Remove telemetry/third_party/gsutilz

tools/telemetry/third_party/gsutilz/third_party/boto/boto/dynamodb2/table.py

Index: tools/telemetry/third_party/gsutilz/third_party/boto/boto/dynamodb2/table.py
diff --git a/tools/telemetry/third_party/gsutilz/third_party/boto/boto/dynamodb2/table.py b/tools/telemetry/third_party/gsutilz/third_party/boto/boto/dynamodb2/table.py
deleted file mode 100644
index d02ff5c7deb4acbb53925c85ab0e489649192a4d..0000000000000000000000000000000000000000
--- a/tools/telemetry/third_party/gsutilz/third_party/boto/boto/dynamodb2/table.py
+++ /dev/null
@@ -1,1722 +0,0 @@
-import boto
-from boto.dynamodb2 import exceptions
-from boto.dynamodb2.fields import (HashKey, RangeKey,
-                                   AllIndex, KeysOnlyIndex, IncludeIndex,
-                                   GlobalAllIndex, GlobalKeysOnlyIndex,
-                                   GlobalIncludeIndex)
-from boto.dynamodb2.items import Item
-from boto.dynamodb2.layer1 import DynamoDBConnection
-from boto.dynamodb2.results import ResultSet, BatchGetResultSet
-from boto.dynamodb2.types import (NonBooleanDynamizer, Dynamizer, FILTER_OPERATORS,
-                                  QUERY_OPERATORS, STRING)
-from boto.exception import JSONResponseError
-
-
-class Table(object):
-    """
-    Interacts & models the behavior of a DynamoDB table.
-
-    The ``Table`` object represents a set (or rough categorization) of
-    records within DynamoDB. The important part is that all records within the
-    table, while largely-schema-free, share the same schema & are essentially
-    namespaced for use in your application. For example, you might have a
-    ``users`` table or a ``forums`` table.
-    """
-    max_batch_get = 100
-
-    _PROJECTION_TYPE_TO_INDEX = dict(
-        global_indexes=dict(
-            ALL=GlobalAllIndex,
-            KEYS_ONLY=GlobalKeysOnlyIndex,
-            INCLUDE=GlobalIncludeIndex,
-        ), local_indexes=dict(
-            ALL=AllIndex,
-            KEYS_ONLY=KeysOnlyIndex,
-            INCLUDE=IncludeIndex,
-        )
-    )
-
-    def __init__(self, table_name, schema=None, throughput=None, indexes=None,
-                 global_indexes=None, connection=None):
-        """
-        Sets up a new in-memory ``Table``.
-
-        This is useful if the table already exists within DynamoDB & you simply
-        want to use it for additional interactions. The only required parameter
-        is the ``table_name``. However, under the hood, the object will call
-        ``describe_table`` to determine the schema/indexes/throughput. You
-        can avoid this extra call by passing in ``schema`` & ``indexes``.
-
-        **IMPORTANT** - If you're creating a new ``Table`` for the first time,
-        you should use the ``Table.create`` method instead, as it will
-        persist the table structure to DynamoDB.
-
-        Requires a ``table_name`` parameter, which should be a simple string
-        of the name of the table.
-
-        Optionally accepts a ``schema`` parameter, which should be a list of
-        ``BaseSchemaField`` subclasses representing the desired schema.
-
-        Optionally accepts a ``throughput`` parameter, which should be a
-        dictionary. If provided, it should specify a ``read`` & ``write`` key,
-        both of which should have an integer value associated with them.
-
-        Optionally accepts a ``indexes`` parameter, which should be a list of
-        ``BaseIndexField`` subclasses representing the desired indexes.
-
-        Optionally accepts a ``global_indexes`` parameter, which should be a
-        list of ``GlobalBaseIndexField`` subclasses representing the desired
-        indexes.
-
-        Optionally accepts a ``connection`` parameter, which should be a
-        ``DynamoDBConnection`` instance (or subclass). This is primarily useful
-        for specifying alternate connection parameters.
-
-        Example::
-
-            # The simple, it-already-exists case.
-            >>> conn = Table('users')
-
-            # The full, minimum-extra-calls case.
-            >>> from boto import dynamodb2
-            >>> users = Table('users', schema=[
-            ...     HashKey('username'),
-            ...     RangeKey('date_joined', data_type=NUMBER)
-            ... ], throughput={
-            ...     'read':20,
-            ...     'write': 10,
-            ... }, indexes=[
-            ...     KeysOnlyIndex('MostRecentlyJoined', parts=[
-            ...         HashKey('username')
-            ...         RangeKey('date_joined')
-            ...     ]),
-            ... ], global_indexes=[
-            ...     GlobalAllIndex('UsersByZipcode', parts=[
-            ...         HashKey('zipcode'),
-            ...         RangeKey('username'),
-            ...     ],
-            ...     throughput={
-            ...       'read':10,
-            ...       'write":10,
-            ...     }),
-            ... ], connection=dynamodb2.connect_to_region('us-west-2',
-            ...     aws_access_key_id='key',
-            ...     aws_secret_access_key='key',
-            ... ))
-
-        """
-        self.table_name = table_name
-        self.connection = connection
-        self.throughput = {
-            'read': 5,
-            'write': 5,
-        }
-        self.schema = schema
-        self.indexes = indexes
-        self.global_indexes = global_indexes
-
-        if self.connection is None:
-            self.connection = DynamoDBConnection()
-
-        if throughput is not None:
-            self.throughput = throughput
-
-        self._dynamizer = NonBooleanDynamizer()
-
-    def use_boolean(self):
-        self._dynamizer = Dynamizer()
-
-    @classmethod
-    def create(cls, table_name, schema, throughput=None, indexes=None,
-               global_indexes=None, connection=None):
-        """
-        Creates a new table in DynamoDB & returns an in-memory ``Table`` object.
-
-        This will setup a brand new table within DynamoDB. The ``table_name``
-        must be unique for your AWS account. The ``schema`` is also required
-        to define the key structure of the table.
-
-        **IMPORTANT** - You should consider the usage pattern of your table
-        up-front, as the schema can **NOT** be modified once the table is
-        created, requiring the creation of a new table & migrating the data
-        should you wish to revise it.
-
-        **IMPORTANT** - If the table already exists in DynamoDB, additional
-        calls to this method will result in an error. If you just need
-        a ``Table`` object to interact with the existing table, you should
-        just initialize a new ``Table`` object, which requires only the
-        ``table_name``.
-
-        Requires a ``table_name`` parameter, which should be a simple string
-        of the name of the table.
-
-        Requires a ``schema`` parameter, which should be a list of
-        ``BaseSchemaField`` subclasses representing the desired schema.
-
-        Optionally accepts a ``throughput`` parameter, which should be a
-        dictionary. If provided, it should specify a ``read`` & ``write`` key,
-        both of which should have an integer value associated with them.
-
-        Optionally accepts a ``indexes`` parameter, which should be a list of
-        ``BaseIndexField`` subclasses representing the desired indexes.
