An Action can be used to separate functionality and state from a component. For example, if you have two or more components that perform the same function, consider using an Action object to implement the function. An Action object is an action listener that provides not only action-event handling, but also centralized handling of the state of action-event-firing components such as tool bar buttons, menu items, common buttons, and text fields. The state that an action can handle includes text, icon, mnemonic, enabled, and selected status.
To create an Action object, you generally create a subclass of AbstractAction and then instantiate it. In your subclass, you must implement the actionPerformed method to react appropriately when the action event occurs. Here's an example of creating and instantiating an AbstractAction subclass:
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When the action created by the preceding code is attached to a button and a menu item, the button and menu item display the text and icon associated with the action. The L character is used for mnemonics on the button and menu item, and their tool-tip text is set to the SHORT_DESCRIPTION string followed by a representation of the mnemonic key.
For example, we have provided a simple example, ActionDemo.java, which defines three actions. Each action is attached to a button and a menu item. Thanks to the mnemonic values set for each button's action, the key sequence Alt-L activates the left button, Alt-M the middle button, and Alt-R the right button. The tool tip for the left button displays This is the left button. Alt-L. All of this configuration occurs automatically, without the program making explicit calls to set the mnemonic or tool-tip text. As we'll show later, the program does make calls to set the button text, but only to avoid using the values already set by the actions.
Click the leftmost button in the tool bar.
The text area again displays information about the event. Note that although the source of the events is different, both events were detected by the same action listener: the Action object attached to the components.
After you create components using an Action, you might well need to customize them. For example, you might want to customize the appearance of one of the components by adding or deleting the icon or text. For example, ActionDemo.java has no icons in its menus, and no text in its buttons. Here's the code that accomplishes this:
We chose to create an icon-only button and a text-only menu item from the same action by setting the icon property to null and the text to an empty string. However, if a property of the Action changes, the widget may try to reset the icon and text from the Action again.
This table defines the properties that can be set on an action. The second column lists which components automatically use the properties (and what method is specifically called). For example, setting the ACCELERATOR_KEY on an action that is then attached to a menu item, means that JMenuItem.setAccelerator(KeyStroke) is called automatically.
I have attempted to plug the java client into Gatling. It was a bit awkward and I have some concerns that what I am attempting is not really possible. For example, some of the results are a bit dubious.
An action returns a play.mvc.Result value, representing the HTTP response to send to the web client. In this example ok constructs a 200 OK response containing a text/plain response body. For more examples of HTTP responses see play.mvc.Results methods.
The starter workflow sets up the PATH to contain OpenJDK 8 for the x64 platform. If you want to use a different version of Java, or target a different architecture (x64 or x86), you can use the setup-java action to choose a different Java runtime environment.
For example, to use version 11 of the JDK provided by Adoptium for the x64 platform, you can use the setup-java action and configure the java-version, distribution and architecture parameters to '11', 'temurin' and x64.
You can cache your dependencies to speed up your workflow runs. After a successful run, your local Maven repository will be stored in a cache. In future workflow runs, the cache will be restored so that dependencies don't need to be downloaded from remote Maven repositories. You can cache dependencies simply using the setup-java action or can use cache action for custom and more advanced configuration.
Maven will usually create output files like JARs, EARs, or WARs in the target directory. To upload those as artifacts, you can copy them into a new directory that contains artifacts to upload. For example, you can create a directory called staging. Then you can upload the contents of that directory using the upload-artifact action.
In order to create a button in Java, you can use the JButton class from the javax.swing package. First, create an instance of the JButton class and then add it to your user interface using a layout manager.
In order to detect when a button is pressed, you can use an ActionListener. This interface has a method called actionPerformed() that will be called whenever the button is clicked. You can add this ActionListener to your button using the addActionListener() method.
In order to make an action repeat as long as the button is pressed, you can use a while loop inside the actionPerformed() method. The condition of the while loop can be set to check whether the button is still being pressed. This way, the action will continue to repeat until the button is released.
Yes, you can change the action that is performed when the button is pressed by changing the code inside the actionPerformed() method. This method is where the action is defined, so you can add or remove code to modify the action that is performed.
In order to stop the action when the button is released, you can use a flag variable inside the actionPerformed() method. The flag can be set to true when the button is pressed and then set to false when the button is released. You can use this flag to control the execution of the action code.
What java client do you have?
Make sure you have 6.0.0 or 6.1.0 the stable releases. And check the above examples
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RDDs support two types of operations: transformations, which create a new dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset. For example, map is a transformation that passes each dataset element through a function and returns a new RDD representing the results. On the other hand, reduce is an action that aggregates all the elements of the RDD using some function and returns the final result to the driver program (although there is also a parallel reduceByKey that returns a distributed dataset).
All transformations in Spark are lazy, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently. For example, we can realize that a dataset created through map will be used in a reduce and return only the result of the reduce to the driver, rather than the larger mapped dataset.
By default, each transformed RDD may be recomputed each time you run an action on it. However, you may also persist an RDD in memory using the persist (or cache) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting RDDs on disk, or replicated across multiple nodes.
The first line defines a base RDD from an external file. This dataset is not loaded in memory orotherwise acted on: lines is merely a pointer to the file.The second line defines lineLengths as the result of a map transformation. Again, lineLengthsis not immediately computed, due to laziness.Finally, we run reduce, which is an action. At this point Spark breaks the computation into tasksto run on separate machines, and each machine runs both its part of the map and a local reduction,returning only its answer to the driver program.
The Spark RDD API also exposes asynchronous versions of some actions, like foreachAsync for foreach, which immediately return a FutureAction to the caller instead of blocking on completion of the action. This can be used to manage or wait for the asynchronous execution of the action.
One of the most important capabilities in Spark is persisting (or caching) a dataset in memoryacross operations. When you persist an RDD, each node stores any partitions of it that it computes inmemory and reuses them in other actions on that dataset (or datasets derived from it). This allowsfuture actions to be much faster (often by more than 10x). Caching is a key tool foriterative algorithms and fast interactive use. 0852c4b9a8
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