Keeping Two Different Databases Aligned With Two Different Technologies Using an Application-Level Solution

In modern software applications, it’s common to use multiple databases with different technologies for various reasons like scalability, performance, or simply leveraging the best features of a specific database for different types of data. However, keeping these databases aligned can be challenging, especially if they belong to different vendors or employ different storage mechanisms.

This article explores an application-level solution to keep two different databases aligned, discusses the challenges, and provides coding examples to illustrate the solution.

Why Use Multiple Databases?

Using multiple databases is often necessary when:

Separation of concerns: An application may need to use a relational database for transactional data and a NoSQL database for unstructured or semi-structured data.
Performance reasons: A high-throughput, low-latency database like Redis might be used for caching alongside a slower but more consistent database like PostgreSQL.
Cost optimization: Sometimes, you might use a cost-effective database for less critical data while reserving more expensive, high-performance databases for vital information.
Specialized functionality: Different databases often have specialized functionalities, such as full-text search, geospatial data handling, or real-time analytics, which may lead to using different systems side by side.

Challenges of Synchronizing Multiple Databases

While it is beneficial to leverage different databases, ensuring data synchronization across these databases is crucial for data consistency. The challenges include:

Transaction consistency: Ensuring that both databases remain consistent during transactions, especially when dealing with different technologies, is complex.
Handling failures: If one database fails, rolling back changes from another database can be challenging.
Concurrency control: Multiple processes updating the databases concurrently could lead to race conditions or inconsistency if not managed correctly.
Conflict resolution: When the same data exists in two databases, determining which version is the correct one after conflicts occur is non-trivial.
Latency issues: Operations may take longer in one database than another, leading to synchronization lag.

To solve these issues, an application-level solution can be built to align two databases using different technologies.

An Application-Level Approach to Database Synchronization

An application-level solution involves placing the responsibility of synchronization inside the application’s codebase. This solution does not rely on database-specific features but instead builds custom synchronization mechanisms to ensure data consistency.

Key Components of the Solution

Event-driven architecture: Every change to one database generates an event that updates the second database. This approach ensures that both databases are eventually consistent.
Distributed transactions: Use of transaction management techniques like the two-phase commit or eventual consistency to ensure both databases are in sync during a critical operation.
Asynchronous processing: Decouple the updates to different databases to avoid delays caused by slow transactions and handle retries on failure.
Error handling and retry logic: Implement a retry mechanism when synchronization fails.
Audit logging: Keep logs of all operations to make it easier to trace issues if databases fall out of sync.

Step-by-Step Implementation of the Solution

Let’s walk through an implementation that aligns a relational database (PostgreSQL) and a NoSQL database (MongoDB). The solution will ensure data synchronization between both databases using an application-level event-driven approach with a message broker like RabbitMQ for event handling.

Setting Up the Environment

First, set up the two databases:

PostgreSQL: To handle structured data.
MongoDB: To handle unstructured or semi-structured data.

You also need a message broker like RabbitMQ to pass events between services.

bash

# Install PostgreSQL

sudo apt-get install postgresql

# Install MongoDB
sudo apt-get install mongodb# Install RabbitMQ
sudo apt-get install rabbitmq-server

Ensure that your application is configured to connect to both databases and RabbitMQ.

Database Models

Let’s assume you have a User model in both PostgreSQL and MongoDB. The PostgreSQL schema looks like this:

sql

CREATE TABLE users (

id SERIAL PRIMARY KEY,

name VARCHAR(100),

email VARCHAR(100) UNIQUE

);

In MongoDB, the user document schema looks like this:

json

{

"_id": "ObjectId",

"name": "string",

"email": "string",

"profile": {

"bio": "string",

"location": "string"

}

}

While the two structures aren’t identical, some data like name and email must remain consistent across both databases.

Event Generation and Messaging

To keep both databases aligned, we’ll adopt an event-driven architecture where every change in PostgreSQL is published as an event, and a consumer listens to those events to update MongoDB.

