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# NDB Properties

#### Experimental!

NDB is an experimental, innovative, and rapidly changing new feature for App Engine. Unfortunately, being on the bleeding edge means that we may make backwards-incompatible changes to NDB. We will inform the community when this feature is no longer experimental.

An NDB entity model can define properties. Entity properties are a little like data members of Python classes, a structured way to hold data; they are a little like fields in a database schema.

- Introduction
- Types
- Property Options
- Repeated Properties
- Date and Time Properties
- Structured Properties
- Computed Properties

## Introduction

A typical application defines a data model by defining a class that inherits from `Model` with some property class attributes. For example,

    from google.appengine.ext import ndb

    class Account(ndb.Model):
      username = ndb.StringProperty()
      userid = ndb.IntegerProperty()
      email = ndb.StringProperty()

Here, `username`, `userid`, and `email` are properties of `Account`.

There are several other property types. Some are handy for representing dates and times and have convenient auto-update features.

An application can adjust a property's behavior by specifying options on the property; these can ease validation, set defaults, or change query indexing.

A model can have more complex properties. Repeated properties are list-like. Structured properties are object-like. Read-only computed properties are defined via functions; this makes it easy to define a property in terms of one or more other properties. Expando models can define properties dynamically.

## Types

NDB supports the following property types:

Property type | Description
--- | ---
`IntegerProperty` | 64-bit signed integer
`FloatProperty` | Double-precision floating-point number
`BooleanProperty` | Boolean
`StringProperty` | Unicode string; up to 500 characters, indexed
`TextProperty` | Unicode string; unlimited length, not indexed
`BlobProperty` | Uninterpreted byte string: if you set `indexed=True`, up to 500 characters, indexed; if `indexed` is `False` (the default), unlimited length, not indexed. Optional keyword argument: `compressed`.
`DateTimeProperty` | Date and time (see Date and Time Properties)
`DateProperty` | Date (see Date and Time Properties)
`TimeProperty` | Time (see Date and Time Properties)
`GeoPtProperty` | Geographical location. This is a `ndb.GeoPt` object. The object has attributes `lat` and `lon`, both floats. You can construct one with two floats like `ndb.GeoPt(52.37, 4.88)` or with a string `ndb.GeoPt("52.37, 4.88")`. (This is actually the same class as `db.GeoPt`)
`KeyProperty` | Datastore key Optional keyword argument: kind=kind, to require that keys assigned to this property always have the indicated kind. May be a string or a Model subclass.
`BlobKeyProperty` | Blobstore key Corresponds to `BlobReferenceProperty` in the old db API, but the property value is a `BlobKey` instead of a `BlobInfo`; you can construct a `BlobInfo` from it using `BlobInfo(blobkey)`
`UserProperty` | User object.
`StructuredProperty` | Includes one kind of model inside another, by value (see Structured Properties)
`LocalStructuredProperty` | Like `StructuredProperty`, but on-disk representation is an opaque blob and is not indexed (see Structured Properties). Optional keyword argument: `compressed`.
`JsonProperty` | Value is a Python object (such as a list or a dict or a string) that is serializable using Python's `json` module; the Datastore stores the JSON serialization as a blob. Unindexed by default. Optional keyword argument: `compressed`.
`PickleProperty` | Value is a Python object (such as a list or a dict or a string) that is serializable using Python's pickle protocol; the Datastore stores the pickle serialization as a blob. Unindexed by default. Optional keyword argument: `compressed`.
`GenericProperty` | Generic value Used mostly by the `Expando` class, but also usable explicitly. Its type may be any of `int`, `long`, `float`, `bool`, `str`, `unicode`, `datetime`, `Key`, `BlobKey`, `GeoPt`, `User`, `None`.
`ComputedProperty` | Value computed from other properties by a user-defined function. (See Computed Properties.)

Some of these properties have an optional keyword argument, `compressed`. If the property has `compressed=True`, then its data is compressed with gzip on disk. It takes up less space but needs CPU to encode/decode on write and read operations.

