DBMS > Kinetica vs. LeanXcale vs. Riak TS vs. ToroDB

System Properties Comparison Kinetica vs. LeanXcale vs. Riak TS vs. ToroDB

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Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Fully vectorized database across both GPUs and CPUs

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Document store

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.28
Rank	#286	Overall
	#41	Key-value stores
	#130	Relational DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

www.kinetica.com

www.leanxcale.com

github.com/torodb/server

Technical documentation

docs.kinetica.com

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

Developer

Kinetica

LeanXcale

Open Source, formerly Basho Technologies

8Kdata

Initial release

2012

2015

2016

Current release

7.1, August 2021

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

C, C++

Erlang

Java

Server operating systems

Linux

Linux
OS X

All OS with a Java 7 VM

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through Apache Derby

yes, limited

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

HTTP API
Native Erlang Interface

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

C
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

Server-side scripts

Stored procedures

user defined functions

Erlang

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets 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.

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

Access rights for users and roles

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More resources

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