DBMS > Hazelcast vs. Linter vs. Tarantool vs. Tibero

System Properties Comparison Hazelcast vs. Linter vs. Tarantool vs. Tibero

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Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

RDBMS for high security requirements

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

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Key-value store

Relational DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

with Tarantool/GIS extension

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

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

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

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

hazelcast.com

linter.ru

www.tarantool.io

us.tmaxsoft.com/products/tibero

Technical documentation

hazelcast.org/imdg/docs

www.tarantool.io/en/doc

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Hazelcast

relex.ru

TmaxSoft

Initial release

2008

1990

2008

2003

Current release

5.3.6, November 2023

2.10.0, May 2022

6, April 2015

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

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

commercial

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

Java

C and C++

C and Assembler

Server operating systems

All OS with a Java VM

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

BSD
Linux
macOS

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

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

yes

Typing

predefined data types such as float or date

yes

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

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

Full-featured ANSI SQL support

yes

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Open binary protocol

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

proprietary syntax with the possibility to convert from PL/SQL

Lua, C and SQL stored procedures

Persistent Stored Procedure (PSM)

Triggers

yes

Events

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Source-replica replication

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

planned for next version

User concepts

Access control

Role-based access control

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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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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