DBMS > Apache IoTDB vs. BigchainDB vs. Datomic vs. TerminusDB vs. TimesTen

System Properties Comparison Apache IoTDB vs. BigchainDB vs. Datomic vs. TerminusDB vs. TimesTen

Editorial information provided by DB-Engines

Name

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

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Document store
RDF store

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

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

Technical documentation

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

bigchaindb.readthedocs.io/en/latest

docs.datomic.com

terminusdb.github.io/terminusdb/#

docs.oracle.com/database/timesten-18.1

Developer

Apache Software Foundation

Cognitect

DataChemist Ltd.

Oracle, TimesTen Performance Software, HP

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

Initial release

2018

2016

2012

2018

1998

Current release

1.1.0, April 2023

1.0.6735, June 2023

11.0.0, January 2023

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL v3

commercial

limited edition free

Open Source

GPL V3

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Python

Java, Clojure

Prolog, Rust

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

All OS with a Java VM

Linux

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

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (WOQL)

yes

APIs and other access methods

JDBC
Native API

CLI Client
RESTful HTTP API

RESTful HTTP API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Go
Haskell
Java
JavaScript
Python
Ruby

Clojure
Java

JavaScript
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

PL/SQL

Triggers

yes

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

But extensive use of caching in the application peers

Graph Partitioning

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

selectable replication factor

none

But extensive use of caching in the application peers

Journaling Streams

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

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

yes,with MongoDB ord RethinkDB

yes

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

yes

in-memory journaling

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

recommended only for testing and development

yes

User concepts

Access control

yes

Role-based access control

fine grained access rights according to SQL-standard

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