For more complex queries, an app must include index specifications in its configuration.
Programming Google App Engine
The App Engine developer tools help produce this configuration file by watching which queries are performed as you test your application with the provided development web server on your computer. When you upload your app, the datastore knows to make indexes for every query the app performed during testing. You can also edit the index configuration manually. When your application creates new entities and updates existing ones, the datastore updates every corresponding index. This makes queries very fast each query is a simple table scan at the expense of entity updates possibly many tables may need updating for a single change.
In fact, the performance of an index-backed query is not affected by the number of entities in the datastore, only the size of the result set. When an application has many clients attempting to read or write the same data simultaneously, it is imperative that the data always be in a consistent state. When an application updates the properties of a single entity, Cloud Datastore ensures that either every update to the entity succeeds all at once, or the entire update fails and the entity remains the way it was prior to the beginning of the update.
Other users do not see any effects of the change until the change succeeds. In other words, an update of a single entity occurs in a transaction. Each transaction is atomic: An application can read or update multiple entities in a single transaction, but it must tell Cloud Datastore which entities will be updated together when it creates the entities.
The application does this by creating entities in entity groups. Cloud Datastore uses entity groups to control how entities are distributed across servers, so it can guarantee a transaction on a group succeeds or fails completely. In database terms, the datastore natively supports local transactions. When an application calls the datastore API to update an entity, the call returns only after the transaction succeeds or fails, and it returns with knowledge of success or failure.
For updates, this means the service waits for all entities to be updated before returning a result. The application can call the datastore asynchronously, such that the app code can continue executing while the datastore is preparing a result. But the update itself does not return until it has confirmed the change.
The other user must try her operation again, possibly rereading values and calculating the update from fresh data. Contention is expected, so retries are common. In database terms, Cloud Datastore uses optimistic concurrency control: Reading the entity never fails due to contention. The application just sees the entity in its most recent stable state.
Google App Engine - Wikipedia
You can also read multiple entities from the same entity group by using a transaction to ensure that all the data in the group is current and consistent with itself. In most cases, retrying a transaction on a contested entity will succeed. But if an application is designed such that many users might update a single entity, the more popular the application gets, the more likely users will get contention failures.
It is important to design entity groups to avoid a high rate of contention failures even with a large number of users. It is often important to read and write data in the same transaction. For example, the application can start a transaction, read an entity, update a property value based on the last read value, save the entity, and then commit the transaction. In this case, the save action does not occur unless the entire transaction succeeds without conflict with another transaction. If there is a conflict and the app wants to try again, the app should retry the entire transaction: By including the read operation in the transaction, the datastore can assume that related writes and reads from multiple simultaneous requests do not interleave and produce inconsistent results.
With indexes and optimistic concurrency control, Cloud Datastore is designed for applications that need to read data quickly, ensure that the data it sees is in a consistent form, and scale the number of users and the size of the data automatically. While these goals are somewhat different from those of a relational database, they are especially well suited to web applications. Google Cloud Platform and App Engine include several other self-scaling services useful for web applications.
The memory cache or memcache service is a short-term key-value storage service. Its main advantage over the datastore is that it is fast, much faster than the datastore for simple storage and retrieval. The memcache stores values in memory instead of on disk for faster access. It is distributed like the datastore, so every request sees the same set of keys and values.
However, it is not persistent like the datastore: It also has a more limited sense of atomicity and transactionality than the datastore. As the name implies, the memcache service is best used as a cache for the results of frequently performed queries or calculations. Google Cloud Platform provides another storage service specifically for very large values, called Google Cloud Storage. Cloud Storage can also accept large files uploaded by users and offline processes.
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This service is distinct from Cloud Datastore to work around infrastructure limits on request and response sizes between users, application servers, and services. Application code can read values from Cloud Storage in chunks that fit within these limits. Code can also query for metadata about Cloud Storage values. Instances are large, and you only pay for the storage you use and the amount of time an instance is running.
You can even configure instances to turn themselves off when idle, and reactivate when a client attempts to connect. Cloud SQL can be the basis for an always-on web app, or a part of a larger data processing solution. Yet another storage service is dedicated to providing full-text search infrastructure, known simply as the Search service.
As Cloud Datastore stores entities with properties, the Search service stores documents with fields. Your app adds documents to indexes. Unlike the datastore, you can use the Search service to perform faceted text searches over the fields of the documents in an index, including partial string matches, range queries, and Boolean search expressions. The service also supports stemming and tokenization. The service makes HTTP requests to other servers on the Internet, such as to retrieve pages or interact with web services. App Engine applications can send email messages using the Mail service.
The app can send email on behalf of the application itself or on behalf of the user who made the request that is sending the email if the message is from the user. Many web applications use email to notify users, confirm user actions, and validate contact information. An application can also receive email messages. App Engine applications can send and receive instant messages to and from chat services that support the XMPP protocol.
