How to allow instance methods to run as JUnit BeforeClass behavior.

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.

JUnit 4.4 added a new assertion mechanism with the method assertThat(). Have a look and consider using it in place of assertEquals(). assertThat(result, is(42)); assertThat(output, containsString("foo"));

Curator's note: This article originally appeared at WindowsAzure.com. To use this feature and other new Windows Azure capabilities, sign up for the free preview. Just as you can quickly create and deploy a web application created from the gallery, you can also deploy a website created on a workstation with traditional developer tools from Microsoft or other companies. Table of Contents Deployment Options How to: Create a Website Using the Management Portal How to: Create a Website from the Gallery How to: Delete a Website Next Steps Deployment Options Windows Azure supports deploying websites from remote computers using WebDeploy, FTP, GIT or TFS. Many development tools provide integrated support for publication using one or more of these methods and may only require that you provide the necessary credentials, site URL and hostname or URL for your chosen deployment method. Credentials and deployment URLs for all enabled deployment methods are stored in the website's publish profile, a file which can be downloaded in the Windows Azure (Preview) Management Portal from the Quick Start page or the quick glance section of the Dashboard page. If you prefer to deploy your website with a separate client application, high quality open source GIT and FTP clients are available for download on the Internet for this purpose. How to: Create a Website Using the Management Portal Follow these steps to create a website in Windows Azure. Login to the Windows Azure (Preview) Management Portal. Click the Create New icon on the bottom left of the Management Portal. Click the Web Site icon, click the Quick Create icon, enter a value for URL and then click the check mark next to create web site on the bottom right corner of the page. When the website has been created you will see the text Creation of Web Site '[SITENAME]' Completed. Click the name of the website displayed in the list of websites to open the website's Quick Start management page. On the Quick Start page you are provided with options to set up TFS or GIT publishing if you would like to deploy your finished website to Windows Azure using these methods. FTP publishing is set up by default for websites and the FTP Host name is displayed under FTP Hostname on the Quick Start and Dashboard pages. Before publishing with FTP or GIT choose the option to Reset deployment credentials on the Dashboard page. Then specify the new credentials (username and password) to authenticate against the FTP Host or the Git Repository when deploying content to the website. The Configure management page exposes several configurable application settings in the following sections: Framework: Set the version of .NET framework or PHP required by your web application. Diagnostics: Set logging options for gathering diagnostic information for your website in this section. App Settings: Specify name/value pairs that will be loaded by your web application on start up. For .NET sites, these settings will be injected into your .NET configuration AppSettings at runtime, overriding existing settings. For PHP and Node sites these settings will be available as environment variables at runtime. Connection Strings: View connection strings for linked resources. For .NET sites, these connection strings will be injected into your .NET configuration connectionStrings settings at runtime, overriding existing entries where the key equals the linked database name. For PHP and Node sites these settings will be available as environment variables at runtime. Default Documents: Add your web application's default document to this list if it is not already in the list. If your web application contains more than one of the files in the list then make sure your website's default document appears at the top of the list. How to: Create a Website from the Gallery The gallery makes available a wide range of popular web applications developed by Microsoft, third party companies, and open source software initiatives. Web applications created from the gallery do not require installation of any software other than the browser used to connect to the Windows Azure Management Portal. In this tutorial, you'll learn: How to create a new site through the gallery. How to deploy the site through the Windows Azure Portal. You'll build a Word press blog that uses a default template. The following illustration shows the completed application: Note To complete this tutorial, you need a Windows Azure account that has the Windows Azure Web Sites 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. Create a web site in the portal Login to the Windows Azure Management Portal. Click the New icon on the bottom left of the dashboard. Click the Web Site icon, and click From Gallery. Locate and click the WordPress icon in list, and then click Next. On the Configure Your App page, enter or select values for all fields: Enter a URL name of your choice Leave Create a new MySQL database selected in the Database field Select the region closest to you Then click Next. On the Create New Database page, you can specify a name for your new MySQL database or use the default name. Select the region closest to you as the hosting location. Select the box at the bottom of the screen to agree to ClearDB's usage terms for your hosted MySQL database. Then click the check to complete the site creation. After you click Complete Windows Azure will initiate build and deploy operations. While the web site is being built and deployed the status of these operations is displayed at the bottom of the Web Sites page. After all operations are performed, A final status message when the site has been successfully deployed. Launch and manage your WordPress site Click on your new site from the Web Sites page to open the dashboard for the site. On the Dashboard management page, scroll down and click the link on the left under Site Url to open the site’s welcome page. Enter appropriate configuration information required by WordPress and click Install WordPress to finalize configuration and open the web site’s login page. Login to the new WordPress web site by entering the username and password that you specified on the Welcome page. You'll have a new WordPress site that looks similar to the site below. How to: Delete a Website Websites are deleted using the Delete icon in the Windows Azure Management Portal. The Delete icon is available in the Windows Azure Portal when you click Web Sites to list all of your websites and at the bottom of each of the website management pages. Next Steps For more information about Websites, see the following: Walkthrough: Troubleshooting a Website on Windows Azure

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.

