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Sublime: Configure to Open HTML Page in a Web Browser
This article presents steps that is needed to configure Sublime to open the HTML pages you are working, in your preferred web browser. As I started developing AngularJS apps with Sublime, I got stuck at the point where I have to manually go to appropriate folder consisting of HTML file and double-click to open it in browser or, go to existing browser having that page and refresh it. In both the case, it was quite a bit cumbersome. Ideally, I wanted some shortcut keys right from within Sublime which would have helped me open the file in browser. This is where I did some research and found the way out. Following are the steps (for Win platform) to configure your Sublime to open the HTML page in the web browser: Goto Tools > Build System and click on “New Build System”. It opens up a file with default command text such as { “cmd”:["make"] } Copy and paste follow command and save the file as “Chrome.sublime-build { "cmd":["PATH_TO_CHROME_OR_FIREFOX","$file"] } Close Sublime and start again. Goto Tools > Build System and select “Chrome” Write an HTML file and use following shortcut: CTRL + B . The command would open the HTML page that you are working, in a web browser. Happy coding with Sublime.
September 19, 2014
by Ajitesh Kumar
· 100,934 Views · 3 Likes
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MySQL 101: Monitor Disk I/O with pt-diskstats
Originally Written by Muhammad Irfan Here on the Percona Support team we often ask customers to retrieve disk stats to monitor disk IO and to measure block devices iops and latency. There are a number of tools available to monitor IO on Linux. iostat is one of the popular tools and Percona Toolkit, which is free, contains the pt-diskstats tool for this purpose. The pt-diskstats tool is similar to iostat but it’s more interactive and contains extended information. pt-diskstats reports current disk activity and shows the statistics for the last second (which by default is 1 second) and will continue until interrupted. The pt-diskstats tool collects samples of /proc/diskstats. In this post, I will share some examples about how to monitor and check to see if the IO subsystem is performing properly or if any disks are a limiting factor – all this by using the pt-diskstats tool. pt-diskstats output consists on number of columns and in order to interpret pt-diskstats output we need to know what each column represents. rd_s tells about number of reads per second while wr_s represents number of writes per second. rd_rt and wr_rt shows average response time in milliseconds for reads & writes respectively, which is similar to iostat tool output await column but pt-diskstats shows individual response time for reads and writes at disk level. Just a note, modern iostat splits read and write latency out, but most distros don’t have the latest iostat in their systat (or equivalent) package. rd_mrg and wr_mrg are other two important columns in pt-diskstats output. *_mrg is telling us how many of the original operations the IO elevator (disk scheduler) was able to merge to reduce IOPS, so *_mrg is telling us a quite important thing by letting us know that the IO scheduler was able to consolidate many or few operations. If rd_mrg/wr_mrg is high% then the IO workload is sequential on the other hand, If rd_mrg/wr_mrg is a low% then IO workload is all random. Binary logs, redo logs (aka ib_logfile*), undo log and doublewrite buffer all need sequential writes. qtime and stime are last two columns in pt-diskstats output where qtime reflects to time spent in disk scheduler queue i.e. average queue time before sending it to physical device and on the other hand stime is average service time which is time accumulated to process the physical device request. Note, that qtime is not discriminated between reads and writes and you can check if response time is higher for qtime than it signal towards disk scheduler. Also note that service time (stime field and svctm field in in pt-diskstats & iostat output respectively) is not reliable on Linux. If you read the iostat manual you will see it is deprecated. Along with that, there are many other parameters for pt-diskstats – you can found full documentation here. Below is an example of pt-disktats in action. I used the –devices-regex option which prints only device information that matches this Perl regex. $ pt-diskstats --devices-regex=sd --interval 5 #ts device rd_s rd_avkb rd_mb_s rd_mrg rd_cnc rd_rt wr_s wr_avkb wr_mb_s wr_mrg wr_cnc wr_rt busy in_prg io_s qtime stime 1.1 sda 21.6 22.8 0.5 45% 1.2 29.4 275.5 4.0 1.1 0% 40.0 145.1 65% 158 297.1 155.0 2.1 1.1 sdb 15.0 21.0 0.3 33% 0.1 5.2 0.0 0.0 0.0 0% 0.0 0.0 11% 1 15.0 0.5 4.7 1.1 sdc 5.6 10.0 0.1 0% 0.0 5.2 1.9 6.0 0.0 33% 0.0 2.0 3% 0 7.5 0.4 3.6 1.1 sdd 0.0 0.0 0.0 0% 0.0 0.0 0.0 0.0 0.0 0% 0.0 0.0 0% 0 0.0 0.0 0.0 5.0 sda 17.0 14.8 0.2 64% 3.1 66.7 404.9 4.6 1.8 14% 140.9 298.5 100% 111 421.9 277.6 1.9 5.0 sdb 14.0 19.9 0.3 48% 0.1 5.5 0.4 174.0 0.1 98% 0.0 0.0 11% 0 14.4 0.9 2.4 5.0 sdc 3.6 27.1 0.1 61% 0.0 3.5 2.8 5.7 0.0 30% 0.0 2.0 3% 0 6.4 0.7 2.4 5.0 sdd 0.0 0.0 0.0 0% 0.0 0.0 0.0 0.0 0.0 0% 0.0 0.0 0% 0 0.0 0.0 0.0 These are the stats from 7200 RPM SATA disks. As you can see, the write-response time is very high and most of that is made up of IO queue time. This shows the problem exactly. The problem is that the IO subsystem is not able to handle the write workload because the amount of writes that are being performed are way beyond what it can handle. It means the disks cannot service every request concurrently. The workload would actually depend a lot on where the hot data is stored and as we can see in this particular case the workload only hits a single disk out of the 4 disks. A single 7.2K RPM disk can only do about 100 random writes per second which is not a lot considering heavy workload. It’s not particularly a hardware issue but a hardware capacity issue. The kind of workload that is present and the amount of writes that are performed per second are not something that the IO subsystem is able to handle in an efficient manner. Mostly writes are generated on this server as can be seen by the disk stats. Let me show you a second example. Here you can see read latency. rd_rt is consistently between 10ms-30ms. It depends on how fast the disks are spinning and the number of disks. To deal with it possible solutions would be to optimize queries to avoid table scans, use memcached where possible, use SSD’s as it can provide good I/O performance with high concurrency. You will find this post useful on SSD’s from our CEO, Peter Zaitsev. #ts device rd_s rd_avkb rd_mb_s rd_mrg rd_cnc rd_rt wr_s wr_avkb wr_mb_s wr_mrg wr_cnc wr_rt busy in_prg io_s qtime stime 1.0 sdb 33.0 29.1 0.9 0% 1.1 34.7 7.0 10.3 0.1 61% 0.0 0.4 99% 1 40.0 2.2 19.5 1.0 sdb1 0.0 0.0 0.0 0% 0.0 0.0 7.0 