DBMS > BoltDB vs. HBase vs. RocksDB vs. TerarkDB vs. Tkrzw

System Properties Comparison BoltDB vs. HBase vs. RocksDB vs. TerarkDB vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

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

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

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

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

Key-value store

Wide column store

Key-value store

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

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

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

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

Website

github.com/boltdb/bolt

hbase.apache.org

rocksdb.org

github.com/bytedance/terarkdb

dbmx.net/tkrzw

Technical documentation

hbase.apache.org/book.html

github.com/facebook/rocksdb/wiki

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Facebook, Inc.

ByteDance, originally Terark

Mikio Hirabayashi

Initial release

2013

2008

2013

2016

2020

Current release

2.3.4, January 2021

8.11.4, April 2024

0.9.3, August 2020

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache version 2

Open Source

BSD

commercial

restricted open source version available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

Linux

Linux
macOS

Data scheme

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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

SQL

Support of SQL

APIs and other access methods

Java API
RESTful HTTP API
Thrift

C++ API
Java API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

C++
Java

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Single row ACID (across millions of columns)

yes

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

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