I really like Spring, so I tend to use its features to the fullest. However, in some dark corners of its philosophy, I tend to disagree with some of its assumptions. One such assumption is the way database testing should work. In this article, I will explain how to configure your projects to make Spring Test and DBUnit play nice together in a multi-developers environment. Context My basic need is to be able to test some complex queries: before integration tests, I've to validate those queries get me the right results. These are not unit tests per se but let's assilimate them as such. In order to achieve this, I use since a while a framework named DBUnit. Although not maintained since late 2010, I haven't found yet a replacement (be my guest for proposals). I also have some constraints: I want to use TestNG for all my test classes, so that new developers wouldn't think about which test framework to use I want to be able to use Spring Test, so that I can inject my test dependencies directly into the test class I want to be able to see for myself the database state at the end of any of my test, so that if something goes wrong, I can execute my own queries to discover why I want every developer to have its own isolated database instance/schema Considering the last point, our organization let us benefit from a single Oracle schema per developer for those "unit-tests". Basic set up Spring provides the AbstractTestNGSpringContextTests class out-of-the-box. In turn, this means we can apply TestNG annotations as well as @Autowired on children classes. It also means we have access to the underlying applicationContext, but I prefer not to (and don't need to in any case). The structure of such a test would look like this: @ContextConfiguration(location = "classpath:persistence-beans.xml") public class MyDaoTest extends AbstractTestNGSpringContextTests { @Autowired private MyDao myDao; @Test public void whenXYZThenTUV() { ... } } Readers familiar with Spring and TestNG shouldn't be surprised here. Bringing in DBunit DbUnit is a JUnit extension targeted at database-driven projects that, among other things, puts your database into a known state between test runs. [...] DbUnit has the ability to export and import your database data to and from XML datasets. Since version 2.0, DbUnit can also work with very large datasets when used in streaming mode. DbUnit can also help you to verify that your database data match an expected set of values. DBunit being a JUnit extension, it's expected to extend the provided parent class org.dbunit.DBTestCase. In my context, I have to redefine some setup and teardown operation to use Spring inheritance hierarchy. Luckily, DBUnit developers thought about that and offer relevant documentation. Among the different strategies available, my tastes tend toward the CLEAN_INSERT and NONE operations respectively on setup and teardown. This way, I can check the database state directly if my test fails. This updates my test class like so: @ContextConfiguration(locations = {"classpath:persistence-beans.xml", "classpath:test-beans.xml"}) public class MyDaoTest extends AbstractTestNGSpringContextTests { @Autowired private MyDao myDao; @Autowired private IDatabaseTester databaseTester; @BeforeMethod protected void setUp() throws Exception { // Get the XML and set it on the databaseTester // Optional: get the DTD and set it on the databaseTester databaseTester.setSetUpOperation(DatabaseOperation.CLEAN_INSERT); databaseTester.setTearDownOperation(DatabaseOperation.NONE); databaseTester.onSetup(); } @Test public void whenXYZThenTUV() { ... } } Per-user configuration with Spring Of course, we need to have a specific Spring configuration file to inject the databaseTester. As an example, here is one: However, there's more than meets the eye. Notice the databaseTester has to be fed a datasource. Since a requirement is to have a database per developer, there are basically two options: either use a in-memory database or use the same database as in production and provide one such database schema per developer. I tend toward the latter solution (when possible) since it tends to decrease differences between the testing environment and the production environment. Thus, in order for each developer to use its own schema, I use Spring's ability to replace Java system properties at runtime: each developer is characterized by a different user.name. Then, I configure a PlaceholderConfigurer that looks for {user.name}.database.properties file, that will look like so: db.username=myusername1 db.password=mypassword1 db.schema=myschema1 This let me achieve my goal of each developer using its own instance of Oracle. If you want to use this strategy, do not forget to provide a specific database.properties for the Continuous Integration server. Huh oh? Finally, the whole testing chain is configured up to the database tier. Yet, when the previous test is run, everything is fine (or not), but when checking the database, it looks untouched. Strangely enough, if you did load some XML dataset and assert it during the test, it does behaves accordingly: this bears all symptoms of a transaction issue. In fact, when you closely look at Spring's documentation, everything becomes clear. Spring's vision is that the database should be left untouched by running tests, in complete contradiction to DBUnit's. It's achieved by simply rollbacking all changes at the end of the test by default. In order to change this behavior, the only thing to do is annotate the test class with @TransactionConfiguration(defaultRollback=false). Note this doesn't prevent us from specifying specific methods that shouldn't affect the database state on a case-by-case basis with the @Rollback annotation. The test class becomes: @ContextConfiguration(locations = {classpath:persistence-beans.xml", "classpath:test-beans.xml"}) @TransactionConfiguration(defaultRollback=false) public class MyDaoTest extends AbstractTestNGSpringContextTests { @Autowired private MyDao myDao; @Autowired private IDatabaseTester databaseTester; @BeforeMethod protected void setUp() throws Exception { // Get the XML and set it on the databaseTester // Optional: get the DTD and set it on the databaseTester databaseTester.setSetUpOperation(DatabaseOperation.CLEAN_INSERT); databaseTester.setTearDownOperation(DatabaseOperation.NONE); databaseTester.onSetup(); } @Test public void whenXYZThenTUV() { ... } } Conclusion Though Spring and DBUnit views on database testing are opposed, Spring's configuration versatility let us make it fit our needs (and benefits from DI). Of course, other improvements are possible: pushing up common code in a parent test class, etc. To go further: Spring Test documentation DBUnit site Database data verification Database testing best practices Generating DTD from your database schema

Dynamic and static properties are really interesting. Learn about managing dynamic property in Spring.

