Start `piker.storage` subsys: cross-(ts)db middlewares
The plan is to offer multiple tsdb and other storage backends (for a variety of use cases) and expose them similarly to how we do for broker and data providers B)basic_buy_bot
Tyler Goodlet
parent
ae8358a5e7
commit
29211b200d
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@ -718,7 +718,7 @@ async def install_brokerd_search(
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async with portal.open_context(
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brokermod.open_symbol_search
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) as (ctx, cache):
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) as (ctx, _):
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# shield here since we expect the search rpc to be
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# cancellable by the user as they see fit.
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@ -1,5 +1,5 @@
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# piker: trading gear for hackers
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# Copyright (C) Tyler Goodlet (in stewardship for piker0)
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# Copyright (C) Tyler Goodlet (in stewardship for pikers)
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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@ -25,11 +25,9 @@
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'''
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from __future__ import annotations
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from contextlib import asynccontextmanager as acm
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from datetime import datetime
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from pprint import pformat
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from typing import (
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Any,
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Union,
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TYPE_CHECKING,
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)
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import time
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@ -37,31 +35,34 @@ from math import isnan
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from pathlib import Path
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from bidict import bidict
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from msgspec.msgpack import encode, decode
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from msgspec.msgpack import (
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encode,
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decode,
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)
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# import pyqtgraph as pg
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import numpy as np
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import tractor
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from trio_websocket import open_websocket_url
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from anyio_marketstore import (
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from anyio_marketstore import ( # noqa
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open_marketstore_client,
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MarketstoreClient,
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Params,
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)
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import pendulum
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import purerpc
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# TODO: import this for specific error set expected by mkts client
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# import purerpc
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from ..data.feed import maybe_open_feed
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from . import Services
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from ._util import (
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log, # sub-sys logger
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get_console_log,
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)
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if TYPE_CHECKING:
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import docker
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from ._ahab import DockerContainer
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from ._util import (
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log, # sub-sys logger
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get_console_log,
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)
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from . import Services
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from ..data.feed import maybe_open_feed
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from .._profile import Profiler
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from .. import config
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# ahabd-supervisor and container level config
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@ -432,375 +433,6 @@ tf_in_1s = bidict({
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})
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class Storage:
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'''
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High level storage api for both real-time and historical ingest.
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'''
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def __init__(
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self,
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client: MarketstoreClient,
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) -> None:
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# TODO: eventually this should be an api/interface type that
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# ensures we can support multiple tsdb backends.
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self.client = client
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# series' cache from tsdb reads
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self._arrays: dict[str, np.ndarray] = {}
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async def list_keys(self) -> list[str]:
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return await self.client.list_symbols()
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async def search_keys(self, pattern: str) -> list[str]:
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'''
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Search for time series key in the storage backend.
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'''
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...
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async def write_ticks(self, ticks: list) -> None:
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...
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async def load(
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self,
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fqme: str,
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timeframe: int,
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) -> tuple[
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np.ndarray, # timeframe sampled array-series
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datetime | None, # first dt
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datetime | None, # last dt
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]:
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first_tsdb_dt, last_tsdb_dt = None, None
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hist = await self.read_ohlcv(
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fqme,
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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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limit=3000,
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timeframe=timeframe,
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)
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log.info(f'Loaded tsdb history {hist}')
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if len(hist):
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times = hist['Epoch']
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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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pendulum.from_timestamp, [first, last]
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)
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return (
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hist, # array-data
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first_tsdb_dt, # start of query-frame
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last_tsdb_dt, # most recent
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)
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async def read_ohlcv(
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self,
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fqme: str,
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timeframe: int | str,
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end: int | None = None,
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limit: int = int(800e3),
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) -> np.ndarray:
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client = self.client
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syms = await client.list_symbols()
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if fqme not in syms:
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return {}
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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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symbols=fqme,
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timeframe=tfstr,
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attrgroup='OHLCV',
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end=end,
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# limit_from_start=True,
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# TODO: figure the max limit here given the
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# ``purepc`` msg size limit of purerpc: 33554432
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limit=limit,
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)
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for i in range(3):
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try:
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result = await client.query(params)
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break
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except purerpc.grpclib.exceptions.UnknownError as err:
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if 'snappy' in err.args:
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await tractor.breakpoint()
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# indicate there is no history for this timeframe
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log.exception(
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f'Unknown mkts QUERY error: {params}\n'
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f'{err.args}'
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)
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else:
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return {}
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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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# it might make sense to make these structured arrays?
