DBMS > Apache Phoenix vs. GigaSpaces vs. RocksDB

System Properties Comparison Apache Phoenix vs. GigaSpaces vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

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

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Key-value store

Secondary database models

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Trend Chart

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

phoenix.apache.org

www.gigaspaces.com

rocksdb.org

Technical documentation

phoenix.apache.org

docs.gigaspaces.com/latest/landing.html

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

Gigaspaces Technologies

Facebook, Inc.

Initial release

2014

2000

2013

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

15.5, September 2020

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; Commercial licenses available

Open Source

BSD

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

Java, C++, .Net

C++

Server operating systems

Linux
Unix
Windows

Linux
macOS
Solaris
Windows

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-99 for query and DML statements

APIs and other access methods

JDBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

C++ API
Java API

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
C++
Java
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes, event driven architecture

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
Source-replica replication

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Role-based access control

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