========================
Developing new Dialects
========================

.. note::

   When studying this file, it's probably a good idea to also
   familiarize with the  README.unittests.rst file, which discusses
   SQLAlchemy's usage and extension of the pytest test runner.

While SQLAlchemy includes many dialects within the core distribution, the
trend for new dialects should be that they are published as external
projects.   SQLAlchemy has since version 0.5 featured a "plugin" system
which allows external dialects to be integrated into SQLAlchemy using
standard setuptools entry points.  As of version 0.8, this system has
been enhanced, so that a dialect can also be "plugged in" at runtime.

On the testing side, SQLAlchemy includes a "dialect compliance
suite" that is usable by third party libraries, in the source tree
at ``lib/sqlalchemy/testing/suite``.   There's no need for a third party
dialect to run through SQLAlchemy's full testing suite, as a large portion of
these tests do not have dialect-sensitive functionality.  The "dialect
compliance suite" should be viewed as the primary target for new dialects.


Dialect Layout
===============

The file structure of a dialect is typically similar to the following::

    sqlalchemy-<dialect>/
                         setup.py
                         setup.cfg
                         sqlalchemy_<dialect>/
                                              __init__.py
                                              base.py
                                              <dbapi>.py
                                              requirements.py
                         test/
                                              __init__.py
                                              conftest.py
                                              test_suite.py
                                              test_<dialect_specific_test>.py
                                              ...

An example of this structure can be seen in the MS Access dialect at
https://github.com/gordthompson/sqlalchemy-access .

Key aspects of this file layout include:

* setup.py - should specify setuptools entrypoints, allowing the
  dialect to be usable from create_engine(), e.g.::

        entry_points = {
         'sqlalchemy.dialects': [
              'access.pyodbc = sqlalchemy_access.pyodbc:AccessDialect_pyodbc',
              ]
        }

  Above, the entrypoint ``access.pyodbc`` allow URLs to be used such as::

    create_engine("access+pyodbc://user:pw@dsn")

* setup.cfg - this file contains the traditional contents such as
  [tool:pytest] directives, but also contains new directives that are used
  by SQLAlchemy's testing framework.  E.g. for Access::

    [tool:pytest]
    addopts= --tb native -v -r fxX --maxfail=25 -p no:warnings
    python_files=test/*test_*.py

    [sqla_testing]
    requirement_cls=sqlalchemy_access.requirements:Requirements
    profile_file=test/profiles.txt

    [db]
    default=access+pyodbc://admin@access_test
    sqlite=sqlite:///:memory:

  Above, the ``[sqla_testing]`` section contains configuration used by
  SQLAlchemy's test plugin.  The ``[tool:pytest]`` section
  include directives to help with these runners.  When using pytest
  the test/conftest.py file will bootstrap SQLAlchemy's plugin.

* test/conftest.py - This script bootstraps SQLAlchemy's pytest plugin
  into the pytest runner.  This
  script can also be used to install your third party dialect into
  SQLAlchemy without using the setuptools entrypoint system; this allows
  your dialect to be present without any explicit setup.py step needed.
  The other portion invokes SQLAlchemy's pytest plugin::

    from sqlalchemy.dialects import registry
    import pytest

    registry.register("access.pyodbc", "sqlalchemy_access.pyodbc", "AccessDialect_pyodbc")

    pytest.register_assert_rewrite("sqlalchemy.testing.assertions")

    from sqlalchemy.testing.plugin.pytestplugin import *

  Where above, the ``registry`` module, introduced in SQLAlchemy 0.8, provides
  an in-Python means of installing the dialect entrypoint(s) without the use
  of setuptools, using the ``registry.register()`` function in a way that
  is similar to the ``entry_points`` directive we placed in our ``setup.py``.
  (The ``pytest.register_assert_rewrite`` is there just to suppress a spurious
  warning from pytest.)

* requirements.py - The ``requirements.py`` file is where directives
  regarding database and dialect capabilities are set up.
  SQLAlchemy's tests are often annotated with decorators   that mark
  tests as "skip" or "fail" for particular backends.  Over time, this
  system   has been refined such that specific database and DBAPI names
  are mentioned   less and less, in favor of @requires directives which
  state a particular capability.   The requirement directive is linked
  to target dialects using a ``Requirements`` subclass.   The custom
  ``Requirements`` subclass is specified in the ``requirements.py`` file
  and   is made available to SQLAlchemy's test runner using the
  ``requirement_cls`` directive   inside the ``[sqla_testing]`` section.

