DBMS > HEAVY.AI vs. Manticore Search vs. Microsoft Azure AI Search vs. Snowflake vs. TerminusDB

System Properties Comparison HEAVY.AI vs. Manticore Search vs. Microsoft Azure AI Search vs. Snowflake vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Multi-storage database for search, including full-text search.

Search-as-a-service for web and mobile app development

Cloud-based data warehousing service for structured and semi-structured data

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Spatial DBMS

Time Series DBMS

using the Manticore Columnar Library

Vector DBMS

Document store
RDF store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.23
Rank	#317	Overall
	#21	Search engines

Score	5.71
Rank	#64	Overall
	#8	Search engines

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	0.22
Rank	#323	Overall
	#28	Graph DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

manticoresearch.com

azure.microsoft.com/en-us/services/search

www.snowflake.com

terminusdb.com

Technical documentation

docs.heavy.ai

manual.manticoresearch.com

learn.microsoft.com/en-us/azure/search

docs.snowflake.net/manuals/index.html

terminusdb.github.io/terminusdb/#

Developer

HEAVY.AI, Inc.

Manticore Software

Microsoft

Snowflake Computing Inc.

DataChemist Ltd.

Initial release

2016

2017

2015

2014

2018

Current release

5.10, January 2022

6.0, February 2023

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

GPL version 2

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++ and CUDA

C++

Prolog, Rust

Server operating systems

Linux

FreeBSD
Linux
macOS
Windows

hosted

Linux

Data scheme

yes

Fixed schema

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Can index from XML

yes

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language

yes

SQL-like query language (WOQL)

APIs and other access methods

JDBC
ODBC
Thrift
Vega

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

RESTful HTTP API

CLI Client
JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

C#
Java
JavaScript
Python

JavaScript (Node.js)
Python

JavaScript
Python

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Implicit feature of the cloud service

yes

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Synchronous replication based on Galera library

yes

Implicit feature of the cloud service

yes

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

in-memory journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

using Azure authentication

Users with fine-grained authorization concept, user roles and pluggable authentication

Role-based access control

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