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

---
title: "Refresh a column"
sidebarTitle: "Column Refresh"
description: "Fill the rows of a computed column that lack values — in process, or as a server-side job."
icon: arrows-rotate
keywords: ["refresh_column", "backfill", "job", "idempotent", "REFRESH COLUMN"]
availability:
  oss: available
  enterprise: varies
  summary: >-
    In the embedded library a refresh runs in your process. On a deployment it
    runs as a server job, which has to be enabled there.
---

A [declared column](/feature-engineering/add-computed-column) starts empty. A
refresh gives values to the rows that lack them.

<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. Two consequences follow, and both
are load-bearing.

**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 -- but unlike a
spreadsheet formula, nothing is recomputed until you refresh, so a derived
column is stale between an update and the next refresh.

## 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.
`function_refresh` for one filled by a registered [function](/management/function). {/* if functions launched */}
</Note>

## As a job {#async}

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

Against a deployment 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.

### Resourcing the job {#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 on a deployment 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.

{/* if functions launched */}
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.
{/* end if */}

## What to read next {#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>