Chore: Benchmarking

samples/samples/benchmarking.py

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+import threading
+from google.cloud import spanner_v1
+import time
+import math
+
+from google.cloud.spanner_v1 import transaction, pool, _helpers
+from google.cloud import spanner
+import concurrent.futures
+
+# Define your Spanner instance and database information
+project_id = "your_project_id"
+instance_id = "your_instance_id"
+database_id = "your_database_id"
+spanner_client = spanner_v1.Client(project=project_id)
+
+# Create a Spanner database instance
+instance = spanner_client.instance(instance_id)
+pool = pool.FixedSizePool(size = 10, logging_enabled=True)
+database = instance.database(pool=pool, database_id=database_id, close_inactive_transactions=True)
+
+transaction_time = []
+
+
+def calculatePercentile(latencies):
+    # sort the latencies array
+    latencies.sort()
+
+    # calculate p50 (50th percentile)
+    p50Index = math.floor(0.5*len(latencies))
+    p50Latency = latencies[p50Index]
+
+    # calculate p90 (90th percentile)
+    p90Index = math.floor(0.9*len(latencies))
+    p90Latency = latencies[p90Index]
+
+    return [p50Latency, p90Latency]
+
+# [START spanner_query_data]
+def query_data(thread_id):
+    print("running thread ", thread_id)
+    start_time = time.time()
+    time.sleep(10)
+    with database.snapshot() as snapshot:
+        results = snapshot.execute_sql(
+            "SELECT 1 FROM Singers"
+        )
+
+    # for row in results:
+    #     print(row)
+
+    # for row in results:
+    #     print("SingerId: {}, FirstName: {}, LastName: {}".format(*row))
+
+    end_time = time.time()
+    transaction_time.append(end_time-start_time)
+# [END spanner_query_data]
+
+# [START spanner_batch_transaction]
+def batch_transaction(thread_id):
+    print("running thread ", thread_id)
+    start_time = time.time()
+    time.sleep(10)
+    batch_txn = database.batch_snapshot()
+    batches = batch_txn.execute_sql(
+        'SELECT * FROM Singers',
+    )
+    results = []
+    for batch in batches:
+        results.append("SingerId: {}, FirstName: {}, LastName: {}".format(*batch))
+
+    # for batch in batches:
+    #     for row in batch_txn.process_read_batch(batch):
+    #         results.append("SingerId: {}, FirstName: {}, LastName: {}".format(*row))
+    # for result in results:
+    #     print(result)
+
+    end_time = time.time()
+    transaction_time.append(end_time-start_time)
+# [END spanner_batch_transaction]
+
+# [START insert_with_dml]
+def insert_with_dml(i):
+    """Inserts data with a DML statement into the database."""
+    print("running thread ", i)
+    start_time = time.time()
+    time.sleep(10)
+
+    def insert_singers(transaction):
+        row_ct = transaction.execute_update(
+            "INSERT Singers (SingerId, FirstName, LastName) VALUES ({}, 'Google{}', 'India{}')".format(i, i, i)
+        )
+        print("{} record(s) inserted.".format(row_ct))
+
+    database.run_in_transaction(insert_singers)
+    end_time = time.time()
+    transaction_time.append(end_time-start_time)
+# [END insert_with_dml]
+
+# Define the number of threads
+num_threads = 20
+starting = 1
+
+# Create and start the threads
+threads = []
+start = time.time()
+for i in range(starting,starting+num_threads):
+    thread = threading.Thread(target=query_data, args=(i,))
+    thread.start()
+    threads.append(thread)
+
+
+# Wait for all threads to complete
+for thread in threads:
+    thread.join()
+
+print("All threads have completed.")
+end = time.time()
+
+# Print the total execution time
+print("total time taken by the execution: ", end-start)
+
+#Writing transaction time to an output file
+for t in transaction_time:
+    with open ('output.txt', 'a') as file:  
+        file.write(str(t)+"\n")
+# latency = calculatePercentile(transaction_time)
+# print("p50 latency is: ", latency[0])
+# print("p90 latency is: ", latency[1])