piker/storage/cli.py

@@ -386,6 +386,8 @@ def ldshm(
             open_annot_ctl() as actl,
         ):
             shm_df: pl.DataFrame | None = None
+            tf2aids: dict[float, dict] = {}
+
             for (
                 shmfile,
                 shm,
@@ -526,16 +528,17 @@ def ldshm(
                             new_df,
                             step_gaps,
                         )
-
                         # last chance manual overwrites in REPL
-                        await tractor.pause()
+                        # await tractor.pause()
                         assert aids
+                        tf2aids[period_s] = aids
 
                 else:
                     # allow interaction even when no ts problems.
-                    await tractor.pause()
-                    # assert not diff
+                    assert not diff
 
+            await tractor.pause()
+            log.info('Exiting TSP shm anal-izer!')
 
             if shm_df is None:
                 log.error(

piker/storage/nativedb.py

@@ -161,7 +161,13 @@ class NativeStorageClient:
 
     def index_files(self):
         for path in self._datadir.iterdir():
-            if path.name in {'borked', 'expired',}:
+            if (
+                path.is_dir()
+                or
+                '.parquet' not in str(path)
+                # or
+                # path.name in {'borked', 'expired',}
+            ):
                 continue
 
             key: str = path.name.rstrip('.parquet')

piker/tsp/__init__.py

@@ -44,8 +44,10 @@ import trio
 from trio_typing import TaskStatus
 import tractor
 from pendulum import (
+    Interval,
     DateTime,
     Duration,
+    duration as mk_duration,
     from_timestamp,
 )
 import numpy as np
@@ -214,7 +216,8 @@ async def maybe_fill_null_segments(
         # pair, immediately stop backfilling?
         if (
             start_dt
-            and end_dt < start_dt
+            and
+            end_dt < start_dt
         ):
             await tractor.pause()
             break
@@ -262,6 +265,7 @@ async def maybe_fill_null_segments(
         except tractor.ContextCancelled:
             # log.exception
             await tractor.pause()
+            raise
 
     null_segs_detected.set()
     # RECHECK for more null-gaps
@@ -349,7 +353,7 @@ async def maybe_fill_null_segments(
 
 async def start_backfill(
     get_hist,
-    frame_types: dict[str, Duration] | None,
+    def_frame_duration: Duration,
     mod: ModuleType,
     mkt: MktPair,
     shm: ShmArray,
@@ -379,22 +383,23 @@ async def start_backfill(
         update_start_on_prepend: bool = False
         if backfill_until_dt is None:
 
-            # TODO: drop this right and just expose the backfill
-            # limits inside a [storage] section in conf.toml?
-            # when no tsdb "last datum" is provided, we just load
-            # some near-term history.
-            # periods = {
-            #     1: {'days': 1},
-            #     60: {'days': 14},
-            # }
-
-            # do a decently sized backfill and load it into storage.
+            # TODO: per-provider default history-durations?
+            # -[ ] inside the `open_history_client()` config allow
+            #    declaring the history duration limits instead of
+            #    guessing and/or applying the same limits to all?
+            #
+            # -[ ] allow declaring (default) per-provider backfill
+            #     limits inside a [storage] sub-section in conf.toml?
+            #
+            # NOTE, when no tsdb "last datum" is provided, we just
+            # load some near-term history by presuming a "decently
+            # large" 60s duration limit and a much shorter 1s range.
             periods = {
                 1: {'days': 2},
                 60: {'years': 6},
             }
             period_duration: int = periods[timeframe]
-            update_start_on_prepend = True
+            update_start_on_prepend: bool = True
 
             # NOTE: manually set the "latest" datetime which we intend to
             # backfill history "until" so as to adhere to the history
@@ -416,7 +421,6 @@ async def start_backfill(
                 f'backfill_until_dt: {backfill_until_dt}\n'
                 f'last_start_dt: {last_start_dt}\n'
             )
-
             try:
                 (
                     array,
@@ -426,71 +430,114 @@ async def start_backfill(
                     timeframe,
                     end_dt=last_start_dt,
                 )
-
             except NoData as _daterr:
-                # 3 cases:
-                # - frame in the middle of a legit venue gap
-                # - history actually began at the `last_start_dt`
-                # - some other unknown error (ib blocking the
-                #   history bc they don't want you seeing how they
-                #   cucked all the tinas..)
-                if dur := frame_types.get(timeframe):
-                    # decrement by a frame's worth of duration and
-                    # retry a few times.
-                    last_start_dt.subtract(
-                        seconds=dur.total_seconds()
+                orig_last_start_dt: datetime = last_start_dt
+                gap_report: str = (
+                    f'EMPTY FRAME for `end_dt: {last_start_dt}`?\n'
+                    f'{mod.name} -> tf@fqme: {timeframe}@{mkt.fqme}\n'
+                    f'last_start_dt: {orig_last_start_dt}\n\n'
+                    f'bf_until: {backfill_until_dt}\n'
                 )
-                    log.warning(
-                        f'{mod.name} -> EMPTY FRAME for end_dt?\n'
-                        f'tf@fqme: {timeframe}@{mkt.fqme}\n'
-                        'bf_until <- last_start_dt:\n'
-                        f'{backfill_until_dt} <- {last_start_dt}\n'
-                        f'Decrementing `end_dt` by {dur} and retry..\n'
+                # EMPTY FRAME signal with 3 (likely) causes:
+                #
+                # 1. range contains legit gap in venue history
+                # 2. history actually (edge case) **began** at the
+                #    value `last_start_dt`
+                # 3. some other unknown error (ib blocking the
+                #    history-query bc they don't want you seeing how
+                #    they cucked all the tinas.. like with options
+                #    hist)
+                #
+                if def_frame_duration:
+                    # decrement by a duration's (frame) worth of time
+                    # as maybe indicated by the backend to see if we
+                    # can get older data before this possible
+                    # "history gap".
+                    last_start_dt: datetime = last_start_dt.subtract(
+                        seconds=def_frame_duration.total_seconds()
                     )
+                    gap_report += (
+                        f'Decrementing `end_dt` and retrying with,\n'
+                        f'def_frame_duration: {def_frame_duration}\n'
+                        f'(new) last_start_dt: {last_start_dt}\n'
+                    )
+                    log.warning(gap_report)
+                    # skip writing to shm/tsdb and try the next
+                    # duration's worth of prior history.
                     continue
 