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data_set = result.by_symbols()[fqme]
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array = data_set.array
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# XXX: ensure sample rate is as expected
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time = data_set.array['Epoch']
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if len(time) > 1:
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time_step = time[-1] - time[-2]
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ts = tf_in_1s.inverse[data_set.timeframe]
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if time_step != ts:
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log.warning(
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f'MKTS BUG: wrong timeframe loaded: {time_step}'
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'YOUR DATABASE LIKELY CONTAINS BAD DATA FROM AN OLD BUG'
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f'WIPING HISTORY FOR {ts}s'
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)
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await self.delete_ts(fqme, timeframe)
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# try reading again..
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return await self.read_ohlcv(
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fqme,
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timeframe,
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end,
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limit,
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)
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return array
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async def delete_ts(
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self,
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key: str,
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timeframe: Union[int, str | None] = None,
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fmt: str = 'OHLCV',
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) -> bool:
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client = self.client
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syms = await client.list_symbols()
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if key not in syms:
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await tractor.breakpoint()
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raise KeyError(f'`{key}` table key not found in\n{syms}?')
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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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self,
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fqme: str,
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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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limit: int = int(800e3),
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) -> None:
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# build mkts schema compat array for writing
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mkts_dt = np.dtype(_ohlcv_dt)
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mkts_array = np.zeros(
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len(ohlcv),
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dtype=mkts_dt,
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)
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# copy from shm array (yes it's this easy):
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# https://numpy.org/doc/stable/user/basics.rec.html#assignment-from-other-structured-arrays
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mkts_array[:] = ohlcv[[
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'time',
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'open',
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'high',
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'low',
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'close',
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'volume',
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]]
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m, r = divmod(len(mkts_array), limit)
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tfkey = tf_in_1s[timeframe]
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for i in range(m, 1):
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to_push = mkts_array[i-1:i*limit]
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# write to db
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resp = await self.client.write(
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to_push,
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tbk=f'{fqme}/{tfkey}/OHLCV',
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# NOTE: will will append duplicates
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# for the same timestamp-index.
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# TODO: pre-deduplicate?
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isvariablelength=append_and_duplicate,
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)
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log.info(
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f'Wrote {mkts_array.size} datums to tsdb\n'
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)
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for resp in resp.responses:
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err = resp.error
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if err:
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raise MarketStoreError(err)
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if r:
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to_push = mkts_array[m*limit:]
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# write to db
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resp = await self.client.write(
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to_push,
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tbk=f'{fqme}/{tfkey}/OHLCV',
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# NOTE: will will append duplicates
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# for the same timestamp-index.
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# TODO: pre deduplicate?
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isvariablelength=append_and_duplicate,
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)
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log.info(
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f'Wrote {mkts_array.size} datums to tsdb\n'
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)
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for resp in resp.responses:
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err = resp.error
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if err:
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raise MarketStoreError(err)
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# XXX: currently the only way to do this is through the CLI:
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# sudo ./marketstore connect --dir ~/.config/piker/data
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# >> \show mnq.globex.20220617.ib/1Sec/OHLCV 2022-05-15
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# and this seems to block and use up mem..
