Merge pull request #18 from ringoldsdev/chore/20250801/add-profiler

Chore/20250801/add profiler

Author: ringoldsdev

laygo/transformers/strategies/threaded.py

@@ -5,8 +5,9 @@
 from concurrent.futures import Future
 from concurrent.futures import ThreadPoolExecutor
 from concurrent.futures import wait
-from functools import partial
 import itertools
+import threading
+from typing import ClassVar
 
 from laygo.context.types import IContextManager
 from laygo.transformers.strategies.types import ChunkGenerator
@@ -15,26 +16,38 @@
 
 
 class ThreadedStrategy[In, Out](ExecutionStrategy[In, Out]):
+  # Class-level thread pool cache to reuse executors
+  _thread_pools: ClassVar[dict[int, ThreadPoolExecutor]] = {}
+  _pool_lock: ClassVar[threading.Lock] = threading.Lock()
+
   def __init__(self, max_workers: int = 4, ordered: bool = True):
     self.max_workers = max_workers
     self.ordered = ordered
 
+  @classmethod
+  def _get_thread_pool(cls, max_workers: int) -> ThreadPoolExecutor:
+    """Get or create a reusable thread pool for the given worker count."""
+    with cls._pool_lock:
+      if max_workers not in cls._thread_pools:
+        cls._thread_pools[max_workers] = ThreadPoolExecutor(
+          max_workers=max_workers, thread_name_prefix=f"laygo-{max_workers}"
+        )
+      return cls._thread_pools[max_workers]
+
   def execute(self, transformer_logic, chunk_generator, data, context):
     """Execute the transformer on data concurrently.
 
-    It uses the shared context provided by the Pipeline, if available.
+    Uses a reusable thread pool to minimize thread creation overhead.
 
     Args:
+        transformer_logic: The transformation function to apply.
+        chunk_generator: Function to generate data chunks.
         data: The input data to process.
         context: Optional pipeline context for shared state.
 
     Returns:
         An iterator over the transformed data.
     """
-
-    # Since threads share memory, we can pass the context manager directly.
-    # No handle/proxy mechanism is needed, but the locking inside
-    # ParallelContextManager is crucial for thread safety.
     yield from self._execute_with_context(data, transformer_logic, context, chunk_generator)
 
   def _execute_with_context(
@@ -48,13 +61,15 @@ def _execute_with_context(
 
     Args:
         data: The input data to process.
+        transformer: The transformation function to apply.
         shared_context: The shared context for the execution.
+        chunk_generator: Function to generate data chunks.
 
     Returns:
         An iterator over the transformed data.
     """
 
-    def process_chunk(chunk: list[In], shared_context: IContextManager) -> list[Out]:
+    def process_chunk(chunk: list[In]) -> list[Out]:
       """Process a single chunk by passing the chunk and context explicitly.
 
       Args:
@@ -66,49 +81,58 @@ def process_chunk(chunk: list[In], shared_context: IContextManager) -> list[Out]
       """
       return transformer(chunk, shared_context)  # type: ignore
 
-    # Create a partial function with the shared_context "baked in".
-    process_chunk_with_context = partial(process_chunk, shared_context=shared_context)
-
     def _ordered_generator(chunks_iter: Iterator[list[In]], executor: ThreadPoolExecutor) -> Iterator[list[Out]]:
       """Generate results in their original order."""
       futures: deque[Future[list[Out]]] = deque()
-      for _ in range(self.max_workers + 1):
+
+      # Pre-submit initial batch of futures
+      for _ in range(min(self.max_workers, 10)):  # Limit initial submissions
         try:
           chunk = next(chunks_iter)
-          futures.append(executor.submit(process_chunk_with_context, chunk))
+          futures.append(executor.submit(process_chunk, chunk))
         except StopIteration:
           break
+
       while futures:
-        yield futures.popleft().result()
+        # Get the next result and submit the next chunk
+        result = futures.popleft().result()
+        yield result
+
         try:
           chunk = next(chunks_iter)
-          futures.append(executor.submit(process_chunk_with_context, chunk))
+          futures.append(executor.submit(process_chunk, chunk))
         except StopIteration:
           continue
 
     def _unordered_generator(chunks_iter: Iterator[list[In]], executor: ThreadPoolExecutor) -> Iterator[list[Out]]:
       """Generate results as they complete."""
+      # Pre-submit initial batch
       futures = {
-        executor.submit(process_chunk_with_context, chunk)
-        for chunk in itertools.islice(chunks_iter, self.max_workers + 1)
+        executor.submit(process_chunk, chunk) for chunk in itertools.islice(chunks_iter, min(self.max_workers, 10))
       }
+
       while futures:
         done, futures = wait(futures, return_when=FIRST_COMPLETED)
         for future in done:
           yield future.result()
           try:
             chunk = next(chunks_iter)
-            futures.add(executor.submit(process_chunk_with_context, chunk))
+            futures.add(executor.submit(process_chunk, chunk))
           except StopIteration:
             continue
 
-    def result_iterator_manager() -> Iterator[Out]:
-      """Manage the thread pool and yield flattened results."""
-      with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
-        chunks_to_process = chunk_generator(data)
-        gen_func = _ordered_generator if self.ordered else _unordered_generator
-        processed_chunks_iterator = gen_func(chunks_to_process, executor)
-        for result_chunk in processed_chunks_iterator:
-          yield from result_chunk
-
-    return result_iterator_manager()
+    # Use the reusable thread pool instead of creating a new one
+    executor = self._get_thread_pool(self.max_workers)
+    chunks_to_process = chunk_generator(data)
+    gen_func = _ordered_generator if self.ordered else _unordered_generator
+
+    # Process chunks using the reusable executor
+    for result_chunk in gen_func(chunks_to_process, executor):
+      yield from result_chunk
+
+  def __del__(self) -> None:
+    """Shutdown all cached thread pools. Call this during application cleanup."""
+    with self._pool_lock:
+      for pool in self._thread_pools.values():
+        pool.shutdown(wait=True)
+      self._thread_pools.clear()

tests/test_custom_transformer.py

@@ -0,0 +1,30 @@
+from collections.abc import Iterable
+from collections.abc import Iterator
+
+from laygo.context.types import IContextManager
+from laygo.pipeline import Pipeline
+from laygo.transformers.types import BaseTransformer
+
+# In should be an int
+
+
+class MultiplierTransformer(BaseTransformer[int, int]):
+  def __call__(self, data: Iterable[int], context: IContextManager | None = None) -> Iterator[int]:
+    """
+    Takes an iterable of data and yields each item multiplied.
+    """
+
+    multiplier = context["multiplier"] if context and "multiplier" in context else 1
+
+    for item in data:
+      yield item * multiplier
+
+
+class TestCustomTransformer:
+  def test_multiplier_transformer(self):
+    data = [1, 2, 3, 4, 5]
+    expected_output = [2, 4, 6, 8, 10]
+
+    result, _ = Pipeline(data).context({"multiplier": 2}).apply(MultiplierTransformer()).to_list()
+
+    assert result == expected_output, f"Expected {expected_output}, but got {result}"