DBMS > Geode vs. Hive vs. RocksDB

System Properties Comparison Geode vs. Hive vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Key-value store

Relational DBMS

Key-value store

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

Score	1.99
Rank	#133	Overall
	#23	Key-value stores

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Website

geode.apache.org

hive.apache.org

rocksdb.org

Technical documentation

geode.apache.org/docs

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

github.com/facebook/rocksdb/wiki

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Apache Software Foundation

initially developed by Facebook

Facebook, Inc.

Initial release

2002

2012

2013

Current release

1.1, February 2017

3.1.3, April 2022

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

Apache Version 2

Open Source

BSD

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++

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

All OS with a Java VM

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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 (OQL)

SQL-like DML and DDL statements

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

JDBC
ODBC
Thrift

C++ API
Java API

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

C++
Java
PHP
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions and integration of map-reduce

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

yes

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

Foreign keys

Referential integrity

Transaction concepts

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

yes, on a single node

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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 per client and object definable

Access rights for users, groups and roles

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