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# >> \trim mnq.globex.20220617.ib/1Sec/OHLCV 2022-05-15
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# relevant source code for this is here:
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# https://github.com/alpacahq/marketstore/blob/master/cmd/connect/session/trim.go#L14
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# def delete_range(self, start_dt, end_dt) -> None:
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# ...
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@acm
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async def open_storage_client(
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host: str,
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grpc_port: int,
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) -> tuple[Storage, dict[str, np.ndarray]]:
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'''
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Load a series by key and deliver in ``numpy`` struct array format.
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'''
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async with (
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# eventually a storage backend endpoint
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get_client(
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host=host,
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port=grpc_port,
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) as client,
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):
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# slap on our wrapper api
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yield Storage(client)
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@acm
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async def open_tsdb_client(
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fqme: str,
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) -> Storage:
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# TODO: real-time dedicated task for ensuring
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# history consistency between the tsdb, shm and real-time feed..
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# update sequence design notes:
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# - load existing highest frequency data from mkts
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# * how do we want to offer this to the UI?
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# - lazy loading?
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# - try to load it all and expect graphics caching/diffing
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# to hide extra bits that aren't in view?
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# - compute the diff between latest data from broker and shm
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# * use sql api in mkts to determine where the backend should
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# start querying for data?
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# * append any diff with new shm length
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# * determine missing (gapped) history by scanning
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# * how far back do we look?
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# - begin rt update ingest and aggregation
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# * could start by always writing ticks to mkts instead of
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# worrying about a shm queue for now.
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# * we have a short list of shm queues worth groking:
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# - https://github.com/pikers/piker/issues/107
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# * the original data feed arch blurb:
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# - https://github.com/pikers/piker/issues/98
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#
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profiler = Profiler(
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disabled=True, # not pg_profile_enabled(),
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delayed=False,
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)
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# load any user service settings for connecting to
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rootconf, path = config.load(
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'conf',
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touch_if_dne=True,
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)
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tsdbconf = rootconf['network'].get('tsdb')
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# backend = tsdbconf.pop('backend')
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async with (
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open_storage_client(
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**tsdbconf,
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) as storage,
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maybe_open_feed(
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[fqme],
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start_stream=False,
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) as feed,
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):
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profiler(f'opened feed for {fqme}')
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# to_append = feed.hist_shm.array
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# to_prepend = None
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if fqme:
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flume = feed.flumes[fqme]
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symbol = flume.mkt
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if symbol:
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fqme = symbol.fqme
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# diff db history with shm and only write the missing portions
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# ohlcv = flume.hist_shm.array
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# TODO: use pg profiler
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# for secs in (1, 60):
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# tsdb_array = await storage.read_ohlcv(
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# fqme,
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# timeframe=timeframe,
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# )
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# # hist diffing:
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# # these aren't currently used but can be referenced from
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# # within the embedded ipython shell below.
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# to_append = ohlcv[ohlcv['time'] > ts['Epoch'][-1]]
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# to_prepend = ohlcv[ohlcv['time'] < ts['Epoch'][0]]
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# profiler('Finished db arrays diffs')
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_ = await storage.client.list_symbols()
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# log.info(f'Existing tsdb symbol set:\n{pformat(syms)}')
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# profiler(f'listed symbols {syms}')
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yield storage
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# for array in [to_append, to_prepend]:
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# if array is None:
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# continue
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# log.info(
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# f'Writing datums {array.size} -> to tsdb from shm\n'
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# )
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# await storage.write_ohlcv(fqme, array)
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# profiler('Finished db writes')
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async def ingest_quote_stream(
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symbols: list[str],
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brokername: str,
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@ -963,5 +595,3 @@ async def stream_quotes(
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if quotes:
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yield quotes
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|
|
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|
|
@ -0,0 +1,430 @@
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# piker: trading gear for hackers
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# Copyright (C) Tyler Goodlet (in stewardship for pikers)
|
||||
|
||||
# This program is free software: you can redistribute it and/or modify
|
||||
# it under the terms of the GNU Affero General Public License as published by
|
||||
# the Free Software Foundation, either version 3 of the License, or
|
||||
# (at your option) any later version.
|
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|
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# This program is distributed in the hope that it will be useful,
|
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
# GNU Affero General Public License for more details.
|
||||
|
||||
# You should have received a copy of the GNU Affero General Public License
|
||||
# along with this program. If not, see <https://www.gnu.org/licenses/>.
|
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'''
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(time-series) database middle ware layer.
