Python Driver 3.7.0 Released

By Alan Boudreault -  September 13, 2016 | 0 Comments

The DataStax Python Driver 3.7.0 for Apache Cassandra has been released. This release had no specific area of focus, but brings a number of new features and improvements. A complete list of issues is available in the CHANGELOG. Here I will mention some of the new features.

Session request listener and query request size information

In addition to cluster metrics, you can now register a session request listener and use it to track alternative metrics about requests (ie. the request size). See this request analyzer as an example.

Speculative query retries

The driver now implements speculative query retries in order to offer smoother latencies even while experiencing some node hiccups. Idempotent statements can benefit from this mechanism. This is a generally extensible interface, but we have also added a ConstantSpeculativeExecutionPolicy implementation. To enable this feature, you need to set a speculative_execution_policy and mark your statement as idempotent.

from cassandra.cluster import Cluster, ExecutionProfile
from cassandra.policies import ConstantSpeculativeExecutionPolicy
from cassandra.query import SimpleStatement

cluster = Cluster()

# send a new request every 100ms for a maximum of 10 attempts
ep = ExecutionProfile(speculative_execution_policy=ConstantSpeculativeExecutionPolicy(.1, 10))
cluster.add_execution_profile('my_app_ep', ep)
session = cluster.connect('test')

statement = SimpleStatement("SELECT i FROM d WHERE k = 0", is_idempotent=True)
result = session.execute(statement, execution_profile='my_app_ep')

Expose paging state

The ResultSet class exposes a new attribute: the paging_state. It can be useful if you have to resume pagination through stateless requests from your application. To use it, you just need to send the paging_state parameter when executing a new query (session.execute).

query = "SELECT * FROM users"
statement = SimpleStatement(query, fetch_size=10)
results = session.execute(statement)

# save the paging_state somewhere...
session['paging_state'] = results.paging_state

# and use it later to resume the pagination
query = "SELECT * FROM users"
statement = SimpleStatement(query, fetch_size=10)
paging_state = session['paging_state']
results = session.execute(statement, paging_state=paging_state)

EC2 address resolver

In the 3.3.0 release, we introduced a new AddressTranslator interface that allows you to implement your ip addresses translation depending on your environment (ie. public ips versus private ips). We now add an official translator for Amazon EC2 since it is heavily used: the EC2MultiRegionTranslator.

from cassandra.cluster import Cluster
from cassandra.policies import EC2MultiRegionTranslator

cluster = Cluster(['127.0.0.1'], address_translator=EC2MultiRegionTranslator())
session = cluster.connect()

# do stuff...

CQLEngine: support of multiple keyspaces and sessions

Prior to this release, using multiple keyspaces and sessions was a common problematic. We now introduce a new experimental feature to accommodate this use case: the Connections. You can now register multiple connections and switch the context on the fly in your application. Here is an example of the cqlengine connection capabilities:

from cassandra.cqlengine import connection
# ...

CONNS = ['cluster1', 'cluster2']
KEYSPACES = ('client1', 'client2', 'client3', 'client4')

connection.register_connection('cluster1', ['127.0.0.1'], default=True)
connection.register_connection('cluster2', ['127.0.0.50'], lazy_connect=True)

for keyspace in KEYSPACES:
    keyspace_simple(keyspace, 3, connections=CONNS)

class Automobile(Model):
    __connection__ = 'cluster2'  # default connection per model
    manufacturer = columns.Text(primary_key=True)
    year = columns.Integer(primary_key=True)
    model = columns.Text()

# sync the table for all connections and keyspaces
sync_table(Automobile, KEYSPACES, CONNS)

# Select the connection and keyspace via the ContextQuery
with ContextQuery(Automobile, connection='cluster1' keyspace='client2') as A:
    A.objects.create(manufacturer='honda', year=2004, model='civic')

# Read from the default model connection 'cluster2'
print len(Automobile.objects.using(keyspace='client2').get(manufacturer='honda', year=2004))  # 0

# Select the connection and keyspace on the fly
print len(Automobile.objects.using(connection='cluster1', keyspace='client2').all())  # 1

# Select on the model instance
a = Automobile.objects.using(connection='cluster1',keyspace='client2').get(manufacturer='honda', year=2004)
a.using('cluster2').save()  # save on cluster2 rather than cluster1

# Connection select with a BatchQuery
with BatchQuery(connection='cluster1' keyspace='client4') as b:
    A.objects.batch(b).create(manufacturer='honda', year=2004, model='civic')
    A.objects.batch(b).create(manufacturer='honda', year=2005, model='civic')
    A.objects.batch(b).create(manufacturer='honda', year=2006, model='civic')

See the documentation here for more details.

Wrap

As always, thanks to all who provided contributions and bug reports. The continued involvement of the community is appreciated:









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