SOAP headers can be added to a Web service request in different ways, if you use Apache CXF. The way I prefer is the one I’ve mentioned here – as it doesn’t require changes to wsdl or method signatures and it’s much faster as it doesn’t break streaming and the memory overhead is less. The headers in the list are streamed at the appropriate time to the wire according to the databinding object found in the Header object. About SOAP headers, Like any good messaging protocol, SOAP defines the concept of a message header. It is an optional part of any SOAP message. If it exists, the header contains application-specific information (like authentication, payment, etc) about the SOAP message i.e. information about the message, or about the context in which the message is sent, or basically whatever the creator of the message thought was a good idea to put there instead of the actual body of the message. If the Header element is present, it must be the first child element of the Envelope element. /** * @author Singaram Subramanian * */ /* Create a ClientProxyFactoryBean reference and assign it an instance of JaxWsProxyFactoryBean, a factory for creating JAX-WS proxies. This class provides access to the internal properties used to set-up proxies. Using it provides more control than the standard JAX-WS APIs. */ ClientProxyFactoryBean factory = new JaxWsProxyFactoryBean(); factory.setServiceClass(singz.ws.cxf.sample.SampleServiceInterface.class); // Set the web service endpoint URL here factory.setAddress("http://xxx.xxx.com/services/SampleService/v1"); SampleServiceInterface serviceClient = (SampleServiceInterface) factory.create(); // Get the underlying Client object from the proxy object of service interface Client proxy = ClientProxy.getClient(serviceClient); // Creating SOAP headers to the web service request // Create a list for holding all SOAP headers List headersList = new ArrayList(); Header testSoapHeader1 = new Header(new QName("uri:singz.ws.sample", "soapheader1"), "SOAP Header Message 1", new JAXBDataBinding(String.class)); Header testSoapHeader2 = new Header(new QName("uri:singz.ws.sample", "soapheader2"), "SOAP Header Message 2", new JAXBDataBinding(String.class)); headersList.add(testSoapHeader1); headersList.add(testSoapHeader2); // Add SOAP headers to the web service request proxy.getRequestContext().put(Header.HEADER_LIST, headersList); More on this @ http://cxf.apache.org/faq.html#FAQ-HowcanIaddsoapheaderstotherequest%2Fresponse%3F

Exception Testing Why test exception flows? Just like with all of your code, test coverage writes a contract between your code and the business functionality that the code is supposed to produce leaving you with a living documentation of the code along with the added ability to stress the functionality early and often. I won't go into the many benefits of testing instead I will focus on just Exception Testing. There are many ways to test an exception flow thrown from a piece of code. Lets say that you have a guarded method that requires an argument to be not null. How would you test that condition? How do you keep JUnit from reporting a failure when the exception is thrown? This blog covers a few different methods culminating with JUnit's ExpectedException implemented with JUnit's @Rule functionality. The "old" way In a not so distant past the process to test an exception required a dense amount of boilerplate code in which you would start a try/catch block, report a failure if your code did not produce the expected behavior and then catch the exception looking for the specific type. Here is an example: public class MyObjTest { @Test public void getNameWithNullValue() { try { MyObj obj = new MyObj(); myObj.setName(null); fail("This should have thrown an exception"); } catch (IllegalArgumentException e) { assertThat(e.getMessage().equals("Name must not be null")); } } } As you can see from this old example, many of the lines in the test case are just to support the lack of functionality present to specifically test exception handling. One good point to make for the try/catch method is the ability to test the specific message and any custom fields on the expected exception. We will explore this a bit further down with JUnit's ExpectedException and @Rule annotation. JUnit adds expected exceptions JUnit responded back to the users need for exception handling by adding a @Test annotation field "expected". The intention is that the entire test case will pass if the type of exception thrown matched the exception class present in the annotation. public class MyObjTest { @Test(expected = IllegalArgumentException.class) public void getNameWithNullValue() { MyObj obj = new MyObj(); myObj.setName(null); } } As you can see from the newer example, there is quite a bit less boiler plate code and the test is very concise, however, there are a few flaws. The main flaw is that the test condition is too broad. Suppose you have two variables in a signature and both cannot be null, then how do you know which variable the IllegalArgumentException was thrown for? What happens when you have extended a Throwable and need to check for the presence of a field? Keep these in mind as you read further, solutions will follow. JUnit @Rule and ExpectedException If you look at the previous example you might see that you are expecting an IllegalArgumentException to be thrown, but what if you have a custom exception? What if you want to make sure that the message contains a specific error code or message? This is where JUnit really excelled by providing a JUnit @Rule object specifically tailored to exception testing. If you are unfamiliar with JUnit @Rule, read the docs here. ExpectedException JUnit provides a JUnit class ExpectedException intended to be used as a @Rule. The ExpectedException allows for your test to declare that an exception is expected and gives you some basic built in functionality to clearly express the expected behavior. Unlike the @Test(expected) annotation feature, ExpectedException class allows you to test for specific error messages and custom fields via the Hamcrest matchers library. An example of JUnit's ExpectedException import org.junit.rules.ExpectedException; public class MyObjTest { @Rule public ExpectedException thrown = ExpectedException.none(); @Test public void getNameWithNullValue() { thrown.expect(IllegalArgumentException.class); thrown.expectMessage("Name must not be null"); MyObj obj = new MyObj(); obj.setName(null); } } As I eluded to above, the framework allows you to test for specific messages ensuring that the exception being thrown is the case that the test is specifically looking for. This is very helpful when the nullability of multiple arguments is in question. Custom Fields Arguably the most useful feature of the ExpectedException framework is the ability to use Hamcrest matchers to test your custom/extended exceptions. For example, you have a custom/extended exception that is to be thrown in a method and inside the exception has an "errorCode". How do you test that functionality without introducing the boiler plate code from the try/catch block listed above? How about a custom Matcher! This code is available at: https://github.com/mike-ensor/custom-exception-testing Solution: First the test case import org.junit.rules.ExpectedException; public class MyObjTest { @Rule public ExpectedException thrown = ExpectedException.none(); @Test public void someMethodThatThrowsCustomException() { thrown.expect(CustomException.class); thrown.expect(CustomMatcher.hasCode("110501")); MyObj obj = new MyObj(); obj.methodThatThrowsCustomException(); } } Solution: Custom matcher import com.thepixlounge.exceptions.CustomException; import org.hamcrest.Description; import org.hamcrest.TypeSafeMatcher; public class CustomMatcher extends TypeSafeMatcher { public static BusinessMatcher hasCode(String item) { return new BusinessMatcher(item); } private String foundErrorCode; private final String expectedErrorCode; private CustomMatcher(String expectedErrorCode) { this.expectedErrorCode = expectedErrorCode; } @Override protected boolean matchesSafely(final CustomException exception) { foundErrorCode = exception.getErrorCode(); return foundErrorCode.equalsIgnoreCase(expectedErrorCode); } @Override public void describeTo(Description description) { description.appendValue(foundErrorCode) .appendText(" was not found instead of ") .appendValue(expectedErrorCode); } } NOTE: Please visit https://github.com/mike-ensor/custom-exception-testing to get a copy of a working Hamcrest Matcher, JUnit @Rule and ExpectedException. And there you have it, a quick overview of different ways to test Exceptions thrown by your code along with the ability to test for specific messages and fields from within custom exception classes. Please be specific with your test cases and try to target the exact case you have setup for your test, remember, tests can save you from introducing side-effect bugs!