A Robust Splitter Aggregator Design Strategy - Messaging Gateway Adapter Pattern What do we mean by robust? In the context of this article, robustness refers to an ability to manage exception conditions within a flow without immediately returning to the caller. In some processing scenarios n of m responses is good enough to proceed to conclusion. Example processing scenarios that typically have these tendencies are: Quotations for finance, insurance and booking systems. Fan-out publishing systems. Why do we need Robust Splitter Aggregator Designs? First and foremost an introduction to a typical Splitter Aggregator pattern maybe necessary. The Splitter is an EIP pattern that describes a mechanism for breaking composite messages into parts in order that they can be processed individually. A Router is an EIP pattern that describes routing messages into channels - aiming them at specific messaging endpoints. The Aggregator is an EIP pattern that collates and stores a set of messages that belong to a group, and releases them when that group is complete. Together, those three EIP constructs form a powerful mechanism for dividing processing into distinct units of work. Spring Integration (SI) uses the same pattern terminology as EIP and so readers of that methodology will be quite comfortable with Spring Integration Framework constructs. The SI Framework allows significant customisations of all three of those constructs and furthermore, by simply using asynchronous channels as you would in any other multi-threaded configuration, allows those units of work to be executed in parallel. An interesting challenge working with SI Splitter Aggregator designs is building appropriately robust flows that operate predictably in a number of invocation scenarios. A simple splitter aggregator design can be used in many circumstances and operate without heavy customisation of the SI constructs. However, some service requirements demand a more robust processing strategy and therefore more complex configuration. The following sections describe and show what a Simple Splitter Aggregator design actually looks like, the type of processing your design must be able to deal with and then goes on to suggest candidate solutions for more robust processing. A Simple Splitter Aggregator Design The following Splitter Aggregator design shows a simple flow that receives document request messages into messaging gateway, splits the message into two processing routes and then aggregates the response. Note that the diagram has been built from EIP constructs in OmniGraffle rather than being an Integration Graph view from within STS; the channels are missing from the diagram for the sake of brevity. SI Constructs in detail: Messaging Gateways - there are three messaging gateways. A number of configurations are available for gateway specifications but significantly can return business objects, exceptions and nulls (following a timeout). The gateway to the far left is the service gateway for which we are defining the flow. The other two gateways, between the Router and Aggregator, are external systems that will be providing responses to business questions that our flow generates. The Splitter - a single splitter exists and is responsible for consuming the document message and producing a collection of messages for onward processing. The Java signature for the, most often, custom Splitter specifies a single object argument and a collection for return. The Recipient List Router - a single router exists, any appropriate router can be used, chose the one that closely matches your requirements - you can easily route by expression or payload type. The primary purpose of the router is route a collection of messages supplied by the splitter. This is a pretty typical Splitter Aggregator configuration. Aggregator - a single construct that is responsible for collecting messages together in a group in order that further processing can take place on the gateway responses. Although the Aggregator can be configured with attributes and bean definitions to provide alternative grouping and release strategies, most often the default aggregation strategy suffices. Interesting Aspects of Splitter Aggregator Operation Gateway - the inbound gateway, the one on the far left, may or may not have an error handling bean reference defined on it. If it does then that bean will have an opportunity to handle an exceptions thrown within the flow to the right of that gateway. If not, any exception will be thrown straight out of the gateway. Gateway - an optional default-reply-timeout can be set on each of the gateways, there are significant implications for setting this value, ensure that they're well understood. An expired timeout will result in a null being returned from the gateway. This is the very same condition that can lead to a thread getting parked if an upstream gateway also has no default-reply-timeout set. Splitter Input Channel - this can be a simple direct channel or a direct channel with a dispatcher defined on it. If the channel has a dispatcher specified the flow downstream of this point will be asynchronous, multi-threaded. This also changes the upstream gateway semantics as it usually means that an otherwise impotent default-reply-timeout becomes active. Splitter - the splitter must return a single object. The single object returned by the splitter is a collection, a java.util.List. The SI framework will take each member of that list and feed it into the output-channel of the Splitter - as with this example, usually straight into a router. The contract for Splitter List returns is as its use in Java - it may contain zero, one or more elements. If the splitter returns an empty list it's unlikely that the router will have any work to do and so the flow invocation will be complete. However, if the List contains one item, the SI framework will extract that item from the list and push it into the router, if this gets routed successfully, the flow will continue. Router - the router will simply route messages into one of two gateways in this example. Gateways - the two gateways that are used between the Splitter and Aggregator are interesting. In this example I have used the generic gateway EIP pattern to represent a message sub-system but not defined it explicitly - we could use an HTTP outbound gateway, another SI flow or any other external system. Of course, for each of those sub-systems, a number of responses is possible. Depending on the protocol and external system, the message request may fail to send, the response fail to arrive, a long running process invoked, a network error or timeout or a general processing exception. Aggregator - the single aggregator will wait for a number of responses depending on what's been created by the Splitter. In the case where the splitter return list is empty the Aggregator will not get invoked. In the case where the Splitter return list has one entry, the aggregator will be waiting for one gateway response to complete the group. In the case where the Splitter list has n entries the Aggregator will be waiting for n entries to complete the group. Custom correlation strategies, release strategies and message stores can be injected amongst a set of rich configuration aspects. Interesting Aspects of Simple Splitter Aggregator Operation The primary deciding factor for establishing whether this type of simple gateway is adequate for requirements is to understand what happens in the event of failure. If any exception occurring in your SI flow results in the flow invocation being abandoned and that suits your requirements, there's no need to read any further. If, however, you need to continue processing following failure in one of the gateways the remainder of this article may be of more interest. Exceptions, from any source, generated between the splitter and aggregator, will result in an empty or partial group being discarded by the Aggregator. The exception will propagate back to the closest upstream gateway for either handling by a custom bean or re-throwing by the gateway. Note that a custom release strategy on the Aggregator is difficult to use and especially so alongside timeouts but would not help in this case as the exception will propagate back to the leftmost gateway before the aggregator is invoked. It's also possible to configure exception handlers on the innermost gateways, the exception message could be caught but how do you route messages from a custom exception handler into the aggregator to complete the group, inject the aggregator channel definition into the custom exception handler? This is a poor approach and would involve unpacking an exception message payload, copying the original message headers into a new SI message and then adding the original payload - only four or five lines of code, but dirty it is. Following exception generation, exception messages (without modification) cannot be routed into an Aggregator to complete the group. The original message, the one that contains the correlation and sequence ids for the group and group position are buried inside the SI messages exception payload. If processing needs to continue following exception generation, it should be clear that in order to continue processing, the following must take place: the aggregation group needs to be completed, any exceptions must be caught and handled before getting back to the closet upstream gateway, the correlation and sequence identifiers that allow group completion in the aggregator are buried within the exception message payload and will require extraction and setting on the message that's bound for the aggregator A More Robust Solution - Messaging Gateway Adapter Pattern Dealing with exceptions and null returns from gateways naturally leads to a design that implements a wrapper around the messaging gateway. This affords a level of control that would otherwise be very difficult to establish. This adapter technique allows all returns from messaging gateways to be caught and processed as the messaging gateway is injected into the Service Activator and called directly from that. The messaging gateway no longer responds to the aggregator directly, it responds to a custom Java code Spring bean configured in the Service Activator namespace definition. As expected, processing that does not undergo exception will continue as normal. Those flows that experience exception conditions or unexpected or missing responses from messaging gateways need to process messages in such as way that message groups bound for aggregation can be completed. If the Service Activator were to allow the exception to be propagated outside of it's backing bean, the group would not complete. The same applies not just for exceptions but any return object that does not carry the prerequisite group correlation id and sequence headers - this is where the adaptation is applied. Exception messages or null responses from messaging gateways are caught and handled as shown in the following example code: import com.l8mdv.sample.*; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.springframework.integration.Message; import org.springframework.integration.MessageHeaders; import org.springframework.integration.support.MessageBuilder; import org.springframework.util.Assert; public class AvsServiceImpl implements AvsService { private static final Logger logger = LoggerFactory.getLogger(AvsServiceImpl.class); public static final String MISSING_MANDATORY_ARG = "Mandatory argument is missing."; private AvsGateway avsGateway; public AvsServiceImpl(final AvsGateway avsGateway) { this.avsGateway = avsGateway; } public Message service(Message message) { Assert.notNull(message, MISSING_MANDATORY_ARG); Assert.notNull(message.getPayload(), MISSING_MANDATORY_ARG); MessageHeaders requestMessageHeaders = message.getHeaders(); Message responseMessage = null; try { logger.debug("Entering AVS Gateway"); responseMessage = avsGateway.send(message); if (responseMessage == null) responseMessage = buildNewResponse(requestMessageHeaders, AvsResponseType.NULL_RESULT); logger.debug("Exited AVS Gateway"); return responseMessage; } catch (Exception e) { return buildNewResponse(responseMessage, requestMessageHeaders, AvsResponseType.EXCEPTION_RESULT, e); } } private Message buildNewResponse(MessageHeaders requestMessageHeaders, AvsResponseType avsResponseType) { Assert.notNull(requestMessageHeaders, MISSING_MANDATORY_ARG); Assert.notNull(avsResponseType, MISSING_MANDATORY_ARG); AvsResponse avsResponse = new AvsResponse(); avsResponse.setError(avsResponseType); return MessageBuilder.withPayload(avsResponse) .copyHeadersIfAbsent(requestMessageHeaders).build(); } private Message buildNewResponse(Message responseMessage, MessageHeaders requestMessageHeaders, AvsResponseType avsResponseType, Exception e) { Assert.notNull(responseMessage, MISSING_MANDATORY_ARG); Assert.notNull(responseMessage.getPayload(), MISSING_MANDATORY_ARG); Assert.notNull(requestMessageHeaders, MISSING_MANDATORY_ARG); Assert.notNull(avsResponseType, MISSING_MANDATORY_ARG); Assert.notNull(e, MISSING_MANDATORY_ARG); AvsResponse avsResponse = new AvsResponse(); avsResponse.setError(avsResponseType, responseMessage.getPayload(), e); return MessageBuilder.withPayload(avsResponse) .copyHeadersIfAbsent(requestMessageHeaders).build(); } } Notice the last line of the catch clause of the exception handling block. This line of code copies the correlation and sequence headers into the response message, this is mandatory if the aggregation group is going to be allowed to complete and will always be necessary following an exception as shown here. Consequences of using this technique There's no doubt that introducing a Messaging Gateway Adapter into SI config makes the configuration more complex to read and follow. The key factor here is that there is no longer a linear progression through the configuration file. This because the Service Activator must forward reference a Gateway or a Gateway defined before it's adapting Service Activator - in both cases the result is the same. Resources Note:- The design for the software that drove creation of this meta-pattern was based on a requirement that a number of external risk assessment services would be accessed by a single, central Risk Assessment Service. In order to satisfy clients of the service, invocation had to take place in parallel and continue despite failure in any one of those external services. This requirement lead to the design of the Messaging Gateway Adapter Pattern for the project. Spring Integration Reference Manual The solution approach for this problem was discussed directly with Mark Fisher (SpringSource) in the context of building Risk Assessment flows for a large US financial institution. Although the configuration and code is protected by NDA and copyright, it's acceptable to express the design intention and similar code in this article.

eclipse comes with a large set of different views: they allow the developer to represent the information in various forms and with different angles. most of these views are navigation oriented: a perfect example for this is the projects view or the outline view . but over time i add more projects, more resources to my project, and at a certain time things get overwhelming. i have a lot of projects, and i do not want to switch between workspace too often. yes, i can open and close projects, but this gets cumbersome too. thankfully, there is a solution in eclipse: working sets . working sets allow me to group elements for display in views. with that, i can do operations on a set of elements in that working set. especially as i’m using many projects the same time, working sets are a big help to focus on the right set of things at a time. i can define a set of things i want to look at, work with, or whatever: it allows me to get be productive in the universe of my environment. building/compiling a working set a nice feature of using working sets is to build a set of projects. instead of selecting a set of projects and then to compile them together, i use a working set. i use the menu project > build working set > select working set… to create or change working sets: menu to select working set if i have no working set defined, then this will show the following dialog where i can press new… to create a new one: creating new working set to create a working set of c/c++ projects, i select c/c++ and press next : new c c++ working set next i give a name and select the project(s) which shall be in my working set, and press finish : defining working set to build my set of projects, i can select the working set and press ok : selected working set search in a working set it is possible to limit the search to a working set. for this i can choose a working set as scope in the search dialogs: search in a working set managing working sets to manage working sets, i press ctrl+3 (see quick access ) and choose manage working sets… : ctrl+3 with manage working sets… note: i can add extra tool-bars and menus for working sets too (this is explained later). then i can manage my working sets: manage working set configuration project view filtering with working sets i can filter the projects shown in the project view based on working sets. for this i select the small triangle and select/define a working set or choose one from the most recently used sets: working sets for project view with this i can easily filter and focus on a subset of projects: project view with applied working set really cool tip: i’m using working sets as well to avoid too many workspaces. instead of having projects spread over different workspaces, i can keep them in one workspace and use working sets instead. there is an added benefit of using working sets: having too many projects open at the same time in eclipse can slow down the ide: using working sets allows me just to switch quickly between the set of projects i’m working on. but it does not stop at filtering by projects: you can filter even things inside the project structure. i simply deselect things i don’t want to see and can focus on what is important for me: filtering project files export and import of working sets note: import and export of working sets is not part of the standard codewarrior eclipse distribution. you get the import/export feature installed with the mqx plugins (www.freescale.com/mqx) or with the anyedit plugins (http://andrei.gmxhome.de/anyedit/index.html). to export a working set, i use the menu file > export > other > export working set : export working sets with mqx plugins note: the anyedit plugins come as well with an import/expert working set wizard. the file format is different, and the mqx plugin allows drag&drop of the file into eclipse. this gives the following dialog where i can specify the file name and the root of projects: export working set dialog this will store the settings in an xml file. importing the working set is done with file > import > other > import working set . tip: i’m using *.wsd extension for working sets. that way i can simply drag&drop the file into eclipse to import it. other kinds of working sets working sets do not stop at projects and files: select working set type i can create working sets of breakpoints or analysis/trace points. or i can create working sets of any resource files or tasks. the possibilities are nearly endless and depend as well on the extra plugins installed. window working sets and now back to the really cool part. one question remains: what are window working sets? window working sets the thing is that every view and dialog has its own working set setting. in my example below i use a working set ‘coldfire’ for the projects view, but my search dialog has a ‘kinetis’ working set configured: two different working sets sometimes i want this, but not always. what i need is a ‘global’ working set. and here the window working sets comes to rescue me. for this i’m going to add some menus and toolbars to make it really easy… for this i choose the menu window > customize perspecti ve. in the command groups i enable ‘window working set’. additionally it is a good idea to enable ‘working set manipulation’ as well: window working set commands the same way i can enable the toolbar and menu visibility. this gives me added tool-bars to switch between working sets and to add/remove things from a working set quickly: working set toolbars in a similar way, it gives me menu access as well: working sets menu and here is the trick: using ‘ window working set ‘ really means ‘ using the global workbench working set ‘. to select the global workbench working set, i use the toolbar icon to switch between window (or workbench) working sets: selecting global window or workbench working set in the individual views i choose to use the window working sets instead a selection of working sets: selected global window or workbench working set now my working set settings are shared and common for all views: if i switch the working set, it will switch for all views where i have set it to ‘window working set’: window working set applied to multiple views that way my working set is the same across views, and switching between different project settings is done with a simple mouse click. summary working sets are an extremely powerful feature to get focus on a subset of things inside eclipse, based on my workflow. as with many great eclipse features, i need to know about it until you really appreciate the power of it. who knows how many other hidden treasures are buried in eclipse i hope this article helps to save you a few mouse clicks. happy work-setting but over time i add more projects, more resources to my project, and at a certain time things get overwhelming. i have a lot of projects, and i do not want to switch between workspace too often.