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- APIs for read, write, delete, replicate over multiple
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db systems.
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- backend agnostic tick msg ingest machinery.
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- broadcast systems for fan out of real-time ingested
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data to live consumers.
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- test harness utilities for data-processing verification.
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|
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'''
|
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from __future__ import annotations
|
||||
from contextlib import asynccontextmanager as acm
|
||||
from datetime import datetime
|
||||
# from pprint import pformat
|
||||
from typing import (
|
||||
Union,
|
||||
)
|
||||
|
||||
import tractor
|
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import numpy as np
|
||||
from anyio_marketstore import (
|
||||
Params,
|
||||
)
|
||||
import pendulum
|
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import purerpc
|
||||
|
||||
from . import config
|
||||
from ..service.marketstore import (
|
||||
MarketstoreClient,
|
||||
tf_in_1s,
|
||||
mk_tbk,
|
||||
_ohlcv_dt,
|
||||
MarketStoreError,
|
||||
)
|
||||
from ..data.feed import maybe_open_feed
|
||||
from ..log import get_logger
|
||||
from .._profile import Profiler
|
||||
|
||||
|
||||
log = get_logger(__name__)
|
||||
|
||||
|
||||
class Storage:
|
||||
'''
|
||||
High level storage api for both real-time and historical ingest.
|
||||
|
||||
'''
|
||||
def __init__(
|
||||
self,
|
||||
client: MarketstoreClient,
|
||||
|
||||
) -> None:
|
||||
# TODO: eventually this should be an api/interface type that
|
||||
# ensures we can support multiple tsdb backends.
|
||||
self.client = client
|
||||
|
||||
# series' cache from tsdb reads
|
||||
self._arrays: dict[str, np.ndarray] = {}
|
||||
|
||||
async def list_keys(self) -> list[str]:
|
||||
return await self.client.list_symbols()
|
||||
|
||||
async def search_keys(self, pattern: str) -> list[str]:
|
||||
'''
|
||||
Search for time series key in the storage backend.
|
||||
|
||||
'''
|
||||
...
|
||||
|
||||
async def write_ticks(self, ticks: list) -> None:
|
||||
...
|
||||
|
||||
async def load(
|
||||
self,
|
||||
fqme: str,
|
||||
timeframe: int,
|
||||
|
||||
) -> tuple[
|
||||
np.ndarray, # timeframe sampled array-series
|
||||
datetime | None, # first dt
|
||||
datetime | None, # last dt
|
||||
]:
