The Internet doesn't lack expositions on REST architecture, RESTful services, and their implementation in Java. But, here is another one. Why? Because I couldn't find something concise enough to point readers of the eValhalla blog series. What is REST? The acronym stands for Representational State Transfer. It refers to an architectural style (or pattern) thought up by one of the main authors of the HTTP protocol. Don't try to infer what the phrase "representational state transfer" could possibly mean. It sounds like there's some transfer of state that's going on between systems, but that's a bit of a stretch. Mostly, there's transfer of resources between clients and servers. The clients initiate requests and get back responses. The responses are resources in some standard media type such as XML or JSON or HTML. But, and that's a crucial aspect of the paradigm, the interaction itself is stateless. That's a major architectural departure from the classic client-server model of the 90s. Unlike classic client-server, there's no notion of a client session here. REST is offered not as a procotol, but as an architectural paradigm. However, in reality we are pretty much talking about HTTP of which REST is an abstraction. The core aspects of the architecture are (1) resource identifiers (i.e. URIs); (2) different possible representations of resources, or internet media types (e.g. application/json); (3) CRUD operations support for resources like the HTTP methods GET, PUT, POST and DEL. Resources are in principle decoupled from their identifiers. That means the environment can deliver a cached version or it can load balance somehow to fulfill the request. In practice, we all know URIs are actually addresses that resolve to particular domains so there's at least that level of coupling. In addition, resources are decoupled from their representation. A server may be asked to return HTML or XML or something else. There's content negotiation going on where the server may offer the desired representation or not. The CRUD operations have constraints on their semantics that may or may not appear obvious to you. The GET, PUT and DEL operations require that a resource be identified while POST is supposed to create a new resource. The GET operation must not have side-effects. So all other things being equal, one should be able to invoke GET many times and get back the same result. PUT updates a resource, DEL removes it and therefore they both have side-effects just like POST. On the other hand, just like GET, PUT may be repeated multiple times always to the same effect. In practice, those semantics are roughly followed. The main exception is the POST method which is frequently used to send data to the server for some processing, but without necessarily expecting it to create a new resource. Implementing RESTful services revolves around implementing those CRUD operations for various resources. This can be done in Java with the help of a Java standard API called JAX-RS. REST in Java = JAX-RS = JSR 311 In the Java world, when it comes to REST, we have the wonderful JAX-RS. And I'm not being sarcastic! This is one of those technologies that the Java Community Process actually got right, unlike so many other screw ups. The API is defined as JSR 311 and it is at version 1.1, with work on version 2.0 under way. The beauty of JAX-RS is that it is almost entirely driven by annotations. This means you can turn almost any class into a RESTful service. You can simply turn a POJO into a REST endpoint by annotating it with JSR 311 annotations. Such an annotated POJOs is called a resource class in JAX-RS terms. Some of the JAX-RS annotations are at the class level, some at the method level and others at the method parameter level. Some are available both at class and method levels. Ultimately the annotations combine to make a given Java method into a RESTful endpoint accessible at an HTTP-based URL. The annotations must specify the following elements: The relative path of the Java method - this is accomplished with @Path annotation. What the HTTP verb is, i.e. what CRUD operation is being performed - this is done by specifying one of @GET, @PUT, @POST or @DELETE annotations. The media type accepted (i.e. the representation format) - @Consumes annotation. The media type returned - @Produces annotation. The two last ones are optional. If omitted, then all media types are assumed possible. Let's look at a simple example and take it apart: import javax.ws.rs.*; @Path("/mail") @Produces("application/json") public class EmailService { @POST @Path("/new") public String sendEmail(@FormParam("subject") String subject, @FormParam("to") String to, @FormParam("body") String body) { return "new email sent"; } @GET @Path("/new") public String getUnread() { return "[]"; } @DELETE @Path("/{id}") public String deleteEmail(@PathParam("id") int emailid) { return "delete " + id; } @GET @Path("/export") @Produces("text/html") public String exportHtml(@QueryParam("searchString") @DefaultValue("") String search) { return "..."; } } The class define a RESTful interface for a hypothetical HTTP-based email service. The top-level path mail is relative to the root application path. The root application path is associated with the JAX-RS javax.ws.rs.core.Application that you extend to plugin into the runtime environment. Then we've declared with the @Produces annotation that all methods in that service produce JSON. This is just a class-default that one can override for individual methods like we've done in the exportHtml method. The sendMail method defines a typical HTTP post where the content is sent as an HTML form. The intent here would be to post to http://myserver.com/mail/new a form for a new email that should be sent out. As you can see, the API allows you to bind each separate form field to a method parameter. Note also that you have a different method for the exact same path. If you do an HTTP get at /mail/new, the Java method annotated with @GET will be called instead. Presumably the semantics of get /mail/new would be to obtain the list of unread emails. Next, note how the path of the deleteEmail method is parametarized by an integer id of the email to delete. The curly braces indicate that "id" is actually a parameter. The value of that parameter is bound to the whatever is annotated with @PathParam("id"). Thus if we do an HTTP delete at http://myserver.com/mail/453 we would be calling the deleteEmail method with argument emailid=453. Finally, the exportHtml method demonstrates how we can get a handle on query parameters. When you annotate a parameter with @QueryParam("x") the value is taken from the HTTP query parameter named x. The @DefaultValue annotation provides a default in case that query parameter is missing. So, calling http://myserver.org/mail/export?searchString=RESTful will call the exportHtml method with a parameter search="RESTful". To expose this service, first we need to write an implementation of javax.ws.rs.core.Application. That's just a few lines: public class MyRestApp extends javax.ws.rs.core.Application { public Set>Class> getClasses() { HashSet S = new HashSet(); S.add(EmailService.class); return S; } } How this gets plugged into your server depends on your JAX-RS implementation. Before we leave the API, I should mentioned that there's more to it. You do have access to a Request and Response objects. You have annotations to access other contextual information and metadata like HTTP headers, cookies etc. And you can provide custom serialization and deserialization between media types and Java objects. RESTful vs Web Services Web services (SOAP, WSDL) were heavily promoted in the past decade, but they didn't become as ubiquitous as their fans had hoped. Blame XML. Blame the rigidity of the XML Schema strong typing. Blame the tremendous overhead, the complexity of deploying and managing a web service. Or, blame the frequent compatibility nightmares between implementations. Reasons are not hard to find and the end result is that RESTful services are much easier to develop and use. But there is a flip side! The simplicity of RESTful services means that one has less guidance in how to map application logic to a REST API. One of the issues is that instead of the programmatic types we have in programming languages, we have the Java primitives and media types. Fortunately, JAX-RS allows to implement whatever conversions we want between actual Java method arguments and what gets sent on the wire. The other issue is the limited set of operations that a REST service can offer. While with web services, you define the operation and its semantics just as in a general purpose programming language, with RESTful you're stuck with get, put, post and delete. So, free from the type mismatch nightmare, but tied into only 4 possible operations. This is not as bad as it seems if you view those operations as abstract, meta operations. The key point when designing RESTful services, whether you are exposing existing application logic or creating a new one, is to think in terms of data resources. That's not so hard since most of what common business applications do is manipulate data. First, because every single thing is identified as a resource, one must come up with an appropriate naming schema. Because URIs are hierarchical, it is easy to devise a nested structure like /productcategory/productname/version/partno. Second, one must decide what kinds of representations are to be supported, both in output and input. For a modern AJAX webpp, we'd mostly use JSON. I would recommend JSON over XML even in a B2B setting where servers talk to each other. Finally, one must categorize business operation as one of GET, PUT, POST and DELETE. This is probably a bit less intuitive, but it's just a matter of getting used to. For example, instead of thinking about a "Checkout Shopping Cart" operation, think about POSTing a new order. Instead of thinking about a "Login User" operation think about GETing an authentication token. In general, every business operation manipulates some data in some way. Therefore, every business operation can fit into this crude CRUD model. Clearly, most read-only operations should be a GET. However, sometimes you have to send a large chunk of data to the server in which case you should use POST. For example you could post some very time consuming query that require a lot of text to specify. Then the resource you are creating is for example the query result. Another way to decide if you should POST or no is if you have a unique resource identifier. If not, then use POST. Obviously, operations that cause some data to be removed should be a DELETE. The operations that "store" data are PUT and again POST. Deciding between those two is easy: use PUT whenever you are modifying an existing resource for which you have an identifier. Otherwise, use POST. Implementations & Resources There are several implementations to choose from. Since, I haven't tried them all, I can't offer specific comments. Most of them used to require a servlet containers. The Restlet framework by Jerome Louvel never did, and that's why I liked it. Its documentation leaves to be desired and if you look at its code, it's over-architected to a comical degree, but then what ambitious Java framework isn't. Another newcomer that is strictly about REST and seems lightweight is Wink, an Apache incubated project. I haven't tried it, but it looks promising. And of course, one should not forget the reference implementation Jersey. Jersey has the advantage of being the most up-to-date with the spec at any given time. Originally it was dependent on Tomcat. Nowadays, it seems it can run standalone so it's on par with Restlet which I mentioned first because that's what I have mostly used. Here are some further reading resources, may their representational state be transferred to your brain and properly encoded from HTML/PDF to a compact and efficient neural net: The Wikipedia article on REST is not in very good shape, but still a starting point if you want to dig deeper into the conceptual framework. Refcard from Dzone.com: http://refcardz.dzone.com/refcardz/rest-foundations-restful#refcard-download-social-buttons-display Wink's User Guide seems well written. Since it's an implementation of JAX-RS, it's a good documentation of that technology. https://dzone.com/articles/putting-java-rest: A fairly good show-and-tell introduction to the JAX-RS API, with a link in there to a more in-depth description of REST concepts by the same author. Worth the read. http://jcp.org/en/jsr/detail?id=311: The official JSR 311 page. Download the specification and API Javadocs from there. http://jsr311.java.net/nonav/javadoc/index.html: Online access of JSR 311 Javadocs. If you know of something better, something nice, please post it in a comment and I'll include in this list. PS: I'm curious if people start new projects with Servlets, JSP/JSF these days? I would be curious as to what the rationale would be to pick those over AJAX + RESTful services communication via JSON. As I said above, this entry is intended to help readers of the eValhalla blogs series which chronicles the development of the eValhalla project following precisely the AJAX+REST model.

This is the second article in the series of "Become a Java GC Expert". In the first issue Understanding Java Garbage Collection we have learned about the processes for different GC algorithms, about how GC works, what Young and Old Generation is, what you should know about the 5 types of GC in the new JDK 7, and what the performance implications are for each of these GC types. In this article, I will explain how JVM is actually running Garbage Collection in the real time. What is GC Monitoring? Garbage Collection Monitoring refers to the process of figuring out how JVM is running GC. For example, we can find out: when an object in young has moved to old and by how much, or when stop-the-world has occurred and for how long. GC monitoring is carried out to see if JVM is running GC efficiently, and to check if additional GC tuning is necessary. Based on this information, the application can be edited or GC method can be changed (GC tuning). How to Monitor GC? There are different ways to monitor GC, but the only difference is how the GC operation information is shown. GC is done by JVM, and since the GC monitoring tools disclose the GC information provided by JVM, you will get the same results no matter how you monitor GC. Therefore, you do not need to learn all methods to monitor GC, but since it only requires a little amount of time to learn each GC monitoring method, knowing a few of them can help you use the right one for different situations and environments. The tools or JVM options listed below cannot be used universally regardless of the HVM vendor. This is because there is no need for a "standard" for disclosing GC information. In this example we will use HotSpot JVM (Oracle JVM). Since NHN is using Oracle (Sun) JVM, there should be no difficulties in applying the tools or JVM options that we are explaining here. First, the GC monitoring methods can be separated into CUI and GUI depending on the access interface. The typical CUI GC monitoring method involves using a separate CUI application called "jstat", or selecting a JVM option called "verbosegc" when running JVM. GUI GC monitoring is done by using a separate GUI application, and three most commonly used applications would be "jconsole", "jvisualvm" and "Visual GC". Let's learn more about each method. jstat jstat is a monitoring tool in HotSpot JVM. Other monitoring tools for HotSpot JVM are jps and jstatd. Sometimes, you need all three tools to monitor a Java application. jstat does not provide only the GC operation information display. It also provides class loader operation information or Just-in-Time compiler operation information. Among all the information jstat can provide, in this article we will only cover its functionality to monitor GC operating information. jstat is located in $JDK_HOME/bin, so if java or javac can run without setting a separate directory from the command line, so can jstat. You can try running the following in the command line. $> jstat –gc $ 1000 S0C S1C S0U S1U EC EU OC OU PC PU YGC YGCT FGC FGCT GCT 3008.0 3072.0 0.0 1511.1 343360.0 46383.0 699072.0 283690.2 75392.0 41064.3 2540 18.454 4 1.133 19.588 3008.0 3072.0 0.0 1511.1 343360.0 47530.9 699072.0 283690.2 75392.0 41064.3 2540 18.454 4 1.133 19.588 3008.0 3072.0 0.0 1511.1 343360.0 47793.0 699072.0 283690.2 75392.0 41064.3 2540 18.454 4 1.133 19.588 $> Just like in the example, the real type data will be output along with the following columns: S0C S1C S0U S1U EC EU OC OU PC. vmid (Virtual Machine ID), as its name implies, is the ID for the VM. Java applications running either on a local machine or on a remote machine can be specified using vmid. The vmid for Java application running on a local machine is called lvmid (Local vmid), and usually is PID. To find out the lvmid, you can write the PID value using a ps command or Windows task manager, but we suggest jps because PID and lvmid does not always match. jps stands for Java PS. jps shows vmids and main method information. Just like ps shows PIDs and process names. Find out the vmid of the Java application that you want to monitor by using jps, then use it as a parameter in jstat. If you use jps alone, only bootstrap information will show when several WAS instances are running in one equipment. We suggest that you use ps -ef | grep java command along with jps. GC performance data needs constant observation, therefore when running jstat, try to output the GC monitoring information on a regular basis. For example, running "jstat –gc 1000" (or 1s) will display the GC monitoring data on the console every 1 second. "jstat –gc 1000 10" will display the GC monitoring information once every 1 second for 10 times in total. There are many options other than -gc, among which GC related ones are listed below. Option Name Description gc It shows the current size for each heap area and its current usage (Ede, survivor, old, etc.), total number of GC performed, and the accumulated time for GC operations. gccapactiy It shows the minimum size (ms) and maximum size (mx) of each heap area, current size, and the number of GC performed for each area. (Does not show current usage and accumulated time for GC operations.) gccause It shows the "information provided by -gcutil" + reason for the last GC and the reason for the current GC. gcnew Shows the GC performance data for the new area. gcnewcapacity Shows statistics for the size of new area. gcold Shows the GC performance data for the old area. gcoldcapacity Shows statistics for the size of old area. gcpermcapacity Shows statistics for the permanent area. gcutil Shows the usage for each heap area in percentage. Also shows the total number of GC performed and the accumulated time for GC operations. Only looking at frequency, you will probably use -gcutil (or -gccause), -gc and -gccapacity the most in that order. -gcutil is used to check the usage of heap areas, the number of GC performed, and the total accumulated time for GC operations, while -gccapacity option and others can be used to check the actual size allocated. You can see the following output by using the -gc option: S0C S1C … GCT 1248.0 896.0 … 1.246 1248.0 896.0 … 1.246 … … … … Different jstat options show different types of columns, which are listed below. Each column information will be displayed when you use the "jstat option" listed on the right. Column Description Jstat Option S0C Displays the current size of Survivor0 area in KB -gc -gccapacity -gcnew -gcnewcapacity S1C Displays the current size of Survivor1 area in KB -gc -gccapacity -gcnew -gcnewcapacity S0U Displays the current usage of Survivor0 area in KB -gc -gcnew S1U Displays the current usage of Survivor1 area in KB -gc -gcnew EC Displays the current size of Eden area in KB -gc -gccapacity -gcnew -gcnewcapacity EU Displays the current usage of Eden area in KB -gc -gcnew OC Displays the current size of old area in KB -gc -gccapacity -gcold -gcoldcapacity OU Displays the current usage of old area in KB -gc -gcold PC Displays the current size of permanent area in KB -gc -gccapacity -gcold -gcoldcapacity -gcpermcapacity PU Displays the current usage of permanent area in KB -gc -gcold YGC The number of GC event occurred in young area -gc -gccapacity -gcnew -gcnewcapacity -gcold -gcoldcapacity -gcpermcapacity -gcutil -gccause YGCT The accumulated time for GC operations for Yong area -gc -gcnew -gcutil -gccause FGC The number of full GC event occurred -gc -gccapacity -gcnew -gcnewcapacity -gcold -gcoldcapacity -gcpermcapacity -gcutil -gccause FGCT The accumulated time for full GC operations -gc -gcold -gcoldcapacity -gcpermcapacity -gcutil -gccause GCT The total accumulated time for GC operations -gc -gcold -gcoldcapacity -gcpermcapacity -gcutil -gccause NGCMN The minimum size of new area in KB -gccapacity -gcnewcapacity NGCMX The maximum size of max area in KB -gccapacity -gcnewcapacity NGC The current size of new area in KB -gccapacity -gcnewcapacity OGCMN The minimum size of old area in KB -gccapacity -gcoldcapacity OGCMX The maximum size of old area in KB -gccapacity -gcoldcapacity OGC The current size of old area in KB -gccapacity -gcoldcapacity PGCMN The minimum size of permanent area in KB -gccapacity -gcpermcapacity PGCMX The maximum size of permanent area in KB -gccapacity -gcpermcapacity PGC The current size of permanent generation area in KB -gccapacity -gcpermcapacity PC The current size of permanent area in KB -gccapacity -gcpermcapacity PU The current usage of permanent area in KB -gc -gcold LGCC The cause for the last GC occurrence -gccause GCC The cause for the current GC occurrence -gccause TT Tenuring threshold. If copied this amount of times in young area (S0 ->S1, S1->S0), they are then moved to old area. -gcnew MTT Maximum Tenuring threshold. If copied this amount of times inside young arae, then they are moved to old area. -gcnew DSS Adequate size of survivor in KB -gcnew The advantage of jstat is that it can always monitor the GC operation data of Java applications running on local/remote machine, as long as a console can be used. From these items, the following result is output when –gcutil is used. At the time of GC tuning, pay careful attention to YGC, YGCT, FGC, FGCT and GCT. S0 S1 E O P YGC YGCT FGC FGCT GCT 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 These items are important because they show how much time was spent in running GC. In this example, YGC is 217 and YGCT is 0.928. So, after calculating the arithmetical average, you can see that it required about 4 ms (0.004 seconds) for each young GC. Likewise, the average full GC time us 33ms. But the arithmetical average often does not help analyzing the actual GC problem. This is due to the severe deviations in GC operation time. (In other words, if the average time is 0.067 seconds for a full GC, one GC may have lasted 1 ms while the other one lasted 57 ms.) In order to check the individual GC time instead of the arithmetical average time, it is better to use -verbosegc. -verbosegc -verbosegc is one of the JVM options specified when running a Java application. While jstat can monitor any JVM application that has not specified any options, -verbosegc needs to be specified in the beginning, so it could be seen as an unnecessary option (since jstat can be used instead). However, as -verbosegc displays easy to understand output results whenever a GC occurs, it is very helpful for monitoring rough GC information. jstat -verbosegc Monitoring Target Java application running on a machine that can log in to a terminal, or a remote Java application that can connect to the network by using jstatd Only when -verbogc was specified as a JVM starting option Output information Heap status (usage, maximum size, number of times for GC/time, etc.) Size of ew and old area before/after GC, and GC operation time Output Time Every designated time Whenever GC occurs Whenever useful When trying to observe the changes of the size of heap area When trying to see the effect of a single GC The followings are other options that can be used with -verbosegc. -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps (from JDK 6 update 4) If only -verbosegc is used, then -XX:+PrintGCDetails is applied by default. Additional options for –verbosgc are not exclusive and can be mixed and used together. When using -verbosegc, you can see the results in the following format whenever a minor GC occurs. [GC [: -> , secs] -> , secs] ] Collector Name of Collector Used for minor gc starting occupancy1 The size of young area before GC ending occupancy1 The size of young area after GC pause time1 The time when the Java application stopped running for minor GC starting occupancy3 The total size of heap area before GC ending occupancy3 The total size of heap area after GC pause time3 The time when the Java application stopped running for overall heap GC, including major GC This is an example of -verbosegc output for minor GC: S0 S1 E O P YGC YGCT FGC FGCT GCT 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 0.00 66.44 54.12 10.58 86.63 217 0.928 2 0.067 0.995 This is the example of output results after an Full GC occurred. [Full GC [Tenured: 3485K->4095K(4096K), 0.1745373 secs] 61244K->7418K(63104K), [Perm : 10756K->10756K(12288K)], 0.1762129 secs] [Times: user=0.19 sys=0.00, real=0.19 secs] If a CMS collector is used, then the following CMS information can be provided as well. As -verbosegc option outputs a log every time a GC event occurs, it is easy to see the changes of the heap usage rates caused by GC operation. (Java) VisualVM + Visual GC Java Visual VM is a GUI profiling/monitoring tool provided by Oracle JDK. Figure 1: VisualVM Screenshot. Instead of the version that is included with JDK, you can download Visual VM directly from its website. For the sake of convenience, the version included with JDK will be referred to as Java VisualVM (jvisualvm), and the version available from the website will be referred to as Visual VM (visualvm). The features of the two are not exactly identical, as there are slight differences, such as when installing plug-ins. Personally, I prefer the Visual VM version, which can be downloaded from the website. After running Visual VM, if you select the application that you wish to monitor from the window on the left side, you can find the "Monitoring" tab there. You can get the basic information about GC and Heap from this Monitoring tab. Though the basic GC status is also available through the basic features of VisualVM, you cannot access detailed information that is available from either jstat or -verbosegc option. If you want the detailed information provided by jstat, then it is recommended to install the Visual GC plug-in. Visual GC can be accessed in real time from the Tools menu. Figure 2: Viusal GC Installation Screenshot. By using Visual GC, you can see the information provided by running jstatd in a more intuitive way. Figure 3: Visual GC execution screenshot. HPJMeter HPJMeter is convenient for analyzing -verbosegc output results. If Visual GC can be considered as the GUI equivalent of jstat, then HPJMeter would be the GUI equivalent of -verbosgc. Of course, GC analysis is just one of the many features provided by HPJMeter. HPJMeter is a performance monitoring tool developed by HP. It can be used in HP-UX, as well as Linux and MS Windows. Originally, a tool called HPTune used to provide the GUI analysis feature for -verbosegc. However, since the HPTune feature has been integrated into HPJMeter since version 3.0, there is no need to download HPTune separately. When executing an application, the -verbosegc output results will be redirected to a separate file. You can open the redirected file with HPJMeter, which allows faster and easier GC performance data analysis through the intuitive GUI. Figure 4: HPJMeter.

