Full Text Search Index

The full text search (FTS) index (a.k.a. inverted index) provides efficient text search by mapping terms to the documents containing them. It's designed for high-performance text search with support for various scoring algorithms and phrase queries.

Index Details

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Storage Layout

The FTS index consists of multiple files storing the token dictionary, document information, and posting lists:

1. tokens.lance - Token dictionary mapping tokens to token IDs
2. docs.lance - Document metadata including token counts
3. invert.lance - Compressed posting lists for each token
4. metadata.lance - Index metadata and configuration

An FTS index may contain multiple partitions. Each partition has its own set of token, document, and posting list files, prefixed with the partition ID (e.g. part_0_tokens.lance, part_0_docs.lance, part_0_invert.lance). The metadata.lance file lists all partition IDs in the index. At query time, every partition must be searched and the results combined to produce the final ranked output. Fewer partitions generally means better query performance, since each partition requires its own token dictionary lookup and posting list scan. The number of partitions is controlled by the training configuration -- specifically LANCE_FTS_TARGET_SIZE determines how large each merged partition can grow (see Training Process for details).

Token Dictionary File Schema

Document File Schema

FTS List File Schema

Metadata File Schema

The metadata file contains JSON-serialized configuration and partition information:

InvertedIndexParams Structure

Tokenizers

The full text search index supports multiple tokenizer types for different text processing needs:

Base Tokenizers

Jieba Tokenizer (Chinese)

Jieba is a popular Chinese text segmentation library that uses a dictionary-based approach with statistical methods for word segmentation.

• Configuration: Uses a config.json file in the model directory
• Models: Must be downloaded and placed in the Lance home directory under jieba/
• Usage: Specify as jieba/<model_name> or just jieba for the default model
• Config Structure:
  jsonCopy

  {
    "main": "path/to/main/dictionary",
    "users": ["path/to/user/dict1", "path/to/user/dict2"]
  }
  

• Features:
  • Accurate word segmentation for Simplified and Traditional Chinese
  • Support for custom user dictionaries
  • Multiple segmentation modes (precise, full, search engine)

Lindera Tokenizer (Japanese)

Lindera is a morphological analysis tokenizer specifically designed for Japanese text. It provides proper word segmentation for Japanese, which doesn't use spaces between words.

• Configuration: Uses a config.yml file in the model directory
• Models: Must be downloaded and placed in the Lance home directory under lindera/
• Usage: Specify as lindera/<model_name> where <model_name> is the subdirectory containing the model files
• Features:
  • Morphological analysis with part-of-speech tagging
  • Dictionary-based tokenization
  • Support for custom user dictionaries

Token Filters

Token filters are applied in sequence after the base tokenizer:

Supported Languages

For stemming and stop word removal, the following languages are supported: Arabic, Danish, Dutch, English, Finnish, French, German, Greek, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil, Turkish

Document Type

Lance supports 2 kinds of documents: text and json. Different document types have different tokenization rules, and parse tokens in different format.

Text Type

Text type includes text and list of text. Tokens are generated by base_tokenizer.

The example below shows how text document is parsed into tokens.

Tom lives in San Francisco.

The tokens are below.

Tom
lives
in
San
Francisco

Json Type

Json is a nested structure, lance breaks down json document into tokens in triplet format path,type,value. The valid types are: str, number, bool, null.

In scenarios where the triplet value is a str, the text value will be further tokenized using the base_tokenizer, resulting in multiple triplet tokens.

During querying, the Json Tokenizer uses the triplet format instead of the json format, which simplifies the query syntax.

The example below shows how the json document is tokenized. Assume we have the following json document:

{
  "name": "Lance",
  "legal.age": 30,
  "address": {
    "city": "San Francisco",
    "zip:us": 94102
  }
}

After parsing, the document will be tokenized into the following tokens:

name,str,Lance
legal.age,number,30
address.city,str,San
address.city,str,Francisco
address.zip:us,number,94102

Then we do full text search in triplet format. To search for "San Francisco," we can search with one of the triplets below:

address.city:San Francisco
address.city:San
address.city:Francisco

Training Process

Building an FTS index is a multi-phase pipeline: the source column is scanned, documents are tokenized in parallel, intermediate results are spilled to part files on disk, and the part files are merged into final output partitions.

Phase 1: Tokenization

The input column is read as a stream of record batches and dispatched to a pool of tokenizer worker tasks. Each worker tokenizes documents independently, accumulating tokens, posting lists, and document metadata in memory.

When a worker's accumulated data reaches the partition size limit or the document count hits u32::MAX, it flushes the data to disk as a set of part files (part_<id>_tokens.lance, part_<id>_invert.lance, part_<id>_docs.lance). A single worker may produce multiple part files if it processes enough data.

Phase 2: Merge

After all workers finish, the part files are merged into output partitions. Part files are streamed with bounded buffering so that not all data needs to be loaded into memory at once. For each part file, the token dictionaries are unified, document sets are concatenated, and posting lists are rewritten with adjusted IDs.

When a merged partition reaches the target size, it is written to the destination store and a new one is started. After all part files are consumed the final partition is flushed, and a metadata.lance file is written listing the partition IDs and index parameters.

Configuration

Memory and Performance Considerations

Memory usage is primarily determined by two factors:

• LANCE_FTS_NUM_SHARDS -- Each worker holds an independent in-memory buffer. Peak memory is roughly NUM_SHARDS * PARTITION_SIZE plus the overhead of token dictionaries and posting list structures.
• LANCE_FTS_PARTITION_SIZE -- Larger values reduce the number of part files and make the merge phase cheaper. Smaller values reduce per-worker memory at the cost of more part files.

Merge phase memory is bounded by the streaming approach: part files are loaded one at a time with a small concurrency buffer. The merged partition's in-memory size is bounded by LANCE_FTS_TARGET_SIZE.

Building an FTS index requires temporary disk space to store the part files generated during tokenization. The amount of temporary space depends heavily on whether position information is enabled. An index with with_position: true stores the position of every token occurrence in every document, which can easily require 10x the size of the original column or more in temporary disk space. An index without positions tends to be smaller than the original column and will typically need less than 2x the size of the column in total disk space.

Performance tips:

• Larger LANCE_FTS_TARGET_SIZE produces fewer output partitions, which is beneficial for query performance because queries must scan every partition's token dictionary. When memory allows, prefer fewer, larger partitions.
• with_position: true significantly increases index size because term positions are stored for every occurrence. Only enable it when phrase queries are needed.
• The ngram tokenizer generates many more tokens per document than word-level tokenizers, so expect larger index sizes and higher memory usage.

Distributed Training

The FTS index supports distributed training where different worker nodes each index a subset of the data and the results are assembled afterward.

1. Each distributed worker is assigned a fragment mask ((fragment_id as u64) << 32) that is OR'd into the partition IDs it generates, ensuring globally unique IDs across workers.
2. Workers set skip_merge: true so they write their part files directly without running the merge phase.
3. Instead of a single metadata.lance, each worker writes per-partition metadata files named part_<id>_metadata.lance.
4. After all workers finish, a coordinator merges the metadata files: it collects all partition IDs, remaps them to a sequential range starting from 0 (renaming the corresponding data files), and writes the final unified metadata.lance.

This allows each worker to operate independently during the tokenization phase. Only the final metadata merge requires a single-node step, and it is lightweight since it only renames files and writes a small metadata file.

Accelerated Queries

Lance SDKs provide dedicated full text search APIs to leverage the FTS index capabilities. These APIs support complex query types beyond simple token matching, enabling sophisticated text search operations. Here are the query types enabled by the FTS index: