DBMS > Drizzle vs. Firebase Realtime Database vs. HugeGraph vs. Spark SQL

System Properties Comparison Drizzle vs. Firebase Realtime Database vs. HugeGraph vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

A fast-speed and highly-scalable Graph DBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

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	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

firebase.google.com/products/realtime-database

github.com/hugegraph
hugegraph.apache.org

spark.apache.org/sql

Technical documentation

firebase.google.com/docs/database

hugegraph.apache.org/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Drizzle project, originally started by Brian Aker

Google

acquired by Google 2014

Baidu

Apache Software Foundation

Initial release

2008

2012

2018

2014

Current release

7.2.4, September 2012

0.9

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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++

Java

Scala

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux
macOS
Unix

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

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.

Secondary indexes

yes

also supports composite index and range index

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC

Android
iOS
JavaScript API
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

Java
JavaScript
Objective-C

Groovy
Java
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

limited functionality with using 'rules'

asynchronous Gremlin script jobs

Triggers

hooks for callbacks inside the server can be used.

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

yes, based on authentication and database rules

Users, roles and permissions

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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12 December 2019, Paul Andlinger

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