-
-        Optionally accepts a ``global_indexes`` parameter, which should be a
-        list of ``GlobalBaseIndexField`` subclasses representing the desired
-        indexes.
-
-        Optionally accepts a ``connection`` parameter, which should be a
-        ``DynamoDBConnection`` instance (or subclass). This is primarily useful
-        for specifying alternate connection parameters.
-
-        Example::
-
-            >>> users = Table.create('users', schema=[
-            ...     HashKey('username'),
-            ...     RangeKey('date_joined', data_type=NUMBER)
-            ... ], throughput={
-            ...     'read':20,
-            ...     'write': 10,
-            ... }, indexes=[
-            ...     KeysOnlyIndex('MostRecentlyJoined', parts=[
-            ...         RangeKey('date_joined')
-            ... ]), global_indexes=[
-            ...     GlobalAllIndex('UsersByZipcode', parts=[
-            ...         HashKey('zipcode'),
-            ...         RangeKey('username'),
-            ...     ],
-            ...     throughput={
-            ...       'read':10,
-            ...       'write':10,
-            ...     }),
-            ... ])
-
-        """
-        table = cls(table_name=table_name, connection=connection)
-        table.schema = schema
-
-        if throughput is not None:
-            table.throughput = throughput
-
-        if indexes is not None:
-            table.indexes = indexes
-
-        if global_indexes is not None:
-            table.global_indexes = global_indexes
-
-        # Prep the schema.
-        raw_schema = []
-        attr_defs = []
-        seen_attrs = set()
-
-        for field in table.schema:
-            raw_schema.append(field.schema())
-            # Build the attributes off what we know.
-            seen_attrs.add(field.name)
-            attr_defs.append(field.definition())
-
-        raw_throughput = {
-            'ReadCapacityUnits': int(table.throughput['read']),
-            'WriteCapacityUnits': int(table.throughput['write']),
-        }
-        kwargs = {}
-
-        kwarg_map = {
-            'indexes': 'local_secondary_indexes',
-            'global_indexes': 'global_secondary_indexes',
-        }
-        for index_attr in ('indexes', 'global_indexes'):
-            table_indexes = getattr(table, index_attr)
-            if table_indexes:
-                raw_indexes = []
-                for index_field in table_indexes:
-                    raw_indexes.append(index_field.schema())
-                    # Make sure all attributes specified in the indexes are
-                    # added to the definition
-                    for field in index_field.parts:
-                        if field.name not in seen_attrs:
-                            seen_attrs.add(field.name)
-                            attr_defs.append(field.definition())
-
-                kwargs[kwarg_map[index_attr]] = raw_indexes
-
-        table.connection.create_table(
-            table_name=table.table_name,
-            attribute_definitions=attr_defs,
-            key_schema=raw_schema,
-            provisioned_throughput=raw_throughput,
-            **kwargs
-        )
-        return table
-
-    def _introspect_schema(self, raw_schema, raw_attributes=None):
-        """
-        Given a raw schema structure back from a DynamoDB response, parse
-        out & build the high-level Python objects that represent them.
-        """
-        schema = []
-        sane_attributes = {}
-
-        if raw_attributes:
-            for field in raw_attributes:
-                sane_attributes[field['AttributeName']] = field['AttributeType']
-
-        for field in raw_schema:
-            data_type = sane_attributes.get(field['AttributeName'], STRING)
-
-            if field['KeyType'] == 'HASH':
-                schema.append(
-                    HashKey(field['AttributeName'], data_type=data_type)
-                )
-            elif field['KeyType'] == 'RANGE':
-                schema.append(
-                    RangeKey(field['AttributeName'], data_type=data_type)
-                )
-            else:
-                raise exceptions.UnknownSchemaFieldError(
-                    "%s was seen, but is unknown. Please report this at "
-                    "https://github.com/boto/boto/issues." % field['KeyType']
-                )
-
-        return schema
-
-    def _introspect_all_indexes(self, raw_indexes, map_indexes_projection):
-        """
-        Given a raw index/global index structure back from a DynamoDB response,
-        parse out & build the high-level Python objects that represent them.
-        """
-        indexes = []
-
-        for field in raw_indexes:
-            index_klass = map_indexes_projection.get('ALL')
-            kwargs = {
-                'parts': []
-            }
-
-            if field['Projection']['ProjectionType'] == 'ALL':
-                index_klass = map_indexes_projection.get('ALL')
-            elif field['Projection']['ProjectionType'] == 'KEYS_ONLY':
-                index_klass = map_indexes_projection.get('KEYS_ONLY')
-            elif field['Projection']['ProjectionType'] == 'INCLUDE':
-                index_klass = map_indexes_projection.get('INCLUDE')
-                kwargs['includes'] = field['Projection']['NonKeyAttributes']
-            else:
-                raise exceptions.UnknownIndexFieldError(
-                    "%s was seen, but is unknown. Please report this at "
-                    "https://github.com/boto/boto/issues." % \
-                    field['Projection']['ProjectionType']
-                )
-
-            name = field['IndexName']
-            kwargs['parts'] = self._introspect_schema(field['KeySchema'], None)
-            indexes.append(index_klass(name, **kwargs))
-
-        return indexes
-
-    def _introspect_indexes(self, raw_indexes):
-        """
-        Given a raw index structure back from a DynamoDB response, parse
-        out & build the high-level Python objects that represent them.
-        """
-        return self._introspect_all_indexes(
-            raw_indexes, self._PROJECTION_TYPE_TO_INDEX.get('local_indexes'))
-
-    def _introspect_global_indexes(self, raw_global_indexes):
-        """
-        Given a raw global index structure back from a DynamoDB response, parse
-        out & build the high-level Python objects that represent them.
-        """
-        return self._introspect_all_indexes(
-            raw_global_indexes,
-            self._PROJECTION_TYPE_TO_INDEX.get('global_indexes'))
-
-    def describe(self):
-        """
-        Describes the current structure of the table in DynamoDB.
-
-        This information will be used to update the ``schema``, ``indexes``,
-        ``global_indexes`` and ``throughput`` information on the ``Table``. Some
-        calls, such as those involving creating keys or querying, will require
-        this information to be populated.
-
-        It also returns the full raw data structure from DynamoDB, in the
-        event you'd like to parse out additional information (such as the
-        ``ItemCount`` or usage information).
-
-        Example::
-
-            >>> users.describe()
-            {
-                # Lots of keys here...
-            }
-            >>> len(users.schema)
-            2
-
-        """
-        result = self.connection.describe_table(self.table_name)
-
-        # Blindly update throughput, since what's on DynamoDB's end is likely
-        # more correct.
-        raw_throughput = result['Table']['ProvisionedThroughput']
-        self.throughput['read'] = int(raw_throughput['ReadCapacityUnits'])
-        self.throughput['write'] = int(raw_throughput['WriteCapacityUnits'])
-
-        if not self.schema:
-            # Since we have the data, build the schema.