10.3 0.1 61% 0.0 0.4 1% 0 7.0 0.0 0.4 1.0 sdb2 33.0 29.1 0.9 0% 1.1 34.7 0.0 0.0 0.0 0% 0.0 0.0 99% 1 33.0 3.5 30.2 1.0 sdb 81.9 28.5 2.3 0% 1.1 14.0 0.0 0.0 0.0 0% 0.0 0.0 99% 1 81.9 2.0 12.0 1.0 sdb1 0.0 0.0 0.0 0% 0.0 0.0 0.0 0.0 0.0 0% 0.0 0.0 0% 0 0.0 0.0 0.0 1.0 sdb2 81.9 28.5 2.3 0% 1.1 14.0 0.0 0.0 0.0 0% 0.0 0.0 99% 1 81.9 2.0 12.0 1.0 sdb 50.0 25.7 1.3 0% 1.3 25.1 13.0 11.7 0.1 66% 0.0 0.7 99% 1 63.0 3.4 11.3 1.0 sdb1 25.0 21.3 0.5 0% 0.6 25.2 13.0 11.7 0.1 66% 0.0 0.7 46% 1 38.0 3.2 7.3 1.0 sdb2 25.0 30.1 0.7 0% 0.6 25.0 0.0 0.0 0.0 0% 0.0 0.0 56% 0 25.0 3.6 22.2 From the below diskstats output it seems that IO is saturated between both reads and writes. This can be noticed with high value for columns rd_s and wr_s. In this particular case, consider having disks in either RAID 5 (better for read only workload) or RAID 10 array is good option along with battery-backed write cache (BBWC) as single disk can really be bad for performance when you are IO bound. device rd_s rd_avkb rd_mb_s rd_mrg rd_cnc rd_rt wr_s wr_avkb wr_mb_s wr_mrg wr_cnc wr_rt busy in_prg io_s qtime stime sdb1 362.0 27.4 9.7 0% 2.7 7.5 525.2 20.2 10.3 35% 6.4 8.0 100% 0 887.2 7.0 0.9 sdb1 439.9 26.5 11.4 0% 3.4 7.7 545.7 20.8 11.1 34% 9.8 11.9 100% 0 985.6 9.6 0.8 sdb1 576.6 26.5 14.9 0% 4.5 7.8 400.2 19.9 7.8 34% 6.7 10.9 100% 0 976.8 8.6 0.8 sdb1 410.8 24.2 9.7 0% 2.9 7.1 403.1 18.3 7.2 34% 10.8 17.7 100% 0 813.9 12.5 1.0 sdb1 378.4 24.6 9.1 0% 2.7 7.3 506.1 16.5 8.2 33% 5.7 7.6 100% 0 884.4 6.6 0.9 sdb1 572.8 26.1 14.6 0% 4.8 8.4 422.6 17.2 7.1 30% 1.7 2.8 100% 0 995.4 4.7 0.8 sdb1 429.2 23.0 9.6 0% 3.2 7.4 511.9 14.5 7.2 31% 1.2 1.7 100% 0 941.2 3.6 0.9 The following example reflects write heavy activity but write-response time is very good, under 1ms, which shows disks are healthy and capable of handling high number of IOPS. #ts device rd_s rd_avkb rd_mb_s rd_mrg rd_cnc rd_rt wr_s wr_avkb wr_mb_s wr_mrg wr_cnc wr_rt busy in_prg io_s qtime stime 1.0 dm-0 530.8 16.0 8.3 0% 0.3 0.5 6124.0 5.1 30.7 0% 1.7 0.3 86% 2 6654.8 0.2 0.1 2.0 dm-0 633.1 16.1 10.0 0% 0.3 0.5 6173.0 6.1 36.6 0% 1.7 0.3 88% 1 6806.1 0.2 0.1 3.0 dm-0 731.8 16.0 11.5 0% 0.4 0.5 6064.2 5.8 34.1 0% 1.9 0.3 90% 2 6795.9 0.2 0.1 4.0 dm-0 711.1 16.0 11.1 0% 0.3 0.5 6448.5 5.4 34.3 0% 1.8 0.3 92% 2 7159.6 0.2 0.1 5.0 dm-0 700.1 16.0 10.9 0% 0.4 0.5 5689.4 5.8 32.2 0% 1.9 0.3 88% 0 6389.5 0.2 0.1 6.0 dm-0 774.1 16.0 12.1 0% 0.3 0.4 6409.5 5.5 34.2 0% 1.7 0.3 86% 0 7183.5 0.2 0.1 7.0 dm-0 849.6 16.0 13.3 0% 0.4 0.5 6151.2 5.4 32.3 0% 1.9 0.3 88% 3 7000.8 0.2 0.1 8.0 dm-0 664.2 16.0 10.4 0% 0.3 0.5 6349.2 5.7 35.1 0% 2.0 0.3 90% 2 7013.4 0.2 0.1 9.0 dm-0 951.0 16.0 14.9 0% 0.4 0.4 5807.0 5.3 29.9 0% 1.8 0.3 90% 3 6758.0 0.2 0.1 10.0 dm-0 742.0 16.0 11.6 0% 0.3 0.5 6461.1 5.1 32.2 0% 1.7 0.3 87% 1 7203.2 0.2 0.1 Let me show you a final example. I used –interval and –iterations parameters for pt-diskstats which tells us to wait for a number of seconds before printing the next disk stats and to limit the number of samples respectively. If you notice, you will see in 3rd iteration high latency (rd_rt, wr_rt) mostly for reads. Also, you can notice a high value for queue time (qtime) and service time (stime) where qtime is related to disk IO scheduler settings. For MySQL database servers we usually recommends noop/deadline instead of default cfq. $ pt-diskstats --interval=20 --iterations=3 #ts device rd_s rd_avkb rd_mb_s rd_mrg rd_cnc rd_rt wr_s wr_avkb wr_mb_s wr_mrg wr_cnc wr_rt busy in_prg io_s qtime stime 10.4 hda 11.7 4.0 0.0 0% 0.0 1.1 40.7 11.7 0.5 26% 0.1 2.1 10% 0 52.5 0.4 1.5 10.4 hda2 0.0 0.0 0.0 0% 0.0 0.0 0.4 7.0 0.0 43% 0.0 0.1 0% 0 0.4 0.0 0.1 10.4 hda3 0.0 0.0 0.0 0% 0.0 0.0 0.4 107.0 0.0 96% 0.0 0.2 0% 0 0.4 0.0 0.2 10.4 hda5 0.0 0.0 0.0 0% 0.0 0.0 0.7 20.0 0.0 80% 0.0 0.3 0% 0 0.7 0.1 0.2 10.4 hda6 0.0 0.0 0.0 0% 0.0 0.0 0.1 4.0 0.0 0% 0.0 4.0 0% 0 0.1 0.0 4.0 10.4 hda9 11.7 4.0 0.0 0% 0.0 1.1 39.2 10.7 0.4 3% 0.1 2.7 9% 0 50.9 0.5 1.8 10.4 drbd1 11.7 4.0 0.0 0% 0.0 1.1 39.1 10.7 0.4 0% 0.1 2.8 9% 0 50.8 0.5 1.7 20.0 hda 14.6 4.0 0.1 0% 0.0 1.4 39.5 12.3 0.5 26% 0.3 6.4 18% 0 54.1 2.6 2.7 20.0 hda2 0.0 0.0 0.0 0% 0.0 0.0 0.4 9.1 0.0 56% 0.0 42.0 3% 0 0.4 0.0 42.0 20.0 hda3 0.0 0.0 0.0 0% 0.0 0.0 1.5 22.3 0.0 82% 0.0 1.5 0% 0 1.5 1.2 0.3 20.0 hda5 0.0 0.0 0.0 0% 0.0 0.0 1.1 18.9 0.0 79% 0.1 21.4 11% 0 1.1 0.1 21.3 20.0 hda6 0.0 0.0 0.0 0% 0.0 0.0 0.8 10.4 0.0 62% 0.0 1.5 0% 0 0.8 1.3 0.2 20.0 hda9 14.6 4.0 0.1 0% 0.0 1.4 35.8 11.7 0.4 3% 0.2 4.9 18% 0 50.4 0.5 3.5 20.0 drbd1 14.6 4.0 0.1 0% 0.0 1.4 36.4 11.6 0.4 0% 0.2 5.1 17% 0 51.0 0.5 3.4 20.0 hda 0.9 4.0 0.0 0% 0.2 251.9 28.8 61.8 1.7 92% 4.5 13.1 31% 2 29.6 12.8 0.9 20.0 hda2 0.0 0.0 0.0 0% 0.0 0.0 0.6 8.3 0.0 52% 0.1 98.2 6% 0 0.6 48.9 49.3 20.0 hda3 0.0 0.0 0.0 0% 0.0 0.0 2.0 23.2 0.0 83% 0.0 1.4 0% 0 2.0 1.2 0.3 20.0 hda5 0.0 0.0 0.0 0% 0.0 0.0 4.9 249.4 1.2 98% 4.0 13.2 9% 0 4.9 12.9 0.3 20.0 hda6 0.0 0.0 0.0 0% 0.0 0.0 0.0 0.0 0.0 0% 0.0 0.0 0% 0 0.0 0.0 0.0 20.0 hda9 0.9 4.0 0.0 0% 0.2 251.9 21.3 24.2 0.5 32% 0.4 12.9 31% 2 22.2 10.2 9.7 20.0 drbd1 0.9 4.0 0.0 0% 0.2 251.9 30.6 17.0 0.5 0% 0.7 24.1 30% 5 31.4 21.0 9.5 You can see the busy column in pt-diskstats output which is the same as the util column in iostat – which points to utilization. Actually, pt-diskstats is quite similar to the iostat tool but pt-diskstats is more interactive and has more information. The busy percentage is only telling us for how long the IO subsystem was busy, but is not indicating capacity. So the only time you care about %busy is when it’s 100% and at the same time latency (await in iostat and rd_rt/wr_rt in diskstats output) increases over -say- 5ms. You can estimate capacity of your IO subsystem and then look at the IOPS being consumed (r/s + w/s columns). Also, the system can process more than one request in parallel (in case of RAID) so %busy can go beyond 100% in pt-diskstats output. If you need to check disk throughput, block device IOPS run the following to capture metrics from your IO subsystem and see if utilization matches other worrisome symptoms. I would suggest capturing disk stats during peak load. Output can be grouped by sample or by disk using the –group-by option. You can use the sysbench benchmark tool for this purpose to measure database server performance. You will find this link useful for sysbench tool details. $ pt-diskstats --group-by=all --iterations=7200 > /tmp/pt-diskstats.out; Conclusion: pt-diskstats is one of the finest tools from Percona Toolkit. By using this tool you can easily spot disk bottlenecks, measure the IO subsystem and identify how much IOPS your drive can handle (i.e. disk capacity).
September 19, 2014
by Peter Zaitsev
· 5,372 Views
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15 Tools That Make Life Easy for Java Developers
If you use Java for programming, read on to learn about tools like Eclipse IDE, the Java Development Kit, and other must-know tools.
