Troubleshooting Common ExigoBatch Errors and FixesExigoBatch is a powerful tool for batch processing — importing orders, updating inventory, and automating repetitive tasks for ecommerce platforms. Like any integration-heavy system, it can produce a variety of errors arising from data issues, configuration mismatches, network problems, or third-party API changes. This article walks through the most common ExigoBatch errors, how to diagnose them, and practical fixes to get your batches running reliably.
1. General Troubleshooting Approach
Before diving into specific errors, follow a structured troubleshooting workflow:
- Check the batch log immediately after a failure — ExigoBatch logs give timestamps, error codes, and often the offending record number.
- Reproduce the issue with a smaller test file (1–5 records) to isolate the problematic row(s).
- Validate your CSV/XML against the expected schema and required fields.
- Confirm environment-level settings: API keys, endpoints, time zone, account credentials, and permissions.
- If the batch depends on third-party APIs (payment gateways, shipping providers), check their status pages for outages or API changes.
- Use version control for batch templates and transformation scripts so you can roll back recent changes.
2. Authentication and Permission Errors
Symptoms:
- Authentication failed, invalid API key, or ⁄403 HTTP responses in logs.
- Actions succeed for some accounts but fail for others.
Causes:
- Expired/rotated API key or credentials.
- Account-level permissions missing (user lacks write/update access).
- IP whitelisting or firewall rules blocking requests.
Fixes:
- Confirm the API key and secret are correct and not expired. Replace keys in ExigoBatch configuration if rotated.
- Verify the user or service account has the necessary permissions (read/write/create) for the resources being modified.
- If your provider uses IP whitelisting, add ExigoBatch’s outgoing IPs or use a proxy with an allowed IP.
- For OAuth flows, ensure refresh tokens are valid and token refresh logic is working.
3. Schema and Field Validation Errors
Symptoms:
- Batch fails with messages like “Missing required field,” “Invalid data type,” or row-level validation errors.
- Partial success: some rows processed while others are rejected.
Causes:
- CSV/XML columns don’t match expected names or order.
- Missing required fields (e.g., SKU, quantity, customer email).
- Incorrect data types or formats (dates, decimals, country codes).
- Leading/trailing whitespace or invisible characters in fields.
Fixes:
- Use the current schema/template provided by Exigo or your integration docs. Ensure exact column headers and required fields are present.
- For date/time fields, convert to the required format (ISO 8601 is common). Use consistent decimal separators (period vs comma).
- Trim whitespace and remove non-printable characters before upload. Many spreadsheet apps add invisible line breaks—run a cleanup script or use a text editor that shows hidden characters.
- If optional fields are problematic, explicitly include them as blank values or remove the column entirely if the template allows it.
4. Duplicate or Unique Constraint Violations
Symptoms:
- Errors like “Duplicate order ID,” “Unique key violation,” or “Record already exists.”
- Batches partially applied; duplicates cause rollback of dependent rows.
Causes:
- Reprocessing the same file without deduplication.
- Client-side generation of IDs that collide with existing records.
- Concurrency where multiple batch jobs create the same entity.
Fixes:
- Implement an idempotency mechanism — include a unique client-side batch ID or order reference that ExigoBatch can use to detect repeats.
- Query the system for existing records before creation; change logic to update rather than create when appropriate.
- Serialize critical operations or use optimistic locking where available.
- Keep a processing log that records successfully processed rows to avoid re-submitting them.
5. Data Mapping and Transformation Errors
Symptoms:
- Records processed but with wrong values (e.g., incorrect SKU mapping, wrong price, mismapped customer fields).
- Downstream failures in fulfillment or billing due to incorrect transformed data.
Causes:
- Mapping configuration errors between source columns and ExigoBatch fields.
- Transformation scripts (e.g., lookups, regex replacements) contain bugs.
- Outdated reference data (SKU lists, pricebooks).
Fixes:
- Review mapping configuration and run a small test with known inputs and expected outputs.
- Add unit tests for transformation scripts and keep them in version control.
- Maintain a synchronized reference dataset (SKUs, tax codes) and validate inputs against it during batching.
- Use logging within transformations to capture before/after values for troubleshooting.
6. File Encoding and Parsing Errors
Symptoms:
- “Unable to parse file,” garbled characters, or Unicode errors.
- Special characters missing or replaced with question marks.
Causes:
- Wrong file encoding (UTF-8 vs ANSI/Windows-1252).
