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.
- Python 3.8+
- SQLAlchemy 1.4 or 2.x
- hdbcli
Install from the Python Package Index:
$ pip install sqlalchemy-hanasqlalchemy-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.typessqlalchemy_hana.errors
For these, only exported members (part of __all__ ) are guaranteed to be stable.
- SAP HANA Cloud
- SAP HANA
- SAP HANA, express edition
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.
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:
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:
hdbuserstore SET <KEY> <host:port> <USERNAME> <PASSWORD>In case of a tenant database, you may use:
from sqlalchemy import create_engine
engine = engine = create_engine('hana://user:pass@host/tenant_db_name')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.
t = Table('my_table', metadata, Column('id', Integer), hana_table_type = 'COLUMN')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.
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.
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....
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.
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'))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.
For each unique constraint an index is created in SAP HANA, this may lead to unexpected behavior in programs using reflection.
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:
| SQLAlchemy type | HANA type |
|---|---|
| DATETIME | TIMESTAMP |
| NUMERIC | DECIMAL |
| String | NVARCHAR |
| Unicode | NVARCHAR |
| TEXT | NCLOB |
| BINARY | VARBINARY |
| DOUBLE_PRECISION | DOUBLE |
| Uuid | NVARCHAR(32) |
| LargeBinary | BLOB |
| UnicodeText | 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.
sqlalchemy-hana supports the regexp_match and regexp_replace functions provided by SQLAlchemy.
The default parameter style for the sqlalchemy-hana dialect is qmark, where SQL is rendered using the following style:
WHERE my_column = ?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:
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 columnSAP HANA supports two computed/calculated columns:
- <col> AS <expr>: the column is fully virtual and the expression is evaluated with each SELECT
- <col> GENERATED ALWAYS AS <expr>: 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.
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.
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 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 sqlalchemy/sqlalchemy#8321).
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 columns are fully supported but not reflection of those. Therefore, alembic support for identity columns is reduced to creation of those.
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:
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.
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:
$ pip install sqlalchemy-hana[alembic]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.
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 foundWe recommend the usage of pyenv to install a proper 3.11 python version for development.
pyenv install 3.11python311 -m venv venvsource venv/bin/activatepip install -U pippip 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.
Pre-Submit: Linters, formatters and test matrix Post-Submit: Linters and formatters
- 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
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.
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.
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.
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.
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