Fixes the dirty-pool-checkout bug surfaced by Margaret Hamilton's
system-wide audit (Critical #1).
The bug: ConnectionPool.release() returned connections with open
server-side transactions still active. Request A's uncommitted
INSERTs would be inherited by Request B reusing the same connection -
B's commit would land A's writes permanently; B's rollback would
silently lose them. Same shape as psycopg2's pre-2.5 dirty-pool bug.
The fix: pool.release() now rolls back any open transaction before
returning the connection to the idle list. The rollback runs OUTSIDE
the pool lock since it's a wire round-trip - the connection is
already off the idle list and counted in _total, so no other thread
can grab it during the rollback window. If the rollback itself fails
(dead socket, etc.), the connection is evicted rather than recycled.
Async path covered automatically: AsyncConnectionPool.release()
delegates to the sync pool's release via _to_thread.
Margaret Hamilton review pass surfaced two findings, both addressed:
* Silent rollback failure: added a WARNING log via logging.getLogger
("informix_db.pool") so evictions are debuggable. First logger in
the project.
* Async cancellation race: the fix doesn't introduce the
asyncio.wait_for race (Critical #2, deferred to Phase 27), but it
adds a code path that can trigger it. Documented loudly in
pool.release() docstring, aio.py module docstring, and USAGE.md
async section. Recommendation: use read_timeout on the connection
instead of asyncio.wait_for until Phase 27 lands.
Two new regression tests in tests/test_pool.py:
* test_uncommitted_writes_invisible_to_next_acquirer (the bug)
* test_committed_writes_survive_pool_checkout (no over-correction)
Verified the regression test catches the bug: stashed the fix, ran
the test - it fails with "B sees 1 rows - leaked across pool
checkout boundary" - confirming it tests the real failure mode.
Total tests: 72 unit + 226 integration + 28 benchmark = 326.
Deferred to Phase 27 per Hamilton audit:
* Critical #2 (concurrency / per-connection wire lock)
* High #3 (async cancellation routes to broken=True)
* High #4 (bare except in _raise_sq_err drain)
* High #5 (no cursor finalizers - server-side resource leaks)
Third-pass optimization on parse_tuple_payload's hot loop. Previous
phases removed redundant work; this one removes correct-but-wasteful
work: the if/elif chain checked branches in implementation order, not
frequency order. Fixed-width types (INT, FLOAT, DATE, BIGINT - the most
common columns in real queries) sat at the bottom, paying ~7 frozenset
misses per column.
Changes (src/informix_db/_resultset.py):
* Added _FIXED_WIDTH_TYPES = frozenset(FIXED_WIDTHS.keys()) at module
load.
* New fast-path branch at the TOP of parse_tuple_payload's loop body
that handles every _FIXED_WIDTH_TYPES column inline: one frozenset
check, one dict lookup, one decode, continue. Skips every other
branch.
* Cleaned up the bottom fall-through; it now genuinely only catches
unknown types.
Performance vs Phase 24 baseline:
* parse_tuple_5cols_iso8859: 1659 ns -> 1400 ns (-16%)
* parse_tuple_5cols_utf8: 1649 ns -> 1341 ns (-19%)
Cumulative vs Phase 21 baseline (before any optimization):
* parse_tuple_5cols: 2796 ns -> 1400 ns (-50%) - HALF the time
* decode_int: 230 ns -> 139 ns (-40%)
Margaret Hamilton review surfaced one HIGH finding addressed before
tagging:
* H: The fast-path optimization assumes every FIXED_WIDTHS key is
decodable WITHOUT qualifier inspection (encoded_length etc.). True
today, but a future contributor adding a fixed-width type that
needs qualifier bits (like DATETIME does) would silently get wrong
decode behavior - Lauren-Bug class failure.
Fix: added INVARIANT comment to FIXED_WIDTHS in converters.py AND
added tests/test_resultset_invariants.py with three CI tripwire
tests:
- _FIXED_WIDTH_TYPES is disjoint from every other dispatch branch
- Every FIXED_WIDTHS key has a DECODERS entry
- DECODERS keys stay < 0x100 (Phase 24 collision-free guarantee)
The tests carry instructions: if one fires, don't update the test
to match - either restore the property or refactor the optimization.
