Getting started with Cloud Spanner in Python

Objectives

This tutorial walks you through the following steps using the Cloud Spanner client library for Python:

  • Create a Cloud Spanner instance and database.
  • Write, read, and execute SQL queries on data in the database.
  • Update the database schema.
  • Update data using a read-write transaction.
  • Add a secondary index to the database.
  • Use the index to read and execute SQL queries on data.
  • Retrieve data using a read-only transaction.

Costs

This tutorial uses Cloud Spanner, which is a billable component of the Google Cloud Platform. For information on the cost of using Cloud Spanner, see Pricing.

Before you begin

  1. Complete the steps described in Set up, which covers creating and setting a default Google Cloud Platform project, enabling billing, enabling the Cloud Spanner API, and setting up OAuth 2.0 to get authentication credentials to use the Cloud Spanner API.
    In particular, ensure that you run gcloud auth application-default login to set up your local development environment with authentication credentials.

  2. Follow the instructions in Setting Up a Python Development Environment.

  3. Clone the sample app repository to your local machine:

    git clone https://github.com/GoogleCloudPlatform/python-docs-samples
    

    Alternatively, you can download the sample as a zip file and extract it.

  4. Change to the directory that contains the Cloud Spanner sample code:

    cd python-docs-samples/spanner/cloud-client
    
  5. Create an isolated Python environment, and install dependencies:

    virtualenv env
    source env/bin/activate
    pip install -r requirements.txt
    

Create an instance

When you first use Cloud Spanner, you must create an instance, which is an allocation of resources that are used by Cloud Spanner databases. When you create an instance, you choose an instance configuration, which determines where your data is stored, and also the number of nodes to use, which determines the amount of serving and storage resources in your instance.

Execute the following command to create a Cloud Spanner instance in the region us-central1 with 1 node:

gcloud spanner instances create test-instance --config=regional-us-central1 \
    --description="Test Instance" --nodes=1

Note that this creates an instance with the following characteristics:

  • Instance ID test-instance
  • Display name Test Instance
  • Instance configuration regional-us-central1 (Regional configurations store data in one region, while multi-region configurations distribute data across multiple regions. Learn more in Instances.)
  • Node count of 1 (node_count corresponds to the amount of serving and storage resources available to databases in the instance. Learn more in Node count.)

You should see:

Creating instance...done.

Look through sample files

The samples repo contains a sample that shows how to use Cloud Spanner with Python.

Take a look through the snippets.py file, which shows how to use Cloud Spanner. The code shows how to create and use a new database. The data uses the example schema shown in the Schema and data model page.

Create a database

Create a database called example-db in the instance called test-instance by running the following at the command line.

python snippets.py test-instance --database-id example-db create_database

You should see:

Created database example-db on instance test-instance

You have just created a Cloud Spanner database. The following is the code that created the database.

def create_database(instance_id, database_id):
    """Creates a database and tables for sample data."""
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)

    database = instance.database(database_id, ddl_statements=[
        """CREATE TABLE Singers (
            SingerId     INT64 NOT NULL,
            FirstName    STRING(1024),
            LastName     STRING(1024),
            SingerInfo   BYTES(MAX)
        ) PRIMARY KEY (SingerId)""",
        """CREATE TABLE Albums (
            SingerId     INT64 NOT NULL,
            AlbumId      INT64 NOT NULL,
            AlbumTitle   STRING(MAX)
        ) PRIMARY KEY (SingerId, AlbumId),
        INTERLEAVE IN PARENT Singers ON DELETE CASCADE"""
    ])

    operation = database.create()

    print('Waiting for operation to complete...')
    operation.result()

    print('Created database {} on instance {}'.format(
        database_id, instance_id))

The code also defines two tables, Singers and Albums, for a basic music application. These tables are used throughout this page. Take a look at the example schema if you haven't already.

The next step is to write data to your database.

Create a database client

Before you can do reads or writes, you must create a Client. You can think of a Client as a database connection: all of your interactions with Cloud Spanner must go through a Client. Typically you create a Client when your application starts up, then you re-use that Client to read, write, and execute transactions. The following code shows how to create a client.

