DBMS > Amazon DocumentDB vs. Hive vs. IRONdb vs. RDF4J vs. Teradata Aster

System Properties Comparison Amazon DocumentDB vs. Hive vs. IRONdb vs. RDF4J vs. Teradata Aster

Editorial information provided by DB-Engines

Name

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

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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.

Platform for big data analytics on multistructured data sources and types

Primary database model

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

aws.amazon.com/documentdb

hive.apache.org

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

rdf4j.org

Technical documentation

aws.amazon.com/documentdb/resources

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

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

rdf4j.org/documentation

Developer

Apache Software Foundation

initially developed by Facebook

Circonus LLC.

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

Teradata

Initial release

2019

2012

2017

2004

2005

Current release

3.1.3, April 2022

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

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++

Java

Server operating systems

hosted

All OS with a Java VM

Linux

Linux
OS X
Unix
Windows

Linux

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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.

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

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

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC
Thrift

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

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
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, in Lua

yes

R packages

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Automatic, metric affinity per node

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

selectable replication factor

configurable replication factor, datacenter aware

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

query execution via MapReduce

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

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.

User concepts

Access control

Access rights for users and roles

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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