sqlalchemy-spanner 1.3.0

SQLAlchemy dialect integrated into Cloud Spanner database

Spanner dialect for SQLAlchemy represents an interface API designed to make it possible to control Cloud Spanner databases with SQLAlchemy API. The dialect is built on top of the Spanner DB API, which is designed in accordance with PEP-249.

Known limitations are listed here. All supported features have been tested and verified to work with the test configurations. There may be configurations and/or data model variations that have not yet been covered by the tests and that show unexpected behavior. Please report any problems that you might encounter by creating a new issue.

• Cloud Spanner product documentation

• SQLAlchemy product documentation

Quick Start

In order to use this package, you first need to go through the following steps:

1. Select or create a Cloud Platform project.

2. Enable billing for your project.

3. Enable the Google Cloud Spanner API.

4. Setup Authentication.

Installation

Stable released version of the package is available on PyPi:

pip install sqlalchemy-spanner

To install an in-development version of the package, clone its Git-repository:

git clone https://github.com/googleapis/python-spanner-sqlalchemy.git

Next install the package from the package setup.py file:

python setup.py install

During setup the dialect will be registered with entry points.

A Minimal App

Database URL

In order to connect to a database one have to use its URL on connection creation step. SQLAlchemy 1.3 and 1.4 versions have a bit of difference on this step in a dialect prefix part:

# for SQLAlchemy 1.3:
spanner:///projects/project-id/instances/instance-id/databases/database-id

# for SQLAlchemy 1.4:
spanner+spanner:///projects/project-id/instances/instance-id/databases/database-id

Create a table

from sqlalchemy import (
    Column,
    Integer,
    MetaData,
    String,
    Table,
    create_engine,
)

engine = create_engine(
    "spanner:///projects/project-id/instances/instance-id/databases/database-id"
)
metadata = MetaData(bind=engine)

user = Table(
    "users",
    metadata,
    Column("user_id", Integer, primary_key=True),
    Column("user_name", String(16), nullable=False),
)

metadata.create_all(engine)

Insert a row

import uuid

from sqlalchemy import (
    MetaData,
    Table,
    create_engine,
)

engine = create_engine(
    "spanner:///projects/project-id/instances/instance-id/databases/database-id"
)
user = Table("users", MetaData(bind=engine), autoload=True)
user_id = uuid.uuid4().hex[:6].lower()

with engine.begin() as connection:
    connection.execute(user.insert(), {"user_id": user_id, "user_name": "Full Name"})

Read

from sqlalchemy import MetaData, Table, create_engine, select

engine = create_engine(
    "spanner:///projects/project-id/instances/instance-id/databases/database-id"
)
table = Table("users", MetaData(bind=engine), autoload=True)

with engine.begin() as connection:
    for row in connection.execute(select(["*"], from_obj=table)).fetchall():
        print(row)

Migration

SQLAlchemy uses Alembic tool to organize database migrations.

Spanner dialect doesn’t provide a default migration environment, it’s up to user to write it. One thing to be noted here - one should explicitly set alembic_version table not to use migration revision id as a primary key:

with connectable.connect() as connection:
    context.configure(
        connection=connection,
        target_metadata=target_metadata,
        version_table_pk=False,  # don't use primary key in the versions table
    )

As Spanner restricts changing a primary key value, not setting the version_table_pk flag to False can cause migration problems. If alembic_versions table was already created with a primary key, setting the flag to False will not work, because the flag is only applied on table creation.

Notice that DDL statements in Spanner are not transactional. They will not be automatically reverted in case of a migration fail. Also Spanner encourage use of the autocommit_block() for migrations in order to prevent DDLs from aborting migration transactions with schema modifications.

Warning!

A migration script can produce a lot of DDL statements. If each of the statements is executed separately, performance issues can occur. To avoid it, it’s highly recommended to use the Alembic batch context feature to pack DDL statements into groups of statements.

