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## Summary Split out from #3354 Adds `LsmWriteSpec` and `Table::set_lsm_write_spec` / `unset_lsm_write_spec` to install and clear the spec that selects Lance's MemWAL LSM-style write path for `merge_insert`. `LsmWriteSpec` offers three sharding strategies, all built on Lance's `InitializeMemWalBuilder`: - `LsmWriteSpec::bucket(column, num_buckets)` — hash-bucket sharding by the single-column unenforced primary key. - `LsmWriteSpec::identity(column)` — identity sharding by the raw value of a scalar column. - `LsmWriteSpec::unsharded()` — a single MemWAL shard. Each can be refined with `with_maintained_indexes(...)` (indexes the MemWAL keeps up to date as rows are appended) and `with_writer_config_defaults(...)` (default `ShardWriter` configuration recorded in the MemWAL index, so every writer starts from the same defaults). All variants require the table to have an unenforced primary key. - `set_lsm_write_spec` installs the spec by initializing the MemWAL index; `unset_lsm_write_spec` removes it (dropping the MemWAL index), reverting to the standard `merge_insert` path. `unset` is idempotent. - Bindings: Python (`LsmWriteSpec.bucket` / `.identity` / `.unsharded`, `set_lsm_write_spec` / `unset_lsm_write_spec`) and TypeScript (`setLsmWriteSpec` with `specType` `"bucket"` / `"identity"` / `"unsharded"`). `RemoteTable` returns `NotSupported`. The actual `merge_insert` LSM dispatch and `ShardWriter` write path are a follow-up — this PR only installs and clears the spec.
LanceDB Python SDK
A Python library for LanceDB.
Installation
pip install lancedb
Preview Releases
Stable releases are created about every 2 weeks. For the latest features and bug fixes, you can install the preview release. These releases receive the same level of testing as stable releases, but are not guaranteed to be available for more than 6 months after they are released. Once your application is stable, we recommend switching to stable releases.
pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
Usage
Basic Example
import lancedb
db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>')
table = db.open_table('my_table')
results = table.search([0.1, 0.3]).limit(20).to_list()
print(results)
Development
See CONTRIBUTING.md for information on how to contribute to LanceDB.