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cassandra.protocol

Protocol Features

.. module:: cassandra.protocol

Custom Payloads

Native protocol version 4+ allows for a custom payload to be sent between clients and custom query handlers. The payload is specified as a string:binary_type dict holding custom key/value pairs.

By default these are ignored by the server. They can be useful for servers implementing a custom QueryHandler.

See :meth:`.Session.execute`, :meth:`.Session.execute_async`, :attr:`.ResponseFuture.custom_payload`.

.. autoclass:: _ProtocolHandler

    .. autoattribute:: message_types_by_opcode
        :annotation: = {default mapping}

    .. automethod:: encode_message

    .. automethod:: decode_message

Faster Deserialization

When python-driver is compiled with Cython, it uses a Cython-based deserialization path to deserialize messages. By default, the driver will use a Cython-based parser that returns lists of rows similar to the pure-Python version. In addition, there are two additional ProtocolHandler classes that can be used to deserialize response messages: LazyProtocolHandler and NumpyProtocolHandler. They can be used as follows:

from cassandra.protocol import NumpyProtocolHandler, LazyProtocolHandler
from cassandra.query import tuple_factory
s.client_protocol_handler = LazyProtocolHandler   # for a result iterator
s.row_factory = tuple_factory  #required for Numpy results
s.client_protocol_handler = NumpyProtocolHandler  # for a dict of NumPy arrays as result

These protocol handlers comprise different parsers, and return results as described below:

  • ProtocolHandler: this default implementation is a drop-in replacement for the pure-Python version. The rows are all parsed upfront, before results are returned.
  • LazyProtocolHandler: near drop-in replacement for the above, except that it returns an iterator over rows, lazily decoded into the default row format (this is more efficient since all decoded results are not materialized at once)
  • NumpyProtocolHandler: deserializes results directly into NumPy arrays. This facilitates efficient integration with analysis toolkits such as Pandas.