-            raw_schema = result['Table'].get('KeySchema', [])
-            raw_attributes = result['Table'].get('AttributeDefinitions', [])
-            self.schema = self._introspect_schema(raw_schema, raw_attributes)
-
-        if not self.indexes:
-            # Build the index information as well.
-            raw_indexes = result['Table'].get('LocalSecondaryIndexes', [])
-            self.indexes = self._introspect_indexes(raw_indexes)
-
-        # Build the global index information as well.
-        raw_global_indexes = result['Table'].get('GlobalSecondaryIndexes', [])
-        self.global_indexes = self._introspect_global_indexes(raw_global_indexes)
-
-        # This is leaky.
-        return result
-
-    def update(self, throughput=None, global_indexes=None):
-        """
-        Updates table attributes and global indexes in DynamoDB.
-
-        Optionally accepts a ``throughput`` parameter, which should be a
-        dictionary. If provided, it should specify a ``read`` & ``write`` key,
-        both of which should have an integer value associated with them.
-
-        Optionally accepts a ``global_indexes`` parameter, which should be a
-        dictionary. If provided, it should specify the index name, which is also
-        a dict containing a ``read`` & ``write`` key, both of which
-        should have an integer value associated with them. If you are writing
-        new code, please use ``Table.update_global_secondary_index``.
-
-        Returns ``True`` on success.
-
-        Example::
-
-            # For a read-heavier application...
-            >>> users.update(throughput={
-            ...     'read': 20,
-            ...     'write': 10,
-            ... })
-            True
-
-            # To also update the global index(es) throughput.
-            >>> users.update(throughput={
-            ...     'read': 20,
-            ...     'write': 10,
-            ... },
-            ... global_secondary_indexes={
-            ...     'TheIndexNameHere': {
-            ...         'read': 15,
-            ...         'write': 5,
-            ...     }
-            ... })
-            True
-        """
-
-        data = None
-
-        if throughput:
-            self.throughput = throughput
-            data = {
-                'ReadCapacityUnits': int(self.throughput['read']),
-                'WriteCapacityUnits': int(self.throughput['write']),
-            }
-
-        gsi_data = None
-
-        if global_indexes:
-            gsi_data = []
-
-            for gsi_name, gsi_throughput in global_indexes.items():
-                gsi_data.append({
-                    "Update": {
-                        "IndexName": gsi_name,
-                        "ProvisionedThroughput": {
-                            "ReadCapacityUnits": int(gsi_throughput['read']),
-                            "WriteCapacityUnits": int(gsi_throughput['write']),
-                        },
-                    },
-                })
-
-        if throughput or global_indexes:
-            self.connection.update_table(
-                self.table_name,
-                provisioned_throughput=data,
-                global_secondary_index_updates=gsi_data,
-            )
-
-            return True
-        else:
-            msg = 'You need to provide either the throughput or the ' \
-                  'global_indexes to update method'
-            boto.log.error(msg)
-
-            return False
-
-    def create_global_secondary_index(self, global_index):
-        """
-        Creates a global index in DynamoDB after the table has been created.
-
-        Requires a ``global_indexes`` parameter, which should be a
-        ``GlobalBaseIndexField`` subclass representing the desired index.
-
-        To update ``global_indexes`` information on the ``Table``, you'll need
-        to call ``Table.describe``.
-
-        Returns ``True`` on success.
-
-        Example::
-
-            # To create a global index
-            >>> users.create_global_secondary_index(
-            ...     global_index=GlobalAllIndex(
-            ...         'TheIndexNameHere', parts=[
-            ...             HashKey('requiredHashkey', data_type=STRING),
-            ...             RangeKey('optionalRangeKey', data_type=STRING)
-            ...         ],
-            ...         throughput={
-            ...             'read': 2,
-            ...             'write': 1,
-            ...         })
-            ... )
-            True
-
-        """
-
-        if global_index:
-            gsi_data = []
-            gsi_data_attr_def = []
-
-            gsi_data.append({
-                "Create": global_index.schema()
-            })
-
-            for attr_def in global_index.parts:
-                gsi_data_attr_def.append(attr_def.definition())
-
-            self.connection.update_table(
-                self.table_name,
-                global_secondary_index_updates=gsi_data,
-                attribute_definitions=gsi_data_attr_def
-            )
-
-            return True
-        else:
-            msg = 'You need to provide the global_index to ' \
-                  'create_global_secondary_index method'
-            boto.log.error(msg)
-
-            return False
-
-    def delete_global_secondary_index(self, global_index_name):
-        """
-        Deletes a global index in DynamoDB after the table has been created.
-
-        Requires a ``global_index_name`` parameter, which should be a simple
-        string of the name of the global secondary index.
-
-        To update ``global_indexes`` information on the ``Table``, you'll need
-        to call ``Table.describe``.
-
-        Returns ``True`` on success.
-
-        Example::
-
-            # To delete a global index
-            >>> users.delete_global_secondary_index('TheIndexNameHere')
-            True
-
-        """
-
-        if global_index_name:
-            gsi_data = [
-                {
-                    "Delete": {
-                        "IndexName": global_index_name
-                    }
-                }
-            ]
-
-            self.connection.update_table(
-                self.table_name,
-                global_secondary_index_updates=gsi_data,
-            )
-
-            return True
-        else:
-            msg = 'You need to provide the global index name to ' \
-                  'delete_global_secondary_index method'
-            boto.log.error(msg)
-
-            return False
-
-    def update_global_secondary_index(self, global_indexes):
-        """
-        Updates a global index(es) in DynamoDB after the table has been created.
-
-        Requires a ``global_indexes`` parameter, which should be a
-        dictionary. If provided, it should specify the index name, which is also
-        a dict containing a ``read`` & ``write`` key, both of which
-        should have an integer value associated with them.
-
-        To update ``global_indexes`` information on the ``Table``, you'll need
-        to call ``Table.describe``.
-
-        Returns ``True`` on success.
-
-        Example::
-
-            # To update a global index
-            >>> users.update_global_secondary_index(global_indexes={
-            ...     'TheIndexNameHere': {
-            ...         'read': 15,
-            ...         'write': 5,
-            ...     }
-            ... })
-            True
-
-        """
-
-        if global_indexes:
-            gsi_data = []
-
-            for gsi_name, gsi_throughput in global_indexes.items():
-                gsi_data.append({
-                    "Update": {
-                        "IndexName": gsi_name,
-                        "ProvisionedThroughput": {
-                            "ReadCapacityUnits": int(gsi_throughput['read']),
-                            "WriteCapacityUnits": int(gsi_throughput['write']),
-                        },
-                    },
-                })
-
-            self.connection.update_table(
-                self.table_name,
-                global_secondary_index_updates=gsi_data,
-            )
-            return True
-        else:
-            msg = 'You need to provide the global indexes to ' \
-                  'update_global_secondary_index method'
-            boto.log.error(msg)
-
-            return False
-
-    def delete(self):
-        """
-        Deletes a table in DynamoDB.
