Common Issues

Connection issues

"Cannot read property '1' of null" error

This error typically appears when you're trying to import from a shared base link instead of a shareable grid view link. Coupler.io requires a view-level link, not a base-level link.

Fix:

1. Go back to Airtable.

2. Open the specific view (table/grid) you want to import.

3. Click Share view.

4. Select Create a shareable grid view link, and copy that URL instead.

5. Update your data flow with the correct link.

Data won't load after connecting the link

If you've pasted the correct shareable link but no data appears after running the data flow, the link may have expired or the view may have restrictions.

Fix:

1. Verify the shareable link still works by opening it in a new browser tab.

2. If the view is password-protected, make sure you've entered the password in the "Protected view password" field.

3. Try creating a new shareable link from Airtable and update your data flow.

4. Run a manual test import to confirm the connection.

"SheetAPI error" or "Invalid values" message

This error can occur if your Airtable view contains fields with special characters, errors, or unsupported content (like embedded objects).

Fix:

1. Check your Airtable view for any cells displaying #ERROR! or [object Object].

2. Delete or fix those problematic cells in Airtable.

3. Run the data flow again after cleaning up the data.

4. If the issue persists, try exporting just a few columns at a time to isolate which field is causing the problem.

Missing data

Only some rows or columns are importing

Coupler.io imports only the data visible in your selected Airtable view. If rows or columns are missing, they may be hidden or filtered out in Airtable.

Fix:

1. Go to your Airtable view and remove any active filters.

2. Unhide any hidden columns or records.

3. Make sure the view shows all the data you want to import.

4. Run your data flow again after updating the view.

Zeros and empty values show as blank

Some Airtable field types (like numbers or checkboxes) may import as blank instead of displaying 0 or FALSE. This is a formatting difference between Airtable and some destinations like Google Sheets.

Fix:

1. This is expected behavior for certain field types.

2. In Google Sheets, you can format these cells or use a formula like =IF(ISBLANK(A1),0,A1) to replace blanks with 0.

3. If the data is critical, verify directly in Airtable that the value actually exists.

Multi-select or linked record fields show strange delimiters

If you choose a delimiter that appears in your actual data, the values may split incorrectly or appear concatenated.

Fix:

1. Edit your data flow settings and choose a different delimiter (use a character that doesn't appear in your data, like a pipe | or semicolon ;).

2. Run the data flow again to refresh the data with the new delimiter.

Permission errors

"Access denied" when trying to connect to Airtable view

This error means Coupler.io cannot access your Airtable view link, usually because the link has expired or the view has been deleted.

Fix:

1. Verify the Airtable view still exists in your base.

2. Create a new shareable link (the old one may have expired).

3. Update your data flow with the fresh link.

4. Test the new link by opening it in an incognito browser tab to confirm it's public.

Password-protected view won't import

If you have the password but the import still fails, the password may be incorrect, or there may be a compatibility issue.

Fix:

1. Double-check the password in Airtable.

2. Make sure there are no leading or trailing spaces in the password field.

3. If the password contains special characters, ensure they're entered exactly as set in Airtable.

4. Test by trying to manually access the view with the same password to confirm it works.

Data discrepancies

Dates show different times than in Airtable

Coupler.io imports dates based on your destination's time zone setting (if any), not Airtable's. If your destination and Airtable are in different time zones, times will appear shifted.

Fix:

1. Go to your destination and check time zone settings.

2. Change it to match your intended time zone (or the time zone you want the data displayed in).

3. Run the data flow again to refresh the dates.

Note: Custom formula fields in Airtable will preserve their custom-calculated times.

Large data imports cause timeout errors

If you're importing very large Airtable views (many rows and columns), the import may time out and fail.

Fix:

1. Break your data flows into smaller views – create separate Airtable views with filtered data and import them as separate importers.

2. Reduce the number of columns by hiding unnecessary ones in your Airtable view before importing.

3. Contact support if your data flow consistently times out; there may be server-side optimizations available.

Entire sheet gets overwritten on each refresh

Coupler.io fully rewrites your destination sheet with fresh data from Airtable on every import. This is by design to keep data in sync, but it means any manual edits or additional columns in your Sheet will be lost.

Fix:

1. Create a separate sheet for imported Airtable data and keep your custom analysis in another sheet.

2. Use formulas like =VLOOKUP() or =INDEX/MATCH() to reference the import sheet from your analysis sheet.

3. Set up your import sheet with no manual edits—treat it as a temporary data layer.

Rate limits

Getting rate-limited or throttled during large imports

If you're importing very large Airtable views with thousands of rows, you may hit rate limits from either Airtable or your destination.

Fix:

1. Schedule large imports during off-peak hours (early morning or late evening).

2. Split your import into multiple smaller views or tables.

3. Reduce the frequency of automatic refreshes if you're running many data flows simultaneously.

4. Contact Coupler.io support if you're consistently hitting limits—they may be able to optimize your setup.

"Timeout" or "ddb3" errors on automatic refreshes

Occasional timeout errors can happen when your Airtable view is very large.

Fix:

1. These errors are usually temporary. Try running the data flow again manually after a few minutes.

2. If you see this error frequently, consider scheduling automatic refreshes during quieter times.

3. Contact support if the timeouts persist across multiple refreshes.