DBMS > BoltDB vs. Riak KV vs. Teradata Aster vs. Tkrzw vs. VoltDB

System Properties Comparison BoltDB vs. Riak KV vs. Teradata Aster vs. Tkrzw vs. VoltDB

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

An embedded key-value store for Go.

Distributed, fault tolerant key-value store

Platform for big data analytics on multistructured data sources and types

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

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

Primary database model

Key-value store

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

Relational DBMS

Key-value store

Relational DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

github.com/boltdb/bolt

dbmx.net/tkrzw

www.voltdb.com

Technical documentation

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

docs.voltdb.com

Developer

OpenSource, formerly Basho Technologies

Teradata

Mikio Hirabayashi

VoltDB Inc.

Initial release

2013

2009

2005

2020

2010

Current release

3.2.0, December 2022

0.9.3, August 2020

11.3, April 2022

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache version 2, commercial enterprise edition

commercial

Open Source

Apache Version 2.0

Open Source

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

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

C++

Java, C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X

Linux

Linux
macOS

Linux
OS X

for development

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

only a subset of SQL 99

APIs and other access methods

HTTP API
Native Erlang Interface

ADO.NET
JDBC
ODBC
OLE DB

Java API
JDBC
RESTful HTTP/JSON API

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

C
C#
C++
Java
Python
R

C++
Java
Python
Ruby

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

Erlang

R packages

Java

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

FOREIGN KEY constraints are not supported

Transaction concepts

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

yes

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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, using Riak Security

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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