-
-        **IMPORTANT** - Be careful when using this method, there is no undo.
-
-        Returns ``True`` on success.
-
-        Example::
-
-            >>> users.delete()
-            True
-
-        """
-        self.connection.delete_table(self.table_name)
-        return True
-
-    def _encode_keys(self, keys):
-        """
-        Given a flat Python dictionary of keys/values, converts it into the
-        nested dictionary DynamoDB expects.
-
-        Converts::
-
-            {
-                'username': 'john',
-                'tags': [1, 2, 5],
-            }
-
-        ...to...::
-
-            {
-                'username': {'S': 'john'},
-                'tags': {'NS': ['1', '2', '5']},
-            }
-
-        """
-        raw_key = {}
-
-        for key, value in keys.items():
-            raw_key[key] = self._dynamizer.encode(value)
-
-        return raw_key
-
-    def get_item(self, consistent=False, attributes=None, **kwargs):
-        """
-        Fetches an item (record) from a table in DynamoDB.
-
-        To specify the key of the item you'd like to get, you can specify the
-        key attributes as kwargs.
-
-        Optionally accepts a ``consistent`` parameter, which should be a
-        boolean. If you provide ``True``, it will perform
-        a consistent (but more expensive) read from DynamoDB.
-        (Default: ``False``)
-
-        Optionally accepts an ``attributes`` parameter, which should be a
-        list of fieldname to fetch. (Default: ``None``, which means all fields
-        should be fetched)
-
-        Returns an ``Item`` instance containing all the data for that record.
-        
-        Raises an ``ItemNotFound`` exception if the item is not found.
-
-        Example::
-
-            # A simple hash key.
-            >>> john = users.get_item(username='johndoe')
-            >>> john['first_name']
-            'John'
-
-            # A complex hash+range key.
-            >>> john = users.get_item(username='johndoe', last_name='Doe')
-            >>> john['first_name']
-            'John'
-
-            # A consistent read (assuming the data might have just changed).
-            >>> john = users.get_item(username='johndoe', consistent=True)
-            >>> john['first_name']
-            'Johann'
-
-            # With a key that is an invalid variable name in Python.
-            # Also, assumes a different schema than previous examples.
-            >>> john = users.get_item(**{
-            ...     'date-joined': 127549192,
-            ... })
-            >>> john['first_name']
-            'John'
-
-        """
-        raw_key = self._encode_keys(kwargs)
-        item_data = self.connection.get_item(
-            self.table_name,
-            raw_key,
-            attributes_to_get=attributes,
-            consistent_read=consistent
-        )
-        if 'Item' not in item_data:
-            raise exceptions.ItemNotFound("Item %s couldn't be found." % kwargs)
-        item = Item(self)
-        item.load(item_data)
-        return item
-
-    def has_item(self, **kwargs):
-        """
-        Return whether an item (record) exists within a table in DynamoDB.
-
-        To specify the key of the item you'd like to get, you can specify the
-        key attributes as kwargs.
-
-        Optionally accepts a ``consistent`` parameter, which should be a
-        boolean. If you provide ``True``, it will perform
-        a consistent (but more expensive) read from DynamoDB.
-        (Default: ``False``)
-
-        Optionally accepts an ``attributes`` parameter, which should be a
-        list of fieldnames to fetch. (Default: ``None``, which means all fields
-        should be fetched)
-
-        Returns ``True`` if an ``Item`` is present, ``False`` if not.
-
-        Example::
-
-            # Simple, just hash-key schema.
-            >>> users.has_item(username='johndoe')
-            True
-
-            # Complex schema, item not present.
-            >>> users.has_item(
-            ...     username='johndoe',
-            ...     date_joined='2014-01-07'
-            ... )
-            False
-
-        """
-        try:
-            self.get_item(**kwargs)
-        except (JSONResponseError, exceptions.ItemNotFound):
-            return False
-
-        return True
-
-    def lookup(self, *args, **kwargs):
-        """
-        Look up an entry in DynamoDB. This is mostly backwards compatible
-        with boto.dynamodb. Unlike get_item, it takes hash_key and range_key first,
-        although you may still specify keyword arguments instead.
-
-        Also unlike the get_item command, if the returned item has no keys
-        (i.e., it does not exist in DynamoDB), a None result is returned, instead
-        of an empty key object.
-
-        Example::
-            >>> user = users.lookup(username)
-            >>> user = users.lookup(username, consistent=True)
-            >>> app = apps.lookup('my_customer_id', 'my_app_id')
-
-        """
-        if not self.schema:
-            self.describe()
-        for x, arg in enumerate(args):
-            kwargs[self.schema[x].name] = arg
-        ret = self.get_item(**kwargs)
-        if not ret.keys():
-            return None
-        return ret
-
-    def new_item(self, *args):
-        """
-        Returns a new, blank item
-
-        This is mostly for consistency with boto.dynamodb
-        """
-        if not self.schema:
-            self.describe()
-        data = {}
-        for x, arg in enumerate(args):
-            data[self.schema[x].name] = arg
-        return Item(self, data=data)
-
-    def put_item(self, data, overwrite=False):
-        """
-        Saves an entire item to DynamoDB.
-
-        By default, if any part of the ``Item``'s original data doesn't match
-        what's currently in DynamoDB, this request will fail. This prevents
-        other processes from updating the data in between when you read the
-        item & when your request to update the item's data is processed, which
-        would typically result in some data loss.
-
-        Requires a ``data`` parameter, which should be a dictionary of the data
-        you'd like to store in DynamoDB.
-
-        Optionally accepts an ``overwrite`` parameter, which should be a
-        boolean. If you provide ``True``, this will tell DynamoDB to blindly
-        overwrite whatever data is present, if any.
-
-        Returns ``True`` on success.
-
-        Example::
-
-            >>> users.put_item(data={
-            ...     'username': 'jane',
-            ...     'first_name': 'Jane',
-            ...     'last_name': 'Doe',
-            ...     'date_joined': 126478915,
-            ... })
-            True
-
-        """
-        item = Item(self, data=data)
-        return item.save(overwrite=overwrite)
-
-    def _put_item(self, item_data, expects=None):
-        """
-        The internal variant of ``put_item`` (full data). This is used by the
-        ``Item`` objects, since that operation is represented at the
-        table-level by the API, but conceptually maps better to telling an
-        individual ``Item`` to save itself.
-        """
-        kwargs = {}
-
-        if expects is not None:
-            kwargs['expected'] = expects
-
-        self.connection.put_item(self.table_name, item_data, **kwargs)
-        return True
-
-    def _update_item(self, key, item_data, expects=None):
-        """
-        The internal variant of ``put_item`` (partial data). This is used by the
-        ``Item`` objects, since that operation is represented at the
-        table-level by the API, but conceptually maps better to telling an
-        individual ``Item`` to save itself.
