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  • Disk IO causing high load on Xen/CentOS guest

    - by Peter Lindqvist
    I'm having serious issues with a xen based server, this is on the guest partition. It's a paravirtualized CentOS 5.5. The following numbers are taken from top while copying a large file over the network. If i copy the file another time the speed decreases in relation to load average. So the second time it's half the speed of the first time. It needs some time to cool off after this. Load average slowly decreases until it's once again usable. ls / takes about 30 seconds. top - 13:26:44 up 13 days, 21:44, 2 users, load average: 7.03, 5.08, 3.15 Tasks: 134 total, 2 running, 132 sleeping, 0 stopped, 0 zombie Cpu(s): 0.0%us, 0.1%sy, 0.0%ni, 25.3%id, 74.5%wa, 0.0%hi, 0.0%si, 0.1%st Mem: 1048752k total, 1041460k used, 7292k free, 3116k buffers Swap: 2129912k total, 40k used, 2129872k free, 904740k cached PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 1506 root 10 -5 0 0 0 S 0.3 0.0 0:03.94 cifsd 1 root 15 0 2172 644 556 S 0.0 0.1 0:00.08 init Meanwhile the host is ~0.5 load avg and steady over time. ~50% wait Server hardware is dual xeon, 3gb ram, 170gb scsi 320 10k rpm, and shouldn't have any problems with copying files over the network. disk = [ "tap:aio:/vm/dev01.img,xvda,w" ] I also get these in the log INFO: task syslogd:1350 blocked for more than 120 seconds. "echo 0 > /proc/sys/kernel/hung_task_timeout_secs" disables this message. syslogd D 00062E4F 2208 1350 1 1353 1312 (NOTLB) c0ef0ed0 00000286 6e71a411 00062e4f c0ef0f18 00000009 c0f20000 6e738bfd 00062e4f 0001e7ec c0f2010c c181a724 c1abd200 00000000 ffffffff c0ef0ecc c041a180 00000000 c0ef0ed8 c03d6a50 00000000 00000000 c03d6a00 00000000 Call Trace: [<c041a180>] __wake_up+0x2a/0x3d [<ee06a1ea>] log_wait_commit+0x80/0xc7 [jbd] [<c043128b>] autoremove_wake_function+0x0/0x2d [<ee065661>] journal_stop+0x195/0x1ba [jbd] [<c0490a32>] __writeback_single_inode+0x1a3/0x2af [<c04568ea>] do_writepages+0x2b/0x32 [<c045239b>] __filemap_fdatawrite_range+0x66/0x72 [<c04910ce>] sync_inode+0x19/0x24 [<ee09b007>] ext3_sync_file+0xaf/0xc4 [ext3] [<c047426f>] do_fsync+0x41/0x83 [<c04742ce>] __do_fsync+0x1d/0x2b [<c0405413>] syscall_call+0x7/0xb ======================= I have tried disabling irqbalanced as suggested here but it does not seem to make any difference.

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  • What does transactions per seconds for a load balancer mean

    - by Anurag
    I was looking at the product matrix of webmux 592G(load balancer). It says maximum connections per sec = 2.8M Maximum number of transactions = 100,000 What does the above numbers mean. Does above means that load balancer can have 2.8M connections open but only 100K of them will be active per seconds. Also incase any one has used webmux 592G do you guys know in practice how many connections it can have open and what qps it can serve

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  • Alternatives for load balancing Tomcat webapp?

    - by Mahriman
    We wish to enable some form of load balancing for a Tomcat webapp we're currently using today. Unfortunately, I know almost to nothing about Tomcat load balancing, clustering and so on. Can anyone share resources that cover the different alternatives, give some handy pointers (maybe some solutions work better in certain types of environment?) or just some tips on solutions to try out? We're currently running Tomcat 5.5 if that makes any difference in features, however no critical obstacles for upgrading to 6.

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  • Servers behind load balancer

    - by Tom
    We have a CISCO hardware load balancer with two web servers behind it. We'd like to force some URLs to only be served by one of the machines. Firstly, is the job of the load balancer? or would a better approach be create a subdomain such as http://assets.example.com which would be automatically be routed to one of the servers?

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  • Windows 2003 :: Performance Monitoring :: Simple/Stupid Tutorial

    - by BSI Support
    I have a half dozen front-end servers all running IIS 6.0-based/hosted applications. (primary .NET 2.0 web apps.) Basically, I'd like to take some basic performance data from each one, through such into a spreadsheet, and compare. CPU load, RAM load, whatever... If anyone can point out a very simple/stupid "here's how you do that" type of tutorial, that would be wonderful.

