"""Core thread safety and event loop handling tests.

This module tests the critical thread pool configuration and event loop
integration that enables the async wrapper to work correctly.

Test Organization:
==================
- TestEventLoopHandling: Event loop creation and management across threads
- TestThreadPoolConfiguration: Thread pool limits and concurrent execution

Key Testing Focus:
==================
1. Event loop isolation between threads
2. Thread-safe callback scheduling
3. Thread pool size limits
4. Concurrent operation handling
5. Thread-local storage isolation

Why This Matters:
=================
The Cassandra driver uses threads for I/O, while our wrapper provides
async/await interface. This requires careful thread and event loop
management to prevent:
- Deadlocks between threads and event loops
- Event loop conflicts
- Thread pool exhaustion
- Race conditions in callbacks
"""

import asyncio
import threading
from unittest.mock import AsyncMock, Mock, patch

import pytest

from async_cassandra.utils import get_or_create_event_loop, safe_call_soon_threadsafe

# Test constants
MAX_WORKERS = 32
_thread_local = threading.local()


class TestEventLoopHandling:
    """
    Test event loop management in threaded environments.

    The async wrapper must handle event loops correctly across
    multiple threads since Cassandra driver callbacks may come
    from any thread in the executor pool.
    """

    @pytest.mark.core
    @pytest.mark.quick
    async def test_get_or_create_event_loop_main_thread(self):
        """
        Test getting event loop in main thread.

        What this tests:
        ---------------
        1. In async context, returns the running loop
        2. Doesn't create a new loop when one exists
        3. Returns the correct loop instance

        Why this matters:
        ----------------
        The main thread typically has an event loop (from asyncio.run
        or pytest-asyncio). We must use the existing loop rather than
        creating a new one, which would cause:
        - Event loop conflicts
        - Callbacks lost in wrong loop
        - "Event loop is closed" errors
        """
        # In async context, should return the running loop
        expected_loop = asyncio.get_running_loop()
        result = get_or_create_event_loop()
        assert result == expected_loop

    @pytest.mark.core
    def test_get_or_create_event_loop_worker_thread(self):
        """
        Test creating event loop in worker thread.

        What this tests:
        ---------------
        1. Worker threads create new event loops
        2. Created loop is stored thread-locally
        3. Loop is properly initialized
        4. Thread can use its own loop

        Why this matters:
        ----------------
        Cassandra driver uses a thread pool for I/O operations.
        When callbacks fire in these threads, they need a way to
        communicate results back to the main async context. Each
        worker thread needs its own event loop to:
        - Schedule callbacks to main loop
        - Handle thread-local async operations
        - Avoid conflicts with other threads

        Without this, callbacks from driver threads would fail.
        """
        result_loop = None

        def worker():
            nonlocal result_loop
            # Worker thread should create a new loop
            result_loop = get_or_create_event_loop()
            assert result_loop is not None
            assert isinstance(result_loop, asyncio.AbstractEventLoop)

        thread = threading.Thread(target=worker)
        thread.start()
        thread.join()

        assert result_loop is not None

    @pytest.mark.core
    @pytest.mark.critical
    def test_thread_local_event_loops(self):
        """
        Test that each thread gets its own event loop.

        What this tests:
        ---------------
        1. Multiple threads each get unique loops
        2. Loops don't interfere with each other
        3. Thread-local storage works correctly
        4. No loop sharing between threads

        Why this matters:
        ----------------
        Event loops are not thread-safe. Sharing loops between
        threads would cause:
        - Race conditions
        - Corrupted event loop state
        - Callbacks executed in wrong thread
        - Deadlocks and hangs

        This test ensures our thread-local storage pattern
        correctly isolates event loops, which is critical for
        the driver's thread pool to work with async/await.
        """
        loops = []

        def worker():
            loop = get_or_create_event_loop()
            loops.append(loop)

        threads = []
        for _ in range(5):
            thread = threading.Thread(target=worker)
            threads.append(thread)
            thread.start()

        for thread in threads:
            thread.join()

        # Each thread should have created a unique loop
        assert len(loops) == 5
        assert len(set(id(loop) for loop in loops)) == 5

    @pytest.mark.core
    async def test_safe_call_soon_threadsafe(self):
        """
        Test thread-safe callback scheduling.

