Cache the Session, Not the Data: 40x Faster ML Iteration with a Shared Sidecar at Meta#

Rohil Bansal, Eric Fu (Meta)

• Why: A small, sharply executed piece of systems engineering with a measured ~40x improvement (391s → 9s) on the developer inner loop — the kind of operational win that transfers to any ML platform with a preprocessing cold-start tax.
• What: The Shared Sidecar (SSC), an out-of-process daemon caching warmed data-preprocessing sessions across local runs, rolled out to all Meta developer workstations by default with no correctness regressions (from abstract only).
• How: Sessions are keyed on a hash of input dataframe + preprocessing plan; matching reuses a pre-warmed session and skips session setup, query optimization, metadata lookup, and reader warm-up. Lifecycle is bounded (8h idle eviction, 1h process timeout, capped session count) with graceful degradation if the daemon dies.
• Where applicable: General technique, not Meta-specific — any environment where preprocessing startup dominates iteration. The 5-minute cold start is environment-dependent, so measure your own baseline first. Single-benchmark evidence (a CTR model); the "no correctness regressions" claim is the authors'.