SQS As a Buffer To Database Writes | Overview.

SQS As a Buffer To Database Writes - Overview.

Scope:

• Intro,
• Why recommends SQS as a Buffer Before a Database,
• Architecture Overview,
• How It Works,
• Key SQS Features for Buffering,
• Best Practices,
• Example Use Case,
• Alternatives.

Intro:

• Using Amazon SQS as a buffer to database writes is a best practice for decoupling components, improving system resilience, and smoothing out write spikes to a database (e.g., RDS, Aurora, DynamoDB, etc.).

 Why twtech recommends the use of SQS as a Buffer Before a Database

1. Decoupling: Producers (e.g., web servers, microservices) don't directly interact with the database.
2. Throttling & Load Smoothing: Prevents database overload during traffic spikes.
3. Retry Handling: Built-in retries, visibility timeout, and DLQs improve reliability.
4. Scalability: Consumers can scale independently based on queue depth.

 Architecture Overview

How It Works

1. Producer sends data/messages to SQS instead of directly writing to the database.
2. SQS Queue temporarily stores messages (buffering).
3. Consumers (e.g., EC2 workers, AWS Lambda, or containers) poll the queue.
4. Each consumer:
• Parses the message.
• Validates/transforms the data.
• Writes it to the database.
• Deletes the message from the queue upon success.

 Key SQS Features for Buffering

Feature	Benefit
Visibility Timeout	Prevents duplicate writes during processing.
Dead-Letter Queue	Catches failed messages for debugging or retry.
Long Polling	Reduces empty receives and API costs.
FIFO Queue	(Optional) Preserves order and guarantees exactly-once processing.

 Best Practices

1. Set Appropriate Visibility Timeout
   Long enough to allow DB write to complete but short enough to retry if consumer fails.
2. Use DLQ
   Route failed messages to a Dead-Letter Queue to avoid data loss and facilitate debugging.
3. Idempotency
   Ensure twtech DB writes are idempotent (e.g., use deduplication keys or primary keys to avoid duplicates).
4. Autoscaling Consumers
   Scale your consumer fleet based on SQS queue depth using ASG or Lambda concurrency.
5. Backpressure Monitoring
   Set up CloudWatch alarms for high queue depth — indicates consumers aren’t keeping up.

 Example Use Case

A high-traffic web app accepts user uploads and metadata. Rather than writing directly to the DB:

• App pushes metadata to SQS.
• Consumer workers pick up the messages and insert the metadata into PostgreSQL.
• If the DB is slow or temporarily unreachable, the SQS buffer prevents loss and retries later.

 Alternatives

Option	When to Consider
Kinesis Fan-out	When twtech needs to Fan-out real-time streaming and analytics.
Lambda + SQS	For serverless and auto-scaling consumer logic.
SNS + SQS	When twtech wants messages to multiple consumers (e.g., DB + analytics).