DBMS > GridDB vs. Manticore Search vs. TerminusDB vs. Vertica

System Properties Comparison GridDB vs. Manticore Search vs. TerminusDB vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Scalable in-memory time series database optimized for IoT and Big Data

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

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Key-value store
Relational DBMS

Time Series DBMS

using the Manticore Columnar Library

Document store
RDF store

Spatial DBMS
Time Series DBMS

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

Score	1.91
Rank	#123	Overall
	#10	Time Series DBMS

Score	0.23
Rank	#301	Overall
	#21	Search engines

Score	0.19
Rank	#311	Overall
	#27	Graph DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

Technical documentation

docs.griddb.net

manual.manticoresearch.com

terminusdb.github.io/terminusdb/#

vertica.com/documentation

Developer

Toshiba Corporation

Manticore Software

DataChemist Ltd.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2013

2017

2018

2005

Current release

5.1, August 2022

6.0, February 2023

11.0.0, January 2023

12.0.3, January 2023

License

Commercial or Open Source

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

GPL version 2

Open Source

GPL V3

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Prolog, Rust

C++

Server operating systems

Linux

FreeBSD
Linux
macOS
Windows

Linux

Data scheme

yes

Fixed schema

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

Secondary indexes

yes

full-text index on all search fields

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language

SQL-like query language (WOQL)

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

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

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

JavaScript
Python

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Graph Partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Synchronous replication based on Galera library

Journaling Streams

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID at container level

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

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

Access rights for users can be defined per database

Role-based access control

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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