influxdb: WIP on magic automatic interface

to run:

    python3 -c 'import my.core.influxdb as I; import my.hypothesis as H; I.magic_fill(H.highlights)'

my/core/influxdb.py

@@ -1,8 +1,7 @@
 '''
 TODO doesn't really belong to 'core' morally, but can think of moving out later
 '''
-from typing import Iterable, Any, Optional
-
+from typing import Iterable, Any, Optional, Dict
 
 from .common import LazyLogger, asdict, Json
 
@@ -14,7 +13,7 @@ class config:
     db = 'db'
 
 
-def fill(it: Iterable[Any], *, measurement: str, reset: bool=False) -> None:
+def fill(it: Iterable[Any], *, measurement: str, reset: bool=False, dt_col: str='dt') -> None:
     # todo infer dt column automatically, reuse in stat?
     # it doesn't like dots, ends up some syntax error?
     measurement = measurement.replace('.', '_')
@@ -31,19 +30,38 @@ def fill(it: Iterable[Any], *, measurement: str, reset: bool=False) -> None:
     if reset:
         client.delete_series(database=db, measurement=measurement)
 
+    # TODO need to take schema here...
+    cache: Dict[str, bool] = {}
+    def good(f, v) -> bool:
+        c = cache.get(f)
+        if c is not None:
+            return c
+        t = type(v)
+        r = t in {str, int}
+        cache[f] = r
+        if not r:
+            logger.warning('%s: filtering out %s=%s because of type %s', measurement, f, v, t)
+        return r
+
+    def filter_dict(d: Json) -> Json:
+        return {f: v for f, v in d.items() if good(f, v)}
+
     def dit() -> Iterable[Json]:
         for i in it:
             d = asdict(i)
             tags: Optional[Json] = None
-            tags = d.get('tags') # meh... handle in a more robust manner
-            if tags is not None:
+            tags_ = d.get('tags') # meh... handle in a more robust manner
+            if tags_ is not None and isinstance(tags_, dict): # FIXME meh.
                 del d['tags']
+                tags = tags_
 
             # TODO what to do with exceptions??
             # todo handle errors.. not sure how? maybe add tag for 'error' and fill with emtpy data?
-            dt = d['dt'].isoformat()
-            del d['dt']
-            fields = d
+            dt = d[dt_col].isoformat()
+            del d[dt_col]
+
+            fields = filter_dict(d)
+
             yield dict(
                 measurement=measurement,
                 # TODO maybe good idea to tag with database file/name? to inspect inconsistencies etc..
@@ -52,7 +70,7 @@ def fill(it: Iterable[Any], *, measurement: str, reset: bool=False) -> None:
                 # "fields are data and tags are metadata"
                 tags=tags,
                 time=dt,
-                fields=d,
+                fields=fields,
             )
 
 
@@ -64,3 +82,28 @@ def fill(it: Iterable[Any], *, measurement: str, reset: bool=False) -> None:
         logger.debug('writing next chunk %s', chl[-1])
         client.write_points(chl, database=db)
     # todo "Specify timestamp precision when writing to InfluxDB."?
+
+
+def magic_fill(it) -> None:
+    assert callable(it)
+    name = f'{it.__module__}:{it.__name__}'
+
+    from itertools import tee
+    from more_itertools import first, one
+    it = it()
+    it, x = tee(it)
+    f = first(x, default=None)
+    if f is None:
+        logger.warning('%s has no data', name)
+        return
+
+    # TODO can we reuse pandas code or something?
+    #
+    from .pandas import _as_columns
+    schema = _as_columns(type(f))
+
+    from datetime import datetime
+    dtex = RuntimeError(f'expected single datetime field. schema: {schema}')
+    dtf = one((f for f, t in schema.items() if t == datetime), too_short=dtex, too_long=dtex)
+
+    fill(it, measurement=name, reset=True, dt_col=dtf)