Making Queries

Users of versions < 0.4, please read this post before upgrading: Breaking Changes

Retrieving objects

Once you’ve populated Cassandra with data, you’ll probably want to retrieve some of it. This is accomplished with QuerySet objects. This section will describe how to use QuerySet objects to retrieve the data you’re looking for.

Retrieving all objects

The simplest query you can make is to return all objects from a table.

This is accomplished with the .all() method, which returns a QuerySet of all objects in a table

Using the Person example model, we would get all Person objects like this:

all_objects = Person.objects.all()

Retrieving objects with filters

Typically, you’ll want to query only a subset of the records in your database.

That can be accomplished with the QuerySet’s .filter(\*\*) method.

For example, given the model definition:

class Automobile(Model):
    manufacturer = columns.Text(primary_key=True)
    year = columns.Integer(primary_key=True)
    model = columns.Text()
    price = columns.Decimal()

...and assuming the Automobile table contains a record of every car model manufactured in the last 20 years or so, we can retrieve only the cars made by a single manufacturer like this:

q = Automobile.objects.filter(manufacturer='Tesla')

You can also use the more convenient syntax:

q = Automobile.objects(Automobile.manufacturer == 'Tesla')

We can then further filter our query with another call to .filter

q = q.filter(year=2012)

Note: all queries involving any filtering MUST define either an ‘=’ or an ‘in’ relation to either a primary key column, or an indexed column.

Accessing objects in a QuerySet

There are several methods for getting objects out of a queryset

  • iterating over the queryset
    for car in Automobile.objects.all(): something to the car instance
  • list index
    q = Automobile.objects.all()
    q[0] #returns the first result
    q[1] #returns the second result
  • list slicing
    q = Automobile.objects.all()
    q[1:] #returns all results except the first
    q[1:9] #returns a slice of the results

    Note: CQL does not support specifying a start position in it’s queries. Therefore, accessing elements using array indexing / slicing will load every result up to the index value requested

  • calling get() on the queryset
    q = Automobile.objects.filter(manufacturer='Tesla')
    q = q.filter(year=2012)
    car = q.get()

    this returns the object matching the queryset

  • calling first() on the queryset
    q = Automobile.objects.filter(manufacturer='Tesla')
    q = q.filter(year=2012)
    car = q.first()

    this returns the first value in the queryset

Filtering Operators

Equal To

The default filtering operator.

q = Automobile.objects.filter(manufacturer='Tesla')
q = q.filter(year=2012)  #year == 2012

In addition to simple equal to queries, cqlengine also supports querying with other operators by appending a __<op> to the field name on the filtering call

in (__in)

q = Automobile.objects.filter(manufacturer='Tesla')
q = q.filter(year__in=[2011, 2012])

> (__gt)

q = Automobile.objects.filter(manufacturer='Tesla')
q = q.filter(year__gt=2010)  # year > 2010

# or the nicer syntax

q.filter(Automobile.year > 2010)

>= (__gte)

q = Automobile.objects.filter(manufacturer='Tesla')
q = q.filter(year__gte=2010)  # year >= 2010

# or the nicer syntax

q.filter(Automobile.year >= 2010)

< (__lt)

q = Automobile.objects.filter(manufacturer='Tesla')
q = q.filter(year__lt=2012)  # year < 2012

# or...

q.filter(Automobile.year < 2012)

<= (__lte)

q = Automobile.objects.filter(manufacturer='Tesla')
q = q.filter(year__lte=2012)  # year <= 2012

q.filter(Automobile.year <= 2012)

TimeUUID Functions

In addition to querying using regular values, there are two functions you can pass in when querying TimeUUID columns to help make filtering by them easier. Note that these functions don’t actually return a value, but instruct the cql interpreter to use the functions in it’s query.

class cqlengine.query.MinTimeUUID(datetime)

returns the minimum time uuid value possible for the given datetime

class cqlengine.query.MaxTimeUUID(datetime)

returns the maximum time uuid value possible for the given datetime


class DataStream(Model):
    time    = cqlengine.TimeUUID(primary_key=True)
    data    = cqlengine.Bytes()

min_time = datetime(1982, 1, 1)
max_time = datetime(1982, 3, 9)

DataStream.filter(time__gt=cqlengine.MinTimeUUID(min_time), time__lt=cqlengine.MaxTimeUUID(max_time))

Token Function

Token functon may be used only on special, virtual column pk__token, representing token of partition key (it also works for composite partition keys). Cassandra orders returned items by value of partition key token, so using cqlengine.Token we can easy paginate through all table rows.



class Items(Model):
    id      = cqlengine.Text(primary_key=True)
    data    = cqlengine.Bytes()

query = Items.objects.all().limit(10)

first_page = list(query);
last = first_page[-1]
next_page = list(query.filter(pk__token__gt=cqlengine.Token(

QuerySets are immutable

When calling any method that changes a queryset, the method does not actually change the queryset object it’s called on, but returns a new queryset object with the attributes of the original queryset, plus the attributes added in the method call.


