About Manticore Search

Ease of use

We believe that nowadays any technology vendor’s key priority should be to make it as easy to use as possible. We don’t want our users, be it a developer or a devops to become experts in databases or search engines or have a PhD to be able to use Manticore products. We assume you have other things to do rather than spending hours trying to understand how this or that setting affects this or that functionality. Hence, Manticore Search should work fine in most cases even on defaults.

SQL / JSON

We love SQL. There can’t be anything simpler to use when you are just preparing your search query: WHERE, GROUP BY, ORDER BY - most developers are used to these things since they’ve been in use for decades. You can use SQL to do any kind of query in Manticore Search. At the same time we understand that when it comes to coding your queries in your application it may be easier to use more structured protocols than an SQL string. That’s why Manticore Search also speaks JSON. We also maintain Manticore Search bindings for various programming languages to make integration even easier.

Multi-model

Manticore Search supports 2 storages: row-wise (like MySQL and Postgres) and columnar (like Redshift, Clickhouse, BigQuery, Druid), which allows Manticore to be blazingly fast on small data, when it can fit into RAM and still fast when your data is a way larger than the RAM size.

Manticore Search also supports 2 configuration modes: plain when you specify all your tables in a configuration file (like you most likely do in k8s yamls) and real-time when you create your tables only using CREATE/ALTER/DROP TABLE.

Performance and resource consumption

We believe that performance and resource consumption matter. That’s why Manticore Search is written in C++ - the programming language used to write operating systems, browsers and other software where performance and resource consumption is especially important. Writing in C++ is hard and takes time, but it allows us to make sure Manticore Search doesn’t consume more RAM than really needed and than it can use your CPU as efficiently as possible. According to the Fair database benchmarks https://db-benchmarks.com Manticore Search is: 15x faster than Elasticsearch on small data, 4x faster on big data, 29x faster for log analytics.

Easy scalability

Driven by the industry’s high standards and our clients’ needs, we made Manticore easily scalable, so no matter what kind of search tasks you need to solve: search on a small site, analytics over billions of log records or building multilingual directory with petabyte data coming from different sources, you can do it with Manticore Search. We believe that such things as replication and search load distribution should be really simple to use and maintain.

Powerful full-text capabilities

Manticore Search, being a purely full-text search engine initially has outstanding full-text capabilities: over 20 full-text operators and more than 20 ranking factors, various built-in rankers and an expression-based custom ranker, text stemming, lemmatization, stopwords, synonyms, wordforms, low-level characters mapping, proper Chinese segmentation, easy text highlighting, ranking and tokenization plugins and many more.

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 be accessed via ProxySQL.

Open source

We are open source lovers and believe that such technology as a database should be open source. Manticore Search and other publicly available Manticore products are all under OSI-approved open source licences. We are on GitHub.