DBMS > Apache IoTDB vs. IRONdb vs. Microsoft Azure Synapse Analytics vs. RDF4J vs. TerarkDB

System Properties Comparison Apache IoTDB vs. IRONdb vs. Microsoft Azure Synapse Analytics vs. RDF4J vs. TerarkDB

Editorial information provided by DB-Engines

Name

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

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

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

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

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	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

iotdb.apache.org

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

azure.microsoft.com/services/synapse-analytics

rdf4j.org

github.com/bytedance/terarkdb

Technical documentation

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

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

docs.microsoft.com/azure/synapse-analytics

rdf4j.org/documentation

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Apache Software Foundation

Circonus LLC.

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

ByteDance, originally Terark

Initial release

2018

2017

2016

2004

2016

Current release

1.1.0, April 2023

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

yes

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++

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

hosted

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

schema-free

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

SQL

Support of SQL

SQL-like query language

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

yes

APIs and other access methods

JDBC
Native API

HTTP API

ADO.NET
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

C++ API
Java API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

C#
Java
PHP

Java
PHP
Python

C++
Java

Server-side scripts

Stored procedures

yes

yes, in Lua

Transact SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Automatic, metric affinity per node

Sharding, horizontal 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

configurable replication factor, datacenter aware

yes

none

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 per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

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

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