If you've read some of my other articles on this blog you probably know I'm a fan of HTML5. With HTML5 we get all this interesting functionality, directly in the browser, in a way that, eventually, is standard across browsers. One of the new HTML5 APIs that is slowly moving through the standardization process is the Web Audio API. With this API, currently only supported in Chrome, we get access to all kinds of interesting audio components you can use to create, modify and visualize sounds (such as the following spectrogram). So why do I start with visualizations? It looks nice, that's one reason, but not the important one. This API provides a number of more complex components, whose behavior is much easier to explain when you can see what happens. With a filter you can instantly see whether some frequencies are filtered, instead of trying to listen to the resulting audio for thse changes. There are many interesting examples that use this API. The problem is, though, that getting started with this API and with digital signal processing (DSP) usually isn't explained. In this article I'll walk you through a couple of steps that shows how to do the following: Create a signal volume meter Visualize the frequencies using a spectrum analyzer And show a time based spectrogram We start with the basic setup that we can use as the basis for the components we'll create. Setting up the basic If we want to experiment with sound, we need some sound source. We could use the microphone (as we'll do later in this series), but to keep it simple, for now we'll just use an mp3 as our input. To get this working using web audio we have to take the following steps: Load the data Read it in a buffer node and play the sound Load the data With the web audio we can use different types of audio sources. We've got a MediaElementAudioSourceNode that can be used to use the audio provided by a media element. There's also a MediaStreamAudioSourceNode. With this audio source node we can use the microphone as input (see my previous article on sound recognition). Finally there is the AudioBufferSourceNode. With this node we can load the data from an existing audio file (e.g mp3) and use that as input. For this example we'll use this last approach. // create the audio context (chrome only for now) var context = new webkitAudioContext(); var audioBuffer; var sourceNode; // load the sound setupAudioNodes(); loadSound("wagner-short.ogg"); function setupAudioNodes() { // create a buffer source node sourceNode = context.createBufferSource(); // and connect to destination sourceNode.connect(context.destination); } // load the specified sound function loadSound(url) { var request = new XMLHttpRequest(); request.open('GET', url, true); request.responseType = 'arraybuffer'; // When loaded decode the data request.onload = function() { // decode the data context.decodeAudioData(request.response, function(buffer) { // when the audio is decoded play the sound playSound(buffer); }, onError); } request.send(); } function playSound(buffer) { sourceNode.buffer = buffer; sourceNode.noteOn(0); } // log if an error occurs function onError(e) { console.log(e); } In this example you can see a couple of functions. The setupAudioNodes function creates a BufferSource audio node and connects it to the destination. The loadSound function shows how you can load an audio file. The buffer which is passed into the playSound function contains decoded audio that can be used by the web audio API. In this example I use an .ogg file, for a complete overview of the formats supported look at: https://sites.google.com/a/chromium.org/dev/audio-video Play the sound To play this audio file, all we have to do is turn the source node on, this is done in the playSound function: function playSound(buffer) { sourceNode.buffer = buffer; sourceNode.noteOn(0); } You can test this out at the following page: Example 1: Loading and playing a sound with Web Audio API. When you open that page, you'll hear some music. Nothing to spectacular for now, but nevertheless an easy way to load audio that'll use for the rest of this article. The first item on our list was the volume meter. Create a volume meter One of the basic scenario's, and often one of the first steps someone new to this API tries to create, is a simple signal volume meter (or an UV meter). I expected this to be a standard component in this API, where I could just read off the signal strength as a property. But, no such node exists. But not to worry, with the components that are available, it's pretty easy (not straightforward, but easy nevertheless) to get an indication of the signal strength of your audio file. Int this section we'll create the following simple volume meter: As you can see this is a simple volume meter where we measure the signal strength for the left and the right audio channel. This is drawn on the canvas, but you could have also used divs or svg to visualize this. Lets start with a single volume meter, instead of one for each channel. For this we need to do the following: Create an analyzer node: With this node we get realtime information about the data that is processed. This data we use to determine the signal strength Create a javascript node: We use this node as a timer to update the volume meters with new information Connect everything together Analyser node With the analyser node we can perform real-time frequency and time domain analysis. From the specification: a node which is able to provide real-time frequency and time-domain analysis information. The audio stream will be passed un-processed from input to output. I won't go into the mathematical details behind this node, since there are many articles out there that explain how this works (a good one is the chapter on fourier transformation from here). What you should now about this node is that it splits up the signal in frequency buckets and we get the amplitude (the signal strenght) for each set of frequencies (the bucket). The best way to understand this, is to skip a bit ahead in this article and look at the frequency distribution we'll create later on. This image plots the result from the analyser node. The frequencies increase from left to right, and the height of the bar shows the strength of that specific frequency bucket. More on this later on in the article. For now we don't want to see the strength of the separate frequency buckets, but the strength of the total signal. For this we'll just add all the strenghts from each bucket and divide it by the number of buckets. First we need to create an analyzer node // setup a analyzer analyser = context.createAnalyser(); analyser.smoothingTimeConstant = 0.3; analyser.fftSize = 1024; This creates an analyzer node whose result will be used to create the volume meter. We use a smoothingTimeConstant to make the meter less jittery. With this variable we use input from a longer time period to calculate the amplitudes, this results in a more smooth meter. The fftSize determine how many buckets we get containing frequency information. If we have a fftSize of 1024 we get 512 buckets (more info on this in the book on DPS and fourier transformations). When this node receives a stream of data, it analyzes this stream and provides us with information about the frequencies in that signal and their strengths. We now need a timer to update the meter at regular intervals. We could use the standard javascript setInterval function, but since we're looking at the Web Audio API lets use one of its nodes. The JavaScriptNode. The javascript node With the javascriptnode we can process the raw audio data directly from javascript. We can use this to write our own analyzers or complex components. We're not going to do that, though. When creating the javascript node, you can specify the interval at which it is called. We'll use that feature to update the meter at regulat intervals. Creating a javascript node is very easy. // setup a javascript node javascriptNode = context.createJavaScriptNode(2048, 1, 1); This will create a javascriptnode that is called whenever the 2048 frames have been sampled. Since our data is sampled at 44.1k, this function will be called approximately 21 times a second. Now what happens when this function is called: // when the javascript node is called // we use information from the analyzer node // to draw the volume javascriptNode.onaudioprocess = function() { // get the average, bincount is fftsize / 2 var array = new Uint8Array(analyser.frequencyBinCount); analyser.getByteFrequencyData(array); var average = getAverageVolume(array) // clear the current state ctx.clearRect(0, 0, 60, 130); // set the fill style ctx.fillStyle=gradient; // create the meters ctx.fillRect(0,130-average,25,130); } function getAverageVolume(array) { var values = 0; var average; var length = array.length; // get all the frequency amplitudes for (var i = 0; i < length; i++) { values += array[i]; } average = values / length; return average; } In these two functions we calculate the average and draw the meter directly on the canvas (using a gradient so we have nice colors). Now all we have to do is connect the output from the audiosource to the analyser, the analyser to the javasource node (and if we want audio to hear, we also need to connect something to the destionation). Connect everything together Connecting everything together is easy: function setupAudioNodes() { // setup a javascript node javascriptNode = context.createJavaScriptNode(2048, 1, 1); // connect to destination, else it isn't called javascriptNode.connect(context.destination); // setup a analyzer analyser = context.createAnalyser(); analyser.smoothingTimeConstant = 0.3; analyser.fftSize = 1024; // create a buffer source node sourceNode = context.createBufferSource(); // connect the source to the analyser sourceNode.connect(analyser); // we use the javascript node to draw at a specific interval. analyser.connect(javascriptNode); // and connect to destination, if you want audio sourceNode.connect(context.destination); } And that's it. This will draw a single volume meter, for the complete signal. Now what do we do when we want to have a volume meter for each channel. For this we use a ChannelSplitter. Let's dive right into the code to connect everything: function setupAudioNodes() { // setup a javascript node javascriptNode = context.createJavaScriptNode(2048, 1, 1); // connect to destination, else it isn't called javascriptNode.connect(context.destination); // setup a analyzer analyser = context.createAnalyser(); analyser.smoothingTimeConstant = 0.3; analyser.fftSize = 1024; analyser2 = context.createAnalyser(); analyser2.smoothingTimeConstant = 0.0; analyser2.fftSize = 1024; // create a buffer source node sourceNode = context.createBufferSource(); splitter = context.createChannelSplitter(); // connect the source to the analyser and the splitter sourceNode.connect(splitter); // connect one of the outputs from the splitter to // the analyser splitter.connect(analyser,0,0); splitter.connect(analyser2,1,0); // we use the javascript node to draw at a // specific interval. analyser.connect(javascriptNode); // and connect to destination sourceNode.connect(context.destination); } As you can see we don't really change much. We introduce a new node, the splitter node. This node splits the sound into a left and a right channel. These channels can be processed separately. With this layout the following happens: The audiosource creates a signal based on the buffered audio. This signal is sent to the splitter, who splits the signal into a left and right stream. Each of these two streams is processed by their own realtime analyser. From the javascript node, we now get the information from both analysers and plot both meters I've shown step 1 through 3, let's quickly move on the step 4. For this we simply add the following to the onaudioprocess node: javascriptNode.onaudioprocess = function() { // get the average for the first channel var array = new Uint8Array(analyser.frequencyBinCount); analyser.getByteFrequencyData(array); var average = getAverageVolume(array); // get the average for the second channel var array2 = new Uint8Array(analyser2.frequencyBinCount); analyser2.getByteFrequencyData(array2); var average2 = getAverageVolume(array2); // clear the current state ctx.clearRect(0, 0, 60, 130); // set the fill style ctx.fillStyle=gradient; // create the meters ctx.fillRect(0,130-average,25,130); ctx.fillRect(30,130-average2,25,130); } And now we've got two signal meters, one for each channel. Example 2: Visualize the signal strength with a volume meter. Or view the result on youtube: Now lets see how we can get the view of the frequencies I showed earlier. Create a frequency spectrum With all the work we already did in the previous section, creating a frequency spectrum overview is now very easy. We're going to aim for this: We set up the nodes just like we did in the first example: function setupAudioNodes() { // setup a javascript node javascriptNode = context.createJavaScriptNode(2048, 1, 1); // connect to destination, else it isn't called javascriptNode.connect(context.destination); // setup a analyzer analyser = context.createAnalyser(); analyser.smoothingTimeConstant = 0.3; analyser.fftSize = 512; // create a buffer source node sourceNode = context.createBufferSource(); sourceNode.connect(analyser); analyser.connect(javascriptNode); // sourceNode.connect(context.destination); } So this time we don't split the channels and we set the fftSize to 512. This means we get 256 bars that represent our frequency. We now just need to alter the onaudioprocess method and the gradient we use: var gradient = ctx.createLinearGradient(0,0,0,300); gradient.addColorStop(1,'#000000'); gradient.addColorStop(0.75,'#ff0000'); gradient.addColorStop(0.25,'#ffff00'); gradient.addColorStop(0,'#ffffff'); // when the javascript node is called // we use information from the analyzer node // to draw the volume javascriptNode.onaudioprocess = function() { // get the average for the first channel var array = new Uint8Array(analyser.frequencyBinCount); analyser.getByteFrequencyData(array); // clear the current state ctx.clearRect(0, 0, 1000, 325); // set the fill style ctx.fillStyle=gradient; drawSpectrum(array); } function drawSpectrum(array) { for ( var i = 0; i < (array.length); i++ ){ var value = array[i]; ctx.fillRect(i*5,325-value,3,325); } }; In the drawSpectrum function we iterate over the array, and draw a vertical bar based on the value. That's it. For a live example, click on the following link: Example 3: Visualize the frequency spectrum. Or view it on youtube: And then the final one. The spectrogram. Time based spectrogram When you run the previous demo you see the strength of the various frequency buckets in real time. While this is a nice visualization, it doesn't allow you to analyze information over a period of time. If you want to do that you can create a spectrogram. With a spectrogram we plot a single line for each measurement. The y-axis represents the frequency, the x-asis the time and the color of a pixel the strength of that frequency. It can be used to analyze the received audio, and also creates nice looking images. The good thing, is that to output this data we don't have to change much from what we've already got in place. The only function that'll change is the onaudioprocess node and we'll create a slightly different analyser. analyser = context.createAnalyser(); analyser.smoothingTimeConstant = 0; analyser.fftSize = 1024; The enalyser we create here has an fftSize of 1024, this means we get 512 frequency buckets with strengths. So we can draw a spectrogram that has a height of 512 pixels. Also note that the smoothingTimeConstant is set to 0. This means we don't use any of the previous results in the analysis. We want to show the real information, not provide a smooth volume meter or frequency spectrum analysis. The easiest way to draw a spectrogram is by just start drawing the line at the left, and for each new set of frequencies increase the x-coordinate by one. The problem is that this will quickly fill up our canvas, and we'll only be able to see the first half a minute of the audio. To fix this, we need some creative canvas copying. The complete code for drawing the spectrogram is shown here: // create a temp canvas we use for copying and scrolling var tempCanvas = document.createElement("canvas"), tempCtx = tempCanvas.getContext("2d"); tempCanvas.width=800; tempCanvas.height=512; // used for color distribution var hot = new chroma.ColorScale({ colors:['#000000', '#ff0000', '#ffff00', '#ffffff'], positions:[0, .25, .75, 1], mode:'rgb', limits:[0, 300] }); ... // when the javascript node is called // we use information from the analyzer node // to draw the volume javascriptNode.onaudioprocess = function () { // get the average for the first channel var array = new Uint8Array(analyser.frequencyBinCount); analyser.getByteFrequencyData(array); // draw the spectrogram if (sourceNode.playbackState == sourceNode.PLAYING_STATE) { drawSpectrogram(array); } } function drawSpectrogram(array) { // copy the current canvas onto the temp canvas var canvas = document.getElementById("canvas"); tempCtx.drawImage(canvas, 0, 0, 800, 512); // iterate over the elements from the array for (var i = 0; i < array.length; i++) { // draw each pixel with the specific color var value = array[i]; ctx.fillStyle = hot.getColor(value).hex(); // draw the line at the right side of the canvas ctx.fillRect(800 - 1, 512 - i, 1, 1); } // set translate on the canvas ctx.translate(-1, 0); // draw the copied image ctx.drawImage(tempCanvas, 0, 0, 800, 512, 0, 0, 800, 512); // reset the transformation matrix ctx.setTransform(1, 0, 0, 1, 0, 0); } To draw the spectrogram we do the following: We copy what is currently drawn to a hidden canvas Next we draw a line of the current values at the far right of the canvas We set the translate on the canvas to -1 We copy the copied information back to the original canvas (that is now drawn 1 pixel to the left) And reset the transformation matrix See a running example here: Example 4: Create a spectrogram Or view it here: Last thing I'd like to mention regarding the code is the chroma.js library I used for the colors. If you ever need to draw something color or gradient related (e.g maps, strengths, levels) you can easily create color scales with this library. Two final pointers, I know I'll get questions about: Volume could be represented as a magnitude, just didn't want to complicate matters for this. The spectogram doesn't use logarithmic scales. Once again, didn't want to complicate things

