Why Are Loaded Rows Higher Than Extracted Rows?
Question
Why does my connection's Sync History chart show more loaded rows than extracted rows after a successful sync, even though the destination data is correct?
Environment
All connectors
Answer
This behavior can occur when a successful sync loads data extracted during a previous canceled, failed, or interrupted sync.
We track these metrics at different stages of the sync:
- Extracted rows counts the rows extracted from the source during a sync.
- Loaded rows counts the rows loaded into the destination during a sync, including any data extracted during previous syncs.
If a sync stops after extracting data, that data may remain in the pipeline queue and load during a subsequent sync. For example:
| Sync | Extracted Rows | Loaded Rows |
|---|---|---|
| Sync 1 (Cancelled) | 5,000,000 | 0 |
| Sync 2 (Successful) | 10,000,000 | 15,000,000 |
In this example, the successful sync loads 10 million newly extracted rows and 5 million rows extracted during the canceled sync. This difference doesn't necessarily indicate duplicate records or a data discrepancy in the destination.
To check whether previous syncs explain the difference:
- In Fivetran, go to the relevant connection overview page.
- Select the Status tab.
- In the Event type list, select the successful sync.
- Review the Extracted rows and Loaded rows counts.
- Select any preceding canceled, failed, or interrupted syncs and review their counts.
- Check whether rows extracted but not loaded during those earlier syncs account for the additional Loaded Rows in the successful sync.
When you view a successful sync's details, the Extracted rows and Loaded rows are raw counts before deduplication. They don't necessarily represent unique records in the source or destination tables. For more information, see Table details drawer.
If previous sync activity accounts for the difference and the destination data is correct, no action is required. If previous sync activity doesn't explain the discrepancy, or you find missing or duplicate records in the destination, contact our support team.
For more information, see the Fivetran Sync Overview.