September 19, 2014
by Michael Georgiou
· 132,551 Views · 3 Likes
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BackBone Tutorial - Part 7: Understanding Backbone.js Routes and History
in this article, we will try to look at routes in backbone.js. we will try to understand how routes can be useful in a large scale single page applications and how we can use routes to perform action based on requested url. background we have been using web application for more than 2 decades now. this has made us tuned to some of the functionalities that the websites provide. one such functionality is to be able to copy the url and use it for the viewing the exact application area that we were viewing before. another example is the use of browser navigation buttons to navigate back and forth the pages. when we create single page applications, there is only one page being rendered on the screen. there is no separate url for each page. the browser is not loading the separate pages for separate screens. so how can we still perform the above mentioned operations even with a single page application. the answer is backbone routes. link to complete series: backbone tutorial – part 1: introduction to backbone.js [ ^ ] backbone tutorial – part 2: understanding the basics of backbone models [ ^ ] backbone tutorial – part 3: more about backbone models [ ^ ] backbone tutorial – part 4: crud operations on backbonejs models using http rest service [ ^ ] backbone tutorial – part 5: understanding backbone.js collections [ ^ ] backbone tutorial – part 6: understanding backbone.js views [ ^ ] backbone tutorial – part 7: understanding backbone.js routes and history [ ^ ] using the code backbone routes and history provides us the mechanism by which we can copy the urls and use them to reach the exact view. it also enables us to use browser navigation with single page applications. actually routes facilitate the possibility of having deep copied urls and history provides the possibility of using the browser navigation. life without router let us try to create a simple application that is not using the router. lets create three simple views and these views will be rendered in the same area on our application based on user selection. let create 3 very simple views. var view1 = backbone.view.extend({ initialize: function() { this.render(); }, render: function() { this.$el.html(this.model.get('message') + " from the view 1"); return this; } }); var view2 = backbone.view.extend({ initialize: function() { this.render(); }, render: function() { this.$el.html(this.model.get('message') + " from the view 2"); return this; } }); var view3 = backbone.view.extend({ initialize: function() { this.render(); }, render: function() { this.$el.html(this.model.get('message') + " from the view 3"); return this; } }); now we need a view that will contain the view and render it whenever the user makes a choice on the screen. var containerview = backbone.view.extend({ mychildview: null, render: function() { this.$el.html("greeting area"); this.$el.append(this.mychildview.$el); return this; } }); var containerview = backbone.view.extend({ mychildview: null, render: function() { this.$el.html("greeting area"); this.$el.append(this.mychildview.$el); return this; } }); now we need a view that will contain the view and render it whenever the user makes a choice on the screen. now lets create a simple div on the ui which will be used as elto this containerview. we will then position three buttons on the ui which will let the user to change the view. below code shows the application setup that is creating the container view and the functions that will get invoked when the user selects the view from screen. var greeting = new greetmodel({ message: "hello world" }); var container = new containerview({ el: $("#appcontainer"), model: greeting }); var view1 = null; var view2 = null; var view3 = null; function showview1() { if (view1 == null) { view1 = new view1({ model: greeting }); } container.mychildview = view1; container.render(); } function showview2() { if (view2 == null) { view2 = new view2({ model: greeting }); } container.mychildview = view2; container.render(); } function showview3() { if (view3 == null) { view3 = new view3({ model: greeting }); } container.mychildview = view3; container.render(); } now lets run the application and see the results. when we click on the buttons we can see that the actual view is getting changes but the url is not getting changes. that would mean that there is no way, i can copy a url and directly go to any view. also, the second thing to note here is that if we press the browser back button, the application will go away(since its still on the same single page from the browser’s perspective). note: please download and run the sample code to see this in action. hello backbone routes now the above problem can very easily be solved using backbone routesand history. so lets try to first look at what are backbone routes. backbone routes are simple objects that are handle the incoming route value from the url and the invoke any function. lets create a very simple route class for our application. var myrouter = backbone.router.extend({ }); in our route class we will have to define the routes that our application will support and how we want to handle them. so first lets create a simple route where only the url is present. this usually is the starting page of our application. for our application lets just open view1 whenever nothing is present in the route. then if the request is for any specific view we will simply invoke the function which will take care of rendering the appropriate view. var myrouter = backbone.router.extend({ greeting: null, container: null, view1: null, view2: null, view3: null, initialize: function() { this.greeting = new greetmodel({ message: "hello world" }); this.container = new containerview({ el: $("#rappcontainer"), model: this.greeting }); }, routes: { "": "handleroute1", "view1": "handleroute1", "view2": "handleroute2", "view3": "handleroute3" }, handleroute1: function () { if (this.view1 == null) { this.view1 = new view1({ model: this.greeting }); } this.container.mychildview = this.view1; this.container.render(); }, handleroute2: function () { if (this.view2 == null) { this.view2 = new view2({ model: this.greeting }); } this.container.mychildview = this.view2; this.container.render(); }, handleroute3: function () { if (this.view3 == null) { this.view3 = new view3({ model: this.greeting }); } this.container.mychildview = this.view3; this.container.render(); } }); now this route class contains the complete logic of handling the url requests and rendering the view accordingly. not only this, we can see that the code which was written in a global scope earlier i.e. the controller and view creation all that is put inside the route now. this would also mean that routes not only provide us deep copyable urls but also could provide more options to have better structured code(since we can have multiple route classes and each route class can handle all the respective views for the defined routes). backbone history and instantiating routes backbone history is a global router that will keep track of the history and let us enable the routing in the application. to instantiate a route and start tracking the navigation history, we need to simply create the router class and call backbone.history.start for let the backbone start listening to routes and manage history. $(document).ready(function () { router = new myrouter(); backbone.history.start(); }) invoking and requesting routes a route can either be invoked from the other parts of the application or it can simply be requested by the user. invoking route: application wants to navigate to a specific route (this can be done by navigating to a route by calling the navigate function: router.navigate('view1'); route request: user enters the fully qualified url (this will work seamlessly) let us run the application and see the result. passing parameters in the routes we can also pass parameters in the route. lets us try to create a new route where the user will request for a view in a parameterized manner. parameters can be defined as “ route/:param” var myrouter = backbone.router.extend({ greeting: null, container: null, view1: null, view2: null, view3: null, initialize: function () { this.greeting = new greetmodel({ message: "hello world" }); this.container = new containerview({ el: $("#rappcontainer"), model: this.greeting }); }, routes: { "": "handleroute1", "view/:viewid": "handlerouteall" }, handlerouteall: function (viewid) { if (viewid == 1) { this.handleroute1(); } else if (viewid == 2) { this.handleroute2(); } else if (viewid == 3) { this.handleroute3(); } }, handleroute1: function () { if (this.view1 == null) { this.view1 = new view1({ model: this.greeting }); } this.container.mychildview = this.view1; this.container.render(); }, handleroute2: function () { if (this.view2 == null) { this.view2 = new view2({ model: this.greeting }); } this.container.mychildview = this.view2; this.container.render(); }, handleroute3: function () { if (this.view3 == null) { this.view3 = new view3({ model: this.greeting }); } this.container.mychildview = this.view3; this.container.render(); } }); the above route can be invoked by passing view/2 as url. the viewid passed to the router will be 2. having optional parameters in routes we can also pass optional parameters in the routes, lets try to pass a simple parameter in the above defined route and see how it works. optional parameters can be defined as “ route(/:param)“. var myrouter = backbone.router.extend({ routes: { "": "handleroute1", "view1": "handleroute1", "view2": "handleroute2", "view3": "handleroute3", "view/:viewid(/:msg)": "handlerouteall" }, handlerouteall: function (viewid, msg) { if (msg) { alert(msg); } } }); in the above code, if we pass the second parameter i.e. view/2/test, the alert will be shown else not. note: the route definition can also contain complex regex based patterns if we need one route to handle multiple urls based on some regular expression. point of interest in this article we saw backbone.js routes. we saw how routes enable us to create bookmarkable urls and will let the user request a view based on url. we also looked at how we can use browser navigation by using backbone history. this has been written from a beginner’s perspective. i hope this has been informative. download sample code for this article: backboneroutessample
September 18, 2014
by Rahul Rajat Singh
· 10,552 Views
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Link to Files and Folders in Eclipse
eclipse projects have the nice features that they can link to files and folders: so instead of having the physical file, it is just a pointer to a file. this is very cool as that way i can point to shared files, or keep files in a common place referenced from projects, and so on. linked folder and file in eclipse as with most things in eclipse, there is not a single way how to do things. so i’m showing in this post several ways how to link to files and folders. creating a new link select the folder/project where to create a link to a file. use the context menu with n ew > file (or the menu file > new > other > general > file ): new file that opens a dialog to create a new file. i can select which project/folder i want to use for this: new file dialog next, click on ‘advanced’, enable ‘link to file in the file system’ and browse to the file you want to link to: link to file this will set the link to the file: specified link to file as an absolute path (c:\….) is probably not really what you want, there are eclipse path variables you can use: path variables :idea: note that these are eclipse path variables (not eclipse build variables), see “ eclipse build variables “. to use the path variable for the link, use the ‘extend…’ button and then select the file, then press ok: extending path variable this now uses a path variable for that link. note that it shows as well to which file it resolves: resolved path variable for linked file press finish, and the link will be created. note the small ‘arrow’ in the icon (see “ icon and label decorators in eclipse “) to show a linked file: linked file modifying a link if you right-click on that linked file and select ‘properties’ of it, you can see that it is really a linked file, with the link information, and you can change/edit that link any time: linked file properties linked folder as for link to files, its possible to create ‘link to folders’. it works the same way: select the folder, then use file > new > folder: creating new folder use default location : this creates a normal folder. virtual folder : this does not create a physical folder, but a virtual ‘container’ where i can place links or other virtual folders. this is useful to organize links and virtual folders, without the need for a physical folder. linked folder : like linked files, this links to a folder. :idea: the cool thing about linked (source) folders is: when i add new files to that folder where it links to, the projects with that linked folder to it will ‘see’ the extra files too, and that way new files are automatically added to the project. i do this many times, and it is like a ‘library’ folder for me: i can add a new source file to that ‘library’ folder, and every project linking to that folder will automatically have it added. :-) deleting links linked files and linked folders can be deleted from the project too. in that case, the destination of the link is *not* deleted, only the link: deleting a link to a folder using drag & drop as mentioned at the beginning: there are multiple ways to do the same thing in eclipse. instead using the top menu, or using the context menu, i can use drag & drop. to create links, i need to hold the ctrl key: drag and drop with ctrl pressed to create a link :idea: notice that during the drag&drop with ctrl key pressed the icon gets a ‘+’ to show copy/linking mode. when i drop the file: i get the usual choices how i want to create the link: link to files and folders dialog drag&drop of files and folders do not work inside eclipse. what works as well under windows is to drag&drop a file or folder from the windows explorer :-). summary links to files and folders are a cool thing in eclipse, and they can be created with menus or even simpler with drag&drop. this is not limited to inside eclipse: i can drag&drop from outside with the windows explorer and that way can link to files and folders everywhere :-) happy linking :-)