- Misdeclared file type (e.g., Excel file saved as CSV with semicolons).
- Line-ending mismatches between OSs (LF vs CRLF).
Fixes:
- Always save and upload files as UTF-8 without BOM unless ExigoBatch requires another encoding.
- Open the file in a plain-text editor to inspect delimiters and headers. Ensure the correct delimiter (comma, tab, semicolon) is used.
- Normalize line endings before upload. Many scripting languages and editors can do this automatically.
- If using Excel, export as CSV UTF-8 and confirm quoting around fields with commas.
7. Network and Timeout Issues
Symptoms:
- Timeouts, partial uploads, 5xx server errors, or intermittent failures.
- Long-running batches fail mid-process.
Causes:
- API rate limits or throttling by the target service.
- Large payloads exceeding allowed size or exceeding execution time.
- Unreliable network or temporary provider outages.
Fixes:
- Implement retry logic with exponential backoff for transient errors.
- Chunk large files into smaller batches and process sequentially or in controlled parallelism.
- Respect provider rate limits; add throttling in your batching process.
- Monitor network health and schedule large batch runs during off-peak hours.
8. Transactional Failures and Rollbacks
Symptoms:
- Entire batch rolls back after a single row failure.
- Inconsistent state where some related resources exist while others do not.
Causes:
- Atomic transactions configured for entire batch processing.
- Dependent operations where failure in step N causes rollback.
Fixes:
- Where acceptable, switch to partial-commit modes that allow successful rows to persist while rejected rows are reported.
- Break dependent changes into separate, idempotent steps: create core record first, then update ancillary data in subsequent batches.
- Improve error reporting so failing rows are isolated and logged for retry.
9. Third-Party Integration Errors
Symptoms:
- Failures referencing payment gateway, shipping provider, tax engine, or external services.
- Errors begin after a vendor API change or certificate update.
Causes:
- API contract changes, authentication updates, deprecated endpoints, or certificate expiry.
- Rate limits or unexpected provider-side throttling.
Fixes:
- Check vendor release notes and update integration code to match new API contracts.
- Ensure TLS certificates are up to date and that your environment trusts the provider’s CA chain.
- Implement vendor-specific retries and proper error handling for transient 5xx responses.
10. Performance and Scalability Problems
Symptoms:
- Slow processing, high memory/CPU usage, long queue backlogs.
- Time-sensitive tasks missing SLAs.
Causes:
- Inefficient parsing or transformation logic.
- Unbounded parallelism causing resource contention.
- Large volumes processed at peak times.
Fixes:
- Profile batch processing to find bottlenecks (parsing, DB writes, network calls). Optimize the slowest components.
- Use worker pools and limit concurrency based on observed resource limits.
- Cache static reference data in memory for repeated lookups (SKU lists, tax rules).
- Consider offloading heavy operations (e.g., large image handling) to asynchronous background jobs.
11. Logging, Monitoring, and Alerting Best Practices
- Centralize logs (structured JSON logs) with correlation IDs for each batch to trace related events.
- Emit metrics: success rate, average processing time, failure counts by error type.
- Alert on sudden increases in failures, processing time spikes, or API error rates.
- Keep a dashboard for queue depth and recent batch status to spot regressions early.
12. Sample Troubleshooting Checklist (Quick)
- Inspect batch log and note error codes and row numbers.
- Run a small test file replicating the error.
- Validate file schema, encoding, and delimiters.
- Confirm credentials, permissions, and provider status.
- Check for duplicates and ensure idempotency.
- Retry with exponential backoff for transient network errors.
- Isolate transformations and test mapping logic.
- Document fixes and add regression tests.
13. When to Contact Support
Contact ExigoBatch support or your integration partner when:
- Errors reference internal server codes or stack traces you can’t resolve.
- You suspect a provider-side outage or need account-level changes.
- You need a feature change (idempotency support, partial-commit behavior) that the platform must implement.
Include in your support ticket:
- Full batch log, sample failing rows, timestamp, and batch template version.
- Environment details (API keys obfuscated, endpoint URLs, account IDs).
- Steps to reproduce and a small test file that triggers the issue.
Troubleshooting ExigoBatch effectively is mostly about methodical isolation: reproduce the problem with minimal data, validate inputs against the schema, and progressively expand testing while monitoring logs and provider status. With idempotency, clear mappings, robust retries, and good observability, most common errors can be prevented or quickly resolved.
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