Store named datetime columns as INTEGER microseconds (datetime_columns)

.gitignore

@@ -1,8 +1,6 @@
 # Python
 __pycache__/
 *.py[cod]
-*.pyo
-*.pyd
 *.egg
 *.egg-info/
 dist/
@@ -40,9 +38,7 @@ Thumbs.db
 .env.*
 
 # sqlmem cache (incl. WAL sidecars from disk-backed mode)
-cache.db
+cache.db*
-cache.db-wal
-cache.db-shm
 
 # Agents
 AGENTS.md

CHANGELOG.md

@@ -6,6 +6,24 @@ All notable changes to this project will be documented in this file.
 
 ---
 
+## [1.12.0] - 2026-06-09
+
+### ⚠️ Breaking
+- **`SCHEMA_VERSION` bumped `3` → `4`** — on upgrade the existing cache is wiped automatically (disk mode wipes the file in place, in-memory discards the backup) and reloaded from the source on next use. For a large cache (e.g. a multi-hundred-million-row table) the full reload can take a while; deploy in a maintenance window.
+- **`datetime_columns` change the public output contract for the chosen columns** — a column listed in `datetime_columns` is stored and returned as an **INTEGER (microseconds since the Unix epoch, UTC)**, not an ISO `TEXT` string. This is opt-in per column, so no table is affected unless you name its columns; consumers that read or filter such a column must adapt (compare against integer µs, or convert on read).
+
+### Added
+- **`datetime_columns=` parameter on `CachingEngine` / `CacheManager`** — `datetime_columns={"VW_X": ["CHANGE_DATE"]}` stores the named datetime columns as INTEGER µs-since-epoch instead of ~28-byte ISO `TEXT`. Saves ~20 bytes per row and makes index comparisons on the column operate on native integers instead of string collation — worthwhile for a pure datetime column on a very large table (e.g. a delta change column that is also range-scanned).
+  - `_coerce.to_sqlite_datetime()` converts datetimes (and ISO/`date` values) to exact integer microseconds via integer arithmetic (no float rounding); a naive datetime is treated as UTC, `None` passes through.
+  - `load_table` declares those columns `INTEGER` and `upsert_rows` coerces them the same way, so full loads and delta upserts agree on the on-disk representation.
+  - The delta high-watermark for such a column is the stored integer; `delta._bind_watermark(..., epoch_us=True)` reconstructs a real UTC `datetime` before binding, so the source still receives a typed timestamp (and the watermark fix from 1.8.0 keeps holding).
+
+### Changed
+- `pyproject.toml` — bumped version to `1.12.0`.
+- `CacheManager.max_value` / `set_last_synced_at` now accept/return `int` watermarks alongside `str` (the INTEGER-µs watermark round-trips through the `last_synced_at` TEXT column as its digit string).
+
+---
+
 ## [1.11.0] - 2026-06-09
 
 ### Added

README.md

@@ -284,6 +284,23 @@ engine = CachingEngine(
 - Every entry is applied as `PRAGMA <key> = <value>` when the cache connection opens. **Unknown or inapplicable pragmas are silently ignored** by SQLite, so a bad value degrades gracefully instead of crashing startup.
 - **`page_size` and `auto_vacuum` are layout pragmas** — they only take effect on a *fresh* file (set before the first table). On an existing cache, `page_size` is ignored with a one-time warning; use [`hard_reset()`](#manual-cache-control) to rebuild the file with the new value.
 
