informix-driver 2026.5.8

Pure-Python driver for IBM Informix IDS — speaks the SQLI wire protocol over raw sockets. No CSDK, no JVM, no native libraries.

informix-db

Pure-Python driver for IBM Informix IDS, speaking the SQLI wire protocol over raw sockets. No IBM Client SDK. No JVM. No native libraries. PEP 249 compliant; sync + async APIs; built-in connection pool; TLS support.

To our knowledge this is the first pure-socket Informix driver in any language — every other Informix driver (IfxPy, the legacy informixdb, ODBC bridges, JPype/JDBC, Perl DBD::Informix) wraps either IBM's CSDK or the JDBC JAR.

pip install informix-driver

Imports as informix_db (the distribution name is informix-driver because the legacy informixdb package on PyPI from 2008 reserves close-by names — same separation Pillow uses with import PIL). Requires Python ≥ 3.10.

Status

Production ready. Every finding from a system-wide failure-mode audit (data correctness, wire safety, resource leaks, concurrency, async cancellation) has been addressed:

Severity	Finding	Status
Critical	Pool returns connections with open transactions	Fixed (Phase 26)
Critical	Unsynchronized wire path → PDU interleaving	Fixed (Phase 27) — per-connection wire lock
High	Async cancellation leaks running workers onto recycled connections	Fixed (Phase 27)
High	_raise_sq_err bare-except masks wire desync	Fixed (Phase 28)
High	Cursor finalizers — server-side resources leak on mid-fetch raise	Fixed (Phase 28+29)
Medium	5 hardening items	Fixed (Phase 28+30)

0 critical, 0 high, 0 medium audit findings remain. Every architectural change went through a Margaret Hamilton-style review focused on silent-failure modes, recovery paths, and documented invariants. Each documented invariant is paired with either a runtime guard or a CI tripwire test.

Test coverage: 300+ tests across unit / integration / benchmark suites. Integration tests run against the official IBM Informix Developer Edition Docker image (15.0.1.0.3DE).

Quick start

import informix_db

with informix_db.connect(
    host="db.example.com", port=9088,
    user="informix", password="...",
    database="mydb", server="informix",
) as conn:
    cur = conn.cursor()
    cur.execute("SELECT id, name FROM users WHERE id = ?", (42,))
    user_id, name = cur.fetchone()

Async (FastAPI / aiohttp / asyncio)

import asyncio
from informix_db import aio

async def main():
    pool = await aio.create_pool(
        host="db.example.com", user="informix", password="...",
        database="mydb",
        min_size=1, max_size=10,
    )
    async with pool.connection() as conn:
        cur = await conn.cursor()
        await cur.execute("SELECT id, name FROM users WHERE id = ?", (42,))
        row = await cur.fetchone()
    await pool.close()

asyncio.run(main())

Connection pool (sync)

import informix_db

pool = informix_db.create_pool(
    host="db.example.com", user="informix", password="...",
    database="mydb",
    min_size=1, max_size=10, acquire_timeout=5.0,
)

with pool.connection() as conn:
    cur = conn.cursor()
    cur.execute("...")

pool.close()

TLS

import ssl

# Production: bring your own context
ctx = ssl.create_default_context(cafile="/path/to/ca.pem")
informix_db.connect(host="...", port=9089, ..., tls=ctx)

# Dev / self-signed: tls=True disables verification
informix_db.connect(host="127.0.0.1", port=9089, ..., tls=True)

Informix uses dedicated TLS-enabled listener ports (configured server-side in sqlhosts) rather than STARTTLS upgrade — point port at the TLS listener (often 9089) when tls is enabled.