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  • Do i need a dedicated server for load balancing?

    - by Ben
    I'm completely new to the concept of load balancing so i hope this question isn't a "stupid question" because i've been searching around and im having a hard time understanding this. So to my understanding, in order to load balance, i need a separate machine with an ip address i can direct all traffic to. I initially thought i needed to rent 3 dedicated servers, one for load balancing and the other two as backend servers. Would a dedicated server be too much for a load balancer or do hosting companies have special types of computers for that process? Then i read somewhere else that i can install a load balance software in both of the two servers and configure it in a way that doesn't require me to rent another machine/dedicated server for load balancing. So im a bit confuse on how to actually implement a load balancer and whether or not i need a dedicated server for the sole purpose of acting as a load balancing machine. Also, i was recommended to use HAproxy so i'll be heading that direction for load balancing.

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  • Big Data – Buzz Words: What is Hadoop – Day 6 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is NoSQL. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – Hadoop. What is Hadoop? Apache Hadoop is an open-source, free and Java based software framework offers a powerful distributed platform to store and manage Big Data. It is licensed under an Apache V2 license. It runs applications on large clusters of commodity hardware and it processes thousands of terabytes of data on thousands of the nodes. Hadoop is inspired from Google’s MapReduce and Google File System (GFS) papers. The major advantage of Hadoop framework is that it provides reliability and high availability. What are the core components of Hadoop? There are two major components of the Hadoop framework and both fo them does two of the important task for it. Hadoop MapReduce is the method to split a larger data problem into smaller chunk and distribute it to many different commodity servers. Each server have their own set of resources and they have processed them locally. Once the commodity server has processed the data they send it back collectively to main server. This is effectively a process where we process large data effectively and efficiently. (We will understand this in tomorrow’s blog post). Hadoop Distributed File System (HDFS) is a virtual file system. There is a big difference between any other file system and Hadoop. When we move a file on HDFS, it is automatically split into many small pieces. These small chunks of the file are replicated and stored on other servers (usually 3) for the fault tolerance or high availability. (We will understand this in the day after tomorrow’s blog post). Besides above two core components Hadoop project also contains following modules as well. Hadoop Common: Common utilities for the other Hadoop modules Hadoop Yarn: A framework for job scheduling and cluster resource management There are a few other projects (like Pig, Hive) related to above Hadoop as well which we will gradually explore in later blog posts. A Multi-node Hadoop Cluster Architecture Now let us quickly see the architecture of the a multi-node Hadoop cluster. A small Hadoop cluster includes a single master node and multiple worker or slave node. As discussed earlier, the entire cluster contains two layers. One of the layer of MapReduce Layer and another is of HDFC Layer. Each of these layer have its own relevant component. The master node consists of a JobTracker, TaskTracker, NameNode and DataNode. A slave or worker node consists of a DataNode and TaskTracker. It is also possible that slave node or worker node is only data or compute node. The matter of the fact that is the key feature of the Hadoop. In this introductory blog post we will stop here while describing the architecture of Hadoop. In a future blog post of this 31 day series we will explore various components of Hadoop Architecture in Detail. Why Use Hadoop? There are many advantages of using Hadoop. Let me quickly list them over here: Robust and Scalable – We can add new nodes as needed as well modify them. Affordable and Cost Effective – We do not need any special hardware for running Hadoop. We can just use commodity server. Adaptive and Flexible – Hadoop is built keeping in mind that it will handle structured and unstructured data. Highly Available and Fault Tolerant – When a node fails, the Hadoop framework automatically fails over to another node. Why Hadoop is named as Hadoop? In year 2005 Hadoop was created by Doug Cutting and Mike Cafarella while working at Yahoo. Doug Cutting named Hadoop after his son’s toy elephant. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – MapReduce. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Finding the maximum value/date across columns