September 18, 2014
by Erich Styger
· 14,286 Views
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5 Error Tracking Tools Java Developers Should Know
Raygun, Stack Hunter, Sentry, Takipi and Airbrake: Modern developer tools to help you crush bugs before bugs crush your app With the Java ecosystem going forward, web applications serving growing numbers of requests and users’ demand for high performance - comes a new breed of modern development tools. A fast paced environment with rapid new deployments requires tracking errors and gaining insight to an application's behavior on a level traditional methods can’t sustain. In this post we’ve decided to gather 5 of those tools, see how they integrate with Java and find out what kind of tricks they have up their sleeves. It’s time to smash some bugs. Raygun Mindscape’s Raygun is a web based error management system that keeps track of exceptions coming from your apps. It supports various desktop, mobile and web programming languages, including Java, Scala, .NET, Python, PHP, and JavaScript. Besides that, sending errors to Raygun is possible through a REST API and a few more Providers (that’s how they call language and framework integrations) came to life thanks to developer community involvement. Key Features: Error grouping - Every occurrence of a bug is presented within one group with access to single instances of it, including its stack trace. Full text search - Error groups and all collected data is searchable. View app activity - Every action on an error group is displayed for all your team to see: status updates, comments and more. Affected users - Counts of affected users appear by each error. External integrations - Github, Bitbucket, Asana, JIRA, HipChat and many more. The Java angle: To use Raygun with Java, you’ll need to add some dependencies to your pom.xml file if you’re using Maven or add the jars manually. The second step would be to add an UncaughtExceptionHandler that would create an instance of RaygunClient and send your exceptions to it. In addition, you can also add custom data fields to your exceptions and send them together to Raygun. The full walkthrough is available here. Behind the curtain: Meet Robie Robot, the certified operator of Raygun. As in, the actual ray gun. Check it out on: https://raygun.io Sentry Started as a side-project, Sentry is an open-source web based solution that serves as a real time event logging and aggregation platform. It monitors errors and displays when, where and to whom they happen, promising to do so without relying solely on user feedback. Supported languages and frameworks include Ruby, Python, JS, Java, Django, iOS, .NET and more. Key Features: See the impact of new deployments in real time Provide support to specific users interrupted by an error Detect and thwart fraud as its attempted - notifications of unusual amounts of failures on purchases, authentication, and other sensitive areas External Integrations - GitHub, HipChat, Heroku, and many more The Java angle: Sentry’s Java client is called Raven and supports major existing logging frameworks like java.util.logging, Log4j, Log4j2 and Logback with Slf4j. An independent method to send events directly to Sentry is also available. To set up Sentry for Java with Logback for example, you’ll need to add the dependencies manually or through Maven, then add a new Sentry appender configuration and you’re good to do. Instructions are available here. Behind the curtain: Sentry was an internal project at Disqus back in 2010 to solve exception logging on a Django application by Chris Jennings and David Cramer Check it out on: https://www.getsentry.com/ Takipi Unlike most of the other tools, Takipi is far more than a stack trace prettifier. It was built with a simple objective in mind: Telling developers exactly when and why production code breaks. Whenever a new exception is thrown or a log error occurs – Takipi captures it and shows you the variable state which caused it, across methods and machines. Takipi will overlay this over the actual code which executed at the moment of error – so you can analyze the exception as if you were there when it happened. Key features: Detect – Caught/uncaught exceptions, Http and logged errors. Prioritize – How often errors happen across your cluster, if they involve new or modified code, and whether that rate is increasing. Analyze – See the actual code and variable state, even across different machines and applications. Easy to install - No code or configuration changes needed. Less than 2% overhead. The Java angle: Takipi was built for production environments in Java and Scala. The installation takes less than 1min, and includes attaching a Java agent to your JVM. Behind the curtain: Each exception type and error has a unique monster that represents it. You can find these monster here. Check it out on: http://www.takipi.com/ Airbrake Another tool that has put exception tracking on its eyesights is Rackspace’s Airbrake, taking on the mission of “No More Searching Log Files”. It provides users with a web based interface that includes a dashboard with error details and an application specific view. Supported languages include Ruby, PHP, Java, .NET, Python and even… Swift. Key Features: Detailed stack traces, grouping by error type, users and environment variables Team productivity - Filter importance errors from the noise Team collaboration - See who’s causing bugs and whose fixing them External Integrations - HipChat, GitHub, JIRA, Pivotal and over 30 more The Java angle: Airbrake officially supports only Log4j, although a Logback library is also available. Log4j2 support is currently lacking. The installation procedure is similar to Sentry, adding a few dependencies manually or through Maven, adding an appender, and you’re ready to start. Similarly, a direct way to send messages to Airbrake is also available with AirbrakeNotice and AirbrakeNotifier. More details are available here. Behind the curtain: Airbrake was acquired by Exceptional, which then got acquired by Rackspace. Check it out on: https://airbrake.io/ StackHunter Currently in beta, Stack Hunter provides a self hosted tool to track your Java exceptions. A change of scenery from the past hosted tools. Other than that, it aims to provide a similar feature set to inform developers of their exceptions and help solve them faster. Key Features: A single self hosted web interface to view all exceptions Collections of stack trace data and context including key metrics such as total exceptions, unique exceptions, users affected, & sessions affected Instant email alerts when exceptions occur Exceptions grouping by root cause The Java angle: Built specifically for Java, StackHunter runs on any servlet container running Java 6 or above. Installation includes running StackHunter on a local servlet, configuring an outgoing mail server for alerts, and configuring the application you’re wishing to log. Full instructions are available here. Behind the curtain: StackHunter is developed by Dele Taylor, who also works on Data Pipeline - a tool for transforming and migrating data in Java. Check it out on: http://stackhunter.com/ Bonus: ABRT Another approach to error tracking worth mentioning is used by ABRT, an automatic bug detection and reporting tool from the Fedora ecosystem, which is a Red Hat sponsored community project. Unlike the 5 tools we covered here, this one is intended to be used not only by app developers - but their users as well. Reporting bugs back to Red Hat with richer context that otherwise would have been harder to understand and debug. The Java angle: Support for Java exceptions is still in its proof of concept stage. A Java connector developed by Jakub Filák is available here. Behind the curtain: ABRT is an open-source project developed by Red Hat. Check it out on: https://github.com/abrt/abrt Did we miss any other tools? How do you keep track of your exceptions? Please let me know in the comments section below.
September 18, 2014
by Chen Harel
· 8,842 Views · 2 Likes
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Semihosting with GNU ARM Embedded (LaunchPad) and GNU ARM Eclipse Debug Plugins
in “ semihosting with kinetis design studio ” i used printf() to exchange text and data between the target board and the host using the debug connection. kinetis design studio (kds) has that semihosting baked into its libraries. what about if using the gnu arm embedded (launchpad) tools and libraries (see “ switching arm gnu tool chain and libraries in kinetis design studio “)? actually it requires two more steps, but is very easy too. semihosting output there are three things to be in place to use semihosting with the gnu arm embedded (launchpad) libraries: option in the gnu linker settings enabling semihosting in the debugger settings initializing the gnu libraries linker option to enable semihosting for the gnu arm embedded ( launchpad ) libraries, i need to add --specs=rdimon.specs to the linker options: linker option to enable semihosting in case i’m using newlib-nano and want to use printf() and/or scanf() with floating point support, i need to pull in some symbols explicitly with the linker options ‘u': -u _scanf_float -u _printf_float debugger settings in the gnu arm eclipse plugins, i need to enable semihosting. segger j-link for segger j-link, i enable the console in the launch configuration: allocated semihosting console for segger additionally i enable semihosting options in the startup options of the debugger: enabled semihosting in the startup options for segger p&e multilink for p&e the following settings are used: semihosting settings for pne settings for openocd the following settings are used for openocd: openocd semihosting settings initializing the gnu libraries if you would now try to use semihosting with running the debugger, you probably will get error messages like this (e.g. from segger j-link): warning: semihosting command sys_flen failed. handle is 0. warning: semihosting command sys_write failed. handle is 0. warning: semihosting command sys_write failed. handle is 0. warning: semihosting command sys_write failed. handle is 0. the reason is that the semihosting needs to be enabled by the application. i need to call initialise_monitor_handles() before i’m using printf() : 1 2 3 4 5 6 7 8 extern void initialise_monitor_handles( void ); /* prototype */ int main( void ) { initialise_monitor_handles(); /* initialize handles */ for (;;) { printf ( "hello world!\r\n" ); } } with this, i can use printf() and scanf() through a debugger connection. semihosting printf output summary while i don’t like printf() for many reasons, sometimes it is useful to exchange data with the host. using semihosting no physical connection is required, as the communication goes through the debugger. it is somewhat intrusive, and adds code and data overhead, but the gnu arm embedded (launchpad) libraries (both newlib and newlib-nano) have semihosting built-in. it is a matter to enable it in the linker and debugger settings, and to initialize the handles in the application. happy semihosting :-)
September 17, 2014
by Erich Styger
· 8,793 Views
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Lambdas and Side Effects
Overview Java 8 has added features such as lambdas and type inference. This makes the language less verbose and cleaner, however it comes with more side effects as you don't have to be as explicit in what you are doing. The return type of a lambda matters Java 8 infers the type of a closure. One way it does this is to look at the return type (or whether anything is returned) This can have a surprising side effect. Consider this code. ExecutorService es = Executors.newSingleThreadExecutor(); es.submit(() -> { try(Scanner scanner = new Scanner(new FileReader("file.txt"))) { String line = scanner.nextLine(); process(line); } return null; }); This code compiles fine. However, the line return null; appears redundant and you might be tempted to remove it. However if you remove the line, you get an error. Error:(12, 39) java: unreported exception java.io.FileNotFoundException; must be caught or declared to be thrown This is complaining about the use of FileReader. What has the return null got to do with catching an uncaught exception !? Type inference. ExecutorService.submit() is an overloaded method. It has two methods which take one argument. ExecutorService.submit(Runnable runnable); ExecutorService.submit(Callable callable); Both these methods take no arguments, so how does the javac compiler infer the type of the lambda? It looks at the return type. If you return null; it is aCallable however if nothing is returned, not even null, it is a Runnable. Callable and Runnable have another important difference. Callable throws checked exceptions, however Runnable doesn't allow checked exceptions to be thrown. The side effect of returning null is that you don't have to handle checked exceptions, these will be stored in the Future submit() returns. If you don't return anything, you have to handle checked exceptions. Conclusion While lambdas and type inference remove significant amounts of boiler plate code, you can find more edge cases, where the hidden details of what the compiler infers can be slightly confusing. Footnote You can be explicit about type inference with a cast. Consider this Callable calls = (Callable & Serializable) () -> { return null; } if (calls instanceof Serializable) // is true This cast has a number of side effects. Not only does the call() method return anInteger and a marker interface added, the code generated for the lambda changes i.e. it adds a writeObject() and readObject() method to support serialization of the lambda. Note: Each call site creates a new class meaning the details of this cast is visible at runtime via reflection.
September 16, 2014
by Peter Lawrey
· 8,042 Views · 1 Like
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A Closer Look at the MySQL ibdata1 Disk Space Issue and Big Tables
A recurring customer issue seen by the Percona Support team involves how to make the ibdata1 file “shrink” within MySQL. I'll show you how to handle big tables.
