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https://github.com/sap/sqlalchemy-hana

SQLAlchemy Dialect for SAP HANA
https://github.com/sap/sqlalchemy-hana

hana orm python sap-hana sqlalchemy

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SQLAlchemy Dialect for SAP HANA

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README 

        
SQLAlchemy dialect for SAP HANA

===============================



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    :target: https://api.reuse.software/info/github.com/SAP/sqlalchemy-hana



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This dialect allows you to use the SAP HANA database with SQLAlchemy.

It uses ``hdbcli`` to connect to SAP HANA.

Please notice that sqlalchemy-hana isn't an official SAP product and isn't covered by SAP support.



Prerequisites

-------------

* Python 3.8+

* SQLAlchemy 1.4 or 2.x

* `hdbcli `_



Install

-------

Install from the Python Package Index:



.. code-block:: bash



    $ pip install sqlalchemy-hana



Versioning

----------

sqlalchemy-hana follows the semantic versioning standard, meaning that breaking changes will

only be added in major releases.

Please note, that only the following modules are considered to be part of the public API



- ``sqlalchemy_hana.types``

- ``sqlalchemy_hana.errors``



For these, only exported members (part of ``__all__`` ) are guaranteed to be stable.



Supported HANA Versions/Editions

--------------------------------

* SAP HANA Cloud

* SAP HANA

* SAP HANA, express edition



Getting started

---------------

If you do not have access to a SAP HANA server, you can also use the

`SAP HANA Express edition `_.



After installation of sqlalchemy-hana, you can create a engine which connects to a SAP HANA

instance. This engine works like all other engines of SQLAlchemy.



.. code-block:: python



    from sqlalchemy import create_engine

    engine = create_engine('hana://username:[email protected]:30015')



Alternatively, you can use HDB User Store to avoid entering connection-related information manually

each time you want to establish a connection to an SAP HANA database:



.. code-block:: python



    from sqlalchemy import create_engine

    engine = create_engine('hana://userkey=my_user_store_key')



You can create your user key in the user store using the following command:



.. code-block::



	hdbuserstore SET    



In case of a tenant database, you may use:



.. code-block:: python



    from sqlalchemy import create_engine

    engine = engine = create_engine('hana://user:pass@host/tenant_db_name')



Usage

-----



Special CREATE TABLE argument

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Sqlalchemy-hana provides a special argument called “hana_table_type” which can be used to

specify the type of table one wants to create with SAP HANA (i.e. ROW/COLUMN).

The default table type depends on your SAP HANA configuration and version.



.. code-block:: python



    t = Table('my_table', metadata, Column('id', Integer), hana_table_type = 'COLUMN')



Case Sensitivity

~~~~~~~~~~~~~~~~

In SAP HANA, all case insensitive identifiers are represented using uppercase text.

In SQLAlchemy on the other hand all lower case identifier names are considered to be case insensitive.

The sqlalchemy-hana dialect converts all case insensitive and case sensitive identifiers to the

right casing during schema level communication.

In the sqlalchemy-hana dialect, using an uppercase name on the SQLAlchemy side indicates a case

sensitive identifier, and SQLAlchemy will quote the name,which may cause case mismatches between

data received from SAP HANA.

Unless identifier names have been truly created as case sensitive (i.e. using quoted names),

all lowercase names should be used on the SQLAlchemy side.



LIMIT/OFFSET Support

~~~~~~~~~~~~~~~~~~~~

SAP HANA supports both ``LIMIT`` and ``OFFSET``, but it only supports ``OFFSET`` in conjunction with

``LIMIT`` i.e. in the select statement the offset parameter cannot be set without the ``LIMIT``

clause, hence in sqlalchemy-hana if the user tries to use offset without limit, a limit of

``2147384648`` would be set, this has been done so that the users can smoothly use ``LIMIT`` or

``OFFSET`` as in other databases that do not have this limitation.

``2147384648`` was chosen, because it is the maximum number of records per result set.



RETURNING Support

~~~~~~~~~~~~~~~~~

Sqlalchemy-hana does not support ``RETURNING`` in the ``INSERT``, ``UPDATE`` and ``DELETE``

statements to retrieve result sets of matched rows from ``INSERT``, ``UPDATE`` and ``DELETE``

statements because newly generated primary key values are neither fetched nor returned automatically

in SAP HANA and SAP HANA does not support the syntax ``INSERT... RETURNING...``.



Reflection

~~~~~~~~~~

The sqlalchemy-hana dialect supports all reflection capabilities of SQLAlchemy.

The Inspector used for the SAP HANA database is an instance of ``HANAInspector`` and offers an

additional method which returns the OID (object id) for the given table name.



.. code-block:: python



    from sqlalchemy import create_engine, inspect



    engine = create_engine("hana://username:[email protected]:30015")

    insp = inspect(engine)  # will be a HANAInspector

    print(insp.get_table_oid('my_table'))



Foreign Key Constraints

~~~~~~~~~~~~~~~~~~~~~~~

In SAP HANA the following ``UPDATE`` and ``DELETE`` foreign key referential actions are available:



* RESTRICT

* CASCADE

* SET NULL

* SET DEFAULT



The foreign key referential option ``NO ACTION`` does not exist in SAP HANA.

The default is ``RESTRICT``.



UNIQUE Constraints

~~~~~~~~~~~~~~~~~~

For each unique constraint an index is created in SAP HANA, this may lead to unexpected behavior

in programs using reflection.



