BCOData
A Python client library for interacting with Business Central OData API.
Features
- Async/await support for efficient API calls
- Automatic rate limiting and concurrency control
- Built-in retry mechanism for failed requests
- Query builder for constructing OData queries
- Comprehensive error handling
- Detailed logging with loguru
- Automatic pagination handling for large datasets
- Type hints and static type checking support
Requirements
- Python >= 3.12
- Dependencies:
- aiolimiter >= 1.2.1
- httpx >= 0.28.1
- loguru >= 0.7.3
- tenacity >= 9.1.2
Installation
pip install bcodata
Quick Start
import asyncio
from bcodata import Client, QueryBuilder
async def main():
# Initialize the client
base_url = "https://api.businesscentral.dynamics.com/v2.0/tenant/companies/company/environment/api/v2.0"
credentials = ("username", "password")
async with Client(base_url, credentials) as client:
# Create a query using QueryBuilder
query = (
QueryBuilder()
.filter("Name eq 'John'") # Filter by name
.select(["Name", "Age", "Email"]) # Select specific fields
.top(10) # Limit to 10 results
.skip(20) # Skip first 20 results
.order_by("Age", descending=True) # Sort by age descending
)
# Fetch data using the query
data = await client.get_data("users", query.build())
print(f"Retrieved {len(data)} items")
if __name__ == "__main__":
asyncio.run(main())
Documentation
Query Builder
The QueryBuilder class provides a fluent interface for constructing OData queries:
Filtering
# Simple equality
query = QueryBuilder().filter("Name eq 'John'")
# Multiple conditions
query = QueryBuilder().filter("Age gt 18 and City eq 'New York'")
# Using functions
query = QueryBuilder().filter("startswith(Name, 'J')")
Field Selection
# Select specific fields
query = QueryBuilder().select(["Name", "Age", "Email"])
# Combine with other parameters
query = (
QueryBuilder()
.select(["Name", "Age"])
.filter("Age gt 18")
)
Pagination
# Get first 10 items
query = QueryBuilder().top(10)
# Skip first 20 items
query = QueryBuilder().skip(20)
# Combine for pagination
query = (
QueryBuilder()
.top(10)
.skip(20) # Get items 21-30
)
Sorting
# Sort ascending
query = QueryBuilder().order_by("Name")
# Sort descending
query = QueryBuilder().order_by("Age", descending=True)
Client Configuration
The Client class accepts the following configuration parameters:
base_url(str): The base URL of the Business Central OData APIcredentials(tuple[str, str] | None): Username and password for authenticationmax_rate(int): Maximum number of requests per time period (default: 10)time_period(int): Time period in seconds for rate limiting (default: 1)max_concurrency(int): Maximum number of concurrent requests (default: 5)max_retries(int): Number of retry attempts for failed requests (default: 3)base_retry_delay(int): Base delay between retries in seconds (default: 1)timeout(int): Request timeout in seconds (default: 90)
Example:
client = Client(
base_url="https://api.businesscentral.dynamics.com/v2.0/tenant/companies/company/environment/api/v2.0",
credentials=("username", "password"),
max_rate=20, # Maximum requests per time period
time_period=1, # Time period in seconds
max_concurrency=5, # Maximum concurrent requests
max_retries=3, # Number of retries for failed requests
base_retry_delay=1, # Base delay between retries in seconds
timeout=90, # Request timeout in seconds
)
Error Handling
from bcodata.exceptions import (
ODataConnectionError,
ODataHTTPError,
ODataJSONDecodeError,
ODataTimeoutError,
ODataRequestError
)
async with Client(base_url, credentials) as client:
try:
data = await client.get_data("users", query.build())
except ODataHTTPError as e:
print(f"HTTP Error: {e.status_code} - {e.response_content}")
except ODataConnectionError as e:
print(f"Connection Error: {e}")
except ODataTimeoutError as e:
print(f"Timeout Error: {e}")
except ODataJSONDecodeError as e:
print(f"JSON Decode Error: {e}")
except ODataRequestError as e:
print(f"Request Error: {e}")
Concurrent Data Fetching
async def fetch_multiple_endpoints(client: Client):
# Define the endpoints to fetch
endpoints = [
"companies",
"customers",
"items",
"salesOrders"
]
# Create tasks for each endpoint
tasks = [client.get_data(endpoint) for endpoint in endpoints]
# Execute all tasks concurrently
results = await asyncio.gather(*tasks)
# Process results
for endpoint, data in zip(endpoints, results):
print(f"Retrieved {len(data)} records from {endpoint}")
Examples
Check out the examples directory for comprehensive examples demonstrating various features and use cases.
Development
Setup
- Clone the repository:
git clone https://github.com/yourusername/bcodata.git
cd bcodata
- Create and activate a virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
- Install development dependencies:
pip install -e ".[dev]"
Code Quality
The project uses:
- Ruff for linting and formatting
- Type hints for static type checking
- Comprehensive test suite
Run the linter:
ruff check .
Format code:
ruff format .
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Metadata
Release files for bcodata 0.1.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| bcodata-0.1.3.tar.gz | 17.1 kB | With provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| bcodata-0.1.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 26.9 kB
Release files / bcodata-0.1.3.tar.gz
| Download URL | bcodata-0.1.3.tar.gz |
|---|---|
| Size | 17.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
1353c56e79aa93d228147991395c0896da415a769d217f46ced52a167c703ac0
|
|
BLAKE2b-256 checksum How to use checksums |
2113745b44c1f407142b90f256704711641789cc606acdcd14642eee19287322
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on May 29, 2025.
Transparency logRelease files / bcodata-0.1.3-py3-none-any.whl
| Download URL | bcodata-0.1.3-py3-none-any.whl |
|---|---|
| Size | 9.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
079b855969ca3c58ef6db6d1ba296b4e2b07104b3f2aaf9df10397c155fc8fed
|
|
BLAKE2b-256 checksum How to use checksums |
feb6c9196613396c903707914dda652502ddc93b51ef97ae02835de15e15b9c1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on May 29, 2025.
Transparency logSecurity for bcodata 0.1.3
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 2 files in this release have been uploaded with provenance metadata.
Attestation for bcodata-0.1.3.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on May 29, 2025.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.