don't like this approach as it complicates everything else. C# Driver / Classes: I'd like to use the NoRM driver and so far I think i'll try to create a product dto that contains all properties (grouped within detail classes like book details, except for those properties that should be displayed on list views etc.). In the app I'll use BookBehavior / DvdBehaviour which are wrappers around a product dto but only expose the revelent Properties. My questions now: Are my performance concerns with the many columns approach valid? Did I overlook something and there is a much better way to do it in an RDBMS? Is MongoDb on Windows stable enough? Does my approach with different behaviour wrappers make sense?

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  • Using memcache together with conventional cache

    - by Industrial
    Hi! Here's the deal. We would have taken the complete static html road to solve performance issues, but since the site will be partially dynamic, this won't work out for us. What we have thought of instead is using memcache + eAccelerator to speed up PHP and take care of caching for the most used data. Here's our two approaches that we have thought of right now: Using memcache on all<< major queries and leaving it alone to do what it does best. Usinc memcache for most commonly retrieved data, and combining with a standard harddrive-stored cache for further usage. The major advantage of only using memcache is of course the performance, but as users increases, the memory usage gets heavy. Combining the two sounds like a more natural approach to us, even though the theoretical compromize in performance. Memcached appears to have some replication features available as well, which may come handy when it's time to increase the nodes. What approach should we use? - Is it stupid to compromize and combine the two methods? Should we insted be focusing on utilizing memcache and instead focusing on upgrading the memory as the load increases with the number of users? Thanks a lot!

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  • Using memory-based cache together with conventional cache

    - by Industrial
    Hi! Here's the deal. We would have taken the complete static html road to solve performance issues, but since the site will be partially dynamic, this won't work out for us. What we have thought of instead is using memcache + eAccelerator to speed up PHP and take care of caching for the most used data. Here's our two approaches that we have thought of right now: Using memcache on all<< major queries and leaving it alone to do what it does best. Usinc memcache for most commonly retrieved data, and combining with a standard harddrive-stored cache for further usage. The major advantage of only using memcache is of course the performance, but as users increases, the memory usage gets heavy. Combining the two sounds like a more natural approach to us, even though the theoretical compromize in performance. Memcached appears to have some replication features available as well, which may come handy when it's time to increase the nodes. What approach should we use? - Is it stupid to compromize and combine the two methods? Should we insted be focusing on utilizing memcache and instead focusing on upgrading the memory as the load increases with the number of users? Thanks a lot!

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  • Interchange structured data between Haskell and C

    - by Eonil
    First, I'm a Haskell beginner. I'm planning integrating Haskell into C for realtime game. Haskell does logic, C does rendering. To do this, I have to pass huge complexly structured data (game state) from/to each other for each tick (at least 30 times per second). So the passing data should be lightweight. This state data may laid on sequential space on memory. Both of Haskell and C parts should access every area of the states freely. In best case, the cost of passing data can be copying a pointer to a memory. In worst case, copying whole data with conversion. I'm reading Haskell's FFI(http://www.haskell.org/haskellwiki/FFICookBook#Working_with_structs) The Haskell code look specifying memory layout explicitly. I have a few questions. Can Haskell specify memory layout explicitly? (to be matched exactly with C struct) Is this real memory layout? Or any kind of conversion required? (performance penalty) If Q#2 is true, Any performance penalty when the memory layout specified explicitly? What's the syntax #{alignment foo}? Where can I find the document about this? If I want to pass huge data with best performance, how should I do that? *PS Explicit memory layout feature which I said is just C#'s [StructLayout] attribute. Which is specifying in-memory position and size explicitly. http://www.developerfusion.com/article/84519/mastering-structs-in-c/ I'm not sure Haskell has matching linguistic construct matching with fields of C struct.

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  • Using EhCache for session.createCriteria(...).list()

    - by James Smith
    I'm benchmarking the performance gains from using a 2nd level cache in Hibernate (enabling EhCache), but it doesn't seem to improve performance. In fact, the time to perform the query slightly increases. The query is: session.createCriteria(MyEntity.class).list(); The entity is: @Entity @Cache(usage = CacheConcurrencyStrategy.NONSTRICT_READ_WRITE) public class MyEntity { @Id @GeneratedValue private long id; @Column(length=5000) private String data; //---SNIP getters and setters--- } My hibernate.cfg.xml is: <!-- all the normal stuff to get it to connect & map the entities plus:--> <property name="hibernate.cache.region.factory_class"> net.sf.ehcache.hibernate.EhCacheRegionFactory </property> The MyEntity table contains about 2000 rows. The problem is that before adding in the cache, the query above to list all entities took an average of 65 ms. After the cache, they take an average of 74 ms. Is there something I'm missing? Is there something extra that needs to be done that will increase performance?

