Manticore Search: High‑Performance Open‑Source Search Engine Overview and Usage

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 database with excellent full‑text search capabilities.

On GitHub, Manticore Search has attracted over 3.7k stars and a large user base. Its developers claim it is a strong alternative to Elasticsearch and may eventually replace the ELK stack.

Independent testing shows that in certain scenarios Manticore Search can be up to 15 times faster than Elasticsearch. Detailed benchmark results are available at Manticore Search blog .

Advantages

Very fast performance, e.g., 182× faster than MySQL on small data, 29× faster than Elasticsearch for log analysis, and up to 15× faster on small datasets.

Modern multithreaded architecture and efficient query parallelism fully utilize CPU cores.

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

Row‑store for small‑to‑medium data and columnar storage via Manticore Columnar Library for very large datasets.

Automatic creation of efficient secondary indexes.

Cost‑optimized query optimizer for best performance.

SQL‑based interface compatible with the MySQL protocol, allowing use of standard MySQL clients.

Client libraries for PHP, Python, JavaScript, Java, Elixir, Go, etc.

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

Fast startup, minimal memory usage, and low‑level optimizations thanks to C++ implementation.

Real‑time insertion; newly added documents are immediately searchable.

Interactive tutorials for easy learning.

Built‑in replication and load‑balancing for reliability.

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

Supports transactions and binlog for safe writes, though not fully ACID‑compliant.

Built‑in tools and SQL commands for simple backup and restore.

Companies such as Craigslist, Socialgist, PubChem, Rozetka, and many others use Manticore Search for efficient search and stream filtering.

Usage

The Docker image is available on Docker Hub:

https://hub.docker.com/r/manticoresearch/manticore/

To run Manticore Search in Docker:

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 running, you can create tables, insert data, and perform searches. Example SQL 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');

Full documentation and source code are available at https://github.com/manticoresoftware/manticoresearch .