add execute_concurrent_async and expose execute_concurrent_* in Session

cassandra/cluster.py

@@ -2725,6 +2725,98 @@ def execute_async(self, query, parameters=None, trace=False, custom_payload=None
         future.send_request()
         return future
 
+    def execute_concurrent(self, statements_and_parameters, concurrency=100, raise_on_first_error=True, results_generator=False, execution_profile=EXEC_PROFILE_DEFAULT):
+        """
+        Executes a sequence of (statement, parameters) tuples concurrently.  Each
+        ``parameters`` item must be a sequence or :const:`None`.
+
+        The `concurrency` parameter controls how many statements will be executed
+        concurrently.  When :attr:`.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
+        :meth:`.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 :attr:`~.Cluster.protocol_version`
+        is 3 or higher, you can safely experiment with higher levels of concurrency.
+
+        If `raise_on_first_error` is left as :const:`True`, execution will stop
+        after the first failed statement and the corresponding exception will be
+        raised.
+
+        `results_generator` controls how the results are returned.
+
+        * If :const:`False`, the results are returned only after all requests have completed.
+        * If :const:`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
+          on-the-fly).
+
+        `execution_profile` argument is the execution profile to use for this
+        request, it is passed directly to :meth:`Session.execute_async`.
+
+        A sequence of ``ExecutionResult(success, result_or_exc)`` namedtuples is returned
+        in the same order that the statements were passed in.  If ``success`` is :const:`False`,
+        there was an error executing the statement, and ``result_or_exc``
+        will be an :class:`Exception`.  If ``success`` is :const:`True`, ``result_or_exc``
+        will be the query result.
+
+        Example usage::
+
+            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 = session.execute_concurrent(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[0])  # 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.
+        """
+        from cassandra.concurrent import execute_concurrent
+        return execute_concurrent(self, statements_and_parameters, concurrency, raise_on_first_error, results_generator, execution_profile)
+
+    def execute_concurrent_with_args(self, statement, parameters, *args, **kwargs):
+        """
+        Like :meth:`~cassandra.concurrent.execute_concurrent()`, but takes a single
+        statement and a sequence of parameters.  Each item in ``parameters``
+        should be a sequence or :const:`None`.
+
+        Example usage::
+
+            statement = session.prepare("INSERT INTO mytable (a, b) VALUES (1, ?)")
+            parameters = [(x,) for x in range(1000)]
+            session.execute_concurrent_with_args(statement, parameters, concurrency=50)
+        """
+        from cassandra.concurrent import execute_concurrent_with_args
+        return execute_concurrent_with_args(self, statement, parameters, *args, **kwargs)
+
+    def execute_concurrent_async(self, statements_and_parameters, concurrency=100, raise_on_first_error=False, execution_profile=EXEC_PROFILE_DEFAULT):
+        """
+        Asynchronously executes a sequence of (statement, parameters) tuples concurrently.
+
+        Args:
+            session: Cassandra session object.
+            statement_and_parameters: Iterable of (prepared CQL statement, bind parameters) tuples.
+            concurrency (int, optional): Number of concurrent operations. Default is 100.
+            raise_on_first_error (bool, optional): If True, execution stops on the first error. Default is True.
+            execution_profile (ExecutionProfile, optional): Execution profile to use. Default is EXEC_PROFILE_DEFAULT.
+
+        Returns:
+            A `Future` object that will be completed when all operations are done.
+        """
+        from cassandra.concurrent import execute_concurrent_async
+        return execute_concurrent_async(self, statements_and_parameters, concurrency, raise_on_first_error, execution_profile)
+
     def execute_graph(self, query, parameters=None, trace=False, execution_profile=EXEC_PROFILE_GRAPH_DEFAULT, execute_as=None):
         """
         Executes a Gremlin query string or GraphStatement synchronously,

cassandra/concurrent.py

@@ -13,77 +13,23 @@
 # limitations under the License.
 
 
+import logging
 from collections import namedtuple
+from concurrent.futures import Future
 from heapq import heappush, heappop
 from itertools import cycle
 from threading import Condition
-import sys
 
 from cassandra.cluster import ResultSet, EXEC_PROFILE_DEFAULT
 
-import logging
 log = logging.getLogger(__name__)
 
 
 ExecutionResult = namedtuple('ExecutionResult', ['success', 'result_or_exc'])
 
 def execute_concurrent(session, statements_and_parameters, concurrency=100, raise_on_first_error=True, results_generator=False, execution_profile=EXEC_PROFILE_DEFAULT):
     """
-    Executes a sequence of (statement, parameters) tuples concurrently.  Each
-    ``parameters`` item must be a sequence or :const:`None`.
-
-    The `concurrency` parameter controls how many statements will be executed
-    concurrently.  When :attr:`.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
-    :meth:`.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 :attr:`~.Cluster.protocol_version`
-    is 3 or higher, you can safely experiment with higher levels of concurrency.
-
-    If `raise_on_first_error` is left as :const:`True`, execution will stop
-    after the first failed statement and the corresponding exception will be
-    raised.
-
-    `results_generator` controls how the results are returned.
-
-    * If :const:`False`, the results are returned only after all requests have completed.
-    * If :const:`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
-      on-the-fly).
-
-    `execution_profile` argument is the execution profile to use for this
-    request, it is passed directly to :meth:`Session.execute_async`.
-
-    A sequence of ``ExecutionResult(success, result_or_exc)`` namedtuples is returned
-    in the same order that the statements were passed in.  If ``success`` is :const:`False`,
-    there was an error executing the statement, and ``result_or_exc`` will be
-    an :class:`Exception`.  If ``success`` is :const:`True`, ``result_or_exc``
-    will be the query result.
-
-    Example usage::
-
-        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[0])  # 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.
+    See :meth:`.Session.execute_concurrent`.
     """
     if concurrency <= 0:
         raise ValueError("concurrency must be greater than 0")
@@ -216,14 +162,50 @@ def _results(self):
 
