> ## Documentation Index > Fetch the complete documentation index at: https://lancedb-bcbb4faf-mintlify-6c016f70.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Lance format > Open-source lakehouse format for multimodal AI. [Lance](https://lance.org/) is an open-source, columnar lakehouse format for multimodal AI. It provides a file format, table format, and lightweight catalog spec, allowing developers to build a complete open lakehouse on top of object storage. Building on top of open foundations and optimizing the format for random access (without compromising scan performance) enables high-performance vector search, full-text search, indexing, and feature engineering capabilities. [LanceDB](/enterprise) builds on these capabilities so teams can work with one multimodal data layer instead of moving data across separate storage, search, feature, and training systems. Visit the Lance format documentation to learn more about its design, features, and how it enables the multimodal lakehouse. ## Capabilities of the Lance format | Capability | What it enables | | ------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | | Multimodal storage | Store images, video, audio, text, embeddings, annotations, metadata, features, and more, all in one table. | | First-class blob API | Store large binary objects such as images, video, audio, and model artifacts in blob columns with lazy reads and streaming byte access. | | Fast random access and scans | Sample, shuffle, and retrieve individual rows efficiently without giving up high-throughput sequential reads. | | Flexible data evolution | Add, drop, rename, or alter columns as datasets change, often without rewriting existing data files. | | Versioned tables | Reproduce experiments, restore previous states, and tie downstream artifacts to the exact table version they used. | | Hybrid search and indexing | Combine vector search, full-text search, and scalar filters on the same dataset with Lance indexes. | | Open lakehouse interoperability | Build on object storage and connect Lance tables to open engines such as PyTorch, Ray, Spark, Trino, DuckDB and Polars. | ## Key concepts The following concepts are core to the Lance format: **Arrow-native, columnar storage** and **interoperability** with the open lakehouse ecosystem (including other file formats and compute engines). **Zero-copy** data evolution, meaning you can easily add derived columns (like features or embeddings) at a later time, **without full table rewrites**. Only new data is written; expensive existing data (like images/videos) remain untouched. Data is **versioned**, with each insert operation creating a new version of the dataset and an update to the manifest that tracks versions via metadata ### Data versioning Data in Lance tables are versioned -- this helps keep LanceDB scalable and consistent. We do not immediately blow away old versions when creating new ones because other clients might be in the middle of querying the old version. It's important to retain older versions for as long as they might be queried. Each version contains metadata and just the new/updated data in your transaction. So if you have 100 versions, they aren't 100 duplicates of the same data. However, they do have 100x the metadata overhead of a single version, which can result in slower queries. ### Data compaction As you insert more data, your dataset will grow and you'll need to perform compaction to maintain query throughput (i.e., keep latencies down to a minimum). Compaction is the process of merging fragments together to reduce the amount of metadata that needs to be managed, and to reduce the number of files that need to be opened while scanning the dataset. Running compaction on a Lance dataset will do the following: * Remove deleted rows from fragments * Remove dropped columns from fragments * Merge small fragments into larger ones Compaction focuses on read performance, not immediate disk reclamation. During compaction, Lance writes new compacted files while older files are still referenced by previous table versions. This means disk usage can increase temporarily until old versions are cleaned up. ### Data deletion and recovery Although Lance allows you to delete rows from a dataset, it does not actually delete the data immediately. It simply marks the row as deleted in the `DataFile` that represents a fragment. For a given version of the dataset, each fragment can have up to one deletion file (if no rows were ever deleted from that fragment, it will not have a deletion file). This is important to keep in mind because it means that the data is still there, and can be recovered if needed, as long as that version still exists based on your backup policy. Lance is a separate open source project. Check out its documentation to learn more.