DBMS > BigObject vs. Kingbase vs. Vitess vs. XTDB

System Properties Comparison BigObject vs. Kingbase vs. Vitess vs. XTDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

Scalable, distributed, cloud-native DBMS, extending MySQL

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.45
Rank	#262	Overall
	#123	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

bigobject.io

www.kingbase.com.cn

vitess.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.bigobject.io

vitess.io/docs

www.xtdb.com/docs

Developer

BigObject, Inc.

BeiJing KINGBASE Information technologies inc.

The Linux Foundation, PlanetScale

Juxt Ltd.

Initial release

2015

1999

2013

2019

Current release

V8.0, August 2021

15.0.2, December 2022

1.19, September 2021

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Apache Version 2.0, commercial licenses available

Open Source

MIT License

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 and Java

Clojure

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
Windows

Docker
Linux
macOS

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

yes

with proprietary extensions

limited SQL, making use of Apache Calcite

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

ADO.NET
JDBC
MySQL protocol
ODBC

HTTP REST
JDBC

Supported programming languages

.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Clojure
Java

Server-side scripts

Stored procedures

Lua

user defined functions

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Multi-source replication
Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Made in China 2025 is back, with a new name and a focus on database companies – The China Project
19 December 2022, The China Project

Opening preparation - Alekhine defense, Saemisch variation
18 April 2016, Chess.com

Backup & Recovery Solutions from China
4 August 2022, Хабр

provided by Google News

Vitess, the database clustering system powering YouTube, graduates CNCF incubation
5 November 2019, SiliconANGLE News

PlanetScale Unveils Distributed MySQL Database Service Based on Vitess
18 May 2021, Datanami

They scaled YouTube — now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

Massively Scaling MySQL Using Vitess
19 February 2019, InfoQ.com

PlanetScale review: Horizontally scalable MySQL in the cloud
1 September 2021, InfoWorld

provided by Google News

Share this page