For my own interest I started exploring Spring REST Services with GWT. It took some time to figure it out and then I came accross RestyGWT. With the help of RestyGWT, I managed to integrate GWT with Spring REST Services. My idea was to keep the GWT way of creating the service and serviceAsync and yet make a REST call. Below are the steps which will help achieve above. My preffered development environment is Eclipse, so as a prerequisite you must have Eclipse with Maven support installed. Lets Begin, Create a Maven project, goto File-->New-->Other. In the wizard type "Maven". Select Maven Project and click on Next. In the "Select project name and location" page of the wizard, make sure that "Create a simple project (skip archetype selection)" option is checked, hit "Next" to continue with default values. In the "Enter group id for the artifact" page of the wizard, enter values for group id and artifactid. Select the packaging as "war", hit "Finish" to exit the wizard and to create your project. Modify the POM file to add required dependencies as below 4.0.0 com.sagar.restgwt RestGWT 0.0.1-SNAPSHOT war com.google.gwt gwt-servlet ${gwt.version} runtime com.google.gwt gwt-user ${gwt.version} provided org.fusesource.restygwt restygwt 1.2 javax.ws.rs jsr311-api 1.1 provided org.codehaus.jackson jackson-mapper-asl 1.4.1 org.springframework spring-core ${org.springframework.version} org.springframework spring-web ${org.springframework.version} org.springframework spring-webmvc ${org.springframework.version} . 2.5.0-rc1 1.6 3.1.1.RELEASE UTF-8 ${project.build.directory}\${project.build.finalName} restgwt ${webappDirectory}/WEB-INF/classes org.codehaus.mojo gwt-maven-plugin 2.5.0-rc1 compile test generateAsync ${webappDirectory} org.apache.maven.plugins maven-compiler-plugin ${java-version} ${java-version} true "Update Project Configuration" by Right clicking on your project-->Maven. The below steps assumes that your are aware of the GWT project structure. Create your GWT module. This can be done by installing GWT plugin for Eclipse. Once the GWT module is ready update your .gwt.xml with the below given content. In the client package create the service to make REST Call. With this approach we dont have to create the ServiceAsync interface. We will be creating our service interface by extending the "RestService", provided by RestyGWT. Code Snippet: InfoService.Java [ A service interface to make REST call. ] @Path("/service") public interface InfoService extends RestService { public static class Util { private static InfoService instance; public static InfoService getService() { if (instance == null) { instance = GWT.create(InfoService.class); } Resource resource = new Resource(GWT.getModuleBaseURL() + "service"); ((RestServiceProxy) instance).setResource(resource); return instance; } } @GET @Path("/loadInfo") @Consumes(MediaType.APPLICATION_JSON) @Produces(MediaType.APPLICATION_JSON) public void getInfo(MethodCallback callback); } OrderConfirmation.java [ Model which will be returned as a response. ] public class OrderConfirmation { public String message; public Long ready_time; /** * Example of how to create an instance of a JsonEncoderDecoder for a data * transfer object. */ public interface OrderConfirmationJED extends JsonEncoderDecoder { } @Override public String toString() { if (GWT.isClient()) { OrderConfirmationJED jed = GWT.create(OrderConfirmationJED.class); return jed.encode(this).toString(); } return super.toString(); } } RestGWT.java [ GWT module entrypoint to see things running. ] public class RestGWT implements EntryPoint { public void onModuleLoad() { Button button = new Button("Click Me"); button.addClickHandler(new ClickHandler() { @Override public void onClick(ClickEvent event) { InfoService.Util.getService().getInfo(new MethodCallback() { @Override public void onSuccess(Method method, OrderConfirmation response) { RootPanel.get().add(new Label(response.toString())); } @Override public void onFailure(Method method, Throwable exception) { GWT.log("Error"); } }); } }); RootPanel.get().add(button); } } Create the Spring managed controller as below, this should be presnt in the "server" package as per GWT project structure. Also you can notice that we don't have to implement our service interface. RestGWTController.java [ Spring managed controller. ] @Controller public class RestGWTController { @RequestMapping(value = "/loadInfo", method = RequestMethod.GET, headers = "Accept=application/json") public @ResponseBody OrderConfirmation handleRequest(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { GreetingServiceEndpoint endpoint = greetingService.getGreetingServiceEndpointPort(); OrderConfirmation confirmation = new OrderConfirmation(); confirmation.message = endpoint.sayHello(); confirmation.ready_time = System.currentTimeMillis() + 1000 * 60 * 30; return confirmation; } } Settings required for Spring src/main/webapp/WEB-INF/web.xml Rest GWT This is web-project for RestGWT contextConfigLocation /WEB-INF/applicationContext.xml org.springframework.web.context.ContextLoaderListener Spring MVC Dispatcher Servlet org.springframework.web.servlet.DispatcherServlet contextConfigLocation /WEB-INF/classes/action-servlet.xml 1 Spring MVC Dispatcher Servlet /restgwt/service/* RestGWT.html src/main/webapp/WEB-INF/applicationCotext.xml src/main/webapp/WEB-INF/classes/action-servlet.xml Now all configuration is completed. To build the application right click on your project → Run As → Maven Install. This will create the war file in your project's target/restgwt folder. To test this approach we are going to deploy our web application to an Apache Tomcat 7 server. To launch the application point your browser to the following address http://localhost:8080/restgwt/ Enjoy Coding Nilabh