Data types

~~~~~~~~~~

As with all SQLAlchemy dialects, all UPPERCASE types that are known to be valid with SAP HANA are

importable from the top level dialect, whether they originate from sqlalchemy types or from the

local dialect.

Therefore all supported types are part of the ``sqlalchemy_hana.types`` module and can be used from

there.



sqlalchemy-hana aims to support as many SQLAlchemy types as possible and to fallback to a similar

type of the requested type is not supported in SAP HANA.

The following table shows the mapping:



.. list-table::

    :header-rows: 1



    * - SQLAlchemy type

      - HANA type

    * - DATETIME

      - TIMESTAMP

    * - NUMERIC

      - DECIMAL

    * - String

      - NVARCHAR

    * - Unicode

      - NVARCHAR

    * - TEXT

      - NCLOB

    * - BINARY

      - VARBINARY

    * - DOUBLE_PRECISION

      - DOUBLE

    * - Uuid

      - NVARCHAR(32) / VARBINARY(16)

    * - LargeBinary

      - BLOB

    * - UnicodeText

      - NCLOB

    * - JSON

      - NCLOB



Please note, that some types might not support a length, precision or scale, even if the SQLAlchemy

type class accepts them.

The type compiler will then just ignore these arguments are render a type which will not lead to a

SQL error.



The ``ARRAY`` datatype is not supported because ``hdbcli`` does not yet provide support for it.



The ``JSON`` datatype only supports saving/updating field contents, but no json-based filters/deep indexing,

as these are not supported by SAP HANA.



The ``Uuid`` (note the casing) supports a special flag ``as_varbinary``.

If set to true (by default false), the UUID will be stored as a ``VARBINARY(16)`` instead of a ``NVARCHAR(32)``.

This does not effect the python side, meaning depending on the ``as_uuid`` flag, either uuid

objects or strings are used.

To use this feature in a database agnostic way, use

``UuidType = Uuid().with_variant(sqlalchemy_hana.types.Uuid(as_varbinary=True), "hana")``.

Note, that SAP HANA offers two UUID functions

(`NEWUID `_

and `SYSUUID `_

) which can be used to generate e.g. default values like

``Column('id', Uuid, server_default=func.NEWUID)``.



Regex

~~~~~

sqlalchemy-hana supports the ``regexp_match`` and ``regexp_replace``

functions provided by SQLAlchemy.



Bound Parameter Styles

~~~~~~~~~~~~~~~~~~~~~~

The default parameter style for the sqlalchemy-hana dialect is ``qmark``, where SQL is rendered

using the following style:



.. code-block:: sql



    WHERE my_column = ?



Boolean

~~~~~~~

By default, sqlalchemy-hana uses native boolean types.

However, older versions of sqlalchemy-hana used integer columns to represent these values leading

to a compatibility gap.

To *disable* native boolean support, add ``use_native_boolean=False`` to ``create_engine``.



Users are encouraged to switch to native booleans.

This can be e.g. done by using ``alembic``:



.. code-block:: python



    from sqlalchemy import false



    # assuming a table TAB with a tinyint column named valid

    def upgrade() -> None:

        op.add_column(Column("TAB", Column('valid_tmp', Boolean, server_default=false())))

        op.get_bind().execute("UPDATE TAB SET valid_tmp = TRUE WHERE valid = 1")

        op.drop_column("TAB", "valid")

        op.get_bind().execute("RENAME COLUMN TAB.valid_tmp to valid")

        # optionally, remove also the server default by using alter column



Computed columns

~~~~~~~~~~~~~~~~

SAP HANA supports two computed/calculated columns:



*  AS : the column is fully virtual and the expression is evaluated with each SELECT

*  GENERATED ALWAYS AS : the expression is evaluated during insertion and the value

    is stored in the table



By default, sqlalchemy-hana creates a ``GENERATED ALWAYS AS`` if a ``Computed`` column is used.

If ``Computed(persisted=False)`` is used, a fully virtual column using ``AS`` is created.



Views

~~~~~

sqlalchemy-hana supports the creation and usage of SQL views.



The views are not bound to the metadata object, therefore each needs to be created/dropped manually

using ``CreateView`` and ``DropView``.

By using the helper function ``view``, a ``TableClause`` object is generated which can be used in

select statements.

The returned object has the same primary keys as the underlying selectable.



Views can also be used in ORM and e.g. assigned to the ``__table__`` attribute of declarative base

classes.



For general information about views, please refer to

`this page `_.



