Extract R analysis into modular backend abstraction#153
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Create bedboss/bedstat/backends/ package with StatBackend ABC and RStatBackend implementation. Move R logic from bedstat.py into r_backend.py (no rewrite, just extraction). bedstat() becomes a thin dispatcher that delegates to the configured backend via factory. bedboss.py reads backend from bbagent.config.config.analysis.backend and passes it through. All R scripts and r_service.py untouched. Identical behavior when backend="r" (the default). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Move magic strings "r" and "gtars" to BACKEND_R / BACKEND_GTARS constants in const.py. Also fixes black formatting in __init__.py. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Architecture change: batch orchestrators build a stats backend once and
pass it through the call chain as a StatBackend instance, instead of
creating a new backend per file inside bedstat().
Before: bedstat(backend="r", r_service=..., ...) reconstructed the
backend on every call. Fine for R because r_service was passed IN and
the RStatBackend constructor was cheap, but broken for future backends
that need per-instance caches (GtarsPyStatBackend's reference cache
would be wiped between files).
After: callers build one backend for their batch, pass it to bedstat()
for each file, and cleanup() at the end. Backend-internal resources
(R service, cached references) are held on the backend instance and
amortized across the whole batch.
Changes:
- bedstat(): signature takes `backend: StatBackend` instead of
`backend: str + r_service`; no longer calls create_backend internally
- Add build_backend(name) helper to backends/__init__.py that hides
backend-specific prerequisites (e.g. starts RServiceManager for "r")
- StatBackend base: add __enter__/__exit__ so backends work as context
managers (with build_backend("r") as backend: ...)
- run_all / insert_pep / reprocess_all / upload_all / _upload_gse:
build backend at batch-orchestrator level, pass through, cleanup
when done
- cli.py `bedstat` standalone command: build+use via `with` block
- run_all accepts optional backend=None for single-file standalone use;
builds + cleans up locally if caller didn't provide one
This is a breaking change — no backward compat shim. The backend
abstraction is pre-merge (PR #153).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
Refactor bedboss bedstat into a modular backend system. Zero behavior change — all existing R-based analysis works identically.
What changed
New package:
bedboss/bedstat/backends/base.py:StatBackendABC withcompute()andcleanup()methods, context manager protocolr_backend.py:RStatBackend— extracts current R logic frombedstat.py(move, not rewrite)__init__.py:create_backend(name)low-level factory,build_backend(name)high-level constructor that handles backend-specific prerequisites (e.g. startsRServiceManagerfor R)Refactored orchestration:
bedstat.py: signature changed frombedstat(backend="r", r_service=..., ...)tobedstat(backend: StatBackend, ...)— takes an instance, not a stringbedboss.py,insert_pep,reprocess_all,upload_all: build one backend at batch-orchestrator level viabuild_backend(), pass through tobedstat()for each file, cleanup when donecli.py: standalonebedstatcommand useswithblock for automatic cleanupWhy this matters:
Backend instances hold resources across a batch (R keeps its subprocess alive, future backends can cache reference data). The old pattern reconstructed backends per file, which prevented cross-file amortization.
Constants
BACKEND_R = "r"andBACKEND_GTARS = "gtars"inconst.py(gtars backend comes in PR Add gtars genomicdist backend #155)Test plan
RServiceManagerlifecycle unchanged (created inbuild_backend, cleaned up by caller)🤖 Generated with Claude Code