    - by AtulThakor
    While working on some code recently I discovered a neat little trick to find the maximum value across several columns….. So the starting point was finding the maximum date across several related tables and storing the maximum value against an aggregated record. Here's the sample setup code: USE TEMPDB IF OBJECT_ID('CUSTOMER') IS NOT NULL BEGIN DROP TABLE CUSTOMER END IF OBJECT_ID('ADDRESS') IS NOT NULL BEGIN DROP TABLE ADDRESS END IF OBJECT_ID('ORDERS') IS NOT NULL BEGIN DROP TABLE ORDERS END SELECT 1 AS CUSTOMERID, 'FREDDY KRUEGER' AS NAME, GETDATE() - 10 AS DATEUPDATED INTO CUSTOMER SELECT 100000 AS ADDRESSID, 1 AS CUSTOMERID, '1428 ELM STREET' AS ADDRESS, GETDATE() -5 AS DATEUPDATED INTO ADDRESS SELECT 123456 AS ORDERID, 1 AS CUSTOMERID, GETDATE() + 1 AS DATEUPDATED INTO ORDERS .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; }   Now the code used a function to determine the maximum date, this performed poorly. After considering pivoting the data I opted for a case statement, this seemed reasonable until I discovered other areas which needed to determine the maximum date between 5 or more tables which didn't scale well. The final solution involved using the value clause within a sub query as followed. SELECT C.CUSTOMERID, A.ADDRESSID, (SELECT MAX(DT) FROM (Values(C.DATEUPDATED),(A.DATEUPDATED),(O.DATEUPDATED)) AS VALUE(DT)) FROM CUSTOMER C INNER JOIN ADDRESS A ON C.CUSTOMERID = A.CUSTOMERID INNER JOIN ORDERS O ON O.CUSTOMERID = C.CUSTOMERID .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } As you can see the solution scales well and can take advantage of many of the aggregate functions!

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  • Master Data Management for Location Data - Oracle Site Hub

    - by david.butler(at)oracle.com
    Most MDM discussions cover key domains such as customer, supplier, product, service, and reference data. It is usually understood that these domains have complex structures and hundreds if not thousands of attributes that need governing. Location, on the other hand, strikes most people as address data. How hard can that be? But for many industries, locations are complex, and site information is critical to efficient operations and relevant analytics. Retail stores and malls, bank branches, construction sites come to mind. But one of the best industries for illustrating the power of a site mastering application is Oil & Gas.   Oracle's Master Data Management solution for location data is the Oracle Site Hub. It is a location mastering solution that enables organizations to centralize site and location specific information from heterogeneous systems, creating a single view of site information that can be leveraged across all functional departments and analytical systems.   Let's take a look at the location entities the Oracle Site Hub can manage for the Oil & Gas industry: organizations, property, land, buildings, roads, oilfield, service center, inventory site, real estate, facilities, refineries, storage tanks, vendor locations, businesses, assets; project site, area, well, basin, pipelines, critical infrastructure, offshore platform, compressor station, gas station, etc. Any site can be classified into multiple hierarchies, like organizational hierarchy, operational hierarchy, geographic hierarchy, divisional hierarchies and so on. Any site can also be associated to multiple clusters, i.e. collections of sites, and these can be used as a foundation for driving reporting, analysis, organize daily work, etc. Hierarchies can also be used to model entities which are structured or non-structured collections of nodes, like for example routes, pipelines and more. The User Defined Attribute Framework provides the needed infrastructure to add single row attributes groups like well base attributes (well IDs, well type, well structure and key characterizing measures, and more) and well geometry, and multi row attribute groups like well applications, permits, production data, activities, operations, logs, treatments, tests, drills, treatments, and KPIs. Site Hub can also model areas, lands, fields, basins, pools, platforms, eco-zones, and stratigraphic layers as specific sites, tracking their base attributes, aliases, descriptions, subcomponents and more. Midstream entities (pipelines, logistic sites, pump stations) and downstream entities (cylinders, tanks, inventories, meters, partner's sites, routes, facilities, gas stations, and competitor sites) can also be easily modeled, together with their specific attributes and relationships. Site Hub can store any type of unstructured data associated to a site. This could be stored directly or on an external content management solution, like Oracle Universal Content Management. Considering a well, for example, Site Hub can store any relevant associated multimedia file such as: CAD drawings of the well profile, structure and/or parts, engineering documents, contracts, applications, permits, logs, pictures, photos, videos and more. For any site entity, Site Hub can associate all the related assets and equipments at the site, as well as all relationships between sites, between a site and multiple parties, and between a site and any purchasable or sellable item, over time. Items can be equipment, instruments, facilities, services, products, production entities, production facilities (pipelines, batteries, compressor stations, gas plants, meters, separators, etc.), support facilities (rigs, roads, transmission or radio towers, airstrips, etc.), supplier products and services, catalogs, and more. Items can just be associated to sites using standard Site Hub features, or they can be fully mastered by implementing Oracle Product Hub. Site locations (addresses or geographical coordinates) are also managed with out-of-the-box address geo-coding capabilities coupled with Google Maps integration to deliver powerful mapping capabilities and spatial data analysis. Locations can be shared between different sites. Centered on the site location, any site can also have associated areas. Site Hub can master any site location specific information, like for example cadastral, ownership, jurisdictional, geological, seismic and more, and any site-centric area specific information, like for example economical, political, risk, weather, logistic, traffic information and more. Now if anyone ever asks you why locations need MDM, think about how all these Oil & Gas entities and attributes would translate into your business locations. To learn more about Oracle's full MDM solution for the digital oil field, here is a link to Roberto Negro's outstanding whitepaper: Oracle Site Master Data Management for mastering wells and other PPDM entities in a digital oilfield context  