Learn about the structure of JVM, how it works, executes Java bytecode, the order of execution, examples of common mistakes and their solutions, new Java SE 7 features.

The content of this article was originally written by Tae Jin Gu on the Cubrid blog. When there is an obstacle, or when a Java based Web application is running much slower than expected, we need to use thread dumps. If thread dumps feel like very complicated to you, this article may help you very much. Here I will explain what threads are in Java, their types, how they are created, how to manage them, how you can dump threads from a running application, and finally how you can analyze them and determine the bottleneck or blocking threads. This article is a result of long experience in Java application debugging. Java and Thread A web server uses tens to hundreds of threads to process a large number of concurrent users. If two or more threads utilize the same resources, a contention between the threads is inevitable, and sometimes deadlock occurs. Thread contention is a status in which one thread is waiting for a lock, held by another thread, to be lifted. Different threads frequently access shared resources on a web application. For example, to record a log, the thread trying to record the log must obtain a lock and access the shared resources. Deadlock is a special type of thread contention, in which two or more threads are waiting for the other threads to complete their tasks in order to complete their own tasks. Different issues can arise from thread contention. To analyze such issues, you need to use the thread dump. A thread dump will give you the information on the exact status of each thread. Background Information for Java Threads Thread Synchronization A thread can be processed with other threads at the same time. In order to ensure compatibility when multiple threads are trying to use shared resources, one thread at a time should be allowed to access the shared resources by using thread synchronization. Thread synchronization on Java can be done using monitor. Every Java object has a single monitor. The monitor can be owned by only one thread. For a thread to own a monitor that is owned by a different thread, it needs to wait in the wait queue until the other thread releases its monitor. Thread Status In order to analyze a thread dump, you need to know the status of threads. The statuses of threads are stated on java.lang.Thread.State. Figure 1: Thread Status. NEW: The thread is created but has not been processed yet. RUNNABLE: The thread is occupying the CPU and processing a task. (It may be in WAITING status due to the OS's resource distribution.) BLOCKED: The thread is waiting for a different thread to release its lock in order to get the monitor lock. WAITING: The thread is waiting by using a wait, join or park method. TIMED_WAITING: The thread is waiting by using a sleep, wait, join or park method. (The difference from WAITING is that the maximum waiting time is specified by the method parameter, and WAITING can be relieved by time as well as external changes.) Thread Types Java threads can be divided into two: daemon threads; and non-daemon threads. Daemon threads stop working when there are no other non-daemon threads. Even if you do not create any threads, the Java application will create several threads by default. Most of them are daemon threads, mainly for processing tasks such as garbage collection or JMX. A thread running the 'static void main(String[] args)’ method is created as a non-daemon thread, and when this thread stops working, all other daemon threads will stop as well. (The thread running this main method is called the VM thread in HotSpot VM.) Getting a Thread Dump We will introduce the three most commonly used methods. Note that there are many other ways to get a thread dump. A thread dump can only show the thread status at the time of measurement, so in order to see the change in thread status, it is recommended to extract them from 5 to 10 times with 5-second intervals. Getting a Thread Dump Using jstack In JDK 1.6 and higher, it is possible to get a thread dump on MS Windows using jstack. Use PID via jps to check the PID of the currently running Java application process. [user@linux ~]$ jps -v 25780 RemoteTestRunner -Dfile.encoding=UTF-8 25590 sub.rmi.registry.RegistryImpl 2999 -Dapplication.home=/home1/user/java/jdk.1.6.0_24 -Xms8m 26300 sun.tools.jps.Jps -mlvV -Dapplication.home=/home1/user/java/jdk.1.6.0_24 -Xms8m Use the extracted PID as the parameter of jstack to obtain a thread dump. [user@linux ~]$ jstack -f 5824 A Thread Dump Using jVisualVM Generate a thread dump by using a program such as jVisualVM. Figure 2: A Thread Dump Using visualvm. The task on the left indicates the list of currently running processes. Click on the process for which you want the information, and select the thread tab to check the thread information in real time. Click the Thread Dump button on the top right corner to get the thread dump file. Generating in a Linux Terminal Obtain the process pid by using ps -ef command to check the pid of the currently running Java process. [user@linux ~]$ ps - ef | grep java user 2477 1 0 Dec23 ? 00:10:45 ... user 25780 25361 0 15:02 pts/3 00:00:02 ./jstatd -J -Djava.security.policy=jstatd.all.policy -p 2999 user 26335 25361 0 15:49 pts/3 00:00:00 grep java Use the extracted pid as the parameter of kill –SIGQUIT(3) to obtain a thread dump. Thread Information from the Thread Dump File "pool-1-thread-13" prio=6 tid=0x000000000729a000 nid=0x2fb4 runnable [0x0000000007f0f000] java.lang.Thread.State: RUNNABLE at java.net.SocketInputStream.socketRead0(Native Method) at java.net.SocketInputStream.read(SocketInputStream.java:129) at sun.nio.cs.StreamDecoder.readBytes(StreamDecoder.java:264) at sun.nio.cs.StreamDecoder.implRead(StreamDecoder.java:306) at sun.nio.cs.StreamDecoder.read(StreamDecoder.java:158) - locked <0x0000000780b7e688> (a java.io.InputStreamReader) at java.io.InputStreamReader.read(InputStreamReader.java:167) at java.io.BufferedReader.fill(BufferedReader.java:136) at java.io.BufferedReader.readLine(BufferedReader.java:299) - locked <0x0000000780b7e688> (a java.io.InputStreamReader) at java.io.BufferedReader.readLine(BufferedReader.java:362) ) Thread name: When using Java.lang.Thread class to generate a thread, the thread will be named Thread-(Number), whereas when using java.util.concurrent.ThreadFactory class, it will be named pool-(number)-thread-(number). Priority: Represents the priority of the threads. Thread ID: Represents the unique ID for the threads. (Some useful information, including the CPU usage or memory usage of the thread, can be obtained by using thread ID.) Thread status: Represents the status of the threads. Thread callstack: Represents the call stack information of the threads. Thread Dump Patterns by Type When Unable to Obtain a Lock (BLOCKED) This is when the overall performance of the application slows down because a thread is occupying the lock and prevents other threads from obtaining it. In the following example, BLOCKED_TEST pool-1-thread-1 thread is running with <0x0000000780a000b0> lock, while BLOCKED_TEST pool-1-thread-2 and BLOCKED_TEST pool-1-thread-3 threads are waiting to obtain <0x0000000780a000b0> lock. Figure 3: A thread blocking other threads. "BLOCKED_TEST pool-1-thread-1" prio=6 tid=0x0000000006904800 nid=0x28f4 runnable [0x000000000785f000] java.lang.Thread.State: RUNNABLE at java.io.FileOutputStream.writeBytes(Native Method) at java.io.FileOutputStream.write(FileOutputStream.java:282) at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:65) at java.io.BufferedOutputStream.flush(BufferedOutputStream.java:123) - locked <0x0000000780a31778> (a java.io.BufferedOutputStream) at java.io.PrintStream.write(PrintStream.java:432) - locked <0x0000000780a04118> (a java.io.PrintStream) at sun.nio.cs.StreamEncoder.writeBytes(StreamEncoder.java:202) at sun.nio.cs.StreamEncoder.implFlushBuffer(StreamEncoder.java:272) at sun.nio.cs.StreamEncoder.flushBuffer(StreamEncoder.java:85) - locked <0x0000000780a040c0> (a java.io.OutputStreamWriter) at java.io.OutputStreamWriter.flushBuffer(OutputStreamWriter.java:168) at java.io.PrintStream.newLine(PrintStream.java:496) - locked <0x0000000780a04118> (a java.io.PrintStream) at java.io.PrintStream.println(PrintStream.java:687) - locked <0x0000000780a04118> (a java.io.PrintStream) at com.nbp.theplatform.threaddump.ThreadBlockedState.monitorLock(ThreadBlockedState.java:44) - locked <0x0000000780a000b0> (a com.nbp.theplatform.threaddump.ThreadBlockedState) at com.nbp.theplatform.threaddump.ThreadBlockedState$1.run(ThreadBlockedState.java:7) at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) at java.lang.Thread.run(Thread.java:662) Locked ownable synchronizers: - <0x0000000780a31758> (a java.util.concurrent.locks.ReentrantLock$NonfairSync) "BLOCKED_TEST pool-1-thread-2" prio=6 tid=0x0000000007673800 nid=0x260c waiting for monitor entry [0x0000000008abf000] java.lang.Thread.State: BLOCKED (on object monitor) at com.nbp.theplatform.threaddump.ThreadBlockedState.monitorLock(ThreadBlockedState.java:43) - waiting to lock <0x0000000780a000b0> (a com.nbp.theplatform.threaddump.ThreadBlockedState) at com.nbp.theplatform.threaddump.ThreadBlockedState$2.run(ThreadBlockedState.java:26) at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) at java.lang.Thread.run(Thread.java:662) Locked ownable synchronizers: - <0x0000000780b0c6a0> (a java.util.concurrent.locks.ReentrantLock$NonfairSync) "BLOCKED_TEST pool-1-thread-3" prio=6 tid=0x00000000074f5800 nid=0x1994 waiting for monitor entry [0x0000000008bbf000] java.lang.Thread.State: BLOCKED (on object monitor) at com.nbp.theplatform.threaddump.ThreadBlockedState.monitorLock(ThreadBlockedState.java:42) - waiting to lock <0x0000000780a000b0> (a com.nbp.theplatform.threaddump.ThreadBlockedState) at com.nbp.theplatform.threaddump.ThreadBlockedState$3.run(ThreadBlockedState.java:34) at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) at java.lang.Thread.run(Thread.java:662) Locked ownable synchronizers: - <0x0000000780b0e1b8> (a java.util.concurrent.locks.ReentrantLock$NonfairSync) When in Deadlock Status This is when thread A needs to obtain thread B's lock to continue its task, while thread B needs to obtain thread A's lock to continue its task. In the thread dump, you can see that DEADLOCK_TEST-1 thread has 0x00000007d58f5e48 lock, and is trying to obtain 0x00000007d58f5e60 lock. You can also see that DEADLOCK_TEST-2 thread has 0x00000007d58f5e60 lock, and is trying to obtain 0x00000007d58f5e78 lock. Also, DEADLOCK_TEST-3 thread has 0x00000007d58f5e78 lock, and is trying to obtain 0x00000007d58f5e48 lock. As you can see, each thread is waiting to obtain another thread's lock, and this status will not change until one thread discards its lock. Figure 4: Threads in a Deadlock status. "DEADLOCK_TEST-1" daemon prio=6 tid=0x000000000690f800 nid=0x1820 waiting for monitor entry [0x000000000805f000] java.lang.Thread.State: BLOCKED (on object monitor) at com.nbp.theplatform.threaddump.ThreadDeadLockState$DeadlockThread.goMonitorDeadlock(ThreadDeadLockState.java:197) - waiting to lock <0x00000007d58f5e60> (a com.nbp.theplatform.threaddump.ThreadDeadLockState$Monitor) at com.nbp.theplatform.threaddump.ThreadDeadLockState$DeadlockThread.monitorOurLock(ThreadDeadLockState.java:182) - locked <0x00000007d58f5e48> (a com.nbp.theplatform.threaddump.ThreadDeadLockState$Monitor) at com.nbp.theplatform.threaddump.ThreadDeadLockState$DeadlockThread.run(ThreadDeadLockState.java:135) Locked ownable synchronizers: - None "DEADLOCK_TEST-2" daemon prio=6 tid=0x0000000006858800 nid=0x17b8 waiting for monitor entry [0x000000000815f000] java.lang.Thread.State: BLOCKED (on object monitor) at com.nbp.theplatform.threaddump.ThreadDeadLockState$DeadlockThread.goMonitorDeadlock(ThreadDeadLockState.java:197) - waiting to lock <0x00000007d58f5e78> (a com.nbp.theplatform.threaddump.ThreadDeadLockState$Monitor) at com.nbp.theplatform.threaddump.ThreadDeadLockState$DeadlockThread.monitorOurLock(ThreadDeadLockState.java:182) - locked <0x00000007d58f5e60> (a com.nbp.theplatform.threaddump.ThreadDeadLockState$Monitor) at com.nbp.theplatform.threaddump.ThreadDeadLockState$DeadlockThread.run(ThreadDeadLockState.java:135) Locked ownable synchronizers: - None "DEADLOCK_TEST-3" daemon prio=6 tid=0x0000000006859000 nid=0x25dc waiting for monitor entry [0x000000000825f000] java.lang.Thread.State: BLOCKED (on object monitor) at com.nbp.theplatform.threaddump.ThreadDeadLockState$DeadlockThread.goMonitorDeadlock(ThreadDeadLockState.java:197) - waiting to lock <0x00000007d58f5e48> (a com.nbp.theplatform.threaddump.ThreadDeadLockState$Monitor) at com.nbp.theplatform.threaddump.ThreadDeadLockState$DeadlockThread.monitorOurLock(ThreadDeadLockState.java:182) - locked <0x00000007d58f5e78> (a com.nbp.theplatform.threaddump.ThreadDeadLockState$Monitor) at com.nbp.theplatform.threaddump.ThreadDeadLockState$DeadlockThread.run(ThreadDeadLockState.java:135) Locked ownable synchronizers: - None When Continuously Waiting to Receive Messages from a Remote Server The thread appears to be normal, since its state keeps showing as RUNNABLE. However, when you align the thread dumps chronologically, you can see that socketReadThread thread is waiting infinitely to read the socket. Figure 5: Continuous Waiting Status. "socketReadThread" prio=6 tid=0x0000000006a0d800 nid=0x1b40 runnable [0x00000000089ef000] java.lang.Thread.State: RUNNABLE at java.net.SocketInputStream.socketRead0(Native Method) at java.net.SocketInputStream.read(SocketInputStream.java:129) at sun.nio.cs.StreamDecoder.readBytes(StreamDecoder.java:264) at sun.nio.cs.StreamDecoder.implRead(StreamDecoder.java:306) at sun.nio.cs.StreamDecoder.read(StreamDecoder.java:158) - locked <0x00000007d78a2230> (a java.io.InputStreamReader) at sun.nio.cs.StreamDecoder.read0(StreamDecoder.java:107) - locked <0x00000007d78a2230> (a java.io.InputStreamReader) at sun.nio.cs.StreamDecoder.read(StreamDecoder.java:93) at java.io.InputStreamReader.read(InputStreamReader.java:151) at com.nbp.theplatform.threaddump.ThreadSocketReadState$1.run(ThreadSocketReadState.java:27) at java.lang.Thread.run(Thread.java:662) When Waiting The thread is maintaining WAIT status. In the thread dump, IoWaitThread thread keeps waiting to receive a message from LinkedBlockingQueue. If there continues to be no message for LinkedBlockingQueue, then the thread status will not change. Figure 6: Waiting status. "IoWaitThread" prio=6 tid=0x0000000007334800 nid=0x2b3c waiting on condition [0x000000000893f000] java.lang.Thread.State: WAITING (parking) at sun.misc.Unsafe.park(Native Method) - parking to wait for <0x00000007d5c45850> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject) at java.util.concurrent.locks.LockSupport.park(LockSupport.java:156) at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1987) at java.util.concurrent.LinkedBlockingDeque.takeFirst(LinkedBlockingDeque.java:440) at java.util.concurrent.LinkedBlockingDeque.take(LinkedBlockingDeque.java:629) at com.nbp.theplatform.threaddump.ThreadIoWaitState$IoWaitHandler2.run(ThreadIoWaitState.java:89) at java.lang.Thread.run(Thread.java:662) When Thread Resources Cannot be Organized Normally Unnecessary threads will pile up when thread resources cannot be organized normally. If this occurs, it is recommended to monitor the thread organization process or check the conditions for thread termination. Figure 7: Unorganized Threads. How to Solve Problems by Using Thread Dump Example 1: When the CPU Usage is Abnormally High 1. Extract the thread that has the highest CPU usage. [user@linux ~]$ ps -mo pid.lwp.stime.time.cpu -C java PID LWP STIME TIME %CPU 10029 - Dec07 00:02:02 99.5 - 10039 Dec07 00:00:00 0.1 - 10040 Dec07 00:00:00 95.5 From the application, find out which thread is using the CPU the most. Acquire the Light Weight Process (LWP) that uses the CPU the most and convert its unique number (10039) into a hexadecimal number (0x2737). 2. After acquiring the thread dump, check the thread's action. Extract the thread dump of an application with a PID of 10029, then find the thread with an nid of 0x2737. "NioProcessor-2" prio=10 tid=0x0a8d2800 nid=0x2737 runnable [0x49aa5000] java.lang.Thread.State: RUNNABLE at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method) at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:210) at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:65) at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:69) - locked <0x74c52678> (a sun.nio.ch.Util$1) - locked <0x74c52668> (a java.util.Collections$UnmodifiableSet) - locked <0x74c501b0> (a sun.nio.ch.EPollSelectorImpl) at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:80) at external.org.apache.mina.transport.socket.nio.NioProcessor.select(NioProcessor.java:65) at external.org.apache.mina.common.AbstractPollingIoProcessor$Worker.run(AbstractPollingIoProcessor.java:708) at external.org.apache.mina.util.NamePreservingRunnable.run(NamePreservingRunnable.java:51) at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) at java.lang.Thread.run(Thread.java:662) Extract thread dumps several times every hour, and check the status change of the threads to determine the problem. Example 2: When the Processing Performance is Abnormally Slow After acquiring thread dumps several times, find the list of threads with BLOCKED status. " DB-Processor-13" daemon prio=5 tid=0x003edf98 nid=0xca waiting for monitor entry [0x000000000825f000] java.lang.Thread.State: BLOCKED (on object monitor) at beans.ConnectionPool.getConnection(ConnectionPool.java:102) - waiting to lock <0xe0375410> (a beans.ConnectionPool) at beans.cus.ServiceCnt.getTodayCount(ServiceCnt.java:111) at beans.cus.ServiceCnt.insertCount(ServiceCnt.java:43) "DB-Processor-14" daemon prio=5 tid=0x003edf98 nid=0xca waiting for monitor entry [0x000000000825f020] java.lang.Thread.State: BLOCKED (on object monitor) at beans.ConnectionPool.getConnection(ConnectionPool.java:102) - waiting to lock <0xe0375410> (a beans.ConnectionPool) at beans.cus.ServiceCnt.getTodayCount(ServiceCnt.java:111) at beans.cus.ServiceCnt.insertCount(ServiceCnt.java:43) " DB-Processor-3" daemon prio=5 tid=0x00928248 nid=0x8b waiting for monitor entry [0x000000000825d080] java.lang.Thread.State: RUNNABLE at oracle.jdbc.driver.OracleConnection.isClosed(OracleConnection.java:570) - waiting to lock <0xe03ba2e0> (a oracle.jdbc.driver.OracleConnection) at beans.ConnectionPool.getConnection(ConnectionPool.java:112) - locked <0xe0386580> (a java.util.Vector) - locked <0xe0375410> (a beans.ConnectionPool) at beans.cus.Cue_1700c.GetNationList(Cue_1700c.java:66) at org.apache.jsp.cue_1700c_jsp._jspService(cue_1700c_jsp.java:120) Acquire the list of threads with BLOCKED status after getting the thread dumps several times. If the threads are BLOCKED, extract the threads related to the lock that the threads are trying to obtain. Through the thread dump, you can confirm that the thread status stays BLOCKED because <0xe0375410> lock could not be obtained. This problem can be solved by analyzing stack trace from the thread currently holding the lock. There are two reasons why the above pattern frequently appears in applications using DBMS. The first reason is inadequate configurations. Despite the fact that the threads are still working, they cannot show their best performance because the configurations for DBCP and the like are not adequate. If you extract thread dumps multiple times and compare them, you will often see that some of the threads that were BLOCKED previously are in a different state. The second reason is the abnormal connection. When the connection with DBMS stays abnormal, the threads wait until the time is out. In this case, even after extracting the thread dumps several times and comparing them, you will see that the threads related to DBMS are still in a BLOCKED state. By adequately changing the values, such as the timeout value, you can shorten the time in which the problem occurs. Coding for Easy Thread Dump Naming Threads When a thread is created using java.lang.Thread object, the thread will be named Thread-(Number). When a thread is created using java.util.concurrent.DefaultThreadFactory object, the thread will be named pool-(Number)-thread-(Number). When analyzing tens to thousands of threads for an application, if all the threads still have their default names, analyzing them becomes very difficult, because it is difficult to distinguish the threads to be analyzed. Therefore, you are recommended to develop the habit of naming the threads whenever a new thread is created. When you create a thread using java.lang.Thread, you can give the thread a custom name by using the creator parameter. public Thread(Runnable target, String name); public Thread(ThreadGroup group, String name); public Thread(ThreadGroup group, Runnable target, String name); public Thread(ThreadGroup group, Runnable target, String name, long stackSize); When you create a thread using java.util.concurrent.ThreadFactory, you can name it by generating your own ThreadFactory. If you do not need special functionalities, then you can use MyThreadFactory as described below: import java.util.concurrent.ConcurrentHashMap; import java.util.concurrent.ThreadFactory; import java.util.concurrent.atomic.AtomicInteger; public class MyThreadFactory implements ThreadFactory { private static final ConcurrentHashMap POOL_NUMBER = new ConcurrentHashMap(); private final ThreadGroup group; private final AtomicInteger threadNumber = new AtomicInteger(1); private final String namePrefix; public MyThreadFactory(String threadPoolName) { if (threadPoolName == null) { throw new NullPointerException("threadPoolName"); } POOL_NUMBER.putIfAbsent(threadPoolName, new AtomicInteger()); SecurityManager securityManager = System.getSecurityManager(); group = (securityManager != null) ? securityManager.getThreadGroup() : Thread.currentThread().getThreadGroup(); AtomicInteger poolCount = POOL_NUMBER.get(threadPoolName); if (poolCount == null) { namePrefix = threadPoolName + " pool-00-thread-"; } else { namePrefix = threadPoolName + " pool-" + poolCount.getAndIncrement() + "-thread-"; } } public Thread newThread(Runnable runnable) { Thread thread = new Thread(group, runnable, namePrefix + threadNumber.getAndIncrement(), 0); if (thread.isDaemon()) { thread.setDaemon(false); } if (thread.getPriority() != Thread.NORM_PRIORITY) { thread.setPriority(Thread.NORM_PRIORITY); } return thread; } } Obtaining More Detailed Information by Using MBean You can obtain ThreadInfo objects using MBean. You can also obtain more information that would be difficult to acquire via thread dumps, by using ThreadInfo. ThreadMXBean mxBean = ManagementFactory.getThreadMXBean(); long[] threadIds = mxBean.getAllThreadIds(); ThreadInfo[] threadInfos = mxBean.getThreadInfo(threadIds); for (ThreadInfo threadInfo : threadInfos) { System.out.println( threadInfo.getThreadName()); System.out.println( threadInfo.getBlockedCount()); System.out.println( threadInfo.getBlockedTime()); System.out.println( threadInfo.getWaitedCount()); System.out.println( threadInfo.getWaitedTime()); } You can acquire the amount of time that the threads WAITed or were BLOCKED by using the method in ThreadInfo, and by using this you can also obtain the list of threads that have been inactive for an abnormally long period of time. In Conclusion In this article I was concerned that for developers with a lot of experience in multi-thread programming, this material may be common knowledge, whereas for less experienced developers, I felt that I was skipping straight to thread dumps, without providing enough background information about the thread activities. This was because of my lack of knowledge, as I was not able to explain the thread activities in a clear yet concise manner. I sincerely hope that this article will prove helpful for many developers.