|
||||
|
||||
first_tsdb_dt, last_tsdb_dt = None, None
|
||||
hist = await self.read_ohlcv(
|
||||
fqme,
|
||||
# on first load we don't need to pull the max
|
||||
# history per request size worth.
|
||||
limit=3000,
|
||||
timeframe=timeframe,
|
||||
)
|
||||
log.info(f'Loaded tsdb history {hist}')
|
||||
|
||||
if len(hist):
|
||||
times = hist['Epoch']
|
||||
first, last = times[0], times[-1]
|
||||
first_tsdb_dt, last_tsdb_dt = map(
|
||||
pendulum.from_timestamp, [first, last]
|
||||
)
|
||||
|
||||
return (
|
||||
hist, # array-data
|
||||
first_tsdb_dt, # start of query-frame
|
||||
last_tsdb_dt, # most recent
|
||||
)
|
||||
|
||||
async def read_ohlcv(
|
||||
self,
|
||||
fqme: str,
|
||||
timeframe: int | str,
|
||||
end: int | None = None,
|
||||
limit: int = int(800e3),
|
||||
|
||||
) -> np.ndarray:
|
||||
|
||||
client = self.client
|
||||
syms = await client.list_symbols()
|
||||
|
||||
if fqme not in syms:
|
||||
return {}
|
||||
|
||||
# use the provided timeframe or 1s by default
|
||||
tfstr = tf_in_1s.get(timeframe, tf_in_1s[1])
|
||||
|
||||
params = Params(
|
||||
symbols=fqme,
|
||||
timeframe=tfstr,
|
||||
attrgroup='OHLCV',
|
||||
end=end,
|
||||
# limit_from_start=True,
|
||||
|
||||
# TODO: figure the max limit here given the
|
||||
# ``purepc`` msg size limit of purerpc: 33554432
|
||||
limit=limit,
|
||||
)
|
||||
|
||||
for i in range(3):
|
||||
try:
|
||||
result = await client.query(params)
|
||||
break
|
||||
except purerpc.grpclib.exceptions.UnknownError as err:
|
||||
if 'snappy' in err.args:
|
||||
await tractor.breakpoint()
|
||||
|
||||
# indicate there is no history for this timeframe
|
||||
log.exception(
|
||||
f'Unknown mkts QUERY error: {params}\n'
|
||||
f'{err.args}'
|
||||
)
|
||||
else:
|
||||
return {}
|
||||
|
||||
# TODO: it turns out column access on recarrays is actually slower:
|
||||
# https://jakevdp.github.io/PythonDataScienceHandbook/02.09-structured-data-numpy.html#RecordArrays:-Structured-Arrays-with-a-Twist
|
||||
# it might make sense to make these structured arrays?
|
||||
data_set = result.by_symbols()[fqme]
|
||||
array = data_set.array
|
||||
|
||||
# XXX: ensure sample rate is as expected
|
||||
time = data_set.array['Epoch']
|
||||
if len(time) > 1:
|
||||
time_step = time[-1] - time[-2]
|
||||
ts = tf_in_1s.inverse[data_set.timeframe]
|
||||
|
||||
if time_step != ts:
|
||||
log.warning(
|
||||
f'MKTS BUG: wrong timeframe loaded: {time_step}'
|
||||
'YOUR DATABASE LIKELY CONTAINS BAD DATA FROM AN OLD BUG'
|
||||
f'WIPING HISTORY FOR {ts}s'
|
||||
)
|
||||
await self.delete_ts(fqme, timeframe)
|
||||
|
||||
# try reading again..
|
||||
return await self.read_ohlcv(
|
||||
fqme,
|
||||
timeframe,
|
||||
end,
|
||||
limit,
|
||||
)
|
||||
|
||||
return array
|
||||
|
||||
async def delete_ts(
|
||||
self,
|
||||
key: str,
|
||||
timeframe: Union[int, str | None] = None,
|
||||
fmt: str = 'OHLCV',
|
||||
|
||||
) -> bool:
|
||||
|
||||
client = self.client
|
||||
syms = await client.list_symbols()
|
||||
if key not in syms:
|
||||
await tractor.breakpoint()
|
||||
raise KeyError(f'`{key}` table key not found in\n{syms}?')
