Project Overview

Relevant source files

The following files were used as context for generating this wiki page:

• README.md
• pyproject.toml
• src/flext_tap_ldap/tap.py
• src/flext_tap_ldap/_utilities/_processor.py
• src/flext_tap_ldap/ldif_streams.py
• src/flext_tap_ldap/_utilities/client_support.py
• AGENTS.md

flext-tap-ldap is a specialized Singer Tap designed for extracting data from LDAP (Lightweight Directory Access Protocol) directory services. It is built using the FLEXT framework, utilizing flext-core, flext-ldap, and flext-meltano to provide a typed, enterprise-grade ETL (Extract, Transform, Load) solution. The tap supports standard LDAP directory queries, LDIF (LDAP Data Interchange Format) file processing, and specific enrichments for Oracle Unified Directory (OID) environments.

Sources: README.md:9-11, pyproject.toml:26-31, src/flext_tap_ldap/tap.py:16-25

Core Architecture

The tap is built on the FlextMeltanoAbstractions base class, which provides the standard Singer protocol implementation. It leverages a modular utility system for LDIF processing and client support, delegating complex LDAP operations to the flext-ldif and flext-ldap libraries.

System Components

The following diagram illustrates the relationship between the Tap, its internal utility mixins, and external library dependencies.

Configuration and Discovery

The tap utilizes a centralized configuration schema defined via Pydantic and JSON Schema. It supports various connection parameters and operational flags, such as enabling specific LDIF processing streams.

Settings and Options

Parameter	Type	Default	Description
host	string	-	LDAP server host address.
port	integer	389	LDAP server port.
use_ssl	boolean	False	Whether to use LDAPS (SSL/TLS).
bind_dn	string	-	Distinguished Name for authentication.
base_dn	string	-	The starting point for directory searches.
enable_ldif_streams	boolean	False	Toggle for LDIF file processing streams.
ldif_directory	string	-	Path to search for LDIF files.

Sources: src/flext_tap_ldap/tap.py:53-110

Stream Discovery Logic

During discovery, the tap identifies standard LDAP streams and dynamically adds LDIF or custom streams based on the provided configuration.

LDIF Processing Utilities

The FlextTapLdapUtilitiesProcessorMixin provides a sophisticated toolset for handling LDIF content. It includes three primary sub-classes for processing, validating, and transforming LDAP entries.

Processor, Validator, and Transformer

• Processor: Handles parsing LDIF files or raw strings into structured entry objects. It tracks statistics such as processed_entries, skipped_entries, and parsing errors.
• Validator: Uses flext-ldif capabilities to verify entries against LDAP standards, returning detailed error and warning payloads.
• Transformer: Applies transformation rules including attribute mappings, schema normalization, attribute removal/addition, and value-level mapping (e.g., mapping "IT" to "Information Technology").

Sources: src/flext_tap_ldap/_utilities/_processor.py:145-200, src/flext_tap_ldap/_utilities/_processor.py:315-345, src/flext_tap_ldap/_utilities/_processor.py:359-450

Client and Oracle Support

The tap includes specialized logic for Oracle-specific LDAP environments (Oracle Internet Directory / Unified Directory). The ClientSupport utility manages normalization of entries and URI construction.

Oracle Normalization

The normalize_oracle_entry function performs specific adjustments:

• Maps orclPassword to userPassword.
• Enriches objectClass by adding organizationalUnit if orclContainer is present but organizationalUnit is missing.
• Extends attribute requests to include OID-specific fields like orclPasswordAttribute.

Sources: src/flext_tap_ldap/_utilities/client_support.py:73-110

Operational Flow

The execution flow of the tap follows the standard Singer CLI pattern, allowing for discovery and sync modes via command-line arguments.

Conclusion

flext-tap-ldap provides a robust, framework-aligned integration for LDAP services. By combining standard directory traversal with advanced LDIF processing and Oracle-specific support, it ensures high-fidelity data extraction for complex enterprise identity environments. The project adheres to "Zero-Tolerance" engineering laws as defined in the AGENTS.md governance pointer, ensuring root-cause fixes and strict typing.

Sources: README.md:16-20, AGENTS.md:20-35