DBMS > Apache IoTDB vs. TimesTen vs. Titan vs. XTDB

System Properties Comparison Apache IoTDB vs. TimesTen vs. Titan vs. XTDB

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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

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

In-Memory RDBMS compatible to Oracle

Titan is a Graph DBMS optimized for distributed clusters.

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

iotdb.apache.org

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

github.com/thinkaurelius/titan

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.oracle.com/database/timesten-18.1

github.com/thinkaurelius/titan/wiki

www.xtdb.com/docs

Developer

Apache Software Foundation

Oracle, TimesTen Performance Software, HP

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

Aurelius, owned by DataStax

Juxt Ltd.

Initial release

2018

1998

2012

2019

Current release

1.1.0, April 2023

11 Release 2 (11.2.2.8.0)

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache license, version 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Clojure

Server operating systems

All OS with a Java VM (>= 1.8)

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

Linux
OS X
Unix
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

SQL

Support of SQL

SQL-like query language

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
Native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP REST
JDBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Java
PL/SQL

Clojure
Java
Python

Clojure
Java

Server-side scripts

Stored procedures

yes

PL/SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

yes

via pluggable storage backends

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication
Source-replica replication

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

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

by means of logfiles and checkpoints

yes

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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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