DBMS > Bangdb vs. BoltDB vs. HBase vs. RocksDB vs. VoltDB

System Properties Comparison Bangdb vs. BoltDB vs. HBase vs. RocksDB vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

An embedded key-value store for Go.

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

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

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

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

bangdb.com

github.com/boltdb/bolt

hbase.apache.org

rocksdb.org

www.voltdb.com

Technical documentation

docs.bangdb.com

hbase.apache.org/book.html

github.com/facebook/rocksdb/wiki

docs.voltdb.com

Developer

Sachin Sinha, BangDB

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Facebook, Inc.

VoltDB Inc.

Initial release

2012

2013

2008

2013

2010

Current release

BangDB 2.0, October 2021

2.3.4, January 2021

9.2.1, May 2024

11.3, April 2022

License

Commercial or Open Source

Open Source

BSD 3

Open Source

MIT License

Open Source

Apache version 2

Open Source

BSD

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

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Implementation language

C, C++

Java

C++

Java, C++

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

Linux

Linux
OS X

for development

Data scheme

schema-free

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

only a subset of SQL 99

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

C++ API
Java API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Java
Python

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

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, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

none

Multi-source replication
Source-replica replication

yes

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

Tunable consistency, set CAP knob accordingly

none

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

ACID

yes

Single row ACID (across millions of columns)

yes

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

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, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

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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