The canonical-source pick grouped the window to (type, day, source),
chose each day's source with DISTINCT ON, and joined the choices back to
the rows. Whether that join ran as a hash join or as a nested loop that
rescanned the whole pick for every row was the planner's call, made from
table statistics. On a measurements table without them (a new install,
or an import still under the autoanalyze threshold) it estimated one row
on each side and took the nested loop.
Binding the request's clock as a parameter instead of reading NOW()
flipped which side of that loop the pick landed on: the analytics read
over 6,000 freshly written blood-pressure rows went from 1.6 s to 8.4 s,
and the demo seed's dashboard headline to 20 s. On a ten-year account
without statistics the 90-day read took 4 s with either clock and the
all-time read ran past two minutes.
The pick is now one window function over one scan: FIRST_VALUE of the
source per (type, UTC day) in ladder order, keeping the rows of that
source. Same rows, same order of preference, so every day keeps its
source; the parity suite against the DISTINCT ON statement holds that.
The analytics read on the fixture above takes about 0.1 s, the headline
0.4 s. On the analysed ten-year account the 90-day and all-time reads
are as fast as the join was, and they no longer depend on statistics.
The rows carry one extra column, canonical_source; every reader selects
its columns by name.
A new integration test runs every reader shape under EXPLAIN ANALYZE on
a table with no statistics and bounds the rows the plan produces or
discards per row seeded. The statement count never moved, so a statement
budget could not see this; the join measured 200 to 8,600 row visits
per row seeded and the window stays at 4.
The summaries slice and the comprehensive aggregator took their JS windows
(the DAY-bucket bounds, the rollup slope anchors, lastSeen staleness) from
the process clock and their SQL windows (avg7, avg30, the month before, the
median, the 90-day canonical pick, the cold-path slope evidence) from
NOW(), the database's clock. One response mixed two clocks, so a reading
on a window's edge fell in or out with whichever host answered, and with
the process clock pinned the analytics parity walk changed its avg7 with
the hour it ran at.
Each read now takes one `now` and binds it into its statements
(`request-clock-sql.ts`): `$N::timestamp`, which keeps the driver's
offset-free UTC string as written in any session zone. The canonical-pick
helper takes its window as an instant plus an interval instead of reading
NOW(); the series and calendar readers anchor it on their own `since`, the
BP-in-target read on its 365-day start. The window-mean and live-slope
columns the two aggregates each spelled out now come from one builder.
The streak's local day series read its bounds through `::timestamptz`,
which follows the session zone: outside a UTC session today's reading fell
out of the streak. It reads them as UTC first.
The parity walks for analytics, the dashboard summary and the
comprehensive overview pin a fixed day instead of today: their records are
laid out from the anchor day, so the chronotype's work and free nights and
the weight-by-weekday points moved with the weekday the walk ran on. The
snapshots are unchanged. A new suite pins the clock to four times of day,
far from the database clock, with readings ten minutes either side of
every edge, on both read paths, in a UTC and an Auckland session.
The shared canonical-source CTE sorted every raw reading in its window by
a CASE expression no index can supply; on the benchmark account the pulse
pick over ten years sorted 260,000 rows and spilled to disk. Its order
depends only on (type, source), so the pick now groups the window to its
distinct (type, day, source) triples first and runs DISTINCT ON over
those. The picked source per day is the same by construction; an
integration test runs the old statement beside the new one over a fixture
with overlapping sources, sources off the ladder, tombstones, empty days
and readings either side of midnight, under default and reordered ladders,
for every reader shape, and requires identical rows.
Every app session now sets jit=off. Postgres compiled the larger folds to
machine code, which took 100 to 140 ms of a 220 ms statement and saved
nothing for statements that run once and are bound by reading rows. JIT
never changes a result.
Marc-André Bombeck