# Imports the Google Cloud Client Library.
from google.cloud import spanner

# Instantiate a client.
spanner_client = spanner.Client()

# Your Cloud Spanner instance ID.
instance_id = 'my-instance-id'

# Get a Cloud Spanner instance by ID.
instance = spanner_client.instance(instance_id)

# Your Cloud Spanner database ID.
database_id = 'my-database-id'

# Get a Cloud Spanner database by ID.
database = instance.database(database_id)

# Execute a simple SQL statement.
with database.snapshot() as snapshot:
    results = snapshot.execute_sql('SELECT 1')

    for row in results:
        print(row)

Read more in the Client reference.

Write data with DML

You can insert data using Data Manipulation Language (DML) in a read-write transaction.

You use the execute_update() method to execute a DML statement.

# instance_id = "your-spanner-instance"
# database_id = "your-spanner-db-id"
spanner_client = spanner.Client()
instance = spanner_client.instance(instance_id)
database = instance.database(database_id)

def insert_singers(transaction):
    row_ct = transaction.execute_update(
        "INSERT Singers (SingerId, FirstName, LastName) VALUES "
        "(12, 'Melissa', 'Garcia'), "
        "(13, 'Russell', 'Morales'), "
        "(14, 'Jacqueline', 'Long'), "
        "(15, 'Dylan', 'Shaw')"
    )
    print("{} record(s) inserted.".format(row_ct))

database.run_in_transaction(insert_singers)

Run the sample using the insert_with_dml argument.

python snippets.py test-instance --database-id example-db insert_with_dml

You should see:

4 record(s) inserted.

Write data with mutations

You can also insert data using mutations.

You write data using a Batch object. A Batch object is a container for mutation operations. A mutation represents a sequence of inserts, updates, and deletes that Cloud Spanner applies atomically to different rows and tables in a Cloud Spanner database.

The insert() method in the Batch class adds one or more insert mutations to the batch. All mutations in a single batch are applied atomically.

This code shows how to write the data using mutations:

def insert_data(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`.
    """
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    with database.batch() as batch:
        batch.insert(
            table='Singers',
            columns=('SingerId', 'FirstName', 'LastName',),
            values=[
                (1, u'Marc', u'Richards'),
                (2, u'Catalina', u'Smith'),
                (3, u'Alice', u'Trentor'),
                (4, u'Lea', u'Martin'),
                (5, u'David', u'Lomond')])

        batch.insert(
            table='Albums',
            columns=('SingerId', 'AlbumId', 'AlbumTitle',),
            values=[
                (1, 1, u'Total Junk'),
                (1, 2, u'Go, Go, Go'),
                (2, 1, u'Green'),
                (2, 2, u'Forever Hold Your Peace'),
                (2, 3, u'Terrified')])

    print('Inserted data.')

Run the sample using the insert_data argument.

python snippets.py test-instance --database-id example-db insert_data

You should see:

Inserted data.

Query data using SQL

Cloud Spanner supports a native SQL interface for reading data, which you can access on the command line using the gcloud command-line tool or programmatically using the Cloud Spanner client library for Python.

On the command line

Execute the following SQL statement to read the values of all columns from the Albums table:

gcloud spanner databases execute-sql example-db --instance=test-instance \
    --sql='SELECT SingerId, AlbumId, AlbumTitle FROM Albums'

The result should be:

SingerId AlbumId AlbumTitle
1        1       Total Junk
1        2       Go, Go, Go
2        1       Green
2        2       Forever Hold Your Peace
2        3       Terrified

Using the Cloud Spanner client library for Python

In addition to executing a SQL statement on the command line, you can issue the same SQL statement programmatically using the Cloud Spanner client library for Python.

Use the execute_sql() method of a Snapshot object to run the SQL query. To get a Snapshot object, call the snapshot() method of the Database class in a with statement.

Here's how to issue the query and access the data:

def query_data(instance_id, database_id):
    """Queries sample data from the database using SQL."""
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    with database.snapshot() as snapshot:
        results = snapshot.execute_sql(
            'SELECT SingerId, AlbumId, AlbumTitle FROM Albums')

        for row in results:
            print(u'SingerId: {}, AlbumId: {}, AlbumTitle: {}'.format(*row))

Run the sample using the query_data argument.

python snippets.py test-instance --database-id example-db query_data

You should see the following result:

SingerId: 2, AlbumId: 2, AlbumTitle: Forever Hold Your Peace
SingerId: 1, AlbumId: 2, AlbumTitle: Go, Go, Go
SingerId: 2, AlbumId: 1, AlbumTitle: Green
SingerId: 2, AlbumId: 3, AlbumTitle: Terrified
SingerId: 1, AlbumId: 1, AlbumTitle: Total Junk

Query using a SQL parameter

You can include custom values in SQL statements using parameters. Here is an example of using @lastName as a parameter in the WHERE clause to query records containing a specific value for LastName.

