MongoDB: How to retry on read/write failure (with examples)

Introduction

MongoDB is a powerful non-relational database that is highly popular for its flexibility, scalability, and capability to deal with high-volume data. However, when working with any database system in a real-world scenario, one is bound to encounter occasional read and write failures. These could stem from a range of issues, including transient network hiccups, replication lag, or system overload. In such cases, implementing a retry mechanism can ensure that your application maintains high durability and availability. This tutorial will guide you on how to effectively retry on read/write failures in MongoDB with practical code examples.

Understanding Potential Failures

Before diving into the retry algorithms, it’s crucial to understand the potential points of failure. In MongoDB, these failures could happen due to:

• Temporary network issues
• Primary replica set member unavailability
• Write conflicts
• Exceeded write concern timeouts
• Maintenance or backups causing brief disconnections

Basic Retry on a Connection Failure

Let’s start with a basic example of retrying a connection attempt when there’s a failure. For demonstration purposes, we’ll use Python’s pymongo library.

import pymongo
from pymongo.errors import ConnectionFailure
import time

# Function to establish a MongoDB Connection with retries
def establish_connection(retry_n_times, retry_delay):
    for attempt in range(retry_n_times):
        try:
            # Replace with your connection properties
            client = pymongo.MongoClient("your_connection_string")
            print("Connection successful!")
            return client
        except ConnectionFailure:
            print(f"Connection attempt {attempt + 1} of {retry_n_times} failed. Retrying in {retry_delay} seconds...")
            time.sleep(retry_delay)
    raise Exception("Maximum retry attempts reached. Failed to connect to MongoDB.")

# Example usage
client = establish_connection(5, 10)

This example tries to establish a connection five times with a ten-second delay between attempts. If the connection is not successful after five attempts, it raises an exception.

Retry on Write Operation Failure

Now, let’s look at how to implement a retry mechanism for write operations. It’s similar to handling connection retries, but here, the focus is on handling exceptions related to write operations likeDuplicateKeyError, WriteError, and WriteConcernError.

from pymongo import MongoClient
from pymongo.errors import WriteError, WriteConcernError

client = MongoClient("your_connection_string")
db = client.test_database
collection = db.test_collection

def write_with_retry(document, retry_n_times, retry_delay):
    for attempt in range(retry_n_times):
        try:
            result = collection.insert_one(document)
            print(f"Write successful: {result.inserted_id}")
            return result
        except (WriteError, WriteConcernError) as e:
            print(f"Attempt {attempt + 1} of {retry_n_times}: Write failed due to {str(e)}. Retrying in {retry_delay} seconds...")
            time.sleep(retry_delay)
    raise Exception("Maximum retry attempts reached. Failed to write document.")

# Example
try:
    write_result = write_with_retry({'_id': 1, 'name': 'John Doe'}, 3, 5)
except Exception as e:
    print(e)

In this case, we are trying to insert a document three times with a five-second delay after encountering a write error. If after three attempts the write still fails, we raise an exception.

Advanced Retry Strategy: Exponential Backoff

If you’re looking for a more sophisticated approach, you can implement exponential backoff. This strategy employs a delay between retry attempts that doubles after each failure, possibly with a cap and with some randomness to prevent the “thundering herd” problem.

import random

# Exponential backoff with jitter
def backoff_with_jitter(base_delay=1, max_delay=60, factor=2):
    delay = base_delay
    while True:
        # Wait with randomized jitter
        yield delay + random.uniform(0, base_delay)
        # Compute next delay
        delay = min(delay * factor, max_delay)

# Retry function using exponential backoff with jitter
def write_with_advanced_retry(document):
    retry_delays = backoff_with_jitter()
    for attempt in enumerate(retry_delays, start=1):
        try:
            result = collection.insert_one(document)
            print("Write successful")
            return result
        except (WriteError, WriteConcernError):
            delay = next(retry_delays)
            print(f"Attempt {attempt[0]}: Write failed. Retrying in {delay:.2f} seconds...")
            time.sleep(delay)

# Example
try:
    write_result = write_with_advanced_retry({'_id': 2, 'name': 'Jane Roe'})
except StopIteration:
    print("Maximum retry attempts reached. Failed to write document.")

This code snippet employs an exponential backoff function that is used as an iterator for delay intervals. The generator will raise a StopIteration exception when the maximum delay is reached.

Conclusion

This tutorial has covered some basic strategies for retrying operations in MongoDB, including exponential backoff for more advanced use cases, and has provided examples in Python using pymongo. Implementing suitable retry logic in your application can significantly increase its robustness and fault tolerance, particularly in distributed environments or in cases involving unreliable networks.