About Manticore Search

Ease of use

We believe that in today's world, the primary focus of any technology vendor should be ease of use. Our goal is for our users, whether they are developers or devops, to not have to become experts in databases or search engines, or hold a PhD, in order to effectively use Manticore products. We understand that our users have other important tasks to attend to, rather than spending hours trying to understand the intricacies of various settings and their effects on functionality. Therefore, we strive to ensure that Manticore Search works seamlessly and efficiently with minimal configuration, even with default settings.

SQL / JSON

We love SQL. It's impossible to find anything more straightforward when preparing your search query. Most developers are familiar with WHERE, GROUP BY, and ORDER BY as they've been in use for decades. With Manticore Search, you can perform any type of query using SQL. Additionally, Manticore Search also understands the MySQL protocol, allowing you to use MySQL clients for your queries. However, we understand that coding queries in your application may be more comfortable with more structured protocols than an SQL string. That's why Manticore Search also supports JSON. Additionally, we provide Manticore Search bindings for various programming languages to make integration even more seamless. Learn more in the documentation .

Multi-model

Manticore Search offers support for both row-wise storage (similar to MySQL and Postgres) and columnar storage (similar to Redshift, Clickhouse, BigQuery, and Druid), making it incredibly fast for small data sets that can fit in RAM and still maintaining speed when your data is significantly larger than the RAM size .

Additionally, Manticore Search offers two configuration modes: plain, in which all tables are specified in a configuration file (similar to k8s yamls), and real-time, in which tables are created, altered, and dropped using CREATE/ALTER/DROP TABLE commands. Learn more in the documentation .

Performance and resource consumption

Performance and resource consumption matter. Written in C++, Manticore Search makes efficient use of memory and CPU. In the current reproducible Fair Database Benchmarks , Manticore Search 27.1.5 is 340x faster than MySQL 9.7.1 for 1.1M Hacker News comments, 3.85x faster than tuned Elasticsearch 9.4.3 for 100M+ comments, and 5.03x faster than tuned Elasticsearch 9.4.3 for selected DevOps queries on 10M Nginx logs. On that Nginx dataset, it ingested data in 5m 46s vs 10m 15s while averaging 3.98 GB vs 36.98 GB RAM. Results vary by workload.

Advanced search capabilities

Manticore Search offers powerful search capabilities. It combines advanced full-text search with fuzzy matching, smart query suggestions, filtering, faceting, and other tools that help you create a smooth and engaging search experience for your users. Learn more in the documentation .

Elasticsearch alternative

Manticore Search serves as a robust alternative to Elasticsearch. It can seamlessly replace Elasticsearch by emulating its interface to some extent, allowing you to continue using familiar tools like Kibana or Logstash. This compatibility makes it easy to migrate your existing Elasticsearch-based projects to Manticore Search, leveraging its performance benefits without significant changes to your infrastructure.

Authentication and authorization

Manticore Search includes built-in authentication and fine-grained authorization. Protect SQL/MySQL, HTTP/HTTPS, and replication-related access with user credentials or bearer tokens, then grant only the permissions each application, ingestion job, or administrator needs. Authentication is opt-in, so you can introduce it when your deployment is ready. Learn more in the documentation .

Easy scalability

Driven by the industry's high standards and our clients' needs, we designed Manticore to be easily scalable. This means that regardless of the size or complexity of your search task - whether it's a small site, analytics over billions of log records, or building a multilingual directory with petabyte data from different sources - you can accomplish it with Manticore Search. Additionally, we believe that features such as replication and search load distribution should be straightforward to use and maintain. Learn more in the documentation .

Powerful full-text capabilities

Manticore Search, initially designed as a pure full-text search engine, boasts exceptional full-text capabilities: with over 20 full-text operators and more than 20 ranking factors, it offers a variety of built-in rankers and an expression-based custom ranker. Additionally, it includes features such as text stemming, lemmatization, stopwords, synonyms, wordforms, low-level character mapping, proper Chinese segmentation, easy text highlighting, ranking, and tokenization plugins, among many others. Learn more in the documentation .

Integrations

We believe that it should be easy to integrate Manticore Search into your existing stack. That's why Manticore can read data from MySQL, Postgres, MSSQL, ODBC, XML, CSV, TSV out of the box. It can also be integrated with MySQL as an engine or accessed via ProxySQL. Manticore Search also integrates seamlessly with Logstash, Apache Superset, Grafana, and other open-source tools. Learn more in the documentation .

Open source

As open source enthusiasts, we firmly believe that technology such as databases should be open source. With this in mind, we have made Manticore Search and all other Manticore products publicly available under OSI-approved open source licenses. You can find us on GitHub .

Vector and hybrid search

Manticore Search enables vector search for features like similarity search, recommendations, semantic search, and hybrid retrieval. By leveraging embeddings (data representations in a high-dimensional space) it helps create intelligent and precise search experiences. Using KNN (k-nearest neighbor) search with the efficient HNSW algorithm, Manticore supports flexible vector configurations and similarity metrics such as cosine similarity, inner product, and squared L2 distance. Integration is seamless with SQL and JSON APIs, ensuring easy compatibility with existing workflows. Learn more in the documentation .

Conversational search

Manticore Search can power conversational search over your existing vectorized data. With an LLM provider, it retrieves relevant documents through KNN search, builds context from them, retains conversation history, and returns answers with the supporting source rows. Configure chat models and ask questions with SQL, or use the HTTP JSON /search endpoint; customize answer instructions for citations, tone, format, and response length. Learn more in the documentation .