-        """
-        raw_key = self._encode_keys(key)
-        kwargs = {}
-
-        if expects is not None:
-            kwargs['expected'] = expects
-
-        self.connection.update_item(self.table_name, raw_key, item_data, **kwargs)
-        return True
-
-    def delete_item(self, expected=None, conditional_operator=None, **kwargs):
-        """
-        Deletes a single item. You can perform a conditional delete operation
-        that deletes the item if it exists, or if it has an expected attribute
-        value.
-
-        Conditional deletes are useful for only deleting items if specific
-        conditions are met. If those conditions are met, DynamoDB performs
-        the delete. Otherwise, the item is not deleted.
-
-        To specify the expected attribute values of the item, you can pass a
-        dictionary of conditions to ``expected``. Each condition should follow
-        the pattern ``<attributename>__<comparison_operator>=<value_to_expect>``.
-
-        **IMPORTANT** - Be careful when using this method, there is no undo.
-
-        To specify the key of the item you'd like to get, you can specify the
-        key attributes as kwargs.
-
-        Optionally accepts an ``expected`` parameter which is a dictionary of
-        expected attribute value conditions.
-
-        Optionally accepts a ``conditional_operator`` which applies to the
-        expected attribute value conditions:
-
-        + `AND` - If all of the conditions evaluate to true (default)
-        + `OR` - True if at least one condition evaluates to true
-
-        Returns ``True`` on success, ``False`` on failed conditional delete.
-
-        Example::
-
-            # A simple hash key.
-            >>> users.delete_item(username='johndoe')
-            True
-
-            # A complex hash+range key.
-            >>> users.delete_item(username='jane', last_name='Doe')
-            True
-
-            # With a key that is an invalid variable name in Python.
-            # Also, assumes a different schema than previous examples.
-            >>> users.delete_item(**{
-            ...     'date-joined': 127549192,
-            ... })
-            True
-
-            # Conditional delete
-            >>> users.delete_item(username='johndoe',
-            ...                   expected={'balance__eq': 0})
-            True
-        """
-        expected = self._build_filters(expected, using=FILTER_OPERATORS)
-        raw_key = self._encode_keys(kwargs)
-
-        try:
-            self.connection.delete_item(self.table_name, raw_key,
-                                        expected=expected,
-                                        conditional_operator=conditional_operator)
-        except exceptions.ConditionalCheckFailedException:
-            return False
-
-        return True
-
-    def get_key_fields(self):
-        """
-        Returns the fields necessary to make a key for a table.
-
-        If the ``Table`` does not already have a populated ``schema``,
-        this will request it via a ``Table.describe`` call.
-
-        Returns a list of fieldnames (strings).
-
-        Example::
-
-            # A simple hash key.
-            >>> users.get_key_fields()
-            ['username']
-
-            # A complex hash+range key.
-            >>> users.get_key_fields()
-            ['username', 'last_name']
-
-        """
-        if not self.schema:
-            # We don't know the structure of the table. Get a description to
-            # populate the schema.
-            self.describe()
-
-        return [field.name for field in self.schema]
-
-    def batch_write(self):
-        """
-        Allows the batching of writes to DynamoDB.
-
-        Since each write/delete call to DynamoDB has a cost associated with it,
-        when loading lots of data, it makes sense to batch them, creating as
-        few calls as possible.
-
-        This returns a context manager that will transparently handle creating
-        these batches. The object you get back lightly-resembles a ``Table``
-        object, sharing just the ``put_item`` & ``delete_item`` methods
-        (which are all that DynamoDB can batch in terms of writing data).
-
-        DynamoDB's maximum batch size is 25 items per request. If you attempt
-        to put/delete more than that, the context manager will batch as many
-        as it can up to that number, then flush them to DynamoDB & continue
-        batching as more calls come in.
-
-        Example::
-
-            # Assuming a table with one record...
-            >>> with users.batch_write() as batch:
-            ...     batch.put_item(data={
-            ...         'username': 'johndoe',
-            ...         'first_name': 'John',
-            ...         'last_name': 'Doe',
-            ...         'owner': 1,
-            ...     })
-            ...     # Nothing across the wire yet.
-            ...     batch.delete_item(username='bob')
-            ...     # Still no requests sent.
-            ...     batch.put_item(data={
-            ...         'username': 'jane',
-            ...         'first_name': 'Jane',
-            ...         'last_name': 'Doe',
-            ...         'date_joined': 127436192,
-            ...     })
-            ...     # Nothing yet, but once we leave the context, the
-            ...     # put/deletes will be sent.
-
-        """
-        # PHENOMENAL COSMIC DOCS!!! itty-bitty code.
-        return BatchTable(self)
-
-    def _build_filters(self, filter_kwargs, using=QUERY_OPERATORS):
-        """
-        An internal method for taking query/scan-style ``**kwargs`` & turning
-        them into the raw structure DynamoDB expects for filtering.
-        """
-        if filter_kwargs is None:
-            return
-
-        filters = {}
-
-        for field_and_op, value in filter_kwargs.items():
-            field_bits = field_and_op.split('__')
-            fieldname = '__'.join(field_bits[:-1])
-
-            try:
-                op = using[field_bits[-1]]
-            except KeyError:
-                raise exceptions.UnknownFilterTypeError(
-                    "Operator '%s' from '%s' is not recognized." % (
-                        field_bits[-1],
-                        field_and_op
-                    )
-                )
-
-            lookup = {
-                'AttributeValueList': [],
-                'ComparisonOperator': op,
-            }
-
-            # Special-case the ``NULL/NOT_NULL`` case.
-            if field_bits[-1] == 'null':
-                del lookup['AttributeValueList']
-
-                if value is False:
-                    lookup['ComparisonOperator'] = 'NOT_NULL'
-                else:
-                    lookup['ComparisonOperator'] = 'NULL'
-            # Special-case the ``BETWEEN`` case.
-            elif field_bits[-1] == 'between':
-                if len(value) == 2 and isinstance(value, (list, tuple)):
-                    lookup['AttributeValueList'].append(
-                        self._dynamizer.encode(value[0])
-                    )
-                    lookup['AttributeValueList'].append(
-                        self._dynamizer.encode(value[1])
-                    )
-            # Special-case the ``IN`` case
-            elif field_bits[-1] == 'in':
-                for val in value:
-                    lookup['AttributeValueList'].append(self._dynamizer.encode(val))
-            else:
-                # Fix up the value for encoding, because it was built to only work
-                # with ``set``s.
-                if isinstance(value, (list, tuple)):
-                    value = set(value)
-                lookup['AttributeValueList'].append(
-                    self._dynamizer.encode(value)
-                )
-
-            # Finally, insert it into the filters.
-            filters[fieldname] = lookup
-
-        return filters
-
-    def query(self, limit=None, index=None, reverse=False, consistent=False,
-              attributes=None, max_page_size=None, **filter_kwargs):
-        """
-        **WARNING:** This method is provided **strictly** for
-        backward-compatibility. It returns results in an incorrect order.
-
-        If you are writing new code, please use ``Table.query_2``.
