Integration Architecture and Data Flow
Core Integration Components
The Trackingmore-Odoo integration employs a distributed architecture with three primary components. Odoo 18 serves as the central hub for order management and customer data. Trackingmore operates as the specialized carrier aggregation service, normalizing data from hundreds of shipping providers. A custom integration module bridges these systems, handling bidirectional data synchronization and business logic.
This module consists of several key technical elements. The core engine is an Odoo model that extends the stock.picking object with tracking-specific fields and methods. A scheduled action executes periodic API calls to Trackingmore for status updates. Webhook endpoints receive push notifications from Trackingmore for real-time event processing. Redis provides caching for carrier metadata and rate limit management.
Data Flow Patterns
The integration implements two distinct data flow patterns to balance performance and real-time requirements. For initial tracking number submission, Odoo initiates outbound API calls to Trackingmore whenever a delivery order confirms. This synchronous operation ensures immediate tracking registration. The system sends package references, carrier codes, and customer email addresses to Trackingmore’s tracking/create endpoint.
For status updates, the architecture leverages webhooks for event-driven notifications. Trackingmore pushes shipment events to a secured endpoint within your Odoo instance. This approach eliminates the need for continuous polling and provides near-instant status propagation. The webhook payload contains the complete tracking history, which the integration parses and maps to Odoo’s delivery states.
Synchronization Mechanisms
The system maintains data consistency through multiple synchronization layers. A PostgreSQL database table stores the mapping between Odoo picking IDs and Trackingmore tracking numbers. This mapping table enables bidirectional lookups when processing webhook events or manual sync requests. The integration implements idempotent operations to handle duplicate webhook deliveries without creating duplicate records.
For recovery scenarios, the system includes a batch synchronization process. A daily scheduled job identifies tracking numbers missing recent updates and queries Trackingmore’s API for current status. This process compensates for missed webhook events or system downtime. The job processes records in batches of 100 to respect API rate limits while ensuring complete data coverage.
Step-by-Step Configuration
Trackingmore API Setup
Begin by creating a Trackingmore account and generating your API credentials. Navigate to the Trackingmore dashboard and access the API management section. Create a new API key with permissions for tracking creation, status retrieval, and webhook management. Record this key securely, as you will embed it in your Odoo configuration.
The API key requires specific configuration for optimal security and functionality. Set the allowed IP addresses to restrict access to your Odoo server’s public IP. Configure rate limit alerts to notify you before hitting API constraints. Enable webhook support and note the verification token, which secures your webhook endpoints against unauthorized access.
Install the Trackingmore Python SDK to simplify API interactions. Add the dependency to your Odoo module’s requirements.txt file. The SDK handles authentication, request signing, and error retries automatically. Initialize the client with your API key in your module’s init.py file to make it available throughout your integration code.
Odoo Module Development
Create a new Odoo module named shipment_trackingmore to encapsulate all integration logic. Define the module manifest with dependencies on stock, sale, and base_automation. The manifest should declare data files for views, security rules, and scheduled actions. This modular approach ensures clean separation from core Odoo functionality.
Extend the stock.picking model with tracking-specific fields. Add fields for Trackingmore tracking ID, carrier code, and last sync timestamp. Create selection fields for enhanced status tracking beyond Odoo’s default states. These extensions provide the data structure necessary for comprehensive tracking management.
Develop the core integration model with methods for API communication. Implement create_trackingmore_shipment to register new packages with Trackingmore. Build sync_tracking_status to retrieve current shipment states. Add helper methods for carrier code mapping and data validation. These methods form the backbone of your integration logic.
Authentication Configuration
Implement secure credential management using Odoo’s configuration parameters. Store the Trackingmore API key in Odoo’s system parameters rather than hardcoded values. This approach enables environment-specific configurations and enhances security. Create a settings page for administrators to update credentials without code changes.
Configure API client initialization with proper error handling. The client should validate connectivity to Trackingmore during Odoo startup. Implement exponential backoff for connection failures to ensure robust startup behavior. Log authentication successes and failures for security monitoring and troubleshooting.
Set up webhook authentication using the verification token from Trackingmore. The token ensures that only legitimate Trackingmore events process through your system. Implement token validation in your webhook controller before processing any incoming requests. Reject requests without valid signatures to prevent injection attacks.
