DBMS > Altibase vs. HugeGraph vs. Oracle vs. Solr vs. TimesTen

System Properties Comparison Altibase vs. HugeGraph vs. Oracle vs. Solr vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

A fast-speed and highly-scalable Graph DBMS

Widely used RDBMS

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

In-Memory RDBMS compatible to Oracle

Primary database model

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

Spatial DBMS

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

Score	1.02
Rank	#189	Overall
	#88	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

altibase.com

github.com/hugegraph
hugegraph.apache.org

www.oracle.com/database

solr.apache.org

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

hugegraph.apache.org/docs

docs.oracle.com/en/database

solr.apache.org/resources.html

docs.oracle.com/database/timesten-18.1

Developer

Altibase

Baidu

Oracle

Apache Software Foundation

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

1999

2018

1980

2006

1998

Current release

v7.3, 2023, August 2023

0.9

23c, September 2023

9.6.1, May 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java

C and C++

Java

Server operating systems

AIX
HP-UX
Linux

Linux
macOS
Unix

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

All OS with a Java VM

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

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

Schemaless in JSON and XML columns

yes

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

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

also supports composite index and range index

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

ANSI SQL-92

yes

with proprietary extensions

Solr Parallel SQL Interface

yes

APIs and other access methods

JDBC
ODBC
Proprietary protocol

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Java API
RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C
C++
Java

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

stored procedures and stored functions

asynchronous Gremlin script jobs

PL/SQL

also stored procedures in Java possible

Java plugins

PL/SQL

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Sharding, horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Multi-source replication
Source-replica replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

can be realized in PL/SQL

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Users, roles and permissions

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

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