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  • lapply slower than for-loop when used for a BiomaRt query. Is that expected?

    - by ptocquin
    I would like to query a database using BiomaRt package. I have loci and want to retrieve some related information, let say description. I first try to use lapply but was surprise by the time needed for the task to be performed. I thus tried a more basic for-loop and get a faster result. Is that expected or is something wrong with my code or with my understanding of apply ? I read other posts dealing with *apply vs for-loop performance (Here, for example) and I was aware that improved performance should not be expected but I don't understand why performance here is actually lower. Here is a reproducible example. 1) Loading the library and selecting the database : library("biomaRt") athaliana <- useMart("plants_mart_14") athaliana <- useDataset("athaliana_eg_gene",mart=athaliana) 2) Querying the database : loci <- c("at1g01300", "at1g01800", "at1g01900", "at1g02335", "at1g02790", "at1g03220", "at1g03230", "at1g04040", "at1g04110", "at1g05240" ) I create a function for the use in lapply : foo <- function(loci) { getBM("description","tair_locus",loci,athaliana) } When I use this function on the first element : > system.time(foo(cwp_loci[1])) utilisateur système écoulé 0.020 0.004 1.599 When I use lapply to retrieve the data for all values : > system.time(lapply(loci, foo)) utilisateur système écoulé 0.220 0.000 16.376 I then created a new function, adding a for-loop : foo2 <- function(loci) { for (i in loci) { getBM("description","tair_locus",loci[i],athaliana) } } Here is the result : > system.time(foo2(loci)) utilisateur système écoulé 0.204 0.004 10.919 Of course, this will be applied to a big list of loci, so the best performing option is needed. I thank you for assistance. EDIT Following recommendation of @MartinMorgan Simply passing the vector loci to getBM greatly improves the query efficiency. Simpler is better. > system.time(lapply(loci, foo)) utilisateur système écoulé 0.236 0.024 110.512 > system.time(foo2(loci)) utilisateur système écoulé 0.208 0.040 116.099 > system.time(foo(loci)) utilisateur système écoulé 0.028 0.000 6.193

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  • Best (Java) book for understanding 'under the bonnet' for programming?

    - by Ben
    What would you say is the best book to buy to understand exactly how programming works under the hood in order to increase performance? I've coded in assembly at university, I studied computer architecture and I obviously did high level programming, but what I really dont understand is things like: -what is happening when I perform a cast -whats the difference in performance if I declare something global as opposed to local? -How does the memory layout for an ArrayList compare with a Vector or LinkedList? -Whats the overhead with pointers? -Are locks more efficient than using synchronized? -Would creating my own array using int[] be faster than using ArrayList -Advantages/disadvantages of declaring a variable volatile I have got a copy of Java Performance Tuning but it doesnt go down very low and it contains rather obvious things like suggesting a hashmap instead of using an ArrayList as you can map the keys to memory addresses etc. I want something a bit more Computer Sciencey, linking the programming language to what happens with the assembler/hardware. The reason im asking is that I have an interview coming up for a job in High Frequency Trading and everything has to be as efficient as possible, yet I cant remember every single possible efficiency saving so i'd just like to learn the fundamentals. Thanks in advance

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  • Phonegap: Will my mobile app 'feel' faster or slower once ported to phonegap?

    - by user15872
    So I'm designing everything in mobile Safari and I know that phonegap is essentially a stripped webview but... Question: Will my application will run better in phonegap? (revised below) a)I imagine my navigation and core app will load faster as the scripts and images are on the hard drive. Is this True? b)I assume since they've been working on it for 2 years now that they may have made some optimizations to make it quicker than just an average safari window. Is this true? (Assuming both html5/js/css code bases are pretty much the same and app is running on iOS.) Update: Sorry, I meant to compare apples to slightly different apples. Question 1 revised: Will my app see any performance benefits running with in a phonegap environment vs standard mobile safari? (compare mobile - to mobile) 1b) In what ways, other than loading time has phonegap optimized performance over standard mobile safari? Follow ups: 1) Are there any pitfalls, other than large libraries, that may cause phonegap to suffer a serious performance hit vs stand mobile safari? 2) Can I mix native and webview rendering? (i.e the top half of my app is rendered in with html/css/js and the bottom half native)

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  • Why use shorter VARCHAR(n) fields?