        What this tests:
        ---------------
        1. Callbacks can be scheduled from same thread
        2. Callback executes in the target loop
        3. Arguments are passed correctly
        4. Callback runs asynchronously

        Why this matters:
        ----------------
        This is the bridge between driver threads and async code:
        - Driver completes query in thread pool
        - Needs to deliver result to async context
        - Must use call_soon_threadsafe for safety

        The safe wrapper handles edge cases like closed loops.
        """
        result = []

        def callback(value):
            result.append(value)

        loop = asyncio.get_running_loop()

        # Schedule callback from same thread
        safe_call_soon_threadsafe(loop, callback, "test1")

        # Give callback time to execute
        await asyncio.sleep(0.1)

        assert result == ["test1"]

    @pytest.mark.core
    def test_safe_call_soon_threadsafe_from_thread(self):
        """
        Test scheduling callback from different thread.

        What this tests:
        ---------------
        1. Callbacks work across thread boundaries
        2. Target loop receives callback correctly
        3. Synchronization works (via Event)
        4. No race conditions or deadlocks

        Why this matters:
        ----------------
        This simulates the real scenario:
        - Main thread has async event loop
        - Driver thread completes I/O operation
        - Driver thread schedules callback to main loop
        - Result delivered safely across threads

        This is the core mechanism that makes the async
        wrapper possible - bridging sync callbacks to async.
        """
        result = []
        event = threading.Event()

        def callback(value):
            result.append(value)
            event.set()

        loop = asyncio.new_event_loop()

        def run_loop():
            asyncio.set_event_loop(loop)
            loop.run_forever()

        loop_thread = threading.Thread(target=run_loop)
        loop_thread.start()

        try:
            # Schedule from different thread
            def worker():
                safe_call_soon_threadsafe(loop, callback, "test2")

            worker_thread = threading.Thread(target=worker)
            worker_thread.start()
            worker_thread.join()

            # Wait for callback
            event.wait(timeout=1)
            assert result == ["test2"]

        finally:
            loop.call_soon_threadsafe(loop.stop)
            loop_thread.join()
            loop.close()

    @pytest.mark.core
    def test_safe_call_soon_threadsafe_closed_loop(self):
        """
        Test handling of closed event loop.

        What this tests:
        ---------------
        1. Closed loop is handled gracefully
        2. No exception is raised
        3. Callback is silently dropped
        4. System remains stable

        Why this matters:
        ----------------
        During shutdown or error scenarios:
        - Event loop might be closed
        - Driver callbacks might still arrive
        - Must not crash the application
        - Should fail silently rather than propagate

        This defensive programming prevents crashes during
        shutdown sequences or error recovery.
        """
        loop = asyncio.new_event_loop()
        loop.close()

        # Should handle gracefully
        safe_call_soon_threadsafe(loop, lambda: None)
        # No exception should be raised


class TestThreadPoolConfiguration:
    """
    Test thread pool configuration and limits.

    The Cassandra driver uses a thread pool for I/O operations.
    These tests ensure proper configuration and behavior under load.
    """

    @pytest.mark.core
    @pytest.mark.quick
    def test_max_workers_constant(self):
        """
        Test MAX_WORKERS is set correctly.

        What this tests:
        ---------------
        1. Thread pool size constant is defined
        2. Value is reasonable (32 threads)
        3. Constant is accessible

        Why this matters:
        ----------------
        Thread pool size affects:
        - Maximum concurrent operations
        - Memory usage (each thread has stack)
        - Performance under load

        32 threads is a balance between concurrency and
        resource usage for typical applications.
        """
        assert MAX_WORKERS == 32

    @pytest.mark.core
    def test_thread_pool_creation(self):
        """
        Test thread pool is created with correct parameters.