Comments rot when nobody reads them; tests fail loudly.
baseline.json refreshed; 72 unit + 224 integration + 28 bench = 324
tests; ruff clean.
Second pass of hot-path optimization on parse_tuple_payload. Two changes
to converters.py:
1. Split decode() into public + internal. Added _decode_base(base_tc,
raw, encoding) that takes an already-base-typed code and skips the
redundant base_type() call. Public decode() is now a one-line
wrapper. parse_tuple_payload's 4 call sites swapped to use
_decode_base directly. _fastpath.py's external decode() caller is
unaffected.
2. Pre-compiled struct.Struct unpackers. The fixed-width integer/float
decoders (_decode_smallint, _decode_int, _decode_bigint,
_decode_smfloat, _decode_float, _decode_date) switched from per-call
struct.unpack(fmt, raw) to module-level bound methods like
_UNPACK_INT = struct.Struct("!i").unpack. Format-string parsed once
at module load. Measured 37% faster than per-call struct.unpack on
CPython 3.13 micro.
Performance vs Phase 23 baseline:
* decode_int: 173 ns -> 139 ns (-20%)
* decode_bigint: 188 ns -> 150 ns (-20%)
* parse_tuple_5cols: 2047 ns -> 1592 ns (-22%)
* 1k-row SELECT: 1255 us -> 989 us (-21%)
Cumulative vs original Phase 21 baseline:
* decode_int: 230 ns -> 139 ns (-40%)
* parse_tuple_5cols: 2796 ns -> 1592 ns (-43%)
* 1k-row SELECT: 1477 us -> 989 us (-33%)
Real-world fetch ceiling: 358K rows/sec -> ~620K rows/sec.
Margaret Hamilton review surfaced one HIGH-severity finding addressed
before tagging:
* H: The no-collision guarantee that makes _decode_base safe is
structural but undocumented (all DECODERS keys are ≤ 0xFF, all flag
bits are ≥ 0x100, so flagged inputs cannot coincidentally match).
Added load-bearing INVARIANT comment at DECODERS dict explaining
the constraint and what to do if violated. Cross-referenced from
_decode_base's docstring for bidirectional traceability.
baseline.json refreshed; all 224 integration tests pass; ruff clean.
Per-row decode is hit on every row of every SELECT. The original code
had three forms of waste in the inner loop:
1. Redundant base_type() call. ColumnInfo.type_code is already
base-typed by parse_describe at construction; calling base_type()
again per column per row was pure waste. Single largest savings.
2. IntFlag->int conversions inline (~10x per iteration). Lifted to
module-level _TC_X constants.
3. Lazy imports inside the loop body (_decode_datetime, _decode_interval,
BlobLocator, ClobLocator, RowValue, CollectionValue). Moved to top.
Plus three precomputed frozensets (_LENGTH_PREFIXED_SHORT_TYPES,
_COMPOSITE_UDT_TYPES, _NUMERIC_TYPES) replace inline tuple-membership
checks. _COLLECTION_KIND_MAP is now MappingProxyType (actually frozen).
Performance:
* parse_tuple_5cols: 2796 ns -> 2030 ns (-27%)
* select_bench_table_all (1k rows): 1477 us -> 1198 us (-19%)
* Codec micro-bench, cold connect, executemany: unchanged
Real-world fetch ceiling on a single connection: 350K rows/sec ->
490K rows/sec.
Margaret Hamilton review surfaced four cleanup items, all addressed
before tagging:
* H1: cursor._dereference_blob_columns had the same redundant
base_type() call - stripped for consistency.
* M1: documented the load-bearing invariant at parse_describe (the
single producer site) so future contributors have a grep target.
* M2: _COLLECTION_KIND_MAP wrapped in MappingProxyType.
* L1: stale line-number comment fixed to point at the INVARIANT
comment instead.
baseline.json refreshed; all 224 integration tests pass; ruff clean.
The docs/USAGE.md predated Phases 17-21, so anyone landing on PyPI was
missing scrollable cursors, locale/Unicode, the autocommit cliff
finding, and the type-mapping reference.
