caite.info Biography EFFICIENT ANDROID THREADING PDF

Efficient android threading pdf

Sunday, April 21, 2019 admin Comments(0)

Multithreading is essential if you want to create an Android app with a great user experience, but how do you know which techniques can help solve your. efficient android threading asynchronous processing techniques for android applications. PDF File: Efficient Android Threading Asynchronous Processing. Multithreading is essential if you want to create an Android app with a great user experience, but how do you know which techniques can help.


Author: AMADA LAPUSNAK
Language: English, Spanish, Portuguese
Country: Macedonia
Genre: Academic & Education
Pages: 302
Published (Last): 12.05.2016
ISBN: 512-8-56823-523-4
ePub File Size: 21.76 MB
PDF File Size: 9.13 MB
Distribution: Free* [*Regsitration Required]
Downloads: 34971
Uploaded by: KATHYRN

Media, Inc. Efficient Android Threading, the cover image of . Preface. Efficient Android Threading explores how to achieve robust and reliable. caite.info Let Your Life Efficient Android Threading Asynchronous Processing Techniques for. Pipes. Basic Pipe Use. Example: Text Processing on a Worker Thread. 42 . Shared Memory. Signaling. BlockingQueue. Android Message.

The Android OS then uses this file to decide how to integrate your app into the device's overall user experience. If you have any question about this opinionated list, do not hesitate to contact me vinta on Twitter or open an issue on GitHub. In the majority of cases, desktop apps have a single entry point from a desktop or program launcher, then run as a single, monolithic process. This is where the Room persistence library comes to the rescue. Natural Language Processing Libraries for working with human languages. Product Number:

Ordering Code: SPEC Code: Code Name: Kaby Lake. Lithography refers to the semiconductor technology used to manufacture an integrated circuit, and is reported in nanometer nm , indicative of the size of features built on the semiconductor.

Cores is a hardware term that describes the number of independent central processing units in a single computing component die or chip. A Thread, or thread of execution, is a software term for the basic ordered sequence of instructions that can be passed through or processed by a single CPU core.

Processor Base Frequency describes the rate at which the processor's transistors open and close. The processor base frequency is the operating point where TDP is defined. Frequency is measured in gigahertz GHz , or billion cycles per second. CPU Cache is an area of fast memory located on the processor. A bus is a subsystem that transfers data between computer components or between computers.

Thermal Design Power TDP represents the average power, in watts, the processor dissipates when operating at Base Frequency with all cores active under an Intel-defined, high-complexity workload.

Refer to Datasheet for thermal solution requirements.

Threading efficient pdf android

Configurable TDP-up Frequency is a processor operating mode where the processor behavior and performance is modified by raising TDP and the processor frequency to fixed points. Configurable TDP-up is a processor operating mode where the processor behavior and performance is modified by raising TDP and the processor frequency to fixed points. The use of Configurable TDP-up is typically executed by the system manufacturer to optimize power and performance.

Configurable TDP-up is the average power, in watts, that the processor dissipates when operating at the Configurable TDP-up frequency under an Intel-defined, high-complexity workload. Configurable TDP-down Frequency is a processor operating mode where the processor behavior and performance is modified by lowering TDP and the processor frequency to fixed points. Configurable TDP-down is a processor operating mode where the processor behavior and performance is modified by lowering TDP and the processor frequency to fixed points.

The use of Configurable TDP-down is typically executed by the system manufacturer to optimize power and performance.

Configurable TDP-down is the average power, in watts, that the processor dissipates when operating at the Configurable TDP-down frequency under an Intel-defined, high-complexity workload. Embedded Options Available indicates products that offer extended purchase availability for intelligent systems and embedded solutions.

See your Intel representative for details. Find products with Embedded Options Available. The number of memory channels refers to the bandwidth operation for real world application. Note that ECC memory support requires both processor and chipset support.