-        """
-        reverse = not reverse
-        return self.query_2(limit=limit, index=index, reverse=reverse,
-                            consistent=consistent, attributes=attributes,
-                            max_page_size=max_page_size, **filter_kwargs)
-
-    def query_2(self, limit=None, index=None, reverse=False,
-                consistent=False, attributes=None, max_page_size=None,
-                query_filter=None, conditional_operator=None,
-                **filter_kwargs):
-        """
-        Queries for a set of matching items in a DynamoDB table.
-
-        Queries can be performed against a hash key, a hash+range key or
-        against any data stored in your local secondary indexes. Query filters
-        can be used to filter on arbitrary fields.
-
-        **Note** - You can not query against arbitrary fields within the data
-        stored in DynamoDB unless you specify ``query_filter`` values.
-
-        To specify the filters of the items you'd like to get, you can specify
-        the filters as kwargs. Each filter kwarg should follow the pattern
-        ``<fieldname>__<filter_operation>=<value_to_look_for>``. Query filters
-        are specified in the same way.
-
-        Optionally accepts a ``limit`` parameter, which should be an integer
-        count of the total number of items to return. (Default: ``None`` -
-        all results)
-
-        Optionally accepts an ``index`` parameter, which should be a string of
-        name of the local secondary index you want to query against.
-        (Default: ``None``)
-
-        Optionally accepts a ``reverse`` parameter, which will present the
-        results in reverse order. (Default: ``False`` - normal order)
-
-        Optionally accepts a ``consistent`` parameter, which should be a
-        boolean. If you provide ``True``, it will force a consistent read of
-        the data (more expensive). (Default: ``False`` - use eventually
-        consistent reads)
-
-        Optionally accepts a ``attributes`` parameter, which should be a
-        tuple. If you provide any attributes only these will be fetched
-        from DynamoDB. This uses the ``AttributesToGet`` and set's
-        ``Select`` to ``SPECIFIC_ATTRIBUTES`` API.
-
-        Optionally accepts a ``max_page_size`` parameter, which should be an
-        integer count of the maximum number of items to retrieve
-        **per-request**. This is useful in making faster requests & prevent
-        the scan from drowning out other queries. (Default: ``None`` -
-        fetch as many as DynamoDB will return)
-
-        Optionally accepts a ``query_filter`` which is a dictionary of filter
-        conditions against any arbitrary field in the returned data.
-
-        Optionally accepts a ``conditional_operator`` which applies to the
-        query filter conditions:
-
-        + `AND` - True if all filter conditions evaluate to true (default)
-        + `OR` - True if at least one filter condition evaluates to true
-
-        Returns a ``ResultSet``, which transparently handles the pagination of
-        results you get back.
-
-        Example::
-
-            # Look for last names equal to "Doe".
-            >>> results = users.query(last_name__eq='Doe')
-            >>> for res in results:
-            ...     print res['first_name']
-            'John'
-            'Jane'
-
-            # Look for last names beginning with "D", in reverse order, limit 3.
-            >>> results = users.query(
-            ...     last_name__beginswith='D',
-            ...     reverse=True,
-            ...     limit=3
-            ... )
-            >>> for res in results:
-            ...     print res['first_name']
-            'Alice'
-            'Jane'
-            'John'
-
-            # Use an LSI & a consistent read.
-            >>> results = users.query(
-            ...     date_joined__gte=1236451000,
-            ...     owner__eq=1,
-            ...     index='DateJoinedIndex',
-            ...     consistent=True
-            ... )
-            >>> for res in results:
-            ...     print res['first_name']
-            'Alice'
-            'Bob'
-            'John'
-            'Fred'
-
-            # Filter by non-indexed field(s)
-            >>> results = users.query(
-            ...     last_name__eq='Doe',
-            ...     reverse=True,
-            ...     query_filter={
-            ...         'first_name__beginswith': 'A'
-            ...     }
-            ... )
-            >>> for res in results:
-            ...     print res['first_name'] + ' ' + res['last_name']
-            'Alice Doe'
-
-        """
-        if self.schema:
-            if len(self.schema) == 1:
-                if len(filter_kwargs) <= 1:
-                    if not self.global_indexes or not len(self.global_indexes):
-                        # If the schema only has one field, there's <= 1 filter
-                        # param & no Global Secondary Indexes, this is user
-                        # error. Bail early.
-                        raise exceptions.QueryError(
-                            "You must specify more than one key to filter on."
-                        )
-
-        if attributes is not None:
-            select = 'SPECIFIC_ATTRIBUTES'
-        else:
-            select = None
-
-        results = ResultSet(
-            max_page_size=max_page_size
-        )
-        kwargs = filter_kwargs.copy()
-        kwargs.update({
-            'limit': limit,
-            'index': index,
-            'reverse': reverse,
-            'consistent': consistent,
-            'select': select,
-            'attributes_to_get': attributes,
-            'query_filter': query_filter,
-            'conditional_operator': conditional_operator,
-        })
-        results.to_call(self._query, **kwargs)
-        return results
-
-    def query_count(self, index=None, consistent=False, conditional_operator=None,
-                    query_filter=None, scan_index_forward=True, limit=None,
-                    exclusive_start_key=None, **filter_kwargs):
-        """
-        Queries the exact count of matching items in a DynamoDB table.
-
-        Queries can be performed against a hash key, a hash+range key or
-        against any data stored in your local secondary indexes. Query filters
-        can be used to filter on arbitrary fields.
-
-        To specify the filters of the items you'd like to get, you can specify
-        the filters as kwargs. Each filter kwarg should follow the pattern
-        ``<fieldname>__<filter_operation>=<value_to_look_for>``. Query filters
-        are specified in the same way.
-
-        Optionally accepts an ``index`` parameter, which should be a string of
-        name of the local secondary index you want to query against.
-        (Default: ``None``)
-
-        Optionally accepts a ``consistent`` parameter, which should be a
-        boolean. If you provide ``True``, it will force a consistent read of
-        the data (more expensive). (Default: ``False`` - use eventually
-        consistent reads)
-
-        Optionally accepts a ``query_filter`` which is a dictionary of filter
-        conditions against any arbitrary field in the returned data.
-
-        Optionally accepts a ``conditional_operator`` which applies to the
-        query filter conditions:
-
-        + `AND` - True if all filter conditions evaluate to true (default)
-        + `OR` - True if at least one filter condition evaluates to true
-
-        Optionally accept a ``exclusive_start_key`` which is used to get
-        the remaining items when a query cannot return the complete count.
-
-        Returns an integer which represents the exact amount of matched
-        items.
-
-        :type scan_index_forward: boolean
-        :param scan_index_forward: Specifies ascending (true) or descending
-            (false) traversal of the index. DynamoDB returns results reflecting
-            the requested order determined by the range key. If the data type
-            is Number, the results are returned in numeric order. For String,
-            the results are returned in order of ASCII character code values.
-            For Binary, DynamoDB treats each byte of the binary data as
-            unsigned when it compares binary values.