 def execute_concurrent_with_args(session, statement, parameters, *args, **kwargs):
     """
-    Like :meth:`~cassandra.concurrent.execute_concurrent()`, but takes a single
-    statement and a sequence of parameters.  Each item in ``parameters``
-    should be a sequence or :const:`None`.
+    See :meth:`.Session.execute_concurrent_with_args`.
+    """
+    return execute_concurrent(session, zip(cycle((statement,)), parameters), *args, **kwargs)
 
-    Example usage::
 
-        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)
+class ConcurrentExecutorFutureResults(ConcurrentExecutorListResults):
+    def __init__(self, session, statements_and_params, execution_profile, future):
+        super().__init__(session, statements_and_params, execution_profile)
+        self.future = future
+
+    def _put_result(self, result, idx, success):
+        super()._put_result(result, idx, success)
+        with self._condition:
+            if self._current == self._exec_count:
+                if self._exception and self._fail_fast:
+                    self.future.set_exception(self._exception)
+                else:
+                    sorted_results = [r[1] for r in sorted(self._results_queue)]
+                    self.future.set_result(sorted_results)
+
+
+def execute_concurrent_async(
+    session,
+    statements_and_parameters,
+    concurrency=100,
+    raise_on_first_error=False,
+    execution_profile=EXEC_PROFILE_DEFAULT
+):
     """
-    return execute_concurrent(session, zip(cycle((statement,)), parameters), *args, **kwargs)
+    See :meth:`.Session.execute_concurrent_async`.
+    """
+    # Create a Future object and initialize the custom ConcurrentExecutor with the Future
+    future = Future()
+    executor = ConcurrentExecutorFutureResults(
+        session=session,
+        statements_and_params=statements_and_parameters,
+        execution_profile=execution_profile,
+        future=future
+    )
+
+    # Execute concurrently
+    try:
+        executor.execute(concurrency=concurrency, fail_fast=raise_on_first_error)
+    except Exception as e:
+        future.set_exception(e)
+
+    return future

tests/unit/test_concurrent.py

@@ -24,7 +24,7 @@
 import platform
 
 from cassandra.cluster import Cluster, Session
-from cassandra.concurrent import execute_concurrent, execute_concurrent_with_args
+from cassandra.concurrent import execute_concurrent, execute_concurrent_with_args, execute_concurrent_async
 from cassandra.pool import Host
 from cassandra.policies import SimpleConvictionPolicy
 from tests.unit.utils import mock_session_pools
@@ -239,6 +239,58 @@ def validate_result_ordering(self, results):
                 self.assertLess(last_time_added, current_time_added)
             last_time_added = current_time_added
 
+    def insert_and_validate_list_async(self, reverse, slowdown):
+        """
+        This utility method will execute submit various statements for execution using execute_concurrent_async,
+        then invoke a separate thread to execute the callback associated with the futures registered
+        for those statements. The parameters will toggle various timing, and ordering changes.
+        Finally it will validate that the results were returned in the order they were submitted
+        :param reverse: Execute the callbacks in the opposite order that they were submitted
+        :param slowdown: Cause intermittent queries to perform slowly
+        """
+        our_handler = MockResponseResponseFuture(reverse=reverse)
+        mock_session = Mock()
+        statements_and_params = zip(cycle(["INSERT INTO test3rf.test (k, v) VALUES (%s, 0)"]),
+                                    [(i, ) for i in range(100)])
+        mock_session.execute_async.return_value = our_handler
+
+        t = TimedCallableInvoker(our_handler, slowdown=slowdown)
+        t.start()
+        try:
+            future = execute_concurrent_async(mock_session, statements_and_params)
+            results = future.result()
+            self.validate_result_ordering(results)
+        finally:
+            t.stop()
+
+    def test_results_ordering_async_forward(self):
+        """
+        This tests the ordering of our execute_concurrent_async function
+        when queries complete in the order they were executed.
+        """
+        self.insert_and_validate_list_async(False, False)
+
+    def test_results_ordering_async_reverse(self):
+        """
+        This tests the ordering of our execute_concurrent_async function
+        when queries complete in the reverse order they were executed.
+        """
+        self.insert_and_validate_list_async(True, False)
+
+    def test_results_ordering_async_forward_slowdown(self):
+        """
+        This tests the ordering of our execute_concurrent_async function
+        when queries complete in the order they were executed, with slow queries mixed in.
+        """
+        self.insert_and_validate_list_async(False, True)
+
+    def test_results_ordering_async_reverse_slowdown(self):
+        """
+        This tests the ordering of our execute_concurrent_async function
+        when queries complete in the reverse order they were executed, with slow queries mixed in.
+        """
+        self.insert_and_validate_list_async(True, True)
+
     @mock_session_pools
     def test_recursion_limited(self):
         """