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  • NSNumberFormatter customize?

    - by Frederick C. Lee
    I wish to use NSNumberFormatter to merely attached a percent ('%') to the supplied number WITHOUT having it multiplied by 100. The canned kCFNumberFormatterPercentStyle automatically x100 which I don't want. For example, converting 5.0 to 5.0% versus 500%. Using the following: NSNumberFormatter *percentFormatter = [[NSNumberFormatter alloc] init]; [percentFormatter setNumberFormat:@"##0.00%;-##0.00%"]; But 'setNumberFormat' doesn't exist in NSNumberFomatter. I need to use this NSNumberFormatter for my Core-Plot label. How can I customize NSNumberFormat? Ric.

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  • How to integrate an dynamically generated JSON Object into an other object?

    - by Marco Ciarfaglia
    How can I put this JSON Object in the function or object below? // this function generates an JSON Object dynamically $(".n_ListTitle").each(function(i, v) { var node = $(this); var nodeParent = node.parent(); var nodeText = node.text(); var nodePrice = node.siblings('.n_ListPrice'); var prodPrice = $(nodePrice).text(); var prodId = nodeParent.attr('id').replace('ric', ''); var prodTitle = nodeText; var json = { id : prodId, price : prodPrice, currency : "CHF", name : prodTitle }; return json; }); TDConf.Config = { products : [ // here should be inserted the JSON Object {id: "[product-id1]", price:"[price1]", currency:"[currency1]", name:"[product-name1]"}, {id: "[product-id2]", price:"[price2]", currency:"[currency2]", name:"[product-name2]"}, ... })], containerTagId :"..." }; If it is not understandable please ask :) Thanks in advance for helping me to figure out!

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  • How to extract img src, title and alt from html using php?

    - by Sam
    I would like to create a page where all images which reside on my website are listed with title and alternative representation. I already wrote me a little program to find and load all html files, but now I am stuck at how to extract src, title and alt from the html < img src="/image/fluffybunny.jpg" title="Harvey the bunny" alt="a cute little fluffy bunny"/ I guess this should be done with some regex, but since the order of the tags may vary, and I need all of them, I don't really know how to parse this in an elegant way (I could do it the hard char by char way, but thats painful).

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  • How do I make a expanding textbox?

    - by jpjp
    I want to make a textbook where it starts out as a given width/height. Then if users type more then the given amount of space, the textbox expands downward. How do I go about doing this? Do I use css? The basic textbox just displays a scroll bar when users pass the number of rows allow. How do I make it so the textbox expands the rows by say 5 more? <form method="post" action=""> <textarea name="comments" cols="50" rows="5"></textarea><br> <input type="submit" value="Submit" /> </form> How do i use the example that Robert Harvey mentioned? I never used javascript before..

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  • Invitation: Oracle EMEA Analytics & Data Integration Partner Forum, 12th November 2012, London (UK)

    - by rituchhibber
    Oracle PartnerNetwork | Account | Feedback INVITATIONORACLE EMEA ANALYTICS & DATA INTEGRATION PARTNER FORUM MONDAY 12TH NOVEMBER, 2012 IN LONDON (UK) Dear partner Come to hear the latest news from Oracle OpenWorld about Oracle BI & Data Integration, and propel your business growth as an Oracle partner. This event should appeal to BI or Data Integration specialised partners, Executives, Sales, Pre-sales and Solution architects: with a choice of participation in the plenary day and then a set of special interest (technical) sessions. The follow on breakout sessions from the 13th November provide deeper dives and technical training for those of you who wish to stay for more detailed and hands-on workshops.Keynote: Andrew Sutherland, SVP Oracle Technology. Data Integration can bring great value to your customers by moving data to transform their business experiences in Oracle pan-EMEA Data Integration business development and opportunities for partners. Hot agenda items will include: The Fusion Middleware Stack: Engineered to work together A complete Analytics and Data Integration Solution Architecture: Big Data and Little Data combined In-Memory Analytics for Extreme Insight Latest Product Development roadmap for Data Integration and Analytics Venue: Oracles London CITY Moorgate OfficesDuring this event you can learn about partner success stories, participate in an array of break-out sessions, exchange information with other partners and enjoy a vibrant panel discussion. Places are limited, Register your seat today! To register to this event CLICK HERE Note: Registration for the conference and the deeper dives and technical training is free of charge to OPN member Partners, but you will be responsible for your own travel and hotel expenses. Event Schedule November 12th:Day 1 Main Plenary Session : Full day, starting 10.30 am.Oracle Hosted Dinner in the Evening November 13th:onwards Architecture Masterclass : IM Reference Architecture – Big Data and Little Data combined(1 day) BI-Apps Bootcamp(4-days) Oracle Data Integrator and Oracle Enterprise Data Quality workshop(1-day) Golden Gate Workshop(1-day) For further information and detail download the Agenda (pdf) or contact Michael Hallett at [email protected] look forward to seeing you in there. Best regards, Mike HallettAlliances and Channels DirectorBI & EPM Oracle EMEAM.No: +44 7831 276 989 [email protected] Duncan HarveyBusiness Development Directorfor Data IntegrationM.No: +420 608 283 [email protected] Milomir VojvodicBusiness Development Manager for Data IntegrationM.No: +420 608 283 [email protected] Copyright © 2012, Oracle and/or its affiliates. All rights reserved. Contact PBC | Legal Notices and Terms of Use | Privacy

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  • How to count the most recent value based on multiple criteria?

