Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://cloud.google.com/appengine/docs/standard/python/ndb below:

The Python 2 App Engine NDB Client Library Overview | App Engine standard environment for Python 2

The Google Datastore NDB Client Library allows App Engine Python apps to connect to Datastore. The NDB client library builds on the older

adding the following data store features:

• The StructuredProperty class, which allows entities to have nested structure.
• Integrated automatic caching, which typically gives fast and inexpensive reads via an in-context cache and Memcache.
• Supports both asynchronous APIs for concurrent actions in addition to synchronous APIs.

This page provides an introduction and overview of the App Engine NDB client library. For information about how to migrate to Cloud NDB, which supports Python 3, please see Migrating to Cloud NDB.

Datastore stores data objects, called entities. An entity has one or more properties, named values of one of several supported data types. For example, a property can be a string, an integer, or a reference to another entity.

Each entity is identified by a key, an identifier unique within the application's datastore. The key can have a parent, another key. This parent can itself have a parent, and so on; at the top of this "chain" of parents is a key with no parent, called the root.

Entities whose keys have the same root form an entity group or group. If entities are in different groups, then changes to those entities might sometimes seem to occur "out of order". If the entities are unrelated in your application's semantics, that's fine. But if some entities' changes should be consistent, your application should make them part of the same group when creating them.

The following entity-relationship diagram and code sample show how a Guestbook can have multiple Greetings, which each have content and date properties.

This relationship is implemented in the code sample below.

A model is a class that describes a type of entity, including the types and configuration for its properties. It's roughly analogous to a table in SQL. An entity can be created by calling the model's class constructor and then stored by calling the put() method.

This sample code defines the model class Greeting. Each Greeting entity has two properties: the text content of the greeting and the date the greeting was created.

To create and store a new greeting, the application creates a new Greeting object and calls its put() method.

To make sure that greetings in a guestbook don't appear "out of order" the application sets a parent key when creating a new Greeting. Thus, the new greeting will be in the same entity group as other greetings in the same guestbook. The application uses this fact when querying: it uses an ancestor query.

An application can query to find entities that match some filters.

A typical NDB query filters entities by kind. In this example, query_book generates a query that returns Greeting entities. A query can also specify filters on entity property values and keys. As in this example, a query can specify an ancestor, finding only entities that "belong to" some ancestor. A query can specify sort order. If a given entity has at least one (possibly null) value for every property in the filters and sort orders and all the filter criteria are met by the property values, then that entity is returned as a result.

Every query uses an index, a table that contains the results for the query in the desired order. The underlying Datastore automatically maintains simple indexes (indexes that use only one property).

It defines its complex indexes in a configuration file, index.yaml. The development web server automatically adds suggestions to this file when it encounters queries that do not yet have indexes configured.

You can tune indexes manually by editing the file before uploading the application. You can update the indexes separately from uploading the application by running gcloud app deploy index.yaml. If your datastore has many entities, it takes a long time to create a new index for them; in this case, it's wise to update the index definitions before uploading code that uses the new index. You can use the Administration Console to find out when the indexes have finished building.

This index mechanism supports a wide range of queries and is suitable for most applications. However, it does not support some kinds of queries common in other database technologies. In particular, joins aren't supported.

NDB writes data in steps:

• In the Commit phase, the underlying Datastore service records the changes.
• NDB invalidates its caches of the affected entity/entities. Thus, future reads will read from (and cache) the underlying Datastore instead of reading stale values from the cache.
• Finally, perhaps seconds later, the underlying Datastore applies the change. It makes the change visible to global queries and eventually- consistent reads.

The NDB function that writes the data (for example, put()) returns after the cache invalidation; the Apply phase happens asynchronously.

If there is a failure during the Commit phase, there are automatic retries, but if failures continue, your application receives an exception. If the Commit phase succeeds but the Apply fails, the Apply is rolled forward to completion when one of the following occurs:

• Periodic Datastore "sweeps" check for uncompleted Commit jobs and apply them.
• The next write, transaction, or strongly-consistent read in the impacted entity group causes the not-yet-applied changes to be applied before the read, write, or transaction.

This behavior affects how and when data is visible to your application. The change might not be completely applied to the underlying Datastore a few hundred milliseconds or so after the NDB function returns. A non-ancestor query performed while a change is being applied might see an inconsistent state, that is, part but not all of the change.

The NDB Client Library can group multiple operations in a single transaction. The transaction cannot succeed unless every operation in the transaction succeeds; if any of the operations fail, the transaction is automatically rolled back. This is especially useful for distributed web applications, where multiple users might be accessing or manipulating the same data at the same time.

NDB uses Memcache as a cache service for "hot spots" in the data. If the application reads some entities often, NDB can read them quickly from cache.

To use NDB with the Django web framework, add google.appengine.ext.ndb.django_middleware.NdbDjangoMiddleware to the MIDDLEWARE_CLASSES entry in your Django settings.py file. It's best to insert it in front of any other middleware classes, because some other middleware might make datastore calls and those won't be handled properly if that middleware is invoked before this middleware. You can learn more about Django middleware.

Learn more about:

• Creating entities in NDB.
• How transactions are processed in Datastore.
• How to create and format a query with the NDB Client Library.
• Caching data using NDB and the underlying Memcache infrastructure.
• Administrating and managing stored data in Datastore.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4