DBMS > 4D vs. HEAVY.AI vs. Manticore Search vs. Microsoft Azure Cosmos DB vs. SpatiaLite

System Properties Comparison 4D vs. HEAVY.AI vs. Manticore Search vs. Microsoft Azure Cosmos DB vs. SpatiaLite

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

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.

Globally distributed, horizontally scalable, multi-model database service

Spatial extension of SQLite

Primary database model

Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Spatial DBMS

Secondary database models

Spatial DBMS

Time Series DBMS

using the Manticore Columnar Library

Spatial DBMS

Relational DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

www.4d.com

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

manticoresearch.com

azure.microsoft.com/services/cosmos-db

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

developer.4d.com

docs.heavy.ai

manual.manticoresearch.com

learn.microsoft.com/azure/cosmos-db

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

4D, Inc

HEAVY.AI, Inc.

Manticore Software

Microsoft

Alessandro Furieri

Initial release

1984

2016

2017

2014

2008

Current release

v20, April 2023

5.10, January 2022

6.0, February 2023

5.0.0, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

GPL version 2

commercial

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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++

Server operating systems

OS X
Windows

Linux

FreeBSD
Linux
macOS
Windows

hosted

server-less

Data scheme

yes

Fixed schema

schema-free

yes

Typing

predefined data types such as float or date

yes

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

yes

JSON types

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.

yes

Can index from XML

Secondary indexes

yes

full-text index on all search fields

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

close to SQL 92

yes

SQL-like query language

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC
Thrift
Vega

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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

4D proprietary IDE
PHP

All languages supporting JDBC/ODBC/Thrift
Python

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

yes

user defined functions

JavaScript

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

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

Sharding

Implicit feature of the cloud service

none

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

Multi-item ACID transactions with snapshot isolation within a partition

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 capabilities

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

yes

User concepts

Access control

Users and groups

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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