-
-        If ScanIndexForward is not specified, the results are returned in
-            ascending order.
-
-        :type limit: integer
-        :param limit: The maximum number of items to evaluate (not necessarily
-            the number of matching items).
-
-        Example::
-
-            # Look for last names equal to "Doe".
-            >>> users.query_count(last_name__eq='Doe')
-            5
-
-            # Use an LSI & a consistent read.
-            >>> users.query_count(
-            ...     date_joined__gte=1236451000,
-            ...     owner__eq=1,
-            ...     index='DateJoinedIndex',
-            ...     consistent=True
-            ... )
-            2
-
-        """
-        key_conditions = self._build_filters(
-            filter_kwargs,
-            using=QUERY_OPERATORS
-        )
-
-        built_query_filter = self._build_filters(
-            query_filter,
-            using=FILTER_OPERATORS
-        )
-
-        count_buffer = 0
-        last_evaluated_key = exclusive_start_key
-
-        while True:
-            raw_results = self.connection.query(
-                self.table_name,
-                index_name=index,
-                consistent_read=consistent,
-                select='COUNT',
-                key_conditions=key_conditions,
-                query_filter=built_query_filter,
-                conditional_operator=conditional_operator,
-                limit=limit,
-                scan_index_forward=scan_index_forward,
-                exclusive_start_key=last_evaluated_key
-            )
-
-            count_buffer += int(raw_results.get('Count', 0))
-            last_evaluated_key = raw_results.get('LastEvaluatedKey')
-            if not last_evaluated_key or count_buffer < 1:
-                break
-
-        return count_buffer
-
-    def _query(self, limit=None, index=None, reverse=False, consistent=False,
-               exclusive_start_key=None, select=None, attributes_to_get=None,
-               query_filter=None, conditional_operator=None, **filter_kwargs):
-        """
-        The internal method that performs the actual queries. Used extensively
-        by ``ResultSet`` to perform each (paginated) request.
-        """
-        kwargs = {
-            'limit': limit,
-            'index_name': index,
-            'consistent_read': consistent,
-            'select': select,
-            'attributes_to_get': attributes_to_get,
-            'conditional_operator': conditional_operator,
-        }
-
-        if reverse:
-            kwargs['scan_index_forward'] = False
-
-        if exclusive_start_key:
-            kwargs['exclusive_start_key'] = {}
-
-            for key, value in exclusive_start_key.items():
-                kwargs['exclusive_start_key'][key] = \
-                    self._dynamizer.encode(value)
-
-        # Convert the filters into something we can actually use.
-        kwargs['key_conditions'] = self._build_filters(
-            filter_kwargs,
-            using=QUERY_OPERATORS
-        )
-
-        kwargs['query_filter'] = self._build_filters(
-            query_filter,
-            using=FILTER_OPERATORS
-        )
-
-        raw_results = self.connection.query(
-            self.table_name,
-            **kwargs
-        )
-        results = []
-        last_key = None
-
-        for raw_item in raw_results.get('Items', []):
-            item = Item(self)
-            item.load({
-                'Item': raw_item,
-            })
-            results.append(item)
-
-        if raw_results.get('LastEvaluatedKey', None):
-            last_key = {}
-
-            for key, value in raw_results['LastEvaluatedKey'].items():
-                last_key[key] = self._dynamizer.decode(value)
-
-        return {
-            'results': results,
-            'last_key': last_key,
-        }
-
-    def scan(self, limit=None, segment=None, total_segments=None,
-             max_page_size=None, attributes=None, conditional_operator=None,
-             **filter_kwargs):
-        """
-        Scans across all items within a DynamoDB table.
-
-        Scans can be performed against a hash key or a hash+range key. You can
-        additionally filter the results after the table has been read but
-        before the response is returned by using query filters.
-
-        To specify the filters of the items you'd like to get, you can specify
-        the filters as kwargs. Each filter kwarg should follow the pattern
-        ``<fieldname>__<filter_operation>=<value_to_look_for>``.
-
-        Optionally accepts a ``limit`` parameter, which should be an integer
-        count of the total number of items to return. (Default: ``None`` -
-        all results)
-
-        Optionally accepts a ``segment`` parameter, which should be an integer
-        of the segment to retrieve on. Please see the documentation about
-        Parallel Scans (Default: ``None`` - no segments)
-
-        Optionally accepts a ``total_segments`` parameter, which should be an
-        integer count of number of segments to divide the table into.
-        Please see the documentation about Parallel Scans (Default: ``None`` -
-        no segments)
-
-        Optionally accepts a ``max_page_size`` parameter, which should be an
-        integer count of the maximum number of items to retrieve
-        **per-request**. This is useful in making faster requests & prevent
-        the scan from drowning out other queries. (Default: ``None`` -
-        fetch as many as DynamoDB will return)
-
-        Optionally accepts an ``attributes`` parameter, which should be a
-        tuple. If you provide any attributes only these will be fetched
-        from DynamoDB. This uses the ``AttributesToGet`` and set's
-        ``Select`` to ``SPECIFIC_ATTRIBUTES`` API.
-
-        Returns a ``ResultSet``, which transparently handles the pagination of
-        results you get back.
-
-        Example::
-
-            # All results.
-            >>> everything = users.scan()
-
-            # Look for last names beginning with "D".
-            >>> results = users.scan(last_name__beginswith='D')
-            >>> for res in results:
-            ...     print res['first_name']
-            'Alice'
-            'John'
-            'Jane'
-
-            # Use an ``IN`` filter & limit.
-            >>> results = users.scan(
-            ...     age__in=[25, 26, 27, 28, 29],
-            ...     limit=1
-            ... )
-            >>> for res in results:
-            ...     print res['first_name']
-            'Alice'
-
-        """
-        results = ResultSet(
-            max_page_size=max_page_size
-        )
-        kwargs = filter_kwargs.copy()
-        kwargs.update({
-            'limit': limit,
-            'segment': segment,
-            'total_segments': total_segments,
-            'attributes': attributes,
-            'conditional_operator': conditional_operator,
-        })
-        results.to_call(self._scan, **kwargs)
-        return results
-
-    def _scan(self, limit=None, exclusive_start_key=None, segment=None,
-              total_segments=None, attributes=None, conditional_operator=None,
-              **filter_kwargs):
-        """
-        The internal method that performs the actual scan. Used extensively
-        by ``ResultSet`` to perform each (paginated) request.
-        """
-        kwargs = {
-            'limit': limit,
-            'segment': segment,
-            'total_segments': total_segments,
-            'attributes_to_get': attributes,
-            'conditional_operator': conditional_operator,
-        }
-
-        if exclusive_start_key:
-            kwargs['exclusive_start_key'] = {}
-
-            for key, value in exclusive_start_key.items():
-                kwargs['exclusive_start_key'][key] = \
-                    self._dynamizer.encode(value)
-
-        # Convert the filters into something we can actually use.
