Search Results

Search found 4636 results on 186 pages for 'red gate'.

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

Page 36/186 | < Previous Page | 32 33 34 35 36 37 38 39 40 41 42 43  | Next Page >

  • (Python) Converting a dictionary to a list?

    - by Daria Egelhoff
    So I have this dictionary: ScoreDict = {"Blue": {'R1': 89, 'R2': 80}, "Brown": {'R1': 61, 'R2': 77}, "Purple": {'R1': 60, 'R2': 98}, "Green": {'R1': 74, 'R2': 91}, "Red": {'R1': 87, 'Lon': 74}} Is there any way how I can convert this dictionary into a list like this: ScoreList = [['Blue', 89, 80], ['Brown', 61, 77], ['Purple', 60, 98], ['Green', 74, 91], ['Red', 87, 74]] I'm not too familiar with dictionaries, so I really need some help here. Thanks in advance!

    Read the article

  • Displaying bitmaps in relative positions

    - by JonF
    I'd like to put a couple images on a surfaceview. I understand that the screen sizes of android devices can vary, so I don't think I can just use an x y position or I might end up placing it off different screens. Say I want to put two boxes in the center of the screen, a blue one and a red one. The blue one is to the left of the red one. How can I accomplish that while accounting for different screen sizes?

    Read the article

  • R: Plotting a graph with different colors of points based on advanced criteria

    - by balconydoor
    What I would like to do is a plot (using ggplot), where the x axis represent years which have a different colour for the last three years in the plot than the rest. The last three years should also meet a certain criteria and based on this the last three years can either be red or green. The criteria is that the mean of the last three years should be less (making it green) or more (making it red) than the 66%-percentile of the remaining years. So far I have made two different functions calculating the last three year mean: LYM3 <- function (x) { LYM3 <- tail(x,3) mean(LYM3$Data,na.rm=T) } And the 66%-percentile for the remaining: perc66 <- function(x) { percentile <- head(x,-3) quantile(percentile$Data, .66, names=F,na.rm=T) } Here are two sets of data that can be used in the calculations (plots), the first which is an example from my real data where LYM3(df1) < perc66(df1) and the second is just made up data where LYM3 perc66. df1<- data.frame(Year=c(1979:2010), Data=c(347261.87, 145071.29, 110181.93, 183016.71, 210995.67, 205207.33, 103291.78, 247182.10, 152894.45, 170771.50, 206534.55, 287770.86, 223832.43, 297542.86, 267343.54, 475485.47, 224575.08, 147607.81, 171732.38, 126818.10, 165801.08, 136921.58, 136947.63, 83428.05, 144295.87, 68566.23, 59943.05, 49909.08, 52149.11, 117627.75, 132127.79, 130463.80)) df2 <- data.frame(Year=c(1979:2010), Data=c(sample(50,29,replace=T),75,75,75)) Here’s my code for my plot so far: plot <- ggplot(df1, aes(x=Year, y=Data)) + theme_bw() + geom_point(size=3, aes(colour=ifelse(df1$Year<2008, "black",ifelse(LYM3(df1) < perc66(df1),"green","red")))) + geom_line() + scale_x_continuous(breaks=c(1980,1985,1990,1995,2000,2005,2010), limits=c(1978,2011)) plot As you notice it doesn’t really do what I want it to do. The only thing it does seem to do is that it turns the years before 2008 into one level and those after into another one and base the point colour off these two levels. Since I don’t want this year to be stationary either, I made another tiny function: fun3 <- function(x) { df <- subset(x, Year==(max(Year)-2)) df$Year } So the previous code would have the same effect as: geom_point(size=3, aes(colour=ifelse(df1$Year<fun3(df1), "black","red"))) But it still does not care about my colours. Why does it make the years into levels? And how come an ifelse function doesn’t work within another one in this case? How would it be possible to the arguments to do what I like? I realise this might be a bit messy, asking for a lot at the same time, but I hope my description is pretty clear. It would be helpful if someone could at least point me in the right direction. I tried to put the code for the plot into a function as well so I wouldn’t have to change the data frame at all functions within the plot, but I can’t get it to work. Thank you!

    Read the article

  • Why border of <tr> not showing in IE?

    - by metal-gear-solid
    Why border of tfoot tr:first-child not showing in IE. I'm checking in IE7. font-weight:bold; background:yellow is showing in IE but border not table { border-collapse: collapse; border-spacing: 0; } table tfoot tr:first-child {font-weight:bold; background:yellow; border-top:2px solid red; border-bottom:2px solid red;}

    Read the article

  • convert json string into array or object...

    - by qulzam
    I get some json data form the web which is like: [{"pk": 1, "model": "stock.item", "fields": {"style": "f/s", "name": "shirt", "color": "red", "sync": 1, "fabric_code": "0012", "item_code": "001", "size": "34"}}, {"pk": 2, "model": "stock.item", "fields": {"style": "febric", "name": "Trouser", "color": "red", "sync": 1, "fabric_code": "fabric code", "item_code": "0123", "size": "44"}}] How can i use it in the C# winforms desktop application. I already get this data in the form of string. All types of answer are welcome.

    Read the article

  • Why is fulltextsearch for phrase ignored in SQL Server?

    - by cpt.oneeye
    I am executing the following SQL statement on an indexed SQL Server 2008 R2 database. SELECT * FROM mydatabase WHERE (CONTAINS(ColumnA,'"The Apple is red"')) The problem is that it returns too many entries. It also returns entries where 'ColumnA' contains only one of the words ('Apple' or 'is' or 'red'...) and not only the entries which contains the exact phrase. According to MSDN this should be the way to search for a phrase. Thanks cpt.oneeye

    Read the article

  • Can a CSS class inherit one or more other classes?