+#### INTEGER datetime columns (`datetime_columns=`)
+
+A pure datetime column stored as an ISO `TEXT` string costs ~28 bytes per row and compares by string collation. For a large table you can store named datetime columns as **INTEGER microseconds since the Unix epoch** instead — 8 bytes, native integer comparison:
+
+```python
+engine = CachingEngine(
+    base_engine,
+    delta={"VW_P_PRATVALUES": DeltaConfig("CHANGE_DATE", ["PRATVALUE_ID"])},
+    datetime_columns={"VW_P_PRATVALUES": ["CHANGE_DATE"]},
+)
+```
+
+- **Opt-in per column.** Only the columns you name change; everything else keeps the default lossless `TEXT` storage.
+- ⚠️ **It changes the output contract for those columns** — `execute()` returns them as `int` (µs since epoch), not ISO strings, and a `WHERE` on them must compare against integer µs. Don't list a column your callers read as a string.
+- The delta watermark is handled transparently: it is persisted as the integer and bound back to a real `datetime` for the source query, so incremental refresh keeps working.
+- ⚠️ This is a **breaking on-disk change** (`SCHEMA_VERSION` 4): an existing cache is wiped and reloaded on first start after enabling it — schedule a maintenance window for a large reload.
+
 ## Manual cache control
 
 ```python
@@ -374,6 +391,7 @@ engine = CachingEngine(
     fetch_batch=10000,                 # SQLMEM_FETCH_BATCH
     dialect="tsql",                    # SQLMEM_SQL_DIALECT
     pragmas={"mmap_size": 32 * 1024**3, "page_size": 8192},  # disk-mode SQLite tuning
+    datetime_columns={"orders": ["created_at"]},  # store these as INTEGER µs (opt-in)
     blocking_startup_refresh=False,    # block startup until caught up? (default: no)
 )
 ```

project.md

@@ -218,10 +218,11 @@ SQLMEM_DEBUG=true   # DEBUG level — podrobný výpis každého dotazu, cache o
 - [x] **Ladění SQLite vrstvy (`pragmas=`)**: `CachingEngine(..., pragmas={...})` aplikuje libovolné PRAGMA na cache spojení (`mmap_size`, `cache_size`, `temp_store`, `page_size`, `auto_vacuum`). `page_size` a `auto_vacuum` jsou layout-pragmata — platí jen na čerstvém souboru (page_size na existující cache se ignoruje s warningem). Neznámá pragmata SQLite tiše ignoruje.
 - [x] **`hard_reset()`**: smaže on-disk soubor (+ WAL/SHM) a otevře nový s aktuálními pragmaty — na rozdíl od `reset()` umožní změnit `page_size`/`auto_vacuum`. Jen disk mód (v memory módu fallback na `reset()`).
 - [x] **`vacuum(incremental=, pages=)`**: údržbový VACUUM cache souboru — inkrementální (uvolní volné stránky po delta `INSERT OR REPLACE`, vyžaduje `auto_vacuum=INCREMENTAL`) nebo plný (přepíše soubor, jen v maintenance okně). V memory módu no-op.
+- [x] **Nativní INTEGER ukládání datetime sloupců (`datetime_columns=`)**: `datetime_columns={"VW_X": ["CHANGE_DATE"]}` — vyjmenované datetime sloupce se ukládají jako INTEGER µs-od-epochy místo ~28 B ISO TEXT (úspora místa + nativní celočíselné porovnání indexu). Opt-in per sloupec → mění výstupní kontrakt jen u zvolených sloupců (vrací int, ne ISO string). Breaking: `SCHEMA_VERSION` 3→4, cache se při upgrade smaže a načte znovu. Watermark se persistuje jako int a `_bind_watermark(epoch_us=True)` ho rekonstruuje zpět na `datetime` pro zdroj.
 
 ## TODO — budoucí funkce
 
-- [ ] **Nativní INTEGER ukládání datetime sloupců (`datetime_columns=`)** — `CHANGE_DATE` apod. jako µs-od-epochy INTEGER místo 28 B ISO TEXT (úspora místa + rychlejší porovnání indexu). Breaking (`SCHEMA_VERSION` 3→4, wipe cache). Plán pro 1.12.0.
+- _(zatím žádné otevřené položky)_
 
 ---
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "sqlmem"
-version = "1.11.0"
+version = "1.12.0"
 description = ""
 authors = [
     {name = "jan.doubravsky@gmail.com"}

src/sqlmem/_coerce.py

@@ -15,6 +15,8 @@ from typing import Any
 
 Params = tuple | list | dict | None
 
+_EPOCH = datetime.datetime(1970, 1, 1, tzinfo=datetime.timezone.utc)
+
 
 def to_sqlite(value: Any) -> Any:
     if isinstance(value, decimal.Decimal):
@@ -28,6 +30,29 @@ def to_sqlite(value: Any) -> Any:
     return value
 
