lance

Crates.iolance
lib.rslance
version0.10.15
sourcesrc
created_at2022-07-28 07:11:32.95739
updated_at2024-04-18 20:03:56.434762
descriptionA columnar data format that is 100x faster than Parquet for random access.
homepage
repositoryhttps://github.com/lancedb/lance
max_upload_size
id634289
size1,458,242
Rob Meng (chebbyChefNEQ)

documentation

README

# Rust Implementation of Lance Data Format

Lance Logo **A new columnar data format for data science and machine learning**

## Installation Install using cargo: ```shell cargo install lance ``` ## Examples ### Create dataset Suppose `batches` is an Arrow `Vec` and schema is Arrow `SchemaRef`: ```rust use lance::{dataset::WriteParams, Dataset}; let write_params = WriteParams::default(); let mut reader = RecordBatchIterator::new( batches.into_iter().map(Ok), schema ); Dataset::write(reader, &uri, Some(write_params)).await.unwrap(); ``` ### Read ```rust let dataset = Dataset::open(path).await.unwrap(); let mut scanner = dataset.scan(); let batches: Vec = scanner .try_into_stream() .await .unwrap() .map(|b| b.unwrap()) .collect::>() .await; ``` ### Take ```rust let values: Result = dataset.take(&[200, 199, 39, 40, 100], &projection).await; ``` ### Vector index Assume "embeddings" is a FixedSizeListArray ```rust use ::lance::index::vector::VectorIndexParams; let params = VectorIndexParams::default(); params.num_partitions = 256; params.num_sub_vectors = 16; // this will Err if list_size(embeddings) / num_sub_vectors does not meet simd alignment dataset.create_index(&["embeddings"], IndexType::Vector, None, ¶ms, true).await; ``` ## 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.
Commit count: 1475

cargo fmt