Curator's note: This tutorial originally appeared at the Windows Azure Java Developer Center. With Windows Azure, you can use a virtual machine to provide server capabilities. As an example, a virtual machine running on Windows Azure can be configured to host a Java application server, such as Apache Tomcat. On completing this guide, you will have an understanding of how to create a virtual machine running on Windows Azure and configure it to run a Java application server. You will learn: How to create a virtual machine. How to remotely log in to your virtual machine. How to install a JDK on your virtual machine. How to install a Java application server on your virtual machine. How to create an endpoint for your virtual machine. How to open a port in the firewall for your application server. For purposes of this tutorial, an Apache Tomcat application server will be installed on a virtual machine. The completed installation will result in a Tomcat installation such as the following. Note To complete this tutorial, you need a Windows Azure account that has the Windows Azure Virtual Machines feature enabled. You can create a free trial account and enable preview features in just a couple of minutes. For details, see Create a Windows Azure account and enable preview features. To create a virtual machine Log in to the Windows Azure Preview Management Portal. Click New. Click Virtual machine. Click Quick create. In the Create virtual machine screen, enter a value for DNS name. From the Image dropdown list, select an image, such as Windows Server 2008 R2 SP1. Enter a password in the New password field, and re-enter it in the Confirm field. This is the Administrator account password. Remember this password, you will use it when you remotely log in to the virtual machine. From the Location drop down list, select the data center location for your virtual machine; for example, West US. Your screen will look similar to the following. Click Create virtual machine. Your virtual machine will be created. You can monitor the status in the Virtual machines section of the management portal. To remotely log in to your virtual machine Log in to the Preview Management Portal. Click Virtual Machines, and then select the MyTestVM1 virtual machine that you previously created. On the command bar, click Connect. Click Open to use the remote desktop protocol file that was automatically created for the virtual machine Click Connect to proceed with the connection process. Type the password that you specified as the password of the Administrator account when you created the virtual machine, and then click OK. Click Yes to verify the identity of the virtual machine. To install a JDK on your virtual machine You can copy a Java Developer Kit (JDK) to your virtual machine, or install a JDK through an installer. For purposes of this tutorial, a JDK will be installed from Oracle's site. Log in to your virtual machine. Within your browser, open http://www.oracle.com/technetwork/java/javase/downloads/index.html. Click the Download button for the JDK that you want to download. For purposes of this tutorial, the Download button for the Java SE 6 Update 32 JDK was used. Accept the license agreement. Click the download executable for Windows x64 (64-bit). Follow the prompts and respond as needed to install the JDK to your virtual machine. To install a Java application server on your virtual machine You can copy a Java application server to your virtual machine, or install a Java application server through an installer. For purposes of this tutorial, a Java application server will be installed by copying a zip file from Apache's site. Log in to your virtual machine. Within your browser, open http://tomcat.apache.org/download-70.cgi. Double-click 64-bit Windows zip. (This tutorial used the zip for Tomcat Apache 7.0.27.) When prompted, choose to save the zip. When the zip is saved, open the folder that contains the zip and double-click the zip. Extract the zip. For purposes of this tutorial, the path used was C:\program files\apache-tomcat-7.0.27-windows-x64. To run the Java application server privately on your virtual machine The following steps show you how to run the Java application server and test it within the virtual machine's browser. It won't be usable by external computers until you create an endpoint and open a port (those steps are described later). Log in to your virtual machine. Add the JDK bin folder to the Pathenvironment variable: Click Windows Start. Right-click Computer. Click Properties. Click Advanced system settings. Click Advanced. Click Environment variables. In the System variables section, click the Path variable and then click Edit. Add a trailing ; to the Path variable value (if there is not one already) and then add c:\program files\java\jdk\bin to the end of the Path variable value (adjust the path as needed if you did not use c:\program files\java\jdk as the path for your JDK installation). Press OK on the opened dialogs to save your Path change. Set the JAVA_HOMEenvironment variable: Click Windows Start. Right-click Computer. Click Properties. Click Advanced system settings. Click Advanced. Click Environment variables. In the System variables section, click New. Create a variable named JRE_HOME and set its value to c:\program files\java\jdk\jre (adjust the path as needed if you did not use c:\program files\java\jdk as the path for your JDK installation). Press OK on the open dialogs to save your JRE_HOME environment variable. Start Tomcat: Open a command prompt. Change the current directory to the Apache Tomcat binfolder. For example: cd c:\program files\apache-tomcat-7.0.27-windows-x64\apache-tomcat-7.0.27\bin (Adjust the path as needed if you used a differrent installation path for Tomcat.) Run catalina.bat start. You should now see Tomcat running if you run the virtual machine's browser and open http://localhost:8080. To see Tomcat running from external machines, you'll need to create an endpoint and open a port. To create an endpoint for your virtual machine Log in to the Preview Management Portal. Click Virtual machines. Click the name of the virtual machine that is running your Java application server. Click Endpoints. Click Add endpoint. In the Add endpoint dialog, ensure Add endpoint is checked and click the Next button. In the New endpoint detailsdialog Specify a name for the endpoint; for example, HttpIn. Specify TCP for the protocol. Specify 80 for the public port. Specify 8080for the private port. Your screen should look similar to the following: Click the Check button to close the dialog. Your endpoint will now be created. To open a port in the firewall for your virtual machine Log in to your virtual machine. Click Windows Start. Click Control Panel. Click System and Security, click Windows Firewall, and then click Advanced Settings. Click Inbound Rules and then click New Rule. For the new rule, select Port for the Rule type and click Next. Select TCP for the protocol and specify 8080 for the port, and click Next. Choose Allow the connection and click Next. Ensure Domain, Private, and Public are checked for the profile and click Next. Specify a name for the rule, such as HttpIn (the rule name is not required to match the endpoint name, however), and then click Finish. At this point, your Tomcat web site should now be viewable from an external browser, using a URL of the form http://your_DNS_name.cloudapp.net, where your_DNS_name is the DNS name you specified when you created the virtual machine. Application lifecycle considerations You could create your own application web archive (WAR) and add it to the webapps folder. For example, create a basic Java Service Page (JSP) dynamic web project and export it as a WAR file, copy the WAR to the Apache Tomcat webapps folder on the virtual machine, then run it in a browser. This tutorial runs Tomcat through a command prompt where catalina.bat start was called. You may instead want to run Tomcat as a service, a key benefit being to have it automatically start if the virtual machine is rebooted. To run Tomcat as a service, you can install it as a service via the service.bat file in the Apache Tomcat bin folder, and then you could set it up to run automatically via the Services snap-in. You can start the Services snap-in by clicking Windows Start, Administrative Tools, and then Services. If you run service.bat install MyTomcat in the Apache Tomcat bin folder, then within the Services snap-in, your service name will appear as Apache Tomcat MyTomcat. By default when the service is installed, it will be set to start manually. To set it to start automatically, double-click the service in the Services snap-in and set Startup Type to Automatic, as shown in the following. You'll need to start the service the first time, which you can do through the Services snap-in (alternatively, you can reboot the virtual machine). Close the running occurrence of catalina.bat start if it is still running before starting the service.