    - by chryss
    It is frequently advised to choose database field sizes to be as narrow as possible. I am wondering to what degree this applies to SQL Server 2005 VARCHAR columns: Storing 10-letter English words in a VARCHAR(255) field will not take up more storage than in a VARCHAR(10) field. Are there other reasons to restrict the size of VARCHAR fields to stick as closely as possible to the size of the data? I'm thinking of Performance: Is there an advantage to using a smaller n when selecting, filtering and sorting on the data? Memory, including on the application side (C++)? Style/validation: How important do you consider restricting colunm size to force non-sensical data imports to fail (such as 200-character surnames)? Anything else? Background: I help data integrators with the design of data flows into a database-backed system. They have to use an API that restricts their choice of data types. For character data, only VARCHAR(n) with n <= 255 is available; CHAR, NCHAR, NVARCHAR and TEXT are not. We're trying to lay down some "good practices" rules, and the question has come up if there is a real detriment to using VARCHAR(255) even for data where real maximum sizes will never exceed 30 bytes or so. Typical data volumes for one table are 1-10 Mio records with up to 150 attributes. Query performance (SELECT, with frequently extensive WHERE clauses) and application-side retrieval performance are paramount.

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  • Dealing with large number of text strings

    - by Fadrian
    My project when it is running, will collect a large number of string text block (about 20K and largest I have seen is about 200K of them) in short span of time and store them in a relational database. Each of the string text is relatively small and the average would be about 15 short lines (about 300 characters). The current implementation is in C# (VS2008), .NET 3.5 and backend DBMS is Ms. SQL Server 2005 Performance and storage are both important concern of the project, but the priority will be performance first, then storage. I am looking for answers to these: Should I compress the text before storing them in DB? or let SQL Server worry about compacting the storage? Do you know what will be the best compression algorithm/library to use for this context that gives me the best performance? Currently I just use the standard GZip in .NET framework Do you know any best practices to deal with this? I welcome outside the box suggestions as long as it is implementable in .NET framework? (it is a big project and this requirements is only a small part of it) EDITED: I will keep adding to this to clarify points raised I don't need text indexing or searching on these text. I just need to be able to retrieve them in later stage for display as a text block using its primary key. I have a working solution implemented as above and SQL Server has no issue at all handling it. This program will run quite often and need to work with large data context so you can imagine the size will grow very rapidly hence every optimization I can do will help.

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  • SQL Server: Why use shorter VARCHAR(n) fields?

    - by chryss
    It is frequently advised to choose database field sizes to be as narrow as possible. I am wondering to what degree this applies to SQL Server 2005 VARCHAR columns: Storing 10-letter English words in a VARCHAR(255) field will not take up more storage than in a VARCHAR(10) field. Are there other reasons to restrict the size of VARCHAR fields to stick as closely as possible to the size of the data? I'm thinking of Performance: Is there an advantage to using a smaller n when selecting, filtering and sorting on the data? Memory, including on the application side (C++)? Style/validation: How important do you consider restricting colunm size to force non-sensical data imports to fail (such as 200-character surnames)? Anything else? Background: I help data integrators with the design of data flows into a database-backed system. They have to use an API that restricts their choice of data types. For character data, only VARCHAR(n) with n <= 255 is available; CHAR, NCHAR, NVARCHAR and TEXT are not. We're trying to lay down some "good practices" rules, and the question has come up if there is a real detriment to using VARCHAR(255) even for data where real maximum sizes will never exceed 30 bytes or so. Typical data volumes for one table are 1-10 Mio records with up to 150 attributes. Query performance (SELECT, with frequently extensive WHERE clauses) and application-side retrieval performance are paramount.

