sqlalchemy-mate 0.0.8

A library extend sqlalchemy module, makes CRUD easier.

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Welcome to ``sqlalchemy_mate`` Documentation
==============================================================================

A library extend sqlalchemy module, makes CRUD easier.


Features
------------------------------------------------------------------------------

.. contents::
:local:
:depth: 1


Read Database Credential Safely
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. contents::
:local:
:depth: 1

Put your database connection credential in your source code is always a **BAD IDEA**.

``sqlalchemy_mate`` provides several options to allow loading credential easily.


From json file
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

API :meth:`~sqlalchemy_mate.credential.Credential.from_json`.

You need to specify two things:

1. path to json file.
2. field path to the data. If your connect info is nested deeply in the json, you can use the dot notation json path to point to it.

content of json:

.. code-block:: python

{
"credentials": {
"db1": {
"host": "example.com",
"port": 1234,
"database": "test",
"username": "admin",
"password": "admin",
},
"db2": {
...
}
}
}

code:

.. code-block:: python

from sqlalchemy_mate import EngineCreator

ec = EngineCreator.from_json(
json_file="path-to-json-file",
json_path="credentials.db1", # dot notation json path
)
engine = ec.create_postgresql_psycopg2()

**Default data fields** are ``host``, ``port``, ``database``, ``username``, ``password``.

If your json schema is different, you need to add the ``key_mapping`` to **specify the field name mapping**:

.. code-block:: python

ec = EngineCreator.from_json(
json_file="...",
json_path="...",
key_mapping={
"host": "your-host-field",
"port": "your-port-field",
"database": "your-database-field",
"username": "your-username-field",
"password": "your-password-field",
}
)


From ``$HOME/.db.json``
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

API :meth:`~sqlalchemy_mate.credential.Credential.from_home_db_json`.

You can put lots of database connection info in a ``.db.json`` file in your ``$HOME`` directory.

.. code-block:: python

from sqlalchemy_mate import EngineCreator

ec = EngineCreator.from_home_db_json(identifier="db1")
engine = ec.create_postgresql_psycopg2()

``$HOME/.db.json`` **assumes flat json schema**, but you can use dot notation json path for ``identifier`` to adapt any json schema:

.. code-block:: python

{
"identifier1": {
"host": "example.com",
"port": 1234,
"database": "test",
"username": "admin",
"password": "admin",
},
"identifier2": {
...
}
}


From json file on AWS S3
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
API :meth:`~sqlalchemy_mate.credential.Credential.from_s3_json`.

This is similar to ``from_json``, but the json is stored on AWS S3.

.. code-block:: python

from sqlalchemy_mate import EngineCreator

ec = EngineCreator.from_s3_json(
bucket_name="my-bucket", key="db.json",
json_path="identifier1",
aws_profile="my-profile",
)
engine = ec.create_redshift()


From Environment Variable
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
API :meth:`~sqlalchemy_mate.credential.Credential.from_env`.

You can put your credentials in Environment Variable. For example:

.. code-block:: bash

export DB_DEV_HOST="..."
export DB_DEV_PORT="..."
export DB_DEV_DATABASE="..."
export DB_DEV_USERNAME="..."
export DB_DEV_PASSWORD="..."

.. code-block:: python

from sqlalchemy_mate import EngineCreator

ec = EngineCreator.from_env(prefix="DB_DEV")
engine = ec.create_redshift()

If you want to read database credential safely from cloud, for example, AWS EC2, AWS Lambda, you can use AWS KMS to decrypt your credentials

.. code-block:: python

# leave aws_profile=None if you are on cloud
ec = EngineCreator.from_env(prefix="DB_DEV", kms_decrypt=True, aws_profile="xxx")
engine = ec.create_redshift()


Smart Insert
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In bulk insert, if there are some rows having primary_key conflict, the classic solution is:

.. code-block:: python

for row in data:
try:
engine.execute(table.insert(), row)
except sqlalchemy.sql.IntegrityError:
pass

It is like one-by-one insert, which is super slow.

``sqlalchemy_mate`` uses ``smart_insert`` strategy to try with smaller bulk insert, which has higher probabily to work. As a result, total number of commits are greatly reduced.

With sql expression:

.. code-block:: python

from sqlalchemy_mate import inserting

engine = create_engine(...)
t_users = Table(
"users", metadata,
Column("id", Integer),
...
)
# lots of data
data = [{"id": 1, "name": "Alice}, {"id": 2, "name": "Bob"}, ...]

inserting.smart_insert(engine, t_users, data)


With ORM:

.. code-block:: python

from sqlalchemy_mate import ExtendedBase

Base = declarative_base()

class User(Base, ExtendedBase): # inherit from ExtendedBase
...

# lots of users
data = [User(id=1, name="Alice"), User(id=2, name="Bob"), ...]

User.smart_insert(engine_or_session, data) # That's it


Smart Update / Upsert
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Automatically update value by primary key.

.. code-block:: python

# in sql expression
from sqlalchemy_mate import updating

updating.update_all(engine, table, data)
updating.upsert_all(engine, table, data)

# in ORM

User.update_all(engine_or_session, user_list)
User.upsert_all(engine_or_session, user_list)


.. _install:

Install
------------------------------------------------------------------------------

``sqlalchemy_mate`` is released on PyPI, so all you need is:

.. code-block:: console

$ pip install sqlalchemy_mate

To upgrade to latest version:

.. code-block:: console

$ pip install --upgrade sqlalchemy_mate