September 16, 2014
by Peter Zaitsev
· 7,974 Views
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Customizing HttpMessageConverters with Spring Boot and Spring MVC
Exposing a REST based endpoint for a Spring Boot application or for that matter a straight Spring MVC application is straightforward, the following is a controller exposing an endpoint to create an entity based on the content POST'ed to it: @RestController @RequestMapping("/rest/hotels") public class RestHotelController { .... @RequestMapping(method=RequestMethod.POST) public Hotel create(@RequestBody @Valid Hotel hotel) { return this.hotelRepository.save(hotel); } } Internally Spring MVC uses a component called a HttpMessageConverter to convert the Http request to an object representation and back. A set of default converters are automatically registered which supports a whole range of different resource representation formats - json, xml for instance. Now, if there is a need to customize the message converters in some way, Spring Boot makes it simple. As an example consider if the POST method in the sample above needs to be little more flexible and should ignore properties which are not present in the Hotel entity - typically this can be done by configuring the Jackson ObjectMapper, all that needs to be done with Spring Boot is to create a new HttpMessageConverter bean and that would end up overriding all the default message converters, this way: @Bean public MappingJackson2HttpMessageConverter mappingJackson2HttpMessageConverter() { MappingJackson2HttpMessageConverter jsonConverter = new MappingJackson2HttpMessageConverter(); ObjectMapper objectMapper = new ObjectMapper(); objectMapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false); jsonConverter.setObjectMapper(objectMapper); return jsonConverter; } This works well for a Spring-Boot application, however for straight Spring MVC applications which do not make use of Spring-Boot, configuring a custom converter is a little more complicated - the default converters are not registered by default and an end user has to be explicit about registering the defaults: @Configuration public class WebConfig extends WebMvcConfigurationSupport { @Bean public MappingJackson2HttpMessageConverter customJackson2HttpMessageConverter() { MappingJackson2HttpMessageConverter jsonConverter = new MappingJackson2HttpMessageConverter(); ObjectMapper objectMapper = new ObjectMapper(); objectMapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false); jsonConverter.setObjectMapper(objectMapper); return jsonConverter; } @Override public void configureMessageConverters(List> converters) { converters.add(customJackson2HttpMessageConverter()); super.addDefaultHttpMessageConverters(); } } Here WebMvcConfigurationSupport provides a way to more finely tune the MVC tier configuration of a Spring based application. In the configureMessageConverters method, the custom converter is being registered and then an explicit call is being made to ensure that the defaults are registered also. A little more work than for a Spring-Boot based application.
September 15, 2014
by Biju Kunjummen
· 148,038 Views · 14 Likes
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More Metrics in Apache Camel 2.14
Apache Camel 2.14 is being released later this month. There is a slight holdup due some Apache infrastructure issue which is being worked on. This blog post is to talk about one of the new functions we have added to this release. Thanks to Lauri Kimmel who donated a camel-metrics component, we integrated with the excellent codehale metrics library. So I took this component one step further and integrated it with the Camel routes so we have additional metrics about the route performances using codehale metrics. This allows end users to seamless feed Camel routing information together with existing data they are gathering using codehale metrics. Also take note we have a lot of existing metrics from camel-core which of course is still around. What codehale brings to the table is that they have additional statistical data which we do not have in camel-core. To use the codehale metics all you need to do is: add camel-metrics component enable route metrics in XML or Java code To enable in XML you declare a as shown below: &;t;bean id="metricsRoutePolicyFactory" class="org.apache.camel.component.metrics. routepolicy.MetricsRoutePolicyFactory"/> And doing so in Java code is easy as well by calling this method on your CamelContext. context.addRoutePolicyFactory(new MetricsRoutePolicyFactory()); Now performance metrics is only useable if you have a way of displaying them, and for that you can use hawtio. Notice you can use any kind of monitoring tooling which can integrate with JMX, as the metrics is available over JMX. The actual data is 100% codehale json format, where a piece of the data is shown in the figure below. Sample of the route metrics JSON data The next release of hawtio supports Camel 2.14 and automatic detects if you have enabled route metrics and if so, then shows a sub, where the information can be seen in real time in a graphical charts. hawtio have detected that we have route metrics enabled, and shows a sub tab where we can see the data in real time The screenshot above is from the new camel-example-servlet-rest-tomcat which we ship out of the box. This example demonstrates another new functionality in Camel 2.14 which is the Rest DSL (I will do a blog about that later). This example enables the route metrics out of the box, so what I did was to deploy this example together with hawtio (the hawtio-default WAR) in Apache Tomcat 8. With hawtio you can also build custom dashboards, so here at the end I have put together a dashboard with various screens from hawtio to have a custom view of a Camel application. hawtio dashboard with Camel route and metrics as well control panel to control the route(s), and the logs in the bottom.
September 15, 2014
by Claus Ibsen
· 9,624 Views
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Python 101: An Intro to Pony ORM
The Pony ORM project is another object relational mapper package for Python. They allow you to query a database using generators. They also have an online ER Diagram Editor that is supposed to help you create a model. They are also one of the only Python packages I’ve seen with a multi-licensing scheme where you can develop using a GNU license or purchase a license for non-open source work. See their website for additional details. In this article, we will spend some time learning the basics of this package. Getting Started Since this project is not included with Python, you will need to download and install it. If you have pip, then you can just do this: pip install pony Otherwise you’ll have to download the source and install it via its setup.py script. Creating the Database We will start out by creating a database to hold some music. We will need two tables: Artist and Album. Let’s get started! import datetime import pony.orm as pny database = pny.Database("sqlite", "music.sqlite", create_db=True) ######################################################################## class Artist(database.Entity): """ Pony ORM model of the Artist table """ name = pny.Required(unicode) albums = pny.Set("Album") ######################################################################## class Album(database.Entity): """ Pony ORM model of album table """ artist = pny.Required(Artist) title = pny.Required(unicode) release_date = pny.Required(datetime.date) publisher = pny.Required(unicode) media_type = pny.Required(unicode) # turn on debug mode pny.sql_debug(True) # map the models to the database # and create the tables, if they don't exist database.generate_mapping(create_tables=True) Pony ORM will create our primary key for us automatically if we don’t specify one. To create a foreign key, all you need to do is pass the model class into a different table, as we did in the Album class. Each Required field takes a Python type. Most of our fields are unicode, with one being a datatime object. Next we turn on debug mode, which will output the SQL that Pony generates when it creates the tables in the last statement. Note that if you run this code multiple times, you won’t recreate the table. Pony will check to see if the tables exist before creating them. If you run the code above, you should see something like this get generated as output: GET CONNECTION FROM THE LOCAL POOL PRAGMA foreign_keys = false BEGIN IMMEDIATE TRANSACTION CREATE TABLE "Artist" ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "name" TEXT NOT NULL ) CREATE TABLE "Album" ( "id" INTEGER PRIMARY KEY AUTOINCREMENT, "artist" INTEGER NOT NULL REFERENCES "Artist" ("id"), "title" TEXT NOT NULL, "release_date" DATE NOT NULL, "publisher" TEXT NOT NULL, "media_type" TEXT NOT NULL ) CREATE INDEX "idx_album__artist" ON "Album" ("artist") SELECT "Album"."id", "Album"."artist", "Album"."title", "Album"."release_date", "Album"."publisher", "Album"."media_type" FROM "Album" "Album" WHERE 0 = 1 SELECT "Artist"."id", "Artist"."name" FROM "Artist" "Artist" WHERE 0 = 1 COMMIT PRAGMA foreign_keys = true CLOSE CONNECTION Wasn’t that neat? Now we’re ready to learn how to add data to our database. How to Insert / Add Data to Your Tables Pony makes adding data to your tables pretty painless. Let’s take a look at how easy it is: import datetime import pony.orm as pny from models import Album, Artist #---------------------------------------------------------------------- @pny.db_session def add_data(): """""" new_artist = Artist(name=u"Newsboys") bands = [u"MXPX", u"Kutless", u"Thousand Foot Krutch"] for band in bands: artist = Artist(name=band) album = Album(artist=new_artist, title=u"Read All About It", release_date=datetime.date(1988,12,01), publisher=u"Refuge", media_type=u"CD") albums = [{"artist": new_artist, "title": "Hell is for Wimps", "release_date": datetime.date(1990,07,31), "publisher": "Sparrow", "media_type": "CD" }, {"artist": new_artist, "title": "Love Liberty Disco", "release_date": datetime.date(1999,11,16), "publisher": "Sparrow", "media_type": "CD" }, {"artist": new_artist, "title": "Thrive", "release_date": datetime.date(2002,03,26), "publisher": "Sparrow", "media_type": "CD"} ] for album in albums: a = Album(**album) if __name__ == "__main__": add_data() # use db_session as a context manager with pny.db_session: a = Artist(name="Skillet") You will note that we need to use a decorator caled db_session to work with the database. It takes care of opening a connection, committing the data and closing the connection. You can also use it as a context manager, which is demonstrated at the very end of this piece of code. Using Basic Queries to Modify Records with Pony ORM In this section, we will learn how to make some basic queries and modify a few entries in our database. ] import pony.orm as pny from models import Artist, Album with pny.db_session: band = Artist.get(name="Newsboys") print band.name for record in band.albums: print record.title # update a record band_name = Artist.get(name="Kutless") band_name.name = "Beach Boys" Here we use the db_session as a context manager. We make a query to get an artist object from the database and print its name. Then we loop over the artist’s albums that are also contained in the returned object. Finally, we change one of the artist’s names. Let’s try querying the database using a generator: result = pny.select(i.name for i in Artist) result.show() If you run this code, you should see something like the following: i.name -------------------- Newsboys MXPX Beach Boys Thousand Foot Krutch The documentation has several other examples that are worth checking out. Note that Pony also supports using SQL itself via its select_by_sql and get_by_sql methods. How to Delete Records in Pony ORM Deleting records with Pony is also pretty easy. Let’s remove one of the bands from the database: import pony.orm as pny from models import Artist with pny.db_session: band = Artist.get(name="MXPX") band.delete() Once more we use db_session to access the database and commit our changes. We use the band object’s delete method to remove the record. You will need to dig to find out if Pony supports cascading deletes where if you delete the Artist, it will also delete all the Albums that are connected to it. According to the docs, if the field is Required, then cascade is enabled. Wrapping Up Now you know the basics of using the Pony ORM package. I personally think the documentation needs a little work as you have to dig a lot to find some of the functionality that I felt should have been in the tutorials. Overall though, the documentation is still a lot better than most projects. Give it a go and see what you think! Additional Resources Pony ORM’s website Pony documentation SQLAlchemy Tutorial An Intro to peewee
