googleapis
diff --git a/‎packages/google-cloud-bigquery-storage/google/cloud/bigquery_storage_v1beta1/reader.py‎
Lines changed: 182 additions & 26 deletions b/‎packages/google-cloud-bigquery-storage/google/cloud/bigquery_storage_v1beta1/reader.py‎
Lines changed: 182 additions & 26 deletions
@@ -15,7 +15,6 @@
 from __future__ import absolute_import
 
 import collections
-import itertools
 import json
 
 try:
@@ -38,6 +37,7 @@
     google.api_core.exceptions.ServiceUnavailable,
 )
 _FASTAVRO_REQUIRED = "fastavro is required to parse Avro blocks"
+_PANDAS_REQUIRED = "pandas is required to create a DataFrame"
 
 
 class ReadRowsStream(object):
@@ -156,9 +156,7 @@ def rows(self, read_session):
         if fastavro is None:
             raise ImportError(_FASTAVRO_REQUIRED)
 
-        avro_schema, _ = _avro_schema(read_session)
-        blocks = (_avro_rows(block, avro_schema) for block in self)
-        return itertools.chain.from_iterable(blocks)
+        return ReadRowsIterable(self, read_session)
 
     def to_dataframe(self, read_session, dtypes=None):
         """Create a :class:`pandas.DataFrame` of all rows in the stream.
@@ -192,29 +190,186 @@ def to_dataframe(self, read_session, dtypes=None):
         if fastavro is None:
             raise ImportError(_FASTAVRO_REQUIRED)
         if pandas is None:
-            raise ImportError("pandas is required to create a DataFrame")
+            raise ImportError(_PANDAS_REQUIRED)
 
-        if dtypes is None:
-            dtypes = {}
+        return self.rows(read_session).to_dataframe(dtypes=dtypes)
+
+
+class ReadRowsIterable(object):
+    """An iterable of rows from a read session.
+
+    Args:
+        reader (google.cloud.bigquery_storage_v1beta1.reader.ReadRowsStream):
+            A read rows stream.
+        read_session (google.cloud.bigquery_storage_v1beta1.types.ReadSession):
+            A read session. This is required because it contains the schema
+            used in the stream blocks.
+    """
+
+    # This class is modelled after the google.cloud.bigquery.table.RowIterator
+    # and aims to be API compatible where possible.
+
+    def __init__(self, reader, read_session):
+        self._status = None
+        self._reader = reader
+        self._read_session = read_session
+
+    @property
+    def total_rows(self):
+        """int: Number of estimated rows in the current stream.
+
+        May change over time.
+        """
+        return getattr(self._status, "estimated_row_count", None)
+
+    @property
+    def pages(self):
+        """A generator of all pages in the stream.
+
+        Returns:
+            types.GeneratorType[google.cloud.bigquery_storage_v1beta1.ReadRowsPage]:
+                A generator of pages.
+        """
+        # Each page is an iterator of rows. But also has num_items, remaining,
+        # and to_dataframe.
+        avro_schema, column_names = _avro_schema(self._read_session)
+        for block in self._reader:
+            self._status = block.status
+            yield ReadRowsPage(avro_schema, column_names, block)
+
+    def __iter__(self):
+        """Iterator for each row in all pages."""
+        for page in self.pages:
+            for row in page:
+                yield row
+
+    def to_dataframe(self, dtypes=None):
+        """Create a :class:`pandas.DataFrame` of all rows in the stream.
+
+        This method requires the pandas libary to create a data frame and the
+        fastavro library to parse row blocks.
+
+        .. warning::
+            DATETIME columns are not supported. They are currently parsed as
+            strings in the fastavro library.
+
+        Args:
+            dtypes ( \
+                Map[str, Union[str, pandas.Series.dtype]] \
+            ):
+                Optional. A dictionary of column names pandas ``dtype``s. The
+                provided ``dtype`` is used when constructing the series for
+                the column specified. Otherwise, the default pandas behavior
+                is used.
+
+        Returns:
+            pandas.DataFrame:
+                A data frame of all rows in the stream.
+        """
+        if pandas is None:
+            raise ImportError(_PANDAS_REQUIRED)
 
