An application can use queries to search the Datastore for entities that match specific search criteria called filters.
An application can use queries to search the Datastore for entities that match specific search criteria called filters. For example, an application that keeps track of several guestbooks could use a query to retrieve messages from one guestbook, ordered by date:
from google.appengine.ext import ndb
class Greeting(ndb.Model): """Models an individual Guestbook entry with content and date.""" content = ndb.StringProperty() date = ndb.DateTimeProperty(auto_now_add=True) @classmethod def query_book(cls, ancestor_key): return cls.query(ancestor=ancestor_key).order(-cls.date)
class MainPage(webapp2.RequestHandler): GREETINGS_PER_PAGE = 20 def get(self): guestbook_name = self.request.get('guestbook_name') ancestor_key = ndb.Key('Book', guestbook_name or '*notitle*') greetings = Greeting.query_book(ancestor_key).fetch( self.GREETINGS_PER_PAGE) self.response.out.write('<html><body>') for greeting in greetings: self.response.out.write( '<blockquote>%s</blockquote>' % cgi.escape(greeting.content)) self.response.out.write('</body></html>')
Some queries are more complex than others; the datastore needs pre-built
indexes for these.
These pre-built indexes are specified in a configuration file,
On the development server, if you run a query that needs an index that
you haven't specified, the development server automatically adds it to
But in your web site, a query that needs a not-yet-specified index fails.
Thus, the typical development cycle is to try a new query on the development
server and then update the web site to use the automatically-changed
You can update
index.yaml separately from uploading
the application (
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.
The App Engine Datastore natively supports filters for
exact matches (the == operator) and
comparisons (the <, <=, > and >= operators).
It supports combining multiple filters using a Boolean
AND operation, with some limitations (see below).
In addition to the native operators,
the API supports the
combining groups of filters using the Boolean
which test for equality to one of a list of possible values
(like Python's 'in' operator).
These operations don't map 1:1 to the Datastore's
native operations; thus they are
a little quirky and slow, relatively.
They are implemented using in-memory merging of
result streams. Note that
p != v is
implemented as "p < v OR p > v".
(This matters for
Limitations: The Datastore enforces some restrictions on queries. Violating these will cause it to raise exceptions. For example, combining too many filters, using inequalities for multiple properties, or combining an inequality with a sort order on a different property are all currently disallowed. Also filters referencing multiple properties sometimes require secondary indexes to be configured.
Unsupported: The Datastore does not directly support substring matches, case-insensitive matches, or so-called full-text search. There are ways to implement case-insensitive matches and even full-text search using computed properties.
Filtering by Property Values
Recall the Account class from NDB Properties:
class Account(ndb.Model): username = ndb.StringProperty() userid = ndb.IntegerProperty() email = ndb.StringProperty()
Usually you don't want to retrieve all entities of a given kind; you want only those with a specific value or range of values for some property.
Property objects overload some operators to return filter expressions that can be used to control a query: for example, to find all Account entities whose userid property has the exact value 42, you can use the expression
query = Account.query(Account.userid == 42)
(If you are sure that there was just one
userid, you might prefer to use
as a key.
is faster than
NDB supports these operations:
property == value
property < value
property <= value
property > value
property >= value
property != value
To filter for an inequality, you can use syntax like the following:
query = Account.query(Account.userid >= 40)
This finds all Account entities whose
userid property is
greater than or equal to 40.
You can specify multiple filters:
query = Account.query(Account.userid >= 40, Account.userid < 50)
This combines the specified filter arguments, returning all Account entities whose userid value is greater than or equal to 40 and less than 50.
Note: As mentioned earlier, the Datastore rejects queries using inequality filtering on more than one property.
Instead of specifying an entire query filter in a single expression, you may find it more convenient to build it up in steps: for example:
query1 = Account.query() # Retrieve all Account entitites query2 = query1.filter(Account.userid >= 40) # Filter on userid >= 40 query3 = query2.filter(Account.userid < 50) # Filter on userid < 50 too
query3 is equivalent to the
query variable from the
previous example. Note that query objects are immutable, so the
query2 does not affect
and the construction of
query3 does not affect
The != and IN Operations
Recall the Article class from NDB Properties:
class Article(ndb.Model): title = ndb.StringProperty() stars = ndb.IntegerProperty() tags = ndb.StringProperty(repeated=True)
!= (not-equal) and
operations are implemented by combining other filters using the
OR operation. The first of these,
property != value
is implemented as
(property < value) OR (property > value)
query = Article.query(Article.tags != 'perl')
is equivalent to
query = Article.query(ndb.OR(Article.tags < 'perl', Article.tags > 'perl'))
Perhaps surprisingly, this query does not search for
Article entities that don't include 'perl' as a tag!
