DBMS > Geode vs. HugeGraph vs. IRONdb vs. RocksDB vs. TimesTen

System Properties Comparison Geode vs. HugeGraph vs. IRONdb vs. RocksDB vs. TimesTen

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

A fast-speed and highly-scalable Graph DBMS

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

In-Memory RDBMS compatible to Oracle

Primary database model

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

Score	1.86
Rank	#134	Overall
	#24	Key-value stores

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

geode.apache.org

github.com/hugegraph
hugegraph.apache.org

www.circonus.com/solutions/time-series-database/

rocksdb.org

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

Technical documentation

geode.apache.org/docs

hugegraph.apache.org/docs

docs.circonus.com/irondb/category/getting-started

github.com/facebook/rocksdb/wiki

docs.oracle.com/database/timesten-18.1

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Baidu

Circonus LLC.

Facebook, Inc.

Oracle, TimesTen Performance Software, HP

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

Initial release

2002

2018

2017

2013

1998

Current release

1.1, February 2017

0.9

V0.10.20, January 2018

9.2.1, May 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

Apache Version 2.0

commercial

Open Source

BSD

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

Java

C and C++

C++

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

Linux
macOS
Unix

Linux

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

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language (OQL)

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API

C++ API
Java API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

Groovy
Java
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C
C++
Go
Java
Perl
Python
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

asynchronous Gremlin script jobs

yes, in Lua

PL/SQL

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Automatic, metric affinity per node

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

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

configurable replication factor, datacenter aware

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

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

yes, on a single node

ACID

yes

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

User concepts

Access control

Access rights per client and object definable

Users, roles and permissions

fine grained access rights according to SQL-standard

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