HTML5 Color Picker (canvas) In our new tutorial we are going to create an easy but effective color picker using HTML5. I think that you have already seen different jQuery versions of colorpicker, our goal today is to create something similar, and even better. In order to make it more unique, there are 5 different colorwheels which you can use. If you are ready – let’s start. This would be a good time to test the demos and download the sources: Live Demo 1 Live Demo 2 Live Demo 3 Live Demo 4 Live Demo 5 download in package If you are ready – let’s start coding ! Step 1. HTML Our first step is the html markup: R G B RGB HEX As you see, our color picker consists of two main components: the preview element and the hidden (by default) color picker element. Once we click by preview element – we will display color picker. Step 2. JS Our next step – is javascript. Please review our result code: js/script.js $(function(){ var bCanPreview = true; // can preview // create canvas and context objects var canvas = document.getElementById('picker'); var ctx = canvas.getContext('2d'); // drawing active image var image = new Image(); image.onload = function () { ctx.drawImage(image, 0, 0, image.width, image.height); // draw the image on the canvas } // select desired colorwheel var imagesrc="images/colorwheel1.png"; switch ($(canvas).attr('var')) { case '2': imagesrc="images/colorwheel2.png"; break; case '3': imagesrc="images/colorwheel3.png"; break; case '4': imagesrc="images/colorwheel4.png"; break; case '5': imagesrc="images/colorwheel5.png"; break; } image.src = imageSrc; $('#picker').mousemove(function(e) { // mouse move handler if (bCanPreview) { // get coordinates of current position var canvasOffset = $(canvas).offset(); var canvasX = Math.floor(e.pageX - canvasOffset.left); var canvasY = Math.floor(e.pageY - canvasOffset.top); // get current pixel var imageData = ctx.getImageData(canvasX, canvasY, 1, 1); var pixel = imageData.data; // update preview color var pixelColor = "rgb("+pixel[0]+", "+pixel[1]+", "+pixel[2]+")"; $('.preview').css('backgroundColor', pixelColor); // update controls $('#rVal').val(pixel[0]); $('#gVal').val(pixel[1]); $('#bVal').val(pixel[2]); $('#rgbVal').val(pixel[0]+','+pixel[1]+','+pixel[2]); var dColor = pixel[2] + 256 * pixel[1] + 65536 * pixel[0]; $('#hexVal').val('#' + ('0000' + dColor.toString(16)).substr(-6)); } }); $('#picker').click(function(e) { // click event handler bCanPreview = !bCanPreview; }); $('.preview').click(function(e) { // preview click $('.colorpicker').fadeToggle("slow", "linear"); bCanPreview = true; }); }); As you can see – there are only 64 lines of our colorpicker, so, as usual, in the beginning we create new canvas and context objects, then – draw an color wheel on the context. As you see – there is small switch case to select desired image (of colorwheel), I decided to use a new attribute for canvas object: ‘var’. So, you can easily change this colorwheel with different ‘var’ value, example: or or or or Well, finally, we have to add event handlers to next events: mousemove (by picker), click (by picker) and click (by preview). As you remember we have to display and hide color picker when we click at Preview element. In order to achieve it – I use ‘fadeToggle’ jQuery function (which was added in version 1.4.4): $('.preview').click(function(e) { // preview click $('.colorpicker').fadeToggle("slow", "linear"); bCanPreview = true; }); When we move our mouse over the Picker object – we should refresh information about current color, and, once we click at the Picker object – we should fix current color (or – disable preview by mousemove): $('#picker').mousemove(function(e) { // mouse move handler if (bCanPreview) { // get coordinates of current position var canvasOffset = $(canvas).offset(); var canvasX = Math.floor(e.pageX - canvasOffset.left); var canvasY = Math.floor(e.pageY - canvasOffset.top); // get current pixel var imageData = ctx.getImageData(canvasX, canvasY, 1, 1); var pixel = imageData.data; // update preview color var pixelColor = "rgb("+pixel[0]+", "+pixel[1]+", "+pixel[2]+")"; $('.preview').css('backgroundColor', pixelColor); // update controls $('#rVal').val(pixel[0]); $('#gVal').val(pixel[1]); $('#bVal').val(pixel[2]); $('#rgbVal').val(pixel[0]+','+pixel[1]+','+pixel[2]); var dColor = pixel[2] + 256 * pixel[1] + 65536 * pixel[0]; $('#hexVal').val('#' + ('0000' + dColor.toString(16)).substr(-6)); } }); $('#picker').click(function(e) { // click event handler bCanPreview = !bCanPreview; }); Step 3. CSS There are CSS styles of our color picker: /* colorpicker styles */ .colorpicker { background-color: #222222; border-radius: 5px 5px 5px 5px; box-shadow: 2px 2px 2px #444444; color: #FFFFFF; font-size: 12px; position: absolute; width: 460px; } #picker { cursor: crosshair; float: left; margin: 10px; border: 0; } .controls { float: right; margin: 10px; } .controls > div { border: 1px solid #2F2F2F; margin-bottom: 5px; overflow: hidden; padding: 5px; } .controls label { float: left; } .controls > div input { background-color: #121212; border: 1px solid #2F2F2F; color: #DDDDDD; float: right; font-size: 10px; height: 14px; margin-left: 6px; text-align: center; text-transform: uppercase; width: 75px; } .preview { background: url("../images/select.png") repeat scroll center center transparent; border-radius: 3px; box-shadow: 2px 2px 2px #444444; cursor: pointer; height: 30px; width: 30px; } Live Demo 1 Live Demo 2 Live Demo 3 Live Demo 4 Live Demo 5 download in package Conclusion We have just created our own small and effective color picker with HTML5 (canvas). I hope that you like it. I will be glad to see your questions and comments. Good luck!

In Groovy we can use the drop() and take() methods to get elements from a collection or String object. Since Groovy 1.8.7 we also can use the dropWhile() and takeWhile() methods and use a closure to define a condition to stop dropping or taking elements. With the dropWhile() method we drop elements or characters until the condition in the closure is true. And the takeWhile() method returns elements from a collection or characters from a String until the condition of the closure is true. In the following example we see how we can use the methods: def s = "Groovy Rocks!" assert s.takeWhile { it != 'R' } == 'Groovy ' assert s.dropWhile { it != 'R' } == 'Rocks!' def list = 0..10 assert 0..4 == list.takeWhile { it < 5 } assert 5..10 == list.dropWhile { it < 5 } def m = [name: 'mrhaki', loves: 'Groovy', worksAt: 'JDriven'] assert [name: 'mrhaki'] == m.takeWhile { key, value -> key.length() == 4 } assert [loves: 'Groovy', worksAt: 'JDriven'] == m.dropWhile { it.key == 'name' } (Code is written with Groovy 2.0.4)

