Google Cloud Spanner Python Samples

This directory contains samples for Google Cloud Spanner. 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.

Setup

Authentication

Authentication is typically done through Application Default Credentials, which means you do not have to change the code to authenticate as long as your environment has credentials. You have a few options for setting up authentication:

1. When running locally, use the Google Cloud SDK

   gcloud beta auth application-default login

2. When running on App Engine or Compute Engine, credentials are already set-up. However, you may need to configure your Compute Engine instance with additional scopes.

3. You can create a Service Account key file. This file can be used to authenticate to Google Cloud Platform services from any environment. To use the file, set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path to the key file, for example:

   export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service_account.json

Install Dependencies

1. Install pip and virtualenv if you do not already have them.

2. Create a virtualenv. Samples are compatible with Python 2.7 and 3.4+.

   $ virtualenv env
   $ source env/bin/activate

3. Install the dependencies needed to run the samples.

   $ pip install -r requirements.txt

Samples

Snippets

To run this sample:

$ python snippets.py

usage: snippets.py [-h] [--database-name DATABASE_NAME]
                   instance_name
                   {insert_data,query_data,read_data,update_data,read_write_transaction,query_data_with_index,read_data_with_index,read_data_with_storing_index}
                   ...

This application demonstrates how to do basic operations using Cloud
Spanner.

For more information, see the README.rst under /spanner.

positional arguments:
  instance_name         Your Cloud Spanner instance name.
  {insert_data,query_data,read_data,update_data,read_write_transaction,query_data_with_index,read_data_with_index,read_data_with_storing_index}
    insert_data         Inserts sample data into the given database. The
                        database and table must already exist and can be
                        created using `create_database`.
    query_data          Queries sample data from the database using SQL.
    read_data           Reads sample data from the database.
    update_data         Updates sample data in the database. This updates the
                        `MarketingBudget` column which must be created before
                        running this sample. Run the following query on your
                        database to create the column: ALTER TABLE Albums ADD
                        COLUMN MarketingBudget INT64
    read_write_transaction
                        Performs a read-write transaction to update two sample
                        records in the database. This will transfer 200,000
                        from the `MarketingBudget` field for the first Album
                        to the second Album. If the `MarketingBudget` is too
                        low, it will raise an exception. Before running this
                        sample, you will need to run the `update_data` sample
                        to populate the fields.
    query_data_with_index
                        Inserts sample data into the given database. The
                        database and table must already exist and can be
                        created using `create_database`.
    read_data_with_index
                        Inserts sample data into the given database. The
                        database and table must already exist and can be
                        created using `create_database`.
    read_data_with_storing_index
                        Inserts sample data into the given database. The
                        database and table must already exist and can be
                        created using `create_database`.

optional arguments:
  -h, --help            show this help message and exit
  --database-name DATABASE_NAME
                        Your Cloud Spanner database name.

The client library

This sample uses the `Google Cloud Client Library for Python`_. You can read the documentation for more details on API usage and use GitHub to `browse the source`_ and `report issues`_.