Processor Graphics indicates graphics processing circuitry integrated into the processor, providing the graphics, compute, media, and display capabilities. The maximum amount of memory accessible to processor graphics. Processor graphics operates on the same physical memory as the CPU subject to OS, driver, and other system limitations. Graphics Output defines the interfaces available to communicate with display devices.

System or device display resolution is dependent on multiple system design factors; actual resolution may be lower on your system. System or device display resolution is dependent on multiple system design factors; actual resolution may be lower on your device. PCI Express Revision is the version supported by the processor. Peripheral Component Interconnect Express or PCIe is a high-speed serial computer expansion bus standard for attaching hardware devices to a computer.

Using our current implementation, if the user rotates the device or leaves and immediately returns to the app, the existing UI becomes visible instantly because the repository retrieves data from our in-memory cache. However, what happens if the user leaves the app and comes back hours later, after the Android OS has killed the process? By relying on our current implementation in this situation, we need to fetch the data again from the network.

This refetching process isn't just a bad user experience; it's also wasteful because it consumes valuable mobile data.

You could fix this issue by caching the web requests, but that creates a key new problem: What happens if the same user data shows up from another type of request, such as fetching a list of friends?

The app would show inconsistent data, which is confusing at best. For example, our app might show two different versions of the same user's data if the user made the list-of-friends request and the single-user request at different times. Our app would need to figure out how to merge this inconsistent data. The proper way to handle this situation is to use a persistent model.

This is where the Room persistence library comes to the rescue. Room is an object-mapping library that provides local data persistence with minimal boilerplate code. At compile time, it validates each query against your data schema, so broken SQL queries result in compile-time errors instead of runtime failures.

Room abstracts away some of the underlying implementation details of working with raw SQL tables and queries. It also allows you to observe changes to the database's data, including collections and join queries, exposing such changes using LiveData objects.

It even explicitly defines execution constraints that address common threading issues, such as accessing storage on the main thread. To use Room, we need to define our local schema. First, we add the Entity annotation to our User data model class and a PrimaryKey annotation to the class's id field. These annotations mark User as a table in our database and id as the table's primary key:. Then, we create a database class by implementing RoomDatabase for our app:. Notice that UserDatabase is abstract.

Room automatically provides an implementation of it.

Intel® Core™ i5-7200U Processor

For details, see the Room documentation. We now need a way to insert user data into the database. For this task, we create a data access object DAO. Room knows when the database is modified and automatically notifies all active observers when the data changes. Because Room uses LiveData , this operation is efficient; it updates the data only when there is at least one active observer. This small-scoped update demonstrates the flexibility that our app's architecture provides.

It's also great for testing, because we can provide a fake UserRepository and test our production UserProfileViewModel at the same time. If users wait a few days before returning to an app that uses this architecture, it's likely that they'll see out-of-date information until the repository can fetch updated information.

Depending on your use case, you may not want to show this out-of-date information. Instead, you can display placeholder data, which shows dummy values and indicates that your app is currently fetching and loading up-to-date information.

Guide to app architecture

For example, if our backend has another endpoint that returns a list of friends, the same user object could come from two different API endpoints, maybe even using different levels of granularity. If the UserRepository were to return the response from the Webservice request as-is, without checking for consistency, our UIs could show confusing information because the version and format of data from the repository would depend on the endpoint most recently called. For this reason, our UserRepository implementation saves web service responses into the database.

Changes to the database then trigger callbacks on active LiveData objects. Using this model, the database serves as the single source of truth , and other parts of the app access it using our UserRepository. Regardless of whether you use a disk cache, we recommend that your repository designate a data source as the single source of truth for the rest of your app.

In some use cases, such as pull-to-refresh, it's important for the UI to show the user that there's currently a network operation in progress. It's good practice to separate the UI action from the actual data because the data might be updated for various reasons.

From the UI's perspective, the fact that there's a request in flight is just another data point, similar to any other piece of data in the User object itself. We can use one of the following strategies to display a consistent data-updating status in the UI, regardless of where the request to update the data came from:. Change getUser to return an object of type LiveData. This object would include the status of the network operation. For an example, see the NetworkBoundResource implementation in the android-architecture-components GitHub project.