-            # broker says there never was or is no more history to pull
-            except DataUnavailable:
-                log.warning(
-                    f'NO-MORE-DATA in range?\n'
-                    f'`{mod.name}` halted history:\n'
-                    f'tf@fqme: {timeframe}@{mkt.fqme}\n'
-                    'bf_until <- last_start_dt:\n'
-                    f'{backfill_until_dt} <- {last_start_dt}\n'
-                )
+                else:
+                    # await tractor.pause()
+                    raise DataUnavailable(gap_report)
 
-                # ugh, what's a better way?
-                # TODO: fwiw, we probably want a way to signal a throttle
-                # condition (eg. with ib) so that we can halt the
-                # request loop until the condition is resolved?
-                if timeframe > 1:
-                    await tractor.pause()
+            # broker says there never was or is no more history to pull
+            except DataUnavailable as due:
+                message: str = due.args[0]
+                log.warning(
+                    f'Provider {mod.name!r} halted backfill due to,\n\n'
+
+                    f'{message}\n'
+
+                    f'fqme: {mkt.fqme}\n'
+                    f'timeframe: {timeframe}\n'
+                    f'last_start_dt: {last_start_dt}\n'
+                    f'bf_until: {backfill_until_dt}\n'
+                )
+                # UGH: what's a better way?
+                # TODO: backends are responsible for being correct on
+                # this right!?
+                # -[ ] in the `ib` case we could maybe offer some way
+                #     to halt the request loop until the condition is
+                #     resolved or should the backend be entirely in
+                #     charge of solving such faults? yes, right?
                 return
 
+            time: np.ndarray = array['time']
             assert (
-                array['time'][0]
+                time[0]
                 ==
                 next_start_dt.timestamp()
             )
 
-            diff = last_start_dt - next_start_dt
-            frame_time_diff_s = diff.seconds
+            assert time[-1] == next_end_dt.timestamp()
+
+            expected_dur: Interval = last_start_dt - next_start_dt
 
             # frame's worth of sample-period-steps, in seconds
             frame_size_s: float = len(array) * timeframe
-            expected_frame_size_s: float = frame_size_s + timeframe
-            if frame_time_diff_s > expected_frame_size_s:
-
+            recv_frame_dur: Duration = (
+                from_timestamp(array[-1]['time'])
+                -
+                from_timestamp(array[0]['time'])
+            )
+            if (
+                (lt_frame := (recv_frame_dur < expected_dur))
+                or
+                (null_frame := (frame_size_s == 0))
+                # ^XXX, should NEVER hit now!
+            ):
                 # XXX: query result includes a start point prior to our
                 # expected "frame size" and thus is likely some kind of
                 # history gap (eg. market closed period, outage, etc.)
                 # so just report it to console for now.
+                if lt_frame:
+                    reason = 'Possible GAP (or first-datum)'
+                else:
+                    assert null_frame
+                    reason = 'NULL-FRAME'
+
+                missing_dur: Interval = expected_dur.end - recv_frame_dur.end
                 log.warning(
-                    'GAP DETECTED:\n'
-                    f'last_start_dt: {last_start_dt}\n'
-                    f'diff: {diff}\n'
-                    f'frame_time_diff_s: {frame_time_diff_s}\n'
+                    f'{timeframe}s-series {reason} detected!\n'
+                    f'fqme: {mkt.fqme}\n'
+                    f'last_start_dt: {last_start_dt}\n\n'
+                    f'recv interval: {recv_frame_dur}\n'
+                    f'expected interval: {expected_dur}\n\n'
+
+                    f'Missing duration of history of {missing_dur.in_words()!r}\n'
+                    f'{missing_dur}\n'
                 )
+                # await tractor.pause()
 
             to_push = diff_history(
                 array,
@@ -565,22 +612,27 @@ async def start_backfill(
             # long-term storage.
             if (
                 storage is not None
-                and write_tsdb
+                and
+                write_tsdb
             ):
                 log.info(
                     f'Writing {ln} frame to storage:\n'
                     f'{next_start_dt} -> {last_start_dt}'
                 )
 