API Endpoint Implementation
Create Trackingmore webhook endpoints within your Odoo module. Develop a controller with routes for tracking updates and carrier notifications. The controller should parse JSON payloads, validate signatures, and queue events for background processing. This design prevents webhook timeouts while maintaining responsive API endpoints.
Implement the tracking/create endpoint for submitting new shipments to Trackingmore. The method should extract carrier information from Odoo’s delivery methods and map them to Trackingmore’s carrier codes. Include customer email addresses for Trackingmore’s notification system. Handle duplicate submission errors gracefully to maintain data integrity.
Build batch synchronization endpoints for manual intervention. Develop an API endpoint that accepts a date range and synchronizes all relevant shipments. This tool proves invaluable for data recovery scenarios or initial bulk imports. The endpoint should return progress information for long-running operations.
Configuration Validation
Develop comprehensive configuration checks to identify setup issues early. Create a diagnostic page that tests API connectivity, webhook configuration, and data mapping consistency. The diagnostic should verify that Trackingmore carriers match Odoo’s delivery methods. Display clear error messages with remediation steps for common configuration problems.
Implement health checks for ongoing monitoring. Create scheduled tasks that verify API quota availability and webhook functionality. These checks should alert administrators before issues impact operations. Track metrics like sync success rates and API response times for performance monitoring.
Document configuration dependencies and prerequisites. Create installation checklists that cover Odoo version compatibility, required Python packages, and necessary system resources. Include troubleshooting guides for common deployment scenarios across different hosting environments.
Data Mapping and Transformation
Carrier Code Mapping
Establish precise mapping between Odoo delivery methods and Trackingmore carrier codes. Odoo identifies carriers using internal names like “fedex” or “ups,” while Trackingmore uses standardized codes like “fedex” and “ups.” Create a mapping table that translates between these systems, accounting for regional variations and custom carriers.
Implement the carrier mapping as a configurable reference table within Odoo. This approach enables administrators to add new carriers without code changes. The table should include both the Odoo delivery method ID and the corresponding Trackingmore code. Include validation to ensure each Odoo carrier maps to exactly one Trackingmore identifier.
Handle edge cases like unknown carriers or multiple Trackingmore codes for a single Odoo method. Develop fallback logic that uses package tracking number patterns to infer the correct carrier. For ambiguous cases, implement a manual resolution process that flags unmappable shipments for administrator review.
Status Transformation Logic
Trackingmore provides detailed status updates that require transformation into Odoo’s delivery states. Map Trackingmore statuses like “pending,” “in transit,” and “delivered” to Odoo’s stock.picking states. Create intermediate states for edge cases like “exception” and “return to sender” that don’t have direct Odoo equivalents.
Develop status transformation rules that consider both the current Trackingmore status and historical events. For example, a “delivered” status should only update the Odoo state if preceded by “out for delivery” events. This logic prevents state oscillations and ensures accurate status representation.
Implement business rules for special status conditions. Create automated actions for “exception” statuses that generate Odoo activities for customer service teams. Develop escalation procedures for packages stuck in “in transit” status beyond expected delivery dates. These rules transform raw tracking data into actionable business intelligence.
Data Enrichment and Augmentation
Enhance basic tracking data with additional context from both systems. Merge Trackingmore’s detailed checkpoint information with Odoo’s customer and order data. Create comprehensive tracking views that show delivery progress alongside order details and customer information.
Develop data augmentation processes that enrich tracking information with business context. Calculate estimated delivery dates based on carrier service levels and shipping dates. Add geographic context by mapping checkpoint locations to regions or cities. This enriched data provides more meaningful tracking experiences for customers.
Implement cross-reference tables that maintain relationships between distributed identifiers. Store the mapping between Odoo picking IDs, Trackingmore tracking numbers, and sales order references. These relationships enable bidirectional navigation between systems and support comprehensive reporting across the order fulfillment lifecycle.
Error Handling and Resilience
Common API Errors
The Trackingmore API returns specific error codes that require targeted handling. HTTP 429 signals rate limit exhaustion, triggering backoff algorithms in your integration. HTTP 400 indicates invalid request parameters, often due to malformed tracking numbers or unsupported carrier codes. HTTP 500 errors represent Trackingmore service issues, requiring retry mechanisms.