September 12, 2014
by Mike Driscoll
· 8,889 Views
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Introducing BIRT iHub F-Type: Installing on Windows
Originally written by Virgil Dodson Actuate recently released a new, free BIRT server called the BIRT iHub F-Type. It incorporates all the functionality of BIRT iHub and is limited only by the capacity of output it can deliver on a daily basis. It is ideal for departmental and smaller scale applications. When BIRT F-Type reaches its maximum output capacity, additional capacity can be purchased on a subscription based model. Some of the key features of BIRT iHub F-Type that will help improve your BIRT content applications are: Interactivity – Allow end-users to modify and personalize reports, and answer questions themselves. Scheduling – Automate report generation based on rules and calendar, and then notify users. Sharing – Secure document management and distribution that allows users to only access content/data they are entitled to. Excel Emitter – Export as native Excel (not CSV) with formulas/pivot tables/worksheets/charts. Integration – JavaScript API to embed dynamic reports and visualizations in your web app. Downloading BIRT iHub F-Type Before we get started with the installation process, we need to download BIRT iHub F-Type. There are three downloads available: Windows, Linux, and a VMware image. This blog will cover the Windows installation. If you’re installing either of the other types, you’ll find links to guides for them at the bottom of this blog post. Once you click on your chosen download, you’ll be asked to register. If you’ve already registered, click the “Click to Login” button. If not, fill out the short registration form to get started. Next, read and accept the license agreement. Once you’ve done that, click the checkbox, and a link for the download will appear. Click that to start your download. At this point, you should also receive an email with an activation code. Be sure to check your spam folder if you don’t see it in your inbox. Installing BIRT iHub F-Type After the download is complete, launch the executable file named ActuateBIRTiHubFType.exe. A welcome message will appear. Press Next to continue. You must read and accept the license agreement on the next screen. Choose a destination folder for the installation. The default is C:\Actuate\BIRTiHub. If you have existing BIRT designs that depend on a JDBC database driver, you can optionally specify the folder where these drivers are located. Press Next to continue. Once the installation has finished, press Finish to launch the BIRT iHub F-Type. A desktop shortcut is also created that points to the iHub F-Type URL at http://localhost:8700/iportal. The first time you launch the BIRT iHub F-Type, you will need to activate it. Enter the activation code that you should have received in an e-mail. After entering a valid activation code, you should receive a message that the code was accepted and the BIRT iHub F-Type should start initializing services. Once that has completed, you will be presented with the login screen. The default user name is “administrator” and the password is blank for your first log in. You’ll be able to change this after you have logged in. Press “Log In” to continue. The first time you launch the BIRT iHub F-Type, you will be in tutorial mode which will help you get started loading your BIRT content and required resources. You can bypass the tutorial mode at any time by pressing the “Exit Tutorial” button at the top right. Select a BIRT design (*.rptdesign) file and press the Upload button. If you don’t have a BIRT design, you can download a sample from the link on the same page. The BIRT design file is automatically inspected and if there are any dependent files needed, like images, data files, BIRT report libraries, CSS styles, or other linked BIRT designs, you will be asked to upload those files as well. Once your BIRT design and dependent files are uploaded, your BIRT report will be displayed in the BIRT iHub F-Type and is now ready to explore. Thanks for reading. Now, it’s time to unleash the full power of BIRT into your application. If you have any questions or comments, please feel free to use the comments section below or visit the BIRT iHub F-Type forum. -Virgil For more blogs in the “Introducing BIRT iHub F-Type” series, see the list below: Installing iHub F-Type: Linux | VMWare Image
September 12, 2014
by Michael Singer
· 7,010 Views
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How to Load an Existing Email Message & Modify its Contents in Java Apps
This technical tip shows how to java developers can load and modify an existing email messages inside their java application using Aspose.Email Java API. Aspose.Email API allows developer to load any existing email message and modify its contents before saving back to the disk. One notable point is to specify the MessageFormat while loading the email message from the disk. In addition, it is important to specify the correct MailMessageSaveType while saving the message back to disk. The following sequence of steps lets you modify an existing email message: Create an instance of the MailMessage class. Load an existing message using the MailMessage class' load(), specifying the email' MessageFormat. Get the subject using the getSubject() method, modify it and set it using the MailMessage class' setSubject() method. Get the body using the getHtmlBody() method, modify it and set it using the MailMessage class' setHtmlBody() method. Create an instance of the MailAddressCollection class. Get recipients from the TO field into the MailAddressCollection object using the MailMessage class' getTo() method. Add or remove recipients using the MailAddressCollection collection's add() and remove() methods. Get recipients from the CC field into the MailAddressCollection object using the MailMessage class' getCC() method. Add or remove recipients using the MailAddressCollection collection's add() and remove() methods. Call the MailMessage class' save() method to save the file to disk in MSG format by specifying the correct MailMessageSaveType. //Adding Attachments to a New Email Message public static void main(String[] args) { // Base folder for reading and writing files String strBaseFolder = "D:\\Data\\Aspose\\resources\\"; //Initialize and Load an existing MSG file by specifying the MessageFormat MailMessage email = MailMessage.load(strBaseFolder + "anEmail.msg", MessageFormat.getMsg()); //Initialize a String variable to get the Email Subject String subject = email.getSubject(); //Append some more information to Subject subject = subject + " This text is added to the existing subject"; //Set the Email Subject email.setSubject(subject); //Initialize a String variable to get the Email's HTML Body String body = email.getHtmlBody(); //Apppend some more information to the Body variable body = body + " This text is added to the existing body"; //Set the Email Body email.setHtmlBody(body); //Initialize MailAddressCollection object MailAddressCollection contacts = new MailAddressCollection(); //Retrieve Email's TO list contacts = email.getTo(); //Check if TO list has some values if (contacts.size() > 0) { //Remove the first email address contacts.remove(0); //Add another email address to collection contacts.add("[email protected]"); } //Set the collection as Email's TO list email.setTo(contacts); //Initialize MailAddressCollection contacts = new MailAddressCollection(); //Retrieve Email's CC list contacts = email.getCC(); //Add another email address to collection contacts.add("[email protected]"); //Set the collection as Email's CC list email.setCC(contacts); //Save the Email message to disk by specifying the MessageFormat email.save(strBaseFolder + "message.msg", MailMessageSaveType.getOutlookMessageFormat()); } //Loading a Message with Load Options //To load a message with specific load options, Aspose.Email provides the MessageLoadOptions class that can be used as follow: MesageLoadOptions options = new MesageLoadOptions(); options.PrefferedTextEncoding = Encoding.getEncoding(1252); options.setMessageFormat(MessageFormat.getMsg()); MailMessage eml = MailMessage.Load("EMAIL_497563\\test3.msg", options);
September 10, 2014
by David Zondray
· 1,773 Views
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NetBeans IDE 8.0.1 Now Available for Download
The NetBeans Team has released NetBeans IDE 8.0.1, with significant enhancements to features relating to HTML5, JavaScript, and CSS3. An update to NetBeans IDE 8.0, this release includes the following notable changes: Modularity and enterprise JavaScript development support via integration of RequireJS Tools for working seamlessly with Grunt, Karma, and Bower Enhanced Java Editor, PHP Editor, and Git Tools Support for new versions of WebLogic, GlassFish, Tomcat, WildFly, and PrimeFaces Performance improvements and bug fixes Complete list of features: http://wiki.netbeans.org/NewAndNoteworthyNB801 To get the recent changes: Download and install NetBeans 8.0.1, which includes the recently released GlassFish 4.1 OR If you already have NetBeans IDE 8.0 installed, launch the IDE and an update notification will appear. Alternatively, choose Help | Check for Updates. Click the alert-box to install the updates. For details on upgrading, see the YouTube screencast "How to Upgrade to NetBeans IDE 8.0.1 from NetBeans IDE 8.0" on the NetBeans YouTube channel. NetBeans IDE 8.0.1 is available in English, Brazilian Portuguese, Japanese, Russian, and Simplified Chinese. We welcome feedback about your use of NetBeans software. Share your thoughts on the NetBeans mailing lists and forums; and keep track of NetBeans news by subscribing to the NetBeans Weekly Newsletter and following NetBeans on Twitter, Facebook, and YouTube.
September 10, 2014
by Geertjan Wielenga
· 36,913 Views
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How to Run HTML files in your Browser from GitHub
if you have a .html file in a github repository and want to view that page directly, you would typically download or clone the repo to your local hard drive and run it from there. there is an easier way simply navigate to the repo in your github account that contains a html file as shown below: right-click the index.html file and select copy link address. you should have a url similar to the following structure: https://github.com///blob/master/index.html enter rawgit.com as the name implies, rawgit shows serves the raw files directly from github. to use it simply use the following format: https://rawgit.com///master/index.html if you want to use it in production, you can use: https://cdn.rawgit.com///master/index.html that was easy now, wasn’t it!
September 10, 2014
by Michael Crump
· 11,409 Views
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Hibernate Bytecode Enhancement
Now that you know the basics of Hibernate dirty checking, we can dig into enhanced dirty checking mechanisms.