 
+def to_sqlite_datetime(value: Any) -> int | None:
+    """Store a datetime as INTEGER microseconds since the Unix epoch (UTC).
+
+    Used for columns the caller marks via ``datetime_columns``: 8 bytes as an
+    INTEGER instead of a ~28-byte ISO ``TEXT`` string, and integer comparison on
+    the change column instead of string collation. ``None`` passes through; a
+    naive datetime is treated as UTC. A non-datetime value is parsed from its ISO
+    string form (so ``date``/ISO-``str`` inputs work too); anything unparseable
+    becomes ``None``.
+    """
+    if value is None:
+        return None
+    if isinstance(value, datetime.datetime):
+        if value.tzinfo is None:
+            value = value.replace(tzinfo=datetime.timezone.utc)
+        delta = value - _EPOCH  # exact integer arithmetic (no float rounding)
+        return delta.days * 86_400_000_000 + delta.seconds * 1_000_000 + delta.microseconds
+    try:
+        return to_sqlite_datetime(datetime.datetime.fromisoformat(str(value)))
+    except (TypeError, ValueError):
+        return None
+
+
 def coerce_row(row: tuple) -> tuple:
     return tuple(to_sqlite(v) for v in row)
 

src/sqlmem/cache.py

@@ -9,12 +9,12 @@ from pathlib import Path
 from loguru import logger
 
 import sqlmem._meta as _meta
-from ._coerce import coerce_params, coerce_row
+from ._coerce import coerce_params, coerce_row, to_sqlite, to_sqlite_datetime
 from ._sql import quote, quote_list, quote_source
 from .config import FETCH_BATCH_SIZE, SQL_DIALECT
 from .stats import TableState
 
-SCHEMA_VERSION = 3
+SCHEMA_VERSION = 4
 
 
 @dataclass(frozen=True)
@@ -41,6 +41,7 @@ class CacheManager:
         dialect: str = SQL_DIALECT,
         fetch_batch: int = FETCH_BATCH_SIZE,
         pragmas: dict[str, str | int] | None = None,
+        datetime_columns: dict[str, list[str]] | None = None,
     ) -> None:
         self._db_path = db_path
         self._backup_interval = backup_interval
@@ -48,6 +49,8 @@ class CacheManager:
         self._dialect = dialect              # source-DB dialect, for identifier quoting
         self._fetch_batch = fetch_batch      # rows fetched per source batch
         self._pragmas = dict(pragmas or {})  # extra read/layout PRAGMAs (disk mode)
+        # table → columns stored as INTEGER µs-since-epoch instead of ISO TEXT
+        self._datetime_columns = {t: list(c) for t, c in (datetime_columns or {}).items()}
         self._lock = threading.Lock()       # serializes connection access
         self._load_lock = threading.Lock()  # serializes full table loads
         self._states: dict[str, str] = {}   # table → live processing state
@@ -387,6 +390,27 @@ class CacheManager:
                 self._conn.commit()
             logger.debug(f"Index {idx.name!r} ready on {table} ({cols})")
 
+    def _row_coercer(self, table: str, columns: list[str]):
+        """Return a per-row coercer for *columns* in source order.
+
+        Columns registered in ``datetime_columns`` for *table* are coerced to
+        INTEGER µs-since-epoch (``to_sqlite_datetime``); everything else keeps the
+        default stringifying coercion (``to_sqlite``). With no datetime columns it
+        is the plain :func:`coerce_row`, so the common path is unchanged.
+        """
+        dt_cols = set(self._datetime_columns.get(table, ()))
+        dt_idx = {i for i, c in enumerate(columns) if c in dt_cols}
+        if not dt_idx:
+            return coerce_row
+
+        def coerce(row: tuple) -> tuple:
+            return tuple(
+                to_sqlite_datetime(v) if i in dt_idx else to_sqlite(v)
+                for i, v in enumerate(row)
+            )
+
+        return coerce
+
     def load_table(
         self,
         table: str,
@@ -403,7 +427,11 @@ class CacheManager:
         connection lock is only held for the brief per-batch inserts and the swap.
         """
         src_cols = ", ".join(quote_source(c, self._dialect) for c in columns)
-        col_defs = ", ".join(f"{quote(c)} TEXT" for c in columns)
+        dt_cols = set(self._datetime_columns.get(table, ()))
+        col_defs = ", ".join(
+            f"{quote(c)} {'INTEGER' if c in dt_cols else 'TEXT'}" for c in columns
+        )
+        coerce = self._row_coercer(table, columns)
         placeholders = ", ".join("?" * len(columns))
         staging = f"{table}__sqlmem_load"
         q_staging = quote(staging)
@@ -427,7 +455,7 @@ class CacheManager:
                     batch = cursor.fetchmany(self._fetch_batch)  # network outside _lock
                     if not batch:
                         break
-                    clean = [coerce_row(row) for row in batch]
+                    clean = [coerce(row) for row in batch]
                     with self._lock:
                         self._conn.executemany(insert_sql, clean)
                         self._conn.commit()
@@ -518,9 +546,11 @@ class CacheManager:
         row = self._conn.execute(
             "SELECT last_synced_at FROM _sqlmem_tables WHERE table_name = ?", (table,)
         ).fetchone()
+        # Stored in a TEXT column: an INTEGER-µs watermark (datetime_columns) comes
+        # back as its digit string; delta._bind_watermark reconstructs the datetime.
         return row[0] if row else None
 
