DBMS > Machbase Neo vs. Manticore Search vs. Oracle Rdb vs. Tarantool

System Properties Comparison Machbase Neo vs. Manticore Search vs. Oracle Rdb vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

TimeSeries DBMS for AIoT and BigData

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

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Time Series DBMS

Search engine

Relational DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Spatial DBMS

with Tarantool/GIS extension

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

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Website

machbase.com

manticoresearch.com

www.oracle.com/database/technologies/related/rdb.html

www.tarantool.io

Technical documentation

machbase.com/dbms

manual.manticoresearch.com

www.oracle.com/database/technologies/related/rdb-doc.html

www.tarantool.io/en/doc

Developer

Machbase

Manticore Software

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2013

2017

1984

2008

Current release

V8.0, August 2023

6.0, February 2023

7.4.1.1, 2021

2.10.0, May 2022

License

Commercial or Open Source

commercial

free test version available

Open Source

GPL version 2

commercial

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C and C++

Server operating systems

Linux
macOS
Windows

FreeBSD
Linux
macOS
Windows

HP Open VMS

BSD
Linux
macOS

Data scheme

yes

Fixed schema

Flexible Schema (defined schema, partial schema, schema free)

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

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

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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

yes

SQL

Support of SQL

SQL-like query language

yes

Full-featured ANSI SQL support

APIs and other access methods

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

Open binary protocol

Supported programming languages

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

user defined functions

Lua, C and SQL stored procedures

Triggers

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Synchronous replication based on Galera library

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

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

yes, on a single node

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

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

yes

yes, write ahead logging

In-memory capabilities

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

yes

volatile and lookup table

yes, full featured in-memory storage engine with persistence

User concepts

Access control

simple password-based access control

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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