cassandra.query
- Prepared Statements, Batch Statements, Tracing, and Row Factories¶
-
cassandra.query.
tuple_factory
(colnames, rows)[source]¶ Returns each row as a tuple
Example:
>>> from cassandra.query import tuple_factory >>> session = cluster.connect('mykeyspace') >>> session.row_factory = tuple_factory >>> rows = session.execute("SELECT name, age FROM users LIMIT 1") >>> print rows[0] ('Bob', 42)
Changed in version 2.0.0: moved from
cassandra.decoder
tocassandra.query
-
cassandra.query.
named_tuple_factory
(colnames, rows)[source]¶ Returns each row as a namedtuple. This is the default row factory.
Example:
>>> from cassandra.query import named_tuple_factory >>> session = cluster.connect('mykeyspace') >>> session.row_factory = named_tuple_factory >>> rows = session.execute("SELECT name, age FROM users LIMIT 1") >>> user = rows[0] >>> # you can access field by their name: >>> print "name: %s, age: %d" % (user.name, user.age) name: Bob, age: 42 >>> # or you can access fields by their position (like a tuple) >>> name, age = user >>> print "name: %s, age: %d" % (name, age) name: Bob, age: 42 >>> name = user[0] >>> age = user[1] >>> print "name: %s, age: %d" % (name, age) name: Bob, age: 42
Changed in version 2.0.0: moved from
cassandra.decoder
tocassandra.query
-
cassandra.query.
dict_factory
(colnames, rows)[source]¶ Returns each row as a dict.
Example:
>>> from cassandra.query import dict_factory >>> session = cluster.connect('mykeyspace') >>> session.row_factory = dict_factory >>> rows = session.execute("SELECT name, age FROM users LIMIT 1") >>> print rows[0] {u'age': 42, u'name': u'Bob'}
Changed in version 2.0.0: moved from
cassandra.decoder
tocassandra.query
-
cassandra.query.
ordered_dict_factory
(colnames, rows)[source]¶ Like
dict_factory()
, but returns each row as an OrderedDict, so the order of the columns is preserved.Changed in version 2.0.0: moved from
cassandra.decoder
tocassandra.query
-
class
cassandra.query.
Statement
(retry_policy=None, consistency_level=None, routing_key=None, serial_consistency_level=None, fetch_size=<object object>, keyspace=None, custom_payload=None)[source]¶ An abstract class representing a single query. There are three subclasses:
SimpleStatement
,BoundStatement
, andBatchStatement
. These can be passed toSession.execute()
.-
retry_policy
= None¶ An instance of a
cassandra.policies.RetryPolicy
or one of its subclasses. This controls when a query will be retried and how it will be retried.
-
consistency_level
= None¶ The
ConsistencyLevel
to be used for this operation. Defaults toNone
, which means that the default consistency level for the Session this is executed in will be used.
-
fetch_size
= <object object>¶ How many rows will be fetched at a time. This overrides the default of
Session.default_fetch_size
This only takes effect when protocol version 2 or higher is used. See
Cluster.protocol_version
for details.New in version 2.0.0.
-
keyspace
= None¶ The string name of the keyspace this query acts on. This is used when
TokenAwarePolicy
is configured forCluster.load_balancing_policy
It is set implicitly on
BoundStatement
, andBatchStatement
, but must be set explicitly onSimpleStatement
.New in version 2.1.3.
-
custom_payload
= None¶ Custom Payloads to be passed to the server.
These are only allowed when using protocol version 4 or higher.
New in version 2.6.0.
-
routing_key
¶ The
partition_key
portion of the primary key, which can be used to determine which nodes are replicas for the query.If the partition key is a composite, a list or tuple must be passed in. Each key component should be in its packed (binary) format, so all components should be strings.