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  • PERC H710 mini raid controller advanced settings (BIOS)

    - by gregg
    I upgraded from a PERC h310 to an H710 controller on my Dell R620 but didnt get any increase in performance. This is a ESXi host with a 5 disk RAID 5. I noticed when going to the RAID BIOS that the advanced settings section was not activated/checked off. In that section is the strip element size: 64kb (default) read policy: no read ahead and the write policy: write-through. Will checking that section do any harm to the existing raid array or will it simply enable those policies and hopefully boost performance? Or, lastly, is it already using those policies and the checkmark is simply to activate them for changes

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  • Intel® Core™2 Duo Desktop Processor vs Intel® Core™ i3 Desktop Processor?

    - by metal gear solid
    Intel® Core™2 Duo Desktop Processor vs Intel® Core™ i3 Desktop Processor? Which CPU is better to buy ? Intel® Core™ i3-530 Processor (4M Cache, 2.93 GHz) (it supports DDR3 also) or Intel® Core™2 Duo Processor E7500 (3M Cache, 2.93 GHz, 1066 MHz FSB) (it supports DDR2 only ) Although I do not play games on my PC but I need good performance in Adobe Photoshop, Watching Full HD Movies. I need good performance in Multitasking. Along with any of these CPU I would purchase 2 GB x 2 stick of RAM. and I will use Windows 7. and I will use Microsoft VPC images also with MS Virtual PC.

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  • Remove automatic Aero disabling in Windows 7

    - by Jani Hartikainen
    Sometimes when I'm playing games which are heavy on the GPU, Windows decides to helpfully disable aero, causing everything to freeze for a bit and in the worst case, combined with ATI's brilliant drivers, causes the game to crash. So, How do I stop Windows from automatically disabling Aero when playing games? It has absolutely no effect on the performance of the game itself when it does that. Also, I'd like to get rid of the "You should disable Aero to improve performance" helpful hint popup which sometimes shows up. But I suppose getting rid of the first will get rid of the second, assuming anyone knows how.

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  • Why are my uWSGI processes dying immediately?

    - by orokusaki
    I'm using Supervisor and the uWSGI Emperor mode. When I set limit-as to 512 (MB), workers die instantly (respawn, die, respawn, die, every 3/4 of a second or so): [uwsgi] workers = 4 threads = 40 limit-as = 512 harakiri = 20 max-requests = 1600 ... non-performance/memory/processor-related settings ommitted But, if I change limit-as to: [uwsgi] workers = 4 threads = 40 limit-as = 1024 harakiri = 20 max-requests = 1600 ... non-performance/memory/processor-related settings ommitted and restart uwsgi, the problem is gone immediately. In order to put a sham in this, I've modified the setting back to 512, restarted again, and the problem is back immediately. Notes: My app is a simple Django app without much additional Python setup during start-up time.

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  • Is there a Windows equivalent of Unix 'CPU steal time'?

    - by Steffen Opel
    In order to assess performance monitoring accuracy on virtualization platforms, the CPU steal time has become an increasingly relevant metric - see EC2 monitoring: the case of stolen CPU for an instructive summary in the context of Amazon EC2 and IBM's paper on CPU time accounting for a more in-depth technical explanation (including illustrations) of the concept: Steal time is the percentage of time a virtual CPU waits for a real CPU while the hypervisor is servicing another virtual processor. Accordingly, it is exposed in most related Unix/Linux monitoring tools nowadays - see e.g. columns %steal or st in sar or top: st -- Steal Time The amount of CPU 'stolen' from this virtual machine by the hypervisor for other tasks (such as running another virtual machine). I've been unable to figure out how to capture the same metric on Windows though, is this possible already? (Ideally for the Windows 2008 Server R2 AMIs on EC2 and via a respective Windows Performance Counters of course.)

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  • Intel P6100 CPU and Mobile Intel® HM55 Express Chipset

    - by Christopher Painter
    I have an Asus K52F-BBR5 notebook that uses an Intel P6100 ( 2GHZ 15x multiplier) and HM55 Express Chipset. I'm looking to replace it's 3GB with 8GB. The Crucial database seems to indicate that a PC3-8500 CAS 7 and PC3-10666 CAS 9 will both work. I'm not up to date on the latest DDR3 nomencalature and I'm wondering which would provide better performance. The price difference is negligible. Drawing on past experiences from many many years ago I could make an argument for either based on sync/async bus speed arguments and CAS latency differences but the truth is I don't know enough about the HM55 chipset to know which would be the correct choice. Does anyone know the answer or point me to information that would help me make the choice? I'm pretty sure the performance difference will be somewhat negligible also but still I'd like to make the optimal choice.