-        kwargs['scan_filter'] = self._build_filters(
-            filter_kwargs,
-            using=FILTER_OPERATORS
-        )
-
-        raw_results = self.connection.scan(
-            self.table_name,
-            **kwargs
-        )
-        results = []
-        last_key = None
-
-        for raw_item in raw_results.get('Items', []):
-            item = Item(self)
-            item.load({
-                'Item': raw_item,
-            })
-            results.append(item)
-
-        if raw_results.get('LastEvaluatedKey', None):
-            last_key = {}
-
-            for key, value in raw_results['LastEvaluatedKey'].items():
-                last_key[key] = self._dynamizer.decode(value)
-
-        return {
-            'results': results,
-            'last_key': last_key,
-        }
-
-    def batch_get(self, keys, consistent=False, attributes=None):
-        """
-        Fetches many specific items in batch from a table.
-
-        Requires a ``keys`` parameter, which should be a list of dictionaries.
-        Each dictionary should consist of the keys values to specify.
-
-        Optionally accepts a ``consistent`` parameter, which should be a
-        boolean. If you provide ``True``, a strongly consistent read will be
-        used. (Default: False)
-
-        Optionally accepts an ``attributes`` parameter, which should be a
-        tuple. If you provide any attributes only these will be fetched
-        from DynamoDB.
-
-        Returns a ``ResultSet``, which transparently handles the pagination of
-        results you get back.
-
-        Example::
-
-            >>> results = users.batch_get(keys=[
-            ...     {
-            ...         'username': 'johndoe',
-            ...     },
-            ...     {
-            ...         'username': 'jane',
-            ...     },
-            ...     {
-            ...         'username': 'fred',
-            ...     },
-            ... ])
-            >>> for res in results:
-            ...     print res['first_name']
-            'John'
-            'Jane'
-            'Fred'
-
-        """
-        # We pass the keys to the constructor instead, so it can maintain it's
-        # own internal state as to what keys have been processed.
-        results = BatchGetResultSet(keys=keys, max_batch_get=self.max_batch_get)
-        results.to_call(self._batch_get, consistent=consistent, attributes=attributes)
-        return results
-
-    def _batch_get(self, keys, consistent=False, attributes=None):
-        """
-        The internal method that performs the actual batch get. Used extensively
-        by ``BatchGetResultSet`` to perform each (paginated) request.
-        """
-        items = {
-            self.table_name: {
-                'Keys': [],
-            },
-        }
-
-        if consistent:
-            items[self.table_name]['ConsistentRead'] = True
-
-        if attributes is not None:
-            items[self.table_name]['AttributesToGet'] = attributes
-
-        for key_data in keys:
-            raw_key = {}
-
-            for key, value in key_data.items():
-                raw_key[key] = self._dynamizer.encode(value)
-
-            items[self.table_name]['Keys'].append(raw_key)
-
-        raw_results = self.connection.batch_get_item(request_items=items)
-        results = []
-        unprocessed_keys = []
-
-        for raw_item in raw_results['Responses'].get(self.table_name, []):
-            item = Item(self)
-            item.load({
-                'Item': raw_item,
-            })
-            results.append(item)
-
-        raw_unproccessed = raw_results.get('UnprocessedKeys', {})
-
-        for raw_key in raw_unproccessed.get('Keys', []):
-            py_key = {}
-
-            for key, value in raw_key.items():
-                py_key[key] = self._dynamizer.decode(value)
-
-            unprocessed_keys.append(py_key)
-
-        return {
-            'results': results,
-            # NEVER return a ``last_key``. Just in-case any part of
-            # ``ResultSet`` peeks through, since much of the
-            # original underlying implementation is based on this key.
-            'last_key': None,
-            'unprocessed_keys': unprocessed_keys,
-        }
-
-    def count(self):
-        """
-        Returns a (very) eventually consistent count of the number of items
-        in a table.
-
-        Lag time is about 6 hours, so don't expect a high degree of accuracy.
-
-        Example::
-
-            >>> users.count()
-            6
-
-        """
-        info = self.describe()
-        return info['Table'].get('ItemCount', 0)
-
-
-class BatchTable(object):
-    """
-    Used by ``Table`` as the context manager for batch writes.
-
-    You likely don't want to try to use this object directly.
-    """
-    def __init__(self, table):
-        self.table = table
-        self._to_put = []
-        self._to_delete = []
-        self._unprocessed = []
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, type, value, traceback):
-        if self._to_put or self._to_delete:
-            # Flush anything that's left.
-            self.flush()
-
-        if self._unprocessed:
-            # Finally, handle anything that wasn't processed.
-            self.resend_unprocessed()
-
-    def put_item(self, data, overwrite=False):
-        self._to_put.append(data)
-
-        if self.should_flush():
-            self.flush()
-
-    def delete_item(self, **kwargs):
-        self._to_delete.append(kwargs)
-
-        if self.should_flush():
-            self.flush()
-
-    def should_flush(self):
-        if len(self._to_put) + len(self._to_delete) == 25:
-            return True
-
-        return False
-
-    def flush(self):
-        batch_data = {
-            self.table.table_name: [
-                # We'll insert data here shortly.
-            ],
-        }
-
-        for put in self._to_put:
-            item = Item(self.table, data=put)
-            batch_data[self.table.table_name].append({
-                'PutRequest': {
-                    'Item': item.prepare_full(),
-                }
-            })
-
-        for delete in self._to_delete:
-            batch_data[self.table.table_name].append({
-                'DeleteRequest': {
-                    'Key': self.table._encode_keys(delete),
-                }
-            })
-
-        resp = self.table.connection.batch_write_item(batch_data)
-        self.handle_unprocessed(resp)
-
-        self._to_put = []
-        self._to_delete = []
-        return True
-
-    def handle_unprocessed(self, resp):
-        if len(resp.get('UnprocessedItems', [])):
-            table_name = self.table.table_name
-            unprocessed = resp['UnprocessedItems'].get(table_name, [])
-
-            # Some items have not been processed. Stow them for now &
-            # re-attempt processing on ``__exit__``.
-            msg = "%s items were unprocessed. Storing for later."
-            boto.log.info(msg % len(unprocessed))
-            self._unprocessed.extend(unprocessed)
-
-    def resend_unprocessed(self):
-        # If there are unprocessed records (for instance, the user was over
-        # their throughput limitations), iterate over them & send until they're
-        # all there.
-        boto.log.info(
-            "Re-sending %s unprocessed items." % len(self._unprocessed)
-        )
-
-        while len(self._unprocessed):
-            # Again, do 25 at a time.
-            to_resend = self._unprocessed[:25]
-            # Remove them from the list.
-            self._unprocessed = self._unprocessed[25:]
-            batch_data = {
-                self.table.table_name: to_resend
-            }
-            boto.log.info("Sending %s items" % len(to_resend))
-            resp = self.table.connection.batch_write_item(batch_data)
-            self.handle_unprocessed(resp)
-            boto.log.info(
-                "%s unprocessed items left" % len(self._unprocessed)
-            )