  For a third-party dialect, the custom ``Requirements`` class can
  usually specify a simple yes/no answer for a particular system. For
  example, a requirements file that specifies a database that supports
  the RETURNING construct but does not support nullable boolean
  columns might look like this::

      # sqlalchemy_access/requirements.py

      from sqlalchemy.testing.requirements import SuiteRequirements

      from sqlalchemy.testing import exclusions

      class Requirements(SuiteRequirements):
          @property
          def nullable_booleans(self):
              """Target database allows boolean columns to store NULL."""
              # Access Yes/No doesn't allow null
              return exclusions.closed()

          @property
          def returning(self):
              return exclusions.open()

  The ``SuiteRequirements`` class in
  ``sqlalchemy.testing.requirements`` contains a large number of
  requirements rules, which attempt to have reasonable defaults. The
  tests will report on those requirements found as they are run.

  The requirements system can also be used when running SQLAlchemy's
  primary test suite against the external dialect.  In this use case,
  a ``--dburi`` as well as a ``--requirements`` flag are passed to SQLAlchemy's
  test runner so that exclusions specific to the dialect take place::

    cd /path/to/sqlalchemy
    pytest -v \
      --requirements sqlalchemy_access.requirements:Requirements \
      --dburi access+pyodbc://admin@access_test

* test_suite.py - Finally, the ``test_suite.py`` module represents a
  stub test suite, which pulls in the actual SQLAlchemy test suite.
  To pull in the suite as a whole, it can   be imported in one step::

      # test/test_suite.py

      from sqlalchemy.testing.suite import *

  That's all that's needed - the ``sqlalchemy.testing.suite`` package
  contains an ever expanding series of tests, most of which should be
  annotated with specific requirement decorators so that they can be
  fully controlled.  In the case that the decorators are not covering
  a particular test, a test can also be directly modified or bypassed.
  In the example below, the Access dialect test suite overrides the
  ``get_huge_int()`` test::

      from sqlalchemy.testing.suite import *

      from sqlalchemy.testing.suite import IntegerTest as _IntegerTest

      class IntegerTest(_IntegerTest):

          @testing.skip("access")
          def test_huge_int(self):
              # bypass this test because Access ODBC fails with
              # [ODBC Microsoft Access Driver] Optional feature not implemented.
              return

AsyncIO dialects
----------------

As of version 1.4 SQLAlchemy supports also dialects that use
asyncio drivers to interface with the database backend.

SQLAlchemy's approach to asyncio drivers is that the connection and cursor
objects of the driver (if any) are adapted into a pep-249 compliant interface,
using the ``AdaptedConnection`` interface class. Refer to the internal asyncio
driver implementations such as that of ``asyncpg``, ``asyncmy`` and
``aiosqlite`` for examples.

Going Forward
==============

The third-party dialect can be distributed like any other Python
module on PyPI. Links to prominent dialects can be featured within
SQLAlchemy's own documentation; contact the developers (see AUTHORS)
for help with this.

While SQLAlchemy includes many dialects built in, it remains to be
seen if the project as a whole might move towards "plugin" model for
all dialects, including all those currently built in.  Now that
SQLAlchemy's dialect API is mature and the test suite is not far
behind, it may be that a better maintenance experience can be
delivered by having all dialects separately maintained and released.

As new versions of SQLAlchemy are released, the test suite and
requirements file will receive new tests and changes.  The dialect
maintainer would normally keep track of these changes and make
adjustments as needed.

SQLAlchemy
==========

|PyPI| |Python| |Downloads|

.. |PyPI| image:: https://img.shields.io/pypi/v/sqlalchemy
    :target: https://pypi.org/project/sqlalchemy
    :alt: PyPI

.. |Python| image:: https://img.shields.io/pypi/pyversions/sqlalchemy
    :target: https://pypi.org/project/sqlalchemy
    :alt: PyPI - Python Version

.. |Downloads| image:: https://img.shields.io/pypi/dm/sqlalchemy
    :target: https://pypi.org/project/sqlalchemy
    :alt: PyPI - Downloads


The Python SQL Toolkit and Object Relational Mapper

Introduction
-------------

SQLAlchemy is the Python SQL toolkit and Object Relational Mapper
that gives application developers the full power and
flexibility of SQL. SQLAlchemy provides a full suite
of well known enterprise-level persistence patterns,
designed for efficient and high-performing database
access, adapted into a simple and Pythonic domain
language.

