DBMS > Datomic vs. HugeGraph vs. IBM Db2 warehouse vs. Oracle vs. SAP SQL Anywhere

System Properties Comparison Datomic vs. HugeGraph vs. IBM Db2 warehouse vs. Oracle vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A fast-speed and highly-scalable Graph DBMS

Cloud-based data warehousing service

Widely used RDBMS

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

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	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

www.datomic.com

github.com/hugegraph
hugegraph.apache.org

www.ibm.com/products/db2/warehouse

www.oracle.com/database

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

docs.datomic.com

hugegraph.apache.org/docs

docs.oracle.com/en/database

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Cognitect

Baidu

IBM

Oracle

SAP

formerly Sybase

Initial release

2012

2018

2014

1980

1992

Current release

1.0.6735, June 2023

0.9

23c, September 2023

17, July 2015

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, Clojure

Java

C and C++

Server operating systems

All OS with a Java VM

Linux
macOS
Unix

hosted

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

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

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.

Import/export of XML data possible

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

.NET Client API
JDBC
ODBC
OLE DB

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

Clojure
Java

Groovy
Java
Python

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

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

Server-side scripts

Stored procedures

yes

Transaction Functions

asynchronous Gremlin script jobs

PL/SQL, SQL PL

PL/SQL

also stored procedures in Java possible

yes, in C/C++, Java, .Net or Perl

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

yes

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

Sharding

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

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

yes

Multi-source replication
Source-replica replication

Source-replica replication

Database mirroring

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

Immediate Consistency

Eventual 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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

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

yes

User concepts

Access control

Users, roles and permissions

fine grained access rights according to SQL-standard

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