DBMS > Apache Impala vs. Firebase Realtime Database vs. Microsoft Azure Cosmos DB vs. NuoDB vs. ToroDB

System Properties Comparison Apache Impala vs. Firebase Realtime Database vs. Microsoft Azure Cosmos DB vs. NuoDB vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Analytic DBMS for Hadoop

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

Globally distributed, horizontally scalable, multi-model database service

NuoDB is a webscale distributed database that supports SQL and ACID transactions

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Relational DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Document store

Secondary database models

Document store

Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Website

impala.apache.org

firebase.google.com/products/realtime-database

azure.microsoft.com/services/cosmos-db

www.3ds.com/nuodb-distributed-sql-database

github.com/torodb/server

Technical documentation

impala.apache.org/impala-docs.html

firebase.google.com/docs/database

learn.microsoft.com/azure/cosmos-db

doc.nuodb.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

acquired by Google 2014

Microsoft

Dassault Systèmes

originally NuoDB, Inc.

8Kdata

Initial release

2013

2012

2014

2013

2016

Current release

4.1.0, June 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

All OS with a Java 7 VM

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

string, integer, double, boolean, date, object_id

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

Android
iOS
JavaScript API
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Java
JavaScript
Objective-C

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

limited functionality with using 'rules'

JavaScript

Java, SQL

Triggers

Callbacks are triggered when data changes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

data is dynamically stored/cached on the nodes where it is read/written

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

yes

Managed transparently by NuoDB

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Multi-item ACID transactions with snapshot isolation within a partition

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Temporary table

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes, based on authentication and database rules

Access rights can be defined down to the item level

Standard SQL roles/ privileges, Administrative Users

Access rights for users and roles

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