September 10, 2014
by Vlad Mihalcea
· 22,229 Views · 1 Like
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Getting Started with JHipster on OS X
Last week I was tasked with developing a quick prototype that used AngularJS for its client and Spring MVC for its server. A colleague developed the same application using Backbone.js and Spring MVC. At first, I considered using my boot-ionic project as a starting point. Then I realized I didn't need to develop a native mobile app, but rather a responsive web app. My colleague mentioned he was going to use RESThub as his starting point, so I figured I'd use JHipster as mine. We allocated a day to get our environments setup with the tools we needed, then timeboxed our first feature spike to four hours. My first experience with JHipster failed the 10-minute test. I spent a lot of time flailing about with various "npm" and "yo" commands, getting permissions issues along the way. After getting thinks to work with some sudo action, I figured I'd try its Docker development environment. This experience was no better. JHipster seems like a nice project, so I figured I'd try to find the causes of my issues. This article is designed to save you the pain I had. If you'd rather just see the steps to get up and running quickly, skip to the summary. The "npm" and "yo" issues I had seemed to be caused by a bad node/npm installation. To fix this, I removed node and installed nvm. Here's the commands I needed to remove node and npm: sudo rm -rf /usr/local/lib/node_modules sudo rm -rf /usr/local/include/node sudo rm /usr/local/bin/node sudo rm -rf /usr/local/bin/npm sudo rm /usr/local/share/man/man1/node.1 sudo rm -rf /usr/local/lib/dtrace/node.d sudo rm -rf ~/.npm Next, I ran "brew doctor" to make sure Homebrew was still happy. It told me some things were broken: $ brew doctor Warning: Broken symlinks were found. Remove them with `brew prune`: /usr/local/bin/yo /usr/local/bin/ionic /usr/local/bin/grunt /usr/local/bin/bower I ran brew update && brew prune, followed by brew install nvm. Next, I added the following to my ~/.profile: source $(brew --prefix nvm)/nvm.sh To install the latest version of node, I ran the commands below and set the latest version as the default: nvm ls-remote nvm install v0.11.13 nvm alias default v0.11.13 Once I had a fresh version of Node.js, I was able to run JHipster's local installation instructions. npm install -g yo npm install -g generator-jhipster Then I created my project: yo jhipster I was disappointed to find this created all the project files in my current directory, rather than in a subdirectory. I'd recommend you do the following instead: mkdir ~/projectname && cd ~/projectname && yo jhipster Before creating your project, JHipster asks you a number of questions. To see what they are, see its documentation on creating an application. Two things to be aware of: Hot reloading Java code doesn't work well (yet) with Java 8 Its OAuth2 implementation doesn't work with WebSockets In other words, I'd recommend using Java 7 + (cookie-based authentication with websockets) or (oauth2 authentication w/o websockets). After creating my project, I was able to run it using "mvn spring-boot:run" and view it at http://localhost:8080. To get hot-reloading for the client, I ran "grunt server" and opened my browser to http://localhost:9000. JHipster + Docker on OS X I had no luck getting the Docker instructions to work initially. I spent a couple hours on it, then gave up. A couple of days ago, I decided to give it another good ol' college-try. To make sure I figured out everything from scratch, I started by removing Docker. I re-installed Docker and pulled the JHipster image using the following: sudo docker pull jdubois/jhipster-docker The error I got from this was the following: 2014/09/05 19:43:38 Post http:///var/run/docker.sock/images/create?fromImage=jdubois%2Fjhipster-docker&tag=: dial unix /var/run/docker.sock: no such file or directory After doing some research, I learned I needed to run boot2docker init first. Next I ran boot2docker up to start the Docker daemon. Then I copied/pasted "export DOCKER_HOST=tcp://192.168.59.103:2375" into my console and tried to run docker pull again. It failed with the same error. The solution was simpler than you might think: don't use sudo. $ docker pull jdubois/jhipster-docker Pulling repository jdubois/jhipster-docker 01bdc74025db: Pulling dependent layers 511136ea3c5a: Download complete ... The next command that JHipster's documentation recommends is to run the Docker image, forward ports and share folders. When you run it, the terminal seems to hang and trying to ssh into it doesn't work. Others have recently reported a similar issue. I discovered the hanging is caused by a missing "-d" parameter and ssh doesn't work because you need to add a portmap to the VM to expose the port to your host. You can fix this by running the following: boot2docker down VBoxManage modifyvm "boot2docker-vm" --natpf1 "containerssh,tcp,,4022,,4022" VBoxManage modifyvm "boot2docker-vm" --natpf1 "containertomcat,tcp,,8080,,8080" VBoxManage modifyvm "boot2docker-vm" --natpf1 "containergruntserver,tcp,,9000,,9000" VBoxManage modifyvm "boot2docker-vm" --natpf1 "containergruntreload,tcp,,35729,,35729" boot2docker start After making these changes, I was able to start the image and ssh into it. docker run -d -v ~/jhipster:/jhipster -p 8080:8080 -p 9000:9000 -p 35729:35729 -p 4022:22 -t jdubois/jhipster-docker ssh -p 4022 jhipster@localhost I tried creating a new project within the VM (cd /jhipster && yo jhipster), but it failed with the following error: /usr/lib/node_modules/generator-jhipster/node_modules/yeoman-generator/node_modules/mkdirp/index.js:89 throw err0; ^ Error: EACCES, permission denied '/jhipster/src' The fix was giving the "jhipster" user ownership of the directory. sudo chown jhipster /jhipster After doing this, I was able to generate an app and run it using "mvn spring-boot:run" and access it from my Mac at http://localhost:8080. I was also able to run "grunt server" and see it at http://localhost:9000 However, I was puzzled to see that there was nothing in my ~/jhipster directory. After doing some searching, I found that the docker run -v /host/path:/container/path doesn't work on OS X. David Gageot's A Better Boot2Docker on OSX led me to svendowideit/samba, which solved this problem. The specifics are documented in boot2docker's folder sharing section. I shutdown my docker container by running "docker ps", grabbing the first two characters of the id and then running: docker stop [2chars] I started the JHipster container without the -v parameter, used "docker ps" to find its name (backstabbing_galileo in this case), then used that to add samba support. docker run -d -p 8080:8080 -p 9000:9000 -p 35729:35729 -p 4022:22 -t jdubois/jhipster-docker docker run --rm -v /usr/local/bin/docker:/docker -v /var/run/docker.sock:/docker.sock svendowideit/samba backstabbing_galileo Then I was able to connect using Finder > Go > Connect to Server, using the following for the server address: cifs://192.168.59.103/jhipster To make this volume appear in my regular development area, I created a symlink: ln -s /Volumes/jhipster ~/dev/jhipster After doing this, all the files were marked as read-only. To fix, I ran "chmod -R 777 ." in the directory on the server. I noticed that this also worked if I ran it from my Mac's terminal, but it took quite a while to traverse all the files. I noticed a similar delay when loading the project into IntelliJ. Summary Phew! That's a lot of information that can be condensed down into four JHipster + Docker on OS X tips. Make sure your npm installation doesn't require sudo rights. If it does, reinstall using nvm. Add portmaps to your VM to expose ports 4022, 8080, 9000 and 35729 to your host. Change ownership on the /jhipster in the Docker image: sudo chown jhipster /jhipster. Use svendowideit/samba to share your VM's directories with OS X.
September 10, 2014
by Matt Raible
· 13,101 Views
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Garbage Collectors - Serial vs. Parallel vs. CMS vs. G1 (and what's new in Java 8)
The 4 Java Garbage Collectors - How the Wrong Choice Dramatically Impacts Performance The year is 2014 and there are two things that still remain a mystery to most developers - Garbage collection and understanding the opposite sex. Since I don’t know much about the latter, I thought I’d take a whack at the former, especially as this is an area that has seen some major changes and improvements with Java 8, especially with the removal of the PermGen and some new and exciting optimizations (more on this towards the end). When we speak about garbage collection, the vast majority of us know the concept and employ it in our everyday programming. Even so, there’s much about it we don’t understand, and that’s when things get painful. One of the biggest misconceptions about the JVM is that it has one garbage collector, where in fact it provides four different ones, each with its own unique advantages and disadvantages. The choice of which one to use isn’t automatic and lies on your shoulders and the differences in throughput and application pauses can be dramatic. What’s common about these four garbage collection algorithms is that they are generational, which means they split the managed heap into different segments, using the age-old assumptions that most objects in the heap are short lived and should be recycled quickly. As this too is a well-covered area, I’m going to jump directly into the different algorithms, along with their pros and their cons. 1. The Serial Collector The serial collector is the simplest one, and the one you probably won’t be using, as it’s mainly designed for single-threaded environments (e.g. 32 bit or Windows) and for small heaps. This collector freezes all application threads whenever it’s working, which disqualifies it for all intents and purposes from being used in a server environment. How to use it: You can use it by turning on the -XX:+UseSerialGC JVM argument, 2. The Parallel / Throughput collector Next off is the Parallel collector. This is the JVM’s default collector. Much like its name, its biggest advantage is that is uses multiple threads to scan through and compact the heap. The downside to the parallel collector is that it will stop application threads when performing either a minor or full GC collection. The parallel collector is best suited for apps that can tolerate application pauses and are trying to optimize for lower CPU overhead caused by the collector. 3. The CMS Collector Following up on the parallel collector is the CMS collector (“concurrent-mark-sweep”). This algorithm uses multiple threads (“concurrent”) to scan through the heap (“mark”) for unused objects that can be recycled (“sweep”). This algorithm will enter “stop the world” (STW) mode in two cases: when initializing the initial marking of roots (objects in the old generation that are reachable from thread entry points or static variables) and when the application has changed the state of the heap while the algorithm was running concurrently, forcing it to go back and do some final touches to make sure it has the right objects marked. The biggest concern when using this collector is encountering promotion failures which are instances where a race condition occurs between collecting the young and old generations. If the collector needs to promote young objects to the old generation, but hasn’t had enough time to make space clear it, it will have to do so first which will result in a full STW collection - the very thing this CMS collector was meant to prevent. To make sure this doesn’t happen you would either increase the size of the old generation (or the entire heap for that matter) or allocate more background threads to the collector for him to compete with the rate of object allocation. Another downside to this algorithm in comparison to the parallel collector is that it uses more CPU in order to provide the application with higher levels of continuous throughput, by using multiple threads to perform scanning and collection. For most long-running server applications which are adverse to application freezes, that’s usually a good trade off to make. Even so, this algorithm is not on by default. You have to specify XX:+USeParNewGC to actually enable it. If you’re willing to allocate more CPU resources to avoid application pauses this is the collector you’ll probably want to use, assuming that your heap is less than 4Gb in size. However, if it’s greater than 4GB, you’ll probably want to use the last algorithm - the G1 Collector. 4. The G1 Collector The Garbage first collector (G1) introduced in JDK 7 update 4 was designed to better support heaps larger than 4GB. The G1 collector utilizes multiple background threads to scan through the heap that it divides into regions, spanning from 1MB to 32MB (depending on the size of your heap). G1 collector is geared towards scanning those regions that contain the most garbage objects first, giving it its name (Garbage first). This collector is turned on using the –XX:+UseG1GC flag. This strategy the chance of the heap being depleted before background threads have finished scanning for unused objects, in which case the collector will have to stop the application which will result in a STW collection. The G1 also has another advantage that is that it compacts the heap on-the-go, something the CMS collector only does during full STW collections. Large heaps have been a fairly contentious area over the past few years with many developers moving away from the single JVM per machine model to more micro-service, componentized architectures with multiple JVMs per machine. This has been driven by many factors including the desire to isolate different application parts, simplifying deployment and avoiding the cost which would usually come with reloading application classes into memory (something which has actually been improved in Java 8). Even so, one of the biggest drivers to do this when it comes to the JVM stems from the desire to avoid those long “stop the world” pauses (which can take many seconds in a large collection) that occur with large heaps. This has also been accelerated by container technologies like Docker that enable you to deploy multiple apps on the same physical machine with relative ease. Java 8 and the G1 Collector Another beautiful optimization which was just out with Java 8 update 20 for is the G1 Collector String deduplication. Since strings (and their internal char[] arrays) takes much of our heap, a new optimization has been made that enables the G1 collector to identify strings which are duplicated more than once across your heap and correct them to point into the same internal char[] array, to avoid multiple copies of the same string from residing inefficiently within the heap. You can use the -XX:+UseStringDeduplicationJVM argument to try this out. Java 8 and PermGen One of the biggest changes made in Java 8 was removing the permgen part of the heap that was traditionally allocated for class meta-data, interned strings and static variables. This would traditionally require developers with applications that would load significant amount of classes (something common with apps using enterprise containers) to optimize and tune for this portion of the heap specifically. This has over the years become the source of many OutOfMemory exceptions, so having the JVM (mostly) take care if it is a very nice addition. Even so, that in itself will probably not reduce the tide of developers decoupling their apps into multiple JVMs. Each of these collectors is configured and tuned differently with a slew of toggles and switches, each with the potential to increase or decrease throughput, all based on the specific behavior of your app. We’ll delve into the key strategies of configuring each of these in our next posts.
