DBMS > GBase vs. Hive vs. Kinetica vs. Sequoiadb

System Properties Comparison GBase vs. Hive vs. Kinetica vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Fully vectorized database across both GPUs and CPUs

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

Document store
Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

docs.kinetica.com

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

General Data Technology Co., Ltd.

Apache Software Foundation

initially developed by Facebook

Kinetica

Sequoiadb Ltd.

Initial release

2004

2012

2013

Current release

GBase 8a, GBase 8s, GBase 8c

3.1.3, April 2022

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

Server: AGPL; Client: Apache V2

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, Java, Python

Java

C, C++

C++

Server operating systems

Linux

All OS with a Java VM

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

Standard with numerous extensions

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC
Thrift

JDBC
ODBC
RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

C++
Java
PHP
Python

C++
Java
JavaScript (Node.js)
Python

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions and integration of map-reduce

user defined functions

JavaScript

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

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

GPU vRAM or System RAM

User concepts

Access control

yes

Access rights for users, groups and roles

Access rights for users and roles on table level

simple password-based access control

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More resources

DB-Engines blog posts

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