If you look around the web for examples of using Spring 3.1’s built in caching then you’ll usually bump into Spring’s SimpleCacheManager, which the Guys at Spring say is “Useful for testing or simple caching declarations”. I actually prefer to think of SimpleCacheManager as lightweight rather than simple; useful in those situations where you want a small in memory cache on a per JVM basis. If the Guys at Spring were running a supermarket then SimpleCacheManagerwould be in their own brand ‘basics’ product range. If, on the other hand, you need a heavy duty cache, one that’s scalable, persistent and distributed, then Spring also comes with a built in ehCache wrapper. The good news is that swapping between Spring's caching implementations is easy. In theory it’s all a matter of configuration and, to prove the theory correct, I took the sample code from my Caching and @Cacheable blog and ran it using an EhCache implementation. The configuration steps are similar to those described in my last blog Caching and Config in that you still need to specify: ...in your Spring config file to switch caching on. You also need to define a bean with an id of cacheManager, only this time you reference Spring’s EhCacheCacheManager class instead of SimpleCacheManager. The example above demonstrates an EhCacheCacheManager configuration. Notice that it references a second bean with an id of 'ehcache'. This is configured as follows: "ehcache" has two properties: configLocation and shared. 'configLocation' is an optional attribute that’s used to specify the location of an ehcache configuration file. In my test code I used the following example file: ...which creates two caches: a default cache and one named “employee”. If this file is missing then the EhCacheManagerFactoryBean simply picks up a default ehcache config file: ehcache-failsafe.xml, which is located in ehcache’s ehcache-core jar file. The other EhCacheManagerFactoryBean attribute is 'shared'. This is supposed to be optional as the documentation states that it defines "whether the EHCache CacheManager should be shared (as a singleton at the VM level) or independent (typically local within the application). Default is 'false', creating an independent instance.” However, if this is set to false then you’ll get the following exception: org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'org.springframework.cache.interceptor.CacheInterceptor#0': Cannot resolve reference to bean 'cacheManager' while setting bean property 'cacheManager'; nested exception is org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'cacheManager' defined in class path resource [ehcache-example.xml]: Cannot resolve reference to bean 'ehcache' while setting bean property 'cacheManager'; nested exception is org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'ehcache' defined in class path resource [ehcache-example.xml]: Invocation of init method failed; nested exception is net.sf.ehcache.CacheException: Another unnamed CacheManager already exists in the same VM. Please provide unique names for each CacheManager in the config or do one of following: 1. Use one of the CacheManager.create() static factory methods to reuse same CacheManager with same name or create one if necessary 2. Shutdown the earlier cacheManager before creating new one with same name. The source of the existing CacheManager is: InputStreamConfigurationSource [stream=java.io.BufferedInputStream@424c414] at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveReference(BeanDefinitionValueResolver.java:328) at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveValueIfNecessary(BeanDefinitionValueResolver.java:106) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.applyPropertyValues(AbstractAutowireCapableBeanFactory.java:1360) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.populateBean(AbstractAutowireCapableBeanFactory.java:1118) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.doCreateBean(AbstractAutowireCapableBeanFactory.java:517) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.createBean(AbstractAutowireCapableBeanFactory.java:456) ... stack trace shortened for clarity at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.runTests(RemoteTestRunner.java:683) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.run(RemoteTestRunner.java:390) at org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.main(RemoteTestRunner.java:197) Caused by: org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'cacheManager' defined in class path resource [ehcache-example.xml]: Cannot resolve reference to bean 'ehcache' while setting bean property 'cacheManager'; nested exception is org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'ehcache' defined in class path resource [ehcache-example.xml]: Invocation of init method failed; nested exception is net.sf.ehcache.CacheException: Another unnamed CacheManager already exists in the same VM. Please provide unique names for each CacheManager in the config or do one of following: 1. Use one of the CacheManager.create() static factory methods to reuse same CacheManager with same name or create one if necessary 2. Shutdown the earlier cacheManager before creating new one with same name. The source of the existing CacheManager is: InputStreamConfigurationSource [stream=java.io.BufferedInputStream@424c414] at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveReference(BeanDefinitionValueResolver.java:328) at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveValueIfNecessary(BeanDefinitionValueResolver.java:106) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.applyPropertyValues(AbstractAutowireCapableBeanFactory.java:1360) ... stack trace shortened for clarity at org.springframework.beans.factory.support.AbstractBeanFactory.getBean(AbstractBeanFactory.java:193) at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveReference(BeanDefinitionValueResolver.java:322) ... 38 more Caused by: org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'ehcache' defined in class path resource [ehcache-example.xml]: Invocation of init method failed; nested exception is net.sf.ehcache.CacheException: Another unnamed CacheManager already exists in the same VM. Please provide unique names for each CacheManager in the config or do one of following: 1. Use one of the CacheManager.create() static factory methods to reuse same CacheManager with same name or create one if necessary 2. Shutdown the earlier cacheManager before creating new one with same name. The source of the existing CacheManager is: InputStreamConfigurationSource [stream=java.io.BufferedInputStream@424c414] at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.initializeBean(AbstractAutowireCapableBeanFactory.java:1455) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.doCreateBean(AbstractAutowireCapableBeanFactory.java:519) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.createBean(AbstractAutowireCapableBeanFactory.java:456) at org.springframework.beans.factory.support.AbstractBeanFactory$1.getObject(AbstractBeanFactory.java:294) at org.springframework.beans.factory.support.DefaultSingletonBeanRegistry.getSingleton(DefaultSingletonBeanRegistry.java:225) at org.springframework.beans.factory.support.AbstractBeanFactory.doGetBean(AbstractBeanFactory.java:291) at org.springframework.beans.factory.support.AbstractBeanFactory.getBean(AbstractBeanFactory.java:193) at org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveReference(BeanDefinitionValueResolver.java:322) ... 48 more Caused by: net.sf.ehcache.CacheException: Another unnamed CacheManager already exists in the same VM. Please provide unique names for each CacheManager in the config or do one of following: 1. Use one of the CacheManager.create() static factory methods to reuse same CacheManager with same name or create one if necessary 2. Shutdown the earlier cacheManager before creating new one with same name. The source of the existing CacheManager is: InputStreamConfigurationSource [stream=java.io.BufferedInputStream@424c414] at net.sf.ehcache.CacheManager.assertNoCacheManagerExistsWithSameName(CacheManager.java:521) at net.sf.ehcache.CacheManager.init(CacheManager.java:371) at net.sf.ehcache.CacheManager.(CacheManager.java:339) at org.springframework.cache.ehcache.EhCacheManagerFactoryBean.afterPropertiesSet(EhCacheManagerFactoryBean.java:104) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.invokeInitMethods(AbstractAutowireCapableBeanFactory.java:1514) at org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.initializeBean(AbstractAutowireCapableBeanFactory.java:1452) ... 55 more ...when you try to run a bunch of unit tests. I think that this comes down to a simple bug Spring’s the ehcache manager factory as it’s trying to create multiple cache instances using new() rather than using, as the exception states, “one of the CacheManager.create() static factory methods" which allows it to reuse same CacheManager with same name. Hence, my first JUnit test works okay, but all others fail. The offending line of code is: this.cacheManager = (this.shared ? CacheManager.create() : new CacheManager()); My full XML config file is listed below for completeness: In using ehcache, the only other configuration details to consider are the Maven dependencies. These are pretty straight forward as the Guys at Ehcache have combined all the various ehcache jars into one Maven POM module. This POM module can be added to your project's POM file using the XML below: net.sf.ehcache ehcache 2.6.0 pom test Finally, the ehcache Jar files are available from both the Maven Central and Sourceforge repositories: sourceforge http://oss.sonatype.org/content/groups/sourceforge/ true true

