DBMS > BigObject vs. GigaSpaces vs. Kyligence Enterprise vs. RocksDB

System Properties Comparison BigObject vs. GigaSpaces vs. Kyligence Enterprise vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

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

A distributed analytics engine for big data, built on top of Apache Kylin

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Key-value store

Secondary database models

Graph DBMS
Search engine

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

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

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

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

Website

bigobject.io

www.gigaspaces.com

kyligence.io/kyligence-enterprise

rocksdb.org

Technical documentation

docs.bigobject.io

docs.gigaspaces.com/latest/landing.html

github.com/facebook/rocksdb/wiki

Developer

BigObject, Inc.

Gigaspaces Technologies

Kyligence, Inc.

Facebook, Inc.

Initial release

2015

2000

2016

2013

Current release

15.5, September 2020

8.11.4, April 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; Commercial licenses available

commercial

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++, .Net

Java

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
macOS
Solaris
Windows

Linux

Data scheme

yes

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-99 for query and DML statements

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

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

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

Supported programming languages

.Net
C++
Java
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Lua

yes

Triggers

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Role-based access control

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