Performance Optimization: Use Static Lookups Instead of Joins
Hi! I wanted to share a powerful and lightweight approach I recently implemented for using static reference data in pipelines—without needing memory-intensive joins or separate file/database reads during runtime. The Challenge In typical scenarios, we handle static or reference data (like lookup tables or code descriptions) by: Reading it from a file or database Performing a Join Snap to enrich the main data stream While effective, joins: Can be memory-heavy, especially with large datasets Add complexity to your pipeline Require both sources to be aligned in structure and timing The New Approach Instead of performing a join, we can: Store static reference data as a JSON file in SnapLogic’s SLDB Load this JSON file in an Expression Library Use filter/map function in your pipeline expressions to fetch data from JSON based on a key No joins. No file readers. Just fast in-memory lookups! Example Sample JSON file (staticData,json) [ { "code": "A1", "desc": "Alpha" }, { "code": "B2", "desc": "Beta" }, { "code": "C3", "desc": "Gamma" } ] Define in Pipeline: Usage in Pipeline: lib.static.filter(x =>x.code == $code_from_source).length > 0 ? lib.static.filter(x =>x.code == $code_from_source)[0].desc : "Unknown" This setup allows you to quickly enrich your data using a simple expression, and the same logic can be reused across multiple pipelines via the library. Benefits Faster: No join processing overhead Simpler pipelines: Fewer snaps and data dependencies Reusable: One JSON file + one function = many pipelines Memory-efficient: Especially helpful when Snaplex memory is a constraint Things to Consider SLDB file size limit: The JSON file stored in SLDB must be under 100MB (SnapLogic’s file size limit for SLDB uploads). Data updates: If your reference data changes frequently (e.g., weekly/monthly), you’ll need to build a separate job or pipeline to overwrite the SLDB file. Search performance: The filter() method checks each item one by one, which can be slow if your JSON has a lot of records. For faster lookups, consider converting the data into a key-value map. Governance: SLDB files have limited access control compared to databases. Ensure your team is aligned on ownership and update responsibility. Maintainability: JSON logic is hardcoded, so changes to structure or logic require modifying the expression library and possibly redeploying affected pipelines. I’ve found this approach especially useful for small to medium-sized static datasets where performance, simplicity, and reusability are key. If you're looking to reduce joins and streamline your pipelines, I highly recommend giving this method a try. To make it easier, I’ve attached a sample pipeline, JSON lookup file, and input CSV so you can see the setup in action. Feel free to explore, adapt, and let me know how it works for you!