-    def set_last_synced_at(self, table: str, value: str | None) -> None:
+    def set_last_synced_at(self, table: str, value: str | int | None) -> None:
         with self._lock:
             self._conn.execute(
                 "UPDATE _sqlmem_tables SET last_synced_at = ? WHERE table_name = ?",
@@ -528,8 +558,11 @@ class CacheManager:
             )
             self._conn.commit()
 
-    def max_value(self, table: str, column: str) -> str | None:
+    def max_value(self, table: str, column: str) -> str | int | None:
-        """Maximum value of *column* across cached rows (the delta watermark)."""
+        """Maximum value of *column* across cached rows (the delta watermark).
+
+        Returns an ``int`` for a datetime column stored as INTEGER µs, else the
+        ISO ``TEXT`` string."""
         row = self._conn.execute(
             f"SELECT MAX({quote(column)}) FROM {quote(table)}"
         ).fetchone()
@@ -539,7 +572,8 @@ class CacheManager:
         """Insert-or-replace one batch of *rows* by the table's unique key."""
         col_list = quote_list(columns)
         placeholders = ", ".join("?" * len(columns))
-        clean_rows = [coerce_row(row) for row in rows]
+        coerce = self._row_coercer(table, columns)
+        clean_rows = [coerce(row) for row in rows]
         with self._lock:
             self._conn.executemany(
                 f"INSERT OR REPLACE INTO {quote(table)} ({col_list}) VALUES ({placeholders})",

src/sqlmem/delta.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass, field
-from datetime import datetime
+from datetime import datetime, timedelta, timezone
 from typing import Any
 
 from loguru import logger
@@ -8,8 +8,10 @@ from ._sql import quote_source
 from .cache import CacheManager
 from .stats import TableState
 
+_EPOCH = datetime(1970, 1, 1, tzinfo=timezone.utc)
 
-def _bind_watermark(watermark: str) -> datetime | str:
+
+def _bind_watermark(watermark: str | int, epoch_us: bool = False) -> datetime | str:
     """Bind the delta watermark back to the source in its native type.
 
     The cache stores the change column as an ISO ``TEXT`` string (see
@@ -22,11 +24,21 @@ def _bind_watermark(watermark: str) -> datetime | str:
     driver send a typed timestamp, so the comparison happens natively with no
     string conversion. Non-datetime change columns (e.g. an integer rowversion)
     don't parse and are passed through unchanged.
+
+    When the change column is stored as INTEGER µs-since-epoch (``datetime_columns``)
+    *epoch_us* is set: the watermark is a microsecond count (an ``int`` or its digit
+    string, since it round-trips through a TEXT column) and is reconstructed into a
+    UTC :class:`~datetime.datetime` so the source still receives a typed timestamp.
     """
+    if epoch_us:
         try:
-        return datetime.fromisoformat(watermark)
+            return _EPOCH + timedelta(microseconds=int(watermark))
         except (TypeError, ValueError):
-        return watermark
+            return watermark if isinstance(watermark, str) else str(watermark)
+    try:
+        return datetime.fromisoformat(watermark)  # type: ignore[arg-type]
+    except (TypeError, ValueError):
+        return watermark if isinstance(watermark, str) else str(watermark)
 
