DBMS > Hive vs. OrientDB vs. OrigoDB

System Properties Comparison Hive vs. OrientDB vs. OrigoDB

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Editorial information provided by DB-Engines

Name

Description

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

Multi-model DBMS (Document, Graph, Key/Value)

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store

Document store
Object oriented DBMS

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

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Score	3.27
Rank	#97	Overall
	#16	Document stores
	#6	Graph DBMS
	#15	Key-value stores

Score	0.03
Rank	#378	Overall
	#51	Document stores
	#18	Object oriented DBMS

Website

hive.apache.org

orientdb.org

origodb.com

Technical documentation

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

www.orientdb.com/docs/last/index.html

origodb.com/docs

Developer

Apache Software Foundation

initially developed by Facebook

OrientDB LTD; CallidusCloud; SAP

Robert Friberg et al

Initial release

2012

2010

2009

under the name LiveDB

Current release

3.1.3, April 2022

3.2.29, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache version 2

Open Source

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

Server operating systems

All OS with a Java VM

All OS with a Java JDK (>= JDK 6)

Linux
Windows

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language, no joins

APIs and other access methods

JDBC
ODBC
Thrift

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

.NET Client API
HTTP API
LINQ

Supported programming languages

C++
Java
PHP
Python

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java, Javascript

yes

Triggers

Hooks

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

depending on model

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

Write ahead log

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

Access rights for users and roles; record level security configurable

Role based authorization

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More resources

DB-Engines blog posts

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