This is the third article in the series of "Become a Java GC Expert". In the first issue Understanding Java Garbage Collection we have learned about the processes for different GC algorithms, about how GC works, what Young and Old Generation is, what you should know about the 5 types of GC in the new JDK 7, and what the performance implications are for each of these GC types. In the second article How to Monitor Java Garbage Collection I have explained how JVM actually runs the Garbage Collection in the real time, how we can monitor GC, and which tools we can use to make this process faster and more effective. In this third article based on real cases as our examples I will show some of the best options you can use for GC tuning. I have written this article under the assumption that you have already understood the previous articles in this series. Therefore, for your further understanding, if you haven't already read the two previous articles, please do so before reading this one. Is GC Tuning Required? Or more precisely is GC tuning required for Java-based services? I should say GC tuning is not always required for all Java-based services. This means a Java-based system in operation has the following options and actions: The memory size has been specified using -Xms and –Xmx options. The -server option is included. Logs such as Timeout log are not left in the system. In other words, if you have not set the memory size and too many Timeout logs are printed, you need to perform GC tuning on your system. But, there is one thing to keep in mind: GC tuning is the last task to be done. Think about the fundamental cause of GC tuning. The Garbage Collector clears an object created in Java. The number of objects necessary to be cleared by the garbage collector as well as the number of GCs to be executed depend on the number of objects which have been created. Therefore, to control the GC performed by your system, you should, first, decrease the number of objects created. There is a saying, "many a little makes a mickle." We need to take care of small things, or they will add up and become something big which is difficult to manage. We need to use and make StringBuilder or StringBuffer a way of life instead of String. And it is better to accumulate as few logs as possible. However, we know that there are some cases we cannot help. We have seen that XML and JSON parsing use the most memory. Even though we use String as little as possible and process logs as well as we can, a huge temporary memory is used for parsing XML or JSON, some 10-100 MB. However, it is difficult not to use XML and JSON. Just understand that it takes too much memory. If application memory usage improves after repeated tunings, you can start GC tuning. I classify the purposes of GC tuning into two. One is to minimize the number of objects passed to the old area; and the other is to decrease Full GC execution time. Minimizing Number of Objects Passed to Old Area Generational GC is the GC provided by Oracle JVM, excluding the G1 GC which can be used from JDK 7 and higher versions. In other words, an object is created in the Eden area and transferred from and to the Survivor area. After that, the objects left are sent to the Old area. Some objects are created in the Eden area and directly passed to the Old area because of their large size. GC in the Old area takes relatively more time than the GC in the New area. Therefore, decreasing the number of objects passed to the Old area can decrease the full GC in frequency. Decreasing the number of objects passed to the Old area may be misunderstood as choosing to leave the object in the New area. However, this is impossible. Instead, you can adjust the size of the New area. Decreasing Full GC Time The execution time of Full GC is relatively longer than that of Minor GC. Therefore, if it takes too much time to execute Full GC (1 second or more), timeout may occur in several connected parts. If you try to decrease the Old area size to decrease Full GC execution time, OutOfMemoryError may occur or the number of Full GCs may increase. Alternatively, if you try to decrease the number of Full GC by increasing the Old area size, the execution time will be increased. Therefore, you need to set the Old area size to a "proper" value. Options Affecting the GC Performance As I have mentioned at the end of Understanding Java Garbage Collection, do not think that "Somebody's got a great performance when he used GC options. Why don't we use that option as he did?" The reason is that the size of objects created and their lifetime is different from one Web service to another. Simply consider, if a task is performed under the conditions of A, B, C, D and E, and the same task is performed under the conditions of only A and B, then which one will be done quicker? From a common-sense standpoint, the answer would be the task which is performed under conditions of A and B. Java GC options are the same. Setting several options does not enhance the speed of executing GC. Rather, it may make it slower. The basic principle of GC tuning is to apply the different GC options to two or more servers and compare them, and then add those options to the server for which the server has demonstrated enhanced performance or better GC time. Keep this in mind. The following table shows options related to memory size among the GC options that can affect performance. Table 1: JVM Options to Be Checked for GC Tuning. Classification Option Description Heap area size -Xms Heap area size when starting JVM -Xmx Maximum heap area size New area size -XX:NewRatio Ratio of New area and Old area -XX:NewSize New area size -XX:SurvivorRatio Ratio of Eden area and Survivor area I frequently use -Xms, -Xmx, and -XX:NewRatio options for GC tuning. -Xms and -Xmx option are particularly required. How you set the NewRatio option makes a significant difference on GC performance. Some people ask how to set the Perm area size? You can set the Perm area size with the -XX:PermSize and -XX:MaxPermSize options but only when OutOfMemoryError occurs and the cause is the Perm area size. Another option that may affect the GC performance is the GC type. The following table shows available options by GC type (based on JDK 6.0). Table 2: Available Options by GC Type. Classification Option Remarks Serial GC -XX:+UseSerialGC Parallel GC -XX:+UseParallelGC -XX:ParallelGCThreads=value Parallel Compacting GC -XX:+UseParallelOldGC CMS GC -XX:+UseConcMarkSweepGC -XX:+UseParNewGC -XX:+CMSParallelRemarkEnabled -XX:CMSInitiatingOccupancyFraction=value -XX:+UseCMSInitiatingOccupancyOnly G1 -XX:+UnlockExperimentalVMOptions -XX:+UseG1GC In JDK 6, these two options must be used together. Except G1 GC, the GC type is changed by setting the option at the first line of each GC type. The most general GC type that does not intrude is Serial GC. It is optimized for client systems. There are a lot of options that affect GC performance. But you can get significant effect by setting the options mentioned above. Remember that setting too many options does not promise enhanced GC execution time. Procedure of GC Tuning The procedure of GC tuning is similar to the general performance improvement procedure. The following is the GC tuning procedure that I use. 1. Monitoring GC status You need to monitor the GC status to check the GC status of the system in operation. Please see various GC monitoring methods in How to Monitor Java Garbage Collection. 2. Deciding whether to tune GC after analyzing the monitoring result After checking the GC status, you should analyze the monitoring result and decide whether to tune GC or not. If the analysis shows that the time taken to execute GC is just 0.1-0.3 seconds. you don't need to waste your time on tuning the GC. However, if the GC execution time is 1-3 seconds, or more than 10 seconds, GC tuning is necessary. But, if you have allocated about 10GB Java memory and it is impossible to decrease the memory size, there is no way to tune GC. Before tuning GC, you need to think about why you need to allocate large memory size. If you have allocated the memory of 1 GB or 2 GB and OutOfMemoryError occurs, you should execute heap dump to verify and remove the cause. Note: Heap dump is a file of the memory that is used to check the objects and data in the Java memory. This file can be created by using the jmap command included in the JDK. While creating the file, the Java process stops. Therefore, do not create this file while the system is operating. Search on the Internet the detailed description on heap dump. For Korean readers, see my book I published last year: The story of troubleshooting for Java developers and system operators (Sangmin Lee, Hanbit Media, 2011, 416 pages). 3. Setting GC type/memory size If you have decided on GC tuning, select the GC type and set the memory size. At this time, if you have several servers, it is important to check the difference of each GC option by setting different GC options for each server. 4. Analyzing results Start analyzing the results after collecting data for at least 24 hours after setting GC options. If you are lucky, you will find the most suitable GC options for the system. If you are not, you should analyze the logs and check how the memory has been allocated. Then you need to find the optimum options for the system by changing the GC type/memory size. 5. If the result is satisfactory, apply the option to all servers and terminate GC tuning. If the GC tuning result is satisfactory, apply the option to all the servers and terminate GC tuning. In the following section, you will see the tasks to be done in each stage. Monitoring GC Status and Analyzing Results The best way to check the GC status of the Web Application Server (WAS) in operation is to use the jstat command. I have explained the jstat command in How To Monitor Java Garbage Collection, so I will describe the data to check in this article. The following example shows a JVM for which GC tuning has not been done (however, it is not the operation server). $ jstat -gcutil 21719 1s S0 S1 E O P YGC YGCT FGC FGCT GCT 48.66 0.00 48.10 49.70 77.45 3428 172.623 3 59.050 231.673 48.66 0.00 48.10 49.70 77.45 3428 172.623 3 59.050 231.673 Here, check the values of YGC and YGCT. Divide YGCT by YGC. Then you get 0.050 seconds (50 ms). It means that it takes average 50 ms to execute GC in the Young area. With that result, you don't need to care about GC for the Young area. And now, check the values of FGCT and FGC. Divide FGCT by FGC. Then you get 19.68 seconds. It means that it takes average 19.68 seconds to execute GC. It may take 19.68 seconds to execute GC three times. Otherwise, it takes 1 second to execute GC two times and 58 seconds for once. In both cases, GC tuning is required. You can easily check GC status by using the jstat command; however, the best way to analyze GC is by generating logs with the –verbosegc option. For a detailed description on how to generate and tools to analyze logs, I have explained it the previous article. HPJMeter is my favorite among tools that are used to analyze the -verbosegc log. It is easy to use and analyze. With HPJmeter you can easily check the distribution of GC execution times and the frequency of GC occurrence. If the GC execution time meets all of the following conditions, GC tuning is not required. Minor GC is processed quickly (within 50 ms). Minor GC is not frequently executed (about 10 seconds). Full GC is processed quickly (within 1 second). Full GC is not frequently executed (once per 10 minutes). The values in parentheses are not the absolute values; they vary according to the service status. Some services may be satisfied with 0.9 seconds of Full GC processing speed, but some may not. Therefore, check the values and decide whether to execute GC tuning or not by considering each service. There is one thing you should be careful of when you check the GC status; do not check the time of Minor GC and Full GC only. You must check the number of GC executions, as well. If the New area size is too small, Minor GC will be too frequently executed (sometimes once or more per 1 second). In addition, the number of objects passed to the Old area increases, causing increased Full GC executions. Therefore, apply the –gccapacity option in the stat command to check how much the area is occupied. Setting GC Type/Memory Size Setting GC Type There are five GC types for Oracle JVM. However, if not JDK 7, one among Parallel GC, Parallel Compacting GC and CMS GC should be selected. There is no principle or rule to decide which one to select. If so, how can we select one? The most recommended way is to apply all three. However, one thing is clear - CMS GC is faster than other Parallel GCs. At this time, if so, just apply CMS GC. However, CMS GC is not always faster. Generally, Full GC of CMS GC is fast, however, when concurrent mode failure occurs, it is slower than other Parallel GCs. Concurrent mode failure Let's take a deeper look into the concurrent mode failure. The biggest difference between Parallel GC and CMS GC is the compaction task. The compaction task is to remove memory fragmentation by compacting memory in order to remove the empty space between allocated memory areas. In the Parallel GC type, the compaction is executed whenever Full GC is executed, taking too much time. However, after executing Full GC, memory can be allocated in a faster way since the next memory can be allocated sequentially. On the contrary, CMS GC does not accompany compaction. Therefore, the CMS GC is executed faster. However, when compaction is not executed, some empty spaces are generated in the memory as before executing Disk Defragmenter. Therefore, there may be no space for large objects. For example, 300 MB is left in the Old area, but some 10 MB objects cannot be sequentially saved in the area. In this case, "Concurrent mode failure" warning occurs and compaction is executed. However, if CMS GC is used, it takes a longer time to execute compaction than other Parallel GCs. And, it may cause another problem. For a more detailed description on concurrent mode failure, see Understanding CMS GC Logs, written by Oracle engineers. In conclusion, you should find the best GC type for your system. Each system requires its proper GC type, so you need to find the best GC type for your system. If you are running six servers, I recommend you to set the same options for each of two servers, add the -verbosegc option, and then analyze the result. Setting Memory Size The following shows the relationship between the memory size, the number of GC execution, and the GC execution time. Large memory size decreases the number of GC executions. increases the GC execution time. Small memory size decreases the GC execution time. increases the number of GC executions. There is no "right" answer to set the memory size to small or large. 10 GB is OK if the server resource is good and Full GC can be completed within 1 second even when the memory has been set to 10 GB. But most servers are not in the status. When the memory is set to 10 GB, it takes about 10 ~ 30 seconds to execute Full GC. Of course, the time may vary according the object size. If so, how we should set the memory size? Generally, I recommend 500 MB. But note that it does not mean that you should set the WAS memory with the –Xms500m and –Xmx500m options. Based on the current status before GC tuning, check the memory size left after Full GC. If there is about 300 MB left after Full GC, it is good to set the memory to 1 GB (300 MB (for default usage) + 500 MB (minimum for the Old area) + 200 MB (for free memory)). That means you should set the memory space with more than 500 MB for the Old area. Therefore, if you have three operation servers, set one server to 1 GB, one to 1.5 GB, and one to 2 GB, and then check the result. Theoretically, GC will be done fast in the order of 1 GB > 1.5 GB > 2 GB, so 1 GB will be the fastest to execute GC. However, it cannot be guaranteed that it takes 1 second to execute Full GC with 1 GB and 2 seconds with 2 GB. The time depends on the server performance and the object size. Therefore, the best way to create the measurement data is to set as many as possible and monitor them. You should set one more thing for setting the memory size: NewRatio. NewRatio is the ratio of the New area and the Old area. If XX:NewRatio=1, New area:Old area is 1:1. For 1 GB, New area:Old area is 500MB: 500MB. If NewRatio is 2, New area:Old area is 1:2. Therefore, as the value gets larger, the Old area size gets larger and the New area size gets smaller. It may not be an important thing, but NewRatio value significantly affects the entire GC performance. If the New area size is small, much memory is passed to the Old area, causing frequent Full GC and taking a long time to handle it. You may simply think that NewRatio 1 would be the best; however, it may not be so. When NewRatio is set to 2 or 3, the entire GC status may be better. And I have seen such cases. What is the fastest way to complete GC tuning? Comparing the results from performance tests is the fastest way to get the result. To set different options for each server and monitor the status, it is recommended to check the data after at least one or two days. However, you should prepare for giving the same load with the operation situation when you execute GC tuning through performance test. And the request ratio such as the URL that gives the load must be identical to that of the operation situation. However, giving accurate load is not easy for the professional performance tester and takes too long time for preparing. Therefore, it is more convenient and easier to apply the options to operation and wait for the result even though it takes a longer time. Analyzing GC Tuning Results After applying the GC option and setting the -verbosegc option, check whether the logs are accumulated as desired with the tail command. If the option is not exactly set and no log is accumulated, you will waste your time. If logs are accumulated as desired, check the result after collecting data for one or two days. The easiest way is to move logs to the local PC and analyze the data by using HPJMeter. In the analysis, focus on the following. The priority is determined by me. The most important item to decide the GC option is Full GC execution time. Full GC execution time Minor GC execution time Full GC execution interval Minor GC execution interval Entire Full GC execution time Entire Minor GC execution time Entire GC execution time Full GC execution times Minor GC execution timesl It is a very lucky case to find the most appropriate GC option, and in most cases, it's not. Be careful when executing GC tuning because OutOfMemoryError may occur if you try to complete GC tuning all at once. Examples of Tuning So far, we have theoretically discussed GC tuning without any examples. Now we will take a look at the examples of GC tuning. Example 1 The following example is GC tuning for Service S. For the newly developed Service S, it took too much time to execute Full GC. See the result of jstat –gcutil. S0 S1 E O P YGC YGCT FGC FGCT GCT 12.16 0.00 5.18 63.78 20.32 54 2.047 5 6.946 8.993 Information to the left Perm area is not important for the initial GC tuning. At this time, the values from the right YGC are important. The average value taken to execute Minor GC and Full GC once is calculated as below. Table 3: Average Time Taken to Execute Minor GC and Full GC for Service S. GC Type GC Execution Times GC Execution Time Average Minor GC 54 2.047 37 s Full GC 5 6.946 1,389 ms 37 ms is not bad for Minor GC. However, 1.389 seconds for Full GC means that timeout may frequently occur when GC occurs in the system of which DB Timeout is set to 1 second. In this case, the system requires GC tuning. First, you should check how the memory is used before starting GC tuning. Use the jstat –gccapacity option to check the memory usage. The result checked from this server is as follows. NGCMN NGCMX NGC S0C S1C EC OGCMN OGCMX OGC OC PGCMN PGCMX PGC PC YGC FGC 212992.0 212992.0 212992.0 21248.0 21248.0 170496.0 1884160.0 1884160.0 1884160.0 1884160.0 262144.0 262144.0 262144.0 262144.0 54 5 The key values are as follows. New area usage size: 212,992 KB Old area usage size: 1,884,160 KB Therefore, the totally allocated memory size is 2 GB, excluding the Perm area, and New area:Old area is 1:9. To check the status in a more detailed way than jstat, the -verbosegc log has been added and three options were set for the three instances as shown below. No other option has been added. NewRatio=2 NewRatio=3 NewRatio=4 After one day, the GC log of the system has been checked. Fortunately, no Full GC has occurred in this system after NewRatio has been set. Why? The reason is that most of the objects created from the system are destroyed soon, so the objects are not passed to the Old area but destroyed in the New area. In this status, it is not necessary to change other options. Just select the best value for NewRatio. So, how can we determine the best value? To get it, analyze the average response time of Minor GC for each NewRatio. The average response time of Minor GC for each option is as follows: NewRatio=2: 45 ms NewRatio=3: 34 ms NewRatio=4: 30 ms We have concluded that NewRatio=4 is the best option since the GC time is the shortest even though the New area size is the smallest. After applying the GC option, the server has no Full GC. For your information, the following is the result of executing jstat –gcutil some days after the JVM of the service had started. S0 S1 E O P YGC YGCT FGC FGCT GCT 8.61 0.00 30.67 24.62 22.38 2424 30.219 0 0.000 30.219 You many think that GC has not frequently occurred since the server has few requests. However, Full GC has not been executed while Minor GC has been executed 2,424 times. Example 2 This example is for Service A. We found that the JVM had not operated for a long time (8 seconds or more) periodically in the Application Performance Manager (APM) in the company. So we executed GC tuning. We were searching for the reason and found that it took a long time to execute Full GC, so we decided to execute GC tuning. As the starting stage of GC tuning, we added the -verbosegc option and the result is as follows. Figure 1: Duration Graph before GC Tuning. The above graph, which shows the duration, is one of the graphs that the HPJMeter automatically provides after analysis. The X-axis shows the time after the JVM has started and the Y-axis shows the response time of each GC. The green dots, the CMS, indicates the Full GC result, and the blue bots, Parallel Scavenge, indicates the Minor GC result. Previous I said that CMS GC would be the fastest. But the above result show that there were some cases which took up to 15 seconds. What has caused such result? Please remember what I said before: CMS gets slower when compaction is executed. In addition, the memory of the service has been set by using –Xms1g and –Xmx4g and the memory allocated was 4 GB. So I changed the GC type from CMS GC to Parallel GC. I changed the memory size to 2 GB and then set the NewRatio to 3. The result of jstat –gcutil after a few hours is as follows. S0 S1 E O P YGC YGCT FGC FGCT GCT 0.00 30.48 3.31 26.54 37.01 226 11.131 4 11.758 22.890 The Full GC time was faster, 3 seconds per one time, compared to 15 seconds for 4 GB. However, 3 seconds is still not so fast. So I created six cases as follows. Case 1: -XX:+UseParallelGC -Xms1536m -Xmx1536m -XX:NewRatio=2 Case 2: -XX:+UseParallelGC -Xms1536m -Xmx1536m -XX:NewRatio=3 Case 3: -XX:+UseParallelGC -Xms1g -Xmx1g -XX:NewRatio=3 Case 4: -XX:+UseParallelOldGC -Xms1536m -Xmx1536m -XX:NewRatio=2 Case 5: -XX:+UseParallelOldGC -Xms1536m -Xmx1536m -XX:NewRatio=3 Case 6: -XX:+UseParallelOldGC -Xms1g -Xmx1g -XX:NewRatio=3 Which one would be the fastest? The result showed that the smaller the memory size was, the better the result was. The following figure shows the duration graph of Case 6, which showed the highest GC improvement. The slowest response time was 1.7 seconds and the average had been changed to within 1 second, showing the improved result. Figure 2: Duration Graph after Applying Case 6. With the result, I changed all GC options of the service to Case 6. However, this change causes OutOfMemoryError at night each day. It is difficult to detail the reason here, but in short, batch data processing made a lack of JVM memory. The related problems are being cleared now. It is very dangerous to analyze the GC logs accumulated for a short time and to apply the result to all servers as executing GC tuning. Keep in mind that GC tuning can be executed without failure only when you analyze the service operation as well as the GC logs. We have reviewed two GC tuning examples to see how GC tuning is executed. As I mentioned, the GC option set in the examples can be identically set for the server which has the same CPU, OS version and JDK version with the service that executes the same functions. However, do not apply the option I did to your services in operation, since they may not work for you. Conclusion I execute GC tuning based on my experiences without executing heap dump and analyzing the memory in detail. Precise memory status analysis may draw the better GC tuning results. However, that kind of analysis may be helpful when the memory is used in the constant and routine pattern. But, if the service is heavily used and there are a lot of memory usage patterns, GC tuning based on reliable previous experience may be recommendable. I have executed the performance test by setting the G1 GC option to some servers, but have not applied to any operation server yet. The G1 GC option shows a faster result than any other GC types. However, it requires to upgrade to JDK 7. In addition, stability is still not guaranteed. Nobody knows if there is any critical bug or not. So the time is not yet ripe for applying the option. After JDK 7 is stabilized (this does not mean that it is not stable) and WAS is optimized for JDK 7, enabling stable application of G1 GC may finally work as expected and some day we may not need the GC tuning. For more detail on GC tuning, search on Slideshare.com for related materials. The most recommendable material is Everything I Ever Learned About JVM Performance Tuning @Twitter, written by Attila Szegedi, a Twitter engineer. Please take the time to read it.

Tony Hoare introduced null references in ALGOL W back in 1965 “simply because it was so easy to implement”. After many years he regretted his decision calling it "my billion dollar mistake". Unfortunately the vast majority of the languages created in the last decades have been built with the same wrong design decision so language designers and software engineers started to look for workarounds to avoid the infamous NullPointerException. Functional languages like Haskell or Scala structurally resolve this problem by wrapping the nullable values in an Option/Maybe monad. Other imperative languages like Groovy introduced a null-safe dereferencing operator (?. operator) to safely navigate values that could be potentially null. A similar feature has been proposed (and then discarded) as part of the project Coin in Java 7. Honestly I don't miss a null safe dereferencing operator in Java even because I can imagine that the majority of developers would start abusing it "just in case". Moreover, since the upcoming Java 8 will have lambda expressions, it will be straightforward to implement an Option monad that, as I hope to show in the remaining part of the post, is a far more powerful and flexible construct. I don't want to delve in category theory and explain what a monad is, even because there are already tons of very goodarticlesdoing this. My purpose is to quickly implement an Option monad using the Java 8 lambda expression syntax and then show how to use it with a very practical example. In Scala, a monad M is any class having the following 3 methods: def map[B](f: A => B): M[B] def flatMap[B](f: A => M[B]): M[B] def filter(p: A => Boolean): M[A] In particular you can think to an Option monad as a wrapper around a, possibly absent, value. So an Option of a generic type A could be define as it follows: import java.util.functions.Predicate; public abstract class Option { public static final None NONE = new None(); public abstract Option map(Func1 f); public abstract Option flatMap(Func1> f); public abstract Option filter(Predicate predicate); public abstract A getOrElse(A def); public static Some some(A value) { return new Some(value); } public static None none() { return NONE; } public static Option asOption(A value) { if (value == null) return none(); else return some(value); } } I also added some convenient factory methods for the Some and None concrete implementations of Option that I will implement later. Here Predicate is a single method interface defined in the new java.util.functions package: public interface Predicate { boolean test(T t); } that is used to determine if the input object matches a given criteria, while Func1 is another single method interface: public interface Func1 { R apply(A1 arg1); } that I defined to represent a more generic function of one argument of type A1 returning a result of type R. The abstract class Option has then two concrete implementations, one representing the absence of a value (something that we are used to wrongly model with the infamous null reference): public class None extends Option { None() { } @Override public Option map(Func1 f) { return NONE; } @Override public Option flatMap(Func1> f) { return NONE; } @Override public Option filter(Predicate predicate) { return NONE; } @Override public A getOrElse(A def) { return def; } } and the other wrapping an actually existing value: public class Some extends Option { private final A value; Some(A value) { this.value = value; } @Override public Option map(Func1 f) { return some(f.apply(value)); } @Override public Option flatMap(Func1> f) { return f.apply(value); } @Override public Option filter(Predicate predicate) { if (predicate.test(value)) return this; else return None.NONE; } @Override public A getOrElse(A def) { return value; } } Now, to try to put the Option at work with a concrete example, let's suppose we have a Map representing a set of named parameters with the corresponding values. We want to develop the method int readPositiveIntParam(Map params, String name) { // TODO ... } that, if the value associated with a given key is a String representing a positive integer returns that integer, but returns zero in all other case. In other words we want the following test to pass: @Test public void testMap() { Map param = new HashMap(); param.put("a", "5"); param.put("b", "true"); param.put("c", "-3"); // the value of the key "a" is a String representing a positive int so return it assertEquals(5, readPositiveIntParam(param, "a")); // returns zero since the value of the key "b" is not an int assertEquals(0, readPositiveIntParam(param, "b")); // returns zero since the value of the key "c" is an int but it is negative assertEquals(0, readPositiveIntParam(param, "c")); // returns zero since there is no key "d" in the map assertEquals(0, readPositiveIntParam(param, "d")); } If we couldn't rely on our Option we should accomplish this task with something similar to this: int readPositiveIntParam(Map params, String name) { String value = params.get(name); if (value == null) return 0; int i = 0; try { i = Integer.parseInt(value); } catch (NumberFormatException nfe) { } if (i < 0) return 0; return i; } too many conditional branches and returning points, isn't it? Using the Option monad we can achieve the same result with a single fluent statement: int readPositiveIntParam(Map params, String name) { return asOption(params.get(name)) .flatMap(FunctionUtils::stringToInt) .filter(i -> i > 0) .getOrElse(0); } where we used an helper static method FunctionUtils.stringToInt() as a function literal, with the :: syntax also introduced in Java 8, defined as: import static Option.*; public class FunctionUtils { public static Option stringToInt(String s) { try { return some(Integer.parseInt(s)); } catch (NumberFormatException nfe) { return none(); } } } This methods tries to convert a String in an int and, if it can't, it returns the None Option. Note that we could also define this behavior inline, while invoking the flatMap() method, using an anonymous lambda expression, but my advice is to develop a small library of utility functions, as I started doing here, in order to leverage the grater reusability allowed by functional programming. I think the comparison of the two readPositiveIntParam methods I provided illustrates well how the extensive use of the Option monad can finally allow us to write completely NullPointerException free software and, more in general, how a bigger employment of functional programming can dramatically reduce its cyclomatic complexity.

