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Add table_kwargs context manager to make pandas/Dask support dialect
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Unlock SQLAlchemy ORM's `__table_args__` on the pandas/Dask `to_sql()`
interface, in order to support CrateDB's special SQL DDL options.
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amotl committed Jun 24, 2024
1 parent 5e39bbf commit a8a61b9
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2 changes: 2 additions & 0 deletions CHANGES.md
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## Unreleased
- Added/reactivated documentation as `sqlalchemy-cratedb`
- Added re-usable patches and polyfills from application adapters
- Added `table_kwargs` context manager to make pandas/Dask support
CrateDB dialect table options.

## 2024/06/13 0.37.0
- Added support for CrateDB's [FLOAT_VECTOR] data type and its accompanying
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30 changes: 30 additions & 0 deletions docs/support.md
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Expand Up @@ -54,6 +54,36 @@ df.to_sql(
)
```


(support-table-kwargs)=
## Context Manager `table_kwargs`

:::{rubric} Background
:::
CrateDB's special SQL DDL options to support [](inv:crate-reference#partitioned-tables),
[](inv:crate-reference#ddl-sharding), or [](inv:crate-reference#ddl-replication)
sometimes can't be configured easily when SQLAlchemy is wrapped into a 3rd-party
framework like pandas or Dask.

:::{rubric} Utility
:::
The `table_kwargs` utility is a context manager that is able to forward CrateDB's
dialect-specific table creation options to the `sa.Table()` constructor call sites
at runtime.

:::{rubric} Synopsis
:::
Using a context manager incantation like outlined below will render a
`PARTITIONED BY ("time")` SQL clause, without touching the call site of
`sa.Table(...)`.
```python
from sqlalchemy_cratedb.support import table_kwargs

with table_kwargs(crate_partitioned_by="time"):
return df.to_sql(...)
```


(support-autoincrement)=
## Synthetic Autoincrement using Timestamps

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3 changes: 2 additions & 1 deletion src/sqlalchemy_cratedb/support/__init__.py
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from sqlalchemy_cratedb.support.pandas import insert_bulk
from sqlalchemy_cratedb.support.pandas import insert_bulk, table_kwargs
from sqlalchemy_cratedb.support.polyfill import check_uniqueness_factory, refresh_after_dml, \
patch_autoincrement_timestamp
from sqlalchemy_cratedb.support.util import refresh_table, refresh_dirty
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refresh_after_dml,
refresh_dirty,
refresh_table,
table_kwargs,
]
49 changes: 49 additions & 0 deletions src/sqlalchemy_cratedb/support/pandas.py
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# However, if you have executed another commercial license agreement
# with Crate these terms will supersede the license and you may use the
# software solely pursuant to the terms of the relevant commercial agreement.
from contextlib import contextmanager
from typing import Any
from unittest.mock import patch

import logging

import sqlalchemy as sa

from sqlalchemy_cratedb import SA_VERSION, SA_2_0

logger = logging.getLogger(__name__)

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cursor = conn._dbapi_connection.cursor()
cursor.execute(sql=sql, bulk_parameters=data)
cursor.close()


@contextmanager
def table_kwargs(**kwargs):
"""
Context manager for adding SQLAlchemy dialect-specific table options at runtime.
In certain cases where SQLAlchemy orchestration is implemented within a
framework, like at this spot [1] in pandas' `SQLTable._create_table_setup`,
it is not easily possible to forward SQLAlchemy dialect options at table
creation time.
In order to augment the SQL DDL statement to make it honor database-specific
dialect options, the only way to work around the unfortunate situation is by
monkey-patching the call to `sa.Table()` at runtime, relaying additional
dialect options through corresponding keyword arguments in their original
`<dialect>_<kwarg>` format [2].
[1] https://github.com/pandas-dev/pandas/blob/v2.2.2/pandas/io/sql.py#L1282-L1285
[2] https://docs.sqlalchemy.org/en/20/core/foundation.html#sqlalchemy.sql.base.DialectKWArgs.dialect_kwargs
"""

if SA_VERSION < SA_2_0:
_init_dist = sa.sql.schema.Table._init

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def _init(self, name, metadata, *args, **kwargs_effective):
kwargs_effective.update(kwargs)
return _init_dist(self, name, metadata, *args, **kwargs_effective)

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with patch("sqlalchemy.sql.schema.Table._init", _init):
yield

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else:
new_dist = sa.sql.schema.Table._new

def _new(cls, *args: Any, **kw: Any) -> Any:
kw.update(kwargs)
table = new_dist(cls, *args, **kw)
return table

with patch("sqlalchemy.sql.schema.Table._new", _new):
yield
73 changes: 73 additions & 0 deletions tests/test_support_pandas.py
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import re
import sys

import pytest
import sqlalchemy as sa
from sqlalchemy.exc import ProgrammingError
from sqlalchemy.orm import sessionmaker

from pueblo.testing.pandas import makeTimeDataFrame

from sqlalchemy_cratedb import SA_VERSION, SA_2_0
from sqlalchemy_cratedb.support.pandas import table_kwargs

TABLE_NAME = "foobar"
INSERT_RECORDS = 42

# Create dataframe, to be used as input data.
df = makeTimeDataFrame(nper=INSERT_RECORDS, freq="S")
df["time"] = df.index


@pytest.mark.skipif(sys.version_info < (3, 8), reason="Feature not supported on Python 3.7 and earlier")
@pytest.mark.skipif(SA_VERSION < SA_2_0, reason="Feature not supported on SQLAlchemy 1.4 and earlier")
def test_table_kwargs_partitioned_by(cratedb_service):
"""
Validate adding CrateDB dialect table option `PARTITIONED BY` at runtime.
"""

engine = cratedb_service.database.engine
session = sessionmaker(bind=engine)()

# Insert records from pandas dataframe.
with table_kwargs(crate_partitioned_by="time"):
df.to_sql(
TABLE_NAME,
engine,
if_exists="replace",
index=False,
)

# Synchronize writes.
cratedb_service.database.refresh_table(TABLE_NAME)

# Inquire table cardinality.
metadata = sa.MetaData()
query = sa.select(sa.func.count()).select_from(sa.Table(TABLE_NAME, metadata))
results = session.execute(query)
count = results.scalar()

# Compare outcome.
assert count == INSERT_RECORDS

# Validate SQL DDL.
ddl = cratedb_service.database.run_sql(f"SHOW CREATE TABLE {TABLE_NAME}")
assert 'PARTITIONED BY ("time")' in ddl[0][0]


@pytest.mark.skipif(sys.version_info < (3, 8), reason="Feature not supported on Python 3.7 and earlier")
@pytest.mark.skipif(SA_VERSION < SA_2_0, reason="Feature not supported on SQLAlchemy 1.4 and earlier")
def test_table_kwargs_unknown(cratedb_service):
"""
Validate behaviour when adding an unknown CrateDB dialect table option.
"""
engine = cratedb_service.database.engine
with table_kwargs(crate_unknown_option="bazqux"):
with pytest.raises(ProgrammingError) as ex:
df.to_sql(
TABLE_NAME,
engine,
if_exists="replace",
index=False,
)
assert ex.match(re.escape("ColumnUnknownException[Column bazqux unknown]"))

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