September 10, 2014
by Chen Harel
· 55,262 Views · 7 Likes
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How JSF Works and how to Debug it - is Polyglot an Alternative?
JSF is not what we often think it is. It's also a framework that can be somewhat tricky to debug, especially when first encountered. In this post let's go over on why that is and provide some JSF debugging techniques. We will go through the following topics: JSF is not what we often think The difficulties of JSF debugging How to debug JSF systematically How JSF Works - The JSF lifecycle Debugging an Ajax request from browser to server and back Debugging the JSF frontend Javascript code Final thoughts - alternatives? (questions to the reader) JSF is not what we often think JSF looks on first look like an enterprise Java/XML frontend framework, but under the hood it really isn't. It's really a polyglot Java/Javascript framework, where the client Javascript part is non-neglectable and also important to understand it. It also has good support for direct HTML/CSS use. JSF developers are on ocasion already polyglot developers, whose primary language is Java but still need to use ocasionally Javascript. The difficulties of JSF debugging When comparing JSF to GWT and AngularJS in a previous post, I found that the (most often used) approach that the framework takes of abstracting HTML and CSS from the developer behind XML adds to the difficulty of debugging, because it creates an extra level of indirection. A more direct approach of using HTML/CSS directly is also possible, but it seems enterprise Java developers tend to stick to XML in most cases, because it's a more familiar technology. Also another problem is that the client side Javascript part of the framework/libraries is not very well documented, and it's often important to understand what is going on. The only way to debug JSF systematically When first encountering JSF, I first tried to approach it from a Java, XML and documentation only. While I could do a part of the work that way, there where frequent situations where that approach was really not sufficient. The conclusion that I got to is that in order to be able to debug JSF applications effectively, an understanding of the following is needed: HTML CSS Javascript HTTP Chrome Dev Tools, Firebug or equivalent The JSF Lifecycle This might sound surprising to developers that work mostly in Java/XML, but this web-centric approach to debugging JSF is the only way that I managed to tackle many requirements that needed some significant component customization, or to be able to fix certain bugs. Let’s start by understanding the inner workings of JSF, so that we can debug it better. The JSF take on MVC The way JSF approaches MVC is that the whole 3 components reside on the server side: The Model is a tree of plain Java objects The View is a server side template defined in XML that is read to build an in-memory view definition The Controller is a Java servlet, that receives each request and processes them through a series of steps The browser is assumed to be simply a rendering engine for the HTML generated at server side. Ajax is achieved by submitting parts of the page for server processing, and requesting a server to ‘repaint’ only portions of the screen, without navigating away from the page. The JSF Lifecycle Once an HTTP request reaches the backend, it gets caught by the JSF Controller that will then process it. The request goes through a series of phases known as the JSF lifecycle, which is essential to understand how JSF works: Design Goals of the JSF Lifecycle The whole point of the lifecycle is to manage MVC 100% on the server side, using the browser as a rendering platform only. The initial idea was to decouple the rendering platform from the server-side UI component model, in order to allow to replace HTML with alternative markup languages by swapping the Render Response phase. This was in the early 2000's when HTML could be soon replaced by XML-based alternatives (that never came to be), and then HTML5 came along. Also browsers where much more qwirkier than what they are today, and the idea of cross-browser Javascript libraries was not widespread. So let’s go through each phase and see how to debug it if needed, starting in the browser. Let's base ourselves in a simple example that uses an Ajax request. A JSF 2 Hello World Example The following is a minimal JSF 2 page, that receives an input text from the user, sends the text via an Ajax request to the backend and refreshes only an output label: JSF 2.2 Hello World Example The page looks like this: Following one Ajax request - to the server and back Let’s click submit in order to trigger the Ajax request, and use the Chrome Dev Tools Network tab (right click and inspect any element on the page).What goes over the wire? This is what we see in the Form Data section of the request: j_idt8:input: Hello World javax.faces.ViewState: -2798727343674530263:954565149304692491 javax.faces.source: j_idt8:j_idt9 javax.faces.partial.event: click javax.faces.partial.execute: j_idt8:j_idt9 j_idt8:input javax.faces.partial.render: j_idt8:output javax.faces.behavior.event: action javax.faces.partial.ajax:true This request says: The new value of the input field is "Hello World", send me a new value for the output field only, and don't navigate away from this page. Let's see how this can be read from the request. As we can see, the new values of the form are submitted to the server, namely the “Hello World” value. This is the meaning of the several entries: javax.faces.ViewState identifies the view from which the request was made. The request is an Ajax request, as indicated by the flag javax.faces.partial.ajax, The request was triggered by a click as defined in javax.faces.partial.event. But what are those j_ strings ? Those are space separated generated identifiers of HTML elements. For example this is how we can see what is the page element corresponding to j_idt8:input, using the Chrome Dev Tools: There are also 3 extra form parameters that use these identifiers, that are linked to UI components: javax.faces.source: The identifier of the HTML element that originated this request, in this case the Id of the submit button. javax.faces.execute: The list of identifiers of the elements whose values are sent to the server for processing, in this case the input text field. javax.faces.render: The list of identifiers of the sections of the page that are to be ‘repainted', in this case the output field only. But what happens when the request hits the server ? JSF lifecycle - Restore View Phase Once the request reaches the server, the JSF controller will inspect the javax.faces.ViewState and identify to which view it refers. It will then build or restore a Java representation of the view, that is somehow similar to the document definition in the browser side. The view will be attached to the request and used throughout. There is usually little need to debug this phase during application development. JSF Lifecycle - Apply Request Values The JSF Controller will then apply to the view widgets the new values received via the request. The values might be invalid at this point. Each JSF component gets a call to it’s decode method in this phase. This method will retrieve the submitted value for the widget in question from the HTTP request and store it on the widget itself. To debug this, let’s put a breakpoint in the decode method of the HtmlInputText class, to see the value “Hello World”: Notice the conditional breakpoint using the HTML clientId of the field we want. This would allow to quickly debug only the decoding of the component we want, even in a large page with many other similar widgets. Next after decoding is the validation phase. JSF Lifecycle - Process Validations In this phase, validations are applied and if the value is found to be in error (for example a date is invalid), then the request bypasses Invoke Application and goes directly to Render Response phase. To debug this phase, a similar breakpoint can be put on method processValidators, or in the validators themselves if you happen to know which ones or if they are custom. JSF Lifecycle - Update Model In this phase, we know all the submitted values where correct. JSF can now update the view model by applying the new values received in the requests to the plain Java objects in the view model. This phase can be debugged by putting a breakpoint in the processUpdates method of the component in question, eventually using a similar conditional breakpoint to break only on the component needed. JSF Lifecycle - Invoke Application This is the simplest phase to debug. The application now has an updated view model, and some logic can be applied on it. This is where the action listeners defined in the XML view definition (the 'action' properties and the listener tags) are executed. JSF Lifecycle - Render Response This is the phase that I end up debugging the most: why is the value not being displayed as we expect it, etc, it all can be found here. In this phase the view and the new model values will be transformed from Java objects into HTML, CSS and eventually Javascript and sent back over the wire to the browser. This phase can be debugged using breakpoints in the encodeBegin, encodeChildren and encodeEnd methods of the component in question. The components will either render themselves or delegate rendering to aRenderer class. Back in the browser It was a long trip, but we are back where we started! This is how the response generated by JSF looks once received in the browser: -8188482707773604502:6956126859616189525> What the Javascript part of the framework will do is to take the contents of the partial response, update by update. Using the Id of the update, the client side JSF callback will search for a component with that Id, delete it from the document and replace it with the new updated version. In this case, "Hello World" will show up on the label next to the Input text field! And so thats how JSF works under the hood. But what about if we need to debug the Javascript part of the framework? Debugging the JSF Javascript Code The Chrome Dev Tools can help debug the client part. For example let’s say that we want to halt the client when an Ajax request is triggered. We need to go to the sources tab, add an XHR (Ajax) breakpoint and trigger the browser action. The debugger will stop and the call stack can be examined: For some frameworks like Primefaces, the Javascript sources might be minified (non human-readable) because they are optimized for size. To solve this, download the source code of the library and do a non minified build of the jar. There are usually instructions for this, otherwise check the project poms. This will install in your Maven repository a jar with non minified sources for debugging. The UI Debug tag: The ui:debug tag allows to view a lot of debugging information using a keyboard shortcut, see here for further details. Final Thoughts JSF is very popular in the enterprise Java world, and it handles a lot of problems well, specially if the UI designers take into account the possibilities of the widget library being used. The problem is that there are usually feature requests that force us to dig deeper into the widgets internal implementation in order to customize them, and this requires HTML, CSS, Javascript and HTTP plus JSF lifecycle knowledge. Is polyglot an alternative? We can wonder that if developers have to know a fair amount about web technologies in order to be able to debug JSF effectively, then it would be simpler to build enterprise front ends (just the client part) using those technologies directly instead. It's possible that a polyglot approach of a Java backend plus a Javascript-only frontend could be proved effective in a nearby future, specially using some sort of a client side MVC framework like Angular. This would require learning more Javascript, (have a look at Javascript for Java developers post if curious), but this is already often necessary to do custom widget development in JSF anyway. Conclusions and some questions to the reader Thanks for reading, please take a moment to share your thoughts on these matters on the comments bellow: do you believe polyglot development (Java/Javascript) is a viable alternative in general, and in your workplace in particular? Did you find one of the GWT-based frameworks (plain GWT, Vaadin, Errai), or the Play Framework to be easier to use and of better productivity?
September 10, 2014
by Vasco Cavalheiro
· 44,691 Views · 5 Likes
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