    - by Joel Martinez
    I feel dumb for having been a web programmer for so long and not knowing the answer to this question, I actually hope it's possible and I just didn't know about rather than what I think is the answer (which is that it's not possible). My question is whether it is possible to make a CSS class that "inherits" from another CSS class (or more than one). For example, say we had: .something { display:inline } .else { background:red } What I'd like to do is something like this: .composite { .something; .else } where the ".composite" class would both display inline and have a red background

    Read the article

  • Do you think its user unfriendly to show error message in tooltips ?

    - by msfanboy
    Hello, when my user enters data validated as wrong a red circle with a white exclamation mark is shown in the right part of the textbox with the wrong data. The error message is only shown when the user hovers the textbox with wrong data. Do you think that is a bad User experience ? I could show the red error message text to the right side of the textboxes if there would still be space...

    Read the article

  • CSS: a:link vs just a (without the :link part)

    - by Rob
    So we're required to use the following order for CSS anchor pseudo-classes a:link { color: red } a:visited { color: blue } a:hover { color: yellow } a:active { color: lime } But my question is why bother with the a:link part? Rather, is there any advantage to the above (other than perhaps clarity) over: a { color:red; } /* notice no :link part */ a:visited { color: blue; } etc.,etc.

    Read the article

  • Is there any C/C++ editor in Linux that shows errors while typing

    - by MetallicPriest
    The Visual C++ editor has a great feature which is that it underlines errors with a red line while typing the code. So for example, if you are using a variable that is not declared, it will underline it with a red curly line. In this way, the programmer can resolve a lot of errors while typing and doesn't have to wait for compiling for noticing them. Now my question is, is there any editor for Linux that has this great feature?

    Read the article

  • input type=image width being ignored

    - by kastulo
    im making an image button like this: <input type="image" src="red.jpeg" width="150px"> but it is displaying the original image size which is much larger, if i put: <img src="red.jpeg" width="150px"> it displays the image 150px wide as i want, what do you guys think is the problem with this? I have tried styling it with a class and CSS but not working either, please help me with this, im going nuts!!

    Read the article

  • How to generate report

    - by user692495
    I have problem in generate report. I use crystal report 8.5 with vb.net 2008,what I want is when I generate report it will appear red value, if the value is more or less than actual value else it will give default value but when I put this code it give me wrong result If {Intake.wheatType} = {Spec.WheatType} AND {Intake.HB43} >={Spec.M_Min} AND{Intake.HB43} >={Spec.M_Max} Then Red Else DefaultAttribute this report is related with two tables, which is table Spec and table Intake. Could anyone help/teach me how to fix this problem

    Read the article

  • Is there a command like pstree for libraries?

    - by flashnode
    I need to determine whether a library named libunaSA.so is being called directly by the process or by another library called libtoki2.so. I guess what I'm looking for is a pstree for libraries. The system is running RHEL 5.3 Beta. This output shows the two libraries in the process map # grep -e toki -e una /proc/2335/maps 0043f000-004ad000 r-xp 00000000 08:02 543465 /usr/lib/libtoki2.so 004ad000-004c5000 rwxp 0006d000 08:02 543465 /usr/lib/libtoki2.so 01185000-01397000 r-xp 00000000 08:02 543503 /usr/lib/libunaSA.so 01397000-013dc000 rwxp 00211000 08:02 543503 /usr/lib/libunaSA.so This output shows that only the libtoki2.so library is in the current cache # ldconfig -p | grep -e una -e toki libtoki2.so (libc6) => /usr/lib/libtoki2.so libtoki.so.4.4.1 (libc6) => /usr/lib/libtoki.so.4.4.1 libtoki.so.2 (libc6) => /usr/lib/libtoki.so.2 I attached strace to the running process but it doesn't provide much output # strace -p 2335 Process 2335 attached - interrupt to quit futex(0xb7ef5bd8, FUTEX_WAIT, 2336, NULL Here's the output to ldd for each library # ldd /usr/lib/libtoki2.so linux-gate.so.1 => (0x00a0a000) libdl.so.2 => /lib/libdl.so.2 (0x001bd000) libstdc++-libc6.2-2.so.3 => /usr/lib/libstdc++-libc6.2-2.so.3 (0x00f3f000) libm.so.6 => /lib/libm.so.6 (0x00b27000) libc.so.6 => /lib/libc.so.6 (0x0043d000) /lib/ld-linux.so.2 (0x00742000) libgcc_s.so.1 => /lib/libgcc_s.so.1 (0x00110000) # ldd /usr/lib/libunaSA.so linux-gate.so.1 => (0x00244000) libpthread.so.0 => /lib/libpthread.so.0 (0x00baf000) libdl.so.2 => /lib/libdl.so.2 (0x007fa000) libstdc++-libc6.2-2.so.3 => /usr/lib/libstdc++-libc6.2-2.so.3 (0x009ce000) libm.so.6 => /lib/libm.so.6 (0x00c96000) libc.so.6 => /lib/libc.so.6 (0x004a2000) /lib/ld-linux.so.2 (0x00742000) libgcc_s.so.1 => /lib/libgcc_s.so.1 (0x00a9f000)

