Add bulk loading Python Sample (#2295)

Adds the following functionality:

* Create bulk_load_csv
* Delete bulk_load_csv
* Create schema.ddl

spanner/cloud-client/bulk_load_csv/README.rst

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+Google Cloud Spanner: Bulk Loading From CSV Python Sample
+===============
+
+``Google Cloud Spanner`` is a highly scalable, transactional, managed, NewSQL database service.
+  Cloud Spanner solves the need for a horizontally-scaling database with consistent global transactions and SQL semantics.
+
+This application demonstrates how to load data from a csv file into a Cloud
+Spanner database.
+
+The data contained in the csv files is sourced from the "Hacker News - Y Combinator" Bigquery `public dataset`_.
+
+.. _public dataset :
+    https://cloud.google.com/bigquery/public-data/
+
+Pre-requisuite
+-----------------------
+Create a database in your Cloud Spanner instance using the `schema`_ in the folder.
+
+.. _schema:
+    schema.ddl
+
+Setup
+------------------------
+
+Authentication
+++++++++++++++
+
+This sample requires you to have authentication setup. Refer to the
+`Authentication Getting Started Guide`_ for instructions on setting up
+credentials for applications.
+
+.. _Authentication Getting Started Guide:
+    https://cloud.google.com/docs/authentication/getting-started
+
+Install Dependencies
+++++++++++++++++++++
+
+#. Install `pip`_ and `virtualenv`_ if you do not already have them. You may want to refer to the `Python Development Environment Setup Guide`_ for Google Cloud Platform for instructions.
+
+   .. _Python Development Environment Setup Guide:
+       https://cloud.google.com/python/setup
+
+#. Create a virtualenv. Samples are compatible with Python 2.7 and 3.4+.
+
+    MACOS/LINUX
+
+    .. code-block:: bash
+ 
+        $ virtualenv env
+        $ source env/bin/activate
+        
+    WINDOWS
+
+    .. code-block:: bash
+ 
+        > virtualenv env
+        > .\env\Scripts\activate
+ 
+#. Install the dependencies needed to run the samples.
+
+    .. code-block:: bash
+ 
+        $ pip install -r requirements.txt
+ 
+.. _pip: https://pip.pypa.io/
+.. _virtualenv: https://virtualenv.pypa.io/
+
+
+To run sample
+-----------------------
+
+ $ python batch_import.py instance_id database_id
+
+positional arguments:
+  instance_id:           Your Cloud Spanner instance ID.
+
+  database_id :          Your Cloud Spanner database ID.

spanner/cloud-client/bulk_load_csv/batch_import.py

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+# Copyright 2019 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This application demonstrates how to do batch operations from a csv file
+# using Cloud Spanner.
+# For more information, see the README.rst.
+
+
+import csv
+import time
+import threading
+import argparse
+from google.cloud import spanner
+
+
+def is_bool_null(file):
+    # This function convertes the boolean values
+    # in the dataset from strings to boolean data types.
+    # It also converts the string Null to a None data
+    # type indicating an empty cell.
+    data = list(csv.reader(file))
+    # Reads each line in the csv file.
+    for line in range(len(data)):
+        for cell in range(len(data[line])):
+            # Changes the string to boolean.
+            if data[line][cell] == 'true':
+                data[line][cell] = eval('True')
+            # Changes blank string to python readable None type.
+            if data[line][cell] == '':
+                data[line][cell] = None
+    return (data)
+
+
+def divide_chunks(lst, n):
+    # This function divides the csv file into chunks so that the mutation will
+    # commit every 500 rows.
+    for i in range(0, len(lst), n):
+        yield lst[i:i + n]
+
+
+def insert_data(database, filepath, table_name, column_names):
+    # This function iterates over the list of files
+    # belonging to the dataset and,
+    # writes each line into cloud spanner using the batch mutation function.
+    with open(filepath, newline='') as file:
+        data = is_bool_null(file)
+        data = tuple(data)
+        l_group = list(divide_chunks(data, 500))
+    # Inserts each chunk of data into database
+    for current_inserts in (l_group):
+        if current_inserts is not None:
+            with database.batch() as batch:
+                batch.insert(
+                    table=table_name,
+                    columns=column_names,
+                    values=current_inserts)
+
+
+def main(instance_id, database_id):
+    # Inserts sample data into the given database.
+    # The database and table must already exist and can be created
+    # using`create_database`.
+    start = time.time()
+    # File paths
+    comments_file = 'hnewscomments.txt'
+    stories_file = 'hnewsstories.txt'
+    # Instantiates a spanner client
+    spanner_client = spanner.Client()
+    instance = spanner_client.instance(instance_id)
+    database = instance.database(database_id)
+    # Sets the Column names.
+    s_columnnames = (
+        'id',
+        'by',
+        'author',
+        'dead',
+        'deleted',
+        'descendants',
+        'score',
+        'text',
+        'time',
+        'time_ts',
+        'title',
+        'url',
+    )
+    c_columnnames = (
+        'id',
+        'by',
+        'author',
+        'dead',
+        'deleted',
+        'parent',
+        'ranking',
+        'text',
+        'time',
+        'time_ts',
+    )
+    # Creates threads
+    t1 = threading.Thread(
+        target=insert_data,
+        args=(database, stories_file, 'stories', s_columnnames))
+    t2 = threading.Thread(
+        target=insert_data,
+        args=(database, comments_file, 'comments', c_columnnames))
+    # Starting threads
+    t1.start()
+    t2.start()
+    # Wait until all threads finish
+    t1.join()
+    t2.join()
+
+    print('Finished Inserting Data.')
+    end = time.time()
+    print('Time: ', end - start)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument('instance_id', help='Your Cloud Spanner instance ID.')
+    parser.add_argument('database_id', help='Your Cloud Spanner database ID.')
+
+    args = parser.parse_args()
+
+    main(args.instance_id, args.database_id)

spanner/cloud-client/bulk_load_csv/batch_import_test.py

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+# Copyright 2019 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This application demonstrates how to do batch operations from a csv file
+# using Cloud Spanner.
+# For more information, see the README.rst.
+"""Test for batch_import"""
+import os
+import pytest
+import batch_import
+from google.cloud import spanner
+
+
+INSTANCE_ID = os.environ['SPANNER_INSTANCE']
+DATABASE_ID = 'hnewsdb'
+
+
+@pytest.fixture(scope='module')
+def spanner_instance():
+    spanner_client = spanner.Client()
+    return spanner_client.instance(INSTANCE_ID)
+
+
+@pytest.fixture
+def example_database():
+    spanner_client = spanner.Client()
+    instance = spanner_client.instance(INSTANCE_ID)
+    database = instance.database(DATABASE_ID)
+
+    if not database.exists():
+        with open('schema.ddl', 'r') as myfile:
+            schema = myfile.read()
+        database = instance.database(DATABASE_ID, ddl_statements=[schema])
+        database.create()
+
+        yield database
+        database.drop()
+
+
+def test_is_bool_null():
+    assert batch_import.is_bool_null(['12', 'true', '', '12',
+                                      'jkl', '']) == [['12'], [True],
+                                                      [], ['12'],
+                                                      ['jkl'], []]
+
+
+def test_divide_chunks():
+    res = list(batch_import.divide_chunks(['12', 'true', '', '12',
+                                           'jkl', ''], 2))
+    assert res == [['12', 'true'], ['', '12'], ['jkl', '']]
+
+
+def test_insert_data(capsys):
+    batch_import.main(INSTANCE_ID, DATABASE_ID)
+    out, _ = capsys.readouterr()
+    assert 'Finished Inserting Data.' in out