-        avro_schema, column_names = _avro_schema(read_session)
         frames = []
-        for block in self:
-            dataframe = _to_dataframe_with_dtypes(
-                _avro_rows(block, avro_schema), column_names, dtypes
-            )
-            frames.append(dataframe)
+        for page in self.pages:
+            frames.append(page.to_dataframe(dtypes=dtypes))
         return pandas.concat(frames)
 
 
-def _to_dataframe_with_dtypes(rows, column_names, dtypes):
-    columns = collections.defaultdict(list)
-    for row in rows:
-        for column in row:
-            columns[column].append(row[column])
-    for column in dtypes:
-        columns[column] = pandas.Series(columns[column], dtype=dtypes[column])
-    return pandas.DataFrame(columns, columns=column_names)
+class ReadRowsPage(object):
+    """An iterator of rows from a read session block.
+
+    Args:
+        avro_schema (fastavro.schema):
+            A parsed Avro schema, using :func:`fastavro.schema.parse_schema`
+        column_names (Tuple[str]]):
+            A read session's column names (in requested order).
+        block (google.cloud.bigquery_storage_v1beta1.types.ReadRowsResponse):
+            A block of data from a read rows stream.
+    """
+
+    # This class is modeled after google.api_core.page_iterator.Page and aims
+    # to provide API compatibility where possible.
+
+    def __init__(self, avro_schema, column_names, block):
+        self._avro_schema = avro_schema
+        self._column_names = column_names
+        self._block = block
+        self._iter_rows = None
+        self._num_items = None
+        self._remaining = None
+
+    def _parse_block(self):
+        """Parse metadata and rows from the block only once."""
+        if self._iter_rows is not None:
+            return
+
+        rows = _avro_rows(self._block, self._avro_schema)
+        self._num_items = self._block.avro_rows.row_count
+        self._remaining = self._block.avro_rows.row_count
+        self._iter_rows = iter(rows)
+
+    @property
+    def num_items(self):
+        """int: Total items in the page."""
+        self._parse_block()
+        return self._num_items
+
+    @property
+    def remaining(self):
+        """int: Remaining items in the page."""
+        self._parse_block()
+        return self._remaining
+
+    def __iter__(self):
+        """A ``ReadRowsPage`` is an iterator."""
+        return self
+
+    def next(self):
+        """Get the next row in the page."""
+        self._parse_block()
+        if self._remaining > 0:
+            self._remaining -= 1
+        return six.next(self._iter_rows)
+
+    # Alias needed for Python 2/3 support.
+    __next__ = next
+
+    def to_dataframe(self, dtypes=None):
+        """Create a :class:`pandas.DataFrame` of rows in the page.
+
+        This method requires the pandas libary to create a data frame and the
+        fastavro library to parse row blocks.
+
+        .. warning::
+            DATETIME columns are not supported. They are currently parsed as
+            strings in the fastavro library.
+
+        Args:
+            dtypes ( \
+                Map[str, Union[str, pandas.Series.dtype]] \
+            ):
+                Optional. A dictionary of column names pandas ``dtype``s. The
+                provided ``dtype`` is used when constructing the series for
+                the column specified. Otherwise, the default pandas behavior
+                is used.
+
+        Returns:
+            pandas.DataFrame:
+                A data frame of all rows in the stream.
+        """
+        if pandas is None:
+            raise ImportError(_PANDAS_REQUIRED)
+
+        if dtypes is None:
+            dtypes = {}
+
+        columns = collections.defaultdict(list)
+        for row in self:
+            for column in row:
+                columns[column].append(row[column])
+        for column in dtypes:
+            columns[column] = pandas.Series(columns[column], dtype=dtypes[column])
+        return pandas.DataFrame(columns, columns=self._column_names)
 
 
 def _avro_schema(read_session):
@@ -242,12 +397,13 @@ def _avro_rows(block, avro_schema):
     """Parse all rows in a stream block.
 
     Args:
-        read_session ( \
-            ~google.cloud.bigquery_storage_v1beta1.types.ReadSession \
+        block ( \
+            ~google.cloud.bigquery_storage_v1beta1.types.ReadRowsResponse \
         ):
-            The read session associated with this read rows stream. This
-            contains the schema, which is required to parse the data
-            blocks.
+            A block containing Avro bytes to parse into rows.
+        avro_schema (fastavro.schema):
+            A parsed Avro schema, used to deserialized the bytes in the
+            block.
 
     Returns:
         Iterable[Mapping]: