DBMS > OrigoDB vs. ReductStore vs. Spark SQL vs. Titan

System Properties Comparison OrigoDB vs. ReductStore vs. Spark SQL vs. Titan

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Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

A fully ACID in-memory object graph database

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Spark SQL is a component on top of 'Spark Core' for structured data processing

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Document store
Object oriented DBMS

Time Series DBMS

Relational DBMS

Graph DBMS

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

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

origodb.com

github.com/reductstore
www.reduct.store

spark.apache.org/sql

github.com/thinkaurelius/titan

Technical documentation

origodb.com/docs

www.reduct.store/docs

spark.apache.org/docs/latest/sql-programming-guide.html

github.com/thinkaurelius/titan/wiki

Developer

Robert Friberg et al

ReductStore LLC

Apache Software Foundation

Aurelius, owned by DataStax

Initial release

2009

under the name LiveDB

2023

2014

2012

Current release

1.9, March 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Business Source License 1.1

Open Source

Apache 2.0

Open Source

Apache license, version 2.0

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++, Rust

Scala

Java

Server operating systems

Linux
Windows

Docker
Linux
macOS
Windows

Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

Typing

predefined data types such as float or date

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

.NET Client API
HTTP API
LINQ

HTTP API

JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

.Net

C++
JavaScript (Node.js)
Python
Rust

Java
Python
R
Scala

Clojure
Java
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

client side managed; servers are not synchronized

yes, utilizing Spark Core

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

depending on model

yes

Relationships in graph

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

In-memory capabilities

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

yes

User concepts

Access control

Role based authorization

User authentification and security via Rexster Graph Server

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More resources

DB-Engines blog posts

Graph DBMS increased their popularity by 500% within the last 2 years
3 March 2015, Paul Andlinger

Graph DBMSs are gaining in popularity faster than any other database category
21 January 2014, Matthias Gelbmann

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