20585a3130
to run:
python3 -c 'import my.core.influxdb as I; import my.hypothesis as H; I.magic_fill(H.highlights)'
109 lines
3.4 KiB
Python
109 lines
3.4 KiB
Python
'''
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TODO doesn't really belong to 'core' morally, but can think of moving out later
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'''
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from typing import Iterable, Any, Optional, Dict
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from .common import LazyLogger, asdict, Json
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logger = LazyLogger(__name__)
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class config:
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db = 'db'
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def fill(it: Iterable[Any], *, measurement: str, reset: bool=False, dt_col: str='dt') -> None:
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# todo infer dt column automatically, reuse in stat?
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# it doesn't like dots, ends up some syntax error?
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measurement = measurement.replace('.', '_')
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# todo autoinfer measurement?
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db = config.db
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from influxdb import InfluxDBClient # type: ignore
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client = InfluxDBClient()
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# todo maybe create if not exists?
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# client.create_database(db)
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# todo should be it be env variable?
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if reset:
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client.delete_series(database=db, measurement=measurement)
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# TODO need to take schema here...
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cache: Dict[str, bool] = {}
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def good(f, v) -> bool:
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c = cache.get(f)
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if c is not None:
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return c
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t = type(v)
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r = t in {str, int}
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cache[f] = r
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if not r:
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logger.warning('%s: filtering out %s=%s because of type %s', measurement, f, v, t)
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return r
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def filter_dict(d: Json) -> Json:
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return {f: v for f, v in d.items() if good(f, v)}
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def dit() -> Iterable[Json]:
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for i in it:
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d = asdict(i)
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tags: Optional[Json] = None
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tags_ = d.get('tags') # meh... handle in a more robust manner
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if tags_ is not None and isinstance(tags_, dict): # FIXME meh.
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del d['tags']
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tags = tags_
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# TODO what to do with exceptions??
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# todo handle errors.. not sure how? maybe add tag for 'error' and fill with emtpy data?
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dt = d[dt_col].isoformat()
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del d[dt_col]
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fields = filter_dict(d)
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yield dict(
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measurement=measurement,
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# TODO maybe good idea to tag with database file/name? to inspect inconsistencies etc..
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# hmm, so tags are autoindexed and might be faster?
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# not sure what's the big difference though
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# "fields are data and tags are metadata"
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tags=tags,
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time=dt,
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fields=fields,
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)
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from more_itertools import chunked
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# "The optimal batch size is 5000 lines of line protocol."
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# some chunking is def necessary, otherwise it fails
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for chi in chunked(dit(), n=5000):
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chl = list(chi)
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logger.debug('writing next chunk %s', chl[-1])
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client.write_points(chl, database=db)
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# todo "Specify timestamp precision when writing to InfluxDB."?
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def magic_fill(it) -> None:
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assert callable(it)
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name = f'{it.__module__}:{it.__name__}'
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from itertools import tee
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from more_itertools import first, one
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it = it()
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it, x = tee(it)
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f = first(x, default=None)
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if f is None:
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logger.warning('%s has no data', name)
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return
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# TODO can we reuse pandas code or something?
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#
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from .pandas import _as_columns
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schema = _as_columns(type(f))
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from datetime import datetime
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dtex = RuntimeError(f'expected single datetime field. schema: {schema}')
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dtf = one((f for f, t in schema.items() if t == datetime), too_short=dtex, too_long=dtex)
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fill(it, measurement=name, reset=True, dt_col=dtf)
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