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As you may have seen from my previous tutorials and case studies, Java Heap Space OutOfMemoryError problems can be complex to pinpoint and resolve. One of the common problems I have observed from Java EE production systems is OutOfMemoryError: unable to create new native thread; error thrown when the HotSpot JVM is unable to further create a new Java thread. This article will revisit this HotSpot VM error and provide you with recommendations and resolution strategies. If you are not familiar with the HotSpot JVM, I first recommend that you look at a high level view of its internal HotSpot JVM memory spaces. This knowledge is important in order for you to understand OutOfMemoryError problems related to the native (C-Heap) memory space. OutOfMemoryError: unable to create new native thread – what is it? Let’s start with a basic explanation. This HotSpot JVM error is thrown when the internal JVM native code is unable to create a new Java thread. More precisely, it means that the JVM native code was unable to create a new “native” thread from the OS (Solaris, Linux, MAC, Windows...). We can clearly see this logic from the OpenJDK 1.6 and 1.7 implementations as per below: Unfortunately at this point you won’t get more detail than this error, with no indication of why the JVM is unable to create a new thread from the OS… HotSpot JVM: 32-bit or 64-bit? Before you go any further in the analysis, one fundamental fact that you must determine from your Java or Java EE environment is which version of HotSpot VM you are using e.g. 32-bit or 64-bit. Why is it so important? What you will learn shortly is that this JVM problem is very often related to native memory depletion; either at the JVM process or OS level. For now please keep in mind that: A 32-bit JVM process is in theory allowed to grow up to 4 GB (even much lower on some older 32-bit Windows versions). For a 32-bit JVM process, the C-Heap is in a race with the Java Heap and PermGen space e.g. C-Heap capacity = 2-4 GB – Java Heap size (-Xms, -Xmx) – PermGen size (-XX:MaxPermSize) A 64-bit JVM process is in theory allowed to use most of the OS virtual memory available or up to 16 EB (16 million TB) As you can see, if you allocate a large Java Heap (2 GB+) for a 32-bit JVM process, the native memory space capacity will be reduced automatically, opening the door for JVM native memory allocation failures. For a 64-bit JVM process, your main concern, from a JVM C-Heap perspective, is the capacity and availability of the OS physical, virtual and swap memory. OK great but how does native memory affect Java threads creation? Now back to our primary problem. Another fundamental JVM aspect to understand is that Java threads created from the JVM requires native memory from the OS. You should now start to understand the source of your problem… The high level thread creation process is as per below: A new Java thread is requested from the Java program & JDK The JVM native code then attempt to create a new native thread from the OS The OS then attempts to create a new native thread as per attributes which include the thread stack size. Native memory is then allocated (reserved) from the OS to the Java process native memory space; assuming the process has enough address space (e.g. 32-bit process) to honour the request The OS will refuse any further native thread & memory allocation if the 32-bit Java process size has depleted its memory address space e.g. 2 GB, 3 GB or 4 GB process size limit The OS will also refuse any further Thread & native memory allocation if the virtual memory of the OS is depleted (including Solaris swap space depletion since thread access to the stack can generate a SIGBUS error, crashing the JVM * http://bugs.sun.com/view_bug.do?bug_id=6302804 In summary: Java threads creation require native memory available from the OS; for both 32-bit & 64-bit JVM processes For a 32-bit JVM, Java thread creation also requires memory available from the C-Heap or process address space Problem diagnostic Now that you understand native memory and JVM thread creation a little better, is it now time to look at your problem. As a starting point, I suggest that your follow the analysis approach below: Determine if you are using HotSpot 32-bit or 64-bit JVM When problem is observed, take a JVM Thread Dump and determine how many Threads are active Monitor closely the Java process size utilization before and during the OOM problem replication Monitor closely the OS virtual memory utilization before and during the OOM problem replication; including the swap memory space utilization if using Solaris OS Proper data gathering as per above will allow you to collect the proper data points, allowing you to perform the first level of investigation. The next step will be to look at the possible problem patterns and determine which one is applicable for your problem case. Problem pattern #1 – C-Heap depletion (32-bit JVM) From my experience, OutOfMemoryError: unable to create new native thread is quite common for 32-bit JVM processes. This problem is often observed when too many threads are created vs. C-Heap capacity. JVM Thread Dump analysis and Java process size monitoring will allow you to determine if this is the cause. Problem pattern #2 – OS virtual memory depletion (64-bit JVM) In this scenario, the OS virtual memory is fully depleted. This could be due to a few 64-bit JVM processes taking lot memory e.g. 10 GB+ and / or other high memory footprint rogue processes. Again, Java process size & OS virtual memory monitoring will allow you to determine if this is the cause. Problem pattern #3 – OS virtual memory depletion (32-bit JVM) The third scenario is less frequent but can still be observed. The diagnostic can be a bit more complex but the key analysis point will be to determine which processes are causing a full OS virtual memory depletion. Your 32-bit JVM processes could be either the source or the victim such as rogue processes using most of the OS virtual memory and preventing your 32-bit JVM processes to reserve more native memory for its thread creation process. Please note that this problem can also manifest itself as a full JVM crash (as per below sample) when running out of OS virtual memory or swap space on Solaris. # # A fatal error has been detected by the Java Runtime Environment: # # java.lang.OutOfMemoryError: requested 32756 bytes for ChunkPool::allocate. Out of swap space? # # Internal Error (allocation.cpp:166), pid=2290, tid=27 # Error: ChunkPool::allocate # # JRE version: 6.0_24-b07 # Java VM: Java HotSpot(TM) Server VM (19.1-b02 mixed mode solaris-sparc ) # If you would like to submit a bug report, please visit: # http://java.sun.com/webapps/bugreport/crash.jsp # --------------- T H R E A D --------------- Current thread (0x003fa800): JavaThread "CompilerThread1" daemon [_thread_in_native, id=27, stack(0x65380000,0x65400000)] Stack: [0x65380000,0x65400000], sp=0x653fd758, free space=501k Native frames: (J=compiled Java code, j=interpreted, Vv=VM code, C=native code) ……………… Native memory depletion: symptom or root cause? You now understand your problem and know which problem pattern you are dealing with. You are now ready to provide recommendations to address the problem…are you? Your work is not done yet, please keep in mind that this JVM OOM event is often just a “symptom” of the actual root cause of the problem. The root cause is typically much deeper so before providing recommendations to your client I recommend that you really perform deeper analysis. The last thing you want to do is to simply address and mask the symptoms. Solutions such as increasing OS physical / virtual memory or upgrading all your JVM processes to 64-bit should only be considered once you have a good view on the root cause and production environment capacity requirements. The next fundamental question to answer is how many threads were active at the time of the OutOfMemoryError? In my experience with Java EE production systems, the most common root cause is actually the application and / or Java EE container attempting to create too many threads at a given time when facing non happy paths such as thread stuck in a remote IO call, thread race conditions etc. In this scenario, the Java EE container can start creating too many threads when attempting to honour incoming client requests, leading to increase pressure point on the C-Heap and native memory allocation. Bottom line, before blaming the JVM, please perform your due diligence and determine if you are dealing with an application or Java EE container thread tuning problem as the root cause. Once you understand and address the root cause (source of thread creations), you can then work on tuning your JVM and OS memory capacity in order to make it more fault tolerant and better “survive” these sudden thread surge scenarios. Recommendations: First perform a JVM Thread Dump analysis and determine the source of all the active threads vs. an established baseline. Determine what is causing your Java application or Java EE container to create so many threads at the time of the failure Please ensure that your monitoring tools closely monitor both your Java VM processes size & OS virtual memory. This crucial data will be required in order to perform a full root cause analysis Do not assume that you are dealing with an OS memory capacity problem. Look at all running processes and determine if your JVM processes are actually the source of the problem or victim of other processes consuming all the virtual memory Revisit your Java EE container thread configuration & JVM thread stack size. Determine if the Java EE container is allowed to create more threads than your JVM process and / or OS can handle Determine if the Java Heap size of your 32-bit JVM is too large, preventing the JVM to create enough threads to fulfill your client requests. In this scenario, you will have to consider reducing your Java Heap size (if possible), vertical scaling or upgrade to a 64-bit JVM Capacity planning analysis to the rescue As you may have seen from my past article on the Top 10 Causes of Java EE Enterprise Performance Problems, lack of capacity planning analysis is often the source of the problem. Any comprehensive load and performance testing exercise should also properly determine the Java EE container threads, JVM & OS native memory requirement for your production environment; including impact measurements of "non-happy" paths. This approach will allow your production environment to stay away from this type of problem and lead to better system scalability and stability in the long run. Please provide any comment and share your experience with JVM native thread troubleshooting.

I am going to try to define immutability and its relation to thread safety.

While me and my friend roshan recently working as a support developers at Australia famous e-commerce website. recently roshan as assign a new bug in this site it’s related to the product synchronize process in the ware house product table and the e-commerce site, his main task was check the quickly the site product table and check with ware house product table product if the either insert new data into a site database, or update an existing record on the site database, Of course, doing a lookup to see if the record exists already and then either updating or inserting would be an expensive process (existing items are defined either by a unique key or a primary key). Luckily, MySQL offers two functions to combat this (each with two very different approaches). 1. REPLACE = DELETE+INSERT 2. INSERT ON DUPLICATE KEY UPDATE = UPDATE + INSERT 1 . REPLACE This syntax is the same as the INSERT function. When dealing with a record with a unique or primary key, REPLACE will either do a DELETE and then an INSERT, or just an INSERT if use this this function will cause a record to be removed, and inserted at the end. It will cause the indexing to get broken apart, decreasing the efficiency of the table. If, however REPLACE INTO ds_product SET pID = 3112, catID = 231, uniCost = 232.50, salePrice = 250.23; 2. ON DUPLICATE KEY UPDATE ON DUPLICATE KEY UPDATE clause to the INSERT function. This one actively hunts down an existing record in the table which has the same UNIQUE or PRIMARY KEY as the one we’re trying to update. If it finds an existing one, you specify a clause for which column(s) you would like to UPDATE. Otherwise, it will do a normal INSERT. INSERT INTO ds_product SET pID = 3112, catID = 231, uniCost = 232.50, salePrice = 250.23, ON DUPLICATE KEY UPDATE uniCost = 232.50, salePrice = 250.23; This should be helpful when trying to create database queries that add and update information, without having to go through the extra step. Thanks Have a Nice Day

Sprache is a very cool lightweight parser library for C#. Today I was experimenting with parsing EasyNetQ connection strings, so I thought I’d have a go at getting Sprache to do it. An EasyNetQ connection string is a list of key-value pairs like this: key1=value1;key2=value2;key3=value3 The motivation for looking at something more sophisticated than simply chopping strings based on delimiters, is that I’m thinking of having more complex values that would themselves need parsing. But that’s for the future, today I’m just going to parse a simple connection string where the values can be strings or numbers (ushort to be exact). So, I want to parse a connection string that looks like this: virtualHost=Copa;username=Copa;host=192.168.1.1;password=abc_xyz;port=12345;requestedHeartbeat=3 … into a strongly typed structure like this: public class ConnectionConfiguration : IConnectionConfiguration { public string Host { get; set; } public ushort Port { get; set; } public string VirtualHost { get; set; } public string UserName { get; set; } public string Password { get; set; } public ushort RequestedHeartbeat { get; set; } } I want it to be as easy as possible to add new connection string items. First let’s define a name for a function that updates a ConnectionConfiguration. A uncommonly used version of the ‘using’ statement allows us to give a short name to a complex type: using UpdateConfiguration = Func; Now lets define a little function that creates a Sprache parser for a key value pair. We supply the key and a parser for the value and get back a parser that can update the ConnectionConfiguration. public static Parser BuildKeyValueParser( string keyName, Parser valueParser, Expression> getter) { return from key in Parse.String(keyName).Token() from separator in Parse.Char('=') from value in valueParser select (Func)(c => { CreateSetter(getter)(c, value); return c; }); } The CreateSetter is a little function that turns a property expression (like x => x.Name) into an Action. Next let’s define parsers for string and number values: public static Parser Text = Parse.CharExcept(';').Many().Text(); public static Parser Number = Parse.Number.Select(ushort.Parse); Now we can chain a series of BuildKeyValueParser invocations and Or them together so that we can parse any of our expected key-values: public static Parser Part = new List> { BuildKeyValueParser("host", Text, c => c.Host), BuildKeyValueParser("port", Number, c => c.Port), BuildKeyValueParser("virtualHost", Text, c => c.VirtualHost), BuildKeyValueParser("requestedHeartbeat", Number, c => c.RequestedHeartbeat), BuildKeyValueParser("username", Text, c => c.UserName), BuildKeyValueParser("password", Text, c => c.Password), }.Aggregate((a, b) => a.Or(b)); Each invocation of BuildKeyValueParser defines an expected key-value pair of our connection string. We just give the key name, the parser that understands the value, and the property on ConnectionConfiguration that we want to update. In effect we’ve defined a little DSL for connection strings. If I want to add a new connection string value, I simply add a new property to ConnectionConfiguration and a single line to the above code. Now lets define a parser for the entire string, by saying that we’ll parse any number of key-value parts: public static Parser> ConnectionStringBuilder = from first in Part from rest in Parse.Char(';').Then(_ => Part).Many() select Cons(first, rest); All we have to do now is parse the connection string and apply the chain of update functions to a ConnectionConfiguration instance: public IConnectionConfiguration Parse(string connectionString) { var updater = ConnectionStringGrammar.ConnectionStringBuilder.Parse(connectionString); return updater.Aggregate(new ConnectionConfiguration(), (current, updateFunction) => updateFunction(current)); } We get lots of nice things out of the box with Sprache, one of the best is the excellent error messages: Parsing failure: unexpected 'x'; expected host or port or virtualHost or requestedHeartbeat or username or password (Line 1, Column 1). Sprache is really nice for this kind of task. I’d recommend checking it out.

(Disclaimer: Based on personal experience and little research, the information might be incomplete.) VisualVM is a great tool for monitoring JVM (5.0+) regarding memory usage, threads, GC, MBeans etc. Let’s see how to use it over SSH to monitor (or even profile, using its sampler) a remote JVM either with JMX or without it. This post is based on Sun JVM 1.6 running on Ubuntu 10 and VisualVM 1.3.3. 1. Communication: JStatD vs. JMX There are two modes of communication between VisualVM and the JVM: Either over the Java Management Extensions (JMX) protocol or over jstatd. jstatd jstatd is a daemon that is distributed with JDK. You start it from the command line (it’s likely necessary to run it as the user running the target JVM or as root) on the target machine and VisualVM will contact it to fetch information about the remote JVMs. Advantages: Can connect to a running JVM, no need to start it with special parameters Disadvantages: Much more limited monitoring capabilities (f.ex. no CPU usage monitoring, not possible to run the Sampler and/or take thread dumps). Ex.: bash> cat jstatd.all.policy grant codebase "file:${java.home}/../lib/tools.jar" { permission java.security.AllPermission; } bash> sudo /path/to/JDK/bin/jstatd -J-Djava.security.policy=jstatd.all.policy # You can specify port with -p number and get more info with -J-Djava.rmi.server.logCalls=true Note: Replace “${java.home}/../lib/tools.jar” with the absolute “/path/to/jdk/lib/tools.jar” if you have only copied but not installed the JDK. If you get the failure Could not create remote object access denied (java.util.PropertyPermission java.rmi.server.ignoreSubClasses write) java.security.AccessControlException: access denied (java.util.PropertyPermission java.rmi.server.ignoreSubClasses write) at java.security.AccessControlContext.checkPermission(AccessControlContext.java:374) then jstatd likely hasn’t been started with the right java.security.policy file (try to provide fully qualified path to it). More info about VisualVM and jstatd from Oracle. JMX Advantages: Using JMX will give you the full power of VisualVM. Disadvantages: Need to start the JVM with some system properties. You will generally want to use something like the following properties when starting the target JVM (though you could also enable SSL and/or require username and password): yourJavaCommand... -Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.port=1098 See Remote JMX Connections. 2. Security: SSH The easiest way to connect to the remote JMX or jstatd over ssh is to use a SOCKS proxy, which standard ssh clients can set up. 2.1 Set Up the SSH Tunnel With SOCKS ssh -v -D 9696 my_server.example.com 2.2 Configure VisualVM to Use the Proxy Tools->Options->Network – Manual Proxy Settings – check it and configure SOCKS Proxy at localhost and port 9696 2.3 Connect VisualVM to the Target File -> Add Remote Host… – type the IP or hostname of the remote machine JStatD Connection You should see logs both in the ssh window (thanks to its “-v”, f.ex. “debug1: Connection to port 9696 forwarding to socks port 0 requested.” and “debug1: channel 3: free: direct-tcpip: listening port 9696 for 10.2.47.71 port 1099, connect from 127.0.0.1 port 61262, nchannels 6“) and in the console where you started jstatd (many, f.ex. “FINER: RMI TCP Connection(23)-10.2.47.71: …“) Wait few minutes after having added the remote host, you should then see the JVMs running there. Available stats: JVM arguments, Monitor: Heap, classes, threads monitoring (but not CPU). Sampler and MBeans require JMX. JMX Right-click on the remote host you have added and select Add JMX Connection …, type the JMX port you have chosen. You should see similar logs as with jstatd. Available stats: Also CPU usage, system properties, detailed Threads report with access to stack traces, CPU sampling (memory sampling not supported). Note: Sampler vs. Profiler The VisualVM’s Sampler excludes time spent in Object.wait and Thread.sleep (f.ex. waiting on I/O). Use the NetBeans Profiler to profile or sample a remote application if you want to have more control or want the possibility to include Object.wait and Thread.sleep time. It requires its Remote Pack (a java agent, i.e. a JAR file) to be in the target JVM (NetBeans’ Attach Wizard can generate the remote pack for you in step 4, Manual integration, and show you the options to pass to the target JVM to use it). You can run the profiler over SSH by forwarding its default port (5140) and attaching to the forwarded port at localhost. (NetBeans version 7.1.1.)