Develop specialized exception classes for each error category. Create RateLimitExceededError with built-in retry timing calculations. Build InvalidRequestError for parameter issues with automatic logging of problematic requests. Implement ServiceUnavailableError for server-side issues with exponential backoff retry logic.
Design error recovery workflows that maintain system stability. For rate limit errors, implement queue-based request processing that respects API constraints. For invalid requests, create isolation mechanisms that prevent single bad records from blocking entire sync operations. These workflows ensure graceful degradation under error conditions.
Data Synchronization Conflicts
Handle conflicts that arise when data changes in both systems simultaneously. Implement optimistic concurrency control using version stamps on tracking records. Detect conflicts when webhook events reference older data than current Odoo records. Develop resolution strategies that prioritize the most recent reliable data source.
Create reconciliation processes for data drift scenarios. Build automated jobs that compare Trackingmore and Odoo states for the same shipments. Flag discrepancies for manual review while maintaining audit trails of all state changes. These processes identify systematic issues in the integration logic.
Develop compensation mechanisms for missed webhook events. Implement periodic consistency checks that identify tracking numbers with stale status in Odoo. Create recovery procedures that query Trackingmore directly for missing updates. These mechanisms ensure eventual consistency despite occasional delivery failures.
System Failure Recovery
Build robust recovery procedures for Odoo server restarts or network outages. Implement checkpoint-based resumption for interrupted batch operations. Store sync progress in persistent storage to enable recovery from exact failure points. These procedures prevent data loss during unexpected downtime.
Create data restoration workflows from backup scenarios. Develop export/import functionality for tracking mappings and sync states. Build verification tools that validate data integrity after restoration. These workflows minimize recovery time during severe failure scenarios.
Design circuit breaker patterns to prevent cascade failures. Monitor error rates and response times from Trackingmore API calls. Automatically disable non-critical sync operations during service degradation. This pattern preserves system stability when external dependencies experience issues.
Testing and Validation
Integration Test Scenarios
Develop comprehensive test cases that validate all integration touchpoints. Create test scenarios for successful tracking creation, status updates, and webhook processing. Build error case tests for invalid credentials, rate limiting, and network timeouts. These tests ensure reliable operation under both normal and edge conditions.
Implement end-to-end tests that simulate complete business workflows. Create test orders with various carrier methods and verify tracking propagation through the entire system. Test bulk operations with hundreds of simultaneous tracking updates to validate performance under load. These tests mirror real-world usage patterns.
Build mock Trackingmore services for isolated testing. Develop mock endpoints that simulate Trackingmore API behavior including error responses and rate limiting. Create configurable mock webhooks that generate test events on demand. This approach enables thorough testing without depending on external services.
Data Validation Procedures
Implement rigorous data validation at every integration point. Validate tracking numbers against carrier-specific format rules before submitting to Trackingmore. Verify webhook payload structure and required fields before processing. Check data type consistency and value ranges for all transformed fields.
Create data quality monitors that track validation failure rates over time. Set up alerts for sudden increases in data rejection rates, which often indicate upstream changes. Implement automated data cleansing for common formatting issues like extra spaces or incorrect case. These procedures maintain high data quality throughout the system.
Develop reconciliation reports that compare data across systems. Build daily reports that identify tracking numbers present in one system but missing in the other. Create consistency checks that verify status alignment between Odoo and Trackingmore. These reports provide ongoing validation of integration health.
Performance Benchmarking
Establish performance baselines for critical integration operations. Measure API response times under various load conditions to establish normal operating ranges. Track webhook processing latency to ensure real-time performance requirements. Monitor batch synchronization throughput to verify scalability.
Create load testing scenarios that simulate peak operational volumes. Generate test data representing holiday season shipment volumes to validate system capacity. Measure resource utilization during high-load operations to identify potential bottlenecks. These tests ensure the integration performs under business-critical conditions.
Implement continuous performance monitoring with actionable alerts. Track metrics like API call duration, webhook processing time, and database query performance. Set up dashboards that visualize these metrics alongside business-level indicators. Configure alerts that trigger before performance degradation impacts users.
