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Dmitry Romanoff
Dmitry Romanoff

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Stress Testing Amazon DocumentDB with Python

#aws #database #python #nosql

If you're working with Amazon DocumentDB and want to evaluate how your cluster performs under load, a stress test is one of the best ways to gain confidence in your setup.

In this post, I’ll walk you through a simple, effective way to stress test your DocumentDB cluster using Python and pymongo.

Stress Testing Amazon DocumentDB with Python

📦 Prerequisites

Before running the script, make sure you have:

  • An active Amazon DocumentDB cluster
  • The global CA certificate from Amazon: 
 curl -o global-bundle.pem https://truststore.pki.rds.amazonaws.com/global/global-bundle.pem
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  • Python 3.7+ installed
  • The pymongo driver: 
 pip install pymongo
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🧠 What This Script Does

The script:

  • Connects to your DocumentDB cluster
  • Spawns multiple threads
  • Randomly performs insert, read, or update operations
  • Measures how long it takes to complete a set of operations

This lets you simulate a concurrent, mixed workload to see how your database performs.

Stress Testing Amazon DocumentDB with Python

💻 The Code 

import random import string import time from pymongo import MongoClient from concurrent.futures import ThreadPoolExecutor, as_completed # Configuration MONGO_URI = ( "mongodb://myadmin:********@" "docdb-dima-1.cluster-xxxxxxxxxxxx.us-east-1.docdb.amazonaws.com:27017" "/?ssl=true&replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false" ) CA_CERT_PATH = "global-bundle.pem" DB_NAME = "stressTestDB" COLLECTION_NAME = "stressCollection" # Connect client = MongoClient(MONGO_URI, tlsCAFile=CA_CERT_PATH) db = client[DB_NAME] collection = db[COLLECTION_NAME] def random_string(length=10): return ''.join(random.choices(string.ascii_letters + string.digits, k=length)) def stress_worker(thread_id): ops = ["insert", "read", "update"] op = random.choice(ops) try: if op == "insert": doc = {"thread": thread_id, "data": random_string(), "value": random.randint(1, 1000)} collection.insert_one(doc) return f"[Thread {thread_id}] Insert OK" elif op == "read": result = collection.find_one({}, {"_id": 0}) return f"[Thread {thread_id}] Read OK: {result}" elif op == "update": collection.update_one({}, {"$set": {"updated": time.time()}}, upsert=True) return f"[Thread {thread_id}] Update OK" except Exception as e: return f"[Thread {thread_id}] ERROR: {e}" def run_stress_test(concurrency=50, iterations=1000): start = time.time() print(f"Starting stress test: {concurrency} threads x {iterations} ops...") with ThreadPoolExecutor(max_workers=concurrency) as executor: futures = [executor.submit(stress_worker, i % concurrency) for i in range(iterations)] for future in as_completed(futures): print(future.result()) duration = time.time() - start print(f"Test completed in {duration:.2f} seconds") if __name__ == "__main__": run_stress_test(concurrency=20, iterations=200)
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🧪 Example Output 

Starting stress test: 20 threads x 200 ops... [Thread 3] Insert OK [Thread 7] Read OK: {'thread': 9, 'data': 'xyz456', 'value': 88} [Thread 12] Update OK ... Test completed in 11.84 seconds
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🛠 Customizing the Test

  • Change concurrency by modifying the concurrency parameter in run_stress_test().
  • Adjust workload size via the iterations parameter.
  • Modify workload types (e.g., remove update or bias toward read) by changing the ops list in stress_worker().

Stress Testing Amazon DocumentDB with Python

⚠️ Important Notes

  • This test writes data — you might want to clean up your test database afterward.
  • Make sure you’re not violating AWS quotas or triggering alarms.
  • Always test in a non-production environment first.

✅ Conclusion

Stress testing your DocumentDB cluster helps you prepare for production-scale workloads. This lightweight script is a great starting point for load testing, performance tuning, or monitoring how your system reacts under pressure.

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