Google Cloud Spanner Python Samples

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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

This sample requires you to have authentication setup. Refer to the Authentication Getting Started Guide for instructions on setting up credentials for applications.
Install Dependencies

Clone python-docs-samples and change directory to the sample directory you want to use.
```
$ git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
```
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.
Create a virtualenv. Samples are compatible with Python 2.7 and 3.4+.
```
$ virtualenv env
$ source env/bin/activate
```
Install the dependencies needed to run the samples.
```
$ pip install -r requirements.txt
```
Samples

Snippets

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To run this sample:
$ python snippets.py

usage: snippets.py [-h] [--database-id DATABASE_ID]
                   instance_id
                   {create_database,insert_data,query_data,read_data,
                   read_stale_data,add_column,update_data,
                   query_data_with_new_column,read_write_transaction,
                   read_only_transaction,add_index,query_data_with_index,
                   read_data_with_index,add_storing_index,
                   read_data_with_storing_index,
                   create_table_with_timestamp,insert_data_with_timestamp,
                   add_timestamp_column,update_data_with_timestamp,
                   query_data_with_timestamp,write_struct_data,
                   query_with_struct,query_with_array_of_struct,
                   query_struct_field,query_nested_struct_field,
                   insert_data_with_dml,update_data_with_dml,
                   delete_data_with_dml,update_data_with_dml_timestamp,
                   dml_write_read_transaction,update_data_with_dml_struct,
                   insert_with_dml,query_data_with_parameter,
                   write_with_dml_transaction,
                   update_data_with_partitioned_dml,
                   delete_data_with_partitioned_dml,update_with_batch_dml,
                   create_table_with_datatypes,insert_datatypes_data,
                   query_data_with_array,query_data_with_bool,
                   query_data_with_bytes,query_data_with_date,
                   query_data_with_float,query_data_with_int,
                   query_data_with_string,
                   query_data_with_timestamp_parameter}
                   ...