    - by Andrew
    I keep a log of phone calls like the following where the F column is LVM = Left Voice Mail, U = Unsuccessful, S = Successful. A1 1 B1 Smith C1 John D1 11/21/2012 E1 8:00 AM F1 LVM A2 2 B2 Smith C2 John D2 11/22/2012 E1 8:15 AM F2 U A3 3 B3 Harvey C3 Luke D3 11/22/2012 E1 8:30 AM F3 S A4 4 B4 Smith C4 John D4 11/22/2012 E1 9:00 AM F4 S A5 5 B5 Smith C5 John D5 11/23/2012 E5 8:00 AM F5 LVM This is a small sample. I actually have over 700 entries. In my line of work, it is important to know how many unsuccessful (LVM or U) calls I have made since the last Successful one (S). Since values in the F column can repeat, I need to take into consideration both the B and C column. Also, since I can make a successful call with a client and then be trying to contact them again, I need to be able to count from the last successful call. My G column is completely open which is where I would like to put a running total for each client (G5 would = 1 ideally while G4 = 0, G3 = 0, G2 = 2, G1 = 1 but I want these values calculated automatically so that I do not have scroll through 700 names).

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  • Top-Rated JavaScript Blogs

    - by Andreas Grech
    I am currently trying to find some blogs that talk (almost solely) on the JavaScript Language, and this is due to the fact that most of the time, bloggers with real life experience at work or at home development can explain more clearly and concisely certain quirks and hidden features than most 'Official Language Specifications' Below find a list of blogs that are JavaScript based (will update the list as more answers flow in): DHTML Kitchen, by Garrett Smith Robert's Talk, by Robert Nyman EJohn, by John Resig (of jQuery) Crockford's JavaScript Page, by Douglas Crockford Dean.edwards.name, by Dean Edwards Ajaxian, by various (@Martin) The JavaScript Weblog, by various SitePoint's JavaScript and CSS Page, by various AjaxBlog, by various Eric Lippert's Blog, by Eric Lippert (talks about JScript and JScript.Net) Web Bug Track, by various (@scunliffe) The Strange Zen Of JavaScript , by Scott Andrew Alex Russell (of Dojo) (@Eran Galperin) Ariel Flesler (@Eran Galperin) Nihilogic, by Jacob Seidelin (@llimllib) Peter's Blog, by Peter Michaux (@Borgar) Flagrant Badassery, by Steve Levithan (@Borgar) ./with Imagination, by Dustin Diaz (@Borgar) HedgerWow (@Borgar) Dreaming in Javascript, by Nosredna spudly.shuoink.com, by Stephen Sorensen Yahoo! User Interface Blog, by various (@Borgar) remy sharp's b:log, by Remy Sharp (@Borgar) JScript Blog, by the JScript Team (@Borgar) Dmitry Baranovskiy’s Web Log, by Dmitry Baranovskiy James Padolsey's Blog (@Kenny Eliasson) Perfection Kills; Exploring JavaScript by example, by Juriy Zaytsev DailyJS (@Ric) NCZOnline (@Kenny Eliasson), by Nicholas C. Zakas Which top-rated blogs am I currently missing from the above list, that you think should be imperative to any JavaScript developer to read (and follow) concurrently?

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  • My First F# program

    - by sudaly
    Hi I just finish writing my first F# program. Functionality wise the code works the way I wanted, but not sure if the code is efficient. I would much appreciate if someone could review the code for me and point out the areas where the code can be improved. Thanks Sudaly open System open System.IO open System.IO.Pipes open System.Text open System.Collections.Generic open System.Runtime.Serialization [<DataContract>] type Quote = { [<field: DataMember(Name="securityIdentifier") >] RicCode:string [<field: DataMember(Name="madeOn") >] MadeOn:DateTime [<field: DataMember(Name="closePrice") >] Price:float } let m_cache = new Dictionary<string, Quote>() let ParseQuoteString (quoteString:string) = let data = Encoding.Unicode.GetBytes(quoteString) let stream = new MemoryStream() stream.Write(data, 0, data.Length); stream.Position <- 0L let ser = Json.DataContractJsonSerializer(typeof<Quote array>) let results:Quote array = ser.ReadObject(stream) :?> Quote array results let RefreshCache quoteList = m_cache.Clear() quoteList |> Array.iter(fun result->m_cache.Add(result.RicCode, result)) let EstablishConnection() = let pipeServer = new NamedPipeServerStream("testpipe", PipeDirection.InOut, 4) let mutable sr = null printfn "[F#] NamedPipeServerStream thread created, Wait for a client to connect" pipeServer.WaitForConnection() printfn "[F#] Client connected." try // Stream for the request. sr <- new StreamReader(pipeServer) with | _ as e -> printfn "[F#]ERROR: %s" e.Message sr while true do let sr = EstablishConnection() // Read request from the stream. printfn "[F#] Ready to Receive data" sr.ReadLine() |> ParseQuoteString |> RefreshCache printfn "[F#]Quot Size, %d" m_cache.Count let quot = m_cache.["MSFT.OQ"] printfn "[F#]RIC: %s" quot.RicCode printfn "[F#]MadeOn: %s" (String.Format("{0:T}",quot.MadeOn)) printfn "[F#]Price: %f" quot.Price