# instance_id = "your-spanner-instance"
# database_id = "your-spanner-db-id"
spanner_client = spanner.Client()
instance = spanner_client.instance(instance_id)
database = instance.database(database_id)

with database.snapshot() as snapshot:
    results = snapshot.execute_sql(
        "SELECT SingerId, FirstName, LastName FROM Singers "
        "WHERE LastName = @lastName",
        params={"lastName": "Garcia"},
        param_types={"lastName": spanner.param_types.STRING})

    for row in results:
        print(u"SingerId: {}, FirstName: {}, LastName: {}".format(*row))

Run the sample using the query_data_with_parameter argument.

python snippets.py test-instance --database-id example-db query_data_with_parameter

You should see the following result:

SingerId: 12, FirstName: Melissa, LastName: Garcia

Read data using the read API

In addition to Cloud Spanner's SQL interface, Cloud Spanner also supports a read interface.

Use the read() method of a Snapshot object to read rows from the database. To get a Snapshot object, call the snapshot() method of the Database class in a with statement. Use a KeySet object to define a collection of keys and key ranges to read.

Here's how to read the data:

def read_data(instance_id, database_id):
    """Reads sample data from the database."""
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    with database.snapshot() as snapshot:
        keyset = spanner.KeySet(all_=True)
        results = snapshot.read(
            table='Albums',
            columns=('SingerId', 'AlbumId', 'AlbumTitle',),
            keyset=keyset,)

        for row in results:
            print(u'SingerId: {}, AlbumId: {}, AlbumTitle: {}'.format(*row))

Run the sample using the read_data argument.

python snippets.py test-instance --database-id example-db read_data

You should see output similar to:

SingerId: 1, AlbumId: 1, AlbumTitle: Total Junk
SingerId: 1, AlbumId: 2, AlbumTitle: Go, Go, Go
SingerId: 2, AlbumId: 1, AlbumTitle: Green
SingerId: 2, AlbumId: 2, AlbumTitle: Forever Hold Your Peace
SingerId: 2, AlbumId: 3, AlbumTitle: Terrified

Update the database schema

Assume you need to add a new column called MarketingBudget to the Albums table. Adding a new column to an existing table requires an update to your database schema. Cloud Spanner supports schema updates to a database while the database continues to serve traffic. Schema updates do not require taking the database offline and they do not lock entire tables or columns; you can continue writing data to the database during the schema update. Read more about supported schema updates and schema change performance in Schema updates.

Add a column

You can add a column on the command line using the gcloud command-line tool or programmatically using the Cloud Spanner client library for Python.

On the command line

Use the following ALTER TABLE command to add the new column to the table:

gcloud spanner databases ddl update example-db --instance=test-instance \
    --ddl='ALTER TABLE Albums ADD COLUMN MarketingBudget INT64'

You should see:

Schema updating...done.

Using the Cloud Spanner client library for Python

Use the update_ddl() method of the Database class to modify the schema:

def add_column(instance_id, database_id):
    """Adds a new column to the Albums table in the example database."""
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    operation = database.update_ddl([
        'ALTER TABLE Albums ADD COLUMN MarketingBudget INT64'])

    print('Waiting for operation to complete...')
    operation.result()

    print('Added the MarketingBudget column.')

Run the sample using the add_column argument.

python snippets.py test-instance --database-id example-db add_column

You should see:

Added the MarketingBudget column.

Write data to the new column

The following code writes data to the new column. It sets MarketingBudget to 100000 for the row keyed by Albums(1, 1) and to 500000 for the row keyed by Albums(2, 2).

def update_data(instance_id, database_id):
    """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

    """
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    with database.batch() as batch:
        batch.update(
            table='Albums',
            columns=(
                'SingerId', 'AlbumId', 'MarketingBudget'),
            values=[
                (1, 1, 100000),
                (2, 2, 500000)])

    print('Updated data.')