Major SQLAlchemy features include:

* An industrial strength ORM, built
  from the core on the identity map, unit of work,
  and data mapper patterns.   These patterns
  allow transparent persistence of objects
  using a declarative configuration system.
  Domain models
  can be constructed and manipulated naturally,
  and changes are synchronized with the
  current transaction automatically.
* A relationally-oriented query system, exposing
  the full range of SQL's capabilities
  explicitly, including joins, subqueries,
  correlation, and most everything else,
  in terms of the object model.
  Writing queries with the ORM uses the same
  techniques of relational composition you use
  when writing SQL.  While you can drop into
  literal SQL at any time, it's virtually never
  needed.
* A comprehensive and flexible system
  of eager loading for related collections and objects.
  Collections are cached within a session,
  and can be loaded on individual access, all
  at once using joins, or by query per collection
  across the full result set.
* A Core SQL construction system and DBAPI
  interaction layer.  The SQLAlchemy Core is
  separate from the ORM and is a full database
  abstraction layer in its own right, and includes
  an extensible Python-based SQL expression
  language, schema metadata, connection pooling,
  type coercion, and custom types.
* All primary and foreign key constraints are
  assumed to be composite and natural.  Surrogate
  integer primary keys are of course still the
  norm, but SQLAlchemy never assumes or hardcodes
  to this model.
* Database introspection and generation.  Database
  schemas can be "reflected" in one step into
  Python structures representing database metadata;
  those same structures can then generate
  CREATE statements right back out - all within
  the Core, independent of the ORM.

SQLAlchemy's philosophy:

* SQL databases behave less and less like object
  collections the more size and performance start to
  matter; object collections behave less and less like
  tables and rows the more abstraction starts to matter.
  SQLAlchemy aims to accommodate both of these
  principles.
* An ORM doesn't need to hide the "R".   A relational
  database provides rich, set-based functionality
  that should be fully exposed.   SQLAlchemy's
  ORM provides an open-ended set of patterns
  that allow a developer to construct a custom
  mediation layer between a domain model and
  a relational schema, turning the so-called
  "object relational impedance" issue into
  a distant memory.
* The developer, in all cases, makes all decisions
  regarding the design, structure, and naming conventions
  of both the object model as well as the relational
  schema.   SQLAlchemy only provides the means
  to automate the execution of these decisions.
* With SQLAlchemy, there's no such thing as
  "the ORM generated a bad query" - you
  retain full control over the structure of
  queries, including how joins are organized,
  how subqueries and correlation is used, what
  columns are requested.  Everything SQLAlchemy
  does is ultimately the result of a developer-
  initiated decision.
* Don't use an ORM if the problem doesn't need one.
  SQLAlchemy consists of a Core and separate ORM
  component.   The Core offers a full SQL expression
  language that allows Pythonic construction
  of SQL constructs that render directly to SQL
  strings for a target database, returning
  result sets that are essentially enhanced DBAPI
  cursors.
* Transactions should be the norm.  With SQLAlchemy's
  ORM, nothing goes to permanent storage until
  commit() is called.  SQLAlchemy encourages applications
  to create a consistent means of delineating
  the start and end of a series of operations.
* Never render a literal value in a SQL statement.
  Bound parameters are used to the greatest degree
  possible, allowing query optimizers to cache
  query plans effectively and making SQL injection
  attacks a non-issue.

Documentation
-------------

Latest documentation is at:

https://www.sqlalchemy.org/docs/

Installation / Requirements
---------------------------

Full documentation for installation is at
`Installation <https://www.sqlalchemy.org/docs/intro.html#installation>`_.

Getting Help / Development / Bug reporting
------------------------------------------

Please refer to the `SQLAlchemy Community Guide <https://www.sqlalchemy.org/support.html>`_.

Code of Conduct
---------------

Above all, SQLAlchemy places great emphasis on polite, thoughtful, and
constructive communication between users and developers.
Please see our current Code of Conduct at
`Code of Conduct <https://www.sqlalchemy.org/codeofconduct.html>`_.

License
-------

SQLAlchemy is distributed under the `MIT license
<https://www.opensource.org/licenses/mit-license.php>`_.

=====================
SQLALCHEMY UNIT TESTS
=====================

Basic Test Running
==================

Tox is used to run the test suite fully.   For basic test runs against
a single Python interpreter::

    tox


Advanced Tox Options
====================

For more elaborate CI-style test running, the tox script provided will
run against various Python / database targets.   For a basic run against
Python 3.8 using an in-memory SQLite database::

    tox -e py38-sqlite

The tox runner contains a series of target combinations that can run
against various combinations of databases.  The test suite can be
run against SQLite with "backend" tests also running against a PostgreSQL
database::

    tox -e py38-sqlite-postgresql

Or to run just "backend" tests against a MySQL database::

    tox -e py38-mysql-backendonly

Running against backends other than SQLite requires that a database of that
vendor be available at a specific URL.  See "Setting Up Databases" below
for details.

The pytest Engine
=================

The tox runner is using pytest to invoke the test suite.   Within the realm of
pytest, SQLAlchemy itself is adding a large series of option and
customizations to the pytest runner using plugin points, to allow for
SQLAlchemy's multiple database support, database setup/teardown and
connectivity, multi process support, as well as lots of skip / database
selection rules.

Running tests with pytest directly grants more immediate control over
database options and test selection.

A generic pytest run looks like::

    pytest -n4

Above, the full test suite will run against SQLite, using four processes.
If the "-n" flag is not used, the pytest-xdist is skipped and the tests will
run linearly, which will take a pretty long time.

The pytest command line is more handy for running subsets of tests and to
quickly allow for custom database connections.  Example::

    pytest --dburi=postgresql+psycopg2://scott:tiger@localhost/test  test/sql/test_query.py

Above will run the tests in the test/sql/test_query.py file (a pretty good
file for basic "does this database work at all?" to start with) against a
running PostgreSQL database at the given URL.

The pytest frontend can also run tests against multiple kinds of databases at
once - a large subset of tests are marked as "backend" tests, which will be run
against each available backend, and additionally lots of tests are targeted at
specific backends only, which only run if a matching backend is made available.
For example, to run the test suite against both PostgreSQL and MySQL at the
same time::

    pytest -n4 --db postgresql --db mysql


Setting Up Databases
====================

The test suite identifies several built-in database tags that run against
a pre-set URL.  These can be seen using --dbs::

    $ pytest --dbs
    Available --db options (use --dburi to override)
                 default    sqlite:///:memory:
                firebird    firebird://sysdba:masterkey@localhost//Users/classic/foo.fdb
                 mariadb    mariadb://scott:tiger@192.168.0.199:3307/test
                   mssql    mssql+pyodbc://scott:tiger^5HHH@mssql2017:1433/test?driver=ODBC+Driver+13+for+SQL+Server
           mssql_pymssql    mssql+pymssql://scott:tiger@ms_2008
                   mysql    mysql://scott:tiger@127.0.0.1:3306/test?charset=utf8mb4
                  oracle    oracle://scott:tiger@127.0.0.1:1521
                 oracle8    oracle://scott:tiger@127.0.0.1:1521/?use_ansi=0
                  pg8000    postgresql+pg8000://scott:tiger@127.0.0.1:5432/test
              postgresql    postgresql://scott:tiger@127.0.0.1:5432/test
    postgresql_psycopg2cffi postgresql+psycopg2cffi://scott:tiger@127.0.0.1:5432/test
                 pymysql    mysql+pymysql://scott:tiger@127.0.0.1:3306/test?charset=utf8mb4
                  sqlite    sqlite:///:memory:
             sqlite_file    sqlite:///querytest.db

Note that a pyodbc URL **must be against a hostname / database name
combination, not a DSN name** when using the multiprocessing option; this is
because the test suite needs to generate new URLs to refer to per-process
databases that are created on the fly.

What those mean is that if you have a database running that can be accessed
by the above URL, you can run the test suite against it using ``--db <name>``.