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  • Boost Solr results based on the field that contained the hit

    - by TomFor
    Hi, I was browsing the web looking for a indexing and search framework and stumbled upon Solr. A functionality that we abolutely need is to boost results based on what field contained the hit. A small example: Consider a record like this: <movie> <title>The Dark Knight</title> <alternative_title>Batman Begins 2</alternative_title> <year>2008</year> <director>Christopher Nolan</director> <plot>Batman, Gordon and Harvey Dent are forced to deal with the chaos unleashed by an anarchist mastermind known only as the Joker, as it drives each of them to their limits.</plot> </movie> I want to combine for example the title, alternative_title and plot fields into one search field, which isn't too difficult after looking at the Solr/Lucene documentation and tutorials. However I also want that movies that have a hit in title have a higher score than hits on alternative_title and those in their turn should score higher than hits in the plot field. Is there any way to indicate this kond of scoring in the xml or do we need to develop some custom scoring algorythm? Please also note that the example I've givnen is fictional end the real data will probably contain 100+ fields. Thanks in advance, Tom

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  • Cross-platform, human-readable, du on root partition that truly ignores other filesystems

    - by nice_line
    I hate this so much: Linux builtsowell 2.6.18-274.7.1.el5 #1 SMP Mon Oct 17 11:57:14 EDT 2011 x86_64 x86_64 x86_64 GNU/Linux df -kh Filesystem Size Used Avail Use% Mounted on /dev/mapper/mpath0p2 8.8G 8.7G 90M 99% / /dev/mapper/mpath0p6 2.0G 37M 1.9G 2% /tmp /dev/mapper/mpath0p3 5.9G 670M 4.9G 12% /var /dev/mapper/mpath0p1 494M 86M 384M 19% /boot /dev/mapper/mpath0p7 7.3G 187M 6.7G 3% /home tmpfs 48G 6.2G 42G 14% /dev/shm /dev/mapper/o10g.bin 25G 7.4G 17G 32% /app/SIP/logs /dev/mapper/o11g.bin 25G 11G 14G 43% /o11g tmpfs 4.0K 0 4.0K 0% /dev/vx lunmonster1q:/vol/oradb_backup/epmxs1q1 686G 507G 180G 74% /rpmqa/backup lunmonster1q:/vol/oradb_redo/bisxs1q1 4.0G 1.6G 2.5G 38% /bisxs1q/rdoctl1 lunmonster1q:/vol/oradb_backup/bisxs1q1 686G 507G 180G 74% /bisxs1q/backup lunmonster1q:/vol/oradb_exp/bisxs1q1 2.0T 1.1T 984G 52% /bisxs1q/exp lunmonster2q:/vol/oradb_home/bisxs1q1 10G 174M 9.9G 2% /bisxs1q/home lunmonster2q:/vol/oradb_data/bisxs1q1 52G 5.2G 47G 10% /bisxs1q/oradata lunmonster1q:/vol/oradb_redo/bisxs1q2 4.0G 1.6G 2.5G 38% /bisxs1q/rdoctl2 ip-address1:/vol/oradb_home/cspxs1q1 10G 184M 9.9G 2% /cspxs1q/home ip-address2:/vol/oradb_backup/cspxs1q1 674G 314G 360G 47% /cspxs1q/backup ip-address2:/vol/oradb_redo/cspxs1q1 4.0G 1.5G 2.6G 37% /cspxs1q/rdoctl1 ip-address2:/vol/oradb_exp/cspxs1q1 4.1T 1.5T 2.6T 37% /cspxs1q/exp ip-address2:/vol/oradb_redo/cspxs1q2 4.0G 1.5G 2.6G 37% /cspxs1q/rdoctl2 ip-address1:/vol/oradb_data/cspxs1q1 160G 23G 138G 15% /cspxs1q/oradata lunmonster1q:/vol/oradb_exp/epmxs1q1 2.0T 1.1T 984G 52% /epmxs1q/exp lunmonster2q:/vol/oradb_home/epmxs1q1 10G 80M 10G 1% /epmxs1q/home lunmonster2q:/vol/oradb_data/epmxs1q1 330G 249G 82G 76% /epmxs1q/oradata lunmonster1q:/vol/oradb_redo/epmxs1q2 5.0G 609M 4.5G 12% /epmxs1q/rdoctl2 lunmonster1q:/vol/oradb_redo/epmxs1q1 5.0G 609M 4.5G 12% /epmxs1q/rdoctl1 /dev/vx/dsk/slaxs1q/slaxs1q-vol1 183G 17G 157G 10% /slaxs1q/backup /dev/vx/dsk/slaxs1q/slaxs1q-vol4 173G 58G 106G 36% /slaxs1q/oradata /dev/vx/dsk/slaxs1q/slaxs1q-vol5 75G 952M 71G 2% /slaxs1q/exp /dev/vx/dsk/slaxs1q/slaxs1q-vol2 9.8G 381M 8.9G 5% /slaxs1q/home /dev/vx/dsk/slaxs1q/slaxs1q-vol6 4.0G 1.6G 2.2G 42% /slaxs1q/rdoctl1 /dev/vx/dsk/slaxs1q/slaxs1q-vol3 4.0G 1.6G 2.2G 42% /slaxs1q/rdoctl2 /dev/mapper/appoem 30G 1.3G 27G 5% /app/em Yet, I equally, if not quite a bit more, also hate this: SunOS solarious 5.10 Generic_147440-19 sun4u sparc SUNW,SPARC-Enterprise Filesystem size used avail capacity Mounted on kiddie001Q_rpool/ROOT/s10s_u8wos_08a 8G 7.7G 1.3G 96% / /devices 0K 0K 0K 0% /devices ctfs 0K 0K 0K 