## Property Options

Most property types support some standard arguments. The first is an optional positional argument specifying the property's Datastore name. You can use this to give the property a different name in the Datastore than from the application's viewpoint. A common use for this is to reduce space in the Datastore, letting the Datastore use abbreviated property names while your code uses longer, more meaningful ones. For example,

    class Employee(ndb.Model):
      full_name = ndb.StringProperty('n')
      retirement_age = ndb.IntegerProperty('r')

This is particularly useful for repeated properties for which you expect many values per entity.

In addition, most property types support the following keyword arguments:

Argument | Type | Default | Description
--- | --- | --- | ---
`indexed` | `bool` | Usually `True` | Include property in Datastore's indexes; if `False`, values cannot be queried but writes are faster. Not all property types support indexing; setting `indexed` to `True` fails for these.
`repeated` | `bool` | `False` | Property value is a Python list containing values of the underlying type (see Repeated Properties).
`required` | `bool` | `False` | Property must have a value specified. Cannot be combined with `repeated=True`.
`default` | Property's underlying type | None | Default value of property if none explicitly specified. Cannot be combined with `repeated=True` or `required=True`.
`choices` | List of values of underlying type | `None` | Optional list of allowable values.
`validator` | Function | `None` | Optional function to validate and possibly coerce the value. Will be called with arguments (prop, value) and should either return the (possibly coerced) value or raise an exception. Calling the function again on a coerced value should not modify the value further. (For example, returning `value.strip()` or `value.lower()` is fine, but not `value + '$'`.) May also return `None`, which means "no change". See also Writing Property Subclasses

## Repeated Properties

Any property with `repeated=True` becomes a repeated property. The property takes a list of values of the underlying type, rather than a single value. For example, the value of a property defined with `IntegerProperty(repeated=True)` is a list of integers.

The Datastore may see multiple values for such a property. A separate index record is created for each value. This affects query semantics; see Querying for Repeated Properties for an example.

This example uses a repeated property:

    class Article(ndb.Model):
      title = ndb.StringProperty()
      stars = ndb.IntegerProperty()
      tags = ndb.StringProperty(repeated=True)

    art = Article(title='Python versus Ruby',
                  stars=3,
                  tags=['python', 'ruby'])
    art.put()

This creates a Datastore entity with the following contents:

    name = 'Python versus Ruby'
    stars = 3
    tags = 'python' # multiple = True
    tags = 'ruby' # multiple = True

When querying for the `tags` property, this entity will satisfy a query for either `'python'` or `'ruby'`.

When updating a repeated property, you can either assign it a new list or mutate the existing list in place. When you assign a new list, the types of the list items are validated immediately. Invalid item types (for example, assigning `[1, 2]` to `art.tags` above) raise an exception. When you mutate the list, the change is not validated immediately. Instead, the value will be validated when you write the entity to the Datastore.

## Date and Time Properties

Three property types are available for storing date- and time-related values:

- `DateProperty`
- `TimeProperty`
- `DateTimeProperty`

These take values belonging to the corresponding classes (`date`, `time`, `datetime`) of the standard Python `datetime` module. The most general of the three is `DateTimeProperty`, which denotes both a calendar date and a time of day; the others are occasionally useful for special purposes requiring just a date (such as a date of birth) or just a time (such as a meeting time). For technical reasons, `DateProperty` and `TimeProperty` are subclasses of `DateTimeProperty`, but you shouldn't depend on this inheritance relationship (and note that it differs from the inheritance relationships between the underlying classes defined by the `datetime` module itself).

Note: App Engine clock times are always expressed in coordinated universal time (UTC). This becomes relevant if you use the current date or time (`datetime.datetime.now()`) as a value or convert between datetime objects and POSIX timestamps or time tuples. However, no explicit time zone information is stored in the Datastore, so if you are careful you can use these to represent local times in any timezone—if you use the current time or the conversions.