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  • Optimal Data Structure for our own API

    - by vermiculus
    I'm in the early stages of writing an Emacs major mode for the Stack Exchange network; if you use Emacs regularly, this will benefit you in the end. In order to minimize the number of calls made to Stack Exchange's API (capped at 10000 per IP per day) and to just be a generally responsible citizen, I want to cache the information I receive from the network and store it in memory, waiting to be accessed again. I'm really stuck as to what data structure to store this information in. Obviously, it is going to be a list. However, as with any data structure, the choice must be determined by what data is being stored and what how it will be accessed. What, I would like to be able to store all of this information in a single symbol such as stack-api/cache. So, without further ado, stack-api/cache is a list of conses keyed by last update: `(<csite> <csite> <csite>) where <csite> would be (1362501715 . <site>) At this point, all we've done is define a simple association list. Of course, we must go deeper. Each <site> is a list of the API parameter (unique) followed by a list questions: `("codereview" <cquestion> <cquestion> <cquestion>) Each <cquestion> is, you guessed it, a cons of questions with their last update time: `(1362501715 <question>) (1362501720 . <question>) <question> is a cons of a question structure and a list of answers (again, consed with their last update time): `(<question-structure> <canswer> <canswer> <canswer> and ` `(1362501715 . <answer-structure>) This data structure is likely most accurately described as a tree, but I don't know if there's a better way to do this considering the language, Emacs Lisp (which isn't all that different from the Lisp you know and love at all). The explicit conses are likely unnecessary, but it helps my brain wrap around it better. I'm pretty sure a <csite>, for example, would just turn into (<epoch-time> <api-param> <cquestion> <cquestion> ...) Concerns: Does storing data in a potentially huge structure like this have any performance trade-offs for the system? I would like to avoid storing extraneous data, but I've done what I could and I don't think the dataset is that large in the first place (for normal use) since it's all just human-readable text in reasonable proportion. (I'm planning on culling old data using the times at the head of the list; each inherits its last-update time from its children and so-on down the tree. To what extent this cull should take place: I'm not sure.) Does storing data like this have any performance trade-offs for that which must use it? That is, will set and retrieve operations suffer from the size of the list? Do you have any other suggestions as to what a better structure might look like?

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  • How do you handle the fetchxml result data?

    - by Luke Baulch
    I have avoided working with fetchxml as I have been unsure the best way to handle the result data after calling crmService.Fetch(fetchXml). In a couple of situations, I have used an XDocument with LINQ to retrieve the data from this data structure, such as: XDocument resultset = XDocument.Parse(_service.Fetch(fetchXml)); if (resultset.Root == null || !resultset.Root.Elements("result").Any()) { return; } foreach (var displayItem in resultset.Root.Elements("result").Select(item => item.Element(displayAttributeName)).Distinct()) { if (displayItem!= null && displayItem.Value != null) { dropDownList.Items.Add(displayItem.Value); } } What is the best way to handle fetchxml result data, so that it can be easily used. Applications such as passing these records into an ASP.NET datagrid would be quite useful.

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  • Generate Entity Data Model from Data Contract

    - by CSmooth.net
    I would like to find a fast way to convert a Data Contract to a Entity Data Model. Consider the following Data Contract: [DataContract] class PigeonHouse { [DataMember] public string housename; [DataMember] public List<Pigeon> pigeons; } [DataContract] class Pigeon { [DataMember] public string name; [DataMember] public int numberOfWings; [DataMember] public int age; } Is there an easy way to create an ADO.NET Entity Data Model from this code?