 
 @dataclass(frozen=True)
@@ -92,9 +104,10 @@ class DeltaRefresher:
             cursor = source_conn.execute(f"SELECT {col_list} FROM {q_table}")
         else:
             change_col = quote_source(cfg.change_column, dialect)
+            epoch_us = cfg.change_column in self._cache._datetime_columns.get(table, ())
             cursor = source_conn.execute(
                 f"SELECT {col_list} FROM {q_table} WHERE {change_col} >= ?",
-                (_bind_watermark(watermark),),
+                (_bind_watermark(watermark, epoch_us),),
             )
 
         # Stream the delta in batches so a large catch-up never materializes at once.

src/sqlmem/engine.py

@@ -66,6 +66,7 @@ class CachingEngine:
         fetch_batch: int | None = None,
         dialect: str | None = None,
         pragmas: dict[str, str | int] | None = None,
+        datetime_columns: dict[str, list[str]] | None = None,
         blocking_startup_refresh: bool = False,
     ) -> None:
         self._source_engine = source_engine
@@ -81,6 +82,7 @@ class CachingEngine:
             dialect=self._dialect,
             fetch_batch=fetch_batch if fetch_batch is not None else FETCH_BATCH_SIZE,
             pragmas=pragmas,
+            datetime_columns=datetime_columns,
         )
         self._registry = ColumnRegistry(self._cache.connection)
         self._stats = StatsCollector()

tests/test_coerce.py

@@ -4,7 +4,7 @@ import uuid
 
 import pytest
 
-from sqlmem._coerce import coerce_params, to_sqlite
+from sqlmem._coerce import coerce_params, to_sqlite, to_sqlite_datetime
 from sqlmem.cache import CacheManager
 
 
@@ -91,6 +91,80 @@ def test_coerce_params_none():
     assert coerce_params(None) is None
 
 
+# --- to_sqlite_datetime (INTEGER µs storage, 1.12.0) ------------------------
+
+
+def test_datetime_to_epoch_micros():
+    # 2026-06-01T10:00:00Z -> microseconds since epoch
+    dt = datetime.datetime(2026, 6, 1, 10, 0, 0, tzinfo=datetime.timezone.utc)
+    expected = int(dt.timestamp() * 1_000_000)
+    assert to_sqlite_datetime(dt) == expected
+
+
+def test_datetime_naive_treated_as_utc():
+    naive = datetime.datetime(2026, 6, 1, 10, 0, 0)
+    aware = naive.replace(tzinfo=datetime.timezone.utc)
+    assert to_sqlite_datetime(naive) == to_sqlite_datetime(aware)
+
+
+def test_datetime_micros_are_exact():
+    dt = datetime.datetime(2026, 6, 5, 14, 54, 24, 823000, tzinfo=datetime.timezone.utc)
+    us = to_sqlite_datetime(dt)
+    # round-trips back to the same instant with no rounding loss
+    back = datetime.datetime(1970, 1, 1, tzinfo=datetime.timezone.utc) + datetime.timedelta(
+        microseconds=us
+    )
+    assert back == dt
+
+
+def test_datetime_none_passes_through():
+    assert to_sqlite_datetime(None) is None
+
+
+def test_datetime_iso_string_parsed():
+    assert to_sqlite_datetime("2026-06-01T10:00:00+00:00") == to_sqlite_datetime(
+        datetime.datetime(2026, 6, 1, 10, 0, 0, tzinfo=datetime.timezone.utc)
+    )
+
+
+def test_datetime_unparseable_is_none():
+    assert to_sqlite_datetime("not a date") is None
+
+
+# --- integration: datetime_columns are stored as INTEGER --------------------
+
+
+def test_datetime_column_stored_as_integer(tmp_path):
+    c = CacheManager(
+        db_path=tmp_path / "cache.db",
+        backup_interval=9999,
+        datetime_columns={"t": ["changed"]},
+    )
+    dt = datetime.datetime(2026, 6, 1, 10, 0, 0, tzinfo=datetime.timezone.utc)
+    c.load_table("t", ["id", "changed"], FakeSource([("1", dt)]))
+
+    # Column declared INTEGER, value stored as µs-since-epoch.
+    coltype = c.connection.execute("PRAGMA table_info(t)").fetchall()
+    types = {row[1]: row[2] for row in coltype}
+    assert types["changed"] == "INTEGER"
+    assert types["id"] == "TEXT"
+    _, out = c.execute_in_memory("SELECT changed FROM t")
+    assert out == [(to_sqlite_datetime(dt),)]
+    c.close()
+
+
+def test_non_datetime_columns_unaffected_by_datetime_columns(tmp_path):
+    c = CacheManager(
+        db_path=tmp_path / "cache.db",
+        backup_interval=9999,
+        datetime_columns={"t": ["changed"]},
+    )
+    c.load_table("t", ["id", "price"], FakeSource([("1", decimal.Decimal("9.99"))]))
+    _, out = c.execute_in_memory("SELECT id, price FROM t")
+    assert out == [("1", "9.99")]  # still TEXT/ISO coercion
+    c.close()
+
+
 # --- integration: values reach the cache through coercion -------------------
 
