DBMS > GigaSpaces vs. H2 vs. Tarantool vs. Warp 10

System Properties Comparison GigaSpaces vs. H2 vs. Tarantool vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Document store
Key-value store
Relational DBMS

Time Series DBMS

Secondary database models

Graph DBMS
Search engine

Spatial DBMS

with Tarantool/GIS extension

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

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

Technical documentation

docs.gigaspaces.com/latest/landing.html

www.h2database.com/html/main.html

www.tarantool.io/en/doc

www.warp10.io/content/02_Getting_started

Developer

Gigaspaces Technologies

Thomas Mueller

SenX

Initial release

2000

2005

2008

2015

Current release

15.5, September 2020

2.2.220, July 2023

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

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

Open Source

Apache License 2.0

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++, .Net

Java

C and C++

Java

Server operating systems

Linux
macOS
Solaris
Windows

All OS with a Java VM

BSD
Linux
macOS

Linux
OS X
Windows

Data scheme

schema-free

yes

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

schema-free

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

Full-featured ANSI SQL support

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

JDBC
ODBC

Open binary protocol

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
C++
Java
Python
Scala

Java

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

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

Lua, C and SQL stored procedures

yes

WarpScript

Triggers

yes, event driven architecture

yes

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

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.

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

With clustering: 2 database servers on different computers operate on identical copies of a database

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

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

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

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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, multi-version concurrency control (MVCC)

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

yes

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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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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