0% /system/contract proc 0K 0K 0K 0% /proc mnttab 0K 0K 0K 0% /etc/mnttab swap 15G 1.8M 15G 1% /etc/svc/volatile objfs 0K 0K 0K 0% /system/object sharefs 0K 0K 0K 0% /etc/dfs/sharetab fd 0K 0K 0K 0% /dev/fd kiddie001Q_rpool/ROOT/s10s_u8wos_08a/var 31G 8.3G 6.6G 56% /var swap 512M 4.6M 507M 1% /tmp swap 15G 88K 15G 1% /var/run swap 15G 0K 15G 0% /dev/vx/dmp swap 15G 0K 15G 0% /dev/vx/rdmp /dev/dsk/c3t4d4s0 3 20G 279G 41G 88% /fs_storage /dev/vx/dsk/oracle/ora10g-vol1 292G 214G 73G 75% /o10g /dev/vx/dsk/oec/oec-vol1 64G 33G 31G 52% /oec/runway /dev/vx/dsk/oracle/ora9i-vol1 64G 33G 31G 59% /o9i /dev/vx/dsk/home 23G 18G 4.7G 80% /export/home /dev/vx/dsk/dbwork/dbwork-vol1 292G 214G 73G 92% /db03/wk01 /dev/vx/dsk/oradg/ebusredovol 2.0G 475M 1.5G 24% /u21 /dev/vx/dsk/oradg/ebusbckupvol 200G 32G 166G 17% /u31 /dev/vx/dsk/oradg/ebuscrtlvol 2.0G 475M 1.5G 24% /u20 kiddie001Q_rpool 31G 97K 6.6G 1% /kiddie001Q_rpool monsterfiler002q:/vol/ebiz_patches_nfs/NSA0304 203G 173G 29G 86% /oracle/patches /dev/odm 0K 0K 0K 0% /dev/odm The people with the authority don't rotate logs or delete packages after install in my environment. Standards, remediation, cohesion...all fancy foreign words to me. ============== How am I supposed to deal with / filesystem full issues across multiple platforms that have a devastating number of mounts? On Red Hat el5, du -x apparently avoids traversal into other filesystems. While this may be so, it does not appear to do anything if run from the / directory. On Solaris 10, the equivalent flag is du -d, which apparently packs no surprises, allowing Sun to uphold its legacy of inconvenience effortlessly. (I'm hoping I've just been doing it wrong.) I offer up for sacrifice my Frankenstein's monster. Tell me how ugly it is. Tell me I should download forbidden 3rd party software. Tell me I should perform unauthorized coreutils updates, piecemeal, across 2000 systems, with no single sign-on, no authorized keys, and no network update capability. Then, please help me make this bastard better: pwd / du * | egrep -v "$(echo $(df | awk '{print $1 "\n" $5 "\n" $6}' | \ cut -d\/ -f2-5 | egrep -v "[0-9]|^$|Filesystem|Use|Available|Mounted|blocks|vol|swap")| \ sed 's/ /\|/g')" | egrep -v "proc|sys|media|selinux|dev|platform|system|tmp|tmpfs|mnt|kernel" | \ cut -d\/ -f1-2 | sort -k2 -k1,1nr | uniq -f1 | sort -k1,1n | cut -f2 | xargs du -shx | \ egrep "G|[5-9][0-9]M|[1-9][0-9][0-9]M" My biggest failure and regret is that it still requires a single character edit for Solaris: pwd / du * | egrep -v "$(echo $(df | awk '{print $1 "\n" $5 "\n" $6}' | \ cut -d\/ -f2-5 | egrep -v "[0-9]|^$|Filesystem|Use|Available|Mounted|blocks|vol|swap")| \ sed 's/ /\|/g')" | egrep -v "proc|sys|media|selinux|dev|platform|system|tmp|tmpfs|mnt|kernel" | \ cut -d\/ -f1-2 | sort -k2 -k1,1nr | uniq -f1 | sort -k1,1n | cut -f2 | xargs du -shd | \ egrep "G|[5-9][0-9]M|[1-9][0-9][0-9]M" This will exclude all non / filesystems in a du search from the / directory by basically munging an egrepped df from a second pipe-delimited egrep regex subshell exclusion that is naturally further excluded upon by a third egrep in what I would like to refer to as "the whale." The munge-fest frantically escalates into some xargs du recycling where -x/-d is actually useful, and a final, gratuitous egrep spits out a list of directories that almost feels like an accomplishment: Linux: 54M etc/gconf 61M opt/quest 77M opt 118M usr/ ##===\ 149M etc 154M root 303M lib/modules 313M usr/java ##====\ 331M lib 357M usr/lib64 ##=====\ 433M usr/lib ##========\ 1.1G usr/share ##=======\ 3.2G usr/local ##========\ 5.4G usr ##<=============Ascending order to parent 94M app/SIP ##<==\ 94M app ##<=======Were reported as 7gb and then corrected by second du with -x. Solaris: 63M etc 490M bb 570M root/cores.ric.20100415 1.7G oec/archive 1.1G root/packages 2.2G root 1.7G oec Guess what? It's really slow. Edit: Are there any bash one-liner heroes out there than can turn my bloated abomination into divine intervention, or at least something resembling gingerly copypasta?