Today I would like to talk about picture manipulation. Not the Direct2D way I used in my previous article but the pure JavaScript way. The test case The test application is simple. On the left a picture to manipulate and on the right the updated result (a sepia tone effect is applied): The page itself is simple and is described as follow: The overall process to apply a sepia tone effect requires you to compute a new value for every pixel of the picture: finalRed= (red * 0.393) + (green * 0.769) + (blue * 0.189); finalGreen = (red * 0.349) + (green * 0.686) + (blue * 0.168); finalBlue= (red * 0.272) + (green * 0.534) + (blue * 0.131); To make it more realistic I added a bit of random in the formula so the final JavaScript code to apply to every pixel is: function noise() { return Math.random() * 0.5 + 0.5; }; function colorDistance(scale, dest, src) { return (scale * dest + (1 - scale) * src); }; var processSepia = function (pixel) { pixel.r = colorDistance(noise(), (pixel.r * 0.393) + (pixel.g * 0.769) + (pixel.b * 0.189), pixel.r); pixel.g = colorDistance(noise(), (pixel.r * 0.349) + (pixel.g * 0.686) + (pixel.b * 0.168), pixel.g); pixel.b = colorDistance(noise(), (pixel.r * 0.272) + (pixel.g * 0.534) + (pixel.b * 0.131), pixel.b); }; Brutal force Obviously the very first solution can consist to the use of a brutal approach with a function that apply the previous code on every pixel. To get access to the pixels, you can use the canvas context with the following code: var source = document.getElementById("source"); source.onload = function () { var canvas = document.getElementById("target"); canvas.width = source.clientWidth; canvas.height = source.clientHeight; tempContext.drawImage(source, 0, 0, canvas.width, canvas.height); var canvasData = tempContext.getImageData(0, 0, canvas.width, canvas.height); var binaryData = canvasData.data; } The binaryData object contains an array of every pixel and can be used to quickly read or write data directly to the canvas. So with this in mind, we can apply the whole effect with the following code: var source = document.getElementById("source"); source.onload = function () { var start = new Date(); var canvas = document.getElementById("target"); canvas.width = source.clientWidth; canvas.height = source.clientHeight; if (!canvas.getContext) { log.innerText = "Canvas not supported. Please install a HTML5 compatible browser."; return; } var tempContext = canvas.getContext("2d"); var len = canvas.width * canvas.height * 4; tempContext.drawImage(source, 0, 0, canvas.width, canvas.height); var canvasData = tempContext.getImageData(0, 0, canvas.width, canvas.height); var binaryData = canvasData.data; processSepia(binaryData, len); tempContext.putImageData(canvasData, 0, 0); var diff = new Date() - start; log.innerText = "Process done in " + diff + " ms (no web workers)"; } The processSepia function is just an variation of the previous one: var processSepia = function (binaryData, l) { for (var i = 0; i < l; i += 4) { var r = binaryData[i]; var g = binaryData[i + 1]; var b = binaryData[i + 2]; binaryData[i] = colorDistance(noise(), (r * 0.393) + (g * 0.769) + (b * 0.189), r); binaryData[i + 1] = colorDistance(noise(), (r * 0.349) + (g * 0.686) + (b * 0.168), g); binaryData[i + 2] = colorDistance(noise(), (r * 0.272) + (g * 0.534) + (b * 0.131), b); } }; With this solution, on my Intel Extreme processor (12 cores), the main process takes 150ms and obviously only use one processor: Adding web workers The best thing you can do when dealing with SIMD (single instruction multiple data) is to use a parallelization approach. Especially when you want to work with low-end hardware (such as phone devices) with limited resources. With JavaScript, to enjoy the power of parallelization, you have to use the Web Workers (my friend David Rousset wrote an excellent paper on this subject: http://blogs.msdn.com/b/davrous/archive/2011/07/15/introduction-to-the-html5-web-workers-the-javascript-multithreading-approach.aspx). Picture processing is a really good candidate for parallelization because (in the case of sepia tone) every processing is independent and so the following approach is possible: To do so, first of all you have to create a tools.js file to be used as a reference by other scripts: function noise() { return Math.random() * 0.5 + 0.5; }; function colorDistance(scale, dest, src) { return (scale * dest + (1 - scale) * src); }; var processSepia = function (binaryData, l) { for (var i = 0; i < l; i += 4) { var r = binaryData[i]; var g = binaryData[i + 1]; var b = binaryData[i + 2]; binaryData[i] = colorDistance(noise(), (r * 0.393) + (g * 0.769) + (b * 0.189), r); binaryData[i + 1] = colorDistance(noise(), (r * 0.349) + (g * 0.686) + (b * 0.168), g); binaryData[i + 2] = colorDistance(noise(), (r * 0.272) + (g * 0.534) + (b * 0.131), b); } }; The processSepia function will be applied to every bunch of the picture by a dedicated worker. The code of each worker is included in a pictureprocessor.js file: importScripts("tools.js"); self.onmessage = function (e) { var canvasData = e.data.data; var binaryData = canvasData.data; var l = e.data.length; var index = e.data.index; processSepia(binaryData, l); self.postMessage({ result: canvasData, index: index }); }; The main point here is that the canvas data (actually a part of it according to the current block to process) is cloned by JavaScript and passed to the worker. The worker is not working on the initial source but on a copy of it (using a specified algorithm: the structured clone algorithm). The copy itself is really quick and limited to a specific part of the picture. The main client page (default.js) has to create 4 workers and give them the right part of the picture. Then every worker will callback a function in the main thread using the messaging API (postMessage / onmessage) to give back the result: var source = document.getElementById("source"); source.onload = function () { var start = new Date(); var canvas = document.getElementById("target"); canvas.width = source.clientWidth; canvas.height = source.clientHeight; // Testing canvas support if (!canvas.getContext) { log.innerText = "Canvas not supported. Please install a HTML5 compatible browser."; return; } var tempContext = canvas.getContext("2d"); var len = canvas.width * canvas.height * 4; // Drawing the source image into the target canvas tempContext.drawImage(source, 0, 0, canvas.width, canvas.height); // If workers are not supported if (!window.Worker) { // Getting all the canvas data var canvasData = tempContext.getImageData(0, 0, canvas.width, canvas.height); var binaryData = canvasData.data; // Processing all the pixel with the main thread processSepia(binaryData, len); // Copying back canvas data to canvas tempContext.putImageData(canvasData, 0, 0); var diff = new Date() - start; log.innerText = "Process done in " + diff + " ms (no web workers)"; return; } // Let say we want to use 4 workers var workersCount = 4; var finished = 0; var segmentLength = len / workersCount; // This is the length of array sent to the worker var blockSize = canvas.height / workersCount; // Height of the picture chunck for every worker // Function called when a job is finished var onWorkEnded = function (e) { // Data is retrieved using a memory clone operation var canvasData = e.data.result; var index = e.data.index; // Copying back canvas data to canvas tempContext.putImageData(canvasData, 0, blockSize * index); finished++; if (finished == workersCount) { var diff = new Date() - start; log.innerText = "Process done in " + diff + " ms"; } }; // Launching every worker for (var index = 0; index < workersCount; index++) { var worker = new Worker("pictureProcessor.js"); worker.onmessage = onWorkEnded; // Getting the picture var canvasData = tempContext.getImageData(0, blockSize * index, canvas.width, blockSize); // Sending canvas data to the worker using a copy memory operation worker.postMessage({ data: canvasData, index: index, length: segmentLength }); } }; Using this technique, the complete process lasts only 80ms (from 150ms) on my computer and obviously uses 4 processors: On my low-end hardware (based on dual core system), the process falls to 500ms (from 900ms). The final code is available here: http://www.catuhe.com/msdn/pictureworkers.zip And the live version is right there: http://www.catuhe.com/msdn/workers/default.html (For comparison, the no web workers version: http://www.catuhe.com/msdn/workers/defaultnoworker.html) A important point to note is that on recent computers the difference can be thin or even in favor of the code without workers. The overhead of the memory copy must be balanced by a complex code used by the workers. The sepia tone could not be enough in some cases. However, the web workers will really be useful on low-end hardware. Porting to Windows 8 Finally I was not able to resist to the pleasure of porting my JavaScript code to create a Windows 8 application. It took me about 10 minutes to create a blank JavaScript project and copy/paste the JavaScript code inside (feel the power of native JavaScript code for Windows 8!) So feel free to grab the Windows 8 app code here: http://www.catuhe.com/msdn/Win8PictureWorkers.zip

have you ever thought about creating your own web games? i'm sure that most of you have already heard about the newest technologies like html5, canvas, webgl, and node.js. but i think that before you start working with these new technologies, you should start developing games with the simplest dom (like html, css, and javascript). i would like to provide you with a collection of such games, so you will be able to investigate and try them out. some of them even work without javascript! 1. whack-a-rat – css only game 2. survivor (1982 commodore 64 game remake) 3. sumon 4. 3d – css puzzle 5. duck hunt 6. dino pairs game 7. cops and robbers – css puzzle 8. cascading cube racer 9. css maze puzzle 10. one-of-a-kind css/js-based game portfolio plus, you can find tutorial about making this portfolio here 11. anigma 12. ninja jarimaru conclusion i hope that our new collection of css3 games was interesting for you. good luck!

This article aims to show you how to quickly fix the most common java security code violations. It assumes that you are familiar with the concept of code rules and violations and how Sonar reports on them. However, if you haven’t heard these terms before then you might take a look at Sonar Concepts or the forthcoming book about Sonar for a more detailed explanation. To get an idea, during Sonar analysis, your project is scanned by many tools to ensure that the source code conforms with the rules you’ve created in your quality profile. Whenever a rule is violated… well a violation is raised. With Sonar you can track these violations with violations drilldown view or in the source code editor. There are hundreds of rules, categorized based on their importance. Ill try, in future posts, to cover as many as I can but for now let’s take a look at some common security rules / violations. There are two pairs of rules (all of them are ranked as critical in Sonar ) we are going to examine right now. 1. Array is Stored Directly ( PMD ) and Method returns internal array ( PMD ) These violations appear in the cases when an internal Array is stored or returned directly from a method. The following example illustrates a simple class that violates these rules. public class CalendarYear { private String[] months; public String[] getMonths() { return months; } public void setMonths(String[] months) { this.months = months; } } To eliminate them you have to clone the Array before storing / returning it as shown in the following class implementation, so noone can modify or get the original data of your class but only a copy of them. public class CalendarYear { private String[] months; public String[] getMonths() { return months.clone(); } public void setMonths(String[] months) { this.months = months.clone(); } } 2. Nonconstant string passed to execute method on an SQL statement (findbugs) and A prepared statement is generated from a nonconstant String (findbugs) Both rules are related to database access when using JDBC libraries. Generally there are two ways to execute an SQL Commants via JDBC connection : Statement and PreparedStatement. There is a lot of discussion about pros and cons but it’s out of the scope of this post. Let’s see how the first violation is raised based on the following source code snippet. Statement stmt = conn.createStatement(); String sqlCommand = "Select * FROM customers WHERE name = '" + custName + "'"; stmt.execute(sqlCommand); You’ve already noticed that the sqlcommand parameter passed to execute method is dynamically created during run-time which is not acceptable by this rule. Similar situations causes the second violation. String sqlCommand = "insert into customers (id, name) values (?, ?)"; Statement stmt = conn.prepareStatement(sqlCommand); You can overcome this problems with three different ways. You can either use StringBuilder or String.format method to create the values of the string variables. If applicable you can define the SQL Commands as Constant in class declaration, but it’s only for the case where the SQL command is not required to be changed in runtime. Let’s re-write the first code snippet using StringBuilder Statement stmt = conn.createStatement(); stmt.execute(new StringBuilder("Select FROM customers WHERE name = '"). append(custName). append("'").toString()); and using String.format Statement stmt = conn.createStatement(); String sqlCommand = String.format("Select * from customers where name = '%s'", custName); stmt.execute(sqlCommand); For the second example you can just declare the sqlCommand as following private static final SQLCOMMAND = insert into customers (id, name) values (?, ?)"; There are more security rules such as the blocker Hardcoded constant database password but I assume that nobody is still hardcodes passwords in source code files… In following articles I’m going to show you how to adhere to performance and bad practice rules. Until then I’m waiting for your comments or suggestions.

Everyone is thinking why in the world would anyone pick static, when you can be dynamic? Usually the thought process is, "what language am I most proficient in, that can do the job." Totally not a bad way to go about it. Now does this choice affect anything else? Testing? Speed of development? Robustness? Dynamic vs. Static Dynamic languages are languages that don’t necessarily need variables to be declared before they are used. Examples of dynamic languages are Python, Ruby, and PHP. So in dynamic languages the following is possible: num = 10 We have successfully assigned a value to variable without declaring it before hand. Simple enough, try doing this in Java (you can’t). This can *increase* development speed, without having to write boilerplate code. This can somewhat be a double edge sword, since dynamic languages types are checked during runtime, there is no way to tell if there is a bug in code until it is run. I know you can test, but you can’t test for everything. You can’t test for everything. Here is an example albeit trivial. def get_first_problem(problems): for problem in problems: problam = problem + 1 return problam Now if you are raging to some serious dubstep, its easy enough to miss that small typo, you go screw it and do it live, and deploy to production. Python will simply create the new variable and not a single thing will be said. Only you can stop bugs in production! Static languages are languages that variables need to be declared before use and type checking is done at compile time. Examples of static languages include Java, C, and C++. So in static languages the following is enforced static int awesomeNumber; awesomeNumber = 10; Many argue this increases robustness as well as decrease chances of Runtime Errors. Since the compiler will catch those horrible horrible mistakes you made throughout your code. Your methods contracts are tighter, downside to this is crap ton of boilerplate code. Weak and Strong Typing can be often be confused with dynamic and static languages. Weak typed languages can lead to philosophical questions like what does the number 2 added to the word ‘two’ give you? Things like this are possible with a weak typed language. a = 2 b = "2" concatenate(a, b) // Returns "22" add(a, b) // Returns 4 Traditionally languages may place restriction on what transaction may occur for example in a strong typed language adding a string and integer will result in a type error as shown below. >>> a = 10 >>> b = 'ten' >>> a + b Traceback (most recent call last): File "", line 1, in TypeError: unsupported operand type(s) for +: 'int' and 'str' >>> Conclusion Regardless of where you land on this discussion, claiming one is better than the other would lead to flame war, but there are places where each is strong. Dynamic languages are good for fast quick development cycles and prototyping, while static languages are better suited to longer development cycles where trivial bugs could be extremely costly (telecommunication systems, air traffic control). For example if some giant company called Moo Corp. spent millions of dollars on QA and Testing and a bug somehow gets into the field, to fix it would mean another round of testing. When sitting in that chair the choice is clear static languages FTW, its a hard job but someone has to milk the cows. Test, test, and test. Just a little food for thought, for when you are starting your next project. You never know what limitations you maybe placing on yourself and your team. What do you do consider when selecting a programming language for a project?