Lance User Guide

cargo add lance

Or add it to Cargo.toml (choose an appropriate version for your project):

[dependencies]
lance = "x.y"

From source (this repository):

maturin develop -m python/Cargo.toml

Minimal intake (ask only what you need)

Collect the minimum information required to avoid wrong guidance:

• Language/API surface: Python / Rust / CLI
• Storage: local filesystem / S3 / other object store
• Workload: scan-only / filter-heavy / vector search / hybrid (vector + filter)
• Vector details (if applicable): dimension, metric (L2/cosine/dot), latency target, recall target
• Update pattern: mostly append / frequent overwrite / frequent deletes/updates
• Data scale: approximate row count and whether there are many small files

If the user does not specify a language, default to Python examples and provide a short mapping to Rust concepts.

Workflow decision tree

1. If the question is "How do I write or update data?": use the Write playbook.
2. If the question is "How do I read / scan / filter?": use the Read playbook.
3. If the question is "How do I do kNN / vector search?": use the Vector search playbook.
4. If the question is "Which index should I use?": consult references/index-selection.md and confirm constraints.
5. If the question is "Why is this slow / why are results missing?": use Troubleshooting and ask for a minimal reproduction.

Primary playbooks (Python)

Write

Prefer lance.write_dataset for most user workflows.

import lance
import pyarrow as pa

vectors = pa.array(
    [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
    type=pa.list_(pa.float32(), 3),
)
table = pa.table({"id": [1, 2], "vector": vectors, "category": ["a", "b"]})

ds = lance.write_dataset(table, "my-data.lance", mode="create")
ds = lance.write_dataset(table, "my-data.lance", mode="append")
ds = lance.write_dataset(table, "my-data.lance", mode="overwrite")

Validation checklist:

• Re-open and count rows: lance.dataset(uri).count_rows()
• Confirm schema: lance.dataset(uri).schema

Notes:

• Use storage_options={...} when writing to an object store URI.
• If the user mentions non-atomic object stores, mention commit_lock and point them to the user guide.

Read

Use lance.dataset + scanner(...) for pushdowns (projection, filter, limit, nearest).

import lance

ds = lance.dataset("my-data.lance")
tbl = ds.scanner(
    columns=["id", "category"],
    filter="category = 'a' and id >= 10",
    limit=100,
).to_table()

Validation checklist:

• If performance is the concern, ask for a minimal scanner(...) call that reproduces it.
• If correctness is the concern, ask for the exact filter string and whether prefilter is enabled (when using nearest).

Vector search (nearest)

Run vector search with scanner(nearest=...) or to_table(nearest=...).

import lance
import numpy as np

ds = lance.dataset("my-data.lance")
q = np.array([1.0, 2.0, 3.0], dtype=np.float32)
tbl = ds.to_table(nearest={"column": "vector", "q": q, "k": 10})

If combining a filter with vector search, decide whether the filter must run before the vector query:

• Use prefilter=True when the filter is highly selective and correctness (top-k among filtered rows) matters.
• Use prefilter=False when the filter is not very selective and speed matters, and accept that results can be fewer than k.

tbl = ds.scanner(
    nearest={"column": "vector", "q": q, "k": 10},
    filter="category = 'a'",
    prefilter=True,
).to_table()

Build a vector index

Create a vector index with LanceDataset.create_index(...).

Start with a minimal working configuration:

ds = lance.dataset("my-data.lance")
ds = ds.create_index(
    "vector",
    index_type="IVF_PQ",
    target_partition_size=8192,
    num_sub_vectors=16,
)

Then verify:

• ds.describe_indices() (preferred) or ds.list_indices() (can be expensive)
• A small nearest query that uses the index

For parameter selection and tuning, consult references/index-selection.md.

Build a scalar index

Scalar indices speed up scans with filters. Use create_scalar_index for a stable entry point.

ds = lance.dataset("my-data.lance")
ds.create_scalar_index("category", "BTREE", replace=True)

Then verify:

• ds.describe_indices()
• A representative scanner(filter=...) query

To choose a scalar index type (BTREE vs BITMAP vs LABEL_LIST vs NGRAM vs INVERTED, etc.), consult references/index-selection.md.

Troubleshooting patterns

"Vector search + filter returns fewer than k rows"

• Explain the difference between post-filtering and pre-filtering.
• Suggest prefilter=True if the user expects top-k among filtered rows.

"Index creation is slow"

• Confirm vector dimension and num_sub_vectors.
• For IVF_PQ, call out the common pitfall: avoid misaligned dimension / num_sub_vectors (see references/index-selection.md).

"Scan is slow even with a scalar index"

• Ask whether the filter is compatible with the index (equality vs range vs text search).
• Suggest checking whether scalar index usage is disabled (use_scalar_index=False).

Local verification (when a repo checkout is available)

When answering API questions, confirm the exact signature and docstrings locally:

• Python I/O entry points: python/python/lance/dataset.py (write_dataset, LanceDataset.scanner)
• Vector indexing: python/python/lance/dataset.py (create_index)
• Scalar indexing: python/python/lance/dataset.py (create_scalar_index)

Use targeted search:

rg -n "def write_dataset\\b|def create_index\\b|def create_scalar_index\\b|def scanner\\b" python/python/lance/dataset.py

Bundled resources

• Index selection and tuning: references/index-selection.md
• I/O and versioning cheat sheet: references/io-cheatsheet.md
• Runnable minimal example: scripts/python_end_to_end.py