DBMS > Kingbase vs. Riak KV vs. Spark SQL

System Properties Comparison Kingbase vs. Riak KV vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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

Distributed, fault tolerant key-value store

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.45
Rank	#262	Overall
	#123	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.kingbase.com.cn

spark.apache.org/sql

Technical documentation

www.tiot.jp/riak-docs/riak/kv/latest

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

BeiJing KINGBASE Information technologies inc.

OpenSource, formerly Basho Technologies

Apache Software Foundation

Initial release

1999

2009

2014

Current release

V8.0, August 2021

3.2.0, December 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache 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

C and Java

Erlang

Scala

Server operating systems

Linux
Windows

Linux
OS X

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

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

restricted

SQL

Support of SQL

Standard with numerous extensions

SQL-like DML and DDL statements

APIs and other access methods

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

HTTP API
Native Erlang Interface

JDBC
ODBC

Supported programming languages

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

no "single point of failure"

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

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.

User concepts

Access control

fine grained access rights according to SQL-standard

yes, using Riak Security

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