Run the sample using the update_data argument.

python snippets.py test-instance --database-id example-db update_data

You can also execute a SQL query or a read call to fetch the values that you just wrote.

Here's the code to execute the query:

def query_data_with_new_column(instance_id, database_id):
    """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
    """
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    with database.snapshot() as snapshot:
        results = snapshot.execute_sql(
            'SELECT SingerId, AlbumId, MarketingBudget FROM Albums')

        for row in results:
            print(
                u'SingerId: {}, AlbumId: {}, MarketingBudget: {}'.format(*row))

To execute this query, run the sample using the query_data_with_new_column argument.

python snippets.py test-instance --database-id example-db query_data_with_new_column

You should see:

SingerId: 2, AlbumId: 2, MarketingBudget: 500000
SingerId: 1, AlbumId: 2, MarketingBudget: None
SingerId: 2, AlbumId: 1, MarketingBudget: None
SingerId: 2, AlbumId: 3, MarketingBudget: None
SingerId: 1, AlbumId: 1, MarketingBudget: 100000

Update data

You can update data using DML in a read-write transaction.

You use the execute_update() method to execute a DML statement.

# instance_id = "your-spanner-instance"
# database_id = "your-spanner-db-id"

spanner_client = spanner.Client()
instance = spanner_client.instance(instance_id)
database = instance.database(database_id)

def transfer_budget(transaction):
    # Transfer marketing budget from one album to another. Performed in a
    # single transaction to ensure that the transfer is atomic.
    second_album_result = transaction.execute_sql(
        "SELECT MarketingBudget from Albums "
        "WHERE SingerId = 2 and AlbumId = 2"
    )
    second_album_row = list(second_album_result)[0]
    second_album_budget = second_album_row[0]

    transfer_amount = 200000

    # Transaction will only be committed if this condition still holds at
    # the time of commit. Otherwise it will be aborted and the callable
    # will be rerun by the client library
    if second_album_budget >= transfer_amount:
        first_album_result = transaction.execute_sql(
            "SELECT MarketingBudget from Albums "
            "WHERE SingerId = 1 and AlbumId = 1"
        )
        first_album_row = list(first_album_result)[0]
        first_album_budget = first_album_row[0]

        second_album_budget -= transfer_amount
        first_album_budget += transfer_amount

        # Update first album
        transaction.execute_update(
            "UPDATE Albums "
            "SET MarketingBudget = @AlbumBudget "
            "WHERE SingerId = 1 and AlbumId = 1",
            params={"AlbumBudget": first_album_budget},
            param_types={"AlbumBudget": spanner.param_types.INT64}
        )

        # Update second album
        transaction.execute_update(
            "UPDATE Albums "
            "SET MarketingBudget = @AlbumBudget "
            "WHERE SingerId = 2 and AlbumId = 2",
            params={"AlbumBudget": second_album_budget},
            param_types={"AlbumBudget": spanner.param_types.INT64}
        )

        print("Transferred {} from Album2's budget to Album1's".format(
            transfer_amount))

database.run_in_transaction(transfer_budget)

Run the sample using the write_with_dml_transaction argument.

python snippets.py test-instance --database-id example-db write_with_dml_transaction

You should see:

Transferred 200000 from Album2's budget to Album1's

Use a secondary index

Suppose you wanted to fetch all rows of Albums that have AlbumTitle values in a certain range. You could read all values from the AlbumTitle column using a SQL statement or a read call, and then discard the rows that don't meet the criteria, but doing this full table scan is expensive, especially for tables with a lot of rows. Instead you can speed up the retrieval of rows when searching by non-primary key columns by creating a secondary index on the table.

Adding a secondary index to an existing table requires a schema update. Like other schema updates, Cloud Spanner supports adding an index while the database continues to serve traffic. Cloud Spanner automatically backfills the index with your existing data. Backfills might take a few minutes to complete, but you don't need to take the database offline or avoid writing to the indexed table during this process. For more details, see index backfilling.

After you add a secondary index, Cloud Spanner automatically uses it for SQL queries that are likely to run faster with the index. If you use the read interface, you must specify the index that you want to use.

Add a secondary index

You can add an index on the command line using the gcloud command line tool or programmatically using the Cloud Spanner client library for Python.

