DBMS > Apache Kylin vs. Firebase Realtime Database vs. GigaSpaces vs. RocksDB vs. Solr

System Properties Comparison Apache Kylin vs. Firebase Realtime Database vs. GigaSpaces vs. RocksDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

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)

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Document store

Document store
Object oriented DBMS

Values are user defined objects

Key-value store

Search engine

Secondary database models

Graph DBMS
Search engine

Spatial DBMS

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

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

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

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

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

kylin.apache.org

firebase.google.com/products/realtime-database

www.gigaspaces.com

rocksdb.org

solr.apache.org

Technical documentation

kylin.apache.org/docs

firebase.google.com/docs/database

docs.gigaspaces.com/latest/landing.html

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

Google

acquired by Google 2014

Gigaspaces Technologies

Facebook, Inc.

Apache Software Foundation

Initial release

2015

2012

2000

2013

2006

Current release

3.1.0, July 2020

15.5, September 2020

8.11.4, April 2024

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2; Commercial licenses available

Open Source

BSD

Open Source

Apache Version 2

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

Java, C++, .Net

C++

Java

Server operating systems

Linux

hosted

Linux
macOS
Solaris
Windows

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-99 for query and DML statements

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

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

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

Java
JavaScript
Objective-C

.Net
C++
Java
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

Java plugins

Triggers

Callbacks are triggered when data changes

yes, event driven architecture

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

ACID

yes

optimistic locking

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

yes, based on authentication and database rules

Role-based access control

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.