Add LLM-friendly analysis module (PR 3) - #10
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- sparkparse/analyze.py: to_plan_summary() builds a token-efficient dict of per-node runtime metrics (durations, scan paths, join types) correlated from accumulator updates; find_cartesian_joins(), find_largest_scans(), find_repeated_scans(), find_spill() for programmatic/notebook use - tests/test_analyze.py: 23 tests covering structure, content, and edge cases across all three fixture logs https://claude.ai/code/session_01K2vBStnakxoYEd85tQrjhZ
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of per-node runtime metrics (durations, scan paths, join types) correlated
from accumulator updates; find_cartesian_joins(), find_largest_scans(),
find_repeated_scans(), find_spill() for programmatic/notebook use
across all three fixture logs
https://claude.ai/code/session_01K2vBStnakxoYEd85tQrjhZ