-                # always drop the src asset token for
+                # NOTE, always drop the src asset token for
                 # non-currency-pair like market types (for now)
+                #
+                # THAT IS, for now our table key schema is NOT
+                # including the dst[/src] source asset token. SO,
+                # 'tsla.nasdaq.ib' over 'tsla/usd.nasdaq.ib' for
+                # historical reasons ONLY.
                 if mkt.dst.atype not in {
                     'crypto',
                     'crypto_currency',
                     'fiat',  # a "forex pair"
+                    'perpetual_future',  # stupid "perps" from cex land
                 }:
-                    # for now, our table key schema is not including
-                    # the dst[/src] source asset token.
                     col_sym_key: str = mkt.get_fqme(
                         delim_char='',
                         without_src=True,
@@ -685,7 +737,7 @@ async def back_load_from_tsdb(
         last_tsdb_dt
         and latest_start_dt
     ):
-        backfilled_size_s = (
+        backfilled_size_s: Duration = (
             latest_start_dt - last_tsdb_dt
         ).seconds
         # if the shm buffer len is not large enough to contain
@@ -908,6 +960,8 @@ async def tsdb_backfill(
             f'{pformat(config)}\n'
         )
 
+        # concurrently load the provider's most-recent-frame AND any
+        # pre-existing tsdb history already saved in `piker` storage.
         dt_eps: list[DateTime, DateTime] = []
         async with trio.open_nursery() as tn:
             tn.start_soon(
@@ -918,7 +972,6 @@ async def tsdb_backfill(
                 timeframe,
                 config,
             )
-
             tsdb_entry: tuple = await load_tsdb_hist(
                 storage,
                 mkt,
@@ -947,6 +1000,25 @@ async def tsdb_backfill(
                 mr_end_dt,
             ) = dt_eps
 
+            first_frame_dur_s: Duration = (mr_end_dt - mr_start_dt).seconds
+            calced_frame_size: Duration = mk_duration(
+                seconds=first_frame_dur_s,
+            )
+            # NOTE, attempt to use the backend declared default frame
+            # sizing (as allowed by their time-series query APIs) and
+            # if not provided try to construct a default from the
+            # first frame received above.
+            def_frame_durs: dict[
+                int,
+                Duration,
+            ]|None = config.get('frame_types', None)
+            if def_frame_durs:
+                def_frame_size: Duration = def_frame_durs[timeframe]
+                assert def_frame_size == calced_frame_size
+            else:
+                # use what we calced from first frame above.
+                def_frame_size = calced_frame_size
+
             # NOTE: when there's no offline data, there's 2 cases:
             # - data backend doesn't support timeframe/sample
             #   period (in which case `dt_eps` should be `None` and
@@ -977,7 +1049,7 @@ async def tsdb_backfill(
                     partial(
                         start_backfill,
                         get_hist=get_hist,
-                        frame_types=config.get('frame_types', None),
+                        def_frame_duration=def_frame_size,
                         mod=mod,
                         mkt=mkt,
                         shm=shm,

piker/tsp/_anal.py

@@ -616,6 +616,18 @@ def detect_price_gaps(
     # ])
     ...
 
+# TODO: probably just use the null_segs impl above?
+def detect_vlm_gaps(
+    df: pl.DataFrame,
+    col: str = 'volume',
+
+) -> pl.DataFrame:
+
+    vnull: pl.DataFrame = w_dts.filter(
+        pl.col(col) == 0
+    )
+    return vnull
+
 
 def dedupe(
     src_df: pl.DataFrame,
@@ -626,7 +638,6 @@ def dedupe(
 
 ) -> tuple[
     pl.DataFrame,  # with dts
-    pl.DataFrame,  # gaps
     pl.DataFrame,  # with deduplicated dts (aka gap/repeat removal)
     int,  # len diff between input and deduped
 ]:
@@ -639,19 +650,22 @@ def dedupe(
     '''
     wdts: pl.DataFrame = with_dts(src_df)
 
-    # maybe sort on any time field
-    if sort:
-        wdts = wdts.sort(by='time')
-        # TODO: detect out-of-order segments which were corrected!
-        # -[ ] report in log msg
-        # -[ ] possibly return segment sections which were moved?
+    deduped = wdts
 
     # remove duplicated datetime samples/sections
     deduped: pl.DataFrame = wdts.unique(
-        subset=['dt'],
+        # subset=['dt'],
+        subset=['time'],
         maintain_order=True,
     )
 
+    # maybe sort on any time field
+    if sort:
+        deduped = deduped.sort(by='time')
+        # TODO: detect out-of-order segments which were corrected!
+        # -[ ] report in log msg
+        # -[ ] possibly return segment sections which were moved?
+
     diff: int = (
         wdts.height
         -