---
title: "Insert data"
sidebarTitle: "Insert"
description: "Append rows to a table, including datasets too large to hold in memory."
icon: plus
---

Rows are appended with `table.add()`. It accepts the same shapes
[table creation](/ingestion) does — lists of objects, Arrow tables, pandas
and polars frames, Pydantic models — and commits a new table version.

These examples use the table set up in [Ingesting data](/ingestion).

import {
    PyAddFromDataset as AddFromDataset,
    PyCreateTableFromIterator as CreateTableFromIterator,
    TsCreateTableFromIterator as TsCreateTableFromIterator,
    RsCreateTableFromIterator as RsCreateTableFromIterator,
    TsAddProgress,
} from '/snippets/tables.mdx';

## Ingesting large datasets {#large-datasets}

When ingesting large datasets, use `table.add()` on an existing table rather than
passing all data to `create_table()`. The `add()` method auto-parallelizes large
writes, while `create_table(name, data)` does not.

<Tip>
For best performance with large datasets, create an empty table first and then call
`table.add()`. This enables automatic write parallelism for materialized data sources.
</Tip>

### From files (Parquet, CSV, etc.) {#from-files-parquet-csv-etc}
<Badge color="green">Python Only</Badge>

For file-based data, pass a `pyarrow.dataset.Dataset` to `table.add()`. This streams
data from disk without loading the entire dataset into memory.

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {AddFromDataset}
    </CodeBlock>
</CodeGroup>

<Note>
`pa.dataset()` input is currently Python-only. TypeScript and Rust support for
file-based dataset ingestion is tracked in
[lancedb#3173](https://github.com/lancedb/lancedb/issues/3173).
</Note>

### From iterators (custom batch generation) {#from-iterators-custom-batch-generation}

When you need custom batch logic — generating embeddings on the fly, transforming
rows from an external source, etc. — use an iterator of `RecordBatch` objects.

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {CreateTableFromIterator}
    </CodeBlock>

    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {TsCreateTableFromIterator}
    </CodeBlock>

    <CodeBlock filename="Rust" language="Rust" icon="rust">
    {RsCreateTableFromIterator}
    </CodeBlock>
</CodeGroup>

Use this pattern when:

- Your source data already arrives in Arrow batches, readers, datasets, or streams.
- Materializing the entire ingest as one giant in-memory list or array would be too expensive.
- You want to control chunk size explicitly during ingestion.

Python can also consume iterators of other supported types like Pandas DataFrames or Python lists.

### Write parallelism {#write-parallelism}

<Note title="Automatic parallelism">
For materialized data (`pa.Table`, `pd.DataFrame`, `pa.dataset()`), LanceDB
automatically parallelizes large writes — no configuration needed. Auto-parallelism
targets approximately 1M rows or 2GB per write partition.

For streaming sources (iterators, `RecordBatchReader`), LanceDB cannot determine
total size upfront. A `parallelism` parameter to control this manually is planned
but not yet exposed in Python or TypeScript
([tracking issue](https://github.com/lancedb/lancedb/issues/3173)).
</Note>

### Tracking ingestion progress {#tracking-ingestion-progress}
<Badge color="green">TypeScript Only</Badge>

For long-running writes, pass a `progress` callback to `table.add()` to surface
per-batch progress in your UI, logs, or metrics pipeline. The callback fires
once per batch written and once more with `done: true` when the write completes.

Each invocation receives a `WriteProgress` object:

| Field | Description |
|:------|:------------|
| `outputRows` | Rows written so far. |
| `outputBytes` | Bytes written so far. |
| `totalRows` | Expected total rows when the input source reports one. Always set on the final callback. |
| `elapsedSeconds` | Wall-clock seconds since the write started. |
| `activeTasks` | Parallel write tasks currently in flight. |
| `totalTasks` | Total parallel write tasks (the write parallelism). |
| `done` | `true` only for the final callback. |

<CodeGroup>
    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {TsAddProgress}
    </CodeBlock>
</CodeGroup>

A few things to know before you wire this up:

- Back-pressures the writer: callback invocations are serialized and run inline with each batch, so a slow callback will slow the write rather than drop updates. Every batch update is delivered, and the final `done: true` callback always fires (even on error or cancellation). Keep the callback cheap — offload heavy work to a queue you drain elsewhere.
- Errors swallowed: anything your callback throws is logged with `console.warn` and won't abort the write, so keep the callback side-effect-only and don't rely on it for control flow.
- Row totals: `totalRows` is only populated when the input source can report it up front (for example, a materialized `arrow.Table`). For streaming sources it stays `undefined` until the final callback, where it falls back to the actual rows written.