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  • Detecting a Lightweight Core Data Migration

    - by hadronzoo
    I'm using Core Data's automatic lightweight migration successfully. However, when a particular entity gets created during a migration, I'd like to populate it with some data. Of course I could check if the entity is empty every time the application starts, but this seems inefficient when Core Data has a migration framework. Is it possible to detect when a lightweight migration occurs (possibly using KVO or notifications), or does this require implementing standard migrations? I've tried using the NSPersistentStoreCoordinatorStoresDidChangeNotification, but it doesn't fire when migrations occur.

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  • weird problem with load () or live () !!

    - by silversky
    I load a page with load () and then I create dinamically a tag. Then I use live() to bind a click event and fires a function. At the end a call unload (). The problem is that when I load the same page again ( without refresh ) when on click the function will be fired twice. If I exit again (again with unload ()) and load the page again on click will fire 3 times and so on .... A sample of my code is: $('#tab').click(function() { $('#formWrap').load('newPage.php'); }); $('div').after('<p class="ctr" ></p>'); $('p.ctr').live('click', function(e) { if($(e.target).is('[k=lf]')) { console.log ('one'); delete ($this); } else if .... }); function delete () { $.post( 'update.php', data); } I have other $.post inside on this page and also on the above live fnc and all work well. The above one also works but like I said on the second load will fire twice and the 3 times and so on ... The weird part for me is that if replece the console with console.log ('two'); save the page and load the page without refresh it will fire on a different rows - one two - if I unload the page replace the console with console.log ('three'); and load again will fire one two and three. I try to use: $.ajax({ url: 'updateDB.php', data: data, type: 'POST', cache:false }); $.ajaxSetup ({ cache: false }); header("Cache-Control: no-cache"); none of this it's working. And I have this problem only on this fnc. What do you think, it could be the reason, it remembers it remembers the previous action and it fires again?

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  • Getting started with massive data

    - by Max
    I'm a math guy and occasionally do some statistics/machine learning analysis consulting projects on the side. The data I have access to are usually on the smaller side, at most a couple hundred of megabytes (and almost always far less), but I want to learn more about handling and analyzing data on the gigabyte/terabyte scale. What do I need to know and what are some good resources to learn from? Hadoop/MapReduce is one obvious start. Is there a particular programming language I should pick up? (I primarily work now in Python, Ruby, R, and occasionally Java, but it seems like C and Clojure are often used for large-scale data analysis?) I'm not really familiar with the whole NoSQL movement, except that it's associated with big data. What's a good place to learn about it, and is there a particular implementation (Cassandra, CouchDB, etc.) I should get familiar with? Where can I learn about applying machine learning algorithms to huge amounts of data? My math background is mostly on the theory side, definitely not on the numerical or approximation side, and I'm guessing most of the standard ML algorithms don't really scale. Any other suggestions on things to learn would be great!

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  • Efficient data structure design

    - by Sway
    Hi there, I need to match a series of user inputed words against a large dictionary of words (to ensure the entered value exists). So if the user entered: "orange" it should match an entry "orange' in the dictionary. Now the catch is that the user can also enter a wildcard or series of wildcard characters like say "or__ge" which would also match "orange" The key requirements are: * this should be as fast as possible. * use the smallest amount of memory to achieve it. If the size of the word list was small I could use a string containing all the words and use regular expressions. however given that the word list could contain potentially hundreds of thousands of enteries I'm assuming this wouldn't work. So is some sort of 'tree' be the way to go for this...? Any thoughts or suggestions on this would be totally appreciated! Thanks in advance, Matt

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  • Effecient data structure design

    - by Sway
    Hi there, I need to match a series of user inputed words against a large dictionary of words (to ensure the entered value exists). So if the user entered: "orange" it should match an entry "orange' in the dictionary. Now the catch is that the user can also enter a wildcard or series of wildcard characters like say "or__ge" which would also match "orange" The key requirements are: * this should be as fast as possible. * use the smallest amount of memory to achieve it. If the size of the word list was small I could use a string containing all the words and use regular expressions. however given that the word list could contain potentially hundreds of thousands of enteries I'm assuming this wouldn't work. So is some sort of 'tree' be the way to go for this...? Any thoughts or suggestions on this would be totally appreciated! Thanks in advance, Matt

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  • How to view existing data in Core Data?