Spring Integration (SI) Gateways Spring Integration Gateways () provide a semantically rich interface to message sub-systems. Gateways are specified using namespace constructs, these reference a specific Java interface () that is backed by an object dynamically implemented at run-time by the Spring Integration framework. Furthermore, these Java interfaces can, if you so wish, be defined entirely independent of any Spring artefacts - that's both code and configuration. One of the primary advantages of using the SI gateway as an interface to message sub-systems is that it's possible to automatically adopt the benefit of rich, default and customisable, gateway configuration. One such configuration attribute deserves further scrutiny and discussion primarily because it's easy to misunderstand and misconfigure around - default-reply-timeout. Primary Motivator for Gateway Analysis During recent consulting engagements, I've encountered a number of deployments that use Spring Integration Gateway specifications that may, in some circumstances, lead to production operational instability. This has often been in high-pressure environments or those where technology support is not backed by adequate training, testing, review or technology mentoring. How do gateways behave in Spring Integration (R2.0.5) One of the key sections, regarding gateways, in the Spring Integration manual clearly explains gateway semantics. Below is a 2-dimensional table of possible non-standard gateway returns for each of the scenarios that the SI Manual (r2.0.5) refers to. Gateway Non-standard Responses Runtime Events default-reply-timeout=x Single-threaded default-reply-timeout=x Multi-threaded default-reply-timeout=null Single-threaded default-reply-timeout=null Multi-threaded 1. Long Running Process Thread Parked null returned Thread Parked Thread Parked 2. Null Returned Downstream null returned null returned Thread Parked Thread Parked 3. void method Downstream null returned null returned Thread Parked Thread Parked 4. Runtime Exception Error handler invoked or exception thrown. Error handler invoked or exception thrown. Error handler invoked or exception thrown. Error handler invoked or exception thrown. The key parts of this table are the conditions that lead to invoking threads being parked (noted in red), nulls returned (noted in orange) and exceptions (noted in green). Each contributor consists of configuration that is under the developers control, deployed code that is under developers control and conditions that are usually not under developers control. Clearly, the column headings in the table above are divided into two sections; two gateway configuration attributes. The default-reply-timeout is set by the SI configured and is the amount of time that a client call is wiling to wait for a response from the gateway. Secondly, synchronous flows are represented by Single-threaded flows, asynchronous by Multi-threaded flows. A synchronous, or single-threaded flow, is one such as the following: The implicit input channel (gateway-request-channel) has no associated dispatcher configured. An asynchronous, or multi-threaded flow, is one such as the following: The explicit input channel has a dispatcher configured ("taskExecutor"). This task executor specifies a thread pool that supplies threads for execution and whose configuration as above marks a thread boundary. Note: This is not the only way of making channels asynchronous The other configuration attribute referenced is default-reply-timeout, this is set on the gateway namespace configuration such as the example above. Note that both of these runtime aspects are set by the configurer during SI flow design and implementation. They are entirely under developer control. The 'Runtime Events' column indicates gateway relevant runtime events that have to be considered during gateway configuration - these are obviously not under developer control. Trigger conditions for these events are not as unusual as one may hope. 1. Long Running Processes It's not uncommon for thread pools to become exhausted because all pooled threads are waiting for an external resource accessed through a socket, this may be a long running database query, a firewall keeping a connection open despite the server terminating etc. There is significant potential for these types of trigger. Some long-running processes terminate naturally, sometimes they never completed - an application restart is required. 2. Null returned downstream A null may be returned from a downstream SI construct such as a Transformer, Service Activator or Gateway. A Gateway may return null in some circumstances such as following a gateway timeout event. 3. Void method downstream Any custom code invoked during an SI flow may use a void method signature. This can also be caused by configuration in circumstances where flows are determined dynamically at runtime. 4. Runtime Exception RuntimeException's can be triggered during normal operation and are generally handled by catching them at the gateway or allowing them to propagate through. The reason that they are coloured green in the table above is that they are generally much easier to handle than timeouts. Gateway Timeout Handling Strategies There are four possible outcomes from invoking a gateway with a request message, all of these as a result of specific runtime events: a) an ordinary message response, b) an exception message, c) a null or d) no-response. Ordinary business responses and exceptions are straight forward to understand and will not be covered further in this article. The two significant outcomes that will be explored further are strategies for dealing with nulls and no-response. Generally speaking, long running processes either terminate or not. Long running processes that terminate may eventually return a message through the invoked gateway or timeout depending on timeout configuration, in which case a null may be returned. The severity of this as a problem depends on throughput volume, length of long running process and system resources (thread-pool size). Configuration exists for default-reply-timeout In the case where a long running process event is underway and a default-reply-timeout has been set, as long as the long running process completes before the default-reply-timeout expires, there is no problem to deal with. However, if the long running process does not complete before that timeout expires one of three outcomes will apply. Firstly, if the long running process terminates subsequent to the reply timeout expiry, the gateway will have already returned null to the invoker so the null response needs handling by the invoker. The thread handling the long-running process will be returned to the pool. Secondly, if the long running process does not terminate and a reply timeout has been set, the gateway will return null to the gateway invoker but the thread executing the long-running process will not get returned to the pool. Thirdly, and most significantly, if a default-reply-timeout has been configured but the long running process is running on the same thread as the invoker, i.e. synchronous channels supply messages to that process, the thread will not return, the default-reply-timeout has no affect. Assuming the most common processing scenario, a long running process completes either before or after the reply timeout expiry. When a null is returned by the gateway, the invoker is forced to deal with a null response. It's often unacceptable to force gateway consumers to deal with null responses and is not necessary as with a little additional configuration, this can be avoided. Absent Configuration for default-reply-timeout The most significant danger exists around gateways that have no default-reply-timeout configuration set. A long running process or a null returned from downstream will mean that the invoking thread is parked. This is true for both synchronous and asynchronous flows and may ultimately force an application to be restarted because the invoker thread pool is likely to start on a depletion course if this continues to occur. Spring Integration Timeout Handling Design Strategies For those Spring Integration configuration designers that are comfortable with gateway invokers dealing with null responses, exceptions and set default-reply-timeouts on gateways, there's no need to read further. However, if you wish to provide clients of your gateway a more predictable response, a couple of strategies exist for handling null responses from gateways in order that invokers are protected from having to deal with them. Firstly, the simpliest solution is to wrap the gateway with a service activator. The gateway must have the default-reply-timeout attribute value set in order to avoid unnecessary parking of threads. In order to avoid the consequence of long-running threads it's also very prudent to use a dispatcher soon after entry to the gateway - this breaks the thread boundary. Whilst this is a valid technical approach, the impact is that we have forced a different entry point to our message sub-system. Entry is now via a Service Activator rather than a Gateway. A side affect of this change is that the testing entry point changes. Integration tests that would normally reference a gateway to send a message now have to locate the backing implementation for the Service Activator, not ideal. An alternative approach toward solving this problem would be to configure two gateways with a Service Activator between them. Only one of the gateways would be exposed to invokers, the outer one. Both Gateways would reference the same service interface. The outer gateway specification would not specify the default-reply-timeout but would specify the input and output channels in the same way that a single gateway would. The Service Activator between the Gateways would handle null gateway responses and possibly any exceptions if preferred to the gateway error handler approach. An example is as follows: The Service Activator bean (enrollmentServiceGatewayHandler) deals with both null and exception responses from the adapted gateway (enrollmentServiceAdaptedGateway), in the situation where these are generated a business response detailing the error is generated. Spring Integration R2.1 Changes async-executor on gateway spec

After my article Connection Pooling in a Java Web Application with Glassfish and NetBeans IDE, here are the instructions for Tomcat. Requirements NetBeans IDE (this tutorial uses NetBeans 7) Tomcat (this tutorial uses Tomcat 7 that is bundled within NetBeans) MySQL database MySQL Java Driver Steps Assuming your MySQL database is ready, connect to it and create a database. Lets call it connpool: mysql> create database connpool; Now we create and populate the table from which we will fetch the data: mysql> use connpool; mysql> create table data(id int(5) not null unique auto_increment, name varchar(255) not null); mysql> insert into data(name) values("Fred Flintstone"), ("Pink Panther"), ("Wayne Cramp"), ("Johnny Bravo"), ("Spongebob Squarepants"); That is it for the database part. We now create our web application. In NetBeans IDE, click File → New Project... Select Java Web → Web Application: Click Next and give the project the name TomPool. Click Next Choose the server as Tomcat and, since we are not going to use any frameworks, click Finish. The project will be created and the start page, index.jsp, opened for us in the IDE. Now we create the connection pooling parameters. In the Projects window, expand configuration files and open "context.xml". You will see that the IDE has added this code for us: Delete the last line: and then add the following to the context.xml file. I have explained the sections along the way. Make sure you edit your MySQL username and password appropriately: Next, expand the Web Pages node, right-click WEB-INF → New → Other → XML → XML Document. Click Next and type web for the File Name. Click next and choose Well-Formed Document then Finish. You will now have the file "web.xml": Delete everything in the file and paste this code: MySQL Test App DB Connection connpool javax.sql.DataSource Container That is it for the connection pool. We now edit our code to make use of it. Edit index.jsp by adding this code just after the initial coments but before Edit the section of the page: Data in my Connection Pooled Database Now, we test the connection pool by running the application: If you want to have the one connection pool used in multiple applications, you need to edit the following two files: 1. /conf/web.xml Just before the closing tag, add the code DB Connection connpool javax.sql.DataSource Container 2. /conf/context.xml Just before the closing tag, add the code Now you can use the pool without editing XML files in each of your applications. Just use the sample code as given in index.jsp That's it folks!

