Timelog

In-memory, LSM-inspired, time-indexed multimap for Python.

Timelog stores many Python objects per timestamp, supports out-of-order ingest, and answers timestamp/range queries from a native C17 engine through a CPython extension. Current package version: 1.3.0.

License PyPI version Python versions Tests (PR) Packaging (PR) Dependency Review Release (PyPI) Coverage CodeQL Sanitizers OpenSSF Scorecard Python 3.12+

Why Timelog

Timelog is built for timestamp-first workloads where the core operation is "everything in [t1, t2)".
It provides a native in-memory index with snapshot-consistent reads, out-of-order ingestion support, and sequenced range deletes.

At a high level, writes flow through mutable ingest state into immutable layers (memrun, L0, L1), while reads merge across layers with tombstone-aware filtering.
The design is LSM-inspired, but explicitly scoped to an embedded in-memory engine.

Use it when you want a local Python object index optimized for:

  • append-heavy event streams,
  • range scans over integer timestamps,
  • retention via logical deletes/tombstones,
  • concurrent snapshot readers over live Python objects,
  • zero-copy timestamp views for analytics-style scans.

Installation

Install from PyPI:

pip install timelog-lib

Or with uv:

uv add timelog-lib

Distribution name is timelog-lib, import namespace stays timelog:

from timelog import Timelog

Runtime Support

  • Regular CPython 3.12-3.14.
  • Isolated subinterpreters with a per-interpreter GIL.
  • Free-threaded CPython 3.14t (Py_GIL_DISABLED=1) on the supported wheel set; importing Timelog does not re-enable the GIL.
  • Typed package metadata is included (py.typed and _timelog.pyi).

The Python API remains single-writer at the instance level: writes and lifecycle operations must be externally serialized. Independent snapshot readers can run concurrently.

What Changed in 1.2 and 1.3

1.2.0 rebuilt the CPython runtime boundary: _timelog now uses multi-phase module initialization, module-local exceptions and heap types, per-interpreter-safe state recovery, and explicit synchronization for the supported free-threaded wheel family.

1.3.0 keeps that runtime contract and focuses on the hot user paths: auto-timestamp append(obj) moved from Python into C, common positional methods use lower-overhead dispatch, bulk_append() ingests typed timestamp buffers directly, and core lower/upper-bound searches use a measured size-gated branchless path.

Quickstart: Streaming

from timelog import Timelog

log = Timelog.for_streaming(time_unit="ms")

# Auto-timestamp append
log.append({"event": "boot"})

# Operator-style explicit timestamp append
log[1_700_000_000_000] = {"event": "tick"}

# Half-open range query [t1, t2)
rows = list(log[1_700_000_000_000:1_700_000_000_001])
print(rows)

log.close()  # deterministic cleanup; finalizer cleanup is best-effort

Quickstart: Correctness Semantics

from timelog import Timelog

log = Timelog(time_unit="ms")
log[10] = "A"
del log[5:15]              # delete [5, 15)
log[10] = "B"              # later insert at same ts

print(log[10])             # ['B']
print(list(log[0:20]))     # [(10, 'B')]

log.close()  # optional explicit cleanup

Timelog uses sequenced tombstones, so later inserts are not hidden by earlier deletes.

Core Guarantees

  • Time ranges are half-open: [t1, t2).
  • Reads are snapshot-consistent.
  • Concurrency model is single writer plus concurrent readers.
  • Duplicate timestamps are allowed (multimap semantics).
  • Write-path backpressure (TimelogBusyError) indicates the write was accepted; do not blind-retry the same write.
  • close() discards all data. Timelog is in-memory; flush() improves open-instance visibility for readers, not durability.

What Timelog Is (and Isn’t)

Timelog is:

  • an embedded, in-memory timestamp index,
  • optimized for append-heavy ingest and time-range retrieval,
  • implemented in C17 with first-party CPython bindings.

Timelog is not:

  • a durable storage engine,
  • a distributed TSDB,
  • a SQL query engine.

close() discards all data — the engine is in-memory, so nothing survives it. flush() matters while the log is OPEN: it materializes pending writes into immutable segments so zero-copy views() readers can see them.

API Snapshot

Core Python facade surface:

  • Constructors: Timelog(...), for_streaming(...), for_bulk_ingest(...), for_low_latency(...).
  • Writes:
    • append(obj), append(obj, ts=...), append(ts, obj).
    • extend([(ts, obj), ...], mostly_ordered=..., insert_on_error=...).
    • bulk_append(timestamps, objects) for contiguous native-endian int64 buffers plus a same-length list/tuple of payloads.
    • log[ts] = obj, delete(t1, t2), delete(ts), cutoff(ts).
  • Reads:
    • log[t1:t2], log[t1:], log[:t2], log[:].
    • log[ts] / at(ts).
    • named iterators: range, since, until, all, point / equal.
    • iterator helpers: len(it), next_batch(n), and it.view().
  • Introspection and views:
    • stats(), busy_events, extend_skipped, retired_queue_len.
    • views(...) / page_spans(...) for zero-copy timestamp spans.
    • PageSpan.timestamps is a read-only memoryview; PageSpan.objects() lazily exposes the corresponding Python payloads.