Rather, it finds all entities with at least one tag unequal to 'perl'.
For example, the following entity would be included in the results,
even though it has 'perl' as one of its tags:
Article(title='Perl + Python = Parrot', stars=5, tags=['python', 'perl'])
Article(title='Introduction to Perl', stars=3, tags=['perl'])
There is no way to query for entities that do not include a tag equal to 'perl'.
Similarly, the IN operation
property IN [value1, value2, ...]
which tests for membership in a list of possible values, is implemented as
(property == value1) OR (property == value2) OR ...
query = Article.query(Article.tags.IN(['python', 'ruby', 'php']))
is equivalent to
query = Article.query(ndb.OR(Article.tags == 'python', Article.tags == 'ruby', Article.tags == 'php'))
de-duplicate their results: the result stream doesn't
include entity more than once, even if an entity matches two or more subqueries.
Querying for Repeated Properties
Article class defined in the preceding section also
serves as an example of querying for repeated properties. Notably, a filter
Article.tags == 'python'
uses a single value, even though
Article.tags is a
repeated property. You cannot compare repeated properties to list
objects (the Datastore won't understand it), and a filter like
Article.tags.IN(['python', 'ruby', 'php'])
does something completely different from searching for
Article entities whose tags value is the list
['python', 'ruby', 'php']:
it searches for entities whose
tags value (regarded as a list)
contains at least one of those values.
Querying for a value of
None on a repeated property has
undefined behavior; don't do that.
Combining AND and OR Operations
You can nest
OR operations arbitrarily.
query = Article.query(ndb.AND(Article.tags == 'python', ndb.OR(Article.tags.IN(['ruby', 'jruby']), ndb.AND(Article.tags == 'php', Article.tags != 'perl'))))
However, due to
OR's implementation, a query of this form that is
too complex might fail with an exception. You are safer if you normalize these
filters so there is (at most) a single
OR operation at the top of
the expression tree, and a single level of
AND operations below that.
To perform this normalization, you need to remember both your rules of Boolean logic,
and how the
IN filters are actually implemented:
- Expand the
INoperators to their primitive form, where
!=becomes a check for the property being < or > than the value, and
INbecomes a check for the property being == to the first value or the second value or...all the way to the last value in the list.
ORinside it is equivalent to an
ANDs applied to the original
ANDoperands, with a single
ORoperand substituted for the original
OR. For example
AND(a, b, OR(c, d))is equivalent to
OR(AND(a, b, c), AND(a, b, d))
ANDthat has an operand that is itself an
ANDoperation can incorporate the operands of the nested
ANDinto the enclosing
AND. For example
AND(a, b, AND(c, d))is equivalent to
AND(a, b, c, d)
ORthat has an operand that is itself an
ORoperation can incorporate the operands of the nested
ORinto the enclosing
OR. For example
OR(a, b, OR(c, d))is equivalent to
OR(a, b, c, d)
If we apply these transformations in stages to the example filter, using a simpler notation than Python, you get:
- Using rule #1 on the
AND(tags == 'python', OR(tags == 'ruby', tags == 'jruby', AND(tags == 'php', OR(tags < 'perl', tags > 'perl'))))
- Using rule #2 on the innermost
ORnested within an
AND(tags == 'python', OR(tags == 'ruby', tags == 'jruby', OR(AND(tags == 'php', tags < 'perl'), AND(tags == 'php', tags > 'perl'))))
- Using rule #4 on the
ORnested within another
AND(tags == 'python', OR(tags == 'ruby', tags == 'jruby', AND(tags == 'php', tags < 'perl'), AND(tags == 'php', tags > 'perl')))
- Using rule #2 on the remaining
ORnested within an
OR(AND(tags == 'python', tags == 'ruby'), AND(tags == 'python', tags == 'jruby'), AND(tags == 'python', AND(tags == 'php', tags < 'perl')), AND(tags == 'python', AND(tags == 'php', tags > 'perl')))
- Using rule #3 to collapse the remaining nested
OR(AND(tags == 'python', tags == 'ruby'), AND(tags == 'python', tags == 'jruby'), AND(tags == 'python', tags == 'php', tags < 'perl'), AND(tags == 'python', tags == 'php', tags > 'perl'))
For some filters, this normalization can cause a combinatorial
explosion. Consider the
AND of 3
clauses with 2 basic clauses each.
