DBMS > HBase vs. ReductStore vs. TerarkDB vs. VoltDB

System Properties Comparison HBase vs. ReductStore vs. TerarkDB vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

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

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

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

hbase.apache.org

github.com/reductstore
www.reduct.store

github.com/bytedance/terarkdb

www.voltdb.com

Technical documentation

hbase.apache.org/book.html

www.reduct.store/docs

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

docs.voltdb.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

ReductStore LLC

ByteDance, originally Terark

VoltDB Inc.

Initial release

2008

2023

2016

2010

Current release

2.3.4, January 2021

1.9, March 2024

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Business Source License 1.1

commercial

restricted open source version available

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

Java

C++, Rust

C++

Java, C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Docker
Linux
macOS
Windows

Linux
OS X

for development

Data scheme

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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

SQL

Support of SQL

yes

only a subset of SQL 99

APIs and other access methods

Java API
RESTful HTTP API
Thrift

HTTP API

C++ API
Java API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
JavaScript (Node.js)
Python
Rust

C++
Java

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Multi-source replication
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

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

Single row ACID (across millions of columns)

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

User concepts

Access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Users and roles with access to stored procedures

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More resources

DB-Engines blog posts

Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

Why is Hadoop not listed in the DB-Engines Ranking?
13 May 2013, Paul Andlinger

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