DBMS > Bangdb vs. Cubrid vs. HugeGraph vs. SAP HANA vs. Solr

System Properties Comparison Bangdb vs. Cubrid vs. HugeGraph vs. SAP HANA vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

A fast-speed and highly-scalable Graph DBMS

In-memory, column based data store. Available as appliance or cloud service

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

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
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	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

bangdb.com

cubrid.com (korean)
cubrid.org (english)

github.com/hugegraph
hugegraph.apache.org

www.sap.com/products/hana.html

solr.apache.org

Technical documentation

docs.bangdb.com

cubrid.org/manuals

hugegraph.apache.org/docs

help.sap.com/hana

solr.apache.org/resources.html

Developer

Sachin Sinha, BangDB

CUBRID Corporation, CUBRID Foundation

Baidu

SAP

Apache Software Foundation

Initial release

2012

2008

2018

2010

2006

Current release

BangDB 2.0, October 2021

11.0, January 2021

0.9

2.0 SPS07 (April 4, 2023), April 2023

9.6.0, April 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C, C++, Java

Java

Server operating systems

Linux

Linux
Windows

Linux
macOS
Unix

Appliance or cloud-service

All OS with a Java VM

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

Data scheme

schema-free

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

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

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

also supports composite index and range index

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL like support with command line tool

yes

Solr Parallel SQL Interface

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Java
Python

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Groovy
Java
Python

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

Server-side scripts

Stored procedures

Java Stored Procedures

asynchronous Gremlin script jobs

SQLScript, R

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

none

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

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

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

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

Users, roles and permissions

yes

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