Some time ago I came across What's the purpose of AsyncContext.start(...) in Servlet 3.0? question. Quoting the Javadoc of aforementioned method: Causes the container to dispatch a thread, possibly from a managed thread pool, to run the specified Runnable. To remind all of you, AsyncContext is a standard way defined in Servlet 3.0 specification to handle HTTP requests asynchronously. Basically HTTP request is no longer tied to an HTTP thread, allowing us to handle it later, possibly using fewer threads. It turned out that the specification provides an API to handle asynchronous threads in a different thread pool out of the box. First we will see how this feature is completely broken and useless in Tomcat and Jetty - and then we will discuss why the usefulness of it is questionable in general. Our test servlet will simply sleep for given amount of time. This is a scalability killer in normal circumstances because even though sleeping servlet is not consuming CPU, but sleeping HTTP thread tied to that particular request consumes memory - and no other incoming request can use that thread. In our test setup I limited the number of HTTP worker threads to 10 which means only 10 concurrent requests are completely blocking the application (it is unresponsive from the outside) even though the application itself is almost completely idle. So clearly sleeping is an enemy of scalability. @WebServlet(urlPatterns = Array("/*")) class SlowServlet extends HttpServlet with Logging { protected override def doGet(req: HttpServletRequest, resp: HttpServletResponse) { logger.info("Request received") val sleepParam = Option(req.getParameter("sleep")) map {_.toLong} TimeUnit.MILLISECONDS.sleep(sleepParam getOrElse 10) logger.info("Request done") } } Benchmarking this code reveals that the average response times are close to sleep parameter as long as the number of concurrent connections is below the number of HTTP threads. Unsurprisingly the response times begin to grow the moment we exceed the HTTP threads count. Eleventh connection has to wait for any other request to finish and release worker thread. When the concurrency level exceeds 100, Tomcat begins to drop connections - too many clients are already queued. So what about the the fancy AsyncContext.start() method (do not confuse with ServletRequest.startAsync())? According to the JavaDoc I can submit any Runnable and the container will use some managed thread pool to handle it. This will help partially as I no longer block HTTP worker threads (but still another thread somewhere in the servlet container is used). Quickly switching to asynchronous servlet: @WebServlet(urlPatterns = Array("/*"), asyncSupported = true) class SlowServlet extends HttpServlet with Logging { protected override def doGet(req: HttpServletRequest, resp: HttpServletResponse) { logger.info("Request received") val asyncContext = req.startAsync() asyncContext.setTimeout(TimeUnit.MINUTES.toMillis(10)) asyncContext.start(new Runnable() { def run() { logger.info("Handling request") val sleepParam = Option(req.getParameter("sleep")) map {_.toLong} TimeUnit.MILLISECONDS.sleep(sleepParam getOrElse 10) logger.info("Request done") asyncContext.complete() } }) } } We are first enabling the asynchronous processing and then simply moving sleep() into a Runnable and hopefully a different thread pool, releasing the HTTP thread pool. Quick stress test reveals slightly unexpected results (here: response times vs. number of concurrent connections): Guess what, the response times are exactly the same as with no asynchronous support at all (!) After closer examination I discovered that when AsyncContext.start() is called Tomcat submits given task back to... HTTP worker thread pool, the same one that is used for all HTTP requests! This basically means that we have released one HTTP thread just to utilize another one milliseconds later (maybe even the same one). There is absolutely no benefit of calling AsyncContext.start() in Tomcat. I have no idea whether this is a bug or a feature. On one hand this is clearly not what the API designers intended. The servlet container was suppose to manage separate, independent thread pool so that HTTP worker thread pool is still usable. I mean, the whole point of asynchronous processing is to escape the HTTP pool. Tomcat pretends to delegate our work to another thread, while it still uses the original worker thread pool. So why I consider this to be a feature? Because Jetty is "broken" in exactly same way... No matter whether this works as designed or is only a poor API implementation, using AsyncContext.start() in Tomcat and Jetty is pointless and only unnecessarily complicates the code. It won't give you anything, the application works exactly the same under high load as if there was no asynchronous logic at all. But what about using this API feature on correct implementations like IBM WAS? It is better, but still the API as is doesn't give us much in terms of scalability. To explain again: the whole point of asynchronous processing is the ability to decouple HTTP request from an underlying thread, preferably by handling several connections using the same thread. AsyncContext.start() will run the provided Runnable in a separate thread pool. Your application is still responsive and can handle ordinary requests while long-running request that you decided to handle asynchronously are processed in a separate thread pool. It is better, unfortunately the thread pool and thread per connection idiom is still a bottle-neck. For the JVM it doesn't matter what type of threads are started - they still occupy memory. So we are no longer blocking HTTP worker threads, but our application is not more scalable in terms of concurrent long-running tasks we can support. In this simple and unrealistic example with sleeping servlet we can actually support thousand of concurrent (waiting) connections using Servlet 3.0 asynchronous support with only one extra thread - and without AsyncContext.start(). Do you know how? Hint: ScheduledExecutorService. Postscriptum: Scala goodness I almost forgot. Even though examples were written in Scala, I haven't used any cool language features yet. Here is one: implicit conversions. Make this available in your scope: implicit def blockToRunnable[T](block: => T) = new Runnable { def run() { block } } And suddenly you can use code block instead of instantiating Runnable manually and explicitly: asyncContext start { logger.info("Handling request") val sleepParam = Option(req.getParameter("sleep")) map { _.toLong} TimeUnit.MILLISECONDS.sleep(sleepParam getOrElse 10) logger.info("Request done") asyncContext.complete() } Sweet!

This tutorial is for people who want to run an Apache Lucene example with OracleDB in just five minutes.

Within Spring Integration, one form of EIP scatter-gather is provided by the splitter and aggregator constructs.

In my last two blog posts, I showed examples of using Selenium WebDriver to capture screenshots, and running in a headless (no X-server) mode. This example combines the two solutions to capture screenshots inside a virtual display. To achieve this, I use a combination of Selenium WebDriver and pyvirtualdisplay (which uses xvfb) to run a browser in a virtual display and capture screenshots. the setup you need is: Selenium 2 Python bindings: PyPI pyvirtualdisplay Python package (depends on xvfb): PyPI On Debian/Ubuntu Linux systems, you can install everything with: $ sudo apt-get install python-pip xvfb xserver-xephyr $ sudo pip install selenium once you have it setup, the following code example should work: #!/usr/bin/env python from pyvirtualdisplay import Display from selenium import webdriver display = Display(visible=0, size=(800, 600)) display.start() browser = webdriver.Firefox() browser.get('http://www.google.com') browser.save_screenshot('screenie.png') browser.quit() display.stop() this will: launch a virtual display launch Firefox browser inside the virtual display navigate to google.com capture and save a screenshot close the browser stop the virtual display

