DBMS > RethinkDB vs. Riak TS vs. RocksDB vs. Splice Machine vs. Tkrzw

System Properties Comparison RethinkDB vs. Riak TS vs. RocksDB vs. Splice Machine vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Secondary database models

Spatial DBMS

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

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

Technical documentation

rethinkdb.com/docs

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

github.com/facebook/rocksdb/wiki

splicemachine.com/how-it-works

Developer

The Linux Foundation

since July 2017

Open Source, formerly Basho Technologies

Facebook, Inc.

Splice Machine

Mikio Hirabayashi

Initial release

2009

2015

2013

2014

2020

Current release

2.4.1, August 2020

3.0.0, September 2022

9.2.1, May 2024

3.1, March 2021

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 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++

Erlang

C++

Java

C++

Server operating systems

Linux
OS X
Windows

Linux
OS X

Linux

Linux
OS X
Solaris
Windows

Linux
macOS

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

restricted

yes

SQL

Support of SQL

yes, limited

yes

APIs and other access methods

HTTP API
Native Erlang Interface

C++ API
Java API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

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++
Go
Java
Perl
Python
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

Erlang

yes

Java

Triggers

Client-side triggers through changefeeds

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

range based

Sharding

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

yes

Transaction concepts

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

Atomic single-document operations

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

yes, multi-version concurrency control (MVCC)

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

using specific database classes

User concepts

Access control

yes

users and table-level permissions

Access rights for users, groups and roles according to SQL-standard

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