Accelerating Coffea Workflows with Persistent Preprocessing Cache

High-energy physics analysis at scale depends on efficient data processing pipelines. When working with ROOT files in distributed computing environments, even small inefficiencies compound quickly, especially when the same preprocessing work gets repeated across hundreds or thousands of workers. That's the problem we're tackling in this pull request for Coffea: eliminating redundant preprocessing overhead through file-backed caching.

Coffea's preprocessor currently recomputes metadata for ROOT files on every run. While this guarantees freshness, it also means that workflows repeatedly pay the cost of extracting file structure, branch information, and entry counts even when those files haven't changed in weeks or months. In clustered environments, this creates a particularly important issue: preprocessing must complete before actual analysis begins, so workers with lighter preprocessing loads sit idle while stragglers finish. The result is wasted CPU cycles, longer time-to-results, and underutilized infrastructure.

This PR (https://github.com/scikit-hep/coffea/pull/1498) introduces a persistent, pickle-backed cache that captures preprocessing results and intelligently reuses them across runs. The cache file (.coffea_metadata_cache.pkl) lives alongside your workflow code and stores per-file metadata indexed by file path and modification timestamp. On subsequent runs, if a file's metadata hasn't changed, Coffea skips preprocessing entirely and pulls the cached results instead. The implementation integrates cleanly into the existing Runner class: the cache loads on initialization, gets consulted during preprocessing, and writes atomically afterward to ensure consistency. If the cache is missing, corrupted, or stale, the system gracefully falls back to the original in-memory behavior—no workflow disruption, just a slower first run.

The performance gains are substantial. Testing on the UGE cluster shows a dramatic shift in preprocessing time distribution. With the original implementation (left panel), preprocessing takes a large majority of the processing time to extract metadata from scratch. With the persistent cache (right panel), that same step drops to under 6 milliseconds per file—a reduction of several orders of magnitude. This isn't just about speed; it's about resource efficiency. By front-loading preprocessing work into the cache, you eliminate the idle-worker problem and unlock true parallelism for the analysis phase where it matters most.

If you're working with Coffea on large-scale analyses, distributed clusters, or any workflow where preprocessing overhead cuts into your iteration speed, this PR offers a practical path forward. Check out the discussion on GitHub and share your thoughts on the caching strategy. Your production experience will help shape a solution that works reliably across the community's diverse use cases.