    Read the article

  • Multiple Devices connecting to VPN on CentOS server

    - by jfreak53
    I am looking for a solution as to what would be the VPN software for multiple OSes and Devices. I currently have 15 systems to connect to a VPN. I was using Hamachi from LogMeIn but their lack of Android support really upsets me, and their limited support for Linux OSes is also a let down. 90% of my systems are Ubuntu 11+ systems, only 2 are Windows XP. But I also have a few people, maybe 3 that need to connect to it from Android devices. This is where Hamachi has let me down and I want to move to my own VPN solution. The server would be a simple VPS running CentOS. So I need some VPN software that allows connection of those to a Linux based server. I wanted to go with OpenVPN, but I am under the opinion that in any OS you have to have their software to connect to the VPN. Ubuntu supports VPN's out of the gate, but OpenVPN requires extra software to be installed, I don't want this if I can help it. Same with Windows and same with Android. Plus android mostly requires rooted devices for OpenVPN, at least from what I've read. I was looking at maybe L2TP, but I'm not sure how easy it is to get Ubu systems connected with it as I haven't found much on the subject, let alone Window's XP machines. I know Android connects out of the gate to it. I don't know much about L2TP but I know it's a pain to get running in CentOS from what I have read. Now the last option is some sort of software for PPTP but I've never read anything on it and don't know if all systems are compatible with it. What would be your solution to these devices and multiple OSes? OpenVPN seems to be my heading I just don't like it that it always requires software to run and rooted Android Devices. Any solutions for this and install solutions? Maybe a different OS for the server like Ubuntu would make another type of VPN easier?

    Read the article

  • Why my Ldirectord check multiple times on read server every interval?

    - by garconcn
    I have a Ldirectord server and two real servers. My ldirectord used to check the request page on real server once in every interval, but now I found that it check four times. I have monitored the log on both real servers, they have the same problem. Here is my ldirectord configuration: checktimeout=10 checkinterval=5 autoreload=yes logfile="/var/log/ldirectord.log" quiescent=no virtual=192.168.1.100:80 fallback=127.0.0.1:80 real=192.168.1.10:80 gate real=192.168.1.20:80 gate service=http request="lb.html" receive="still alive" scheduler=sh persistent=60 protocol=tcp checktype=negotiate Ldirectord will connect to each real server once every 5 seconds (checkinterval) and request 192.168.0.10:80/test.html (real/request). The access log in real server: 192.168.1.100 - - [13/Jun/2012:10:36:44 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:44 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:44 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:44 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:49 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:49 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:49 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:49 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:54 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:54 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:54 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805" 192.168.1.100 - - [13/Jun/2012:10:36:54 -0700] "GET /lb.html HTTP/1.1" 200 12 "-" "libwww-perl/5.805"

    Read the article

  • Windows Azure: Import/Export Hard Drives, VM ACLs, Web Sockets, Remote Debugging, Continuous Delivery, New Relic, Billing Alerts and More

