Getting Started & Examples
Relevant source files
The following files were used as context for generating this wiki page:
The flext-ldif library provides an enterprise-grade toolkit for processing Lightweight Data Interchange Format (LDIF) files. It is designed around Clean Architecture and Domain-Driven Design principles, offering high-level APIs for parsing, writing, validating, and migrating LDAP data across different server types such as OID, OUD, and OpenLDAP. Sources: src/README.md:1-15, examples/README.md:1-20
This documentation serves as a guide for developers to integrate the library using standardized patterns, including "Railway Oriented Programming" for error handling and context-aware processing. The examples range from basic core operations to complex, real-world migration and validation workflows. Sources: examples/01_basic_usage.py:1-20, examples/README.md:144-165
Library Fundamentals
To begin using the library, developers typically interact with the ldif public facade. The library supports strict type safety through MyPy and leverages the flext-core ecosystem for error handling and correlation tracking. Sources: src/README.md:112-120, examples/01_basic_usage.py:126-135
Core API Surface
The primary operations are exposed through the FlextLdif application service. Key operations include:
- Parsing: Converting LDIF strings or files into structured
Entrymodels. - Writing: Serializing
Entrymodels back into RFC-compliant LDIF format. - Validation: Checking entries against RFC 2849 rules and business logic.
- Migration: Transforming LDIF data between different LDAP server flavors.
Sources: examples/README.md:115-135, src/README.md:122-130
Usage Pattern: Railway Oriented Pipeline
The library utilizes a result-based pattern (often referred to as the "Railway" pattern) to handle errors without extensive try-except blocks. Operations return a Result object that can be chained using map and flat_map. Sources: examples/01_basic_usage.py:100-115, examples/README.md:167-175
The diagram above illustrates the typical railway pipeline where each step only executes if the previous step succeeded.
Sources: examples/01_basic_usage.py:104-120
Common Implementation Examples
1. Basic Parsing and Validation
This example demonstrates how to parse a string and validate its contents using the public facade.
from flext_ldif import ldif, c
def simple_workflow(content: str):
# Parse LDIF content with RFC server type
parse_result = ldif.parse_ldif(content, server_type=c.Ldif.ServerTypes.RFC)
if parse_result.success:
response = parse_result.unwrap()
# Validate entries against business rules
validation_result = ldif.validate_entries(response.entries)
return validation_result
return parse_result
Sources: examples/08_complete_workflow.py:16-30, examples/01_basic_usage.py:80-95
2. Entry Building
Entries can be programmatically constructed using domain models. The Entry model requires a DN (Distinguished Name) and an Attributes collection.
| Component | Description | Source |
|---|---|---|
m.Ldif.Entry | The primary domain entity representing an LDAP record. | src/README.md:65 |
m.Ldif.DN | A value object representing the Distinguished Name. | src/README.md:66 |
m.Ldif.Attributes | A mapping of attribute names to lists of string values. | examples/01_basic_usage.py:53-65 |
Sources: examples/08_complete_workflow.py:53-70, src/README.md:60-70
3. Server Migration
Migration workflows allow moving data between server types while maintaining schema integrity.
Sequence of a standard migration workflow between different LDAP server implementations.
Sources: examples/08_complete_workflow.py:33-50, examples/README.md:55-65
Advanced Processing
Batch & Parallel Processing
For large datasets, the library provides FlextProcessors to handle entries in batches or via parallel execution. This is critical for maintaining performance when processing files up to 1GB. Sources: examples/README.md:85-95, tests/README.md:144-150
Context and Correlation
Integration with flext-core allows operations to be wrapped in a FlextContext. This ensures that all logs and traces generated during the LDIF processing carry a consistent correlation_id. Sources: examples/01_basic_usage.py:122-135, src/README.md:72-75
from flext_core import FlextContext
from flext_ldif import ldif
def tracked_operation():
with FlextContext.new_correlation("req-123"):
# All library logs will now include 'req-123'
return ldif.parse_ldif_file(Path("input.ldif"))
Sources: examples/01_basic_usage.py:127-135
Testing and Quality Assurance
The project emphasizes a "Continuous Green" policy where examples and documentation snippets are validated via the test suite. Sources: AGENTS.md:P0, tests/README.md:20-30
| Test Category | Purpose | Marker |
|---|---|---|
| Unit | Individual component testing with mocked dependencies. | pytest -m unit |
| Integration | Testing interactions between API, domain services, and configuration. | pytest -m integration |
| End-to-End | Complete CLI and file processing workflows. | pytest -m e2e |
| Performance | Benchmarking parsing speed (>1000 entries/sec) and memory usage. | pytest -m performance |
Sources: tests/README.md:40-100, tests/integration/README.md:180-195
Summary
Getting started with flext-ldif involves utilizing the high-level ldif facade to orchestrate parsing, validation, and writing tasks. By following the provided examples—specifically the Railway Oriented pipelines and context-aware workflows—developers can build robust LDAP integration tools that adhere to enterprise standards for type safety, observability, and cross-server compatibility. Sources: examples/README.md:144-165, src/README.md:112-120