lancedb/docs/web/feature-engineering/refresh-column.mdx

---
title: "Refresh a column"
sidebarTitle: "Column Refresh"
description: "Populate a computed column and update it when its inputs change."
icon: arrows-rotate
keywords: ["refresh_column", "backfill", "job", "idempotent", "REFRESH COLUMN"]
availability:
  oss: available
  enterprise: available
  summary: >-
    Both offerings support column refresh. The embedded library runs it in your process; Enterprise runs it as a server job that must be enabled for your Enterprise cluster.
---

Refresh a [computed column](/feature-engineering/add-computed-column) to fill
new rows and recompute values whose inputs changed. The example below continues
with the `doubled` column from the computed-column guide.

<CodeGroup>
```python Python icon="python"
table.refresh_column("doubled")
# RefreshColumnResult(rows_filled=2, version=4)

table.to_arrow().sort_by("x").to_pandas()
#    x  doubled
# 0  1        2
# 1  2        4
```

```sql SQL
REFRESH COLUMN doubled ON my_table
```
</CodeGroup>

The refresh commits ordinary table versions (the values, then a
metadata-only record of the inputs they were computed from), so
[versioning](/management/table#versioning) applies to it like any other write.

<Note>
The SQL form is [`REFRESH COLUMN`](/sql/refresh#refresh-column), with the same resource options.
</Note>

## What a refresh recomputes {#idempotent}

A refresh fills rows that have no value and recomputes rows whose inputs
changed since they were computed. Every other row is left alone, so a second
refresh with nothing changed does nothing. This supports two common workflows.

**Appending is cheap.** Rows added since the last refresh are filled by the
next one; filled rows whose inputs are unchanged are skipped. Backfilling a
column and then ingesting more data does not mean recomputing the backfill.

**A filled value catches up with its input at the next refresh.** If `doubled`
derives from `x` and a row's `x` is later updated, `doubled` keeps its old value
until the next refresh recomputes it.

That is what makes the call idempotent and safe to retry. However, unlike a
spreadsheet formula, nothing is recomputed until you refresh, so a derived
column is stale between an update and the next refresh.

{/*
Draft for automatic computed-column refresh. Keep the released manual-refresh
instructions above until the server feature is available.

## Automatic refresh {#automatic}

Set scheduling options when declaring a computed column:

```python
table.add_columns(
    computed={"doubled": "x * 2"},
    auto_refresh_enabled=True,
    auto_refresh_interval_seconds=300,
)
```

Both options apply to every computed output declared in that call, including
registered Function outputs. Automatic refresh is enabled by default. When
the interval is omitted, the server determines when to refresh. Local tables
persist only an explicitly configured interval; automatic execution requires a
Cloud or Enterprise server with its separate `column_refresh.enabled` switch
turned on. That server switch is disabled in the base configuration.

The interval is a positive minimum delay after the last successful refresh
of that output. A first refresh is immediately eligible. The scheduler still
checks input freshness and waits for computed ancestors to settle; the timer
does not make unchanged inputs require recomputation. Each Function output's
interval is independent, so refreshing a sibling does not postpone it.

Pacing uses retained live Job Registry history. If completed jobs are archived
before a configured interval expires, the scheduler can lose the last-success
timestamp and refresh earlier. Leave archival disabled, or retain live history
for at least the longest configured column interval. Archival is disabled in
the base configuration; its default retention threshold is seven days.

Change a stored setting through SQL:

```sql
ALTER TABLE my_table ALTER COLUMN doubled SET auto_refresh_enabled = false;
ALTER TABLE my_table ALTER COLUMN doubled SET auto_refresh_interval_seconds = 600;
ALTER TABLE my_table ALTER COLUMN doubled SET auto_refresh_enabled = true;
```

Disabling automatic refresh prevents new automatic jobs. Inflight jobs may
finish, and manual refresh remains available. Re-enabling wakes maintenance
to consider accumulated changes under the interval and dependency rules.
*/}

## Refreshing part of a table {#partial}

<Badge color="red">Enterprise</Badge> In SQL a refresh can be scoped to a
predicate, which is useful for filling a slice first and checking it before
committing to the whole table:

```sql
REFRESH COLUMN embedding ON documents WHERE created_at > '2026-01-01'
```

<Note>
**Enterprise job type:** `computed_refresh` for a SQL-expression column.
{/* Draft for the Functions release: `function_refresh` for one filled by a registered [function](/management/function). */}
</Note>

## Run a refresh on Enterprise {#async}

<Badge color="red">Enterprise</Badge>

With an Enterprise cluster that enables computed columns, the refresh runs on the
cluster. `refresh_column` raises
there; use `refresh_column_async`, which returns a [job](/basics/jobs) you can
wait on or poll instead of holding a connection open:

```python
job = table.refresh_column_async("doubled")
result = job.wait()
result.rows_assigned
# 2
job.status()
# 'finished'
```

Poll with `status()`, resolve with `wait()`. `status()` is a point snapshot and
does not raise, which is what makes it safe in a loop; `wait()` raises
`JobFailedError`, `JobCancelledError` or `TimeoutError`.

A long backfill can therefore be started, left, and checked on from a different
process.

### Configure workers and resources {#resources}

SQL exposes the worker budget directly, which is the lever when a backfill is
too slow or too expensive:

```sql
REFRESH COLUMN embedding ON documents
  WITH (cpu_millis = 8000, memory_bytes = 17179869184, gpu_count = 2)
```

`num_workers` sets the count instead of a per-worker budget; the two are
alternatives rather than a pair. `batch_size` controls how many rows a worker
takes at a time, and `error_policy = 'skip'` continues past rows that fail
rather than failing the job.

## When a refresh fails {#failures}

What a failure keeps depends on where the refresh ran. `refresh_column` in your
process writes the column in one commit, so a failure leaves the column as it
was. A refresh job in LanceDB Enterprise commits finished batches as it goes, so some
values can remain after a failure.

Either way, recover by fixing the cause and refreshing again. The new refresh
fills rows that are still empty or whose inputs changed, and normally skips
values that were kept. That is not guaranteed: a refresh can recompute values
that were already written (for example when the record of the inputs they
were computed from was not saved), so do not count on a retry costing only the
rows that did not complete.

{/*
Draft for the Functions release.

For a column filled by a [function](/management/function), the usual cause is
the worker environment rather than the data. See
[environment mismatches](/management/function#mismatch-symptoms) for the
symptoms, which rarely name their cause.
*/}

## Related guides {#next}

<CardGroup cols={2}>
  <Card title="Jobs" icon="hourglass-half" href="/basics/jobs">
    Waiting on, inspecting, and cancelling server-side work.
  </Card>
  <Card title="Refresh a materialized view" icon="table" href="/feature-engineering/materialized-views">
    The same idea, for a whole derived table.
  </Card>
</CardGroup>