Skip to main content

Custom Database Queries

Project description

Few Utility Functions

License Python PyPi Build Status

Install All databases dependencies

pip install ddcDatabases[all]

Install MSSQL

pip install ddcDatabases[mssql]

Install PostgreSQL

pip install ddcDatabases[pgsql]

Databases

  • Parameters for all classes are declared as OPTIONAL falling back to .env file\
  • All examples are using db_utils.py\
  • By default a session is always open\
  • But the engine can be available, examples bellow

SQLITE

class Sqlite(
    file_path: Optional[str] = None,
    echo: Optional[bool] = None,
)

Session

import sqlalchemy as sa
from ddcDatabases import DBUtils, Sqlite
with Sqlite() as session:
    utils = DBUtils(session)
    stmt = sa.select(Table).where(Table.id == 1)
    results = utils.fetchall(stmt)
    for row in results:
        print(row)

Sync Engine

from ddcDatabases import Sqlite
with Sqlite().engine() as engine:
    ...

MSSQL

class MSSQL(        
    host: Optional[str] = None,
    port: Optional[int] = None,
    username: Optional[str] = None,
    password: Optional[str] = None,
    database: Optional[str] = None,
    schema: Optional[str] = None,
    echo: Optional[bool] = None,
    pool_size: Optional[int] = None,
    max_overflow: Optional[int] = None
)

Sync Example

import sqlalchemy as sa
from ddcDatabases import DBUtils, MSSQL
with MSSQL() as session:
    stmt = sa.select(Table).where(Table.id == 1)
    db_utils = DBUtils(session)
    results = db_utils.fetchall(stmt)
    for row in results:
        print(row)

Async Example

import sqlalchemy as sa
from ddcDatabases import DBUtilsAsync, MSSQL
async with MSSQL() as session:
    stmt = sa.select(Table).where(Table.id == 1)
    db_utils = DBUtilsAsync(session)
    results = await db_utils.fetchall(stmt)
    for row in results:
        print(row)

Sync Engine

from ddcDatabases import MSSQL
with MSSQL().engine() as engine:
    ...

Async Engine

from ddcDatabases import MSSQL
async with MSSQL().async_engine() as engine:
    ...

PostgreSQL

class DBPostgres(
    host: Optional[str] = None,
    port: Optional[int] = None,
    username: Optional[str] = None,
    password: Optional[str] = None,
    database: Optional[str] = None,
    echo: Optional[bool] = None,
)

Sync Example

import sqlalchemy as sa
from ddcDatabases import DBUtils, PostgreSQL
with PostgreSQL() as session:
    stmt = sa.select(Table).where(Table.id == 1)
    db_utils = DBUtils(session)
    results = db_utils.fetchall(stmt)
    for row in results:
        print(row)

Async Example

import sqlalchemy as sa
from ddcDatabases import DBUtilsAsync, PostgreSQL
async with PostgreSQL() as session:
    stmt = sa.select(Table).where(Table.id == 1)
    db_utils = DBUtilsAsync(session)
    results = await db_utils.fetchall(stmt)
    for row in results:
        print(row)

Sync Engine

from ddcDatabases import PostgreSQL
with PostgreSQL().engine() as engine:
    ...

Async Engine

from ddcDatabases import PostgreSQL
async with PostgreSQL().async_engine() as engine:
    ...

DBUtils and DBUtilsAsync

  • Take an open session as parameter
  • Can use SQLAlchemy statements
  • Execute function can be used to update, insert or any SQLAlchemy.text
from ddcDatabases import DBUtils
db_utils = DBUtils(session)
db_utils.fetchall(stmt)                     # returns a list of RowMapping
db_utils.fetchvalue(stmt)                   # fetch a single value, returning as string
db_utils.insert(stmt)                       # insert into model table
db_utils.deleteall(model)                   # delete all records from model
db_utils.insertbulk(model, list[dict])      # insert records into model from a list of dicts
db_utils.execute(stmt)                      # this is the actual execute from session

Source Code

Build

poetry build -f wheel

Run Tests and Get Coverage Report

poetry run coverage run --omit=./tests/* --source=./ddcDatabases -m pytest -v && poetry run coverage report

License

Released under the MIT License

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ddcdatabases-1.0.8.tar.gz (7.8 kB view details)

Uploaded Source

Built Distribution

ddcdatabases-1.0.8-py3-none-any.whl (9.7 kB view details)

Uploaded Python 3

File details

Details for the file ddcdatabases-1.0.8.tar.gz.

File metadata

  • Download URL: ddcdatabases-1.0.8.tar.gz
  • Upload date:
  • Size: 7.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for ddcdatabases-1.0.8.tar.gz
Algorithm Hash digest
SHA256 1ac25c469c9b0bd936daceff784c023fc6cdbfe354ca811dfafa7a27b6890aa1
MD5 b9f3c2683388e4d0fa540a013d105075
BLAKE2b-256 bb585132369370649d7a938bace87530c1b2e1e40f8de215fdc458cc0a42792f

See more details on using hashes here.

Provenance

The following attestation bundles were made for ddcdatabases-1.0.8.tar.gz:

Publisher: workflow.yml on ddc/ddcDatabases

Attestations:

File details

Details for the file ddcdatabases-1.0.8-py3-none-any.whl.

File metadata

  • Download URL: ddcdatabases-1.0.8-py3-none-any.whl
  • Upload date:
  • Size: 9.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for ddcdatabases-1.0.8-py3-none-any.whl
Algorithm Hash digest
SHA256 022eb887c9a183d269e8698d6f8a5df1bfd070a5e65aff63fc3bf23b5fc52368
MD5 3f7038f8f37162cc68de00bba6d8c210
BLAKE2b-256 02b7abd945560e5c920647d19eab86ac53729352d12eb7c5642df37971c3fa23

See more details on using hashes here.

Provenance

The following attestation bundles were made for ddcdatabases-1.0.8-py3-none-any.whl:

Publisher: workflow.yml on ddc/ddcDatabases

Attestations:

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page