DBMS > Bangdb vs. HugeGraph vs. Oracle vs. Oracle Coherence vs. Yaacomo

System Properties Comparison Bangdb vs. HugeGraph vs. Oracle vs. Oracle Coherence vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

A fast-speed and highly-scalable Graph DBMS

Widely used RDBMS

Oracles in-memory data grid solution

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Website

bangdb.com

github.com/hugegraph
hugegraph.apache.org

www.oracle.com/database

www.oracle.com/java/coherence

yaacomo.com

Technical documentation

docs.bangdb.com

hugegraph.apache.org/docs

docs.oracle.com/en/database

docs.oracle.com/en/middleware/standalone/coherence

Developer

Sachin Sinha, BangDB

Baidu

Oracle

Q2WEB GmbH

Initial release

2012

2018

1980

2007

2009

Current release

BangDB 2.0, October 2021

0.9

23c, September 2023

14.1, August 2023

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

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 and C++

Java

Server operating systems

Linux

Linux
macOS
Unix

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

All OS with a Java VM

Android
Linux
Windows

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

schema-free

yes

Typing

predefined data types such as float or date

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

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.

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL like support with command line tool

yes

with proprietary extensions

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JCache
JPA
RESTful HTTP API

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

Groovy
Java
Python

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

.Net
C++
Java

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

PL/SQL

also stored procedures in Java possible

Triggers

yes, Notifications (with Streaming only)

yes

Live Events

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

yes

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

Sharding, horizontal partitioning

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

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

Multi-source replication
Source-replica replication

yes, with selectable consistency level

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

isolation level can be parameterized

configurable

ACID

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

optionally

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

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

yes (enterprise version only)

Users, roles and permissions

fine grained access rights according to SQL-standard

authentification to access the cache via certificates or http basic authentication

fine grained access rights according to SQL-standard

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