Key Challenges in Laravel & MySQL Implementations
In Part 1, we explored the fundamentals of Amazon Marketing Stream (AMS) and how to establish a real-time data pipeline using AWS SQS and the Amazon Advertising API. While the setup is straightforward at a conceptual level, real-world implementations – especially for enterprise Amazon sellers – introduce several engineering challenges.
This becomes particularly evident when AMS is integrated into traditional web application stacks such as Laravel with MySQL, which were not originally designed for continuous, high-throughput streaming workloads.
This article dives into the key challenges faced during AMS implementation and outlines scalable architectural strategies to overcome them.
1. Handling High-Volume Data Ingestion
Challenge
- Enterprise Amazon sellers can generate thousands of SQS messages per hour
- Laravel queue workers and cron jobs are not designed for continuous, high-throughput streaming
- Direct ingestion into Laravel causes:
- Queue backlogs
- Increased server load
- Slower user-facing application performance
Solution
Use AWS Lambda to consume and process SQS messages
- Decouple real-time ingestion from the Laravel application
- Allow Lambda to:
- Scale automatically
- Batch-process messages
- Perform lightweight validation and transformation
- Forward processed data to Laravel via API, queue, or intermediate storage
Result: Stable application performance with scalable data ingestion.
2. MySQL Write Bottlenecks
Challenge
- AMS data is:
- Highly granular
- Insert-heavy
- Time-series oriented
- Frequent single-row inserts lead to:
- Table locking
- Slow write performance
- Replication lag
Solution
- Implement batch inserts instead of row-by-row writes
- Design write-optimized tables with minimal indexing
- Separate:
- Raw event data
- Aggregated reporting tables
- Partition large tables by date where applicable
Result: Faster database writes and improved reporting performance.
3. Schema Complexity & Evolving Data Structures
Challenge
- AMS datasets vary by ad type (SP, SB, SD, DSP)
- Amazon frequently adds or modifies metrics
- Rigid MySQL schemas require frequent migrations and code changes
Solution
- Store raw AMS payloads using JSON columns
- Extract only essential fields into structured columns
- Maintain schema flexibility while preserving query efficiency
Result: Reduced maintenance effort and future-proof data ingestion.
Conclusion
Successfully implementing Amazon Marketing Stream in a Laravel + MySQL environment requires rethinking traditional ingestion and storage patterns. By decoupling ingestion with AWS Lambda, optimizing database writes, and adopting flexible schemas, teams can build a scalable and resilient AMS data pipeline without compromising application performance.