.. code-block:: python



    from sqlalchemy import Column, Integer, MetaData, String, Table, select

    from sqlalchemy_hana.elements import CreateView, DropView, view



    engine = None  # a engine bound to a SAP HANA instance

    metadata = MetaData()

    stuff = sa.Table(

        "stuff",

        metadata,

        Column("id", Integer, primary_key=True),

        Column("data", String(50)),

    )



    selectable = select(stuff.c.id, stuff.c.data).where(stuff.c.data == "something")



    with engine.begin() as conn:

        # create a view

        ddl = CreateView("stuff_view", selectable)

        conn.execute(ddl)



        # usage of a view

        stuff_view = view("stuff_view", selectable)

        select(stuff_view.c.id, stuff_view.c.data).all()



        # drop a view

        ddl = DropView("stuff_view")

        conn.execute(ddl)



Upsert

~~~~~~

UPSERT statements are supported with some limitations by sqlalchemy-hana.

Caching is disabled due to implementation details and will not be added until a unified

insert/upsert/merge implementation is available in SQLAlchemy (see https://github.com/sqlalchemy/sqlalchemy/issues/8321).



.. code-block:: python



    from sqlalchemy import Column, Integer, MetaData, String, Table

    from sqlalchemy_hana.elements import upsert



    engine = None  # a engine bound to a SAP HANA instance

    metadata = MetaData()

    stuff = sa.Table(

        "stuff",

        metadata,

        Column("id", Integer, primary_key=True),

        Column("data", String(50)),

    )



    with engine.begin() as conn:

        statement upsert(stuff).values(id=1, data="some").filter_by(id=1)

        conn.execute(statement)



Identity

~~~~~~~~

Identity columns are fully supported but not reflection of those.

Therefore, alembic support for identity columns is reduced to creation of those.



Auto-increment

~~~~~~~~~~~~~~

SAP HANA only supports auto-increment with identity columns, therefore an identity will be rendered

if needed. This means that the  the following constructs are equivalent:



* ``Column('some', Integer, autoincrement=True)``

* ``Column('some', Integer, Identity, autoincrement=True)``

* ``Column('some', Integer, Identity, autoincrement=True)``



Note, that for ``autoincrement=True`` a post-execute statement execution is needed to fetch the

inserted identity value which might affect performance.



As an SQLAlchemy specific alternative, a ``sqlalchemy.schema.Sequence`` can be used to simulate

an auto-increment behavior, as followed:



.. code-block:: python



    t = Table('my_table', metadata, Column('id', Integer, Sequence('id_seq'), primary key=True))



Note, that on SAP HANA side, the column and the sequence are not linked, meaning that the sequence

can be e.g. be incremented w/o an actual insert into the table.



Alembic

-------

The sqlalchemy-hana dialect also contains a dialect for ``alembic``.

This dialect is active as soon as ``alembic`` is installed.

To ensure version compatibility, install sqlalchemy-hana as followed:



.. code-block:: bash



    $ pip install sqlalchemy-hana[alembic]



Error handling for humans

-------------------------

sqlalchemy-hana provides the ``sqlalchemy_hana.errors`` module which contains a set of

special exceptions and wrapper methods.

SQLAlchemy and hdbcli only provide generic exceptions which are sometimes not very helpful and

manual effort is needed to extract the relevant information.

To make this easier, the module provides two wrapper functions which take a SQLAlchemy or hdbcli

error and raise a more specific exception if possible.



.. code-block:: python



    from sqlalchemy_hana.errors import wrap_dbapi_error

    from sqlalchemy.exc import DBAPIError



    try:

        # some sqlalchemy code which might raise a DBAPIError

    except DBAPIError as err:

        wrap_dbapi_error(err)

        # if you reach this line, either the wrapped error of DBAPIError was not a hdbcli error

        # of no more specific exception was found



Development Setup

-----------------

We recommend the usage of ``pyenv`` to install a proper 3.12 python version for development.



* ``pyenv install 3.12``

* ``python311 -m venv venv``

* ``source venv/bin/activate``

* ``pip install -U pip``

* ``pip install -e .[dev,test,alembic]``



To execute the tests, use ``pyenv``.

The linters and formatters can be executed using ``pre-commit``: ``pre-commit run -a``.



Testing

-------

**Pre-Submit**: Linters, formatters and test matrix

**Post-Submit**: Linters and formatters



Release Actions

---------------

* Update the version in the pyproject.toml

* Add an entry in the changelog

* Push a new tag like vX.X.X to trigger the release



Support, Feedback, Contributing

-------------------------------

This project is open to feature requests/suggestions, bug reports etc.

via `GitHub issues `_.

Contribution and feedback are encouraged and always welcome.

For more information about how to contribute, the project structure,

as well as additional contribution information, see our

`Contribution Guidelines `_.



Security / Disclosure

---------------------

If you find any bug that may be a security problem, please follow our instructions at

`in our security policy `_ on how to report it.

Please do not create GitHub issues for security-related doubts or problems.



Code of Conduct

---------------

We as members, contributors, and leaders pledge to make participation in our community a

harassment-free experience for everyone.

By participating in this project, you agree to abide by its

`Code of Conduct `_ at all times.



Licensing

---------

Copyright 2024 SAP SE or an SAP affiliate company and sqlalchemy-hana contributors.

Please see our `LICENSE `_

for copyright and license information.

Detailed information including third-party components and their licensing/copyright information

is available `via the REUSE tool `_.