The Architect's Guide to Mastering Large-Scale RETS and Matrix API Data Synchronization
The RETS and Matrix APIs represent the lifeblood of real estate applications. These APIs seem like a deceitful software challenge. It is not just a simple fetch request to retrieve the datasets with 20,000 listings with numerous high-resolution images. It's a distributed systems challenge. This article aims to provide a scalable architecture to sync data at such a large scale.
The Pitfalls of Simple Solutions
The Monolithic Script Fallacy
The monolithic script fallacy is still the most prevalent error. One script with many iterations to retrieve data sequentially will always blow up. Parsing large datasets in the form of XML or JSON is not memory and CPU friendly because of thousands of records.
Serverless Anti-Patterns
Serverless solutions such as Supabase Edge Functions, Lambda Functions, and Vercel Functions pose critical anti-patterns related to batch ingest operations if used as monolithic processing solutions. These services have hard execution deadlines measured at 150 seconds. A process that requires execution for hours fails to fit within this stipulated framework. Indeed, serverless solutions can be potent if refactored to small worker solutions with a focus on batches.
AI-Generated Code Limitations
Code written by AI reinforces such issues. Although useful for writing boilerplates, AI does not have a nuanced understanding of rate limitations of APIs, memory handling, or error paths. Code working with 100 records fails with 100,000.
Core Architectural Strategy: Divide and Conquer
Hardware Allocation
Hardware allocation is resource intensive. Its deployment should be on a Linux-based environment such as Ubuntu or Alpine in Docker containers. This provides scalability horizontally with Kubernetes or Docker Swarm orchestration.
Parallel Processing
Parallel processing is not negotiable. Split the massive task into individual jobs. It is common knowledge that 1,000 results per request is the norm for most RETS interfaces and Matrix. Make parallel requests for varying pieces. It will take 3 minutes for 100 requests to finish when one request would take 5 hours.
Serverless as Batch Processors
Serverless as batch processors can work well if constrained correctly. It is not one Lambda or Edge Function processing 20,000 records. It is necessary to have a workflow composition layer with Step Functions or something like N8N to achieve three tasks:
First, break tasks into small batches. A batch includes 100 to 500 records. It is designed to be finished within the server-less timeout value.
Secondly, there is one Lambda or Edge Function per batch. Each is totally self-contained and finishes in seconds. This provides for clean shutdowns and cold starts.
Thirdly, chain calls sequentially or in parallel waves. This is handled by the orchestrator.
This is well within the API's limitations and is what makes serverless computing truly shine. It processes data, cleans up gracefully, and then begins again with a clean slate.
Critical Application Components
API Compatibility
API compatibility is critical. This is because a high number of requests can be problematic for free or paid RETS/Matrix solutions. This is because such solutions can neither cope with high requests concurrently coming from one customer nor high-fire rate queries. This is because the strategy removes the challenge. This is because the strategy involves small batches with pauses.
Pagination & Filtering
Pagination and filtering continue to be very important. Paginate with limit and offset parameters exposed directly in the API to limit queries to under-infinite requests. For fault-tolerant execution while combining with stateful workers, it is pertinent to implement a job queue architecture. The orchestrator is able to determine total work to be done and then send it to a distributed job queue such as RabbitMQ, Redis, or SQS. More than one worker service or worker process can then perform execution tasks.
Rate Limit Handling
Rate limit handling is a crucial component. Write the code to handle 429 Too Many Requests errors to automatically rate limit and retry with respect to the Retry-After header. The batching method automatically rate limits to prevent rate limit collisions.
Conclusion
A well-functional RETS and Matrix pipeline is not a script; it is a distributed system. By designing your architecture with workflow orchestration in mind and with job queues and proper error handling to begin with, you construct infrastructure that is resilient and scalable enough to meet the demands of any given real estate site.
Serverless is not disallowed. Serverless is simply refocused as a high-powered method for focused and time-bound batch operations.
Amir Yekta
Software Architect & Team Lead specializing in full-stack development, AI-powered SaaS platforms, and scalable system design. Passionate about solving complex technical challenges and building high-performing engineering teams.