DBMS > BoltDB vs. Hive vs. IRONdb vs. Kinetica vs. RDF4J

System Properties Comparison BoltDB vs. Hive vs. IRONdb vs. Kinetica vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

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

GPU-accelerated database for real-time analysis of large and streaming datasets

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

Primary database model

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

Score	0.58
Rank	#217	Overall
	#34	Key-value stores

Score	70.43
Rank	#16	Overall
	#11	Relational DBMS

Score	0.00
Rank	#335	Overall
	#31	Time Series DBMS

Score	0.76
Rank	#193	Overall
	#97	Relational DBMS

Score	0.53
Rank	#224	Overall
	#9	RDF stores

Website

github.com/boltdb/bolt

hive.apache.org

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

www.kinetica.com

rdf4j.org

Technical documentation

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

docs.circonus.com/irondb

www.kinetica.com/docs

rdf4j.org/documentation

Developer

Apache Software Foundation

initially developed by Facebook

Circonus LLC.

Kinetica

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

Initial release

2013

2012

2017

2012

2004

Current release

3.1.2, August 2019

V0.10.20, January 2018

6.0

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Java

C and C++

C, C++

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

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 DML and DDL statements

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

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
Thrift

HTTP API

JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

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

C++
Java
JavaScript (Node.js)
Python

Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, in Lua

user defined functions

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Automatic, metric affinity per node

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

configurable replication factor, datacenter aware

Source-replica replication

none

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

none

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

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

yes

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

In-memory capabilities

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

yes

GPU vRAM or System RAM

User concepts

Access control

Access rights for users, groups and roles

Access rights for users and roles on table level

More information provided by the system vendor

Specific characteristics

IRONdb is a highly available, distributed Time Series Database. It can support dozens...
» more

Competitive advantages

Unmatched Scalability IRONdb is unique among TSDBs in that it does not use a consensus...
» more

Typical application scenarios

Real Systems Monitoring Monitor your systems infrastructure in real time across thousands...
» more

Key customers

IRONdb serves the needs of the world's largest Time Series Database customers. One...
» more

Market metrics

Only TSDB capable of scaling to billions of metrics. Only TSDB to scale to dozens...
» more

Licensing and pricing models

IRONdb is licensed under a subscription model based on the number of active metrics...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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