cassandra.concurrent- Utilities for Concurrent Statement Execution¶
Executes a sequence of (statement, parameters) tuples concurrently. Each
parameters item must be a sequence or
The concurrency parameter controls how many statements will be executed
Cluster.protocol_version is set to 1 or 2,
it is recommended that this be kept below 100 times the number of
core connections per host times the number of connected hosts (see
Cluster.set_core_connections_per_host()). If that amount is exceeded,
the event loop thread may attempt to block on new connection creation,
substantially impacting throughput. If
is 3 or higher, you can safely experiment with higher levels of concurrency.
If raise_on_first_error is left as
True, execution will stop
after the first failed statement and the corresponding exception will be
results_generator controls how the results are returned.
False, the results are returned only after all requests have completed.
True, a generator expression is returned. Using a generator results in a constrained
memory footprint when the results set will be large – results are yielded
as they return instead of materializing the entire list at once. The trade for lower memory
footprint is marginal CPU overhead (more thread coordination and sorting out-of-order results
A sequence of
ExecutionResult(success, result_or_exc) namedtuples is returned
in the same order that the statements were passed in. If
there was an error executing the statement, and
result_or_exc will be
will be the query result.
select_statement = session.prepare("SELECT * FROM users WHERE id=?") statements_and_params =  for user_id in user_ids: params = (user_id, ) statements_and_params.append((select_statement, params)) results = execute_concurrent( session, statements_and_params, raise_on_first_error=False) for (success, result) in results: if not success: handle_error(result) # result will be an Exception else: process_user(result) # result will be a list of rows
Note: in the case that generators are used, it is important to ensure the consumers do not block or attempt further synchronous requests, because no further IO will be processed until the consumer returns. This may also produce a deadlock in the IO event thread.
execute_concurrent(), but takes a single
statement and a sequence of parameters. Each item in
should be a sequence or
statement = session.prepare("INSERT INTO mytable (a, b) VALUES (1, ?)") parameters = [(x,) for x in range(1000)] execute_concurrent_with_args(session, statement, parameters, concurrency=50)