    - by ScottGu
    Two weeks ago we released a giant set of improvements to Windows Azure, as well as a significant update of the Windows Azure SDK. This morning we released another massive set of enhancements to Windows Azure.  Today’s new capabilities include: Storage: Import/Export Hard Disk Drives to your Storage Accounts HDInsight: General Availability of our Hadoop Service in the cloud Virtual Machines: New VM Gallery, ACL support for VIPs Web Sites: WebSocket and Remote Debugging Support Notification Hubs: Segmented customer push notification support with tag expressions TFS & GIT: Continuous Delivery Support for Web Sites + Cloud Services Developer Analytics: New Relic support for Web Sites + Mobile Services Service Bus: Support for partitioned queues and topics Billing: New Billing Alert Service that sends emails notifications when your bill hits a threshold you define All of these improvements are now available to use immediately (note that some features are still in preview).  Below are more details about them. Storage: Import/Export Hard Disk Drives to Windows Azure I am excited to announce the preview of our new Windows Azure Import/Export Service! The Windows Azure Import/Export Service enables you to move large amounts of on-premises data into and out of your Windows Azure Storage accounts. It does this by enabling you to securely ship hard disk drives directly to our Windows Azure data centers. Once we receive the drives we’ll automatically transfer the data to or from your Windows Azure Storage account.  This enables you to import or export massive amounts of data more quickly and cost effectively (and not be constrained by available network bandwidth). Encrypted Transport Our Import/Export service provides built-in support for BitLocker disk encryption – which enables you to securely encrypt data on the hard drives before you send it, and not have to worry about it being compromised even if the disk is lost/stolen in transit (since the content on the transported hard drives is completely encrypted and you are the only one who has the key to it).  The drive preparation tool we are shipping today makes setting up bitlocker encryption on these hard drives easy. How to Import/Export your first Hard Drive of Data You can read our Getting Started Guide to learn more about how to begin using the import/export service.  You can create import and export jobs via the Windows Azure Management Portal as well as programmatically using our Server Management APIs. It is really easy to create a new import or export job using the Windows Azure Management Portal.  Simply navigate to a Windows Azure storage account, and then click the new Import/Export tab now available within it (note: if you don’t have this tab make sure to sign-up for the Import/Export preview): Then click the “Create Import Job” or “Create Export Job” commands at the bottom of it.  This will launch a wizard that easily walks you through the steps required: For more comprehensive information about Import/Export, refer to Windows Azure Storage team blog.  You can also send questions and comments to the [email protected] email address. We think you’ll find this new service makes it much easier to move data into and out of Windows Azure, and it will dramatically cut down the network bandwidth required when working on large data migration projects.  We hope you like it. HDInsight: 100% Compatible Hadoop Service in the Cloud Last week we announced the general availability release of Windows Azure HDInsight. HDInsight is a 100% compatible Hadoop service that allows you to easily provision and manage Hadoop clusters for big data processing in Windows Azure.  This release is now live in production, backed by an enterprise SLA, supported 24x7 by Microsoft Support, and is ready to use for production scenarios. HDInsight allows you to use Apache Hadoop tools, such as Pig and Hive, to process large amounts of data in Windows Azure Blob Storage. Because data is stored in Windows Azure Blob Storage, you can choose to dynamically create Hadoop clusters only when you need them, and then shut them down when they are no longer required (since you pay only for the time the Hadoop cluster instances are running this provides a super cost effective way to use them).  You can create Hadoop clusters using either the Windows Azure Management Portal (see below) or using our PowerShell and Cross Platform Command line tools: The import/export hard drive support that came out today is a perfect companion service to use with HDInsight – the combination allows you to easily ingest, process and optionally export a limitless amount of data.  We’ve also integrated HDInsight with our Business Intelligence tools, so users can leverage familiar tools like Excel in order to analyze the output of jobs.  You can find out more about how to get started with HDInsight here. Virtual Machines: VM Gallery Enhancements Today’s update of Windows Azure brings with it a new Virtual Machine gallery that you can use to create new VMs in the cloud.  You can launch the gallery by doing New->Compute->Virtual Machine->From Gallery within the Windows Azure Management Portal: The new Virtual Machine Gallery includes some nice enhancements that make it even easier to use: Search: You can now easily search and filter images using the search box in the top-right of the dialog.  For example, simply type “SQL” and we’ll filter to show those images in the gallery that contain that substring. Category Tree-view: Each month we add more built-in VM images to the gallery.  You can continue to browse these using the “All” view within the VM Gallery – or now quickly filter them using the category tree-view on the left-hand side of the dialog.  For example, by selecting “Oracle” in the tree-view you can now quickly filter to see the official Oracle supplied images. MSDN and Supported checkboxes: With today’s update we are also introducing filters that makes it easy to filter out types of images that you may not be interested in. The first checkbox is MSDN: using this filter you can exclude any image that is not part of the Windows Azure benefits for MSDN subscribers (which have highly discounted pricing - you can learn more about the MSDN pricing here). The second checkbox is Supported: this filter will exclude any image that contains prerelease software, so you can feel confident that the software you choose to deploy is fully supported by Windows Azure and our partners. Sort options: We sort gallery images by what we think customers are most interested in, but sometimes you might want to sort using different views. So we’re providing some additional sort options, like “Newest,” to customize the image list for what suits you best. Pricing information: We now provide additional pricing information about images and options on how to cost effectively run them directly within the VM Gallery. The above improvements make it even easier to use the VM Gallery and quickly create launch and run Virtual Machines in the cloud. Virtual Machines: ACL Support for VIPs A few months ago we exposed the ability to configure Access Control Lists (ACLs) for Virtual Machines using Windows PowerShell cmdlets and our Service Management API. With today’s release, you can now configure VM ACLs using the Windows Azure Management Portal as well. You can now do this by clicking the new Manage ACL command in the Endpoints tab of a virtual machine instance: This will enable you to configure an ordered list of permit and deny rules to scope the traffic that can access your VM’s network endpoints. For example, if you were on a virtual network, you could limit RDP access to a Windows Azure virtual machine to only a few computers attached to your enterprise. Or if you weren’t on a virtual network you could alternatively limit traffic from public IPs that can access your workloads: Here is the default behaviors for ACLs in Windows Azure: By default (i.e. no rules specified), all traffic is permitted. When using only Permit rules, all other traffic is denied. When using only Deny rules, all other traffic is permitted. When there is a combination of Permit and Deny rules, all other traffic is denied. Lastly, remember that configuring endpoints does not automatically configure them within the VM if it also has firewall rules enabled at the OS level.  So if you create an endpoint using the Windows Azure Management Portal, Windows PowerShell, or REST API, be sure to also configure your guest VM firewall appropriately as well. Web Sites: Web Sockets Support With today’s release you can now use Web Sockets with Windows Azure Web Sites.  This feature enables you to easily integrate real-time communication scenarios within your web based applications, and is available at no extra charge (it even works with the free tier).  Higher level programming libraries like SignalR and socket.io are also now supported with it. You can enable Web Sockets support on a web site by navigating to the Configure tab of a Web Site, and by toggling Web Sockets support to “on”: Once Web Sockets is enabled you can start to integrate some really cool scenarios into your web applications.  