DBMS > BigObject vs. Cachelot.io vs. GBase vs. SwayDB vs. Ultipa

System Properties Comparison BigObject vs. Cachelot.io vs. GBase vs. SwayDB vs. Ultipa

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

In-memory caching system

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

Technical documentation

docs.bigobject.io

www.ultipa.com/document

Developer

BigObject, Inc.

General Data Technology Co., Ltd.

Simer Plaha

Ultipa

Initial release

2015

2004

2018

2019

Current release

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

commercial

free community edition available

Open Source

Simplified BSD License

commercial

Open Source

GNU Affero GPL V3.0

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

C++

C, Java, Python

Scala

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Standard with numerous extensions

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

Memcached protocol

ADO.NET
C API
JDBC
ODBC

RESTful HTTP API

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

Java
Kotlin
Scala

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by range, list and hash) and vertical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

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

yes

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High-performance computing's role in real-time graph analytics - DataScienceCentral.com
30 January 2024, Data Science Central

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