 

tests/test_delta.py

@@ -1,6 +1,6 @@
 import sqlite3
 import threading
-from datetime import datetime
+from datetime import datetime, timezone
 from types import SimpleNamespace
 
 import pytest
@@ -140,6 +140,18 @@ def test_bind_watermark_passes_through_non_datetime():
     assert _bind_watermark("12345") == "12345"
 
 
+# --- INTEGER µs watermark binding (datetime_columns, 1.12.0) ----------------
+
+
+def test_bind_watermark_epoch_us_reconstructs_datetime():
+    dt = datetime(2026, 6, 5, 14, 54, 24, 823000, tzinfo=timezone.utc)
+    us = int(dt.timestamp() * 1_000_000)
+    # Whether the watermark is an int or its digit string (it round-trips through
+    # the TEXT last_synced_at column), it binds back to the same UTC datetime.
+    assert _bind_watermark(us, epoch_us=True) == dt
+    assert _bind_watermark(str(us), epoch_us=True) == dt
+
+
 class _SpyCursor:
     def __init__(self, rows):
         self._rows = list(rows)
@@ -174,6 +186,46 @@ def test_refresh_binds_watermark_as_datetime(env):
     assert params == (datetime(2026, 6, 5, 14, 54, 24, 823000),)
 
 
+class _RowSource:
+    """Returns fixed rows for any query (for loading datetime-typed source data)."""
+
+    def __init__(self, rows):
+        self._rows = rows
+
+    def execute(self, sql, params=()):
+        return _SpyCursor(self._rows)
+
+
+def test_datetime_column_watermark_stored_as_int_and_bound_back(tmp_path):
+    """A change column declared in datetime_columns is stored as INTEGER µs; the
+    watermark is bound back to a real datetime for the source query."""
+    cache = CacheManager(
+        db_path=tmp_path / "c.db",
+        backup_interval=9999,
+        datetime_columns={"products": ["changed"]},
+    )
+    dt1 = datetime(2026, 6, 1, 10, 0, 0, tzinfo=timezone.utc)
+    dt2 = datetime(2026, 6, 1, 10, 5, 0, tzinfo=timezone.utc)
+    cache.load_table("products", ["id", "changed"], _RowSource([("1", dt1), ("2", dt2)]))
+    cache.create_unique_index("products", ["id"])
+    cache.set_last_synced_at("products", cache.max_value("products", "changed"))
+
+    # Watermark persisted as the max INTEGER µs (digit string out of the TEXT col).
+    wm = cache.get_last_synced_at("products")
+    assert wm == str(int(dt2.timestamp() * 1_000_000))
+
+    refresher = DeltaRefresher(
+        cache, {"products": ResolvedDelta("changed", ["id"])}
+    )
+    spy = _SpySource(rows=[])  # no new rows — just capture the bound watermark
+    refresher.refresh(spy)
+
+    assert spy.bound, "source query was never issued"
+    _, params = spy.bound[-1]
+    assert params == (dt2,)  # bound back as datetime, not an int/string
+    cache.close()
+
+
 # ---------------------------------------------------------------------------
 # Refresh failures are recorded (4.3) so a stuck delta is visible in stats
 # ---------------------------------------------------------------------------