        What this tests:
        ---------------
        1. AsyncCluster respects executor_threads parameter
        2. Thread pool is created with specified size
        3. Configuration flows to driver correctly

        Why this matters:
        ----------------
        Applications need to tune thread pool size based on:
        - Expected query volume
        - Available system resources
        - Latency requirements

        Too few threads: queries queue up, high latency
        Too many threads: memory waste, context switching

        This ensures the configuration works as expected.
        """
        from async_cassandra.cluster import AsyncCluster

        cluster = AsyncCluster(executor_threads=16)
        assert cluster._cluster.executor._max_workers == 16

    @pytest.mark.core
    @pytest.mark.critical
    async def test_concurrent_operations_within_limit(self):
        """
        Test handling concurrent operations within thread pool limit.

        What this tests:
        ---------------
        1. Multiple concurrent queries execute successfully
        2. All operations complete without blocking
        3. Results are delivered correctly
        4. No thread pool exhaustion with reasonable load

        Why this matters:
        ----------------
        Real applications execute many queries concurrently:
        - Web requests trigger multiple queries
        - Batch processing runs parallel operations
        - Background tasks query simultaneously

        The thread pool must handle reasonable concurrency
        without deadlocking or failing. This test simulates
        a typical concurrent load scenario.

        10 concurrent operations is well within the 32 thread
        limit, so all should complete successfully.
        """
        from cassandra.cluster import ResponseFuture

        from async_cassandra.session import AsyncCassandraSession as AsyncSession

        mock_session = Mock()
        results = []

        def mock_execute_async(*args, **kwargs):
            mock_future = Mock(spec=ResponseFuture)
            mock_future.result.return_value = Mock(rows=[])
            mock_future.timeout = None
            mock_future.has_more_pages = False
            results.append(1)
            return mock_future

        mock_session.execute_async.side_effect = mock_execute_async

        async_session = AsyncSession(mock_session)

        # Run operations concurrently
        with patch("async_cassandra.session.AsyncResultHandler") as mock_handler_class:
            mock_handler = Mock()
            mock_handler.get_result = AsyncMock(return_value=Mock(rows=[]))
            mock_handler_class.return_value = mock_handler

            tasks = []
            for i in range(10):
                task = asyncio.create_task(async_session.execute(f"SELECT * FROM table{i}"))
                tasks.append(task)

            await asyncio.gather(*tasks)

        # All operations should complete
        assert len(results) == 10

    @pytest.mark.core
    def test_thread_local_storage(self):
        """
        Test thread-local storage for event loops.

        What this tests:
        ---------------
        1. Each thread has isolated storage
        2. Values don't leak between threads
        3. Thread-local mechanism works correctly
        4. Storage is truly thread-specific

        Why this matters:
        ----------------
        Thread-local storage is critical for:
        - Event loop isolation (each thread's loop)
        - Connection state per thread
        - Avoiding race conditions

        If thread-local storage failed:
        - Event loops would be shared (crashes)
        - State would corrupt between threads
        - Race conditions everywhere

        This fundamental mechanism enables safe multi-threaded
        operation of the async wrapper.
        """
        # Each thread should have its own storage
        storage_values = []

        def worker(value):
            _thread_local.test_value = value
            storage_values.append((_thread_local.test_value, threading.current_thread().ident))

        threads = []
        for i in range(5):
            thread = threading.Thread(target=worker, args=(i,))
            threads.append(thread)
            thread.start()

        for thread in threads:
            thread.join()

        # Each thread should have stored its own value
        assert len(storage_values) == 5
        values = [v[0] for v in storage_values]
        assert sorted(values) == [0, 1, 2, 3, 4]