Check out the new SignalR documentation hub on www.asp.net to learn more about some of the awesome scenarios you can do with it. Web Sites: Remote Debugging Support The Windows Azure SDK 2.2 we released two weeks ago introduced remote debugging support for Windows Azure Cloud Services. With today’s Windows Azure release we are extending this remote debugging support to also work with Windows Azure Web Sites. With live, remote debugging support inside of Visual Studio, you are able to have more visibility than ever before into how your code is operating live in Windows Azure. It is now super easy to attach the debugger and quickly see what is going on with your application in the cloud. Remote Debugging of a Windows Azure Web Site using VS 2013 Enabling the remote debugging of a Windows Azure Web Site using VS 2013 is really easy.  Start by opening up your web application’s project within Visual Studio. Then navigate to the “Server Explorer” tab within Visual Studio, and click on the deployed web-site you want to debug that is running within Windows Azure using the Windows Azure->Web Sites node in the Server Explorer.  Then right-click and choose the “Attach Debugger” option on it: When you do this Visual Studio will remotely attach the debugger to the Web Site running within Windows Azure.  The debugger will then stop the web site’s execution when it hits any break points that you have set within your web application’s project inside Visual Studio.  For example, below I set a breakpoint on the “ViewBag.Message” assignment statement within the HomeController of the standard ASP.NET MVC project template.  When I hit refresh on the “About” page of the web site within the browser, the breakpoint was triggered and I am now able to debug the app remotely using Visual Studio: Note above how we can debug variables (including autos/watchlist/etc), as well as use the Immediate and Command Windows. In the debug session above I used the Immediate Window to explore some of the request object state, as well as to dynamically change the ViewBag.Message property.  When we click the the “Continue” button (or press F5) the app will continue execution and the Web Site will render the content back to the browser.  This makes it super easy to debug web apps remotely. Tips for Better Debugging To get the best experience while debugging, we recommend publishing your site using the Debug configuration within Visual Studio’s Web Publish dialog. This will ensure that debug symbol information is uploaded to the Web Site which will enable a richer debug experience within Visual Studio.  You can find this option on the Web Publish dialog on the Settings tab: When you ultimately deploy/run the application in production we recommend using the “Release” configuration setting – the release configuration is memory optimized and will provide the best production performance.  To learn more about diagnosing and debugging Windows Azure Web Sites read our new Troubleshooting Windows Azure Web Sites in Visual Studio guide. Notification Hubs: Segmented Push Notification support with tag expressions In August we announced the General Availability of Windows Azure Notification Hubs - a powerful Mobile Push Notifications service that makes it easy to send high volume push notifications with low latency from any mobile app back-end.  Notification hubs can be used with any mobile app back-end (including ones built using our Mobile Services capability) and can also be used with back-ends that run in the cloud as well as on-premises. Beginning with the initial release, Notification Hubs allowed developers to send personalized push notifications to both individual users as well as groups of users by interest, by associating their devices with tags representing the logical target of the notification. For example, by registering all devices of customers interested in a favorite MLB team with a corresponding tag, it is possible to broadcast one message to millions of Boston Red Sox fans and another message to millions of St. Louis Cardinals fans with a single API call respectively. New support for using tag expressions to enable advanced customer segmentation With today’s release we are adding support for even more advanced customer targeting.  You can now identify customers that you want to send push notifications to by defining rich tag expressions. With tag expressions, you can now not only broadcast notifications to Boston Red Sox fans, but take that segmenting a step farther and reach more granular segments. This opens up a variety of scenarios, for example: Offers based on multiple preferences—e.g. send a game day vegetarian special to users tagged as both a Boston Red Sox fan AND a vegetarian Push content to multiple segments in a single message—e.g. rain delay information only to users who are tagged as either a Boston Red Sox fan OR a St. Louis Cardinal fan Avoid presenting subsets of a segment with irrelevant content—e.g. season ticket availability reminder to users who are tagged as a Boston Red Sox fan but NOT also a season ticket holder To illustrate with code, consider a restaurant chain app that sends an offer related to a Red Sox vs Cardinals game for users in Boston. Devices can be tagged by your app with location tags (e.g. “Loc:Boston”) and interest tags (e.g. “Follows:RedSox”, “Follows:Cardinals”), and then a notification can be sent by your back-end to “(Follows:RedSox || Follows:Cardinals) && Loc:Boston” in order to deliver an offer to all devices in Boston that follow either the RedSox or the Cardinals. This can be done directly in your server backend send logic using the code below: var notification = new WindowsNotification(messagePayload); hub.SendNotificationAsync(notification, "(Follows:RedSox || Follows:Cardinals) && Loc:Boston"); In your expressions you can use all Boolean operators: AND (&&), OR (||), and NOT (!).  Some other cool use cases for tag expressions that are now supported include: Social: To “all my group except me” - group:id && !user:id Events: Touchdown event is sent to everybody following either team or any of the players involved in the action: Followteam:A || Followteam:B || followplayer:1 || followplayer:2 … Hours: Send notifications at specific times. E.g. Tag devices with time zone and when it is 12pm in Seattle send to: GMT8 && follows:thaifood Versions and platforms: Send a reminder to people still using your first version for Android - version:1.0 && platform:Android For help on getting started with Notification Hubs, visit the Notification Hub documentation center.  Then download the latest NuGet package (or use the Notification Hubs REST APIs directly) to start sending push notifications using tag expressions.  They are really powerful and enable a bunch of great new scenarios. TFS & GIT: Continuous Delivery Support for Web Sites + Cloud Services With today’s Windows Azure release we are making it really easy to enable continuous delivery support with Windows Azure and Team Foundation Services.  Team Foundation Services is a cloud based offering from Microsoft that provides integrated source control (with both TFS and Git support), build server, test execution, collaboration tools, and agile planning support.  It makes it really easy to setup a team project (complete with automated builds and test runners) in the cloud, and it has really rich integration with Visual Studio. With today’s Windows Azure release it is now really easy to enable continuous delivery support with both TFS and Git based repositories hosted using Team Foundation Services.  This enables a workflow where when code is checked in, built successfully on an automated build server, and all tests pass on it – I can automatically have the app deployed on Windows Azure with zero manual intervention or work required. The below screen-shots demonstrate how to quickly setup a continuous delivery workflow to Windows Azure with a Git-based ASP.NET MVC project hosted using Team Foundation Services. Enabling Continuous Delivery to Windows Azure with Team Foundation Services The project I’m going to enable continuous delivery with is a simple ASP.NET MVC project whose source code I’m hosting using Team Foundation Services.  I did this by creating a “SimpleContinuousDeploymentTest” repository there using Git – and then used the new built-in Git tooling support within Visual Studio 2013 to push the source code to it.  Below is a screen-shot of the Git repository hosted within Team Foundation Services: I can access the repository within Visual Studio 2013 and easily make commits with it (as well as branch, merge and do other tasks).  Using VS 2013 I can also setup automated builds to take place in the cloud using Team Foundation Services every time someone checks in code to the repository: The cool thing about this is that I don’t have to buy or rent my own build server – Team Foundation Services automatically maintains its own build server farm and can automatically queue up a build for me (for free) every time someone checks in code using the above settings.  This build server (and automated testing) support now works with both TFS and Git based source control repositories. Connecting a Team Foundation Services project to Windows Azure Once I have a source repository hosted in Team Foundation Services with Automated Builds and Testing set up, I can then go even further and set it up so that it will be automatically deployed to Windows Azure when a source code commit is made to the repository (assuming the Build + Tests pass).  Enabling this is now really easy.  