DBMS > Hive vs. Riak KV vs. Tkrzw vs. Ultipa

System Properties Comparison Hive vs. Riak KV vs. Tkrzw vs. Ultipa

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Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Distributed, fault tolerant key-value store

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

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Relational DBMS

Key-value store

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

Key-value store

Graph DBMS

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

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

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

hive.apache.org

dbmx.net/tkrzw

www.ultipa.com

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

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

www.ultipa.com/document

Developer

Apache Software Foundation

initially developed by Facebook

OpenSource, formerly Basho Technologies

Mikio Hirabayashi

Ultipa

Initial release

2012

2009

2020

2019

Current release

3.1.3, April 2022

3.2.0, December 2022

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache Version 2.0

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

Java

Erlang

C++

Server operating systems

All OS with a Java VM

Linux
OS X

Linux
macOS

Data scheme

yes

schema-free

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.

Secondary indexes

yes

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
Thrift

HTTP API
Native Erlang Interface

RESTful HTTP API

Supported programming languages

C++
Java
PHP
Python

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++
Java
Python
Ruby

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Erlang

Triggers

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

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

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

using specific database classes

User concepts

Access control

Access rights for users, groups and roles

yes, using Riak Security

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More resources

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