Add stress test to check multithreading

Author: Dmitry Yashunin

.gitignore

@@ -9,3 +9,4 @@ var/
 .idea/
 .vscode/
 .vs/
+**.DS_Store

python_bindings/tests/bindings_test_replace.py

@@ -57,7 +57,7 @@ def testRandomSelf(self):
         for l in labels2_deleted:
             hnsw_index.mark_deleted(l[0])
         labels1_found, _ = hnsw_index.knn_query(data1, k=1)
-        items = hnsw_index.get_items(labels1)
+        items = hnsw_index.get_items(labels1_found)
         diff_with_gt_labels = np.mean(np.abs(data1-items))
         self.assertAlmostEqual(diff_with_gt_labels, 0, delta=1e-3)
 

python_bindings/tests/bindings_test_stress_mt_replace.py

@@ -0,0 +1,62 @@
+import unittest
+
+import numpy as np
+
+import hnswlib
+
+
+class RandomSelfTestCase(unittest.TestCase):
+    def testRandomSelf(self):
+        dim = 16
+        num_elements = 1_000
+        max_num_elements = 2 * num_elements
+
+        # Generating sample data
+        # batch 1
+        first_id = 0
+        last_id = num_elements
+        labels1 = np.arange(first_id, last_id)
+        data1 = np.float32(np.random.random((num_elements, dim)))
+        # batch 2
+        first_id += num_elements
+        last_id += num_elements
+        labels2 = np.arange(first_id, last_id)
+        data2 = np.float32(np.random.random((num_elements, dim)))
+        # batch 3
+        first_id += num_elements
+        last_id += num_elements
+        labels3 = np.arange(first_id, last_id)
+        data3 = np.float32(np.random.random((num_elements, dim)))
+
+        # Declaring index
+        for _ in range(100):
+            hnsw_index = hnswlib.Index(space='l2', dim=dim)
+            hnsw_index.init_index(max_elements=max_num_elements, ef_construction=200, M=16, replace_deleted=True)
+
+            hnsw_index.set_ef(100)
+            hnsw_index.set_num_threads(50)
+
+            # Add batch 1 and 2
+            hnsw_index.add_items(data1, labels1)
+            hnsw_index.add_items(data2, labels2)  # maximum number of elements is reached
+
+            # Delete nearest neighbors of batch 2
+            labels2_deleted, _ = hnsw_index.knn_query(data2, k=1)
+            for l in labels2_deleted:
+                hnsw_index.mark_deleted(l[0])
+            labels1_found, _ = hnsw_index.knn_query(data1, k=1)
+            items = hnsw_index.get_items(labels1_found)
+            diff_with_gt_labels = np.mean(np.abs(data1-items))
+            self.assertAlmostEqual(diff_with_gt_labels, 0, delta=1e-3)
+
+            labels2_after, _ = hnsw_index.knn_query(data2, k=1)
+            labels2_after_flat = labels2_after.flatten()
+            labels2_deleted_flat = labels2_deleted.flatten()
+            common = np.intersect1d(labels2_after_flat, labels2_deleted_flat)
+            self.assertTrue(common.size == 0)
+
+            # Replace deleted elements
+            # Maximum number of elements is reached therefore we cannot add new items
+            # but we can replace the deleted ones
+            labels_replaced = hnsw_index.add_items_to_vacant_place(data3, labels3)
+