Add `NativeStorageClient._cache_df()` use it in `.write_ohlcv()` for caching on writes as well

piker/storage/nativedb.py

@@ -236,6 +236,22 @@ class NativeStorageClient:
             datadir=self._datadir,
         )
 
+    def _cache_df(
+        self,
+        fqme: str,
+        df: pl.DataFrame,
+        timeframe: float,
+    ) -> None:
+        # cache df for later usage since we (currently) need to
+        # convert to np.ndarrays to push to our `ShmArray` rt
+        # buffers subsys but later we may operate entirely on
+        # pyarrow arrays/buffers so keeping the dfs around for
+        # a variety of purposes is handy.
+        self._dfs.setdefault(
+            timeframe,
+            {},
+        )[fqme] = df
+
     async def read_ohlcv(
         self,
         fqme: str,
@@ -250,16 +266,11 @@ class NativeStorageClient:
         )
         df: pl.DataFrame = pl.read_parquet(path)
 
-        # cache df for later usage since we (currently) need to
-        # convert to np.ndarrays to push to our `ShmArray` rt
-        # buffers subsys but later we may operate entirely on
-        # pyarrow arrays/buffers so keeping the dfs around for
-        # a variety of purposes is handy.
-        self._dfs.setdefault(
-            timeframe,
-            {},
-        )[fqme] = df
-
+        self._cache_df(
+            fqme=fqme,
+            df=df,
+            timeframe=timeframe,
+        )
         # TODO: filter by end and limit inputs
         # times: pl.Series = df['time']
         array: np.ndarray = tsp.pl2np(
@@ -272,11 +283,15 @@ class NativeStorageClient:
         self,
         fqme: str,
         period: int = 60,
+        load_from_offline: bool = True,
 
     ) -> pl.DataFrame:
         try:
             return self._dfs[period][fqme]
         except KeyError:
+            if not load_from_offline:
+                raise
+
             await self.read_ohlcv(fqme, period)
             return self._dfs[period][fqme]
 
@@ -302,10 +317,18 @@ class NativeStorageClient:
         else:
             df = ohlcv
 
+        self._cache_df(
+            fqme=fqme,
+            df=df,
+            timeframe=timeframe,
+        )
+
         # TODO: in terms of managing the ultra long term data
-        # - use a proper profiler to measure all this IO and
+        # -[ ] use a proper profiler to measure all this IO and
         #   roundtripping!
-        # - try out ``fastparquet``'s append writing:
+        # -[ ] implement parquet append!? see issue:
+        #   https://github.com/pikers/piker/issues/536
+        #   -[ ] try out ``fastparquet``'s append writing:
         #     https://fastparquet.readthedocs.io/en/latest/api.html#fastparquet.write
         start = time.time()
         df.write_parquet(path)