perf: optimize time-series write/read hot paths (10's to 100's of ns savings, 2-25x better)

[mykaul]

Summary

Optimize the serialization and deserialization hot paths most relevant to time-series workloads. Each commit is an independent, benchmarked improvement.

Changes

Commit 1: Microbenchmark suite

• Add benchmarks/bench_timeseries.py — standalone timeit-based microbenchmarks covering DateType.serialize/deserialize, varint_pack/unpack, MonotonicTimestampGenerator, and BoundStatement.bind() for a 5-column time-series schema.

Commit 2: DateType.serialize — replace calendar.timegm with integer arithmetic

• Replace calendar.timegm(v.utctimetuple()) with timedelta arithmetic in cqltypes.py and encoder.py, eliminating the costly struct_time allocation.
• 3–5x faster for datetime serialization.

Commit 3: varint_pack/varint_unpack — use int.to_bytes/int.from_bytes

• Replace the hand-rolled hex-string loop in marshal.py and cython_marshal.pyx with Python 3 builtins int.to_bytes() / int.from_bytes().
• 2–21x faster depending on integer size (larger values see bigger gains).

Commit 4: MonotonicTimestampGenerator — use time.time_ns()

• Replace int(time.time() * 1e6) with time.time_ns() // 1000 for exact microsecond precision and pure integer arithmetic.
• Remove unnecessary lock from __init__.
• ~1.16x faster single-threaded.

Commit 5: Cython-accelerated SerDateType timestamp serializer

• Restore serializers.pyx/.pxd from PR (improvement) serializers: add Cython-optimized serialization for VectorType (up to 30x faster - 823us -> 4us!) #748 branch.
• Add SerDateType that serializes datetime/date values to 8-byte big-endian int64 timestamps using C-level byte-swapping.
• Fix TimestampType → SerDateType mapping so TimestampType (subclass of DateType) correctly uses the Cython fast path.

Benchmark Results (Python 3.14.3)

DateType.serialize (Python path)

Benchmark	Master	Perf	Speedup
datetime (2025)	1056 ns	256 ns	4.1x
datetime (epoch)	929 ns	176 ns	5.3x
date object	1492 ns	511 ns	2.9x

SerDateType (Cython path)

Benchmark	Master	Perf	Speedup
datetime (2025)	237 ns	191 ns	1.24x
datetime (epoch)	139 ns	125 ns	1.11x
date object	452 ns	398 ns	1.14x
raw int	641 ns	635 ns	1.01x

Note: Master baseline used a stale pre-built .so — the .pyx/.pxd sources did not exist on master. The main win from Commit 5 is that serializers.pyx is now source-tracked and TimestampType correctly resolves to the Cython fast path.

varint_pack / varint_unpack

Benchmark	Master	Perf	Speedup
pack small (42)	192 ns	79 ns	2.4x
pack large (2^127)	1158 ns	96 ns	12.1x
unpack large (2^127)	1966 ns	92 ns	21.4x
unpack negative	1209 ns	91 ns	13.3x

MonotonicTimestampGenerator

Benchmark	Master	Perf	Speedup
single-thread call	432 ns	373 ns	1.16x

End-to-end: BoundStatement.bind (5-col time-series row)

Benchmark	Master	Perf	Speedup
bind 5-col row	4486 ns	3157 ns	1.42x

Testing

• 162 unit tests pass, 1 skipped (pre-existing).

[mykaul]

Follow-up: Memoize cql_parameterized_type() on all type classes

Commit: 434465b

What changed

Added lazy memoization to cql_parameterized_type() on all 6 override sites: _CassandraType (base), TupleType, UserType, CompositeType, DynamicCompositeType, and VectorType.

The computed CQL type string is cached in a _cql_type_str class attribute. Since type classes are immutable after apply_parameters(), no invalidation is needed. The pattern is a simple None-sentinel check — no functools overhead.

Benchmark results (Python 3.14, 500k iterations)

Type	Uncached	Cached	Speedup
Int32Type (simple)	157 ns	23 ns	6.9x
MapType<text, int>	464 ns	20 ns	22.9x
SetType<float>	371 ns	20 ns	18.2x
ListType<double>	357 ns	21 ns	17.3x
TupleType<int, text, bool>	509 ns	20 ns	25.0x
MapType<text, list<tuple<int, float, double>>>	636 ns	56 ns	11.4x

Testing

• 607 unit tests passed (10.2s)
• No regressions

[mykaul]

Follow-up: Skip ColDesc creation in bind() when column encryption is disabled

Commit: 3015986

What changed

Split BoundStatement.bind() into two code paths based on whether column_encryption_policy is set:

• Fast path (no encryption — the common case): Calls col_spec.type.serialize(value, proto_version) directly. Eliminates per-column ColDesc namedtuple creation, ce_policy.contains_column() check, and ce_policy.column_type() lookup.
• Encryption path: Unchanged behavior with full ColDesc creation and encryption logic.

Benchmark results (Python 3.14, 200k iterations, inner loop)

Schema	Old	New	Saving	Speedup
3-col (int, double, text)	1,375 ns	523 ns	852 ns	2.63x
5-col time-series	2,226 ns	1,013 ns	1,213 ns	2.20x
8-col wide row	3,495 ns	1,317 ns	2,178 ns	2.65x

Testing

• 607 unit tests passed (10.6s)
• No regressions