DBMS > Firebase Realtime Database vs. Hypertable vs. Ignite vs. Riak TS

System Properties Comparison Firebase Realtime Database vs. Hypertable vs. Ignite vs. Riak TS

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

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

An open source BigTable implementation based on distributed file systems such as Hadoop

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Document store

Wide column store

Key-value store
Relational DBMS

Time Series DBMS

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

firebase.google.com/products/realtime-database

ignite.apache.org

Technical documentation

firebase.google.com/docs/database

apacheignite.readme.io/docs

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Google

acquired by Google 2014

Hypertable Inc.

Apache Software Foundation

Open Source, formerly Basho Technologies

Initial release

2012

2009

2015

Current release

0.9.8.11, March 2016

Apache Ignite 2.6

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

GNU version 3. Commercial license available

Open Source

Apache 2.0

Open Source

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

C++

C++, Java, .Net

Erlang

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X
Solaris
Windows

Linux
OS X

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.

yes

Secondary indexes

yes

restricted

only exact value or prefix value scans

yes

restricted

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes, limited

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

C++ API
Thrift

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP API
Native Erlang Interface

Supported programming languages

Java
JavaScript
Objective-C

C++
Java
Perl
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes (compute grid and cache interceptors can be used instead)

Erlang

Triggers

Callbacks are triggered when data changes

yes (cache interceptors and events)

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

yes (replicated cache)

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

yes

ACID

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

Security Hooks for custom implementations

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More resources

DB-Engines blog posts

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