there are many different web api standards that are turning the web browser into a complete application platform. with websockets we get nice asynchronous communication, various standards allow us access to sensors in laptops and mobile devices and we can even determine how full the battery is. one of the standards i'm really interested in is webrtc. with webrtc we can get real-time audio and video communication between browsers without needing plugins or additional tools. a couple of months ago i wrote about how you can use webrtc to access the webcam and use it for face recognition . at that time, none of the browser allowed you to access the microphone. a couple of months later though, and both the developer version of firefox and developer version of chrome, allow you to access the microphone! so let's see what we can do with this. most of the examples i've seen so far focus on processing the input directly, within the browser, using the web audio api . you get synthesizers, audio visualizations, spectrometers etc. what was missing, however, was a means of recording the audio data and storing it for further processing at the server side. in this article i'll show you just that. i'm going to show you how you can create the following (you might need to enlarge it to read the response from the server): in this screencast you can see the following: a simple html page that access your microphone the speech is recorded and using websockets is sent to a backend the backend combines the audio data and sends it to google's speech to text api the result from this api call is returned to the browser and all this is done without any plugins in the browser! so what's involved to accomplish all this. allowing access to your microphone the first thing you need to do is make sure you've got an up to date version of chrome. i use the dev build, and am currently on this version: since this is still an experimental feature we need to enable this using the chrome flags. make sure the "web audio input" flag is enabled. with this configuration out of the way we can start to access our microphone. access the audio stream from the microphone this is actually very easy: function callback(stream) { var context = new webkitaudiocontext(); var mediastreamsource = context.createmediastreamsource(stream); ... } $(document).ready(function() { navigator.webkitgetusermedia({audio:true}, callback); ... } as you can see i use the webkit prefix functions directly, you could, of course, also use a shim so it is browser independent. what happens in the code above is rather straightforward. we ask, using getusermedia, for access to the microphone. if this is successful our callback gets called with the audio stream as its parameter. in this callback we use the web audio specification to create a mediastreamsource from our microphone. with this mediastreamsource we can do all the nice web audio tricks you can see here . but we don't want that, we want to record the stream and send it to a backend server for further processing. in future versions this will probably be possible directly from the webrtc api, at this time, however, this isn't possible yet. luckily, though, we can use a feature from the web audio api to get access to the raw data. with the javascriptaudionode we can create a custom node, which we can use to access the raw data (which is pcm encoded). before i started my own work on this i searched around a bit and came across the recoder.js project from here: https://github.com/mattdiamond/recorderjs . matt created a recorder that can record the output from web audio nodes, and that's exactly what i needed. all i needed to do now was connect the stream we just created to the recorder library: function callback(stream) { var context = new webkitaudiocontext(); var mediastreamsource = context.createmediastreamsource(stream); rec = new recorder(mediastreamsource); } with this code, we create a recorder from our stream. this recorder provides the following functions: record: start recording from the input stop: stop recording clear: clear the current recording exportwav: export the data as a wav file connect the recorder to the buttons i've created a simple webpage with an output for the text and two buttons to control the recording: the 'record' button starts the recording, and once you hit the 'export' button the recording stops, and is sent to the backend for processing. record button: $('#record').click(function() { rec.record(); ws.send("start"); $("#message").text("click export to stop recording and analyze the input"); // export a wav every second, so we can send it using websockets intervalkey = setinterval(function() { rec.exportwav(function(blob) { rec.clear(); ws.send(blob); }); }, 1000); }); this function (using jquery to connect it to the button) when clicked starts the recording. it also uses a websocket (ws), see further down on how to setup the websocket, to indicate to the backend server to expect a new recording (more on this later). finally when the button is clicked an interval is created that passes the data to the backend, encoded as wav file, every second. we do this to avoid sending too large chunks of data to the backend and improve performance. export button: $('#export').click(function() { // first send the stop command rec.stop(); ws.send("stop"); clearinterval(intervalkey); ws.send("analyze"); $("#message").text(""); }); the export button, bad naming i think when i'm writing this, stops the recording, the interval and informs the backend server that it can send the received data to the google api for further processing. connecting the frontend to the backend to connect the webapplication to the backend server we use websockets. in the previous code fragments you've already seen how they are used. we create them with the following: var ws = new websocket("ws://127.0.0.1:9999"); ws.onopen = function () { console.log("openened connection to websocket"); }; ws.onmessage = function(e) { var jsonresponse = jquery.parsejson(e.data ); console.log(jsonresponse); if (jsonresponse.hypotheses.length > 0) { var bestmatch = jsonresponse.hypotheses[0].utterance; $("#outputtext").text(bestmatch); } } we create a connection, and when we receive a message from the backend we just assume it contains the response to our speech analysis. and that's it for the complete front end of the application. we use getusermedia to access the microphone, use the web audio api to get access to the raw data and communicate with websockets with the backend server. the backend server our backend server needs to do a couple of things. it first needs to combine the incoming chunks to a single audio file, next it needs to convert this to a format google apis expect, which is flac. finally we make a call to the google api and return the response. i've used jetty as the websocket server for this example. if you want to know the details about setting this up, look at the facedetection example. in this article i'll only show the code to process the incoming messages. first step, combine the incoming data the data we receive is encoded as wav (thanks to the recorder.js library we don't have to do this ourselves). in our backend we thus receive sound fragments with a length of one second. we can't just concatenate these together, since wav files have a header that tells how long the fragment is (amongst other things), so we have to combine them, and rewrite the header. lets first look at the code (ugly code, but works good enough for now :) public void onmessage(byte[] data, int offset, int length) { if (currentcommand.equals("start")) { try { // the temporary file that contains our captured audio stream file f = new file("out.wav"); // if the file already exists we append it. if (f.exists()) { log.info("adding received block to existing file."); // two clips are used to concat the data audioinputstream clip1 = audiosystem.getaudioinputstream(f); audioinputstream clip2 = audiosystem.getaudioinputstream(new bytearrayinputstream(data)); // use a sequenceinput to cat them together audioinputstream appendedfiles = new audioinputstream( new sequenceinputstream(clip1, clip2), clip1.getformat(), clip1.getframelength() + clip2.getframelength()); // write out the output to a temporary file audiosystem.write(appendedfiles, audiofileformat.type.wave, new file("out2.wav")); // rename the files and delete the old one file f1 = new file("out.wav"); file f2 = new file("out2.wav"); f1.delete(); f2.renameto(new file("out.wav")); } else { log.info("starting new recording."); fileoutputstream fout = new fileoutputstream("out.wav",true); fout.write(data); fout.close(); } } catch (exception e) { ...} } } this method gets called for each chunk of audio we receive from the browser. what we do here is the following: first, we check whether we have a temp audio file, if not we create it if the file exists we use java's audiosystem to create an audio sequence this sequence is then written to another file the original is deleted and the new one is renamed. we repeat this for each chunk so at this point we have a wav file that keeps on growing for each added chunk. now before we convert this, lets look at the code we use to control the backend. public void onmessage(string data) { if (data.startswith("start")) { // before we start we cleanup anything left over cleanup(); currentcommand = "start"; } else if (data.startswith("stop")) { currentcommand = "stop"; } else if (data.startswith("clear")) { // just remove the current recording cleanup(); } else if (data.startswith("analyze")) { // convert to flac ... // send the request to the google speech to text service ... } } the previous method responded to binary websockets messages. the one shown above responds to string messages. we use this to control, from the browser, what the backend should do. let's look at the analyze command, since that is the interesting one. when this command is issued from the frontend the backend needs to convert the wav file to flac and send it to the google service. convert to flac for the conversion to flac we need an external library since java standard has no support for this. i used the javaflacencoder from here for this. // get an encoder flac_fileencoder flacencoder = new flac_fileencoder(); // point to the input file file inputfile = new file("out.wav"); file outputfile = new file("out2.flac"); // encode the file log.info("start encoding wav file to flac."); flacencoder.encode(inputfile, outputfile); log.info("finished encoding wav file to flac."); easy as that. now we got a flac file that we can send to google for analysis. send to google for analysis a couple of weeks ago i ran across an article that explained how someone analyzed chrome and found out about an undocumented google api you can use for speech to text. if you post a flac file to this url: https://www.google.com/speech-api/v1/recognize?xjerr=1&client=chromium&l... you receive a response like this: { "status": 0, "id": "ae466ffa24a1213f5611f32a17d5a42b-1", "hypotheses": [ { "utterance": "the quick brown fox", "confidence": 0.857393 }] } to do this from java code, using httpclient, you do the following: // send the request to the google speech to text service log.info("sending file to google for speech2text"); httpclient client = new defaulthttpclient(); httppost p = new httppost(url); p.addheader("content-type", "audio/x-flac; rate=44100"); p.setentity(new fileentity(outputfile, "audio/x-flac; rate=44100")); httpresponse response = client.execute(p); f (response.getstatusline().getstatuscode() == 200) { log.info("received valid response, sending back to browser."); string result = new string(ioutils.tobytearray(response.getentity().getcontent())); this.connection.sendmessage(result); } and that are all the steps that are needed.