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  • CLSF & CLK 2013 Trip Report by Jeff Liu

    - by jamesmorris
    This is a contributed post from Jeff Liu, lead XFS developer for the Oracle mainline Linux kernel team. Recently, I attended both the China Linux Storage and Filesystem workshop (CLSF), and the China Linux Kernel conference (CLK), which were held in Shanghai. Here are the highlights for both events. CLSF - 17th October XFS update (led by Jeff Liu) XFS keeps rapid progress with a lot of changes, especially focused on the infrastructure/performance improvements as well as  new feature development.  This can be reflected with a sample statistics among XFS/Ext4+JBD2/Btrfs via: # git diff --stat --minimal -C -M v3.7..v3.12-rc4 -- fs/xfs|fs/ext4+fs/jbd2|fs/btrfs XFS: 141 files changed, 27598 insertions(+), 19113 deletions(-) Ext4+JBD2: 39 files changed, 10487 insertions(+), 5454 deletions(-) Btrfs: 70 files changed, 19875 insertions(+), 8130 deletions(-) What made up those changes in XFS? Self-describing metadata(CRC32c). This is a new feature and it contributed about 70% code changes, it can be enabled via `mkfs.xfs -m crc=1 /dev/xxx` for v5 superblock. Transaction log space reservation improvements. With this change, we can calculate the log space reservation at mount time rather than runtime to reduce the the CPU overhead. User namespace support. So both XFS and USERNS can be enabled on kernel configuration begin from Linux 3.10. Thanks Dwight Engen's efforts for this thing. Split project/group quota inodes. Originally, project quota can not be enabled with group quota at the same time because they were share the same quota file inode, now it works but only for v5 super block. i.e, CRC enabled. CONFIG_XFS_WARN, an new lightweight runtime debugger which can be deployed in production environment. Readahead log object recovery, this change can speed up the log replay progress significantly. Speculative preallocation inode tracking, clearing and throttling. The main purpose is to deal with inodes with post-EOF space due to speculative preallocation, support improved quota management to free up a significant amount of unwritten space when at or near EDQUOT. It support backgroup scanning which occurs on a longish interval(5 mins by default, tunable), and on-demand scanning/trimming via ioctl(2). Bitter arguments ensued from this session, especially for the comparison between Ext4 and Btrfs in different areas, I have to spent a whole morning of the 1st day answering those questions. We basically agreed on XFS is the best choice in Linux nowadays because: Stable, XFS has a good record in stability in the past 10 years. Fengguang Wu who lead the 0-day kernel test project also said that he has observed less error than other filesystems in the past 1+ years, I own it to the XFS upstream code reviewer, they always performing serious code review as well as testing. Good performance for large/small files, XFS does not works very well for small files has already been an old story for years. Best choice (maybe) for distributed PB filesystems. e.g, Ceph recommends delopy OSD daemon on XFS because Ext4 has limited xattr size. Best choice for large storage (>16TB). Ext4 does not support a single file more than around 15.95TB. Scalability, any objection to XFS is best in this point? :) XFS is better to deal with transaction concurrency than Ext4, why? The maximum size of the log in XFS is 2038MB compare to 128MB in Ext4. Misc. Ext4 is widely used and it has been proved fast/stable in various loads and scenarios, XFS just need more customers, and Btrfs is still on the road to be a manhood. Ceph Introduction (Led by Li Wang) This a hot topic.  Li gave us a nice introduction about the design as well as their current works. Actually, Ceph client has been included in Linux kernel since 2.6.34 and supported by Openstack since Folsom but it seems that it has not yet been widely deployment in production environment. Their major work is focus on the inline data support to separate the metadata and data storage, reduce the file access time, i.e, a file access need communication twice, fetch the metadata from MDS and then get data from OSD, and also, the small file access is limited by the network latency. The solution is, for the small files they would like to store the data at metadata so that when accessing a small file, the metadata server can push both metadata and data to the client at the same time. In this way, they can reduce the overhead of calculating the data offset and save the communication to OSD. For this feature, they have only run some small scale testing but really saw noticeable improvements. Test environment: Intel 2 CPU 12 Core, 64GB RAM, Ubuntu 12.04, Ceph 0.56.6 with 200GB SATA disk, 15 OSD, 1 MDS, 1 MON. The sequence read performance for 1K size files improved about 50%. I have asked Li and