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  • Linux memory fragmentation

    - by Raghu
    Hi all, Is there a way to detect memory fragmentation on linux ? This is because on some long running servers I have noticed performance degradation and only after I restart process I see better performance. I noticed it more when using linux huge page support -- are huge pages in linux more prone to fragmentation ? I have looked at /proc/buddyinfo in particular. I want to know whether there are any better ways(not just CLI commands per se, any program or theoretical background would do) to look at it.

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  • CgiModule and FastCgiModule in IIS7

    - by Hari
    My web server is IIS7 running on Windows 2008 Web edition. There are nearly 40 modules when checked pre-installed "Modules". It also having "CgiModule and FastCgiModules". All the websites installed on this server purely runs with ASP.NET technology. Can I remove these two modules to improve performance? Same way, my application uses "Forms Authentication" only. In such case can I delete "Windows Authentication and WindowsAuthenticationModule"?. Also please suggest if any other modules can be deleted to improve performance.

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  • Oracle on NFS vmdk beats native NFS!?

    - by fletch00
    Hi, my colleagues are pursuing this with Netapp and Oracle - but I thought I'd post here on the off chance someone else has seen this We have a RedHat 5 VM (fully up2date) running Oracle 11i with data disks mounted via the VM's linux kernel NFS using Oracle's recommended mount options and the performance is very inconsistent (Querys that should take < 2 seconds sometimes take 60 seconds) Funny thing is we can run the same queries perfectly consistently < 2 seconds on a VMDK residing on SAME NetApp NFS datastore! Makes me wish Oracle and NetApp collaborated as closely as VMware and NetApp did on the Virtual Storage Console we used to perfectly set the NFS options and keep them in compliance... We have tried a few Linux NFS options others have posted and not seen improvement so far. We are now creating VMDK's for the VM to replace the Linux NFS mounted and workaround the issue as our developers need consistent performance ASAP.

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  • ZFS/Btrfs/LVM2-like storage with advanced features on Linux?

    - by Easter Sunshine
    I have 3 identical internal 7200 RPM SATA hard disk drives on a Linux machine. I'm looking for a storage set-up that will give me all of this: Different data sets (filesystems or subtrees) can have different RAID levels so I can choose performance, space overhead, and risk trade-offs differently for different data sets while having a few number of physical disks (very important data can be 3xRAID1, important data can be 3xRAID5, unimportant reproducible data can be 3xRAID0). If each data set has an explicit size or size limit, then the ability to grow and shrink the size limit (offline if need be) Avoid out-of-kernel modules R/W or read-only COW snapshots. If it's a block-level snapshots, the filesystem should be synced and quiesced during a snapshot. Ability to add physical disks and then grow/redistribute RAID1, RAID5, and RAID0 volumes to take advantage of the new spindle and make sure no spindle is hotter than the rest (e.g., in NetApp, growing a RAID-DP raid group by a few disks will not balance the I/O across them without an explicit redistribution) Not required but nice-to-haves: Transparent compression, per-file or subtree. Even better if, like NetApps, analyzes the data first for compressibility and only compresses compressible data Deduplication that doesn't have huge performance penalties or require obscene amounts of memory (NetApp does scheduled deduplication on weekends, which is good) Resistance to silent data corruption like ZFS (this is not required because I have never seen ZFS report any data corruption on these specific disks) Storage tiering, either automatic (based on caching rules) or user-defined rules (yes, I have all-identical disks now but this will let me add a read/write SSD cache in the future). If it's user-defined rules, these rules should have the ability to promote to SSD on a file level and not a block level. Space-efficient packing of small files I tried ZFS on Linux but the limitations were: Upgrading is additional work because the package is in an external repository and is tied to specific kernel versions; it is not integrated with the package manager Write IOPS does not scale with number of devices in a raidz vdev. Cannot add disks to raidz vdevs Cannot have select data on RAID0 to reduce overhead and improve performance without additional physical disks or giving ZFS a single partition of the disks ext4 on LVM2 looks like an option except I can't tell whether I can shrink, extend, and redistribute onto new spindles RAID-type logical volumes (of course, I can experiment with LVM on a bunch of files). As far as I can tell, it doesn't have any of the nice-to-haves so I was wondering if there is something better out there. I did look at LVM dangers and caveats but then again, no system is perfect.