|
||||
|
||||
tbk = mk_tbk((
|
||||
key,
|
||||
tf_in_1s.get(timeframe, tf_in_1s[60]),
|
||||
fmt,
|
||||
))
|
||||
return await client.destroy(tbk=tbk)
|
||||
|
||||
async def write_ohlcv(
|
||||
self,
|
||||
fqme: str,
|
||||
ohlcv: np.ndarray,
|
||||
timeframe: int,
|
||||
append_and_duplicate: bool = True,
|
||||
limit: int = int(800e3),
|
||||
|
||||
) -> None:
|
||||
# build mkts schema compat array for writing
|
||||
mkts_dt = np.dtype(_ohlcv_dt)
|
||||
mkts_array = np.zeros(
|
||||
len(ohlcv),
|
||||
dtype=mkts_dt,
|
||||
)
|
||||
# copy from shm array (yes it's this easy):
|
||||
# https://numpy.org/doc/stable/user/basics.rec.html#assignment-from-other-structured-arrays
|
||||
mkts_array[:] = ohlcv[[
|
||||
'time',
|
||||
'open',
|
||||
'high',
|
||||
'low',
|
||||
'close',
|
||||
'volume',
|
||||
]]
|
||||
|
||||
m, r = divmod(len(mkts_array), limit)
|
||||
|
||||
tfkey = tf_in_1s[timeframe]
|
||||
for i in range(m, 1):
|
||||
to_push = mkts_array[i-1:i*limit]
|
||||
|
||||
# write to db
|
||||
resp = await self.client.write(
|
||||
to_push,
|
||||
tbk=f'{fqme}/{tfkey}/OHLCV',
|
||||
|
||||
# NOTE: will will append duplicates
|
||||
# for the same timestamp-index.
|
||||
# TODO: pre-deduplicate?
|
||||
isvariablelength=append_and_duplicate,
|
||||
)
|
||||
|
||||
log.info(
|
||||
f'Wrote {mkts_array.size} datums to tsdb\n'
|
||||
)
|
||||
|
||||
for resp in resp.responses:
|
||||
err = resp.error
|
||||
if err:
|
||||
raise MarketStoreError(err)
|
||||
|
||||
if r:
|
||||
to_push = mkts_array[m*limit:]
|
||||
|
||||
# write to db
|
||||
resp = await self.client.write(
|
||||
to_push,
|
||||
tbk=f'{fqme}/{tfkey}/OHLCV',
|
||||
|
||||
# NOTE: will will append duplicates
|
||||
# for the same timestamp-index.
|
||||
# TODO: pre deduplicate?
|
||||
isvariablelength=append_and_duplicate,
|
||||
)
|
||||
|
||||
log.info(
|
||||
f'Wrote {mkts_array.size} datums to tsdb\n'
|
||||
)
|
||||
|
||||
for resp in resp.responses:
|
||||
err = resp.error
|
||||
if err:
|
||||
raise MarketStoreError(err)
|
||||
|
||||
# XXX: currently the only way to do this is through the CLI:
|
||||
|
||||
# sudo ./marketstore connect --dir ~/.config/piker/data
|
||||
# >> \show mnq.globex.20220617.ib/1Sec/OHLCV 2022-05-15
|
||||
# and this seems to block and use up mem..
|
||||
# >> \trim mnq.globex.20220617.ib/1Sec/OHLCV 2022-05-15
|
||||
|
||||
# relevant source code for this is here:
|
||||
# https://github.com/alpacahq/marketstore/blob/master/cmd/connect/session/trim.go#L14
|
||||
# def delete_range(self, start_dt, end_dt) -> None:
|
||||
# ...
|
||||
|
||||
|
||||
@acm
|
||||
async def open_storage_client(
|
||||
host: str,
|
||||
grpc_port: int,
|
||||
|
||||
) -> tuple[Storage, dict[str, np.ndarray]]:
|
||||
'''
|
||||
Load a series by key and deliver in ``numpy`` struct array format.
