DBMS > Firebase Realtime Database vs. Hypertable vs. IRONdb vs. RocksDB vs. Sphinx

System Properties Comparison Firebase Realtime Database vs. Hypertable vs. IRONdb vs. RocksDB vs. Sphinx

Editorial information provided by DB-Engines

Name

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

IRONdb seems to be discontinued. Therefore it is excluded 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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

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

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

firebase.google.com/products/realtime-database

www.circonus.com/solutions/time-series-database/

rocksdb.org

sphinxsearch.com

Technical documentation

firebase.google.com/docs/database

docs.circonus.com/irondb/category/getting-started

github.com/facebook/rocksdb/wiki

sphinxsearch.com/docs

Developer

Google

acquired by Google 2014

Hypertable Inc.

Circonus LLC.

Facebook, Inc.

Sphinx Technologies Inc.

Initial release

2012

2009

2017

2013

2001

Current release

0.9.8.11, March 2016

V0.10.20, January 2018

8.11.4, April 2024

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

GNU version 3. Commercial license available

commercial

Open Source

BSD

Open Source

GPL version 2, commercial licence available

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 and C++

C++

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

restricted

only exact value or prefix value scans

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language (SphinxQL)

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

C++ API
Thrift

HTTP API

C++ API
Java API

Proprietary protocol

Supported programming languages

Java
JavaScript
Objective-C

C++
Java
Perl
PHP
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C
C++
Go
Java
Perl
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes, in Lua

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

horizontal partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

configurable replication factor, datacenter aware

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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

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