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  • How to use router QoS?

    - by Nathaniel
    N00b question. How exactly does one use router quality of service settings? I've read up on it a bit but I'm still not exactly sure how to use it. So, my real questions are these: Generally, how does QoS work? How would one use it, say, to guarantee smooth performance in latency sensitive application (cough online gaming cough)? Performance for that sort of stuff bombs out on our connection when somebody is uploading files. I apologize if this is kind of sprawling. Suggestions to clean it up / edits welcome.

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  • High Load mysql on Debian server stops every day. Why?

    - by Oleg Abrazhaev
    I have Debian server with 32 gb memory. And there is apache2, memcached and nginx on this server. Memory load always on maximum. Only 500m free. Most memory leak do MySql. Apache only 70 clients configured, other services small memory usage. When mysql use all memory it stops. And nothing works, need mysql reboot. Mysql configured use maximum 24 gb memory. I have hight weight InnoDB bases. (400000 rows, 30 gb). And on server multithread daemon, that makes many inserts in this tables, thats why InnoDB. There is my mysql config. [mysqld] # # * Basic Settings # default-time-zone = "+04:00" user = mysql pid-file = /var/run/mysqld/mysqld.pid socket = /var/run/mysqld/mysqld.sock port = 3306 basedir = /usr datadir = /var/lib/mysql tmpdir = /tmp language = /usr/share/mysql/english skip-external-locking default-time-zone='Europe/Moscow' # # Instead of skip-networking the default is now to listen only on # localhost which is more compatible and is not less secure. # # * Fine Tuning # #low_priority_updates = 1 concurrent_insert = ALWAYS wait_timeout = 600 interactive_timeout = 600 #normal key_buffer_size = 2024M #key_buffer_size = 1512M #70% hot cache key_cache_division_limit= 70 #16-32 max_allowed_packet = 32M #1-16M thread_stack = 8M #40-50 thread_cache_size = 50 #orderby groupby sort sort_buffer_size = 64M #same myisam_sort_buffer_size = 400M #temp table creates when group_by tmp_table_size = 3000M #tables in memory max_heap_table_size = 3000M #on disk open_files_limit = 10000 table_cache = 10000 join_buffer_size = 5M # This replaces the startup script and checks MyISAM tables if needed # the first time they are touched myisam-recover = BACKUP #myisam_use_mmap = 1 max_connections = 200 thread_concurrency = 8 # # * Query Cache Configuration # #more ignored query_cache_limit = 50M query_cache_size = 210M #on query cache query_cache_type = 1 # # * Logging and Replication # # Both location gets rotated by the cronjob. # Be aware that this log type is a performance killer. #log = /var/log/mysql/mysql.log # # Error logging goes to syslog. This is a Debian improvement :) # # Here you can see queries with especially long duration log_slow_queries = /var/log/mysql/mysql-slow.log long_query_time = 1 log-queries-not-using-indexes # # The following can be used as easy to replay backup logs or for replication. # note: if you are setting up a replication slave, see README.Debian about # other settings you may need to change. #server-id = 1 #log_bin = /var/log/mysql/mysql-bin.log server-id = 1 log-bin = /var/lib/mysql/mysql-bin #replicate-do-db = gate log-bin-index = /var/lib/mysql/mysql-bin.index log-error = /var/lib/mysql/mysql-bin.err relay-log = /var/lib/mysql/relay-bin relay-log-info-file = /var/lib/mysql/relay-bin.info relay-log-index = /var/lib/mysql/relay-bin.index binlog_do_db = 24avia expire_logs_days = 10 max_binlog_size = 100M read_buffer_size = 4024288 innodb_buffer_pool_size = 5000M innodb_flush_log_at_trx_commit = 2 innodb_thread_concurrency = 8 table_definition_cache = 2000 group_concat_max_len = 16M #binlog_do_db = gate #binlog_ignore_db = include_database_name # # * BerkeleyDB # # Using BerkeleyDB is now discouraged as its support will cease in 5.1.12. #skip-bdb # # * InnoDB # # InnoDB is enabled by default with a 10MB datafile in /var/lib/mysql/. # Read the manual for more