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Dan RammerandClaude Opus 5
fe34eb3549
## What Renames the MemWAL surface in two passes, across Rust, Python, TypeScript, and Java, including the wire format. ### 1. MemWAL APIs drop "LSM" The write path these APIs drive is Lance's MemWAL; "LSM" described its shape rather than naming it. Turning it on and off now reads as enabling a feature rather than installing a spec. | Before | After | |---|---| | `set_lsm_write_spec` | `enable_memwal` | | `unset_lsm_write_spec` | `disable_memwal` | | `get_lsm_write_spec` | `get_memwal_spec` | | `flush_lsm` / `compact_lsm` / `checkpoint_lsm` | `flush_memwal` / `compact_memwal` / `checkpoint_memwal` | | `get_lsm_stats` | `get_memwal_stats` | | `close_lsm_writers` | `close_memwal_writers` | | `use_lsm` (query, merge insert) | `use_memwal` | | `LsmWriteSpec` / `LsmStats` | `MemWalWriteSpec` / `MemWalStats` | | Java `LanceDbTableLsm` | `LanceDbTableMemWal` | TypeScript and Java use camelCase (`enableMemWal`, `getMemWalStats`, ...); the napi methods set `js_name` so the generated names keep the `MemWal` casing. flush/compact/checkpoint keep a `_memwal` suffix so `compact_memwal` is not confused with `compact_files` / `optimize`, which compact the base table. Internal names (`LsmMode`, `LsmScanner`, the `merge::lsm` / `query::lsm` modules) are unchanged. ### 2. Stats use SSTable and table shard Aligns the stats surface with the MemWAL naming settled upstream in lance-format/lance#7943 and #7957, where the persisted unit became an **SSTable**. **Generation → SSTable.** A `GenerationStats` describes one flushed MemTable, which is an SSTable. The generation *number* is kept — an SSTable is identified by its generation — so only the noun moved. **Bucket → table shard.** Each entry is one MemWAL shard. "Bucket" names only the hash sharding *transform*, so it was already wrong for `identity` and `year` sharding, which produce shards and no buckets at all. | Before | After | |---|---| | `GenerationStats` | `SsTableStats` | | `BucketStats` | `TableShardStats` | | `stats.buckets` | `stats.table_shards` | | `BucketStats.generations` | `TableShardStats.sstables` | | `include_generation_rows` | `include_sstable_rows` | | `newest_generation` | `newest_sstable_generation` | | `outstanding_generations` | `outstanding_sstables` | Docs and comments follow suit: "seal" → "freeze", "L0" / "flushed generations" → SSTables, "bucket" (meaning a shard) → table shard. `MemWalWriteSpec::bucket`, `num_buckets`, and the `"Sealed"` shard status value are **unchanged** — those name the sharding transform or a server value, not the shard. ## Compatibility Breaking, for an experimental API. The wire format changes with the SDKs, so this must roll out together with the matching server change: - Routes: `/set_lsm_write_spec/` → `/enable_memwal/`, `/unset_lsm_write_spec/` → `/disable_memwal/`, `/get_lsm_write_spec/` → `/get_memwal_spec/`, `/flush_lsm/` → `/flush_memwal/`, `/compact_lsm/` → `/compact_memwal/`, `/get_lsm_stats/` → `/get_memwal_stats/` - Response keys: `lsm_write_spec` → `memwal_spec`, `lsm_stats` → `memwal_stats`, `buckets` → `table_shards`, `generations` → `sstables` - Request fields: `include_generation_rows` → `include_sstable_rows`; `use_lsm` → `use_memwal` on query and merge-insert bodies ## Validation - Rust: `cargo test --features remote -p lancedb --lib --tests` — 1467 lib tests and all integration test targets pass; `cargo clippy` clean - Python: rebuilt the extension; `test_remote_db`, `test_memwal_spec`, `test_merge_insert_lsm`, `test_query`, `test_permutation`, `test_table` — 655 passed; `ruff format` / `ruff check` clean - TypeScript: `pnpm build`; `table`, `merge`, `query` tests — 593 passed; `biome check` clean; `docs/src/js` regenerated with `npm run docs` - Java: `mvn test` — 37 passed (Spotless applied with JDK 11; the pinned google-java-format does not run on JDK 21) 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
The Multimodal Lakehouse for AI
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
Demo: Multimodal Search by Keyword, Vector or with SQL
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⭐ Click here ⭐ to see how fast we're growing!
Key Features:
- Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
- Comprehensive Search: Support for vector similarity search, full-text search and SQL.
- Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
- Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
Products:
- Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
- Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
Ecosystem:
- Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
- Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
- Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
How to Install:
Follow the Quickstart doc to set up LanceDB locally.
API & SDK: We also support Python, Typescript and Rust SDKs
| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/rest |
Join Us and Contribute
We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.
If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.
Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
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