Type support

SQL type	Python type
SMALLINT / INT / BIGINT / SERIAL	int
FLOAT / SMALLFLOAT	float
DECIMAL(p,s) / MONEY	decimal.Decimal
CHAR / VARCHAR / NCHAR / NVCHAR / LVARCHAR	str
BOOLEAN	bool
DATE	datetime.date
DATETIME YEAR TO ...	datetime.datetime / datetime.time / datetime.date
INTERVAL DAY TO FRACTION	datetime.timedelta
INTERVAL YEAR TO MONTH	informix_db.IntervalYM
BYTE / TEXT (legacy in-row blobs)	bytes / str
BLOB / CLOB (smart-LOBs)	informix_db.BlobLocator / informix_db.ClobLocator (read via cursor.read_blob_column, write via cursor.write_blob_column)
ROW(...)	informix_db.RowValue
SET(...) / MULTISET(...) / LIST(...)	informix_db.CollectionValue
NULL	None

Smart-LOB (BLOB / CLOB) read & write

# Read: returns the actual bytes
data = cur.read_blob_column(
    "SELECT data FROM photos WHERE id = ?", (42,)
)

# Write: BLOB_PLACEHOLDER token marks where the BLOB goes
cur.write_blob_column(
    "INSERT INTO photos VALUES (?, BLOB_PLACEHOLDER)",
    blob_data=jpeg_bytes,
    params=(42,),
)

Both work end-to-end in pure Python via the lotofile / filetoblob server functions intercepted at the SQ_FILE (98) wire-protocol level — no native machinery anywhere in the thread of execution. See docs/DECISION_LOG.md §10–11 for the architecture pivot that made this possible.

Direct stored-procedure invocation (fast-path)

# Cleanly close a smart-LOB descriptor opened via SQL
result = conn.fast_path_call(
    "function informix.ifx_lo_close(integer)", lofd
)
# result == [0] on success

The fast-path RPC (SQ_FPROUTINE / SQ_EXFPROUTINE) bypasses PREPARE → EXECUTE → FETCH for direct UDF/SPL calls. Routine handles are cached per-connection, so repeated calls to the same function take a single round-trip.

Server compatibility

Tested against IBM Informix Dynamic Server 15.0.1.0.3DE (the official icr.io/informix/informix-developer-database Docker image). The wire protocol is stable across modern Informix versions; should work against 12.10+ unmodified.

For features that need server-side configuration (smart-LOBs, logged transactions), see docs/DECISION_LOG.md:

• Phase 7 — logged-DB transactions
• Phase 8 — BYTE/TEXT (needs blobspace)
• Phase 10/11 — BLOB/CLOB (needs sbspace + SBSPACENAME config + level-0 archive)

Performance

Single-connection benchmarks against the dev container on loopback:

Operation	Mean	Throughput
decode(int) per cell	139 ns	7.2M ops/sec
parse_tuple_payload per row (5 cols)	1.4 µs	715K rows/sec
SELECT 1 round-trip	~140 µs	~7K queries/sec
1000-row SELECT	~1.0 ms	~990K rows/sec sustained
executemany(1000) in transaction	32 ms	~31,000 rows/sec
Pool acquire + query + release	295 µs	~3.4K queries/sec
Cold connect (login handshake)	11 ms	~90 connections/sec

Performance gotcha: executemany(...) under autocommit=True is 53× slower than the same call inside a single transaction (server flushes the transaction log per row). For bulk loads, autocommit=False (default) + conn.commit() at the end. See docs/USAGE.md for the full performance tips section.

Compared to IfxPy (the C-bound PyPI driver)

Head-to-head benchmarks against IfxPy on identical workloads, same Informix server, matched conditions. Using median + IQR over 10+ rounds to resist outlier-round noise:

Benchmark	IfxPy 3.0.5 (C-bound)	informix-db (pure Python)	Result
Single-row SELECT round-trip	118 µs	114 µs	comparable
~10-row server-side query	130 µs	159 µs	IfxPy 22% faster
Cold connect (login handshake)	11.0 ms	10.5 ms	comparable
executemany(1k) in transaction	23.5 ms	23.2 ms	tied
executemany(10k) in transaction	259 ms	161 ms	informix-db 1.6× faster
executemany(100k) in transaction	2376 ms	1487 ms	informix-db 1.6× faster
SELECT 1k rows	1.2 ms	2.7 ms	IfxPy 2.3× faster
SELECT 10k rows	11.3 ms	25.8 ms	IfxPy 2.3× faster
SELECT 100k rows	112 ms	271 ms	IfxPy 2.4× faster

The honest summary:

• Bulk-insert workloads: informix-db wins 1.6× at scale. The pipelined executemany (Phase 33) sends all N BIND+EXECUTE PDUs before draining responses, eliminating per-row RTT. IfxPy still pays one round-trip per IfxPy.execute(stmt, tuple) call.
• Large-fetch workloads: IfxPy wins 2.3× at scale. Their C-level fetch_tuple decoder is genuinely faster than our Python parse_tuple_payload (~1.1 µs/row vs ~2.7 µs/row). At 100k rows, that 1.6 µs/row gap accumulates into a 160 ms wall-clock difference.
• Small queries: comparable. Both spend ~120 µs waiting for the server; the per-call codec cost is small relative to the round-trip.