InnoDB related options. There are many! # You might want to disable InnoDB to shrink the mysqld process by circa 100MB. #skip-innodb # # * Security Features # # Read the manual, too, if you want chroot! # chroot = /var/lib/mysql/ # # For generating SSL certificates I recommend the OpenSSL GUI "tinyca". # # ssl-ca=/etc/mysql/cacert.pem # ssl-cert=/etc/mysql/server-cert.pem # ssl-key=/etc/mysql/server-key.pem [mysqldump] quick quote-names max_allowed_packet = 500M [mysql] #no-auto-rehash # faster start of mysql but no tab completition [isamchk] key_buffer = 32M key_buffer_size = 512M # # * NDB Cluster # # See /usr/share/doc/mysql-server-*/README.Debian for more information. # # The following configuration is read by the NDB Data Nodes (ndbd processes) # not from the NDB Management Nodes (ndb_mgmd processes). # # [MYSQL_CLUSTER] # ndb-connectstring=127.0.0.1 # # * IMPORTANT: Additional settings that can override those from this file! # The files must end with '.cnf', otherwise they'll be ignored. # !includedir /etc/mysql/conf.d/ Please, help me make it stable. Memory used /etc/mysql # free total used free shared buffers cached Mem: 32930800 32766424 164376 0 139208 23829196 -/+ buffers/cache: 8798020 24132780 Swap: 33553328 44660 33508668 Maybe my problem not in memory, but MySQL stops every day. As you can see, cache memory free 24 gb. Thank to Michael Hampton? for correction. Load overage on server 3.5. Maybe hdd or another problem? Maybe my config not optimal for 30gb InnoDB ? I'm already try mysqltuner and tunung-primer.sh , but they marked all green. Mysqltuner output mysqltuner >> MySQLTuner 1.0.1 - Major Hayden <[email protected]> >> Bug reports, feature requests, and downloads at http://mysqltuner.com/ >> Run with '--help' for additional options and output filtering -------- General Statistics -------------------------------------------------- [--] Skipped version check for MySQLTuner script [OK] Currently running supported MySQL version 5.5.24-9-log [OK] Operating on 64-bit architecture -------- Storage Engine Statistics ------------------------------------------- [--] Status: -Archive -BDB -Federated +InnoDB -ISAM -NDBCluster [--] Data in MyISAM tables: 112G (Tables: 1528) [--] Data in InnoDB tables: 39G (Tables: 340) [--] Data in PERFORMANCE_SCHEMA tables: 0B (Tables: 17) [!!] Total fragmented tables: 344 -------- Performance Metrics ------------------------------------------------- [--] Up for: 8h 18m 33s (14M q [478.333 qps], 259K conn, TX: 9B, RX: 5B) [--] Reads / Writes: 84% / 16% [--] Total buffers: 10.5G global + 81.1M per thread (200 max threads) [OK] Maximum possible memory usage: 26.3G (83% of installed RAM) [OK] Slow queries: 1% (259K/14M) [!!] Highest connection usage: 100% (201/200) [OK] Key buffer size / total MyISAM indexes: 1.5G/5.6G [OK] Key buffer hit rate: 100.0% (6B cached / 1M reads) [OK] Query cache efficiency: 74.3% (8M cached / 11M selects) [OK] Query cache prunes per day: 0 [OK] Sorts requiring temporary tables: 0% (0 temp sorts / 247K sorts) [!!] Joins performed without indexes: 106025 [!!] Temporary tables created on disk: 49% (351K on disk / 715K total) [OK] Thread cache hit rate: 99% (249 created / 259K connections) [!!] Table cache hit rate: 15% (2K open / 13K opened) [OK] Open file limit used: 15% (3K/20K) [OK] Table locks acquired immediately: 99% (4M immediate / 4M locks) [!!] InnoDB data size / buffer pool: 39.4G/5.9G -------- Recommendations ----------------------------------------------------- General recommendations: Run OPTIMIZE TABLE to defragment tables for better performance MySQL started within last 24 hours - recommendations may be inaccurate Reduce or eliminate persistent connections to reduce connection usage Adjust your join queries to always utilize indexes Temporary table size is already large - reduce result set size Reduce your SELECT DISTINCT queries without LIMIT clauses Increase table_cache gradually to avoid file descriptor limits Variables to adjust: max_connections (> 200) wait_timeout (< 600) interactive_timeout (< 600) join_buffer_size (> 5.0M, or always