Explore SQL query optimization and normalization.

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.

This is the first in what will be a series of posts on how to establish a continuous delivery pipeline. The eventual goal is to have an app that we can push out into production anytime we like, safely and with little effort. But we’re going to start small and proceed incrementally, which is the best way to undertake this sort of effort. In this post we’ll start by establishing continuous integration for an arbitrary Java/Maven-based open source library project. It shouldn’t take more than an afternoon to do this more or less from scratch if you’re reasonably familiar with the technologies in question, except for the part where we have to get a Maven repo. We have to wait for Sonatype to approve the repo, but they’re pretty fast about it. I set this up for an open source library project I’m doing called Kite. The details of the project itself aren’t important for this exercise, but the open source library part is: Libraries (as opposed to apps) are easier to deal with, because deployment is just a matter of getting the binary into a Maven repo, as opposed to getting it running on a live server. Open source means that we can get a bunch of infrastructure freebies. Here’s what it will look like at the end of this post: So if you have an open source library you’ve been wanting to develop, now’s the time to get started! Find a place to host your project sources The right host depends on which SCM you prefer. If you like Git, then GitHub and Bitbucket are two obvious (free) choices. Bitbucket supports Mercurial as well, and it supports not just free public repos like GitHub, but free private repos as well. Anyway, I chose GitHub for Kite. Here’s the repo: GitHub Kite repo Set up a continuous integration server The next step is to set up a continuous integration server. Hudson and Jenkins are both pretty popular open source offerings, but I chose Atlassian’s Bamboo because the UI is a lot more polished. Bamboo is free for open source projects, and even if you decide to buy it, it’s only $10 for the starter license (proceeds go to charity), which gives you a local build agent. Anyway, you can’t go wrong with any of those, so choose one that makes sense and set it up. They’re all easy to set up. For Bamboo, you’ll need to install Java, Tomcat (or whichever container you like), Git and Maven 3 on the server as well. Bamboo is a Java web app, which is why you need Java and Tomcat. Bamboo uses Git to pull the code down from GitHub, and Maven to build the code. You’ll need to configure this using the Bamboo admin console, once you’ve installed the packages above, you can do the configuration entirely through the Bamboo UI and it’s very easy. After installing Ubuntu 11.10 server manually, I used Chef to set up everything on my Bamboo server except for deploying the Bamboo WAR itself, since Chef has community cookbooks for Java, Tomcat, Maven and Git. (Note however that currently the URL in the Maven 3 recipe is wrong, so if you go that way then you’ll need to update the URL and the checksum.) Jo Liss has a great tutorial on Chef Solo if you’re interested in giving this a shot. But you don’t have to use Chef. You can just use your native package manager if you prefer to do it that way. Once you have the executables all set up, you’ll need to create a build plan that slurps source code from GitHub and builds it with a Maven 3 task. In the screenshot you’ll notice that I created three separate stages here: a commit stage, an acceptance stage and a deploy stage. The idea, following Continuous Delivery, is to separate the fast-but-not-so-comprehensive feedback part from the slow-but-more-comprehensive feedback, and run those as different stages in the build. That way you know right away in most cases where you break the build (commit stage fails), and you still find out soon enough even in those cases where the breakage involves some kind of integration or business acceptance criterion. So in my Bamboo plan I do the commit stage first, then run integration tests during the acceptance stage, and finally deploy the snapshot build to my Maven repo using mvn deploy if the previous two stages pass. That makes it available to others for continuous integration. Let’s look at the Maven repo part now. Set up a Maven repo Sonatype offers a hosted Nexus-based Maven repo for free to open source projects. Moreover they’ll push your artifacts out to Maven central for you as well. So if that sounds good to you, here are instructions on signing up. JFrog’s Artifactory is an option as well. As you can see from the link, there’s an open source option. We use this at work and it’s pretty nice. For Sonatype, you’ll need to create a JIRA ticket to get the repo, as per the instructions above. Here’s mine. Also, your POM will need to conform to certain requirements; see this example for the POM that I’m using for Kite. Nothing too out-of-the-ordinary, though do take note of the parent POM. Anyway, once you have your Maven repo, practice manually deploying from the command line via mvn deploy just to make sure you’re able to push code. If it works, then you’ll want to try it out from Bamboo. Be sure to update Bamboo’s copy of Maven’s settings.xml so Bamboo can authenticate into the Sonatype repo: sonatype-nexus-snapshots your_username your_password sonatype-nexus-staging your_username your_password Once you have it working, you should be able to push your snapshots over to Sonatype. Here’s the Nexus UI, and here’s the raw directory browser view. Conclusion Though it takes a bit of effort to set this up, at the end of the day you have a good foundation for your continuous delivery efforts. In the next post in this series, we’ll expand our deployment pipeline so it can handle pushing a web application to a live, cloud-based container.

(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.)

this simple cheat sheet contains all the key goals, principals and design patterns that you should be aware of when designing soa services and contains helpful links to places where you can find more in-depth information on each topic. when i was studying for my soa certified architect exams, i kept notes on all the best bits from the course material. after 9 months and several hundred hours of study, i found that there were certain key pieces of information that i kept referring back to time and time again, such as… how do you define service-orientation? what are the goals and strategic benefits of having a service-oriented business? what are the design principals you should apply to soa service design & soa governance? what are the characteristics of soa based businesses – how can you recognise one? what are the most useful soa design patterns and how are they grouped? i thought it might be useful to bring all this information together into one place, so in collaboration with soagrowers we have published a free pdf cheat sheet on soa service design which you can print out and keep close to hand so it’s there whenever you need it. it’s not meant to be an exhaustive guide – it’s just a set of place-holders to remind you of the topics that may be of relevance to you when designing services. however, it should prove useful to any service architect or developer who’s interested in service design or anyone who is going through the same certification programme as i did – even if you just use it as a check-list or aide-mémoire . none of it is particularly technology specific. the same set of goals, principals and patterns can be applied equally to soap based web services , restful services or any other kind of distributed components – that’s the beauty of service-orientation, it’s vendor and technology neutral. in the sheet i’ve also highlighted something that often get’s overlooked when technologists have the lead on soa implementations:- soa has some very attractive and unique business benefits that can only be fully realised when you apply the design paradigm correctly. for my money, it’s this outcome oriented viewpoint (the business case if you like) that really differentiates soa from other tactics like eai/esb, but all too often this message gets lost in the melee . we hope you find it useful. to get your copy of the soa service design cheat sheet, just click on the image below. if you like it please share it (there are handy share buttons on the page below). click on the image to download the pdf get involved. did you find this useful? is there something you think could be added or removed? did you notice how esb is just a small fraction of the bigger picture? let me know your thoughts in the comments below. ————————————————————————— updated: 18/09/2012. i’ve now added a small section on contract first service design, just because it so fundamentally underpins many of the most important goals, principals and patterns used to deliver successful soa. for more information on contract first, see spring-ws’s excellent whitepaper . contract-first isn’t just a soap thing by the way. ‘contract’ in a soa design context means operations, data types, policies and anything else to do with the service’s public facia. so although rest has an implicit contract with predetermined operations (get, put, post, etc.) it still has data type’s and flexible url’s that convey some meaning. therefore, if you want to make a rest architecture more interoperable and less brittle for clients, it helps to plan these datatypes and url’s in advance if you can so they become better standardised and therefore more reusable.

