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.
This sample requires you to have authentication setup. Refer to the Authentication Getting Started Guide for instructions on setting up credentials for applications.
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/activateInstall the dependencies needed to run the samples.
$ pip install -r requirements.txt
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.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.