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BABOSO.. fix last history frame overlap slicing! 

I guess since i started supporting the whole "allow a gap between
the latest tsdb sample and the latest retrieved history frame" the
overlap slicing has been completely borked XD where we've been sticking
in duplicate history samples and this has caused all sorts of down
stream time-series processing issues..

So fix that but ensuring whenever there IS an overlap between history in
the latest frame and the tsdb that we always prefer the latest frame's
data and slice OUT the tsdb's duplicate indices..

CHERRY TO #486
distribute_dis
Tyler Goodlet 2023-12-08 18:56:38 -05:00
parent 2d72a052aa
commit cb941a5554
1 changed files with 27 additions and 7 deletions
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34
piker/data/history.py
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@ -716,9 +716,14 @@ async def tsdb_backfill(
backfill_diff: Duration = mr_start_dt - last_tsdb_dt backfill_diff: Duration = mr_start_dt - last_tsdb_dt
offset_s: float = backfill_diff.in_seconds() offset_s: float = backfill_diff.in_seconds()
# XXX EDGE CASE: when the venue was closed (say over # XXX EDGE CASEs: the most recent frame overlaps with
# the weeknd) causing a timeseries gap, AND the query # prior tsdb history!!
# frames size (eg. for 1s we rx 2k datums ~= 33.33m) IS # - so the latest frame's start time is earlier then
# the tsdb's latest sample.
# - alternatively this may also more generally occur
# when the venue was closed (say over the weeknd)
# causing a timeseries gap, AND the query frames size
# (eg. for ib's 1s we rx 2k datums ~= 33.33m) IS
# GREATER THAN the current venue-market's operating # GREATER THAN the current venue-market's operating
# session (time) we will receive datums from BEFORE THE # session (time) we will receive datums from BEFORE THE
# CLOSURE GAP and thus the `offset_s` value will be # CLOSURE GAP and thus the `offset_s` value will be
@ -727,20 +732,35 @@ async def tsdb_backfill(
# tsdb. In this case we instead only retreive and push # tsdb. In this case we instead only retreive and push
# the series portion missing from the db's data set. # the series portion missing from the db's data set.
if offset_s < 0: if offset_s < 0:
backfill_diff: Duration = mr_end_dt - last_tsdb_dt non_overlap_diff: Duration = mr_end_dt - last_tsdb_dt
offset_s: float = backfill_diff.in_seconds() non_overlap_offset_s: float = backfill_diff.in_seconds()
offset_samples: int = round(offset_s / timeframe) offset_samples: int = round(offset_s / timeframe)
# TODO: see if there's faster multi-field reads: # TODO: see if there's faster multi-field reads:
# https://numpy.org/doc/stable/user/basics.rec.html#accessing-multiple-fields # https://numpy.org/doc/stable/user/basics.rec.html#accessing-multiple-fields
# re-index with a `time` and index field # re-index with a `time` and index field
prepend_start = shm._first.value - offset_samples + 1 if offset_s > 0:
# NOTE XXX: ONLY when there is an actual gap
# between the earliest sample in the latest history
# frame do we want to NOT stick the latest tsdb
# history adjacent to that latest frame!
prepend_start = shm._first.value - offset_samples + 1
to_push = tsdb_history[-prepend_start:]
else:
# when there is overlap we want to remove the
# overlapping samples from the tsdb portion (taking
# instead the latest frame's values since THEY
# SHOULD BE THE SAME) and prepend DIRECTLY adjacent
# to the latest frame!
# TODO: assert the overlap segment array contains
# the same values!?!
prepend_start = shm._first.value
to_push = tsdb_history[-(shm._first.value):offset_samples - 1]
# tsdb history is so far in the past we can't fit it in # tsdb history is so far in the past we can't fit it in
# shm buffer space so simply don't load it! # shm buffer space so simply don't load it!
if prepend_start > 0: if prepend_start > 0:
to_push = tsdb_history[-prepend_start:]
shm.push( shm.push(
to_push, to_push,