The URLs are present in the ``setup.cfg`` file.   You can make your own URLs by
creating a new file called ``test.cfg`` and adding your own ``[db]`` section::

    # test.cfg file
    [db]
    my_postgresql=postgresql://username:pass@hostname/dbname

Above, we can now run the tests with ``my_postgresql``::

    pytest --db my_postgresql

We can also override the existing names in our ``test.cfg`` file, so that we can run
with the tox runner also::

    # test.cfg file
    [db]
    postgresql=postgresql://username:pass@hostname/dbname

Now when we run ``tox -e py27-postgresql``, it will use our custom URL instead
of the fixed one in setup.cfg.

Database Configuration
======================

Step one, the **database chosen for tests must be entirely empty**.  A lot
of what SQLAlchemy tests is creating and dropping lots of tables
as well as running database introspection to see what is there.  If there
are pre-existing tables or other objects in the target database already,
these will get in the way.   A failed test run can also be followed by
 a run that includes the "--dropfirst" option, which will try to drop
all existing tables in the target database.

The above paragraph changes somewhat when the multiprocessing option
is used, in that separate databases will be created instead, however
in the case of Postgresql, the starting database is used as a template,
so the starting database must still be empty.  See below for example
configurations using docker.

The test runner will by default create and drop tables within the default
database that's in the database URL, *unless* the multiprocessing option is in
use via the pytest "-n" flag, which invokes pytest-xdist.   The
multiprocessing option is **enabled by default** when using the tox runner.
When multiprocessing is used, the SQLAlchemy testing framework will create a
new database for each process, and then tear it down after the test run is
complete.    So it will be necessary for the database user to have access to
CREATE DATABASE in order for this to work.   Additionally, as mentioned
earlier, the database URL must be formatted such that it can be rewritten on
the fly to refer to these other databases, which means for pyodbc it must refer
to a hostname/database name combination, not a DSN name.

Several tests require alternate usernames or schemas to be present, which
are used to test dotted-name access scenarios.  On some databases such
as Oracle these are usernames, and others such as PostgreSQL
and MySQL they are schemas.   The requirement applies to all backends
except SQLite and Firebird.  The names are::

    test_schema
    test_schema_2 (only used on PostgreSQL and mssql)

Please refer to your vendor documentation for the proper syntax to create
these namespaces - the database user must have permission to create and drop
tables within these schemas.  Its perfectly fine to run the test suite
without these namespaces present, it only means that a handful of tests which
expect them to be present will fail.

Additional steps specific to individual databases are as follows::

    POSTGRESQL: To enable unicode testing with JSONB, create the
    database with UTF8 encoding::

        postgres=# create database test with owner=scott encoding='utf8' template=template0;

    To include tests for HSTORE, create the HSTORE type engine::

        postgres=# \c test;
        You are now connected to database "test" as user "postgresql".
        test=# create extension hstore;
        CREATE EXTENSION

    Full-text search configuration should be set to English, else
    several tests of ``.match()`` will fail. This can be set (if it isn't so
    already) with:

     ALTER DATABASE test SET default_text_search_config = 'pg_catalog.english'

    For two-phase transaction support, the max_prepared_transactions
    configuration variable must be set to a non-zero value in postgresql.conf.
    See
    https://www.postgresql.org/docs/current/runtime-config-resource.html#GUC-MAX-PREPARED-TRANSACTIONS
    for further background.

    ORACLE: a user named "test_schema" is created in addition to the default
    user.

    The primary database user needs to be able to create and drop tables,
    synonyms, and constraints within the "test_schema" user.   For this
    to work fully, including that the user has the "REFERENCES" role
    in a remote schema for tables not yet defined (REFERENCES is per-table),
    it is required that the test the user be present in the "DBA" role:

        grant dba to scott;

    MSSQL: Tests that involve multiple connections require Snapshot Isolation
    ability implemented on the test database in order to prevent deadlocks that
    will occur with record locking isolation. This feature is only available
    with MSSQL 2005 and greater. You must enable snapshot isolation at the
    database level and set the default cursor isolation with two SQL commands:

     ALTER DATABASE MyDatabase SET ALLOW_SNAPSHOT_ISOLATION ON

     ALTER DATABASE MyDatabase SET READ_COMMITTED_SNAPSHOT ON

Docker Configurations
---------------------

The SQLAlchemy test can run against database running in Docker containers.
This ensures that they are empty and that their configuration is not influenced
by any local usage.

The following configurations are just examples that developers can use to
quickly set up a local environment for SQLAlchemy development. They are **NOT**
intended for production use!