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

For a given WSDL, there are several different ways to generate Java web service code (CXF, Axis2, etc..). And depending on certain settings within the WSDL file and settings used by the relevant build tool, there are different ways of exposing those services described in the WSDL. This post will briefly document the generating of Java code for a WSDL using Maven and the jaxws wsimport plugin. It will also show the difference in the services exposed when using wrapped and non-wrapped bindings. Below is an extract from a pom.xml to generate the Java code: org.codehaus.mojo jaxws-maven-plugin 1.10 wsimport City81SOAPService.wsdl ${basedir}/src/wsdl generate-sources generate-sources ......... ${project.build.directory}/generated-sources/jaxws-wsimport true true true ${basedir}/src/jax-ws-catalog.xml For the below WSDL file, the wsimport plugin will generate the following classes: com\city81\soap\Balance.java com\city81\soap\City81SOAP.java com\city81\soap\City81SOAPImplService.java com\city81\soap\CreateCustomer.java com\city81\soap\CreateCustomerResponse.java com\city81\soap\CreateCustomerResponseType.java com\city81\soap\CreateStatus.java com\city81\soap\ObjectFactory.java com\city81\soap\package-info.java For the above settings, the generated City81SOAP class will be as below: @WebService(name = "City81SOAP", targetNamespace = "http://soap.city81.com/") @SOAPBinding(parameterStyle = SOAPBinding.ParameterStyle.BARE) @XmlSeeAlso({ ObjectFactory.class }) public interface City81SOAP { @WebMethod(action = "http://soap.city81.com/createCustomer") @WebResult(name = "createCustomerResponse", targetNamespace = "http://soap.city81.com/", partName = "params") public CreateCustomerResponse createCustomer(@WebParam(name = "createCustomer", targetNamespace = "http://soap.city81.com/", partName = "params") CreateCustomer params); } The binding style as can be seen from the @SOAPBinding annotation at the head of the class is BARE ie non-wrapped. The method's args and return parameters are in each case represented as a single Java object. CreateCustomer and CreateCustomerResponse. This has happened because in the pom.xml file, there is a bindingDirectory tag which points to a folder containing a binding.xml file. This file, shown below, has an enableWrapperStyle tag and the boolean value of false. false If the boolean was true, or if there was no bindingDirectory tag in the pom.xml file, then the default SOAP binding style would be used ie WRAPPED. This would then result in the below generated City81SOAP class: @WebService(name = "City81SOAP", targetNamespace = "http://soap.city81.com/") @XmlSeeAlso({ ObjectFactory.class }) public interface City81SOAP { @WebMethod(action = "http://soap.city81.com/createCustomer") @RequestWrapper(localName = "createCustomer", targetNamespace = "http://soap.city81.com/", className = "com.city81.soap.CreateCustomer") @ResponseWrapper(localName = "createCustomerResponse", targetNamespace = "http://soap.city81.com/", className = "com.city81.soap.CreateCustomerResponse") public void createCustomer( @WebParam(name = "surname", targetNamespace = "") String surname, @WebParam(name = "firstName", targetNamespace = "") String firstName, @WebParam(name = "balance", targetNamespace = "") Balance balance, @WebParam(name = "customerId", targetNamespace = "", mode = WebParam.Mode.OUT) Holder customerId, @WebParam(name = "status", targetNamespace = "", mode = WebParam.Mode.OUT) Holder status); } The method's args are now individual Java objects and the return parameters are each represented as Holder objects with a WebParam.Mode.OUT value denoting they are return objects. This means that return objects are set as opposed to actually being returned in the method's signature. Another way to specify bindings other than using the binding.xml file is to embed the enableWrapperStyle as a child of the portType but if a WSDL is from a third party, then having to change it every time a new version of the WSDL is released is open to errors. false ... Back to the generated interfaces, and these of course need to be implemented. For an interface with a binding type of BARE, the implemented class would look like below: @WebService(targetNamespace = "http://soap.city81.com/", name = "City81SOAP", portName = "City81SOAPImplPort", serviceName = "City81SOAPImplService") @SOAPBinding(style = SOAPBinding.Style.DOCUMENT, use = SOAPBinding.Use.LITERAL, parameterStyle = SOAPBinding.ParameterStyle.BARE) public class City81SOAPImpl implements City81SOAP { @Override public CreateCustomerResponse createCustomer(CreateCustomer createCustomer) { CreateCustomerResponse createCustomerResponse = new CreateCustomerResponse(); ..... return createCustomerResponse; } } In the case of WRAPPED binding style, the SOAPBinding annotation would include parameterStyle = SOAPBinding.ParameterStyle.WRAPPED and the createCustomer method would be as below: public void createCustomer( String surname, String firstName, Balance balance, Holder customerId, Holder status) { customerId= new Holder("1"); status = new Holder(CreateStatus.CREATE_PENDING); } This post shows that there are different ways to ultimately achieve the same result.