Streaming REST APIs for Large Financial Transaction Exports from Relational Databases
This addresses scalability and responsiveness issues for financial platforms and enterprise systems handling high-volume export operations, but it is incremental as it builds on existing streaming and database cursor techniques.
The paper tackled the problem of high memory consumption and delayed response initiation in traditional REST APIs for large financial transaction exports by proposing a streaming-based architecture, resulting in significantly reduced memory buffering and immediate download initiation for large datasets.
Financial platforms and enterprise systems frequently provide transaction export capabilities to support reporting, reconciliation, auditing, and regulatory compliance workflows. In many environments, these exports involve very large datasets containing hundreds of thousands or even millions of transaction records. Traditional REST API implementations often construct the entire export payload in application memory before transmitting the response to the client, which can lead to high memory consumption and delayed response initiation when processing large datasets. This paper presents a streaming-based REST API architecture that retrieves transaction records incrementally from relational databases and writes them directly to the HTTP response output stream. By integrating database cursor retrieval with progressive HTTP transmission, the proposed design allows export data to be delivered continuously as records are processed rather than after the full dataset has been assembled. The architecture is implemented using a Java-based JAX-RS framework with the StreamingOutput interface and supports multiple financial export formats including CSV, OFX, QFX, and QBO. In practice, the streaming approach significantly reduces memory buffering requirements and allows large export downloads to begin immediately, improving responsiveness and scalability for high-volume export operations.