Debugging & Troubleshooting

Relevant source files

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

• CONTRIBUTING.md
• src/flext_target_oracle/_utilities/errors.py
• examples/03_production_setup.py
• tests/README.md
• examples/README.md
• examples/01_basic_usage.py

Introduction

Debugging and troubleshooting within the flext-target-oracle project involves a multi-layered approach encompassing structured logging, a specialized exception hierarchy, health monitoring, and comprehensive test suites. The system is designed to provide granular visibility into the data loading process, from initial configuration validation to final database commits.

The project utilizes Railway-Oriented Programming (ROP) via the r result pattern, ensuring that errors are handled as first-class citizens rather than side effects. This approach, combined with production-grade monitoring and automated test environments, allows developers to isolate issues across Oracle connectivity, Singer message processing, and DDL/DML execution.

Sources: examples/README.md:38-42, CONTRIBUTING.md:162-166, examples/03_production_setup.py:126-130

Exception Management and Error Metadata

The project implements a structured exception hierarchy where Oracle-specific errors inherit from the flext-core foundation. This ensures consistency across the FLEXT ecosystem while providing specific context for database-related failures.

Structured Error Metadata

Errors are enriched with FlextTargetOracleErrorMetadata, which includes canonical error codes and correlation IDs for cross-service tracking.

Field	Description
code	Canonical error code (e.g., CONNECTION_ERROR, VALIDATION_ERROR).
context	Structured mapping containing specific details like stream_name, user, or dsn.
correlation_id	Identifier used for tracing errors across service boundaries.

Sources: src/flext_target_oracle/_utilities/errors.py:22-38

Exception Classes

The following table describes the primary exception classes used to categorize failures:

Exception Class	Purpose
OracleConnectionError	Failures during database handshake, including DSN and user context.
AuthenticationError	Permission or credential failures, including wallet location if applicable.
SchemaError	Validation failures related to Singer schemas, including schema hashes.
ProcessingError	Runtime failures during record insertion, including record counts and operation types.

Sources: src/flext_target_oracle/_utilities/errors.py:65-155

Observability and Health Monitoring

For production environments, the project provides a ProductionTargetManager that includes built-in health checks and real-time metric collection.

Health Check Workflow

The health check mechanism evaluates the target's initialization state and active database connectivity.

The health status includes a timestamp, status string (healthy, degraded, or unhealthy), and specific check results.
Sources: examples/03_production_setup.py:126-193

Debugging Environment and Setup

Developers can enable verbose debugging by setting specific environment variables to increase log granularity.

Debug Mode Configuration

Setting FLEXT_DEBUG=true enables detailed internal state logging, while FLEXT_LOG_LEVEL=DEBUG captures fine-grained Singer message transitions.

export FLEXT_DEBUG=true
export FLEXT_LOG_LEVEL=DEBUG

Sources: CONTRIBUTING.md:189-192

Common Development Issues

• Import Errors: Usually resolved by ensuring all flext-* dependencies from pyproject.toml are installed via poetry install.
• Oracle Connection: Typically requires verifying the Docker container status via make oracle-logs.
• Type Errors: These are caught during development using mypy and the make type-check command.

Sources: CONTRIBUTING.md:194-197, pyproject.toml:23-35

Troubleshooting via Automated Testing

The test suite is the primary tool for isolating failures in DDL (Data Definition Language) and DML (Data Manipulation Language) operations.

Test Categories and Isolation

The project categorizes tests to isolate environment-specific issues:

Database Lifecycle Management

If integration tests fail, developers can use manual container management commands to inspect the state:

• make oracle-shell: Opens a SQL*Plus shell to inspect the FLEXT_TEST schema.
• make oracle-logs: Displays the Oracle XE container logs to check for database-level errors (e.g., tablespace exhaustion).
• ./scripts/run_tests.sh --keep-db: Prevents the container from stopping after tests, allowing for manual post-mortem inspection.

Sources: tests/README.md:34-44, tests/README.md:73-77

Summary

Troubleshooting flext-target-oracle requires a systematic review of configuration validation, Singer message integrity, and Oracle connectivity. By leveraging the structured FlextTargetOracleExceptions and production HealthStatus metrics, developers can quickly differentiate between environmental connectivity issues, schema validation errors, and runtime processing failures. The combination of ROP error handling and a containerized testing strategy provides the necessary tools for maintaining high-reliability data pipelines.

Sources: examples/03_production_setup.py:355-385, CONTRIBUTING.md:162-166