Add optimized read_parquet path for remote storage

nvtabular/io/dataset.py

@@ -23,7 +23,6 @@
 
 import dask
 import numpy as np
-import py
 from dask.base import tokenize
 from dask.dataframe.core import new_dd_object
 from dask.highlevelgraph import HighLevelGraph
@@ -230,6 +229,9 @@ def __init__(
         self.client = client
         self.schema = schema
 
+        # Cache for "real" (sampled) metadata
+        self._real_meta = {}
+
         # Check if we are keeping data in cpu memory
         self.cpu = cpu
         if not self.cpu:
@@ -281,11 +283,7 @@ def __init__(
                 part_size = int(device_mem_size(kind="total", cpu=self.cpu) * part_mem_fraction)
 
             # Engine-agnostic path handling
-            paths = path_or_source
-            if hasattr(paths, "name"):
-                paths = stringify_path(paths)
-            if isinstance(paths, (py._path.local.LocalPath, Path)):
-                paths = str(paths)
+            paths = stringify_path(path_or_source)
             if isinstance(paths, str):
                 paths = [paths]
             paths = sorted(paths, key=natural_sort_key)
@@ -1107,21 +1105,7 @@ def infer_schema(self, n=1):
 
         dtypes = {}
         try:
-            _ddf = self.to_ddf()
-            dtypes = {
-                col_name: {"dtype": dtype, "is_list": False}
-                for col_name, dtype in _ddf.dtypes.items()
-            }
-            for partition_index in range(_ddf.npartitions):
-                _head = _ddf.partitions[partition_index].head(n)
-
-                if len(_head):
-                    for col in _head.columns:
-                        dtypes[col] = {
-                            "dtype": dispatch._list_val_dtype(_head[col]) or _head[col].dtype,
-                            "is_list": dispatch._is_list_dtype(_head[col]),
-                        }
-
+            dtypes = self.sample_dtypes(n=n, annotate_lists=True)
         except RuntimeError:
             warnings.warn(
                 "Unable to sample column dtypes to infer nvt.Dataset schema, schema is empty."
@@ -1136,20 +1120,30 @@ def infer_schema(self, n=1):
         self.schema = Schema(column_schemas)
         return self.schema
 
-    def sample_dtypes(self, n=1):
+    def sample_dtypes(self, n=1, annotate_lists=False):
         """Return the real dtypes of the Dataset
 
-        Sample the partitions of the underlying Dask collection
-        until a non-empty partition is found. Then, use the first
-        ``n`` rows of that partition to infer dtype info. If no
-        non-empty partitions are found, use the Dask dtypes.
+        Use cached metadata if this operation was
+        already performed. Otherwise, call down to the
+        underlying engine for sampling logic.
         """
-        _ddf = self.to_ddf()
-        for partition_index in range(_ddf.npartitions):
-            _head = _ddf.partitions[partition_index].head(n)
-            if len(_head):
-                return _head.dtypes
-        return _ddf.dtypes
+        if self._real_meta.get(n, None) is None:
+            _real_meta = self.engine.sample_data(n=n)
+            if self.dtypes:
+                _real_meta = _set_dtypes(_real_meta, self.dtypes)
+            self._real_meta[n] = _real_meta
+
+        if annotate_lists:
+            _real_meta = self._real_meta[n]
+            return {
+                col: {
+                    "dtype": dispatch._list_val_dtype(_real_meta[col]) or _real_meta[col].dtype,
+                    "is_list": dispatch._is_list_dtype(_real_meta[col]),
+                }
+                for col in _real_meta.columns
+            }
+
+        return self._real_meta[n].dtypes
 
     @classmethod
     def _bind_dd_method(cls, name):

nvtabular/io/dataset_engine.py

@@ -57,3 +57,18 @@ def validate_dataset(self, **kwargs):
     @classmethod
     def regenerate_dataset(cls, dataset, output_path, columns=None, **kwargs):
         raise NotImplementedError(""" Regenerate a dataset with optimal properties """)
+
+    def sample_data(self, n=1):
+        """Return a sample of real data from the dataset
+
+        Sample the partitions of the underlying Dask collection
+        until a non-empty partition is found. Then, use the first
+        ``n`` rows of that partition to infer dtype info. If no
+        non-empty partitions are found, use the Dask metadata.
+        """
+        _ddf = self.to_ddf()
+        for partition_index in range(_ddf.npartitions):
+            _head = _ddf.partitions[partition_index].head(n)
+            if len(_head):
+                return _head
+        return _ddf._meta