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¶

message InvertedIndexDetails {
  // Marking this field as optional as old versions of the index store blank details and we
  // need to make sure we have a proper optional field to detect this.
  optional string base_tokenizer = 1;
  string language = 2;
  bool with_position = 3;
  optional uint32 max_token_length = 4;
  bool lower_case = 5;
  bool stem = 6;
  bool remove_stop_words = 7;
  bool ascii_folding = 8;
  uint32 min_ngram_length = 9;
  uint32 max_ngram_length = 10;
  bool prefix_only = 11;

}

Storage Layout¶

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

tokens.lance - Token dictionary mapping tokens to token IDs
docs.lance - Document metadata including token counts
invert.lance - Compressed posting lists for each token
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¶

Column	Type	Nullable	Description
`_token`	Utf8	false	The token string
`_token_id`	UInt32	false	Unique identifier for the token

Document File Schema¶

Column	Type	Nullable	Description
`_rowid`	UInt64	false	Document row ID
`_num_tokens`	UInt32	false	Number of tokens in the document

FTS List File Schema¶

Column	Type	Nullable	Description
`_posting`	List	false	Compressed posting lists (delta-encoded row IDs and frequencies)
`_max_score`	Float32	false	Maximum score for the token (for query optimization)
`_length`	UInt32	false	Number of documents containing the token
`_compressed_position`	List>	true	Optional compressed position lists for phrase queries

Metadata File Schema¶

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

Key	Type	Description
`partitions`	Array	List of partition IDs for distributed index organization
`params`	JSON Object	Serialized InvertedIndexParams with tokenizer config

InvertedIndexParams Structure¶

Field	Type	Default	Description
`base_tokenizer`	String	"simple"	Base tokenizer type (see Tokenizers section)
`language`	String	"English"	Language for stemming and stop words
`with_position`	Boolean	false	Store term positions for phrase queries (increases index size)
`max_token_length`	UInt32?	None	Maximum token length (tokens longer than this are removed)
`lower_case`	Boolean	true	Convert tokens to lowercase
`stem`	Boolean	false	Apply language-specific stemming
`remove_stop_words`	Boolean	false	Remove common stop words for the specified language
`ascii_folding`	Boolean	true	Convert accented characters to ASCII equivalents
`min_gram`	UInt32	2	Minimum n-gram length (only for ngram tokenizer)
`max_gram`	UInt32	15	Maximum n-gram length (only for ngram tokenizer)
`prefix_only`	Boolean	false	Generate only prefix n-grams (only for ngram tokenizer)

Tokenizers¶

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

Base Tokenizers¶

Tokenizer	Description	Use Case
simple	Splits on whitespace and punctuation, removes non-alphanumeric characters	General text (default)
whitespace	Splits only on whitespace characters	Preserve punctuation
raw	No tokenization, treats entire text as single token	Exact matching
ngram	Breaks text into overlapping character sequences	Substring/fuzzy search
jieba/*	Chinese text tokenizer with word segmentation	Chinese text
lindera/*	Japanese text tokenizer with morphological analysis	Japanese text

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:

{
  "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:

Filter	Description	Configuration
RemoveLong	Removes tokens exceeding max_token_length	`max_token_length`
LowerCase	Converts tokens to lowercase	`lower_case` (default: true)
Stemmer	Reduces words to their root form	`stem`, `language`
StopWords	Removes common words like "the", "is", "at"	`remove_stop_words`, `language`
AsciiFolding	Converts accented characters to ASCII	`ascii_folding` (default: true)

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¶

Environment Variable	Default	Description
`LANCE_FTS_NUM_SHARDS`	Number of compute-intensive CPUs	Number of parallel tokenizer worker tasks. Higher values increase indexing throughput but use more memory.
`LANCE_FTS_PARTITION_SIZE`	256 (MiB)	Maximum uncompressed size of a worker's in-memory buffer before it is spilled to a part file.
`LANCE_FTS_TARGET_SIZE`	4096 (MiB)	Target uncompressed size for merged output partitions. Fewer, larger partitions improve query performance.

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.

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.
Workers set skip_merge: true so they write their part files directly without running the merge phase.
Instead of a single metadata.lance, each worker writes per-partition metadata files named part_<id>_metadata.lance.
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:

Query Type	Description	Example Usage	Result Type
contains_tokens	Basic token-based search (UDF) with BM25 scoring and automatic result ranking	SQL: `contains_tokens(column, 'search terms')`	AtMost
match	Match query with configurable AND/OR operators and relevance scoring	`{"match": {"query": "text", "operator": "and/or"}}`	AtMost
phrase	Exact phrase matching with position information (requires `with_position: true`)	`{"phrase": {"query": "exact phrase"}}`	AtMost
boolean	Complex boolean queries with must/should/must_not clauses for sophisticated search logic	`{"boolean": {"must": [...], "should": [...]}}`	AtMost
multi_match	Search across multiple fields simultaneously with unified scoring	`{"multi_match": [{"field1": "query"}, ...]}`	AtMost
boost	Boost relevance scores for specific terms or queries by a configurable factor	`{"boost": {"query": {...}, "factor": 2.0}}`	AtMost