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Making Queries

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()
    options = columns.Set(columns.Text)

…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():
        #...do something to the car instance
        pass
    
  • list index
    q = Automobile.objects.all()
    q[0] #returns the first result
    q[1] #returns the second result
    

    Note

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

    • Using negative indices requires a “SELECT COUNT()” to be executed. This has a performance cost on large datasets.

  • 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 slicing will load every result up to the index value requested

    • Using negative indices requires a “SELECT COUNT()” to be executed. This has a performance cost on large datasets.

  • 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)

CONTAINS (__contains)

The CONTAINS operator is available for all collection types (List, Set, Map).

q = Automobile.objects.filter(manufacturer='Tesla')
q.filter(options__contains='backup camera').allow_filtering()

Note that we need to use allow_filtering() since the options column has no secondary index.

LIKE (__like)

The LIKE operator is available for text columns that have a SASI secondary index.

q = Automobile.objects.filter(model__like='%Civic%').allow_filtering()

IS NOT NULL (IsNotNull(column_name))

The IS NOT NULL operator is not yet supported for C*.

q = Automobile.objects.filter(IsNotNull('model'))

Limitations:

  • Currently, cqlengine does not support SASI index creation. To use this feature, you need to create the SASI index using the core driver.

  • Queries using LIKE must use allow_filtering() since the model column has no standard secondary index. Note that the server will use the SASI index properly when executing the query.

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.queryset.MinTimeUUID(datetime)

returns the minimum time uuid value possible for the given datetime

class cqlengine.queryset.MaxTimeUUID(datetime)

returns the maximum time uuid value possible for the given datetime

Example

class DataStream(Model):
    id      = columns.UUID(partition_key=True)
    time    = columns.TimeUUID(primary_key=True)
    data    = columns.Bytes()

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

DataStream.filter(time__gt=functions.MinTimeUUID(min_time), time__lt=functions.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.

See http://cassandra.apache.org/doc/cql3/CQL-3.0.html#tokenFun

Example

class Items(Model):
    id      = columns.Text(primary_key=True)
    data    = columns.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(last.pk)))

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.

Example

#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.

Example

#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))
random.shuffle(items)
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]

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

Row.objects().timeout(5).all()

Create a single row with a 50ms timeout

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

Delete a single row with no timeout

Row(id=1).timeout(None).delete()

Update a single row with no timeout

Row(id=1).timeout(None).update(name='Blake')

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.

Default TTL and Per Query TTL

Model default TTL now relies on the default_time_to_live feature, introduced in Cassandra 2.0. It is not handled anymore in the CQLEngine Model (scylla-driver >=3.6). You can set the default TTL of a table like this:

Example:

class User(Model):
    __options__ = {'default_time_to_live': 20}

    user_id = columns.UUID(primary_key=True)
    ...

You can set TTL per-query if needed. Here are a some examples:

Example:

class User(Model):
    __options__ = {'default_time_to_live': 20}

    user_id = columns.UUID(primary_key=True)
    ...

user = User.objects.create(user_id=1)  # Default TTL 20 will be set automatically on the server

user.ttl(30).update(age=21)            # Update the TTL to 30
User.objects.ttl(10).create(user_id=1)  # TTL 10
User(user_id=1, age=21).ttl(10).save()  # TTL 10

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 cassandra.cqlengine.connection import setup
setup("127.0.0.1", "cqlengine_test")

from cassandra.cqlengine.named import NamedTable
user = NamedTable("cqlengine_test", "user")
user.objects()
user.objects()[0]

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