Added sections to docs/USAGE.md:
* Locale and Unicode - client_locale, Connection.encoding, CLIENT_LOCALE
vs DB_LOCALE, when characters can't fit the codec
* Type mapping reference - full SQL <-> Python type table, NULL
sentinels subsection, IntervalYM
* Performance tips - 53x autocommit-cliff fix, 100x executemany win,
72x pool win, with the actual benchmark numbers from Phase 21.1
* Scrollable cursors - fetch_* API, in-memory vs server-side trade-off,
edge cases (past-end semantics, negative indexing, rownumber)
* Timeouts and keepalive subsection - production starting points
* Environment dictionary subsection - env={} parameter
* Known limitations - explicit table of what doesn't work (named
params, complex UDT bind, GSSAPI, XA) with workarounds; "things
that might surprise you" notes
README.md - added Documentation section linking to docs/USAGE.md
and tests/benchmarks/README.md.
Doc corrections caught during review:
* cursor.rownumber is 0-indexed (impl has always been correct; only
the original docstring wording was loose)
* fetch_* methods work on BOTH scrollable=True and default cursors;
the in-memory path supports them too
USAGE.md grew from 345 lines to 633.
Investigation of the Phase 21 baseline finding that executemany(N) cost
scaled linearly per-row (1.74 ms x N) regardless of batch size.
Root cause: every autocommit=True INSERT forces a server-side
transaction-log flush. Not a wire-protocol bug.
Numbers:
* executemany(1000) autocommit=True: 1.72 s (1.72 ms/row)
* executemany(1000) in single txn: 32 ms (32 us/row)
53x speedup from changing the transaction boundary, not the driver.
Pure protocol overhead is ~32 us/row -> ~31K rows/sec sustained
throughput on a single connection. Comparable to pg8000.
Added test_executemany_1000_rows_in_txn benchmark to make this
visible. Updated README headline numbers and added a "Performance
gotchas" section explaining when autocommit=False matters.
Decision: don't pipeline. The remaining 32 us is already excellent;
the autocommit gotcha is the real user-facing footgun. Docs > code.
If someone reports needing >31K rows/sec single-connection, that
becomes Phase 22.
Fills the highest-priority gap from the test-adequacy audit:
connection-failure recovery. 12 new integration tests using a
thread-based TCP proxy (ControlledProxy) that can be kill()'d at
any moment to simulate network drops or server crashes via TCP RST
(SO_LINGER=0).
Coverage:
* Network drop mid-SELECT — OperationalError, not hang
* Network drop after describe, before fetch
* Network drop during fetch (already-materialized rows still
readable; fresh execute fails)
* Local socket forced-close (kernel-level disconnect simulation)
* I/O error marks connection unusable post-failure
* Pool evicts connection that died mid-`with` block (size drops)
* Pool revives after all idle connections died (health check on
acquire mints fresh)
* Async cancellation via asyncio.wait_for — pool stays usable
* Cursor reusable after SQL error
* Connection survives cursor close after error
* Sustained pool load (50 acquire/release cycles, no leak)
* read_timeout fires on a hung connection within bounds
Catches the failure classes that bite production users:
* Hangs (waiting forever on dead socket)
* Silent corruption (EOF treated as valid tuple)
* Double-fault (cleanup raises after primary error)
* Pool poisoning (broken connection returned to pool)
* Stale cursor reuse across error boundaries
Helper:
* tests/_proxy.py — ControlledProxy: thread-based TCP forwarder
with kill() for fault injection. Two-thread pump model. SO_LINGER=0
for RST-on-close (mimics router drop).
Total: 69 unit + 203 integration = 272 tests.
Remaining gaps from the audit (UTF-8 multibyte locale, server-version
matrix, performance benchmarks) are real but lower-severity. Phase 19
addressed the one most likely to bite production deployments.
Opt-in via conn.cursor(scrollable=True). Opens the cursor with
SQ_SCROLL (24) before SQ_OPEN (6), keeps it open server-side, and
sends SQ_SFETCH (23) per scroll call instead of materializing the
result set up-front.
User-facing API is identical to Phase 17's in-memory scroll
(fetch_first/last/prior/absolute/relative, scroll, rownumber).