#this produces 3 different querysets
#q does not change after it's initial definition
q = Automobiles.objects.filter(year=2012)
tesla2012 = q.filter(manufacturer='Tesla')
honda2012 = q.filter(manufacturer='Honda')

Ordering QuerySets

Since Cassandra is essentially a distributed hash table on steroids, the order you get records back in will not be particularly predictable.

However, you can set a column to order on with the .order_by(column_name) method.


#sort ascending
q = Automobiles.objects.all().order_by('year')
#sort descending
q = Automobiles.objects.all().order_by('-year')

Note: Cassandra only supports ordering on a clustering key. In other words, to support ordering results, your model must have more than one primary key, and you must order on a primary key, excluding the first one.

For instance, given our Automobile model, year is the only column we can order on.

Values Lists

There is a special QuerySet’s method .values_list() - when called, QuerySet returns lists of values instead of model instances. It may significantly speedup things with lower memory footprint for large responses. Each tuple contains the value from the respective field passed into the values_list() call — so the first item is the first field, etc. For example:

items = list(range(20))
for i in items:
    TestModel.create(id=1, clustering_key=i)

values = list(TestModel.objects.values_list('clustering_key', flat=True))
# [19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 0L]

Batch Queries

cqlengine now supports batch queries using the BatchQuery class. Batch queries can be started and stopped manually, or within a context manager. To add queries to the batch object, you just need to precede the create/save/delete call with a call to batch, and pass in the batch object.

Batch Query General Use Pattern

You can only create, update, and delete rows with a batch query, attempting to read rows out of the database with a batch query will fail.

from cqlengine import BatchQuery

#using a context manager
with BatchQuery() as b:
    now =
    em1 = ExampleModel.batch(b).create(example_type=0, description="1", created_at=now)
    em2 = ExampleModel.batch(b).create(example_type=0, description="2", created_at=now)
    em3 = ExampleModel.batch(b).create(example_type=0, description="3", created_at=now)

# -- or --

b = BatchQuery()
now =
em1 = ExampleModel.batch(b).create(example_type=0, description="1", created_at=now)
em2 = ExampleModel.batch(b).create(example_type=0, description="2", created_at=now)
em3 = ExampleModel.batch(b).create(example_type=0, description="3", created_at=now)

# updating in a batch

b = BatchQuery()
em1.description = "new description"
em2.description = "another new description"

# deleting in a batch
b = BatchQuery()

Typically you will not want the block to execute if an exception occurs inside the with block. However, in the case that this is desirable, it’s achievable by using the following syntax:

with BatchQuery(execute_on_exception=True) as b:
    LogEntry.batch(b).create(k=1, v=1)
    mystery_function() # exception thrown in here
    LogEntry.batch(b).create(k=1, v=2) # this code is never reached due to the exception, but anything leading up to here will execute in the batch.

If an exception is thrown somewhere in the block, any statements that have been added to the batch will still be executed. This is useful for some logging situations.

Batch Query Execution Callbacks

In order to allow secondary tasks to be chained to the end of batch, BatchQuery instances allow callbacks to be registered with the batch, to be executed immediately after the batch executes.

Multiple callbacks can be attached to same BatchQuery instance, they are executed in the same order that they are added to the batch.

The callbacks attached to a given batch instance are executed only if the batch executes. If the batch is used as a context manager and an exception is raised, the queued up callbacks will not be run.

def my_callback(*args, **kwargs):

batch = BatchQuery()

batch.add_callback(my_callback, 'positional arg', named_arg='named arg value')

# if you need reference to the batch within the callback,
# just trap it in the arguments to be passed to the callback:
batch.add_callback(my_callback, cqlengine_batch=batch)

# once the batch executes...

# the effect of the above scheduled callbacks will be similar to
my_callback('positional arg', named_arg='named arg value')

Failure in any of the callbacks does not affect the batch’s execution, as the callbacks are started after the execution of the batch is complete.

