DBMS > Hive vs. IRONdb vs. RDF4J vs. TerarkDB vs. TimescaleDB

System Properties Comparison Hive vs. IRONdb vs. RDF4J vs. TerarkDB vs. TimescaleDB

Editorial information provided by DB-Engines

Name

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

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Relational DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

hive.apache.org

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

rdf4j.org

github.com/bytedance/terarkdb

www.timescale.com

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

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

rdf4j.org/documentation

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

docs.timescale.com

Developer

Apache Software Foundation

initially developed by Facebook

Circonus LLC.

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

ByteDance, originally Terark

Timescale

Initial release

2012

2017

2004

2016

2017

Current release

3.1.3, April 2022

V0.10.20, January 2018

2.13.0, November 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

restricted open source version available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Java

C++

Server operating systems

All OS with a Java VM

Linux

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

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

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC
Thrift

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

C++ API
Java API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C++
Java
PHP
Python

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

Java
PHP
Python

C++
Java

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, in Lua

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

configurable replication factor, datacenter aware

none

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

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

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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