pylance 0.3.2

python wrapper for lance-rs

Python bindings for Lance Data Format

:warning: Under heavy development

Lance is a new columnar data format for data science and machine learning

Why you should use Lance

1. Is order of magnitude faster than parquet for point queries and nested data structures common to DS/ML
2. Comes with a fast vector index that delivers sub-millisecond nearest neighbors search performance
3. Is automatically versioned and supports lineage and time-travel for full reproducibility
4. Integrated with duckdb/pandas/polars already. Easily convert from/to parquet in 2 lines of code

Quick start

Installation

pip install pylance

Converting to Lance

import lance

import pandas as pd
import pyarrow as pa
import pyarrow.dataset

df = pd.DataFrame({"a": [5], "b": [10]})
uri = "/tmp/test.parquet"
tbl = pa.Table.from_pandas(df)
pa.dataset.write_dataset(tbl, uri, format='parquet')

parquet = pa.dataset.dataset(uri, format='parquet')
lance.write_dataset(parquet, "/tmp/test.lance")

Reading Lance data

dataset = lance.dataset("/tmp/test.lance")
assert isinstance(dataset, pa.dataset.Dataset)

Pandas

df = dataset.to_table().to_pandas()

DuckDB

import duckdb

tbl = dataset.to_table()  # next release of duckdb will have pushdowns enabled
duckdb.query("SELECT * FROM tbl LIMIT 10").to_df()

Vector search

Download an indexed sift dataset, and unzip it into vec_data.lance

# Get top 10 similar vectors
import lance
import duckdb
import numpy as np

uri = "vec_data.lance"
dataset = lance.dataset(uri)

# Sample 100 query vectors
tbl = dataset.to_table()
sample = duckdb.query("SELECT vector FROM tbl USING SAMPLE 100").to_df()
query_vectors = np.array([np.array(x) for x in sample.vector])

# Get nearest neighbors for all of them
rs = [dataset.to_table(nearest={"column": "vector", 
                                "k": 10, 
                                "q": query_vectors[i, :]}) 
      for i in range(query_vectors.shape[0])]

For the fast indexing capability, you can and run the same code as above. We're working on a more convenient indexing tool via python.

*More distance metrics, supported types, and compute integration coming

Python package details

Install from PyPI: pip install pylance # >=0.3.0 is the new rust-based implementation Install from source: maturin develop (under the /python directory)

Import via: import lance

The python integration is done via pyo3 + custom python code:

1. We make wrapper classes in Rust for Dataset/Scanner/RecordBatchReader that's exposed to python.
2. These are then used by LanceDataset / LanceScanner implementations that extend pyarrow Dataset/Scanner for duckdb compat.
3. Data is delivered via the Arrow C Data Interface

Motivation

Why do we need a new format for data science and machine learning?

1. Reproducibility is a must-have

Versioning and experimentation support should be built into the dataset instead of requiring multiple tools.
It should also be efficient and not require expensive copying everytime you want to create a new version.
We call this "Zero copy versioning" in Lance. It makes versioning data easy without increasing storage costs.

2. Cloud storage is now the default

Remote object storage is the default now for data science and machine learning and the performance characteristics of cloud are fundamentally different.
Lance format is optimized to be cloud native. Common operations like filter-then-take can be order of magnitude faster using Lance than Parquet, especially for ML data.

3. Vectors must be a first class citizen, not a separate thing

The majority of reasonable scale workflows should not require the added complexity and cost of a specialized database just to compute vector similarity. Lance integrates optimized vector indices into a columnar format so no additional infrastructure is required to get low latency top-K similarity search.

4. Open standards is a requirement

The DS/ML ecosystem is incredibly rich and data must be easily accessible across different languages, tools, and environments. Lance makes Apache Arrow integration its primary interface, which means conversions to/from is 2 lines of code, your code does not need to change after conversion, and nothing is locked-up to force you to pay for vendor compute. We need open-source not fauxpen-source.