On the command line

Use the following CREATE INDEX command to add an index to the database:

gcloud spanner databases ddl update example-db --instance=test-instance \
    --ddl='CREATE INDEX AlbumsByAlbumTitle ON Albums(AlbumTitle)'

You should see:

Schema updating...done.

Using the Cloud Spanner client library for Python

Use the update_ddl() method of the Database class to add an index:

def add_index(instance_id, database_id):
    """Adds a simple index to the example database."""
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    operation = database.update_ddl([
        'CREATE INDEX AlbumsByAlbumTitle ON Albums(AlbumTitle)'])

    print('Waiting for operation to complete...')
    operation.result()

    print('Added the AlbumsByAlbumTitle index.')

Run the sample using the add_index argument.

python snippets.py test-instance --database-id example-db add_index

Adding an index can take a few minutes. After the index is added, you should see:

Added the AlbumsByAlbumTitle index.

Read using the index

For SQL queries, Cloud Spanner automatically uses an appropriate index. In the read interface, you must specify the index in your request.

To use the index in the read interface, provide an Index argument to the read() method of a Snapshot object. To get a Snapshot object, call the snapshot() method of the Database class in a with statement.

def read_data_with_index(instance_id, database_id):
    """Reads sample data from the database using 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)

    """
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    with database.snapshot() as snapshot:
        keyset = spanner.KeySet(all_=True)
        results = snapshot.read(
            table='Albums',
            columns=('AlbumId', 'AlbumTitle'),
            keyset=keyset,
            index='AlbumsByAlbumTitle')

        for row in results:
            print('AlbumId: {}, AlbumTitle: {}'.format(*row))

Run the sample using the read_data_with_index argument.

python snippets.py test-instance --database-id example-db read_data_with_index

You should see:

AlbumId: 2, AlbumTitle: Forever Hold Your Peace
AlbumId: 2, AlbumTitle: Go, Go, Go
AlbumId: 1, AlbumTitle: Green
AlbumId: 3, AlbumTitle: Terrified
AlbumId: 1, AlbumTitle: Total Junk

Add an index with a STORING clause

You might have noticed that the read example above did not include reading the MarketingBudget column. This is because Cloud Spanner's read interface does not support the ability to join an index with a data table to look up values that are not stored in the index.

Create an alternate definition of AlbumsByAlbumTitle that stores a copy of MarketingBudget in the index.

On the command line

gcloud spanner databases ddl update example-db --instance=test-instance \
    --ddl='CREATE INDEX AlbumsByAlbumTitle2 ON Albums(AlbumTitle) STORING (MarketingBudget)'

Adding an index can take a few minutes. After the index is added, you should see:

Schema updating...done.

Using the Cloud Spanner client library for Python

Use the update_ddl() method of the Database class to add an index with a STORING clause:

def add_storing_index(instance_id, database_id):
    """Adds an storing index to the example database."""
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    operation = database.update_ddl([
        'CREATE INDEX AlbumsByAlbumTitle2 ON Albums(AlbumTitle)'
        'STORING (MarketingBudget)'])

    print('Waiting for operation to complete...')
    operation.result()

    print('Added the AlbumsByAlbumTitle2 index.')

Run the sample using the add_storing_index argument.

python snippets.py test-instance --database-id example-db add_storing_index

You should see:

Added the AlbumsByAlbumTitle2 index.

Now you can execute a read that fetches all AlbumId, AlbumTitle, and MarketingBudget columns from the AlbumsByAlbumTitle2 index:

def read_data_with_storing_index(instance_id, database_id):
    """Reads sample data from the database using an index with a storing
    clause.

    The index must exist before running this sample. You can add the index
    by running the `add_soring_index` sample or by running this DDL statement
    against your database:

        CREATE INDEX AlbumsByAlbumTitle2 ON Albums(AlbumTitle)
        STORING (MarketingBudget)

    """
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    with database.snapshot() as snapshot:
        keyset = spanner.KeySet(all_=True)
        results = snapshot.read(
            table='Albums',
            columns=('AlbumId', 'AlbumTitle', 'MarketingBudget'),
            keyset=keyset,
            index='AlbumsByAlbumTitle2')

        for row in results:
            print(
                u'AlbumId: {}, AlbumTitle: {}, '
                'MarketingBudget: {}'.format(*row))

