DBMS > Riak TS vs. TimescaleDB vs. ToroDB vs. VoltDB vs. XTDB

System Properties Comparison Riak TS vs. TimescaleDB vs. ToroDB vs. VoltDB vs. XTDB

Editorial information provided by DB-Engines

Name

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

Description

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

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

Primary database model

Secondary database models

Relational DBMS

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

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.timescale.com

github.com/torodb/server

www.voltdb.com

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

Technical documentation

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

docs.timescale.com

docs.voltdb.com

www.xtdb.com/docs

Developer

Open Source, formerly Basho Technologies

Timescale

8Kdata

VoltDB Inc.

Juxt Ltd.

Initial release

2015

2017

2016

2010

2019

Current release

3.0.0, September 2022

2.15.0, May 2024

11.3, April 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL-V3

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Erlang

Java

Java, C++

Clojure

Server operating systems

Linux
OS X

Linux
OS X
Windows

All OS with a Java 7 VM

Linux
OS X

for development

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

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

yes

string, integer, double, boolean, date, object_id

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

restricted

yes

SQL

Support of SQL

yes, limited

yes

full PostgreSQL SQL syntax

yes

only a subset of SQL 99

limited SQL, making use of Apache Calcite

APIs and other access methods

HTTP API
Native Erlang Interface

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
JDBC
RESTful HTTP/JSON API

HTTP REST
JDBC

Supported programming languages

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Clojure
Java

Server-side scripts

Stored procedures

Erlang

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Java

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, across time and space (hash partitioning) attributes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication with hot standby and reads on replicas

Source-replica replication

Multi-source replication
Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

ACID

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

Snapshots and command logging

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

Users and roles with access to stored procedures

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