go-ngram
A Unicode-aware, append-only n-gram index library for Go with memory-efficient string pooling.
What is go-ngram?
go-ngram is a Go library for creating n-gram indexes, which break text into overlapping character sequences to enable fast search and similarity matching. It solves the problem of efficient text indexing and retrieval in Go applications, particularly where memory usage and performance are critical.
Go developers building applications that require text search, autocomplete, or similarity detection, such as search engines, data processing pipelines, or NLP tools.
Developers choose go-ngram for its focus on memory efficiency through string pooling, Unicode support, and a simple, append-only API that avoids complex document management overhead.
Overview
Ngram index for golang
Use Cases
Best For
- Implementing autocomplete or search suggestions in Go applications
- Building text similarity or fuzzy matching systems
- Creating lightweight full-text search indexes without external dependencies
- Processing and indexing multilingual text with Unicode support
- Optimizing memory usage in text-heavy Go services
- Developing NLP or information retrieval tools in Go
Not Ideal For
- Applications requiring data deletion or frequent updates, due to its strict append-only design
- Projects needing built-in document management or advanced search features, as it only provides raw indexing without higher-level abstractions
- Systems that depend on NLP smoothing techniques like Laplace, which are listed as TODO and not yet implemented
- Large-scale distributed search engines requiring out-of-the-box scalability, replication, or complex querying
Pros & Cons
Pros
Supports international characters beyond ASCII, enabling reliable indexing of multilingual text as highlighted in the README.
Uses string pooling and compression to reduce memory overhead and garbage collection pressure, making it efficient for text-heavy applications.
Application agnostic with no built-in document model, allowing developers to integrate it into various use cases without unnecessary complexity.
Prevents data deletion, ensuring a consistent index structure and avoiding fragmentation issues, which simplifies maintenance.
Cons
The append-only design means data cannot be deleted, forcing workarounds like versioning or external filtering for dynamic datasets.
Lacks smoothing functions (e.g., Laplace) mentioned in the TODO, limiting its usefulness for advanced text processing tasks without custom implementation.
Users must build document management and search logic from scratch, as it provides only basic n-gram indexing without out-of-the-box search capabilities.
Frequently Asked Questions
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