Introduction In recent times, many programming languages that run on JVM have emerged. Many of these languages support the concept of writing code in a functional style. Programmers have started realizing the benefits of functional programming and are beginning to rediscover the powerful style of this programming paradigm. The emergence of multiple languages on JVM have only helped to reignite the strong interest in this paradigm. Java at its core is an imperative programming language. However in recent past many new languages like Scala, Clojure, Groovy etc. have become popular which supports functional programming style and yet run on JVM. However none of these languages can be considered as pure functional language since all of them allow Java code to be called from within them and Java on its own is not a functional language. Still they have different degree of support for writing code in functional style and have their own benefits. Functional programming requires different kind of thinking and has its own advantages as compared to imperative programming. It seems that Java has also realized functional programming advantages and is slowly inching towards it. First sign of this can be seen in the form of Lambda Expressions that will be supported in Java 8. Although it's too early to comment on this as the draft for Java 8 is still under review and is expected to be released next year, but it does show that Java has plans of supporting functional programming style going forward. In this article we will first discuss what functional programming is and how it is different from imperative programming. Later we will see where does each of the above mentioned Java based programming languages i.e. Scala, Clojure and Groovy fits in the world of functional programming and what each of them has to offer. And at the last we will sneak-peak into Java 8's lambda expressions. Why Functional Programming? Computers of current era are shipped with multicore processors. Going forward the number of processors in a machine is only going to increase. The code we write today and tomorrow will probably never run on a single processor system. In order to get best out of this, software must be designed to make more and more use of concurrency and hence keep all available processors busy. Java does provide concurrency concepts like threads, synchronization, locks etc. to execute code in parallel. But shared memory multi-threading approach in Java causes more trouble than solving the problem. Java based functional programming languages like Scala, Clojure, Groovy etc. looks into these problems with a different angle and provides less complex and less error-prone solutions as compared to imperative programming. They provide immutability concepts out of the box and hence eliminate need of synchronization and associated risk of deadlocks or livelocks. Concepts like Actors, Agents and DataFlow variables provide high level concurrency abstraction and makes very easy to write concurrent programs. What is Functional Programming? Functional Programming is a concept which treats functions as first class citizens. At the core of functional programming is immutability. It emphasizes on application of functions in contrast to imperative programming style which emphasizes on change in state. Functional programming has no side effects whereas programming in imperative style can result in side-effects. Let's elaborate more on each of these characteristics to understand the concept behind functional programming. Immutable state - The state of an object doesn't change and hence need not be protected or synchronized. That might sound a bit awkward at first, since if nothing changes, one might think that we are not writing a useful program. However that's not what immutable state means. In functional programming, change in state occurs via series of transformations which keeps the object immutable and yet achieves change in state. Functions as first class citizens - There was a major shift in the way programs were written when Object oriented concepts came into picture. Everything was conceptualized as object and any action to be performed was treated as method call on objects. Hence there is a series of method calls executed on objects to get the desired work done. In functional programming world, it's more about thinking in terms of communication chain between functions than method calls on objects. This makes functions as first class citizens of functional programming since everything is modelled around functions. Higher-order functions - Functions in functional programming are higher order functions since following actions can be performed with them. 1. Functions can be passed within functions as arguments. 2. Functions can be created within functions just as objects can be created in functions 3. Functions can be returned from functions Functions with no side-effects - In functional programming, function execution has no side-effects. In other words a function code will always return same result for same argument when called multiple times. It doesn't change anything outside its boundaries and is also not affected by any external change outside it's boundary. It doesn't change input value and can only produce new output. However once the output has been produced and returned by function, it also becomes immutable and cannot be modified by any other function. In other words, they support referential transparency i.e. if a function takes an input and returns some output, multiple invocation of that function at different point of time will always return same output as long as input remains same. This is one of the main motivations behind using functional language as it makes easy to understand and predict behaviour of program. Characteristics like immutability and no side-effects are extremely helpful while writing multi-threaded code and developers need not to worry for synchronizing the state. Hence functional code is very easy to distribute across multiple cores as they don't have any side effects. JVM based Functional Programming Languages There are many JVM based languages which supports functional programming paradigm. However I intend to limit discussion around following. Scala Clojure Groovy Lambda Expressions in Java 8 Lambda Expressions is not a programming language but a feature that will be supported in Java8. The reason for including it in this article is to emphasize on the fact that going forward Java will also support writing code in functional style. Scala Scala is a statically typed multi-paradigm programming language designed to integrate features of object oriented programming and functional programming. Since it is static, one cannot change class definition at run time i.e. one cannot add new methods or variables at run-time. However Scala does provide functional programming concepts i.e. immutability, higher-order functions, nested functions etc. Apart from supporting Java's concurrency model, it also provides concept of Actor model out of the box for event based asynchronous message passing between objects. The code written in Scala gets compiled into very efficient bytecode which can then be executed on JVM. Creating immutable list in Scala is very simple and doesn't require any extra effort. "val" keyword does the trick. val numbers = List(1,2,3,4) Functions can be passed as arguments. Let's see this with an example. Suppose we have a list of 10 numbers and we want to calculate sum of all the numbers in list. val numbers = List(1,2,3,4,5,6,7,8,9,10) val total = numbers.foldLeft(0){(a,b) => a+b } As can be seen in above example, we are passing a function to add two variables "a" and "b" to another function "foldLeft" which is provided by Scala library on collections. We have also not used any iteration logic and temporary variable to calculate the sum. "foldLeft" method eliminates the need to maintain state in temporary variable which would have otherwise be required if we were to write this code in pure Java way (as mentioned below). int total = 0; for(int number in numbers){ total+=number; } Scala function can easily be executed in parallel without any need for synchronization since it does not mutate state. This was just a small example to showcase the power of Scala as functional programming language. There are whole lot of features available in Scala to write code in functional style. Clojure Clojure is a dynamic language with an excellent support for writing code in functional style. It is a dialect of "lisp" programming language with an efficient and robust infrastructure for multithreaded programming. Clojure is predominantly a functional programming language, and features a rich set of immutable, persistent data structures. When mutable state is needed, Clojure offers a software transactional memory system and reactive Agent system that ensure clean, correct multithreaded designs. Apart from this since Clojure is a dynamic language, it allows to modify class definition at run time by adding new methods or modifying existing one at run time. This makes it different from Scala which is a statically typed language. Immutability is in the root of Clojure. To create immutable list just following needs to be done. By default list in Clojure is immutable, so does not require any extra effort. (def numbers (list 1 2 3 4 5 6 7 8 9 10)) To add numbers without maintaining state, reduce function can be used as mentioned below (reduce + 0 '(1 2 3 4 5 6 7 8 9 10)) As can be seen, adding list of numbers just requires one line of code without mutating any state. This is the beauty about functional programming languages and plays an important role for parallel execution. Groovy Groovy is again a dynamic language with some support for functional programming. Amongst the 3 languages, Groovy can be considered weakest in terms of functional programming features. However because of it's dynamic nature and close resemblance to Java, it has been widely accepted and considered good alternative to Java. Groovy does not provide immutable objects out of the box but has excellent support for higher order functions. Immutable objects can be created with annotation @Immutation, but it's far less flexible than immutablity support in Scala and Clojure. In Groovy functions can be passed around just as any other variable in the form of Closures. The same example in Groovy can be written as follows def numbers = [1,2,3,4,5,6,7,8,9,10] def total = numbers.inject(0){a,b -> a+b } However the point to be noted is that variables "numbers" and "total" are not immutable and can be modified at any point of time. Hence writing multithreaded code can be a bit challenging. But Groovy does provide the concept of Actors, Agents and DataFlow variables via library called GPars(Groovy Parallel System) which reduces the challenges associated with multithreaded code to a greater extent. Java8 Lambda Expression Java has finally realized the power of writing code in functional style and is going to support the concept of closures starting from Java8. JSR 335 - Lambda Expressions for the JavaTM Programming Language aims to support programming in a multicore environment by adding closures and related features to the Java language. So it will finally be possible to pass around functions similar to variables in pure Java code. Currently if someone wants to try out and play around lambda expressions, Project Lambda of OpenJDK provides prototype implementation of JSR-335. Following code snippet should run fine with OpenJDK Project Lambda compiler. ExecutorService executor = Executors.newCachedThreadPool(); executor.submit(() -> {System.out.println("I am running")}) As can be seen above, a closure(function) has been passed to executor's submit method. It does not take any argument and hence empty brackets () have been placed. This function just prints "I am running" when executed. Just as we can pass functions to function, it will also be possible to create closure within functions and return closure from function. I would recommend to try out OpenJDK to get a feel of lambda expressions which is going to be part of Java8 Conclusion So this was all about functional programming, it's concepts, benefits and options available on JVM to write function code. Functional programming requires a different mind-set and can be very useful if used correctly. Functional Programming along with Object Oriented Programming can be a jewel in crown. As discussed there are various options available to write code in functional style that can be executed on JVM. Choice depends on various factors and there is no one language that can be considered best in all aspects. However one thing is for sure, going forward we are going to see more and more usage of functional programming.

rate this eclipse is good, but like any other tool: it gets better after i have it customized for my special needs. eclipse stores a lot of settings in the workspace, see my article about copy my workspace settings . but is there a way to apply some settings to every workspace? at least to the new ones? because importing/exporting the settings can get really tedious as i have many workspace. and indeed, there are global settings in eclipse. and i want to have them changed… warning: changing eclipse global preferences might break an eclipse installation. so better have a backup of the changed files at hand! i’m using here the eclipse based codewarrior for mcu10.2 , but things are pretty much the same for any eclipse based product (see the documentation in defining your own global preferences ). question: where are the global preferences stored? the first thing to check is the eclipse\configuration\.settings folder: here some plugins store their global preferences. for example: the recent workspace settings are in org.eclipse.ui.ide.prefs. #fri apr 06 16:46:14 cest 2012 recent_workspaces_protocol=3 max_recent_workspaces=10 show_workspace_selection_dialog=true eclipse.preferences.version=1 recent_workspaces=c\:\\tmp\\wsp_test\nc\:\\tmp\\wsp_10.2 but what about all the other settings? looking at the codewarrior installation, inside the eclipse folder, i find the cwide.ini file. cwide.ini file this file defines the eclipse startup options for launching the ide (cwide.exe for codewarrior). the interesting part is this line: -declipse.plugincustomization=cwide.properties this tells eclipse to use the cwide.properties as a default configuration file. if i inspect that file, it has the following content: org.eclipse.debug.ui/org.eclipse.debug.ui.switch_perspective_on_suspend=always org.eclipse.debug.ui/org.eclipse.debug.ui.switch_to_perspective=always org.eclipse.ui.editors/spellingengine=org.eclipse.cdt.internal.ui.text.spelling.cspellingengine ok, that gives me an idea how settings could look like. but the question is: how to know the settings and syntax? what works (most of the time) is following approach: launch eclipse with a new workspace export the settings using file > export > general > preferences to a file change the setting in window > preferences export the settings using file > export > general > preferences to a different file compare/inspect the exported information and find the settings apply the settings to the cwide.properties file, without the /instance/ part restart the ide and check if it works with a new workspace the last check is necessary as not all settings might work that way, see this forum post . this is maybe best illustrated with an example. i have configured my workspace to use 2 for tab width and to insert spaces for tabs: changed preferences for tabs if i compare the two exported .epf files, this gives me: diffing eclipse preference files that means the two following lines are configuring what i have changed: /instance/org.eclipse.ui.editors/tabwidth=2 /instance/org.eclipse.ui.editors/spacesfortabs=true for the cwide.properties file i need to cut off the /instance/ part, so i have this added to the cwide.properties : # set tab width to 2 org.eclipse.ui.editors/tabwidth=2 # using spaces for tabs org.eclipse.ui.editors/spacesfortabs=true note: preferences are applied in following order: global preferences, then local (workspace) preferences this does not overwrite an existing setting of my workspace. as i can see from above diff, my initial workspace settings do not have any settings for tabwidth and spacesfortabs. creating a new workspace use and apply my new settings. but once i have the them, they will not be overwritten with new global ones. which makes sense: the local settings are winning. note: post a comment if you know an elegant way how to enforce/overwrite workspace settings with global ones.