    - by mshsayem
    Well, may be this question is silly, but I couldn't find a way (except programmatically). I built a project (for iPhone OS 3.0) which uses Core Data. The xcdatamodel file shows the schema description, but I want to see the data in tabular form (like the management studio for mssql server or phpmyadmin for mysql). Is there any way (except coding)? What is that? Also, which file (in disk/device) those data are stored into? [ I built the tutorial (from apple) on Core Data, named Locations. They used this line somewhere in the code: NSURL *storeUrl = [NSURL fileURLWithPath: [[self applicationDocumentsDirectory] stringByAppendingPathComponent: @"Locations.sqlite"]]; But, I did not see any "xxxxx.sqlite" file in project location (nor in the disk).]

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  • Efficient alternatives to merge for larger data.frames R

    - by Etienne Low-Décarie
    I am looking for an efficient (both computer resource wise and learning/implementation wise) method to merge two larger (size1 million / 300 KB RData file) data frames. "merge" in base R and "join" in plyr appear to use up all my memory effectively crashing my system. Example load test data frame and try test.merged<-merge(test, test) or test.merged<-join(test, test, type="all") - The following post provides a list of merge and alternatives: How to join data frames in R (inner, outer, left, right)? The following allows object size inspection: https://heuristically.wordpress.com/2010/01/04/r-memory-usage-statistics-variable/ Data produced by anonym

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  • Generated images fail to load in browser

    - by notJim
    I've got a page on a webapp that has about 13 images that are generated by my application, which is written in the Kohana PHP framework. The images are actually graphs. They are cached so they are only generated once, but the first time the user visits the page, and the images all have to be generated, about half of the images don't load in the browser. Once the page has been requested once and images are cached, they all load successfully. Doing some ad-hoc testing, if I load an individual image in the browser, it takes from 450-700 ms to load with an empty cache (I checked this using Google Chrome's resource tracking feature). For reference, it takes around 90-150 ms to load a cached image. Even if the image cache is empty, I have the data and some of the application's startup tasks cached, so that after the first request, none of that data needs to be fetched. My questions are: Why are the images failing to load? It seems like the browser just decides not to download the image after a certain point, rather than waiting for them all to finish loading. What can I do to get them to load the first time, with an empty cache? Obviously one option is to decrease the load times, and I could figure out how to do that by profiling the app, but are there other options? As I mentioned, the app is in the Kohana PHP framework, and it's running on Apache. As an aside, I've solved this problem for now by fetching the page as soon as the data is available (it comes from a batch process), so that the images are always cached by the time the user sees them. That feels like a kludgey solution to me, though, and I'm curious about what's actually going on.

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  • Browser timing out attempting to load images

    - by notJim
    I've got a page on a webapp that has about 13 images that are generated by my application, which is written in the Kohana PHP framework. The images are actually graphs. They are cached so they are only generated once, but the first time the user visits the page, and the images all have to be generated, about half of the images don't load in the browser. Once the page has been requested once and images are cached, they all load successfully. Doing some ad-hoc testing, if I load an individual image in the browser, it takes from 450-700 ms to load with an empty cache (I checked this using Google Chrome's resource tracking feature). For reference, it takes around 90-150 ms to load a cached image. Even if the image cache is empty, I have the data and some of the application's startup tasks cached, so that after the first request, none of that data needs to be fetched. My questions are: Why are the images failing to load? It seems like the browser just decides not to download the image after a certain point, rather than waiting for them all to finish loading. What can I do to get them to load the first time, with an empty cache? Obviously one option is to decrease the load times, and I could figure out how to do that by profiling the app, but are there other options? As I mentioned, the app is in the Kohana PHP framework, and it's running on Apache. As an aside, I've solved this problem for now by fetching the page as soon as the data is available (it comes from a batch process), so that the images are always cached by the time the user sees them. That feels like a kludgey solution to me, though, and I'm curious about what's actually going on.

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  • find the top K most frequent numbers in a data stream

    - by Jin
    This is more of a data structure question rather than a coding question. If I am fetching a data stream, i.e, I keep receiving float numbers once at a time, how should I keep track of the top K frequent numbers? Here my memory is 4G and I prefer to have less communication with hard drive unless necessary. I think heap is good for updating the max and min. How should I design the data structure? Thanks

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  • Performance problems when running Java desktop applications on Citrix Metaframe

    - by demetriusnunes
    We have a desktop Java application running within a Citrix Metaframe server farm and the performance, specially while starting up the app, is very unreliable. Sometimes it takes 15 seconds and sometimes it takes over a minute. It's really unpredicatable. Is there any way to optimize running Java desktop applications within Citrix Metaframe Terminal server sessions to a more reliable performance level? Are there any optimization directed specifically toward Java, such as pre-load JVMs or something like that? Any help would be greatly appreciated.

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