Each of these properties has two extra Boolean keyword options:

Option | Description
--- | ---
`auto_now_add` | Set property to current date/time when entity is created.
`auto_now` | Set property to current date/time when entity is created and whenever it is updated.

These options cannot be combined with `repeated=True`. Both of them default to `False`; if both are set to `True`, `auto_now` takes precedence. It is possible to override the value for a property with `auto_now_add=True`, but not for one with `auto_now=True`. The automatic value is not generated until the entity is written; that is, these options don't provide dynamic defaults. (These details differ from the old db API.)

Note: When a transaction writing a property with `auto_now_add=True` fails and is later retried, it will reuse the same time value as on the original try rather than update it to the time of the retry. If the transaction fails permanently, the property's value will still be set in the in-memory copy of the entity.

## Structured Properties

You can structure a model's properties. For example, you can define a model class Contact containing a list of addresses, each with internal structure. Structured properties (type `StructuredProperty`) let you do this; for example:

    class Address(ndb.Model):
      type = ndb.StringProperty() # E.g., 'home', 'work'
      street = ndb.StringProperty()
      city = ndb.StringProperty()

    class Contact(ndb.Model):
      name = ndb.StringProperty()
      addresses = ndb.StructuredProperty(Address, repeated=True)

    guido = Contact(name='Guido',
                    addresses=[Address(type='home',
                                       city='Amsterdam'),
                               Address(type='work',
                                       street='Spear St',
                                       city='SF')])

    guido.put()

This creates a single Datastore entity with the following properties:

    name = 'Guido'
    addresses.type = 'home'
    addresses.type = 'work'
    addresses.street = None
    addresses.street = 'Spear St'
    addresses.city = 'Amsterdam'
    addresses.city = 'SF'

Reading back such an entity reconstructs the original `Contact` entity exactly. Although the `Address` instances are defined using the same syntax as for model classes, they are not full-fledged entities. They don't have their own keys in the Datastore. They cannot be retrieved independently of the `Contact` entity to which they belong. An application can, however, query for the values of their individual fields; see Filtering for Structured Property Values. Note that `address.type`, `address.street`, and `address.city` are viewed as parallel arrays from the Datastore's viewpoint, but the NDB library hides this aspect and constructs the corresponding list of `Address` instances.

You can specify the usual property options for structured properties (except `indexed`). The Datastore name is the second positional argument in this case (the first being the model class used to define the substructure).

When you don't need to query for a substructure's internal properties, you can use a local structured property (`LocalStructuredProperty`) instead. If you replace `StructuredProperty` with `LocalStructuredProperty` in the example above, the behavior of the Python code is the same, but the Datastore sees just an opaque blob for each address. The `guido` entity created in the example would be stored as follows:

    name = 'Guido'
    address = <opaque blob for {'type': 'home', 'city': 'Amsterdam'}>
    address = <opaque blob for {'type': 'work', 'city': 'SF',
                                'street': 'Spear St'}>

The entity will be read back correctly. Since properties of this type are always unindexed, you cannot query for address values.

## Computed Properties

Computed properties (`ComputedProperty`) are read-only properties whose value is computed from other property values by an application-supplied function. The computed value is written to the Datastore so that it can be queried and displayed in the Datastore viewer, but the stored value is ignored when the entity is read back from the Datastore; rather, the value is recomputed by calling the function whenever the value is requested. For example:

    class SomeEntity(ndb.Model):
      name = ndb.StringProperty()
      name_lower = ndb.ComputedProperty(lambda self: self.name.lower())

    x = SomeEntity(name='Nick')

This stores an entity with the following property values:

    name = 'Nick'
    name_lower = 'nick'

If we change name to 'Nickie' and ask for the value of `name_lower`, it returns 'nickie':

    x.name = 'Nick'
    assert x.name_lower == 'nick'
    x.name = 'Nickie'
    assert x.name_lower == 'nickie'

Note: Use `ComputedProperty` if the application queries for the computed value. If you just want to use the derived version in Python code, define a regular method or use Python's `@property` built-in.