Only the internal mechanism differs:
| feature | default | scrollable=True
|-------------------|------------------|------------------
| memory | all rows | one row at a time
| round-trips/fetch | 0 (after NFETCH) | 1 per call
| cursor lifetime | closed after exec| open until close()
| best for | sequential iter | random access on
| huge result sets
Wire format (verified against JDBC ScrollProbe capture):
* SQ_SFETCH: [short SQ_ID=4][int 23][short scrolltype]
[int target][int bufSize=4096][short SQ_EOT]
scrolltype: 1=NEXT, 4=LAST, 6=ABSOLUTE
* SQ_SCROLL (24): emitted between CURNAME and SQ_OPEN
* SQ_TUPID (25): response tag with 1-indexed row position;
authoritative source for client-side position tracking
Position tracking uses the server's SQ_TUPID rather than client-
computed indexes. Total row count discovered lazily via SFETCH(LAST)
when negative absolute indexing requires it; cached in
_scroll_total_rows.
Trap on the way: initial SFETCH used SHORT for bufSize → server
hung silently. Same SHORT-vs-INT diagnostic pattern as Phase 4.x's
CURNAME+NFETCH. Captured JDBC trace, byte-diffed against ours,
found the mismatch (bufSize is INT in modern Informix per
isXPSVER8_40 / is2GBFetchBufferSupported).
Tests: 14 integration tests in test_scroll_cursor_server.py
covering lifecycle, sequential fetch, fetch_first/last/prior/
absolute/relative, negative indexing, scroll, empty result sets,
past-end, and random-access on a 100-row result set.
Total: 69 unit + 191 integration = 260 tests.
Adds scroll/random-access methods on Cursor:
* scroll(value, mode='relative'|'absolute') — PEP 249 compatible
* fetch_first() / fetch_last() — jump to result-set ends
* fetch_prior() — step backward (SQL-standard: from past-end yields
the last row, matching JDBC ResultSet.previous() semantics)
* fetch_absolute(n) — 0-indexed jump; negative n indexes from end
* fetch_relative(n) — n-step from current position
* rownumber property — current 0-indexed position
Implementation: replaced _row_iter (single-pass iterator) with
_row_index (random-access index) on the cursor. The result set
is already materialized in _rows during execute(); scroll just
repositions the index. No new wire protocol needed.
For server-side scroll over genuinely huge result sets, SQ_SFETCH
(tag 23) would be needed — JDBC has executeScrollFetch (line 3908)
but we only need it if someone hits the in-memory materialization
ceiling. Phase 18 if so.
Out-of-range scroll raises IndexError per PEP 249. Invalid mode
strings raise ProgrammingError. fetchall() now correctly returns
only the rows from the current position to end (not all rows).
14 new integration tests in test_scroll_cursor.py covering:
* fetchone advancing rownumber sequentially
* fetch_first reset
* fetch_last
* fetch_prior including the past-end-to-last-row semantics
* fetch_absolute with positive and negative indexes
* fetch_relative
* PEP 249 scroll(value, mode='relative'/'absolute')
* IndexError on out-of-range
* ProgrammingError on bad mode
* Empty-result-set edge cases
* fetchall after partial iteration
Total: 69 unit + 177 integration = 246 tests.
Version bump (2026.05.02 → 2026.05.04) reflects the library reaching
feature completeness across Phases 1-16.
Documentation:
* README.md — full rewrite. The previous README was from Phase 1
("cursor() / execute() / fetchone() arrive in Phase 2"). New
README covers: sync + async APIs, connection pool, TLS, full type
matrix, smart-LOBs, fast-path RPC, server-compatibility,
development workflow, and pointers to the protocol research docs.
* docs/USAGE.md — new practical recipe guide. Connecting, cursor
lifecycle, parameter binding, transactions (logged + unlogged),
executemany, smart-LOB read/write, connection pool, async,
TLS, error handling, fast-path RPC, server-side setup steps,
and a migration table from IfxPy / legacy informixdb.
* CHANGELOG.md — new file. Captures the v2026.05.04 release as the
Phase 1-16 completion milestone with a full feature inventory
and known-gap list. Future point-releases append here.
Classifiers updated:
* Development Status: 2 → 4 (Pre-Alpha → Beta)
* Added Framework :: AsyncIO
Keywords: added asyncio, async.
No code changes; tests still pass (69 unit + 163 integration = 232).
Ruff clean.