To set this up with a Windows Azure Web Site simply use the New->Compute->Web Site->Custom Create command inside the Windows Azure Management Portal.  This will create a dialog like below.  I gave the web site a name and then made sure the “Publish from source control” checkbox was selected: When we click next we’ll be prompted for the location of the source repository.  We’ll select “Team Foundation Services”: Once we do this we’ll be prompted for our Team Foundation Services account that our source repository is hosted under (in this case my TFS account is “scottguthrie”): When we click the “Authorize Now” button we’ll be prompted to give Windows Azure permissions to connect to the Team Foundation Services account.  Once we do this we’ll be prompted to pick the source repository we want to connect to.  Starting with today’s Windows Azure release you can now connect to both TFS and Git based source repositories.  This new support allows me to connect to the “SimpleContinuousDeploymentTest” respository we created earlier: Clicking the finish button will then create the Web Site with the continuous delivery hooks setup with Team Foundation Services.  Now every time someone pushes source control to the repository in Team Foundation Services, it will kick off an automated build, run all of the unit tests in the solution , and if they pass the app will be automatically deployed to our Web Site in Windows Azure.  You can monitor the history and status of these automated deployments using the Deployments tab within the Web Site: This enables a really slick continuous delivery workflow, and enables you to build and deploy apps in a really nice way. Developer Analytics: New Relic support for Web Sites + Mobile Services With today’s Windows Azure release we are making it really easy to enable Developer Analytics and Monitoring support with both Windows Azure Web Site and Windows Azure Mobile Services.  We are partnering with New Relic, who provide a great dev analytics and app performance monitoring offering, to enable this - and we have updated the Windows Azure Management Portal to make it really easy to configure. Enabling New Relic with a Windows Azure Web Site Enabling New Relic support with a Windows Azure Web Site is now really easy.  Simply navigate to the Configure tab of a Web Site and scroll down to the “developer analytics” section that is now within it: Clicking the “add-on” button will display some additional UI.  If you don’t already have a New Relic subscription, you can click the “view windows azure store” button to obtain a subscription (note: New Relic has a perpetually free tier so you can enable it even without paying anything): Clicking the “view windows azure store” button will launch the integrated Windows Azure Store experience we have within the Windows Azure Management Portal.  You can use this to browse from a variety of great add-on services – including New Relic: Select “New Relic” within the dialog above, then click the next button, and you’ll be able to choose which type of New Relic subscription you wish to purchase.  For this demo we’ll simply select the “Free Standard Version” – which does not cost anything and can be used forever:  Once we’ve signed-up for our New Relic subscription and added it to our Windows Azure account, we can go back to the Web Site’s configuration tab and choose to use the New Relic add-on with our Windows Azure Web Site.  We can do this by simply selecting it from the “add-on” dropdown (it is automatically populated within it once we have a New Relic subscription in our account): Clicking the “Save” button will then cause the Windows Azure Management Portal to automatically populate all of the needed New Relic configuration settings to our Web Site: Deploying the New Relic Agent as part of a Web Site The final step to enable developer analytics using New Relic is to add the New Relic runtime agent to our web app.  We can do this within Visual Studio by right-clicking on our web project and selecting the “Manage NuGet Packages” context menu: This will bring up the NuGet package manager.  You can search for “New Relic” within it to find the New Relic agent.  Note that there is both a 32-bit and 64-bit edition of it – make sure to install the version that matches how your Web Site is running within Windows Azure (note: you can configure your Web Site to run in either 32-bit or 64-bit mode using the Web Site’s “Configuration” tab within the Windows Azure Management Portal): Once we install the NuGet package we are all set to go.  We’ll simply re-publish the web site again to Windows Azure and New Relic will now automatically start monitoring the application Monitoring a Web Site using New Relic Now that the application has developer analytics support with New Relic enabled, we can launch the New Relic monitoring portal to start monitoring the health of it.  We can do this by clicking on the “Add Ons” tab in the left-hand side of the Windows Azure Management Portal.  Then select the New Relic add-on we signed-up for within it.  The Windows Azure Management Portal will provide some default information about the add-on when we do this.  Clicking the “Manage” button in the tray at the bottom will launch a new browser tab and single-sign us into the New Relic monitoring portal associated with our account: When we do this a new browser tab will launch with the New Relic admin tool loaded within it: We can now see insights into how our app is performing – without having to have written a single line of monitoring code.  The New Relic service provides a ton of great built-in monitoring features allowing us to quickly see: Performance times (including browser rendering speed) for the overall site and individual pages.  You can optionally set alert thresholds to trigger if the speed does not meet a threshold you specify. Information about where in the world your customers are hitting the site from (and how performance varies by region) Details on the latency performance of external services your web apps are using (for example: SQL, Storage, Twitter, etc) Error information including call stack details for exceptions that have occurred at runtime SQL Server profiling information – including which queries executed against your database and what their performance was And a whole bunch more… The cool thing about New Relic is that you don’t need to write monitoring code within your application to get all of the above reports (plus a lot more).  The New Relic agent automatically enables the CLR profiler within applications and automatically captures the information necessary to identify these.  This makes it super easy to get started and immediately have a rich developer analytics view for your solutions with very little effort. If you haven’t tried New Relic out yet with Windows Azure I recommend you do so – I think you’ll find it helps you build even better cloud applications.  Following the above steps will help you get started and deliver you a really good application monitoring solution in only minutes. Service Bus: Support for partitioned queues and topics With today’s release, we are enabling support within Service Bus for partitioned queues and topics. Enabling partitioning enables you to achieve a higher message throughput and better availability from your queues and topics. Higher message throughput is achieved by implementing multiple message brokers for each partitioned queue and topic.  The  multiple messaging stores will also provide higher availability. You can create a partitioned queue or topic by simply checking the Enable Partitioning option in the custom create wizard for a Queue or Topic: Read this article to learn more about partitioned queues and topics and how to take advantage of them today. Billing: New Billing Alert Service Today’s Windows Azure update enables a new Billing Alert Service Preview that enables you to get proactive email notifications when your Windows Azure bill goes above a certain monetary threshold that you configure.  This makes it easier to manage your bill and avoid potential surprises at the end of the month. With the Billing Alert Service Preview, you can now create email alerts to monitor and manage your monetary credits or your current bill total.  To set up an alert first sign-up for the free Billing Alert Service Preview.  Then visit the account management page, click on a subscription you have setup, and then navigate to the new Alerts tab that is available: The alerts tab allows you to setup email alerts that will be sent automatically once a certain threshold is hit.  For example, by clicking the “add alert” button above I can setup a rule to send myself email anytime my Windows Azure bill goes above $100 for the month: The Billing Alert Service will evolve to support additional aspects of your bill as well as support multiple forms of alerts such as SMS.  Try out the new Billing Alert Service Preview today and give us feedback. Summary Today’s Windows Azure release enables a ton of great new scenarios, and makes building applications hosted in the cloud even easier. If you don’t already have a Windows Azure account, you can sign-up for a free trial and start using all of the above features today.  Then visit the Windows Azure Developer Center to learn more about how to build apps with it. Hope this helps, Scott P.S. In addition to blogging, I am also now using Twitter for quick updates and to share links. Follow me at: twitter.com/scottgu

