fix(ingest/partition-executor): Fix deadlock by recomputing ready ite…

…ms (datahub-project#11853)

metadata-ingestion/setup.py

@@ -573,6 +573,7 @@
 
 test_api_requirements = {
     "pytest>=6.2.2",
+    "pytest-timeout",
     # Missing numpy requirement in 8.0.0
     "deepdiff!=8.0.0",
     "PyYAML",

metadata-ingestion/src/datahub/utilities/partition_executor.py

@@ -237,6 +237,11 @@ def __init__(
         process_batch: Callable[[List], None],
         max_per_batch: int = 100,
         min_process_interval: timedelta = _DEFAULT_BATCHER_MIN_PROCESS_INTERVAL,
+        # Why 3 seconds? It's somewhat arbitrary.
+        # We don't want it to be too high, since then liveness suffers,
+        # particularly during a dirty shutdown. If it's too low, then we'll
+        # waste CPU cycles rechecking the timer, only to call get again.
+        read_from_pending_interval: timedelta = timedelta(seconds=3),
     ) -> None:
         """Similar to PartitionExecutor, but with batching.
 
@@ -262,8 +267,10 @@ def __init__(
         self.max_per_batch = max_per_batch
         self.process_batch = process_batch
         self.min_process_interval = min_process_interval
+        self.read_from_pending_interval = read_from_pending_interval
         assert self.max_workers > 1
 
+        self.state_lock = threading.Lock()
         self._executor = ThreadPoolExecutor(
             # We add one here to account for the clearinghouse worker thread.
             max_workers=max_workers + 1,
@@ -362,12 +369,8 @@ def _build_batch() -> List[_BatchPartitionWorkItem]:
                     if not blocking:
                         next_item = self._pending.get_nowait()
                     else:
-                        # Why 3 seconds? It's somewhat arbitrary.
-                        # We don't want it to be too high, since then liveness suffers,
-                        # particularly during a dirty shutdown. If it's too low, then we'll
-                        # waste CPU cycles rechecking the timer, only to call get again.
                         next_item = self._pending.get(
-                            timeout=3,  # seconds
+                            timeout=self.read_from_pending_interval.total_seconds(),
                         )
 
                     if next_item is None:  # None is the shutdown signal
@@ -379,6 +382,9 @@ def _build_batch() -> List[_BatchPartitionWorkItem]:
                             pending_key_completion.append(next_item)
                         else:
                             next_batch.append(next_item)
+
+                    if not next_batch:
+                        next_batch = _find_ready_items()
                 except queue.Empty:
                     if not blocking:
                         break
@@ -452,10 +458,11 @@ def _ensure_clearinghouse_started(self) -> None:
                 f"{self.__class__.__name__} is shutting down; cannot submit new work items."
             )
 
-        # Lazily start the clearinghouse worker.
-        if not self._clearinghouse_started:
-            self._clearinghouse_started = True
-            self._executor.submit(self._clearinghouse_worker)
+        with self.state_lock:
+            # Lazily start the clearinghouse worker.
+            if not self._clearinghouse_started:
+                self._clearinghouse_started = True
+                self._executor.submit(self._clearinghouse_worker)
 
     def submit(
         self,

metadata-ingestion/tests/unit/utilities/test_partition_executor.py

@@ -1,7 +1,11 @@
 import logging
+import math
 import time
 from concurrent.futures import Future
 
+import pytest
+from pydantic.schema import timedelta
+
 from datahub.utilities.partition_executor import (
     BatchPartitionExecutor,
     PartitionExecutor,
@@ -129,23 +133,30 @@ def process_batch(batch):
     }
 
 
+@pytest.mark.timeout(10)
 def test_batch_partition_executor_max_batch_size():
+    n = 20  # Exceed max_pending to test for deadlocks when max_pending exceeded
     batches_processed = []
 
     def process_batch(batch):
         batches_processed.append(batch)
         time.sleep(0.1)  # Simulate batch processing time
 
     with BatchPartitionExecutor(
-        max_workers=5, max_pending=20, process_batch=process_batch, max_per_batch=2
+        max_workers=5,
+        max_pending=10,
+        process_batch=process_batch,
+        max_per_batch=2,
+        min_process_interval=timedelta(seconds=1),
+        read_from_pending_interval=timedelta(seconds=1),
     ) as executor:
         # Submit more tasks than the max_per_batch to test batching limits.
-        for i in range(5):
+        for i in range(n):
             executor.submit("key3", "key3", f"task{i}")
 
     # Check the batches.
     logger.info(f"batches_processed: {batches_processed}")
-    assert len(batches_processed) == 3
+    assert len(batches_processed) == math.ceil(n / 2), "Incorrect number of batches"
     for batch in batches_processed:
         assert len(batch) <= 2, "Batch size exceeded max_per_batch limit"