Run the sample using the read_data_with_storing_index argument.

python snippets.py test-instance --database-id example-db read_data_with_storing_index

You should see output similar to:

AlbumId: 2, AlbumTitle: Forever Hold Your Peace, MarketingBudget: 300000
AlbumId: 2, AlbumTitle: Go, Go, Go, MarketingBudget: None
AlbumId: 1, AlbumTitle: Green, MarketingBudget: None
AlbumId: 3, AlbumTitle: Terrified, MarketingBudget: None
AlbumId: 1, AlbumTitle: Total Junk, MarketingBudget: 300000

Retrieve data using read-only transactions

Suppose you want to execute more than one read at the same timestamp. Read-only transactions observe a consistent prefix of the transaction commit history, so your application always gets consistent data. Use a Snapshot object for executing read-only transactions. To get a Snapshot object, call the snapshot() method of the Database class in a with statement.

The following shows how to run a query and perform a read in the same read-only transaction:

def read_only_transaction(instance_id, database_id):
    """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.
    """
    spanner_client = spanner.Client()
    instance = spanner_client.instance(instance_id)
    database = instance.database(database_id)

    with database.snapshot(multi_use=True) as snapshot:
        # Read using SQL.
        results = snapshot.execute_sql(
            'SELECT SingerId, AlbumId, AlbumTitle FROM Albums')

        print('Results from first read:')
        for row in results:
            print(u'SingerId: {}, AlbumId: {}, AlbumTitle: {}'.format(*row))

        # Perform another read using the `read` method. Even if the data
        # is updated in-between the reads, the snapshot ensures that both
        # return the same data.
        keyset = spanner.KeySet(all_=True)
        results = snapshot.read(
            table='Albums',
            columns=('SingerId', 'AlbumId', 'AlbumTitle',),
            keyset=keyset,)

        print('Results from second read:')
        for row in results:
            print(u'SingerId: {}, AlbumId: {}, AlbumTitle: {}'.format(*row))

Run the sample using the read_only_transaction argument.

python snippets.py test-instance --database-id example-db read_only_transaction

You should see output similar to:

Results from first read:
SingerId: 2, AlbumId: 2, AlbumTitle: Forever Hold Your Peace
SingerId: 1, AlbumId: 2, AlbumTitle: Go, Go, Go
SingerId: 2, AlbumId: 1, AlbumTitle: Green
SingerId: 2, AlbumId: 3, AlbumTitle: Terrified
SingerId: 1, AlbumId: 1, AlbumTitle: Total Junk
Results from second read:
SingerId: 1, AlbumId: 1, AlbumTitle: Total Junk
SingerId: 1, AlbumId: 2, AlbumTitle: Go, Go, Go
SingerId: 2, AlbumId: 1, AlbumTitle: Green
SingerId: 2, AlbumId: 2, AlbumTitle: Forever Hold Your Peace
SingerId: 2, AlbumId: 3, AlbumTitle: Terrified

Cleanup

To avoid incurring additional charges to your Google Cloud Platform account for the resources used in this tutorial, drop the database and delete the instance that you created.

Delete the database

If you delete an instance, all databases within it are automatically deleted. This step shows how to delete a database without deleting an instance (you would still incur charges for the instance).

On the command line

gcloud spanner databases delete example-db --instance=test-instance

Using the GCP Console

  1. Go to the Cloud Spanner Instances page in the Google Cloud Platform Console.

    Go to the Instances page

  2. Click the instance.

  3. Click the database that you want to delete.

  4. In the Database details page, click Delete.

  5. Confirm that you want to delete the database and click Delete.

Delete the instance

Deleting an instance automatically drops all databases created in that instance.

On the command line

gcloud spanner instances delete test-instance

Using the GCP Console

  1. Go to the Cloud Spanner Instances page in the Google Cloud Platform Console.

    Go to the Instances page

  2. Click your instance.

  3. Click Delete.

  4. Confirm that you want to delete the instance and click Delete.

What's next

  • Access Cloud Spanner in a virtual machine instance: create a virtual machine instance with access to your Cloud Spanner database.
  • Learn about authorization and authentication credentials in Getting started with authentication.
  • Learn more Cloud Spanner concepts.
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    Cloud Spanner Documentation