DBMS > Firebase Realtime Database vs. Google BigQuery vs. Hawkular Metrics vs. LokiJS vs. NCache

System Properties Comparison Firebase Realtime Database vs. Google BigQuery vs. Hawkular Metrics vs. LokiJS vs. NCache

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

In-memory JavaScript DBMS

Open-Source and Enterprise in-memory Key-Value Store

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	15.00
Rank	#39	Overall
	#6	Document stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	0.48
Rank	#262	Overall
	#42	Document stores

Score	0.98
Rank	#196	Overall
	#29	Key-value stores

Website

firebase.google.com/products/realtime-database

cloud.google.com/bigquery

www.hawkular.org

github.com/techfort/LokiJS

www.alachisoft.com/ncache

Technical documentation

firebase.google.com/docs/database

cloud.google.com/bigquery/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

techfort.github.io/LokiJS

www.alachisoft.com/resources/docs

Developer

Google

acquired by Google 2014

Google

Community supported by Red Hat

Alachisoft

Initial release

2012

2010

2014

2005

Current release

5.3.3, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Enterprise Edition 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

Java

JavaScript

C#, .NET, .NET Core, Java

Server operating systems

hosted

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Linux
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

via views

yes

SQL

Support of SQL

yes

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP/JSON API

HTTP REST

JavaScript API

IDistributedCache
JCache
LINQ
Proprietary native API

Supported programming languages

Java
JavaScript
Objective-C

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
Python
Ruby

JavaScript

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

limited functionality with using 'rules'

user defined functions

in JavaScript

View functions in JavaScript

support for stored procedures with SQL-Server CLR

Triggers

Callbacks are triggered when data changes

yes

via Hawkular Alerting

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

yes, with selectable consistency level

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

none

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Foreign keys

Referential integrity

Transaction concepts

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

yes

Since BigQuery is designed for querying data

atomic operations within a single collection possible

optimistic locking and pessimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

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Market metrics

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