--- title: "Refresh a computed column" sidebarTitle: "Refresh Computed Column" description: "Populate a computed column and update it when its inputs change." icon: arrows-rotate keywords: ["refresh_column", "backfill", "job", "idempotent", "REFRESH COLUMN"] availability: embedded: unavailable enterprise: available --- 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. Computed columns require LanceDB Enterprise. ```python Python icon="python" job = table.refresh_column_async("doubled") job.wait() table.to_arrow().sort_by("x").to_pandas() # x doubled # 0 1 2 # 1 2 4 ``` ```sql SQL icon="database" REFRESH COLUMN doubled ON my_table ``` `table.refresh_column(...)` (sync) is also a local, blocking form, but it raises on an Enterprise connection -- use `refresh_column_async`, shown above, or the SQL form instead. See [Run a refresh on Enterprise](#async) below for what the returned job supports. 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. The SQL form is [`REFRESH COLUMN`](/sql/dml/refresh-column), with the same resource options. ## What a refresh recomputes {#idempotent} Without a `WHERE` predicate, a refresh fills unassigned rows and recomputes values 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. ## Automatic refresh {#automatic} A computed column can refresh itself on a schedule instead of waiting for an explicit call here. See [Auto Refresh](/enterprise/auto-refresh) for enabling it, the SQL and SDK forms for changing the setting, and how catch-up works when it's re-enabled after being off. ## Refreshing part of a table {#partial} Enterprise 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 SQL icon="database" REFRESH COLUMN embedding ON documents WHERE created_at > '2026-01-01' ``` A filtered refresh recomputes **every matching live row**, including rows whose outputs are already current. Repeating the same predicate repeats those Function calls and their cost. To select only null outputs within a slice: ```sql SQL icon="database" REFRESH COLUMN embedding ON documents WHERE created_at > '2026-01-01' AND embedding IS NULL ``` This skips non-null outputs even when their inputs changed, and can recompute outputs that were successfully computed as `NULL`. Use an unfiltered refresh to let LanceDB track which outputs need updating. **Enterprise job type:** `computed_refresh` for a SQL-expression column, `function_refresh` for one filled by a registered [function](/management/function). ## Run a refresh on Enterprise {#async} Enterprise 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 Python icon="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 SQL icon="database" REFRESH COLUMN embedding ON documents WITH (cpu_millis = 8000, memory_bytes = 17179869184, gpu_count = 2) ``` The resource values cap the Function workers together: this example allows up to 8 CPUs, 16 GiB of memory, and 2 GPUs across the fleet. They are not per-worker allocations or a guaranteed worker count. `num_workers` is only accepted for SQL-expression columns; registered Function columns reject it. When setting CPU or memory, supply all three budget fields, using `gpu_count = 0` for a CPU-only Function. `batch_size` caps rows in one Function call; LanceDB adapts actual call and work sizes. For Function columns, the refresh defaults to `error_policy = 'fail'`. Set `error_policy = 'skip'` to write null outputs for failing rows and record them in the error store, subject to failure limits. See the [SQL refresh options](/sql/dml/refresh-column#options). {/* Draft for the Function retry policy release. For registered Function columns, pass `retry_policy` to `refresh_column_async` to replace the Function's registered retry policy for this refresh. For example, `retry_policy=FunctionRetryPolicy(transient=True, max_attempts=3)` allows three attempts, including the first call. Import `FunctionRetryPolicy` from `lancedb.functions`. Use `FunctionRetryPolicy(max_attempts=1)` to disable user-error retries. Omitting the override preserves the registered policy. This requires a supporting LanceDB Enterprise server as well as an SDK that exposes these options. An older Function image may need re-registration before it accepts a refresh override. Only enable retries when repeating a failed call's partial work is safe, including any external requests made before the error. The attempt budget belongs to one failed Function call. With an explicit retry policy and `error_policy="skip"`, exhaustion in a batch Function writes nulls for the whole failed call and records its errors, subject to failure limits. It does not split the batch to restart the retry budget. Row Functions isolate failures per row. Infrastructure recovery, framework deadlines and cancellation remain separate from this user-error policy. See [Function failure handling](/management/function#failures) for the retry and replay boundaries. */} For a column filled by a registered Function, the Python and Rust job APIs can override the Function's GPU placement for this refresh: ```python classes = db.list_gpu_classes() gpu_class = next(gpu.name for gpu in classes if gpu.gpu_model == "NVIDIA H100") job = table.refresh_column_async( "embedding", gpu_class=gpu_class, gpu_count=4, batch_size=32, ) ``` ```rust use lancedb::{Connection, Table, table::RefreshColumnOptions}; async fn refresh( conn: &Connection, table: &Table, ) -> Result<(), Box> { let gpu_class = conn .list_gpu_classes() .await? .into_iter() .find(|class| class.gpu_model.as_deref() == Some("NVIDIA H100")) .map(|class| class.name) .ok_or("H100 GPU class is not configured")?; let _job = table .refresh_column_async_with_options( "embedding", RefreshColumnOptions::default() .gpu_class(gpu_class) .gpu_count(4) .batch_size(32), ) .await?; Ok(()) } ``` `gpu_count` is the aggregate GPU budget for the refresh's Function workers, not a request for that many GPUs per worker. It must be positive. `gpu_class` must name a class returned by `list_gpu_classes()`; class names are lowercase DNS labels. Omit either option to use the Function's registered class or the server's configured budget. `list_gpu_classes()` requires System Usage permission, and raises `NotImplementedError` when the server predates GPU class discovery. The Rust GPU discovery and refresh controls are not in the latest Rust release. TypeScript does not expose GPU class discovery or placement; a tracking issue is needed before adding equivalent controls. ## When a refresh fails {#failures} What a failure keeps depends on where the refresh ran. The local, blocking `refresh_column` writes the column in one commit, so a failure leaves the column as it was. A refresh job in LanceDB Enterprise -- always `refresh_column_async` or `REFRESH COLUMN` there, never the blocking form -- 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. 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} Waiting on, inspecting, and cancelling server-side work. The same idea, for a whole derived table. Refresh on a schedule instead of calling this yourself.