Make `marketstore` storage api timeframe aware
The `Store.load()`, `.read_ohlcv()` and `.write_ohlcv()` and `.delete_ts()` now can take a `timeframe: Optional[float]` param which is used to look up the appropriate sampling period table-key from `marketstore`.ib_1m_hist
parent
2a866dde65
commit
bf7d5e9a71
piker/data
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@ -387,6 +387,7 @@ class Storage:
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async def load(
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async def load(
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self,
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self,
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fqsn: str,
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fqsn: str,
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timeframe: int,
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) -> tuple[
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) -> tuple[
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dict[int, np.ndarray], # timeframe (in secs) to series
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dict[int, np.ndarray], # timeframe (in secs) to series
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@ -400,12 +401,16 @@ class Storage:
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# on first load we don't need to pull the max
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# on first load we don't need to pull the max
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# history per request size worth.
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# history per request size worth.
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limit=3000,
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limit=3000,
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timeframe=timeframe,
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)
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)
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log.info(f'Loaded tsdb history {tsdb_arrays}')
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log.info(f'Loaded tsdb history {tsdb_arrays}')
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if tsdb_arrays:
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if len(tsdb_arrays):
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fastest = list(tsdb_arrays.values())[0]
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# fastest = list(tsdb_arrays.values())[0]
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times = fastest['Epoch']
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# slowest = list(tsdb_arrays.values())[-1]
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hist = tsdb_arrays[timeframe]
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times = hist['Epoch']
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first, last = times[0], times[-1]
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first, last = times[0], times[-1]
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first_tsdb_dt, last_tsdb_dt = map(
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first_tsdb_dt, last_tsdb_dt = map(
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pendulum.from_timestamp, [first, last]
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pendulum.from_timestamp, [first, last]
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@ -420,9 +425,9 @@ class Storage:
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end: Optional[int] = None,
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end: Optional[int] = None,
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limit: int = int(800e3),
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limit: int = int(800e3),
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) -> tuple[
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) -> dict[
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MarketstoreClient,
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int,
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Union[dict, np.ndarray]
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Union[dict, np.ndarray],
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]:
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]:
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client = self.client
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client = self.client
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syms = await client.list_symbols()
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syms = await client.list_symbols()
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@ -430,7 +435,8 @@ class Storage:
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if fqsn not in syms:
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if fqsn not in syms:
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return {}
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return {}
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tfstr = tf_in_1s[1]
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# use the provided timeframe or 1s by default
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tfstr = tf_in_1s.get(timeframe, tf_in_1s[1])
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params = Params(
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params = Params(
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symbols=fqsn,
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symbols=fqsn,
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@ -463,39 +469,52 @@ class Storage:
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return {}
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return {}
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else:
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else:
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params.set('timeframe', tfstr)
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try:
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result = await client.query(params)
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result = await client.query(params)
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except purerpc.grpclib.exceptions.UnknownError:
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# indicate there is no history for this timeframe
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return {}
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# Fill out a `numpy` array-results map keyed by timeframe
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arrays = {}
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# TODO: it turns out column access on recarrays is actually slower:
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# TODO: it turns out column access on recarrays is actually slower:
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# https://jakevdp.github.io/PythonDataScienceHandbook/02.09-structured-data-numpy.html#RecordArrays:-Structured-Arrays-with-a-Twist
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# https://jakevdp.github.io/PythonDataScienceHandbook/02.09-structured-data-numpy.html#RecordArrays:-Structured-Arrays-with-a-Twist
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# it might make sense to make these structured arrays?
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# it might make sense to make these structured arrays?
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# Fill out a `numpy` array-results map
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arrays = {}
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for fqsn, data_set in result.by_symbols().items():
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for fqsn, data_set in result.by_symbols().items():
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arrays.setdefault(fqsn, {})[
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arrays.setdefault(fqsn, {})[
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tf_in_1s.inverse[data_set.timeframe]
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tf_in_1s.inverse[data_set.timeframe]
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] = data_set.array
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] = data_set.array
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return arrays[fqsn][timeframe] if timeframe else arrays[fqsn]
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return arrays[fqsn]
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async def delete_ts(
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async def delete_ts(
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self,
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self,
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key: str,
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key: str,
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timeframe: Optional[Union[int, str]] = None,
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timeframe: Optional[Union[int, str]] = None,
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fmt: str = 'OHLCV',
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) -> bool:
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) -> bool:
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client = self.client
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client = self.client
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syms = await client.list_symbols()
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syms = await client.list_symbols()
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print(syms)
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print(syms)
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# if key not in syms:
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if key not in syms:
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# raise KeyError(f'`{fqsn}` table key not found?')
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raise KeyError(f'`{key}` table key not found in\n{syms}?')
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return await client.destroy(tbk=key)
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tbk = mk_tbk((
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key,
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tf_in_1s.get(timeframe, tf_in_1s[60]),
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fmt,
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))
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return await client.destroy(tbk=tbk)
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async def write_ohlcv(
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async def write_ohlcv(
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self,
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self,
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fqsn: str,
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fqsn: str,
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ohlcv: np.ndarray,
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ohlcv: np.ndarray,
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timeframe: int,
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append_and_duplicate: bool = True,
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append_and_duplicate: bool = True,
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limit: int = int(800e3),
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limit: int = int(800e3),
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@ -525,7 +544,7 @@ class Storage:
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# write to db
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# write to db
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resp = await self.client.write(
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resp = await self.client.write(
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to_push,
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to_push,
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tbk=f'{fqsn}/1Sec/OHLCV',
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tbk=f'{fqsn}/{tf_in_1s[timeframe]}/OHLCV',
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# NOTE: will will append duplicates
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# NOTE: will will append duplicates
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# for the same timestamp-index.
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# for the same timestamp-index.
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@ -577,6 +596,7 @@ class Storage:
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# def delete_range(self, start_dt, end_dt) -> None:
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# def delete_range(self, start_dt, end_dt) -> None:
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# ...
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# ...
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@acm
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@acm
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async def open_storage_client(
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async def open_storage_client(
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fqsn: str,
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fqsn: str,
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@ -642,7 +662,7 @@ async def tsdb_history_update(
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):
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):
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profiler(f'opened feed for {fqsn}')
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profiler(f'opened feed for {fqsn}')
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to_append = feed.shm.array
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to_append = feed.hist_shm.array
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to_prepend = None
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to_prepend = None
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if fqsn:
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if fqsn:
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@ -651,7 +671,7 @@ async def tsdb_history_update(
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fqsn = symbol.front_fqsn()
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fqsn = symbol.front_fqsn()
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# diff db history with shm and only write the missing portions
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# diff db history with shm and only write the missing portions
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ohlcv = feed.shm.array
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ohlcv = feed.hist_shm.array
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# TODO: use pg profiler
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# TODO: use pg profiler
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tsdb_arrays = await storage.read_ohlcv(fqsn)
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tsdb_arrays = await storage.read_ohlcv(fqsn)
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