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ROFL, even using `pymarketstore`'s json-RPC it's borked.. 

Turns out trying to switch to the old sync client and going back to
using the old json-RPC API (after having had to patch the upstream repo
to not import gRPC machinery to avoid crashes..) I'm basically getting
the exact same issues.

New tinkering results does possibly tell some new stuff:
- the EOF error seems to indeed be due to trying fetch records which haven't been
  written (properly) - like asking for a `end=<epoch_int>` that is
  earlier then the earliest record.
- the "snappy input corrupt" error seems to have something to do with
  the `Params.end` field not being an `int` and/or the int precision not
  being chosen correctly?
  - toying with this a bunch manually shows that the internals of the
    client (particularly `.build_query()` stuff) is parsing/calcing the
    `Epoch` and `Nanoseconds` values out incorrectly.. which is likely
    part of the problem.
  - we also changed `anyio_marketstore.MarketStoreclient.build_query()`
    logic when removing `pandas` a while back, which also seems to be
    part of the problem on the async side, however reverting those
    changes also didn't fix the issue entirely; likely something else
    more subtle going on (maybe with the write vs. read `Epoch` field
    type we pass?).

Despite all this malarky, we're already underway more or less obsoleting
this whole thing with a much less complex approach of using apache
parquet files and modern filesystem tools to get a more flexible and
numerics-native dataframe-oriented tsdb B)
basic_buy_bot
Tyler Goodlet 2023-05-31 18:28:14 -04:00
parent 9859f601ca
commit 7d1cc47db9
1 changed files with 72 additions and 40 deletions
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112
piker/storage/marketstore/__init__.py
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@ -46,7 +46,7 @@ from anyio_marketstore import (
import pendulum
import purerpc
from ..service.marketstore import (
from piker.service.marketstore import (
MarketstoreClient,
tf_in_1s,
mk_tbk,
@ -58,7 +58,7 @@ from anyio_marketstore import ( # noqa
MarketstoreClient,
Params,
)
from ..log import get_logger
from piker.log import get_logger
# from .._profile import Profiler
@ -107,7 +107,6 @@ class MktsStorageClient:
datetime | None, # first dt
datetime | None, # last dt
]:
first_tsdb_dt, last_tsdb_dt = None, None
hist = await self.read_ohlcv(
fqme,
@ -119,10 +118,13 @@ class MktsStorageClient:
log.info(f'Loaded tsdb history {hist}')
if len(hist):
times = hist['Epoch']
# breakpoint()
times: np.ndarray = hist['Epoch']
first, last = times[0], times[-1]
first_tsdb_dt, last_tsdb_dt = map(
pendulum.from_timestamp, [first, last]
pendulum.from_timestamp,
[first, last]
)
return (
@ -135,53 +137,82 @@ class MktsStorageClient:
self,
fqme: str,
timeframe: int | str,
end: int | None = None,
limit: int = int(800e3),
end: float | None = None, # epoch or none
limit: int = int(200e3),
) -> np.ndarray:
client = self.client
syms = await client.list_symbols()
if fqme not in syms:
return {}
# ensure end time is in correct int format!
if (
end
and not isinstance(end, float)
):
end = int(float(end))
# breakpoint()
# use the provided timeframe or 1s by default
tfstr = tf_in_1s.get(timeframe, tf_in_1s[1])
params = Params(
import pymarketstore as pymkts
sync_client = pymkts.Client()
param = pymkts.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,
# limit_from_start=True,
)
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()
reply = sync_client.query(param)
except Exception as err:
if 'no files returned from query parse: None' in err.args:
return []
# indicate there is no history for this timeframe
log.exception(
f'Unknown mkts QUERY error: {params}\n'
f'{err.args}'
)
else:
return {}
raise
# 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
data_set: pymkts.results.DataSet = reply.first()
array: np.ndarray = data_set.array
# 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']
@ -191,19 +222,20 @@ class MktsStorageClient:
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'MKTS BUG: wrong timeframe loaded: {time_step}\n'
'YOUR DATABASE LIKELY CONTAINS BAD DATA FROM AN OLD BUG '
f'WIPING HISTORY FOR {ts}s'
)
await self.delete_ts(fqme, timeframe)
await tractor.breakpoint()
# await self.delete_ts(fqme, timeframe)
# try reading again..
return await self.read_ohlcv(
fqme,
timeframe,
end,
limit,
)
# return await self.read_ohlcv(
# fqme,
# timeframe,
# end,
# limit,
# )
return array