-
serial_consistency_level
¶ The serial consistency level is only used by conditional updates (
INSERT
,UPDATE
andDELETE
with anIF
condition). For those, theserial_consistency_level
defines the consistency level of the serial phase (or “paxos” phase) while the normalconsistency_level
defines the consistency for the “learn” phase, i.e. what type of reads will be guaranteed to see the update right away. For example, if a conditional write has aconsistency_level
ofQUORUM
(and is successful), then aQUORUM
read is guaranteed to see that write. But if the regularconsistency_level
of that write isANY
, then only a read with aconsistency_level
ofSERIAL
is guaranteed to see it (even a read with consistencyALL
is not guaranteed to be enough).The serial consistency can only be one of
SERIAL
orLOCAL_SERIAL
. WhileSERIAL
guarantees full linearizability (with otherSERIAL
updates),LOCAL_SERIAL
only guarantees it in the local data center.The serial consistency level is ignored for any query that is not a conditional update. Serial reads should use the regular
consistency_level
.Serial consistency levels may only be used against Cassandra 2.0+ and the
protocol_version
must be set to 2 or higher.See Lightweight Transactions (Compare-and-set) for a discussion on how to work with results returned from conditional statements.
New in version 2.0.0.
-
-
class
cassandra.query.
SimpleStatement
(query_string, *args, **kwargs)[source]¶ A simple, un-prepared query.
query_string should be a literal CQL statement with the exception of parameter placeholders that will be filled through the parameters argument of
Session.execute()
.All arguments to
Statement
apply to this class as well
-
class
cassandra.query.
PreparedStatement
[source]¶ A statement that has been prepared against at least one Cassandra node. Instances of this class should not be created directly, but through
Session.prepare()
.A
PreparedStatement
should be prepared only once. Re-preparing a statement may affect performance (as the operation requires a network roundtrip).-
bind
(values)[source]¶ Creates and returns a
BoundStatement
instance using values.See
BoundStatement.bind()
for rules on inputvalues
.
-
-
class
cassandra.query.
BoundStatement
(prepared_statement, *args, **kwargs)[source]¶ A prepared statement that has been bound to a particular set of values. These may be created directly or through
PreparedStatement.bind()
.prepared_statement should be an instance of
PreparedStatement
.All arguments to
Statement
apply to this class as well-
prepared_statement
= None¶ The
PreparedStatement
instance that this was created from.
-
values
= None¶ The sequence of values that were bound to the prepared statement.
-
bind
(values)[source]¶ Binds a sequence of values for the prepared statement parameters and returns this instance. Note that values must be:
- a sequence, even if you are only binding one value, or
- a dict that relates 1-to-1 between dict keys and columns
Changed in version 2.6.0:
UNSET_VALUE
was introduced. These can be bound as positional parameters in a sequence, or by name in a dict. Additionally, when using protocol v4+:- short sequences will be extended to match bind parameters with UNSET_VALUE
- names may be omitted from a dict with UNSET_VALUE implied.
Changed in version 3.0.0: method will not throw if extra keys are present in bound dict (PYTHON-178)
-
-
cassandra.query.
UNSET_VALUE
¶ Specifies an unset value when binding a prepared statement.
Unset values are ignored, allowing prepared statements to be used without specify
See https://issues.apache.org/jira/browse/CASSANDRA-7304 for further details on semantics.
New in version 2.6.0.
Only valid when using native protocol v4+
-
class
cassandra.query.
BatchStatement
(batch_type=BatchType.LOGGED, retry_policy=None, consistency_level=None)[source]¶ A protocol-level batch of operations which are applied atomically by default.
New in version 2.0.0.
batch_type specifies The
BatchType
for the batch operation. Defaults toBatchType.LOGGED
.retry_policy should be a
RetryPolicy
instance for controlling retries on the operation.consistency_level should be a
ConsistencyLevel
value to be used for all operations in the batch.custom_payload is a Custom Payloads passed to the server. Note: as Statement objects are added to the batch, this map is updated with any values found in their custom payloads. These are only allowed when using protocol version 4 or higher.
