DBMS > Bangdb vs. Citus vs. HBase vs. MySQL vs. Solr

System Properties Comparison Bangdb vs. Citus vs. HBase vs. MySQL vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

Widely used open source RDBMS

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Wide column store

Relational DBMS

Key/Value like access via memcached API

Search engine

Secondary database models

Spatial DBMS

Document store

Document store
Spatial DBMS

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

Technical documentation

docs.bangdb.com

docs.citusdata.com

hbase.apache.org/book.html

dev.mysql.com/doc

solr.apache.org/resources.html

Developer

Sachin Sinha, BangDB

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2012

2010

2008

1995

2006

Current release

BangDB 2.0, October 2021

8.1, December 2018

2.3.4, January 2021

8.4.0, April 2024

9.6.0, April 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

AGPL, commercial license also available

Open Source

Apache version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

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Implementation language

C, C++

Java

C and C++

Java

Server operating systems

Linux

Linux
Unix
Windows

using Cygwin

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

schema-free, schema definition possible

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

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

yes

options to bring your own types, AVRO

yes

supports customizable data types and automatic typing

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

specific XML type available, but no XML query functionality

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL like support with command line tool

yes

standard, with numerous extensions

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
RESTful HTTP API
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C#
C++
Groovy
Java
PHP
Python
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

Coprocessors in Java

yes

proprietary syntax

Java plugins

Triggers

yes, Notifications (with Streaming only)

yes

User configurable commands triggered on index changes

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

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

ACID

not for MyISAM storage engine

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

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)

fine grained access rights according to SQL-standard

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

Users with fine-grained authorization concept

no user groups or roles

yes

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