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

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

positional arguments:
  instance_id           Your Cloud Spanner instance ID.
  {create_database, insert_data, delete_data, query_data, read_data,
  read_stale_data, add_column, update_data, query_data_with_new_column,
  read_write_transaction, read_only_transaction, add_index,
  query_data_with_index, read_data_with_index, add_storing_index,
  read_data_with_storing_index, create_table_with_timestamp,
  insert_data_with_timestamp, add_timestamp_column,
  update_data_with_timestamp, query_data_with_timestamp,
  write_struct_data, query_with_struct, query_with_array_of_struct,
  query_struct_field, query_nested_struct_field, insert_data_with_dml,
  update_data_with_dml, delete_data_with_dml,
  update_data_with_dml_timestamp, dml_write_read_transaction,
  update_data_with_dml_struct, insert_with_dml, query_data_with_parameter,
  write_with_dml_transaction, update_data_with_partitioned_dml,
  delete_data_with_partitioned_dml, update_with_batch_dml,
  create_table_with_datatypes, insert_datatypes_data,
  query_data_with_array, query_data_with_bool, query_data_with_bytes,
  query_data_with_date, query_data_with_float, query_data_with_int,
  query_data_with_string, query_data_with_timestamp_parameter}
    create_database     Creates a database and tables for sample data.
    insert_data         Inserts sample data into the given database. The
                        database and table must already exist and can be
                        created using `create_database`.
    delete_data         Deletes sample data from the given database. The
                        database, table, and data must already exist and
                        can be created using `create_database` and
                        `insert_data`.
    query_data          Queries sample data from the database using SQL.
    read_data           Reads sample data from the database.
    read_stale_data     Reads sample data from the database. The data is
                        exactly 15 seconds stale.
    add_column          Adds a new column to the Albums table in the example
                        database.
    update_data         Updates sample data in the database. This updates the
                        `MarketingBudget` column which must be created before
                        running this sample. You can add the column by running
                        the `add_column` sample or by running this DDL
                        statement against your database: ALTER TABLE Albums
                        ADD COLUMN MarketingBudget INT64
    query_data_with_new_column
                        Queries sample data from the database using SQL. This
                        sample uses the `MarketingBudget` column. You can add
                        the column by running the `add_column` sample or by
                        running this DDL statement against your database:
                        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 second Album
                        to the first 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.
    read_only_transaction
                        Reads data inside of a read-only transaction. Within
                        the read-only transaction, or "snapshot", the
                        application sees consistent view of the database at a
                        particular timestamp.
    add_index           Adds a simple index to the example database.
    query_data_with_index
                        Queries sample data from the database using SQL and an
                        index. The index must exist before running this
                        sample. You can add the index by running the
                        `add_index` sample or by running this DDL statement
                        against your database: CREATE INDEX AlbumsByAlbumTitle
                        ON Albums(AlbumTitle) This sample also uses the
                        `MarketingBudget` column. You can add the column by
                        running the `add_column` sample or by running this DDL
                        statement against your database: ALTER TABLE Albums
                        ADD COLUMN MarketingBudget INT64
    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`.
    add_storing_index   Adds an storing index to the example 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`.
    create_table_with_timestamp
                        Creates a table with a COMMIT_TIMESTAMP column.
    insert_data_with_timestamp
                        Inserts data with a COMMIT_TIMESTAMP field into a
                        table.
    add_timestamp_column
                        Adds a new TIMESTAMP column to the Albums table in the
                        example database.
    update_data_with_timestamp
                        Updates Performances tables in the database with the
                        COMMIT_TIMESTAMP column. This updates the
                        `MarketingBudget` column which must be created before
                        running this sample. You can add the column by running
                        the `add_column` sample or by running this DDL
                        statement against your database: ALTER TABLE Albums
                        ADD COLUMN MarketingBudget INT64 In addition this
                        update expects the LastUpdateTime column added by
                        applying this DDL statement against your database:
                        ALTER TABLE Albums ADD COLUMN LastUpdateTime TIMESTAMP
                        OPTIONS(allow_commit_timestamp=true)
    query_data_with_timestamp
                        Queries sample data from the database using SQL. This
                        updates the `LastUpdateTime` column which must be
                        created before running this sample. You can add the
                        column by running the `add_timestamp_column` sample or
                        by running this DDL statement against your database:
                        ALTER TABLE Performances ADD COLUMN LastUpdateTime
                        TIMESTAMP OPTIONS (allow_commit_timestamp=true)
    write_struct_data   Inserts sample data that can be used to test STRUCT
                        parameters in queries.
    query_with_struct   Query a table using STRUCT parameters.
    query_with_array_of_struct
                        Query a table using an array of STRUCT parameters.
    query_struct_field  Query a table using field access on a STRUCT
                        parameter.
    query_nested_struct_field
                        Query a table using nested field access on a STRUCT
                        parameter.
    insert_data_with_dml
                        Inserts sample data into the given database using a
                        DML statement.
    update_data_with_dml
                        Updates sample data from the database using a DML
                        statement.
    delete_data_with_dml
                        Deletes sample data from the database using a DML
                        statement.
    update_data_with_dml_timestamp
                        Updates data with Timestamp from the database using
                        a DML statement.
    dml_write_read_transaction
                        First inserts data then reads it from within a
                        transaction using DML.
    update_data_with_dml_struct
                        Updates data with a DML statement and STRUCT
                        parameters.
    insert_with_dml     Inserts data with a DML statement into the
                        database.
    query_data_with_parameter
                        Queries sample data from the database using SQL
                        with a parameter.
    write_with_dml_transaction
                        Transfers part of a marketing budget from one
                        album to another.
    update_data_with_partitioned_dml
                        Update sample data with a partitioned DML
                        statement.
    delete_data_with_partitioned_dml
                        Delete sample data with a partitioned DML
                        statement.
    update_with_batch_dml
                        Updates sample data in the database using Batch
                        DML.
    create_table_with_datatypes
                        Creates a table with supported dataypes.
    insert_datatypes_data
                        Inserts data with supported datatypes into a table.
    query_data_with_array
                        Queries sample data using SQL with an ARRAY
                        parameter.
    query_data_with_bool
                        Queries sample data using SQL with a BOOL
                        parameter.
    query_data_with_bytes
                        Queries sample data using SQL with a BYTES
                        parameter.
    query_data_with_date
                        Queries sample data using SQL with a DATE
                        parameter.
    query_data_with_float
                        Queries sample data using SQL with a FLOAT64
                        parameter.
    query_data_with_int
                        Queries sample data using SQL with a INT64
                        parameter.
    query_data_with_string
                        Queries sample data using SQL with a STRING
                        parameter.
    query_data_with_timestamp_parameter
                        Queries sample data using SQL with a TIMESTAMP
                        parameter.

optional arguments:
  -h, --help            show this help message and exit
  --database-id DATABASE_ID
                        Your Cloud Spanner database ID.
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.