You can accomplish real-time two-way communication directly with a web browser using the Channel service, a clever implementation of the Comet model of browser app communication. Channels allow browsers to keep a network connection open with a remote host to receive real-time messages long after a web page has finished loading. App Engine fits this into its request-based processing model by using a service: When an application decides to send a message to a client or set of clients during its normal processing, it calls the Channel service with the message.
Google Accounts is especially useful for developing applications for your company or organization using Google Apps for Work or Google Apps for Education. You can add your App Engine application to a subdomain of your Apps domain from your Google Apps dashboard, just like any other Google Apps feature.
Of course, there is no obligation to use Google Accounts. You can always build your own account system, or use an OpenID provider. This is useful when building applications for the Google Apps Marketplace, which uses OpenID to integrate with enterprise single sign-on services. App Engine includes built-in support for OAuth, a protocol that makes it possible for users to grant permission to third-party applications to access personal data in another service, without having to share her account credentials with the third party.
Sometimes there is more work to do than there is time to do it. But the user needs a guarantee that the work will get done. For this kind of work, an App Engine app uses task queues. Task queues let you describe work to be done at a later time, outside the scope of the web request. Queues ensure that every task gets done eventually.
If a task fails, the queue retries the task until it succeeds. There are two kinds of task queues: With push queues, each task record represents an HTTP request to a request handler. App Engine issues these requests itself as it processes a push queue. You can configure the rate at which push queues are processed to spread the workload throughout the day. With pull queues, you provide the mechanism, such as a custom computational engine, that takes task records off the queue and does the work. App Engine manages the queuing aspect of pull queues.
A push queue performs a task by calling a request handler. An especially powerful feature of task queues is the ability to enqueue a task within a Cloud Datastore transaction, when called via App Engine. This ensures that the task will be enqueued only if the rest of the datastore transaction succeeds. For example, when a user asks to delete a bunch of records, you can store a receipt of this request in the datastore and enqueue the corresponding task in a single transaction. If the transaction fails, you can report this to the user, and rest assured that neither the receipt nor the task are in the system.
App Engine has another service for executing tasks at specific times of the day, called the scheduled tasks service. The scheduled tasks service can invoke a request handler at a specified time of the day, week, or month, based on a schedule you provide when you upload your application.
Scheduled tasks are useful for doing regular maintenance or sending periodic notification messages. Cloud Datastore, Cloud Storage, memcache, Search, and task queues all store data for an app.
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For example, an app may be serving multiple companies, where each company is to see its own isolated instance of the application, and no company should see any data that belongs to any other company. You could implement this partitioning in the application code, using a company ID as the prefix to every key. But this is prone to error: To better serve this case, these storage services provide this partitioning feature at the infrastructure level. An app can declare it is acting in a namespace by calling an API. All subsequent uses of any of the data services will restrict itself to the namespace automatically.
The app does not need to keep track of which namespace it is in after the initial declaration. The default namespace has a name equal to the empty string. This namespace is distinct from other namespaces. In the services that support it, all data belongs to a namespace. Google provides a rich set of tools and libraries for developing for Cloud Platform and App Engine.
The main tool suite is the Cloud SDK, which, among other things, includes a package installer and updater for the other tools. All of the tools are scriptable, so you can integrate them into deployment scripts and other parts of your development environment. One of the most useful parts of the SDK is the development web server.
This tool runs your application on your local computer and simulates the runtime environment and services. The development server automatically detects changes in your source files and reloads them as needed, so you can keep the server running while you develop the application. You can turn this feature off for testing whether queries have appropriate indexes in the configuration. The development web server includes a built-in browser-based developer console for inspecting and prodding your app.
You use this console to inspect and modify the contents of the simulated data storage services, manage task queue interactions, and simulate non-web events such as incoming email messages. You also use the toolkit to interact with App Engine directly, especially to deploy your application and run it on Cloud Platform. It's basically a fully integrated application environment that's aimed at those that want to reach millions of users in the same way as Google products such as Google Apps and Google Reader have.
For now, account registrations are limited to the first 10, developers, and applications are restricted to the free account limits. On the positive side, it's totally free to get started like all good Google products and you'll receive MB of free space and enough processing power for 5 million monthly page views. However, you'll obviously need programming knowledge to get your app up and running and in addition, you'll have to be quick as testing is limited to the first 10, users. This is an exciting attempt by Google to take a more determined step towards Web 2. Google App Engine enables you to build web applications on the same scalable systems that power Google applications.
No assembly is required. Google App Engine provides a fully-integrated application environment. It's easy to scale and Google App Engine makes it easy to build scalable applications that grow from one user to millions of users without infrastructure headaches. What do you think about Google App Engine? Do you recommend it? Google App Engine allows you to build web View full description.
CONS Requires programming knowledge. Softonic review Is this the new Google App that will revolutionise the way programs are made and open programming up to a wider audience? Xenu's Link Sleuth Excellent, free broken link checker.