Security Considerations
Authentication Security
Implement secure API key management with regular rotation policies. Store Trackingmore API keys in Odoo’s encrypted parameters rather than code or database fields. Develop key rotation procedures that update credentials without service interruption. These practices minimize the risk of credential compromise.
Enforce principle of least privilege for integration access. Create dedicated Odoo user accounts with minimal permissions for integration operations. Restrict database access to only the tables required for tracking functionality. Implement API rate limiting at the Odoo level to prevent abuse through your integration.
Secure webhook endpoints with multiple authentication layers. Validate Trackingmore signatures on every webhook request to ensure legitimate source. Implement IP whitelisting if Trackingmore provides static outbound IP addresses. Add timestamp validation to reject replay attacks. These measures prevent unauthorized data manipulation.
Data Protection
Encrypt sensitive tracking data in transit and at rest. Enforce HTTPS for all API communications with Trackingmore. Implement database encryption for tracking numbers and customer information. Apply Odoo’s field-level encryption for particularly sensitive data elements.
Develop data retention policies that comply with privacy regulations. Automatically purge tracking data after operational requirements expire. Implement anonymization procedures for data used in testing or analytics. Create audit trails that track data access for security monitoring.
Secure customer information shared with Trackingmore. Minimize data exposure by sending only necessary customer details to Trackingmore. Implement data masking for sensitive customer information in logs and debug outputs. These practices reduce privacy risks associated with third-party data sharing.
Compliance Requirements
Address GDPR compliance for EU customer data. Implement data processing agreements with Trackingmore that cover GDPR requirements. Develop customer consent mechanisms for tracking communications. Create data export and deletion procedures to honor customer rights.
Maintain PCI DSS compliance for any payment-related data. Isolate tracking functionality from payment processing systems. Implement strict access controls for any systems handling financial data. Conduct regular security assessments to validate compliance.
Document security protocols for audit purposes. Maintain records of security configurations, access controls, and incident responses. Develop security incident playbooks specific to tracking data breaches. These documents demonstrate due diligence during compliance audits.
Performance Optimization
API Call Efficiency
Implement intelligent polling strategies to minimize API calls. Use webhooks for real-time updates instead of continuous polling for status changes. For necessary polling operations, implement incremental synchronization that only queries changed records. These techniques reduce API consumption and improve responsiveness.
Develop request batching for bulk operations. Group multiple tracking numbers into single API calls where supported by Trackingmore’s API. Implement connection pooling for HTTP requests to reduce TCP overhead. These optimizations improve throughput while respecting rate limits.
Create smart caching layers for reference data. Cache carrier codes and service levels to avoid repetitive API lookups. Implement cache invalidation policies that balance freshness with performance. Use Redis or similar in-memory stores for low-latency data access.
Database Optimization
Optimize database queries for tracking data retrieval. Create indexes on tracking numbers, sync timestamps, and carrier codes to accelerate lookups. Implement query batching to reduce database round trips for webhook processing. These optimizations prevent database bottlenecks during high-volume operations.
Design efficient data structures for tracking history storage. Use JSON fields for checkpoint data to avoid expensive joins. Implement partitioning for large tracking history tables based on date ranges. These techniques maintain query performance as data volumes grow.
Develop database maintenance procedures for ongoing performance. Schedule regular vacuum operations for PostgreSQL to reclaim storage and maintain performance. Implement archiving processes that move historical tracking data to separate tables. These procedures prevent performance degradation over time.
System Resource Management
Implement memory-efficient processing for large data sets. Use generator patterns for batch operations to avoid loading entire data sets into memory. Implement streaming processing for webhook payloads to handle large event volumes. These techniques prevent memory exhaustion during peak loads.
Optimize background job processing for better resource utilization. Use Odoo’s queue job system for asynchronous processing of non-critical operations. Implement job prioritization to ensure time-sensitive tracking updates process first. These approaches improve overall system responsiveness.
Monitor and tune server resources for optimal performance. Configure Odoo workers based on your specific tracking volume and concurrency requirements. Implement load balancing for webhook endpoints to distribute incoming traffic. Set up resource monitoring to identify and address performance constraints proactively.