use indexes with joins) table_cache (> 10000) innodb_buffer_pool_size (>= 39G) Mysql primer output -- MYSQL PERFORMANCE TUNING PRIMER -- - By: Matthew Montgomery - MySQL Version 5.5.24-9-log x86_64 Uptime = 0 days 8 hrs 20 min 50 sec Avg. qps = 478 Total Questions = 14369568 Threads Connected = 16 Warning: Server has not been running for at least 48hrs. It may not be safe to use these recommendations To find out more information on how each of these runtime variables effects performance visit: http://dev.mysql.com/doc/refman/5.5/en/server-system-variables.html Visit http://www.mysql.com/products/enterprise/advisors.html for info about MySQL's Enterprise Monitoring and Advisory Service SLOW QUERIES The slow query log is enabled. Current long_query_time = 1.000000 sec. You have 260626 out of 14369701 that take longer than 1.000000 sec. to complete Your long_query_time seems to be fine BINARY UPDATE LOG The binary update log is enabled Binlog sync is not enabled, you could loose binlog records during a server crash WORKER THREADS Current thread_cache_size = 50 Current threads_cached = 45 Current threads_per_sec = 0 Historic threads_per_sec = 0 Your thread_cache_size is fine MAX CONNECTIONS Current max_connections = 200 Current threads_connected = 11 Historic max_used_connections = 201 The number of used connections is 100% of the configured maximum. You should raise max_connections INNODB STATUS Current InnoDB index space = 214 M Current InnoDB data space = 39.40 G Current InnoDB buffer pool free = 0 % Current innodb_buffer_pool_size = 5.85 G Depending on how much space your innodb indexes take up it may be safe to increase this value to up to 2 / 3 of total system memory MEMORY USAGE Max Memory Ever Allocated : 23.46 G Configured Max Per-thread Buffers : 15.84 G Configured Max Global Buffers : 7.54 G Configured Max Memory Limit : 23.39 G Physical Memory : 31.40 G Max memory limit seem to be within acceptable norms KEY BUFFER Current MyISAM index space = 5.61 G Current key_buffer_size = 1.47 G Key cache miss rate is 1 : 5578 Key buffer free ratio = 77 % Your key_buffer_size seems to be fine QUERY CACHE Query cache is enabled Current query_cache_size = 200 M Current query_cache_used = 101 M Current query_cache_limit = 50 M Current Query cache Memory fill ratio = 50.59 % Current query_cache_min_res_unit = 4 K MySQL won't cache query results that are larger than query_cache_limit in size SORT OPERATIONS Current sort_buffer_size = 64 M Current read_rnd_buffer_size = 256 K Sort buffer seems to be fine JOINS Current join_buffer_size = 5.00 M You have had 106606 queries where a join could not use an index properly You have had 8 joins without keys that check for key usage after each row join_buffer_size >= 4 M This is not advised You should enable "log-queries-not-using-indexes" Then look for non indexed joins in the slow query log. OPEN FILES LIMIT Current open_files_limit = 20210 files The open_files_limit should typically be set to at least 2x-3x that of table_cache if you have heavy MyISAM usage. Your open_files_limit value seems to be fine TABLE CACHE Current table_open_cache = 10000 tables Current table_definition_cache = 2000 tables You have a total of 1910 tables You have 2151 open tables. The table_cache value seems to be fine TEMP TABLES Current max_heap_table_size = 2.92 G Current tmp_table_size = 2.92 G Of 366426 temp tables, 49% were created on disk Perhaps you should increase your tmp_table_size and/or max_heap_table_size to reduce the number of disk-based temporary tables Note! BLOB and TEXT columns are not allow in memory tables. If you are using these columns raising these values might not impact your ratio of on disk temp tables. TABLE SCANS Current read_buffer_size = 3 M Current table scan ratio = 2846 : 1 read_buffer_size seems to be fine TABLE LOCKING Current Lock Wait ratio = 1 : 185 You may benefit from selective use of InnoDB. If you have long running SELECT's against MyISAM tables and perform frequent updates consider setting 'low_priority_updates=1'

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