Zheng Yan (the core developer of Ceph, who also worked on Btrfs) whether Ceph is really stable and can be deployed at production environment for large scale PB level storage, but they can not give a positive answer, looks Ceph even does not spread over Dreamhost (subject to confirmation). From Li, they only deployed Ceph for a small scale storage(32 nodes) although they'd like to try 6000 nodes in the future. Improve Linux swap for Flash storage (led by Shaohua Li) Because of high density, low power and low price, flash storage (SSD) is a good candidate to partially replace DRAM. A quick answer for this is using SSD as swap. But Linux swap is designed for slow hard disk storage, so there are a lot of challenges to efficiently use SSD for swap. SWAPOUT swap_map scan swap_map is the in-memory data structure to track swap disk usage, but it is a slow linear scan. It will become a bottleneck while finding many adjacent pages in the use of SSD. Shaohua Li have changed it to a cluster(128K) list, resulting in O(1) algorithm. However, this apporoach needs restrictive cluster alignment and only enabled for SSD. IO pattern In most cases, the swap io is in interleaved pattern because of mutiple reclaimers or a free cluster is shared by all reclaimers. Even though block layer can merge interleaved IO to some extent, but we cannot count on it completely. Hence the per-cpu cluster is added base on the previous change, it can help reclaimer do sequential IO and the block layer will be easier to merge IO. TLB flush: If we're reclaiming one active page, we should first move the page from active lru list to inactive lru list, and then reclaim the page from inactive lru to swap it out. During the process, we need to clear PTE twice: first is 'A'(ACCESS) bit, second is 'P'(PRESENT) bit. Processors need to send lots of ipi which make the TLB flush really expensive. Some works have been done to improve this, including rework smp_call_functiom_many() or remove the first TLB flush in x86, but there still have some arguments here and only parts of works have been pushed to mainline. SWAPIN: Page fault does iodepth=1 sync io, but it's a little waste if only issue a page size's IO. The obvious solution is doing swap readahead. But the current in-kernel swap readahead is arbitary(always 8 pages), and it always doesn't perform well for both random and sequential access workload. Shaohua introduced a new flag for madvise(MADV_WILLNEED) to do swap prefetch, so the changes happen in userspace API and leave the in-kernel readahead unchanged(but I think some improvement can also be done here). SWAP discard As we know, discard is important for SSD write throughout, but the current swap discard implementation is synchronous. He changed it to async discard which allow discard and write run in the same time. Meanwhile, the unit of discard is also optimized to cluster. Misc: lock contention For many concurrent swapout and swapin , the lock contention such as anon_vma or swap_lock is high, so he changed the swap_lock to a per-swap lock. But there still have some lock contention in very high speed SSD because of swapcache address_space lock. Zproject (led by Bob Liu) Bob gave us a very nice introduction about the current memory compression status. Now there are 3 projects(zswap/zram/zcache) which all aim at smooth swap IO storm and promote performance, but they all have their own pros and cons. ZSWAP It is implemented based on frontswap API and it uses a dynamic allocater named Zbud to allocate free pages. Zbud means pairs of zpages are "buddied" and it can only store at most two compressed pages in one page frame, so the max compress ratio is 50%. Each page frame is lru-linked and can do shink in memory pressure. If the compressed memory pool reach its limitation, shink or reclaim happens. It decompress the page frame into two new allocated pages and then write them to real swap device, but it can fail when allocating the two pages. ZRAM Acts as a compressed ramdisk and used as swap device, and it use zsmalloc as its allocator which has high density but may have fragmentation issues. Besides, page reclaim is hard since it will need more pages to uncompress and free just one page. ZRAM is preferred by embedded system which may not have any real swap device. Now both ZRAM and ZSWAP are in driver/staging tree, and in the mm community there are some disscussions of merging ZRAM into ZSWAP or viceversa, but no agreement yet. ZCACHE Handles file page compression but it is removed out of staging recently. From industry (led by Tang Jie, LSI) An LSI engineer introduced several new produces to us. The first is raid5/6 cards that it use full stripe writes to improve performance. The 2nd one he introduced is SandForce flash controller, who can understand data file types (data entropy) to reduce write amplification (WA) for nearly all writes. It's called