The guard seeded each day's pair six hours before the clock, with the
second source ten minutes later. Run between 05:50 and 06:00 UTC, the
newest day's second pair landed just past midnight, alone on a UTC day
of its own; the pick rightly chose that source for that day, the old
DISTINCT ON statement chose the same, and the assertion that every
series row is the first source failed for those ten minutes of the day,
on every Postgres major.
The pairs now sit at 10:00 and 10:10 UTC from yesterday back, whatever
the clock. With the clock pinned to 05:55 UTC the old fixture fails the
three series shapes and the new one passes, on 16 and 18 alike.
Three readers grouped the same rows twice and joined the second grouping
back to the first: the live buckets joined each hourly-mean day's hour
means to the per-source days, the all-time extremes joined the day
weights the same way, and the doctor report's dense buckets joined each
bucket's latest reading and hour means to the bucket aggregates. On a
measurements table without planner statistics (a new install, or an
import under the autoanalyze threshold) the planner estimated one row on
each side and ran every one of those joins as a nested loop that re-ran
the inner grouping for each outer row.
On ten years of readings over six types (51,000 rows, no statistics) the
dense buckets and the every-type live buckets ran into the 60 s statement
timeout and the all-time extremes took 11 s. With statistics all three
answered in 0.1 to 0.2 s.
Each is now one chain of folds over one scan. The live buckets fold the
readings per local hour and the hours per local day, carrying the day of
hours beside the counts. The all-time extremes take the day weights from
the same windowed rows they count, the shape the pre-fold remainder
already has. The dense buckets carry each bucket's latest reading and each
hour's count and mean on the rows as window values, and one grouping folds
them all. Same fixture without statistics: 0.2 s, 0.1 s and 0.1 s, the same
as with them.
A parity suite runs the joined statements as the reference against the
readers across ladders, zones with whole, half-hour and negative offsets,
grains, overlapping and off-ladder sources, glucose contexts and device
types, tombstones and both DST changes; sums and means agree to
floating-point summation order, everything else exactly. The
unanalysed-table guard now runs the three readers under EXPLAIN ANALYZE:
the joins measured 200 to 3,000 row visits per row stored, the folds about
10.
The dense-bucket loader drops under the 200-line function cap, so its
baseline entry goes.
The blood-pressure series chose each UTC day's source with DISTINCT ON
over the day's floor-passing systolic rows and joined the choices back
to the readings. That is the shape the analytics pick had, with the
same weakness: on a measurements table without planner statistics the
planner estimated one row on each side and ran the join as a nested
loop that rescanned the pick for every reading.
The ten-year series over 6,000 freshly written rows from one source
took 0.57 s for the statement alone, 0.86 s with a second source on
every third day; the plan visited about 4,900 rows per row stored, so
the cost grows with the square of the history.
The pick is now one window over one scan: the day's rows ordered
systolic first, then by ladder rank, then by source name, and the first
row's source taken only when it is a systolic one. The scan starts at
the window's first UTC day, so the pick still sees the whole edge day,
and a day without a floor-passing systolic still shows nothing. Both
fixtures above read in about 10 ms, with or without statistics.
A parity suite runs the old statement as the reference against the new
one across overlapping and off-ladder sources, half readings, sub-floor
rows, tombstones, both DST changes, window starts on and either side of
UTC midnight, and four session zones. The unanalysed-table guard now
covers the series' ten-year, quarter and week reads.
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.
Marc-André Bombeck