At my employer we have a local NuGet server to host all of our internal packages. Occasionally, I’ll be working on a project and realize that I need to tweak something in one of my NuGet packages. Initially, I got into the habit of opening up a second instance of Visual Studio, making the necessary changes and using the NuGet web interface to re-upload the package. I quickly realized that manually uploading the package was too time consuming. Therefore, I started looking for a way to automate the process instead. Eventually that led me to the PowerShell script you see below. $nugetServer = "https://" $apiKey = "" $packageName = "" $latestRelease = nuget list $packageName $version = $latestRelease.split(" ")[1]; $versionTokens = $version.split(".") $buildNumber = [System.Double]::Parse($versionTokens[$versionTokens.Count -1]) $versionTokens[$versionTokens.Count -1] = $buildNumber +1 $newVersion = [string]::join('.', $versionTokens) echo $newVersion get-childitem | where {$_.extension -eq ".nupkg"} | foreach ($_) {remove-item $_.fullname} nuget pack -Version $newVersion $package = get-childitem | where {$_.extension -eq ".nupkg"} nuget push -Source $nugetServer $package $apiKey The script needs a few variables defined in order for it to run. The first variable ($nugetServer) is the URL of the NuGet Server. The second variable ($apiKey) is your personal API key. You can get your API key by logging into your NuGet Server with a browser. After you log in, click on your username in the upper right hand corner. This will take you to your account page. On the bottom of the “My Account” page there is a box which you can click on to make your API key visible. Finally the last variable ($packageName) is the name of the package you are uploading. This can be easily acquired by looking at your project properties and copying the Assembly name from the Application tab. Depending on how your machine is configured you may have the option to Run with PowerShell on your context menu. If not, you can take a look at this blog post in order to configure it manually. Alternatively you can use the following command instead. powershell.exe "\publish.ps1" If you have any problems running the script then please refer to the following TechNet article or send me a question and I’ll be glad to help.

Learn how Apache Camel implements the EIPs and offers a standardized, internal domain-specific language (DSL) to integrate applications.

This article will describe the complete root cause analysis of a recent Java deadlock problem observed from a Weblogic 11g production system running on the IBM JVM 1.6.This case study will also demonstrate the importance of mastering Thread Dump analysis skills; including for the IBM JVM Thread Dump format. Environment specification Java EE server: Oracle Weblogic Server 11g & Spring 2.0 OS: AIX 5.3 Java VM: IBM JRE 1.6.0 Platform type: Portal & ordering application Monitoring and troubleshooting tools JVM Thread Dump (IBM JVM format) Compuware Server Vantage (Weblogic JMX monitoring & alerting) Problem overview A major stuck Threads problem was observed & reported from Compuware Server Vantage and affecting 2 of our Weblogic 11g production managed servers causing application impact and timeout conditions from our end users. Gathering and validation of facts As usual, a Java EE problem investigation requires gathering of technical and non-technical facts so we can either derived other facts and/or conclude on the root cause. Before applying a corrective measure, the facts below were verified in order to conclude on the root cause: · What is the client impact? MEDIUM (only 2 managed servers / JVM affected out of 16) · Recent change of the affected platform? Yes (new JMS related asynchronous component) · Any recent traffic increase to the affected platform? No · How does this problem manifest itself? A sudden increase of Threads was observed leading to rapid Thread depletion · Did a Weblogic managed server restart resolve the problem? Yes, but problem is returning after few hours (unpredictable & intermittent pattern) - Conclusion #1: The problem is related to an intermittent stuck Threads behaviour affecting only a few Weblogic managed servers at the time - Conclusion #2: Since problem is intermittent, a global root cause such as a non-responsive downstream system is not likely Thread Dump analysis – first pass The first thing to do when dealing with stuck Thread problems is to generate a JVM Thread Dump. This is a golden rule regardless of your environment specifications & problem context. A JVM Thread Dump snapshot provides you with crucial information about the active Threads and what type of processing / tasks they are performing at that time. Now back to our case study, an IBM JVM Thread Dump (javacore.xyz format) was generated which did reveal the following Java Thread deadlock condition below: 1LKDEADLOCK Deadlock detected !!! NULL --------------------- NULL 2LKDEADLOCKTHR Thread "[STUCK] ExecuteThread: '8' for queue: 'weblogic.kernel.Default (self-tuning)'" (0x000000012CC08B00) 3LKDEADLOCKWTR is waiting for: 4LKDEADLOCKMON sys_mon_t:0x0000000126171DF8 infl_mon_t: 0x0000000126171E38: 4LKDEADLOCKOBJ weblogic/jms/frontend/FESession@0x07000000198048C0/0x07000000198048D8: 3LKDEADLOCKOWN which is owned by: 2LKDEADLOCKTHR Thread "[STUCK] ExecuteThread: '10' for queue: 'weblogic.kernel.Default (self-tuning)'" (0x000000012E560500) 3LKDEADLOCKWTR which is waiting for: 4LKDEADLOCKMON sys_mon_t:0x000000012884CD60 infl_mon_t: 0x000000012884CDA0: 4LKDEADLOCKOBJ weblogic/jms/frontend/FEConnection@0x0700000019822F08/0x0700000019822F20: 3LKDEADLOCKOWN which is owned by: 2LKDEADLOCKTHR Thread "[STUCK] ExecuteThread: '8' for queue: 'weblogic.kernel.Default (self-tuning)'" (0x000000012CC08B00) This deadlock situation can be translated as per below: - Weblogic Thread #8 is waiting to acquire an Object monitor lock owned by Weblogic Thread #10 - Weblogic Thread #10 is waiting to acquire an Object monitor lock owned by Weblogic Thread #8 Conclusion: both Weblogic Threads #8 & #10 are waiting on each other; forever! Now before going any deeper in this root cause analysis, let me provide you a high level overview on Java Thread deadlocks. Java Thread deadlock overview Most of you are probably familiar with Java Thread deadlock principles but did you really experience a true deadlock problem? From my experience, true Java deadlocks are rare and I have only seen ~5 occurrences over the last 10 years. The reason is that most stuck Threads related problems are due to Thread hanging conditions (waiting on remote IO call etc.) but not involved in a true deadlock condition with other Thread(s). A Java Thread deadlock is a situation for example where Thread A is waiting to acquire an Object monitor lock held by Thread B which is itself waiting to acquire an Object monitor lock held by Thread A. Both these Threads will wait for each other forever. This situation can be visualized as per below diagram: Thread deadlock is confirmed…now what can you do? Once the deadlock is confirmed (most JVM Thread Dump implementations will highlight it for you), the next step is to perform a deeper dive analysis by reviewing each Thread involved in the deadlock situation along with their current task & wait condition.Find below the partial Thread Stack Trace from our problem case for each Thread involved in the deadlock condition: ** Please note that the real application Java package name was renamed for confidentiality purposes ** Weblogic Thread #8 "[STUCK] ExecuteThread: '8' for queue: 'weblogic.kernel.Default (self-tuning)'" J9VMThread:0x000000012CC08B00, j9thread_t:0x00000001299E5100, java/lang/Thread:0x070000001D72EE00, state:B, prio=1 (native thread ID:0x111200F, native priority:0x1, native policy:UNKNOWN) Java callstack: at weblogic/jms/frontend/FEConnection.stop(FEConnection.java:671(Compiled Code)) at weblogic/jms/frontend/FEConnection.invoke(FEConnection.java:1685(Compiled Code)) at weblogic/messaging/dispatcher/Request.wrappedFiniteStateMachine(Request.java:961(Compiled Code)) at weblogic/messaging/dispatcher/DispatcherImpl.syncRequest(DispatcherImpl.java:184(Compiled Code)) at weblogic/messaging/dispatcher/DispatcherImpl.dispatchSync(DispatcherImpl.java:212(Compiled Code)) at weblogic/jms/dispatcher/DispatcherAdapter.dispatchSync(DispatcherAdapter.java:43(Compiled Code)) at weblogic/jms/client/JMSConnection.stop(JMSConnection.java:863(Compiled Code)) at weblogic/jms/client/WLConnectionImpl.stop(WLConnectionImpl.java:843) at org/springframework/jms/connection/SingleConnectionFactory.closeConnection(SingleConnectionFactory.java:342) at org/springframework/jms/connection/SingleConnectionFactory.resetConnection(SingleConnectionFactory.java:296) at org/app/JMSReceiver.receive() …………………………………………………………………… Weblogic Thread #10 "[STUCK] ExecuteThread: '10' for queue: 'weblogic.kernel.Default (self-tuning)'" J9VMThread:0x000000012E560500, j9thread_t:0x000000012E35BCE0, java/lang/Thread:0x070000001ECA9200, state:B, prio=1 (native thread ID:0x4FA027, native priority:0x1, native policy:UNKNOWN) Java callstack: at weblogic/jms/frontend/FEConnection.getPeerVersion(FEConnection.java:1381(Compiled Code)) at weblogic/jms/frontend/FESession.setUpBackEndSession(FESession.java:755(Compiled Code)) at weblogic/jms/frontend/FESession.consumerCreate(FESession.java:1025(Compiled Code)) at weblogic/jms/frontend/FESession.invoke(FESession.java:2995(Compiled Code)) at weblogic/messaging/dispatcher/Request.wrappedFiniteStateMachine(Request.java:961(Compiled Code)) at weblogic/messaging/dispatcher/DispatcherImpl.syncRequest(DispatcherImpl.java:184(Compiled Code)) at weblogic/messaging/dispatcher/DispatcherImpl.dispatchSync(DispatcherImpl.java:212(Compiled Code)) at weblogic/jms/dispatcher/DispatcherAdapter.dispatchSync(DispatcherAdapter.java:43(Compiled Code)) at weblogic/jms/client/JMSSession.consumerCreate(JMSSession.java:2982(Compiled Code)) at weblogic/jms/client/JMSSession.setupConsumer(JMSSession.java:2749(Compiled Code)) at weblogic/jms/client/JMSSession.createConsumer(JMSSession.java:2691(Compiled Code)) at weblogic/jms/client/JMSSession.createReceiver(JMSSession.java:2596(Compiled Code)) at weblogic/jms/client/WLSessionImpl.createReceiver(WLSessionImpl.java:991(Compiled Code)) at org/springframework/jms/core/JmsTemplate102.createConsumer(JmsTemplate102.java:204(Compiled Code)) at org/springframework/jms/core/JmsTemplate.doReceive(JmsTemplate.java:676(Compiled Code)) at org/springframework/jms/core/JmsTemplate$10.doInJms(JmsTemplate.java:652(Compiled Code)) at org/springframework/jms/core/JmsTemplate.execute(JmsTemplate.java:412(Compiled Code)) at org/springframework/jms/core/JmsTemplate.receiveSelected(JmsTemplate.java:650(Compiled Code)) at org/springframework/jms/core/JmsTemplate.receiveSelected(JmsTemplate.java:641(Compiled Code)) at org/app/JMSReceiver.receive() …………………………………………………………… As you can see in the above Thread Strack Traces, such deadlock did originate from our application code which is using the Spring framework API for the JMS consumer implementation (very useful when not using MDB’s). The Stack Traces are quite interesting and revealing that both Threads are in a race condition against the same Weblogic JMS consumer session / connection and leading to a deadlock situation: - Weblogic Thread #8 is attempting to reset and close the current JMS connection - Weblogic Thread #10 is attempting to use the same JMS Connection / Session in order to create a new JMS consumer - Thread deadlock is triggered! Root cause: non Thread safe Spring JMS SingleConnectionFactory implementation A code review and a quick research from Spring JIRA bug database did reveal the following Thread safe defect below with a perfect correlation with the above analysis: # SingleConnectionFactory's resetConnection is causing deadlocks with underlying OracleAQ's JMS connection https://jira.springsource.org/browse/SPR-5987 A patch for Spring SingleConnectionFactory was released back in 2009 which did involve adding proper synchronized{} block in order to prevent Thread deadlock in the event of a JMS Connection reset operation: synchronized (connectionMonitor) { //if condition added to avoid possible deadlocks when trying to reset the target connection if (!started) { this.target.start(); started = true; } } Solution Our team is currently planning to integrate this Spring patch in to our production environment shortly. The initial tests performed in our test environment are positive. Conclusion I hope this case study has helped understand a real-life Java Thread deadlock problem and how proper Thread Dump analysis skills can allow you to quickly pinpoint the root cause of stuck Thread related problems at the code level. Please don’t hesitate to post any comment or question.