|
||||
|
||||
'''
|
||||
from piker.service.marketstore import get_client
|
||||
|
||||
async with (
|
||||
# eventually a storage backend endpoint
|
||||
get_client(
|
||||
host=host,
|
||||
port=grpc_port,
|
||||
) as client,
|
||||
):
|
||||
# slap on our wrapper api
|
||||
yield Storage(client)
|
||||
|
||||
|
||||
# NOTE: pretty sure right now this is only being
|
||||
# called by a CLI entrypoint?
|
||||
@acm
|
||||
async def open_tsdb_client(
|
||||
fqme: str,
|
||||
) -> Storage:
|
||||
|
||||
# TODO: real-time dedicated task for ensuring
|
||||
# history consistency between the tsdb, shm and real-time feed..
|
||||
|
||||
# update sequence design notes:
|
||||
|
||||
# - load existing highest frequency data from mkts
|
||||
# * how do we want to offer this to the UI?
|
||||
# - lazy loading?
|
||||
# - try to load it all and expect graphics caching/diffing
|
||||
# to hide extra bits that aren't in view?
|
||||
|
||||
# - compute the diff between latest data from broker and shm
|
||||
# * use sql api in mkts to determine where the backend should
|
||||
# start querying for data?
|
||||
# * append any diff with new shm length
|
||||
# * determine missing (gapped) history by scanning
|
||||
# * how far back do we look?
|
||||
|
||||
# - begin rt update ingest and aggregation
|
||||
# * could start by always writing ticks to mkts instead of
|
||||
# worrying about a shm queue for now.
|
||||
# * we have a short list of shm queues worth groking:
|
||||
# - https://github.com/pikers/piker/issues/107
|
||||
# * the original data feed arch blurb:
|
||||
# - https://github.com/pikers/piker/issues/98
|
||||
#
|
||||
profiler = Profiler(
|
||||
disabled=True, # not pg_profile_enabled(),
|
||||
delayed=False,
|
||||
)
|
||||
|
||||
# load any user service settings for connecting to
|
||||
rootconf, path = config.load(
|
||||
'conf',
|
||||
touch_if_dne=True,
|
||||
)
|
||||
tsdbconf = rootconf['network'].get('tsdb')
|
||||
# backend = tsdbconf.pop('backend')
|
||||
async with (
|
||||
open_storage_client(
|
||||
**tsdbconf,
|
||||
) as storage,
|
||||
|
||||
maybe_open_feed(
|
||||
[fqme],
|
||||
start_stream=False,
|
||||
|
||||
) as feed,
|
||||
):
|
||||
profiler(f'opened feed for {fqme}')
|
||||
|
||||
# to_append = feed.hist_shm.array
|
||||
# to_prepend = None
|
||||
|
||||
if fqme:
|
||||
flume = feed.flumes[fqme]
|
||||
symbol = flume.mkt
|
||||
if symbol:
|
||||
fqme = symbol.fqme
|
||||
|
||||
# diff db history with shm and only write the missing portions
|
||||
# ohlcv = flume.hist_shm.array
|
||||
|
||||
# TODO: use pg profiler
|
||||
# for secs in (1, 60):
|
||||
# tsdb_array = await storage.read_ohlcv(
|
||||
# fqme,
|
||||
# timeframe=timeframe,
|
||||
# )
|
||||
# # hist diffing:
|
||||
# # these aren't currently used but can be referenced from
|
||||
# # within the embedded ipython shell below.
|
||||
# to_append = ohlcv[ohlcv['time'] > ts['Epoch'][-1]]
|
||||
# to_prepend = ohlcv[ohlcv['time'] < ts['Epoch'][0]]
|
||||
|
||||
# profiler('Finished db arrays diffs')
|
||||
|
||||
_ = await storage.client.list_symbols()
|
||||
# log.info(f'Existing tsdb symbol set:\n{pformat(syms)}')
|
||||
# profiler(f'listed symbols {syms}')
|
||||
yield storage
|
||||
|
||||
# for array in [to_append, to_prepend]:
|
||||
# if array is None:
|
||||
# continue
|
||||
|
||||
# log.info(
|
||||
# f'Writing datums {array.size} -> to tsdb from shm\n'
|
||||
# )
|
||||
# await storage.write_ohlcv(fqme, array)
|
||||
|
||||
# profiler('Finished db writes')
|
||||
Loading…
Reference in New Issue