Example usage:
insert_user = session.prepare("INSERT INTO users (name, age) VALUES (?, ?)") batch = BatchStatement(consistency_level=ConsistencyLevel.QUORUM) for (name, age) in users_to_insert: batch.add(insert_user, (name, age)) session.execute(batch)
You can also mix different types of operations within a batch:
batch = BatchStatement() batch.add(SimpleStatement("INSERT INTO users (name, age) VALUES (%s, %s)"), (name, age)) batch.add(SimpleStatement("DELETE FROM pending_users WHERE name=%s"), (name,)) session.execute(batch)
New in version 2.0.0.
Changed in version 2.1.0: Added serial_consistency_level as a parameter
Changed in version 2.6.0: Added custom_payload as a parameter
-
serial_consistency_level
= None¶ The same as
Statement.serial_consistency_level
, but is only supported when using protocol version 3 or higher.
-
batch_type
= None¶ The
BatchType
for the batch operation. Defaults toBatchType.LOGGED
.
-
-
class
cassandra.query.
BatchType
[source]¶ A BatchType is used with
BatchStatement
instances to control the atomicity of the batch operation.New in version 2.0.0.
-
LOGGED
= BatchType.LOGGED¶ Atomic batch operation.
-
UNLOGGED
= BatchType.UNLOGGED¶ Non-atomic batch operation.
-
COUNTER
= BatchType.COUNTER¶ Batches of counter operations.
-
-
class
cassandra.query.
ValueSequence
[source]¶ A wrapper class that is used to specify that a sequence of values should be treated as a CQL list of values instead of a single column collection when used as part of the parameters argument for
Session.execute()
.This is typically needed when supplying a list of keys to select. For example:
>>> my_user_ids = ('alice', 'bob', 'charles') >>> query = "SELECT * FROM users WHERE user_id IN %s" >>> session.execute(query, parameters=[ValueSequence(my_user_ids)])
-
class
cassandra.query.
QueryTrace
[source]¶ A trace of the duration and events that occurred when executing an operation.
-
request_type
= None¶ A string that very generally describes the traced operation.
-
duration
= None¶ A
datetime.timedelta
measure of the duration of the query.
-
client
= None¶ The IP address of the client that issued this request
This is only available when using Cassandra 2.2+
-
coordinator
= None¶ The IP address of the host that acted as coordinator for this request.
-
parameters
= None¶ A
dict
of parameters for the traced operation, such as the specific query string.
-
started_at
= None¶ A UTC
datetime.datetime
object describing when the operation was started.
-
events
= None¶ A chronologically sorted list of
TraceEvent
instances representing the steps the traced operation went through. This corresponds to the rows insystem_traces.events
for this tracing session.
-
trace_id
= None¶ uuid.UUID
unique identifier for this tracing session. Matches thesession_id
column insystem_traces.sessions
andsystem_traces.events
.
-
populate
(max_wait=2.0, wait_for_complete=True)[source]¶ Retrieves the actual tracing details from Cassandra and populates the attributes of this instance. Because tracing details are stored asynchronously by Cassandra, this may need to retry the session detail fetch. If the trace is still not available after max_wait seconds,
TraceUnavailable
will be raised; if max_wait isNone
, this will retry forever.wait_for_complete=False bypasses the wait for duration to be populated. This can be used to query events from partial sessions.
-
-
class
cassandra.query.
TraceEvent
[source]¶ Representation of a single event within a query trace.
-
description
= None¶ A brief description of the event.
-
datetime
= None¶ A UTC
datetime.datetime
marking when the event occurred.
-
source
= None¶ The IP address of the node this event occurred on.
-
source_elapsed
= None¶ A
datetime.timedelta
measuring the amount of time until this event occurred starting from whensource
first received the query.
-
thread_name
= None¶ The name of the thread that this event occurred on.
-
Raised when complete trace details cannot be fetched from Cassandra.