So, thought it'd be good to talk about another Java library that I like. It's been around for a while and is not perhaps the most exciting library, but it is very very useful. I probably make use of it daily. org.apache.commons.lang.StringUtils StringUtils is part of Apache Commons Lang (http://commons.apache.org/lang/, and as the name suggest it provides some nice utilities for dealing with Strings, going beyond what is offered in java.lang.String. It consists of over 50 static methods, and I'm not going to cover every single one of them, just a selection of methods that I make the most use of. There are two different versions available, the newer org.apache.commons.lang3.StringUtils and the older org.apache.commons.lang.StringUtils. There are not really any significant differences between the two. lang3.StringUtils requires Java 5.0 and is probably the version you'll want to use. public static boolean equals(CharSequence str1, CharSequence str2) Thought I'd start with one of the most straight forward methods. equals. This does exactly what you'd expect, it takes two Strings and returns true if they are identical, or false if they're not. But java.lang.String already has a perfectly good equals method? Why on earth would I want to use a third party implementation? It's a fair question. Let's look at some code, can you see any problems? public void doStuffWithString(String stringParam) { if(stringParam.equals("MyStringValue")) { // do stuff } } That's a NullPointerException waiting to happen! There are a couple of ways around this: public void safeDoStuffWithString1(String stringParam) { if(stringParam != null && stringParam.equals("MyStringValue")) { // do stuff } } public void safeDoStuffWithString2(String stringParm) { if("MyStringValue".equals(stringParam)) { // do stuff } } Personally I'm not a fan of either method. I think null checks pollute code, and to me "MyStringValue".equals(stringParam) just doesn't scan well, it looks wrong. This is where StringUtils.equals comes in handy, it's null safe. It doesn't matter what you pass it, it won't NullPointer on you! So you could rewrite the simple method as follows: public void safeDoStuffWithString3(String stringParam) { if(StringUtils.equals(stringParam,"MyStringValue)) { // do stuff } } It's personal preference, but I think this reads better than the first two examples. There's nothing wrong with them, but I do think StringUtils.equals() is worth considering. isEmpty, isNotEmpty, isBlank, isNotBlank OK, these look pretty self explanatory, I'm guessing they're all null safe? You're probably spotting a pattern here. isEmpty is indeed a null safe replacement for java.lang.String.isEmpty(), and isNotEmpty is it's inverse. So no more null checks: if(myString != null && !myString.isEmpty()) { // urghh // Do stuff with myString } if(StringUtils.isNotEmpty(myString)) { // much nicer // Do stuff with myString } So, why Blank and Empty? There is a difference, isBlank also returns true if the String just contains whitespace, ie... String someWhiteSpace = " \t \n"; StringUtils.isEmpty(someWhiteSpace); // false StringUtils.isBlank(someWhiteSpace); // true public static String[] split(String str, String separatorChars) Right that looks just like String.split(), so this is just a null safe version of the built in Java method? Well, yes it certainly is null safe. Trying to split a null string results in null, and a null separator splits on whitespace. But there is another reason you should consider using StringUtils.split(...), and that's the fact that java.lang.String.split takes a regular expression as a separator. For example the following may not do what you want: public void possiblyNotWhatYouWant() { String contrivedExampleString = "one.two.three.four"; String[] result = contrivedExampleString.split("."); System.out.println(result.length); // 0 } But all I have to do is put a couple of backslashes in front of the '.' and it will work fine. It's not really a big deal is it? Perhaps not, but there's one last advantage to using StringUtils.split, and that's the fact that regular expressions are expensive. In fact when I tested splitting a String on a comma (a fairly common use case in my experience), StingUtils.split runs over four times faster! public static String join(Iterable iterable, String separator) Ah, finally something genuinely useful! Indeed I've never found an elegant way of concatenating strings with a separator, there's always that annoying conditional require to check if want to insert the separator or not. So it's nice there's a utility to this for me. Here's a quick example: String[] numbers = {"one", "two", "three"}; StringUtils.join(numbers,","); // returns "one,two,three" There's also various overloaded versions of join that take Arrays, and Iterators. Ok, I'm convinced. This looks like a pretty useful library, what else can it do? Quite a lot, but like I said earlier I won't bother going through every single method available, I'd just end up repeating what's said in the API documentation. I'd really recommend taking a closer look: http://commons.apache.org/lang/api-3.1/org/apache/commons/lang3/StringUtils.html So basically if you ever need to do something with a String that isn't covered by Java's core String library (and maybe even stuff that is), take a look at StringUtils.

Continuing on the theme of temporary storage for transient messages used within Spring Integration flows, the claim-check model offers configurable storage for message payloads. The advantage in using this Enterprise Integration pattern, compared against header enrichment, is that objects don't have to be packed into the header using a Header Enrichment technique. They can be stored in a local Java Map, an IMDB, cache or anything else that be used to hold data. Several advantages using this approach are evident. Firstly, performance and efficiency. When using header enrichment, if message payloads need to be managed outside of the JVM that generates the enriched message header, the object will not be available unless it's serialised and transported around the distributed application. This could be costly in terms of performance and transport efficiency. The key factor here is the frequency of remote dispatch and the size of the header object. In specific circumstances the claim-check pattern may offer an advantage here, objects can be serialised and/or transformed into a storage specific format and stored internally in memory or externally in a data store. Secondly, accessibility. It's conceivable that message payloads undergoing claim-check processing may need to be accessed by third party applications that are unable to receive Spring Integration messages. The claim-check pattern allows this type of processing to take place. Thirdly, resiliency is offered. A data store can be chosen that guarantees persistence for messages in order that they can be recovered following failure. The following code details how the claim-check pattern can be used: The gateway used is specified as the following Java class: package com.l8mdv.sample; import org.springframework.integration.Message; import org.springframework.integration.annotation.Gateway; public interface ClaimCheckGateway { public static final String CLAIM_CHECK_ID = "CLAIM_CHECK_ID"; @Gateway (requestChannel = "claim-check-in-channel") public Message send(Message message); } Lastly, this can all be tested by using the following JUnit test case: package com.l8mdv.sample; import org.junit.Assert; import org.junit.Test; import org.junit.runner.RunWith; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.integration.Message; import org.springframework.integration.support.MessageBuilder; import org.springframework.test.context.ContextConfiguration; import org.springframework.test.context.junit4.SpringJUnit4ClassRunner; import static com.l8mdv.sample.ClaimCheckGateway.CLAIM_CHECK_ID; @RunWith(SpringJUnit4ClassRunner.class) @ContextConfiguration( locations = {"classpath:META-INF/spring/claim-check.xml"} ) public class ClaimCheckIntegrationTest { @Autowired ClaimCheckGateway claimCheckGateway; @Test public void locatePayloadInHeader() { String payload = "Sample test message."; Message message = MessageBuilder.withPayload(payload).build(); Message response = claimCheckGateway.send(message); Assert.assertTrue(response.getPayload().equals(payload)); Assert.assertTrue(response.getHeaders().get(CLAIM_CHECK_ID) != null); } }

