Manticore Search: A High‑Performance Alternative That Could Overtake Elasticsearch

Manticore Search Overview

Manticore Search is a high‑performance search engine written in C++, created in 2017 as the successor to Sphinx Search. It rewrites much of the original code, fixes hundreds of bugs, and remains open source, providing a modern, fast, lightweight, and feature‑rich full‑text search database.

The project has gathered over 11 k stars on GitHub and its maintainers state that it is a solid replacement for Elasticsearch, with Microsoft’s own tests showing a qualitative performance boost.

In certain scenarios Manticore is up to 15× faster than Elasticsearch; the full benchmark results are available at https://manticoresearch.com/blog/manticore-alternative-to-elasticsearch/.

Advantages

Very fast, making it more cost‑effective than alternatives; e.g., 182× faster than MySQL on small data, 29× faster than Elasticsearch on log analysis, 15× faster on small datasets, 5× faster on medium datasets, and up to 4× faster on large datasets.

Modern multithreaded architecture and efficient query parallelism fully utilize all CPU cores for the fastest response times.

Powerful full‑text search that handles both small and large datasets seamlessly.

Row‑store for small, medium, and large datasets.

Columnar storage via Manticore Columnar Library for datasets that cannot fit entirely in memory.

Automatic creation of efficient secondary indexes, saving time and effort.

Cost‑optimizing query optimizer that tunes search queries for optimal performance.

SQL‑based native syntax compatible with the MySQL protocol, allowing use of any MySQL client.

Simple integration through client libraries for PHP, Python, JavaScript, Java, Elixir, Go, and more.

Built‑in HTTP JSON protocol for flexible data and schema management.

C++ implementation provides fast startup, minimal memory usage, and low‑level optimizations.

Real‑time insertion: newly added documents become instantly searchable.

Built‑in replication and load‑balancing features increase reliability.

Easy data synchronization from MySQL, PostgreSQL, ODBC, XML, CSV, and other sources.

Supports transactions and binlog for safe writes, despite not being fully ACID compliant.

Built‑in tools and SQL commands simplify backup and recovery.

Getting Started with Docker

Docker images are available on Docker Hub. To experiment with Manticore Search in Docker, run:

docker run -e EXTRA=1 --name manticore --rm -d manticoresearch/manticore && \
until docker logs manticore 2>&1 | grep -q "accepting connections"; do \
  sleep 1; \
 done && docker exec -it manticore mysql && docker stop manticore

After the container is ready, you can create tables, insert data, and execute searches. Example commands:

create table movies(title text, year int) morphology='stem_en' html_strip='1' stopwords='en';
insert into movies(title, year) values ('The Seven Samurai', 1954), ('Bonnie and Clyde', 1954), ('Reservoir Dogs', 1992), ('Airplane!', 1980), ('Raging Bull', 1980), ('Groundhog Day', 1993), ('Jurassic Park', 1993), ('Ferris Bueller''s Day Off', 1986);
select highlight(), year from movies where match('the dog');
select highlight(), year from movies where match('days') facet year;
select * from movies where match('google');

Project Repository

https://github.com/manticoresoftware/manticoresearch