Logged vs Unlogged Batches

By default, queries in cqlengine are LOGGED, which carries additional overhead from UNLOGGED. To explicitly state which batch type to use, simply:

from cqlengine.query import BatchType
with BatchQuery(batch_type=BatchType.Unlogged) as b:
    LogEntry.batch(b).create(k=1, v=1)
    LogEntry.batch(b).create(k=1, v=2)

QuerySet method reference

class cqlengine.query.QuerySet

Returns a queryset matching all rows

for user in User.objects().all():

Sets the batch object to run the query on. Note that running a select query with a batch object will raise an exception


Sets the consistency level for the operation. Options may be imported from the top level cqlengine package.

for user in User.objects(id=3).consistency(ONE):

Returns the number of matching rows in your QuerySet

Parameters:values – See Retrieving objects with filters

Returns a QuerySet filtered on the keyword arguments

Parameters:values – See Retrieving objects with filters

Returns a single object matching the QuerySet. If no objects are matched, a DoesNotExist exception is raised. If more than one object is found, a MultipleObjectsReturned exception is raised.

user = User.get(id=1)

Limits the number of results returned by Cassandra.

Note that CQL’s default limit is 10,000, so all queries without a limit set explicitly will have an implicit limit of 10,000

for user in User.objects().limit(100):
Parameters:field_name (string) – the name of the field to order on. Note: the field_name must be a clustering key

Sets the field to order on.

from uuid import uuid1,uuid4

class Comment(Model):
    photo_id = UUID(primary_key=True)
    comment_id = TimeUUID(primary_key=True, default=uuid1) # auto becomes clustering key
    comment = Text()


u = uuid4()
for x in range(5):
    Comment.create(photo_id=u, comment="test %d" % x)

for comment in Comment.objects(photo_id=u):
    print comment.comment_id

for comment in Comment.objects(photo_id=u).order_by("-comment_id"):
    print comment.comment_id

Enables the (usually) unwise practive of querying on a clustering key without also defining a partition key


Allows for custom timestamps to be saved with the record.

Parameters:ttl_in_seconds (int) – time in seconds in which the saved values should expire

Sets the ttl to run the query query with. Note that running a select query with a ttl value will raise an exception


Performs an update on the row selected by the queryset. Include values to update in the update like so:


Passing in updates for columns which are not part of the model will raise a ValidationError. Per column validation will be performed, but instance level validation will not (Model.validate is not called). This is sometimes referred to as a blind update.

For example:

class User(Model):
    id = Integer(primary_key=True)
    name = Text()

setup(["localhost"], "test")

u = User.create(id=1, name="jon")


# sets name to null

The queryset update method also supports blindly adding and removing elements from container columns, without loading a model instance from Cassandra.

Using the syntax .update(column_name={x, y, z}) will overwrite the contents of the container, like updating a non container column. However, adding __<operation> to the end of the keyword arg, makes the update call add or remove items from the collection, without overwriting then entire column.

Given the model below, here are the operations that can be performed on the different container columns:

class Row(Model):
    row_id      = columns.Integer(primary_key=True)
    set_column  = columns.Set(Integer)
    list_column = columns.Set(Integer)
    map_column  = columns.Set(Integer, Integer)


  • add: adds the elements of the given set to the column
  • remove: removes the elements of the given set to the column
# add elements to a set

# remove elements to a set


  • append: appends the elements of the given list to the end of the column
  • prepend: prepends the elements of the given list to the beginning of the column
# append items to a list
Row.objects(row_id=5).update(list_column__append=[6, 7])

# prepend items to a list
Row.objects(row_id=5).update(list_column__prepend=[1, 2])


  • update: adds the given keys/values to the columns, creating new entries if they didn’t exist, and overwriting old ones if they did
# add items to a map
Row.objects(row_id=5).update(map_column__update={1: 2, 3: 4})

Per Query Timeouts

By default all queries are executed with the timeout defined in ~cqlengine.connection.setup() The examples below show how to specify a per-query timeout. A timeout is specified in seconds and can be an int, float or None. None means no timeout.

class Row(Model):
    id = columns.Integer(primary_key=True)
    name = columns.Text()

Fetch all objects with a timeout of 5 seconds


Create a single row with a 50ms timeout

Row(id=1, name='Jon').timeout(0.05).create()

Delete a single row with no timeout


Update a single row with no timeout


Batch query timeouts

with BatchQuery(timeout=10) as b:
    Row(id=1, name='Jon').create()

NOTE: You cannot set both timeout and batch at the same time, batch will use the timeout defined in it’s constructor. Setting the timeout on the model is meaningless and will raise an AssertionError.

Named Tables

Named tables are a way of querying a table without creating an class. They’re useful for querying system tables or exploring an unfamiliar database.

from cqlengine.connection import setup
setup("", "cqlengine_test")

from cqlengine.named import NamedTable
user = NamedTable("cqlengine_test", "user")

# {u'pk': 1, u't': datetime.datetime(2014, 6, 26, 17, 10, 31, 774000)}