**PostgreSQL configuration**::

    # create the container with the proper configuration for sqlalchemy
    docker run --rm -e POSTGRES_USER='scott' -e POSTGRES_PASSWORD='tiger' -e POSTGRES_DB='test' -p 127.0.0.1:5432:5432 -d --name postgres postgres

    # configure the database
    sleep 10
    docker exec -ti postgres psql -U scott -c 'CREATE SCHEMA test_schema; CREATE SCHEMA test_schema_2;' test
    # this last command is optional
    docker exec -ti postgres sed -i 's/#max_prepared_transactions = 0/max_prepared_transactions = 10/g' /var/lib/postgresql/data/postgresql.conf

    # To stop the container. It will also remove it.
    docker stop postgres

**MySQL configuration**::

    # create the container with the proper configuration for sqlalchemy
    docker run --rm -e MYSQL_USER='scott' -e MYSQL_PASSWORD='tiger' -e MYSQL_DATABASE='test' -e MYSQL_ROOT_PASSWORD='password' -p 127.0.0.1:3306:3306 -d --name mysql mysql --character-set-server=utf8mb4 --collation-server=utf8mb4_unicode_ci

    # configure the database
    sleep 20
    docker exec -ti mysql mysql -u root -ppassword -w -e "CREATE DATABASE test_schema CHARSET utf8mb4; GRANT ALL ON test_schema.* TO scott;"

    # To stop the container. It will also remove it.
    docker stop mysql

**MariaDB configuration**::

    # create the container with the proper configuration for sqlalchemy
    docker run --rm -e MARIADB_USER='scott' -e MARIADB_PASSWORD='tiger' -e MARIADB_DATABASE='test' -e MARIADB_ROOT_PASSWORD='password' -p 127.0.0.1:3306:3306 -d --name mariadb mariadb --character-set-server=utf8mb4 --collation-server=utf8mb4_unicode_ci

    # configure the database
    sleep 20
    docker exec -ti mariadb mysql -u root -ppassword -w -e "CREATE DATABASE test_schema CHARSET utf8mb4; GRANT ALL ON test_schema.* TO scott;"

    # To stop the container. It will also remove it.
    docker stop mariadb

**MSSQL configuration**::

    # create the container with the proper configuration for sqlalchemy
    # it will use the Developer version
    docker run --rm -e 'ACCEPT_EULA=Y' -e 'SA_PASSWORD=yourStrong(!)Password' -p 127.0.0.1:1433:1433 -d --name mssql mcr.microsoft.com/mssql/server

    # configure the database
    sleep 20
    docker exec -it mssql /opt/mssql-tools/bin/sqlcmd -S localhost -U sa -P 'yourStrong(!)Password' -Q "sp_configure 'contained database authentication', 1; RECONFIGURE; CREATE DATABASE test CONTAINMENT = PARTIAL; ALTER DATABASE test SET ALLOW_SNAPSHOT_ISOLATION ON; ALTER DATABASE test SET READ_COMMITTED_SNAPSHOT ON; CREATE LOGIN scott WITH PASSWORD = 'tiger^5HHH'; ALTER SERVER ROLE sysadmin ADD MEMBER scott;"
    docker exec -it mssql /opt/mssql-tools/bin/sqlcmd -S localhost -U sa -P 'yourStrong(!)Password' -d test -Q "CREATE SCHEMA test_schema"
    docker exec -it mssql /opt/mssql-tools/bin/sqlcmd -S localhost -U sa -P 'yourStrong(!)Password' -d test -Q "CREATE SCHEMA test_schema_2"

    # To stop the container. It will also remove it.
    docker stop mssql

NOTE: with this configuration the url to use is not the default one configured
in setup, but ``mssql+pymssql://scott:tiger^5HHH@127.0.0.1:1433/test``.  It can
be used with pytest by using ``--db docker_mssql``.

CONFIGURING LOGGING
-------------------
SQLAlchemy logs its activity and debugging through Python's logging package.
Any log target can be directed to the console with command line options, such
as::

    $ ./pytest test/orm/test_unitofwork.py -s \
      --log-debug=sqlalchemy.pool --log-info=sqlalchemy.engine

Above we add the pytest "-s" flag so that standard out is not suppressed.


DEVELOPING AND TESTING NEW DIALECTS
-----------------------------------

See the file README.dialects.rst for detail on dialects.