    Read the article

  • Using R to Analyze G1GC Log Files

    - by user12620111
    Using R to Analyze G1GC Log Files body, td { font-family: sans-serif; background-color: white; font-size: 12px; margin: 8px; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1 { font-size:2.2em; } h2 { font-size:1.8em; } h3 { font-size:1.4em; } h4 { font-size:1.0em; } h5 { font-size:0.9em; } h6 { font-size:0.8em; } a:visited { color: rgb(50%, 0%, 50%); } pre { margin-top: 0; max-width: 95%; border: 1px solid #ccc; white-space: pre-wrap; } pre code { display: block; padding: 0.5em; } code.r, code.cpp { background-color: #F8F8F8; } table, td, th { border: none; } blockquote { color:#666666; margin:0; padding-left: 1em; border-left: 0.5em #EEE solid; } hr { height: 0px; border-bottom: none; border-top-width: thin; border-top-style: dotted; border-top-color: #999999; } @media print { * { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; } body { font-size:12pt; max-width:100%; } a, a:visited { text-decoration: underline; } hr { visibility: hidden; page-break-before: always; } pre, blockquote { padding-right: 1em; page-break-inside: avoid; } tr, img { page-break-inside: avoid; } img { max-width: 100% !important; } @page :left { margin: 15mm 20mm 15mm 10mm; } @page :right { margin: 15mm 10mm 15mm 20mm; } p, h2, h3 { orphans: 3; widows: 3; } h2, h3 { page-break-after: avoid; } } pre .operator, pre .paren { color: rgb(104, 118, 135) } pre .literal { color: rgb(88, 72, 246) } pre .number { color: rgb(0, 0, 205); } pre .comment { color: rgb(76, 136, 107); } pre .keyword { color: rgb(0, 0, 255); } pre .identifier { color: rgb(0, 0, 0); } pre .string { color: rgb(3, 106, 7); } var hljs=new function(){function m(p){return p.replace(/&/gm,"&").replace(/"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("")}while(p!=v.node);s.splice(r,1);while(r'+M[0]+""}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L1){O=D[D.length-2].cN?"":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.rr.keyword_count+r.r){r=s}if(s.keyword_count+s.rp.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((]+|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML=""+y.value+"";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p|=||=||=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"|=||   Using R to Analyze G1GC Log Files   Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z\(\),]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\\( " , " " ); gsub ( "\ \)" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period:     par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight.   plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