See docs/python-api.md for the full behavior contract.

Lifecycle, Threading, and Backpressure

  • Most users should write log = Timelog(...) or use a preset constructor and keep the object for the required scope. A context manager is available but not required.
  • Explicit close() gives deterministic cleanup. If omitted, collection auto-closes on a best-effort basis.
  • Do not call close() concurrently with other operations on the same instance.
  • Release active iterators, PageSpan objects, object views, and exported memoryviews before closing; they hold snapshot pins.
  • Background maintenance can run automatically (maintenance="background") or be controlled manually (maintenance="disabled" + flush() / compact() / maint_step()).
  • TimelogBusyError on write operations means accepted write + pressure signal, not "write lost".

Architecture

Write Path                               Read Path
----------                               ---------
append/extend/delete                     snapshot + query([t1, t2))
      |                                           |
      v                                           v
  Memtable (mutable)  <--------------------  Snapshot view
      | seal
      v
  Memrun (immutable)
      | flush
      v
  L0 Segments (overlap)
      | compact
      v
  L1 Segments (windowed, non-overlap)

Reads plan sources across active + immutable layers, then run k-way merge with tombstone filtering based on sequencing/watermark state.
Flush and compaction bound read fan-out over time.
Deletes are logical tombstones; physical cleanup is deferred to maintenance.

flush() is a visibility operation, not durability: it publishes pending writes into immutable in-memory segments so readers and zero-copy views() can see them. close() always tears down the in-memory engine and discards all records.

Performance at a Glance

Same-harness v1.3 A/B against the v1.2.0 wheel, Linux x86_64, pinned CPU, CPython 3.13.12, median of 5:

Operation v1.2.0 v1.3.0 Change
append(obj) 513.9 ns 117.1 ns 4.39x faster
append(ts, obj) 352.1 ns 103.9 ns 3.39x faster
append(obj, ts=...) 364.7 ns 109.6 ns 3.33x faster
point(ts) 457.1 ns 337.1 ns 1.36x faster
equal(ts) 548.8 ns 429.3 ns 1.28x faster
next_ts(ts) 393.8 ns 299.8 ns 1.31x faster
range(t1, t2) 575.9 ns 458.0 ns 1.26x faster
delete_range(t1, t2) 18,059.6 ns 13,289.3 ns 1.36x faster
delete_before(ts) 109.7 ns 80.8 ns 1.36x faster

New v1.3 ingest fast path:

  • bulk_append(np.int64 array, list): 113.3 ns/record on a 200k-record measured batch.
  • In that benchmark, bulk_append was 2.23x faster than a post-v1.3 per-record append loop and 3.51x faster than extend(zip(...)).

Search-path optimization:

  • Size-gated branchless lower/upper-bound search measured 1.9x-5.0x faster at gated sizes up to 262,144 records, and falls back to the neutral path for very large arrays where it no longer wins.

Historical scale snapshot (2026-02-15, Linux x86_64, CPython 3.13.12, dataset 11,550,000 rows):

  • Batch ingest (A2): 191,105 records/sec.
  • Full scan (B4): 18,088,679 records/sec.
  • Append latency (K1, background): p99 = 672 ns.
  • PageSpan iteration (F1): 1.48B timestamps/sec on the timestamp-only span path.

Results are workload-, configuration-, and hardware-dependent. The current publishable benchmark framing is docs/performance.md; older reports are retained as historical snapshots.

Methodology and context:

  • docs/PERFORMANCE_METHODOLOGY.md
  • docs/performance.md
  • docs/benchmarks/bulk_append.md
  • docs/benchmarks/max_delta_segments.md
  • docs/BENCHMARK_1GB_7PCT_OOO_UNIX.md
  • docs/BENCHMARK_REPORT.md

Complexity claims should be interpreted with stated assumptions. In practice:

  • append path is amortized O(1) at memtable layer,
  • point/range behavior approaches logarithmic seek + linear output scan when source fan-out is bounded by maintenance,
  • delete cost depends on tombstone interval state.

Documentation

  • Index: docs/index.md
  • Release notes: docs/release-notes.md
  • Python API: docs/python-api.md
  • Configuration: docs/configuration.md
  • Error and retry semantics: docs/errors-and-retry-semantics.md
  • Performance methodology: docs/PERFORMANCE_METHODOLOGY.md
  • PyPI/release operations: docs/pypi-release.md

License

MIT. See LICENSE.

Contributing

PRs are welcome. Run core validation locally:

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DTIMELOG_BUILD_PYTHON=ON -DTIMELOG_BUILD_PY_TESTS=ON
cmake --build build --target timelog_e2e_build --config Release -j 2
ctest --test-dir build -C Release --output-on-failure -R '^py_.*_tests$'
cmake -E env PYTHONPATH="$PWD/python" python -m pytest python/tests -q

Package build sanity:

python -m build
python -m twine check dist/*

Project links

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Credits

Author: Timelog Contributors

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