Record Explorer

The Record Explorer lets you browse, search, and inspect every synced record directly in the AlgoBridge UI — without writing SQL or opening a database client.

Opening the Explorer

Go to Explorer in the workspace sidebar and select a mapping from the dropdown, or navigate directly:

/t/:workspaceId/explorer/:mappingId

URLs are bookmarkable — your current filters and selected record are preserved in the URL.

Browsing records

The Explorer shows all rows in the mapped PostgreSQL table, with:

• State filter — filter by _ab_lastop value (PENDING, INSERTED, UPDATED, SYNCED, FAILED)
• Search — full-text search across mapped fields
• FAILED rows sorted first — rows in FAILED state always appear at the top of the list regardless of sort order, so errors are immediately visible

Records load with infinite scroll — keep scrolling to load more rows without pagination clicks.

Inspecting a record

Click any row to expand it. Two sub-tabs appear:

Data tab

Side-by-side comparison of the record as it exists in:

• PostgreSQL — the current values in your mapped table
• Salesforce — the live values fetched from the SF API at the time of inspection

Differences between the two sides are highlighted. This is useful for diagnosing sync lag, field-level conflicts, or mapping misconfigurations.

Timeline tab

The full trigger-log history for this record, in chronological order:

The old/new diff is derived from the hstore payload stored in _trigger_log. For INSERT events, “old” is blank and “new” shows the initial field values. For DELETE events, “new” is blank.

Tip: Use the Timeline to trace exactly what changed in a record and when — useful for debugging unexpected Salesforce field values or auditing data modifications by external systems.

Retrying FAILED records

When a record is in FAILED state, the Retry button appears in both the record list and the expanded row view.

Clicking Retry:

1. Resets the most recent _trigger_log row for this record from FAILED back to NEW
2. The sync engine picks it up on the next 10-second cycle
3. The row’s _ab_err is cleared on a successful retry

Retry is useful for transient failures (network timeouts, temporary Salesforce API errors). If the same record keeps failing after multiple retries, check the error message in _ab_err — it usually points to a Salesforce validation rule, required field, or field-level security issue.

You can also retry all FAILED records for a mapping at once from Monitoring → Salesforce Errors → Retry All.

Field sync stats

The Stats tab on a mapping shows the top 10 most frequently changed fields over the last 30 days, based on trigger-log history. This is useful for understanding which fields are driving sync volume and where to focus optimization.

Stats are cached for 5 minutes and recalculated on each cache miss.