Features and limitations

Interleaved tables

Cloud Spanner dialect includes two dialect-specific arguments for Table constructor, which help to define interleave relations: spanner_interleave_in - a parent table name spanner_inverleave_on_delete_cascade - a flag specifying if ON DELETE CASCADE statement must be used for the interleave relation

An example of interleave relations definition:

team = Table(
    "team",
    metadata,
    Column("team_id", Integer, primary_key=True),
    Column("team_name", String(16), nullable=False),
)
team.create(engine)

client = Table(
    "client",
    metadata,
    Column("team_id", Integer, primary_key=True),
    Column("client_id", Integer, primary_key=True),
    Column("client_name", String(16), nullable=False),
    spanner_interleave_in="team",
    spanner_interleave_on_delete_cascade=True,
)
client.add_is_dependent_on(team)

client.create(engine)

Note: Interleaved tables have a dependency between them, so the parent table must be created before the child table. When creating tables with this feature, make sure to call add_is_dependent_on() on the child table to request SQLAlchemy to create the parent table before the child table.

Unique constraints

Cloud Spanner doesn’t support direct UNIQUE constraints creation. In order to achieve column values uniqueness UNIQUE indexes should be used.

Instead of direct UNIQUE constraint creation:

Table(
    'table',
    metadata,
    Column('col1', Integer),
    UniqueConstraint('col1', name='uix_1')
)

Create a UNIQUE index:

Table(
    'table',
    metadata,
    Column('col1', Integer),
    Index("uix_1", "col1", unique=True),
)

Autocommit mode

Spanner dialect supports both SERIALIZABLE and AUTOCOMMIT isolation levels. SERIALIZABLE is the default one, where transactions need to be committed manually. AUTOCOMMIT mode corresponds to automatically committing of a query right in its execution time.

Isolation level change example:

from sqlalchemy import create_engine

eng = create_engine("spanner:///projects/project-id/instances/instance-id/databases/database-id")
autocommit_engine = eng.execution_options(isolation_level="AUTOCOMMIT")

Automatic transactions retry

In the default SERIALIZABLE mode transactions may fail with Aborted exception. This is a transient kind of errors, which mostly happen to prevent data corruption by concurrent modifications. Though the original transaction becomes non operational, a simple retry of the queries solves the issue.

This, however, may require to manually repeat a long list of operations, executed in the failed transaction. To simplify it, Spanner Connection API tracks all the operations, executed inside current transaction, and their result checksums. If the transaction failed with Aborted exception, the Connection API will automatically start a new transaction and will re-run all the tracked operations, checking if their results are the same as they were in the original transaction. In case data changed, and results differ, the transaction is dropped, as there is no way to automatically retry it.

In AUTOCOMMIT mode automatic transactions retry mechanism is disabled, as every operation is committed just in time, and there is no way an Aborted exception can happen.

Autoincremented IDs

Cloud Spanner doesn’t support autoincremented IDs mechanism due to performance reasons (see for more details). We recommend that you use the Python uuid module to generate primary key fields to avoid creating monotonically increasing keys.

Though it’s not encouraged to do so, in case you need the feature, you can simulate it manually as follows:

with engine.begin() as connection:
    top_id = connection.execute(
        select([user.c.user_id]).order_by(user.c.user_id.desc()).limit(1)
    ).fetchone()
    next_id = top_id[0] + 1 if top_id else 1

    connection.execute(user.insert(), {"user_id": next_id})

Query hints

Spanner dialect supports query hints, which give the ability to set additional query execution parameters. Usage example:

session = Session(engine)

Base = declarative_base()

class User(Base):
    """Data model."""

    __tablename__ = "users"
    id = Column(Integer, primary_key=True)
    name = Column(String(50))


query = session.query(User)
query = query.with_hint(
    selectable=User, text="@{FORCE_INDEX=index_name}"
)
query = query.filter(User.name.in_(["val1", "val2"]))
query.statement.compile(session.bind)

ReadOnly transactions

By default, transactions produced by a Spanner connection are in ReadWrite mode. However, some applications require an ability to grant ReadOnly access to users/methods; for these cases Spanner dialect supports the read_only execution option, which switches a connection into ReadOnly mode:

with engine.connect().execution_options(read_only=True) as connection:
    connection.execute(select(["*"], from_obj=table)).fetchall()

Note that execution options are applied lazily - on the execute() method call, right before it.

ReadOnly/ReadWrite mode of a connection can’t be changed while a transaction is in progress - first you must commit or rollback it.