When normalized, this becomes an
OR of 8
clauses with 3 basic clauses each: that is, 6 terms become 24.
Specifying Sort Orders
You can use the
order() method to specify the order in
which a query returns its results. This method takes a list of
arguments, each of which is either a property object (to be sorted in
ascending order) or its negation (denoting descending order). For example:
query = Greeting.query().order(Greeting.content, -Greeting.date)
This retrieves all
Greeting entities, sorted by
ascending value of their
Runs of consecutive entities with the same content property will be
sorted by descending value of their
You can use multiple
order() calls to the same effect:
query = Greeting.query().order(Greeting.content).order(-Greeting.date)
Note: When combining filters
order(), the Datastore rejects certain combinations.
In particular, when you use an inequality filter, the first sort order
(if any) must specify the same property as the filter.
Also, you sometimes need to configure a secondary index.
Ancestor queries allow you to make strongly consistent queries to the datastore, however entities with the same ancestor are limited to 1 write per second. Here's a simple comparison of the tradeoffs and structure between an ancestor and non-ancestor query using customers and their associated purchases in the datastore.
In the following non-ancestor example, there's one entity in the datastore for each
and one entity in the datastore for each
Purchase, with a
points to the customer.
class Customer(ndb.Model): name = ndb.StringProperty() class Purchase(ndb.Model): customer = ndb.KeyProperty(kind=Customer) price = ndb.IntegerProperty()
To find all the purchases that belong to the customer, you can use the following query:
purchases = Purchase.query( Purchase.customer == customer_entity.key).fetch()
In this case, the datastore offers high write throughput, but only eventual consistency. If a new purchase was added you may get stale data. You can eliminate this behavior using ancestor queries.
For customers and purchases with ancestor queries, you still have the same structure with
two separate entities. The customer part is the same. However, when you create purchases,
you no longer need to specify the
KeyProperty() for purchases anymore. This is
because when you use ancestor queries, you call the customer entity's key when
you create a purchase entity.
class Customer(ndb.Model): name = ndb.StringProperty() class Purchase(ndb.Model): price = ndb.IntegerProperty()
Each purchase has a key, and the customer has its own key as well. However, each purchase key will have the customer_entity's key embedded in it. Remember, this will be limited to one write per ancestor per second. The following creates an entity with an ancestor:
purchase = Purchase(parent=customer_entity.key)
To query for the purchases of a given customer, use the following query.
purchases = Purchase.query(ancestor=customer_entity.key).fetch()
Query objects have the following read-only data attributes:
|kind||Kind name (usually the class name)|
|ancestor||Ancestor specified to query|
Printing a query object (or calling
repr() on it)
produces a nicely-formatted string representation:
print(Employee.query()) # -> Query(kind='Employee') print(Employee.query(ancestor=ndb.Key(Manager, 1))) # -> Query(kind='Employee', ancestor=Key('Manager', 1))
Filtering for Structured Property Values
A query can filter directly for the field values of structured properties.
For example, a query for all contacts with an address whose city is
'Amsterdam' would look like
query = Contact.query(Contact.addresses.city == 'Amsterdam')
If you combine multiple such filters, the filters may match
Address sub-entities within
the same Contact entity.
query = Contact.query(Contact.addresses.city == 'Amsterdam', # Beware! Contact.addresses.street == 'Spear St')
may find contacts with an address whose city is
'Amsterdam' and another (different) address whose street is
'Spear St'. However, at least for equality filters, you can
create a query that returns only results with multiple values in a
query = Contact.query(Contact.addresses == Address(city='San Francisco', street='Spear St'))
If you use this technique, properties of the sub-entity
None are ignored in the query.
If a property has a default value, you have to explicitly set it to
None to ignore it in the query, otherwise the query includes
a filter requiring that property value to be equal to the default.
For example, if the
Address model had a property
default='us', the above
example would only return contacts with country equal to
'us'; to consider contacts with other country values,
you would need to filter for
Address(city='San Francisco', street='Spear St',
If a sub-entity has any property values equal to
they are ignored. Thus, it doesn't make sense to filter for
a sub-entity property value of
Using Properties Named by String
Sometimes you want to filter or order a query based on a property
whose name is
specified by string. For example, if you let the user enter search queries
tags:python, it would be convenient to somehow turn
that into a query like
Article.query(Article."tags" == "python") # does NOT work
If your model is an
Expando, then your
filter can use
Expando uses for dynamic properties:
property_to_query = 'location' query = FlexEmployee.query(ndb.GenericProperty(property_to_query) == 'SF')
GenericProperty also works if your model is not an
Expando, but if you want to ensure that you
are only using defined property names, you can also use the
_properties class attribute
query = Article.query(Article._properties[keyword] == value)
getattr() to get it from the class:
query = Article.query(getattr(Article, keyword) == value)
The difference is that
getattr() uses the "Python name"
of the property while
_properties is indexed by the
"datastore name" of the property. These only differ when the property
was declared with something like
class ArticleWithDifferentDatastoreName(ndb.Model): title = ndb.StringProperty('t')
Here the Python name is
title but the datastore name is
These approaches also work for ordering query results:
expando_query = FlexEmployee.query().order(ndb.GenericProperty('location')) property_query = Article.query().order(Article._properties[keyword])
While a query is in progress, its state is held in an
iterator object. (Most applications won't use them directly; it's
normally more straightforward to call
to manipulate the iterator object.)
There are two basic ways to get such an object:
- using Python's built-in
iter()function on a
- calling the
The first supports the use of a Python
(which implicitly calls the
to loop over a query.
for greeting in greetings: self.response.out.write( '<blockquote>%s</blockquote>' % cgi.escape(greeting.content))
The second way, using the
iter() method, allows you to pass options to the
iterator to affect its behavior. For example, to use a
keys-only query in a
for loop, you can write this:
for key in query.iter(keys_only=True): print(key)
Query iterators have other useful methods:
|Part of Python's iterator protocol.|
|Returns the next result
or raises the exception |
Blocks until the answer to this question is known and buffers the result (if any) until you retrieve it with
May return a false positive (
|Returns a query cursor representing a point just before the
last result returned.|
Raises an exception if no cursor is available (in particular, if the
|Returns a query cursor representing a point just after the last
Raises an exception if no cursor is available (in particular, if the
|Returns a list of indexes used by an executed query, including primary, composite, kind, and single-property indexes.|
A query cursor is a small opaque data structure
representing a resumption point in a query. This is useful
for showing a user a page of results at a time; it's also
useful for handling long jobs that might need to stop and resume.
A typical way to use them is with a query's
It works somewhat like
fetch(), but it returns a triple
(results, cursor, more).
more flag indicates that there are probably more
results; a UI can use this, for example, to suppress a
"Next Page" button or link.
To request subsequent pages, pass the cursor returned by one
fetch_page() call into the next.
Thus, to let the user view all entities matching a query, fetching them a page at a time, your code might look like:
from google.appengine.datastore.datastore_query import Cursor
class List(webapp2.RequestHandler): GREETINGS_PER_PAGE = 10 def get(self): """Handles requests like /list?cursor=1234567.""" cursor = Cursor(urlsafe=self.request.get('cursor')) greets, next_cursor, more = Greeting.query().fetch_page( self.GREETINGS_PER_PAGE, start_cursor=cursor) self.response.out.write('<html><body>') for greeting in greets: self.response.out.write( '<blockquote>%s</blockquote>' % cgi.escape(greeting.content)) if more and next_cursor: self.response.out.write('<a href="/list?cursor=%s">More...</a>' % next_cursor.urlsafe()) self.response.out.write('</body></html>')
Note the use of
Cursor(urlsafe=s) to serialize and
deserialize the cursor.