Here’s how to achieve this:

Write data to PostgreSQL

When an update occurs in PostgreSQL, an event is triggered.

python

# app/db_postgres.py

import psycopg2

def update_user_pg(user_id, name, email):
connection = psycopg2.connect(“dbname=mydb user=myuser”)
cursor = connection.cursor()cursor.execute(“””
UPDATE users SET name = %s, email = %s WHERE id = %s
“””, (name, email, user_id))connection.commit()
cursor.close()
connection.close()# Publish event to RabbitMQ
publish_event(“user.updated”, {“user_id”: user_id, “name”: name, “email”: email})

Publish the event to RabbitMQ

An event producer will send a message whenever a user is updated.

python

# app/event_producer.py

import pika

import json

def publish_event(event_type, payload):
connection = pika.BlockingConnection(pika.ConnectionParameters(‘localhost’))
channel = connection.channel()
channel.queue_declare(queue=‘user_updates’)message = json.dumps({“event”: event_type, “data”: payload})
channel.basic_publish(exchange=”, routing_key=‘user_updates’, body=message)
connection.close()

Consume the event and update MongoDB

A consumer listens to the RabbitMQ queue and updates the MongoDB record accordingly.

python

# app/event_consumer.py

import pika

import json

from pymongo import MongoClient

def update_user_mongo(user_id, name, email):
client = MongoClient(‘mongodb://localhost:27017/’)
db = client[‘mydb’]
users_collection = db[‘users’]# Update the user document
users_collection.update_one(
{“_id”: user_id},
{“$set”: {“name”: name, “email”: email}}
)def callback(ch, method, properties, body):
event = json.loads(body)
if event[‘event’] == ‘user.updated’:
update_user_mongo(
event[‘data’][‘user_id’],
event[‘data’][‘name’],
event[‘data’][’email’]
)# Setting up the consumer
def consume_events():
connection = pika.BlockingConnection(pika.ConnectionParameters(‘localhost’))
channel = connection.channel()
channel.queue_declare(queue=‘user_updates’)channel.basic_consume(queue=‘user_updates’, on_message_callback=callback, auto_ack=True)
channel.start_consuming()

Ensuring Transaction Consistency

If PostgreSQL and MongoDB should remain synchronized as part of the same operation, you must ensure transaction consistency.

One common approach is using a two-phase commit protocol (2PC), which ensures that both databases either commit or rollback together.

Here’s a high-level illustration:

Prepare phase: Both databases are asked if they are ready to commit the transaction.
Commit/Abort phase: If both databases respond positively, the commit is executed; if one fails, both rollback.

Error Handling and Retry Mechanism

If an event cannot be processed or fails, a retry mechanism should be implemented. This could be done by storing the failed event in a dead-letter queue and retrying later, or by implementing an exponential backoff strategy.

Here’s a basic example of retrying failed operations:

python

import time

import random

def process_event(event):
for attempt in range(3): # Retry 3 times
try:
update_user_mongo(event[‘user_id’], event[‘name’], event[’email’])
break # Success
except Exception as e:
print(f”Attempt {attempt + 1} failed. Retrying…”)
time.sleep(2 ** attempt) # Exponential backoff

Audit Logging

To trace inconsistencies, you should log all operations. Use a logging library to store any changes made to either database.

python

import logging

logging.basicConfig(filename=‘db_sync.log’, level=logging.INFO)

def log_event(event):
logging.info(f”Event processed: {event}“)

Conclusion

Aligning two different databases using an application-level solution requires careful planning and consideration of factors such as transaction consistency, error handling, and performance. The event-driven architecture ensures that changes in one database trigger updates in the other, while a message broker like RabbitMQ helps decouple these updates for greater flexibility and scalability.

Through mechanisms like two-phase commit and retries, you can mitigate synchronization failures, making the system more reliable. While such solutions require additional development effort, they allow developers to maintain full control over synchronization logic, ensuring that multiple databases remain aligned and consistent in a distributed, heterogeneous environment.

In a world where multiple databases with different technologies are increasingly common, application-level synchronization provides a flexible and powerful way to ensure data integrity across diverse systems.