DuraWrite and typical WA is 0.5. What's more, if enable its Dynamic Logical Capacity function module, the controller can do data compression which is transparent to upper layer. LSI testing shows that with this virtual capacity enables 1x TB drive can support up to 2x TB capacity, but the application must monitor free flash space to maintain optimal performance and to guard against free flash space exhaustion. He said the most useful application is for datebase. Another thing I think it's worth to mention is that a NV-DRAM memory in NMR/Raptor which is directly exposed to host system. Applications can directly access the NV-DRAM via a memory address - using standard system call mmap(). He said that it is very useful for database logging now. This kind of NVM produces are beginning to appear in recent years, and it is said that Samsung is building a research center in China for related produces. IMHO, NVM will bring an effect to current os layer especially on file system, e.g. its journaling may need to redesign to fully utilize these nonvolatile memory. OCFS2 (led by Canquan Shen) Without a doubt, HuaWei is the biggest contributor to OCFS2 in the past two years. They have posted 46 upstream patches and 39 patches have been merged. Their current project is based on 32/64 nodes cluster, but they also tried 128 nodes at the experimental stage. The major work they are working is to support ATS (atomic test and set), it can be works with DLM at the same time. Looks this idea is inspired by the vmware VMFS locking, i.e, http://blogs.vmware.com/vsphere/2012/05/vmfs-locking-uncovered.html CLK - 18th October 2013 Improving Linux Development with Better Tools (Andi Kleen) This talk focused on how to find/solve bugs along with the Linux complexity growing. Generally, we can do this with the following kind of tools: Static code checkers tools. e.g, sparse, smatch, coccinelle, clang checker, checkpatch, gcc -W/LTO, stanse. This can help check a lot of things, simple mistakes, complex problems, but the challenges are: some are very slow, false positives, may need a concentrated effort to get false positives down. Especially, no static checker I found can follow indirect calls (“OO in C”, common in kernel): struct foo_ops { int (*do_foo)(struct foo *obj); } foo->do_foo(foo); Dynamic runtime checkers, e.g, thread checkers, kmemcheck, lockdep. Ideally all kernel code would come with a test suite, then someone could run all the dynamic checkers. Fuzzers/test suites. e.g, Trinity is a great tool, it finds many bugs, but needs manual model for each syscall. Modern fuzzers around using automatic feedback, but notfor kernel yet: http://taviso.decsystem.org/making_software_dumber.pdf Debuggers/Tracers to understand code, e.g, ftrace, can dump on events/oops/custom triggers, but still too much overhead in many cases to run always during debug. Tools to read/understand source, e.g, grep/cscope work great for many cases, but do not understand indirect pointers (OO in C model used in kernel), give us all “do_foo” instances: struct foo_ops { int (*do_foo)(struct foo *obj); } = { .do_foo = my_foo }; foo>do_foo(foo); That would be great to have a cscope like tool that understands this based on types/initializers XFS: The High Performance Enterprise File System (Jeff Liu) [slides] I gave a talk for introducing the disk layout, unique features, as well as the recent changes.   The slides include some charts to reflect the performances between XFS/Btrfs/Ext4 for small files. About a dozen users raised their hands when I asking who has experienced with XFS. I remembered that when I asked the same question in LinuxCon/Japan, only 3 people raised their hands, but they are Chris Mason, Ric Wheeler, and another attendee. The attendee questions were mainly focused on stability, and comparison with other file systems. Linux Containers (Feng Gao) The speaker introduced us that the purpose for those kind of namespaces, include mount/UTS/IPC/Network/Pid/User, as well as the system API/ABI. For the userspace tools, He mainly focus on the Libvirt LXC rather than us(LXC). Libvirt LXC is another userspace container management tool, implemented as one type of libvirt driver, it can manage containers, create namespace, create private filesystem layout for container, Create devices for container and setup resources controller via cgroup. In this talk, Feng also mentioned another two possible new namespaces in the future, the 1st is the audit, but not sure if it should be assigned to user namespace or not. Another is about syslog, but the question is do we really need it? In-memory Compression (Bob Liu) Same as CLSF, a nice introduction that I have already mentioned above. Misc There were some other talks related to ACPI based memory hotplug, smart wake-affinity in scheduler etc., but my head is not big enough to record all those things. -- Jeff Liu