This allows you to pass a cursor to a client on the web in the
response to one request, and receive it back from the client in a later
fetch_page() method typically returns a cursor even
if there are no more results, but this is not guaranteed: the cursor
value returned may be
None. Note also that because the
more flag is implemented using the iterator's
probably_has_next() method, in rare circumstances it may
True even though the next page is empty.
Some NDB queries don't support query cursors, but you can fix them.
If a query uses
then the query results
won't work with cursors unless ordered by key.
If an application doesn't order the results by key and calls
fetch_page(), it gets a
gets an error, change it to
User.query(User.name.IN(['Joe', 'Jane'])).order(User.name, User.key).fetch_page(N)
Instead of "paging" through query results, you can use a query's
iter() method to get a cursor at a precise point.
To do this, pass
when the iterator is at the right place, call its
to get a cursor that's just after that. (Or, similarly, call
cursor_before() for a cursor just before.)
Note that calling
cursor_before() may make a blocking Datastore call, rerunning
part of the query in order to extract a cursor that points to the middle of
To use a cursor to page backwards through query results, create a reverse query:
# Set up. q = Bar.query() q_forward = q.order(Bar.key) q_reverse = q.order(-Bar.key) # Fetch a page going forward. bars, cursor, more = q_forward.fetch_page(10) # Fetch the same page going backward. r_bars, r_cursor, r_more = q_reverse.fetch_page(10, start_cursor=cursor)
Calling a Function for each Entity ("Mapping")
Suppose you need to get the
entities corresponding to the
entities returned by a query.
You could write something like this:
message_account_pairs =  for message in message_query: key = ndb.Key('Account', message.userid) account = key.get() message_account_pairs.append((message, account))
However, this is pretty inefficient: it waits to fetch a entity, then uses the entity; waits for the next entity, uses the entity. There is a lot of waiting time. Another way is to write a callback function that is mapped over the query results:
def callback(message): key = ndb.Key('Account', message.userid) account = key.get() message_account_pairs = message_query.map(callback) # Now message_account_pairs is a list of (message, account) tuples.
This version will run somewhat faster than the simple
loop above because some concurrency is possible.
However, because the
get() call in
callback() is still synchronous,
the gain is not tremendous.
This is a good place to use
GQL is a SQL-like language for retrieving entities or keys from the App Engine Datastore. While GQL's features are different from those of a query language for a traditional relational database, the GQL syntax is similar to that of SQL. The GQL syntax is described in the GQL Reference.
You can use GQL to construct queries. This is similar to creating a query
Model.query(), but uses GQL syntax to define
the query filter and order. To use it:
Queryobject (the same type as returned by
Model.query()). All the usual methods are available on such
Model.gql(querystring)is a shorthand for
ndb.gql("SELECT * FROM Model " + querystring). Typically, querystring is something like
"WHERE prop1 > 0 AND prop2 = TRUE".
- To query models containing structured properties, you can use
foo.barin your GQL syntax to reference subproperties.
- GQL supports SQL-like parameter bindings. An application can define
a query and then bind values into it:
query = ndb.gql("SELECT * FROM Article WHERE stars > :1") query2 = query.bind(3)
query = ndb.gql("SELECT * FROM Article WHERE stars > :1", 3)
Calling a query's
bind()function returns a new query; it does not change the original.
- If your model class overrides the
_get_kind()class method, your GQL query should use the kind returned by that function, not the class name.
- If a property in your model overrides its name (e.g.,
foo = StringProperty('bar')) your GQL query should use the overridden property name (in the example,
Always use the parameter-binding feature if some values in your query are user-supplied variables. This avoids attacks based on syntactic hacks.
It is an error to query for a model that hasn't been imported (or, more generally, defined).
It is an error to use a property name that is not defined by the model class unless that model is an Expando.
Specifying a limit or offset to the query's
the limit or offset set by GQL's
LIMIT clauses. Don't combine GQL's
If you are accustomed to SQL, beware of false assumptions when using GQL. GQL is translated to NDB's native query API. This is different from a typical Object-Relational mapper (like SQLAlchemy or Django's database support), where the API calls are translated into SQL before they are transmitted to the database server. GQL does not support Datastore modifications (inserts, deletes or updates); it only supports queries.