This article discusses best practices for processing large files in data indexing pipelines, especially for AI applications like RAG or semantic search. It emphasizes understanding processing granularity, as it affects system reliability and resource management. The trade-offs of commit frequency highlight the costs of frequent database writes versus risks associated with committing entire files. The ideal approach typically involves processing entries independently and batching commits to manage resources effectively, while also addressing challenges like interdependent data sources that require new granularity definitions.
When building data indexing pipelines, handling large files efficiently presents unique challenges. For example, patent XML files from the USPTO can contain hundreds of patents in a single file, with each file being over 1GB in size.
Processing granularity determines when and how frequently we commit processed data to storage. This seemingly simple decision has significant implications for system reliability, resource utilization, and recovery capabilities.
Finding the Right Balance: A reasonable processing granularity typically lies between extremes. The default approach is to process each source entry independently and batch commit related entries together.
Challenging Scenarios: Non-Independent Sources (Fan-in) – The default granularity breaks down when source entries are interdependent, requiring new processing units established at an appropriate granularity.
Collection
[
|
...
]