    Read the article

  • jQuery Templates - {Supported Tags}

    - by hajan
    I have started with Introduction to jQuery Templates, then jQuery Templates - tmpl(), template() and tmplItem() functions. In this blog we will see what supported tags are available in the jQuery Templates plugin.Template tags can be used inside template together in combination with HTML tags and plain text, which helps to iterate over JSON data. Up to now, there are several supported tags in jQuery Templates plugin: ${expr} or {{= expr}} {{each itemArray}} … {{/each}} {{if condition}} … {{else}} … {{/if}} {{html …}} {{tmpl …}} {{wrap …}} … {{/wrap}}   - ${expr} or {{= expr}} Is used for insertion of data values in the rendered template. It can evaluate fields, functions or expression. Example: <script id="attendeesTemplate" type="text/html">     <li> ${Name} {{= Surname}} </li>         </script> Either ${Name} or {{= Surname}} (with blank space between =<blankspace>Field) will work.   - {{each itemArray}} … {{/each}} each is everywhere the same "(for)each", used to loop over array or collection Example: <script id="attendeesTemplate" type="text/html">     <li>         ${Name} ${Surname}         {{if speaker}}             (<font color="red">speaks</font>)         {{else}}             (attendee)         {{/if}}                 {{each phones}}                             <br />             ${$index}: <em>${$value}</em>         {{/each}}             </li> </script> So, you see we can use ${$index} and ${$value} to get the current index and value while iterating over the item collection. Alternatively, you can add index,value on the following way: {{each(i,v) phones}}     <br />     ${i}: <em>${v}</em> {{/each}} Result would be: Here is complete working example that you can run and see the result: <html xmlns="http://www.w3.org/1999/xhtml" > <head id="Head1" runat="server">     <title>Nesting and Looping Example :: jQuery Templates</title>     <script src="http://ajax.aspnetcdn.com/ajax/jQuery/jquery-1.4.4.min.js" type="text/javascript"></script>     <script src="http://ajax.aspnetcdn.com/ajax/jquery.templates/beta1/jquery.tmpl.js" type="text/javascript"></script>     <script language="javascript" type="text/javascript">         $(function () {             var attendees = [                 { Name: "Hajan", Surname: "Selmani", speaker: true, phones:[070555555, 071888999, 071222333] },                 { Name: "Someone", Surname: "Surname", phones: [070555555, 071222333] },                 { Name: "Third", Surname: "Thirdsurname", phones: [070555555, 071888999, 071222333] },             ];             $("#attendeesTemplate").tmpl(attendees).appendTo("#attendeesList");         });     </script>     <script id="attendeesTemplate" type="text/html">         <li>             ${Name} ${Surname}             {{if speaker}}                 (<font color="red">speaks</font>)             {{else}}                 (attendee)             {{/if}}                     {{each(i,v) phones}}                 <br />                 ${i}: <em>${v}</em>             {{/each}}                 </li>     </script> </head> <body>     <ol id="attendeesList"></ol>     </body> </html>   - {{if condition}} … {{else}} … {{/if}} Standard if/else statement. Of course, you can use it without the {{else}} if you have such condition to check, however closing the {{/if}} tag is required. Example: {{if speaker}}     (<font color="red">speaks</font>) {{else}}     (attendee) {{/if}} You have this same code block in the above complete example showing the 'each' cycle ;).   - {{html …}} Is used for insertion of HTML markup strings in the rendered template. Evaluates the specified field on the current data item, or the specified JavaScript function or expression. Example: - without {{html …}} <script language="javascript" type="text/javascript">   $(function () {   var attendees = [             { Name: "Hajan", Surname: "Selmani", Info: "He <font color='red'>is the speaker of today's</font> session", speaker: true },         ];   $("#myTemplate").tmpl(attendees).appendTo("#speakers"); }); </script> <script id="myTemplate" type="text/html">     ${Name} ${Surname} <br />     ${Info} </script> Result: - with {{html …}} <script language="javascript" type="text/javascript">   $(function () {   var attendees = [             { Name: "Hajan", Surname: "Selmani", Info: "He <font color='red'>is the speaker of today's</font> session", speaker: true },         ];   $("#myTemplate").tmpl(attendees).appendTo("#speakers"); }); </script> <script id="myTemplate" type="text/html">     ${Name} ${Surname} <br />     {{html Info}} </script> Result:   - {{wrap …}} It’s used for composition and incorporation of wrapped HTML. It’s similar to {{tmpl}} Example: <script id="myTmpl" type="text/html">     <div id="personInfo">     <br />     ${Name} ${Surname}     {{wrap "#myWrapper"}}         <h2>${Info}</h2>         <div>             {{if speaker}}                 (speaker)             {{else}}                 (attendee)             {{/if}}         </div>     {{/wrap}}     </div> </script> <script id="myWrapper" type="text/html">     <table><tbody>         <tr>             {{each $item.html("div")}}                 <td>                     {{html $value}}                 </td>             {{/each}}         </tr>     </tbody></table> </script> All the HTMl content inside the {{wrap}} … {{/wrap}} is available to the $item.html(filter, textOnly) method. In our example, we have defined some standard template and created wrapper which calls the other template with id myWrapper. Then using $item.html(“div”) we find the div tag and render the html value (together with the div tag) inside the <td> … </td>. So, here inside td the <div> <speaker or attendee depending of the condition> </div>  will be rendered. The HTML output from this is:   - {{tmpl …}} Used for composition as template items Example: <script id="myTemplate" type="text/html">     <div id="bookItem">         <div id="bookCover">             {{tmpl "#bookCoverTemplate"}}         </div>         <div id="bookDetails">             <div id="book">                             ${title} - ${author}             </div>             <div id="price">$${price}</div>             <div id="Details">${pages} pgs. - ${year} year</div>         </div>     </div> </script> <script id="bookCoverTemplate" type="text/html">     <img src="${image}" alt="${title} Image" /> </script> In this example, using {{tmpl “#bookCoverTemplate”}} I’m calling another template inside the first template. In the other template I’ve created template for a book cover. The rendered HTML of this is: and   So we have seen example for each of the tags that are right now available in the jQuery Templates (beta) plugin which is created by Microsoft as a contribution to the open source jQuery Project. I hope this was useful blog post for you. Regards, HajanNEXT - jQuery Templates with ASP.NET MVC

    Read the article

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

< Previous Page | 32 33 34 35 36 37 38 39 40 41 42 43  | Next Page >