Stale reads

To use the Spanner Stale Reads with SQLAlchemy you can tweak the connection execution options with a wanted staleness value. For example:

# maximum staleness
with engine.connect().execution_options(
    read_only=True,
    staleness={"max_staleness": datetime.timedelta(seconds=5)}
) as connection:
    connection.execute(select(["*"], from_obj=table)).fetchall()

# exact staleness
with engine.connect().execution_options(
    read_only=True,
    staleness={"exact_staleness": datetime.timedelta(seconds=5)}
) as connection:
    connection.execute(select(["*"], from_obj=table)).fetchall()

# min read timestamp
with engine.connect().execution_options(
    read_only=True,
    staleness={"min_read_timestamp": datetime.datetime(2021, 11, 17, 12, 55, 30)}
) as connection:
    connection.execute(select(["*"], from_obj=table)).fetchall()

# read timestamp
with engine.connect().execution_options(
    read_only=True,
    staleness={"read_timestamp": datetime.datetime(2021, 11, 17, 12, 55, 30)}
) as connection:
    connection.execute(select(["*"], from_obj=table)).fetchall()

Note that the set option will be dropped when the connection is returned back to the pool.

Request priority

In order to use Request Priorities feature in Cloud Spanner, SQLAlchemy provides an execution_options parameter:

from google.cloud.spanner_v1 import RequestOptions

with engine.connect().execution_options(
    request_priority=RequestOptions.Priority.PRIORITY_MEDIUM
) as connection:
    connection.execute(select(["*"], from_obj=table)).fetchall()

DDL and transactions

DDL statements are executed outside the regular transactions mechanism, which means DDL statements will not be rolled back on normal transaction rollback.

Dropping a table

Cloud Spanner, by default, doesn’t drop tables, which have secondary indexes and/or foreign key constraints. In Spanner dialect for SQLAlchemy, however, this restriction is omitted - if a table you are trying to delete has indexes/foreign keys, they will be dropped automatically right before dropping the table.

Data types

Data types table mapping SQLAlchemy types to Cloud Spanner types:

SQLAlchemy	Spanner
INTEGER	INT64
BIGINT	INT64
DECIMAL	NUMERIC
FLOAT	FLOAT64
TEXT	STRING
ARRAY	ARRAY
BINARY	BYTES
VARCHAR	STRING
CHAR	STRING
BOOLEAN	BOOL
DATETIME	TIMESTAMP
NUMERIC	NUMERIC

Other limitations

• WITH RECURSIVE statement is not supported.

• Named schemas are not supported.

• Temporary tables are not supported.

• Numeric type dimensions (scale and precision) are constant. See the docs.

Best practices

When a SQLAlchemy function is called, a new connection to a database is established and a Spanner session object is fetched. In case of connectionless execution these fetches are done for every execute() call, which can cause a significant latency. To avoid initiating a Spanner session on every execute() call it’s recommended to write code in connection-bounded fashion. Once a Connection() object is explicitly initiated, it fetches a Spanner session object and uses it for all the following calls made on this Connection() object.

Non-optimal connectionless use:

# execute() is called on object, which is not a Connection() object
insert(user).values(user_id=1, user_name="Full Name").execute()

Optimal connection-bounded use:

with engine.begin() as connection:
    # execute() is called on a Connection() object
    connection.execute(user.insert(), {"user_id": 1, "user_name": "Full Name"})

Connectionless way of use is also deprecated since SQLAlchemy 2.0 and soon will be removed (see in SQLAlchemy docs).

Running tests

Spanner dialect includes a compliance, migration and unit test suite. To run the tests the nox package commands can be used:

# Run the whole suite
$ nox

# Run a particular test session
$ nox -s migration_test

Running tests on Spanner emulator

The dialect test suite can be runned on Spanner emulator. Several tests, relating to NULL values of data types, are skipped when executed on emulator.

Contributing

Contributions to this library are welcome and encouraged. Please report issues, file feature requests, and send pull requests. See CONTRIBUTING for more information on how to get started.

Note that this project is not officially supported by Google as part of the Cloud Spanner product.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See the Code of Conduct for more information.