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  • How to count each digit in a range of integers?

    - by Carlos Gutiérrez
    Imagine you sell those metallic digits used to number houses, locker doors, hotel rooms, etc. You need to find how many of each digit to ship when your customer needs to number doors/houses: 1 to 100 51 to 300 1 to 2,000 with zeros to the left The obvious solution is to do a loop from the first to the last number, convert the counter to a string with or without zeros to the left, extract each digit and use it as an index to increment an array of 10 integers. I wonder if there is a better way to solve this, without having to loop through the entire integers range. Solutions in any language or pseudocode are welcome. Edit: Answers review John at CashCommons and Wayne Conrad comment that my current approach is good and fast enough. Let me use a silly analogy: If you were given the task of counting the squares in a chess board in less than 1 minute, you could finish the task by counting the squares one by one, but a better solution is to count the sides and do a multiplication, because you later may be asked to count the tiles in a building. Alex Reisner points to a very interesting mathematical law that, unfortunately, doesn’t seem to be relevant to this problem. Andres suggests the same algorithm I’m using, but extracting digits with %10 operations instead of substrings. John at CashCommons and phord propose pre-calculating the digits required and storing them in a lookup table or, for raw speed, an array. This could be a good solution if we had an absolute, unmovable, set in stone, maximum integer value. I’ve never seen one of those. High-Performance Mark and strainer computed the needed digits for various ranges. The result for one millon seems to indicate there is a proportion, but the results for other number show different proportions. strainer found some formulas that may be used to count digit for number which are a power of ten. Robert Harvey had a very interesting experience posting the question at MathOverflow. One of the math guys wrote a solution using mathematical notation. Aaronaught developed and tested a solution using mathematics. After posting it he reviewed the formulas originated from Math Overflow and found a flaw in it (point to Stackoverflow :). noahlavine developed an algorithm and presented it in pseudocode. A new solution After reading all the answers, and doing some experiments, I found that for a range of integer from 1 to 10n-1: For digits 1 to 9, n*10(n-1) pieces are needed For digit 0, if not using leading zeros, n*10n-1 - ((10n-1) / 9) are needed For digit 0, if using leading zeros, n*10n-1 - n are needed The first formula was found by strainer (and probably by others), and I found the other two by trial and error (but they may be included in other answers). For example, if n = 6, range is 1 to 999,999: For digits 1 to 9 we need 6*105 = 600,000 of each one For digit 0, without leading zeros, we need 6*105 – (106-1)/9 = 600,000 - 111,111 = 488,889 For digit 0, with leading zeros, we need 6*105 – 6 = 599,994 These numbers can be checked using High-Performance Mark results. Using these formulas, I improved the original algorithm. It still loops from the first to the last number in the range of integers, but, if it finds a number which is a power of ten, it uses the formulas to add to the digits count the quantity for a full range of 1 to 9 or 1 to 99 or 1 to 999 etc. Here's the algorithm in pseudocode: integer First,Last //First and last number in the range integer Number //Current number in the loop integer Power //Power is the n in 10^n in the formulas integer Nines //Nines is the resut of 10^n - 1, 10^5 - 1 = 99999 integer Prefix //First digits in a number. For 14,200, prefix is 142 array 0..9 Digits //Will hold the count for all the digits FOR Number = First TO Last CALL TallyDigitsForOneNumber WITH Number,1 //Tally the count of each digit //in the number, increment by 1 //Start of optimization. Comments are for Number = 1,000 and Last = 8,000. Power = Zeros at the end of number //For 1,000, Power = 3 IF Power 0 //The number ends in 0 00 000 etc Nines = 10^Power-1 //Nines = 10^3 - 1 = 1000 - 1 = 999 IF Number+Nines <= Last //If 1,000+999 < 8,000, add a full set Digits[0-9] += Power*10^(Power-1) //Add 3*10^(3-1) = 300 to digits 0 to 9 Digits[0] -= -Power //Adjust digit 0 (leading zeros formula) Prefix = First digits of Number //For 1000, prefix is 1 CALL TallyDigitsForOneNumber WITH Prefix,Nines //Tally the count of each //digit in prefix, //increment by 999 Number += Nines //Increment the loop counter 999 cycles ENDIF ENDIF //End of optimization ENDFOR SUBROUTINE TallyDigitsForOneNumber PARAMS Number,Count REPEAT Digits [ Number % 10 ] += Count Number = Number / 10 UNTIL Number = 0 For example, for range 786 to 3,021, the counter will be incremented: By 1 from 786 to 790 (5 cycles) By 9 from 790 to 799 (1 cycle) By 1 from 799 to 800 By 99 from 800 to 899 By 1 from 899 to 900 By 99 from 900 to 999 By 1 from 999 to 1000 By 999 from 1000 to 1999 By 1 from 1999 to 2000 By 999 from 2000 to 2999 By 1 from 2999 to 3000 By 1 from 3000 to 3010 (10 cycles) By 9 from 3010 to 3019 (1 cycle) By 1 from 3019 to 3021 (2 cycles) Total: 28 cycles Without optimization: 2,235 cycles Note that this algorithm solves the problem without leading zeros. To use it with leading zeros, I used a hack: If range 700 to 1,000 with leading zeros is needed, use the algorithm for 10,700 to 11,000 and then substract 1,000 - 700 = 300 from the count of digit 1. Benchmark and Source code I tested the original approach, the same approach using %10 and the new solution for some large ranges, with these results: Original 104.78 seconds With %10 83.66 With Powers of Ten 0.07 A screenshot of the benchmark application: If you would like to see the full source code or run the benchmark, use these links: Complete Source code (in Clarion): http://sca.mx/ftp/countdigits.txt Compilable project and win32 exe: http://sca.mx/ftp/countdigits.zip Accepted answer noahlavine solution may be correct, but l just couldn’t follow the pseudo code, I think there are some details missing or not completely explained. Aaronaught solution seems to be correct, but the code is just too complex for my taste. I accepted strainer’s answer, because his line of thought guided me to develop this new solution.

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