Tech Talks After Hours: Data Buffer Manipulation

In this After Hours edition of Tech Talks, join Jon Golembiewski and Tom Linton as they dive deep into advanced data buffer manipulation techniques in OneStream, revealing powerful strategies for optimizing performance and reducing scripting complexity.

Key Topics Covered

• What is a data buffer: a collection of cells in memory representing a slice of OneStream data, loaded once and held for repeated use within a rule execution
• Three advanced techniques not covered in previous Tech Talks or the Financial Calculations Handbook — all learned from real-world implementation experience since the book was published
• Convert Extended Members: using this method to collapse account relationships within a data buffer, enabling work across parent-child account structures
• Copying data to 'none': a common data buffer manipulation pattern where data is first staged to a null scenario before transformation, giving the rule a clean working surface
• GetFilteredDataBuffer: filtering a data buffer by a specific dimension type using a dictionary of member IDs, allowing the rule to isolate only the cells needed for a particular calculation
• Dictionary structure for GetFilteredDataBuffer: requires a Dictionary<int, object> where the integer key is the member ID and the object value is null — the member ID is what drives the filter
• Data buffer cell loops: iterating over the cells of a data buffer in memory to apply calculation logic cell by cell, rather than issuing repeated database queries for each value
• Load once, query in memory: the core performance pattern — load the full data buffer once at the start of the rule, then pull individual cells or subsets from the in-memory buffer throughout the calculation
• Significant reduction in database calls: querying an in-memory data buffer avoids the overhead of repeated round-trips to the database, which is one of the most expensive operations in any rule
• Per-account or per-product calculation logic: because the data buffer is already in memory, different calculation logic can be applied to each account or product without incurring additional data retrieval cost each time
• Remove zeros and remove members: data buffer methods that can cleanse a dataset before processing — useful for stripping out noise or orphaned members prior to running calculations
• Practical guidance from production experience: the techniques presented reflect patterns Jon encountered on real client projects, making them highly applicable to everyday OneStream development scenarios