DBMS > KeyDB vs. Kinetica vs. NSDb vs. Riak KV vs. TimesTen

System Properties Comparison KeyDB vs. Kinetica vs. NSDb vs. Riak KV vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Fully vectorized database across both GPUs and CPUs

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Distributed, fault tolerant key-value store

In-Memory RDBMS compatible to Oracle

Primary database model

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

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

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

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

github.com/Snapchat/KeyDB
keydb.dev

www.kinetica.com

nsdb.io

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.keydb.dev

docs.kinetica.com

nsdb.io/Architecture

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

docs.oracle.com/database/timesten-18.1

Developer

EQ Alpha Technology Ltd.

Kinetica

OpenSource, formerly Basho Technologies

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2019

2012

2017

2009

1998

Current release

7.1, August 2021

3.2.0, December 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

BSD-3

commercial

Open Source

Apache Version 2.0

Open Source

Apache version 2, commercial enterprise edition

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, C++

Java, Scala

Erlang

Server operating systems

Linux

Linux
macOS

Linux
OS X

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

yes

yes: int, bigint, decimal, string

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.

Secondary indexes

yes

by using the Redis Search module

yes

all fields are automatically indexed

restricted

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protoco

JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

HTTP API
Native Erlang Interface

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C++
Java
JavaScript (Node.js)
Python

Java
Scala

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

Lua

user defined functions

Erlang

PL/SQL

Triggers

yes

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

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

Transaction concepts

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

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

Using Apache Lucene

yes

by means of logfiles and checkpoints

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

yes

User concepts

Access control

simple password-based access control and ACL

Access rights for users and roles on table level

yes, using Riak Security

fine grained access rights according to SQL-standard

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