When to prefer informix-db:

• ETL pipelines, log shipping, bulk writes (1.6× faster at scale)
• Containerized / minimal-dependency environments (50 KB wheel vs IfxPy's 92 MB OneDB tarball + libcrypt.so.1 dependency hell)
• Modern Python (works on 3.10–3.14; IfxPy is broken on Python 3.12+)
• Async / FastAPI workloads (we have native async; IfxPy doesn't)

When IfxPy may be faster:

• Analytical reporting queries pulling 10k+ rows in a single SELECT
• Workloads where the per-row decode cost dominates (wide rows, tight read loops)

These results are reproducible from tests/benchmarks/compare/ — the Dockerfile, bench script, and README walk through every step.

Full methodology, IQR caveats, install gauntlet, and reproduction in tests/benchmarks/compare/README.md.

A note on IfxPy's install gauntlet: getting it to run on a modern system requires Python ≤ 3.11, setuptools <58, permissive CFLAGS, manual download of a 92 MB ODBC tarball, four LD_LIBRARY_PATH directories, and libcrypt.so.1 (deprecated 2018, missing on Arch / Fedora 35+ / RHEL 9). informix-db's install: pip install informix-driver.

Standards & guarantees

• PEP 249 (DB-API 2.0): connect(), Connection, Cursor, description, rowcount, exception hierarchy
• paramstyle = "numeric" (Informix's native ESQL/C convention; ? and :1 both work)
• Threadsafety = 1: threads may share the module but not connections; the pool gives per-thread connection access. Phase 27 added a per-connection wire lock that makes accidental sharing safe (interleaved PDUs serialize correctly), but PEP 249 advice still holds — give each thread its own connection.
• CalVer versioning: YYYY.MM.DD releases. PEP 440 post-releases (.1, .2) for same-day fixes.

Development

The full test + lint workflow is in the Makefile. Quick summary:

make test                     # 77 unit tests (no Docker)
make ifx-up && make test-integration   # 231 integration tests
make bench                    # benchmark suite
make lint                     # ruff

For the smart-LOB tests specifically, the dev container needs additional one-time setup (blobspace + sbspace + level-0 archive). See docs/DECISION_LOG.md §10 for the onspaces / onmode / ontape commands.

Documentation

• docs/USAGE.md — practical recipes: connections, parameter binding, type mapping, transactions, performance tips, scrollable cursors, BLOBs, async, TLS, locale/Unicode, error handling, known limitations
• tests/benchmarks/README.md — performance baselines, headline numbers, how to run regressions
• CHANGELOG.md — phase-by-phase release notes

Project history & design rationale

This driver was built incrementally across 30 phases, each with a focused scope and decision log. The reasoning trail lives in:

• docs/PROTOCOL_NOTES.md — byte-level SQLI wire-format reference
• docs/JDBC_NOTES.md — index into the decompiled IBM JDBC driver, used as a clean-room reference
• docs/DECISION_LOG.md — phase-by-phase architectural decisions, with the why preserved
• docs/CAPTURES/ — annotated socat hex-dump captures

Notable architectural pivots documented in the decision log:

• Phase 10/11 (smart-LOB read/write): used lotofile/filetoblob SQL functions + SQ_FILE protocol intercept instead of the heavier SQ_FPROUTINE + SQ_LODATA stack — ~3x smaller than originally projected
• Phase 7 (logged-DB transactions): discovered Informix requires explicit SQ_BEGIN before each transaction in non-ANSI mode, plus SQ_RBWORK needs a savepoint short payload
• Phase 16 (async): shipped thread-pool wrapping (~250 lines) instead of full I/O abstraction refactor (~2000 lines); functionally equivalent for typical FastAPI workloads

License

MIT.