Provide another public function in the UserRepository class that can return the refresh status of the User. This option is better if you want to show the network status in your UI only when the data-fetching process originated from an explicit user action, such as pull-to-refresh. In the separation of concerns section, we mentioned that one key benefit of following this principle is testability.

User interface and interactions: Use an Android UI instrumentation test. The best way to create this test is to use the Espresso library. You can create the fragment and provide it a mock UserProfileViewModel. Because the fragment communicates only with the UserProfileViewModel , mocking this one class is sufficient to fully test your app's UI.

You only need to mock one class, UserRepository.

You can test the UserRepository using a JUnit test, as well. You need to mock the Webservice and the UserDao.

In these tests, verify the following behavior:.

Efficient Android Threading by Anders Göransson - PDF Drive

Because both Webservice and UserDao are interfaces, you can mock them or create fake implementations for more complex test cases. Test DAO classes using instrumentation tests. Because these instrumentation tests don't require any UI components, they run quickly. For each test, create an in-memory database to ensure that the test doesn't have any side effects, such as changing the database files on disk. This approach isn't recommended, however, because the SQLite version running on the device might differ from the SQLite version on your development machine.

In these tests, avoid making network calls to your backend. It's important for all tests, especially web-based ones, to be independent from the outside world. Several libraries, including MockWebServer , can help you create a fake local server for these tests.

Testing Artifacts: Architecture Components provides a maven artifact to control its background threads. The android. Programming is a creative field, and building Android apps isn't an exception.

Pdf threading efficient android

There are many ways to solve a problem, be it communicating data between multiple activities or fragments, retrieving remote data and persisting it locally for offline mode, or any number of other common scenarios that nontrivial apps encounter.

Although the following recommendations aren't mandatory, it has been our experience that following them makes your code base more robust, testable, and maintainable in the long run:.

In the recommended app architecture section above, we omitted network error and loading states to keep the code snippets simple. This section demonstrates how to expose network status using a Resource class that encapsulate both the data and its state. Because it's common to load data from the network while showing the disk copy of that data, it's good to create a helper class that you can reuse in multiple places.

For this example, we create a class called NetworkBoundResource. It starts by observing the database for the resource. When the entry is loaded from the database for the first time, NetworkBoundResource checks whether the result is good enough to be dispatched or that it should be re-fetched from the network. Note that both of these situations can happen at the same time, given that you probably want to show cached data while updating it from the network.

If the network call completes successfully, it saves the response into the database and re-initializes the stream. If network request fails, the NetworkBoundResource dispatches a failure directly. After saving new data to disk, we re-initialize the stream from the database.

We usually don't need to do that, however, because the database itself happens to dispatch the change. Keep in mind that relying on the database to dispatch the change involves relying on the associated side effects, which isn't good because undefined behavior from these side effects could occur if the database ends up not dispatching changes because the data hasn't changed. Also, don't dispatch the result that arrived from the network because that would violate the single source of truth principle.

After all, maybe the database includes triggers that change data values during a "save" operation. Building TV playback apps. Helping users find content on TV. Recommending TV content. Building TV channels. Android Auto. Android Things. Developer kits. Advanced setup. Build apps. Create a Things app. Communicate with wireless devices.

Configure devices. Interact with peripherals. Build user-space drivers. Manage devices. Create a build. Push an update. Chrome OS devices. Core topics. Interact with other apps. Handling app links. App shortcuts. App widgets. Architecture Components.

Data Binding Library. Paging Library. How-To Guides. Advanced Concepts. Threading in WorkManager. Intents and intent filters. User interface. Improving layout performance. Custom view components. Look and feel. Add the app bar. Control the system UI visibility.

Supporting swipe-to-refresh. Pop-up messages overview. Adding search functionality. Creating backward-compatible UIs.