Cross-site request forgery attacks (CSRF) are very common in web applications and can cause significant harm if allowed. If you have never heard of CSRF I recommend you check out OWASPs page about it. Luckily preventing CSRF attacks is quite simple, I’ll try to show you how they work and how we can defend from them in the least obtrusive way possible in Java based web apps. Imagine you are about to perform a money transfer in your bank’s secure web page, when you click on the transfer option a form page is loaded that allows you to choose the debit and credit accounts, and enter the amount of money to move. When you are satisfied with your options you press “submit” and send the form information to your bank’s web server, which in turns performs the transaction. Now add the following to the picture, a malicious website (which you think harmless of course) is open on another window/tab of your browser while you are innocently moving all your millions in your bank’s site. This evil site knows the bank’s web forms structure, and as you browse through it, it tries to post transactions withdrawing money from your accounts and depositing it on the evil overlord’s accounts, it can do it because you have an open and valid session with the banks site in the same browser! This is the basis for a CSRF attack. One simple and effective way to prevent it is to generate a random (i.e. unpredictable) string when the initial transfer form is loaded and send it to the browser. The browser then sends this piece of data along with the transfer options, and the server validates it before approving the transaction for processing. This way, malicious websites cannot post transactions even if they have access to a valid session in a browser. To implement this mechanism in Java I choose to use two filters, one to create the salt for each request, and another to validate it. Since the users request and subsequent POST or GETs that should be validated do not necessarily get executed in order, I decided to use a time based cache to store a list of valid salt strings. The first filter, used to generate a new salt for a request and store it in the cache can be coded as follows: package com.ricardozuasti.csrf; import com.google.common.cache.Cache; import com.google.common.cache.CacheBuilder; import com.google.common.cache.CacheLoader; import com.google.common.cache.LoadingCache; import java.io.IOException; import java.security.SecureRandom; import java.util.concurrent.ExecutionException; import java.util.concurrent.TimeUnit; import javax.servlet.*; import javax.servlet.http.HttpServletRequest; import org.apache.commons.lang.RandomStringUtils; public class LoadSalt implements Filter { @Override public void doFilter(ServletRequest request, ServletResponse response, FilterChain chain) throws IOException, ServletException { // Assume its HTTP HttpServletRequest httpReq = (HttpServletRequest) request; // Check the user session for the salt cache, if none is present we create one Cache csrfPreventionSaltCache = (Cache) httpReq.getSession().getAttribute("csrfPreventionSaltCache"); if (csrfPreventionSaltCache == null){ csrfPreventionSaltCache = CacheBuilder.newBuilder() .maximumSize(5000) .expireAfterWrite(20, TimeUnit.MINUTES) .build(); httpReq.getSession().setAttribute("csrfPreventionSaltCache", csrfPreventionSaltCache); } // Generate the salt and store it in the users cache String salt = RandomStringUtils.random(20, 0, 0, true, true, null, new SecureRandom()); csrfPreventionSaltCache.put(salt, Boolean.TRUE); // Add the salt to the current request so it can be used // by the page rendered in this request httpReq.setAttribute("csrfPreventionSalt", salt); chain.doFilter(request, response); } @Override public void init(FilterConfig filterConfig) throws ServletException { } @Override public void destroy() { } } I used Guava CacheBuilder to create the salt cache since it has both a size limit and an expiration timeout per entry. To generate the actual salt I used Apache Commons RandomStringUtils, powered by Java 6 SecureRandom to ensure a strong generation seed. This filter should be used in all requests ending in a page that will link, post or call via AJAX a secured transaction, so in most cases it’s a good idea to map it to every request (maybe with the exception of static content such as images, CSS, etc.). It’s mapping in your web.xml should look similar to: ... loadSalt com.ricardozuasti.csrf.LoadSalt ... loadSalt * ... As I said, to validate the salt before executing secure transactions we can write another filter: package com.ricardozuasti.csrf; import com.google.common.cache.Cache; import java.io.IOException; import javax.servlet.*; import javax.servlet.http.HttpServletRequest; public class ValidateSalt implements Filter { @Override public void doFilter(ServletRequest request, ServletResponse response, FilterChain chain) throws IOException, ServletException { // Assume its HTTP HttpServletRequest httpReq = (HttpServletRequest) request; // Get the salt sent with the request String salt = (String) httpReq.getParameter("csrfPreventionSalt"); // Validate that the salt is in the cache Cache csrfPreventionSaltCache = (Cache) httpReq.getSession().getAttribute("csrfPreventionSaltCache"); if (csrfPreventionSaltCache != null && salt != null && csrfPreventionSaltCache.getIfPresent(salt) != null){ // If the salt is in the cache, we move on chain.doFilter(request, response); } else { // Otherwise we throw an exception aborting the request flow throw new ServletException("Potential CSRF detected!! Inform a scary sysadmin ASAP."); } } @Override public void init(FilterConfig filterConfig) throws ServletException { } @Override public void destroy() { } } You should configure this filter for every request that needs to be secure (i.e. retrieves or modifies sensitive information, move money, etc.), for example: ... validateSalt com.ricardozuasti.csrf.ValidateSalt ... validateSalt /transferMoneyServlet ... After configuring both servlets all your secured requests should fail :). To fix it you have to add, to each link and form post that ends in a secure URL, the csrfPreventionSalt parameter containing the value of the request parameter with the same name. For example, in an HTML form within a JSP page: ... ... ... Of course you can write a custom tag, a nice Javascript code or whatever you prefer to inject the new parameter in every needed link/form.

This article will provide you with a tutorial on how you can quickly pinpoint the Java Thread contributors to a high CPU problem on the Windows OS. Windows, like other OS such as Linux, Solaris & AIX allow you to monitor the CPU utilization at the process level but also for individual Thread executing a task within a process. For this tutorial, we created a simple Java program that will allow you to learn this technique in a step by step manner. Troubleshooting tools The following tools will be used below for this tutorial: - Windows Process Explorer (to pinpoint high CPU Thread contributors) - JVM Thread Dump (for Thread correlation and root cause analysis at code level) High CPU simulator Java program The simple program below is simply looping and creating new String objects. It will allow us to perform this CPU per Thread analysis. I recommend that you import it in an IDE of your choice e.g. Eclipse and run it from there. You should observe an increase of CPU on your Windows machine as soon as you execute it. package org.ph.javaee.tool.cpu; /** * HighCPUSimulator * @author Pierre-Hugues Charbonneau * http://javaeesupportpatterns.blogspot.com * */ public class HighCPUSimulator { private final static int NB_ITERATIONS = 500000000; // ~1 KB data footprint private final static String DATA_PREFIX = "datadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadatadata"; /** * @param args */ public static void main(String[] args) { System.out.println("HIGH CPU Simulator 1.0"); System.out.println("Author: Pierre-Hugues Charbonneau"); System.out.println("http://javaeesupportpatterns.blogspot.com/"); try { for (int i = 0; i < NB_ITERATIONS; i++) { // Perform some String manipulations to slowdown and expose looping process... String data = DATA_PREFIX + i; } } catch (Throwable any) { System.out.println("Unexpected Exception! " + any.getMessage() + " [" + any + "]"); } System.out.println("HighCPUSimulator done!"); } } Step #1 – Launch Process Explorer The Process Explorer tool visually shows the CPU usage dynamically. It is good for live analysis. If you need historical data on CPU per Thread then you can also use Windows perfmon with % Processor Time & Thread Id data counters. You can download Process Explorer from the link below: http://technet.microsoft.com/en-us/sysinternals/bb896653 In our example, you can see that the Eclipse javaw.exe process is now using ~25% of total CPU utilization following the execution of our sample program. Step #2 – Launch Process Explorer Threads view The next step is to display the Threads view of the javaw.exe process. Simply right click on the javaw.exe process and select Properties. The Threads view will be opened as per below snapshot: - The first column is the Thread Id (decimal format) - The second column is the CPU utilization % used by each Thread - The third column is also another counter indicating if Thread is running on the CPU In our example, we can see our primary culprit is Thread Id #5996 using ~ 25% of CPU. Step #3 – Generate a JVM Thread Dump At this point, Process Explorer will no longer be useful. The goal was to pinpoint one or multiple Java Threads consuming most of the Java process CPU utilization which is what we achieved. In order to go the next level in your analysis you will need to capture a JVM Thread Dump. This will allow you to correlate the Thread Id with the Thread Stack Trace so you can pinpoint that type of processing is consuming such high CPU. JVM Thread Dump generation can be done in a few manners. If you are using JRockit VM you can simply use the jrcmd tool as per below example: Once you have the Thread Dump data, simply search for the Thread Id and locate the Thread Stack Trace that you are interested in. For our example, the Thread “Main Thread” which was fired from Eclipse got exposed as the primary culprit which is exactly what we wanted to demonstrate. "Main Thread" id=1 idx=0x4 tid=5996 prio=5 alive, native_blocked at org/ph/javaee/tool/cpu/HighCPUSimulator.main (HighCPUSimulator.java:31) at jrockit/vm/RNI.c2java(IIIII)V(Native Method) -- end of trace Step #4 – Analyze the culprit Thread(s) Stack Trace and determine root cause At this point you should have everything that you need to move forward with the root cause analysis. You will need to review each Thread Stack Trace and determine what type of problem you are dealing with. That final step is typically where you will spend most of your time and problem can be simple such as infinite looping or complex such as garbage collection related problems. In our example, the Thread Dump did reveal the high CPU originates from our sample Java program around line 31. As expected, it did reveal the looping condition that we engineered on purpose for this tutorial. for (int i = 0; i < NB_ITERATIONS; i++) { // Perform some String manipulations to slowdown and expose looping process... String data = DATA_PREFIX + i; } I hope this tutorial has helped you understand how you can analyze and help pinpoint root cause of Java CPU problems on Windows OS. Please stay tuned for more updates, the next article will provide you with a Java CPU troubleshooting guide including how to tackle that last analysis step along with common problem patterns.

Looking for the best Eclipse shortcuts? Here are the top 10.