DBMS > Drizzle vs. Galaxybase vs. Spark SQL vs. ToroDB

System Properties Comparison Drizzle vs. Galaxybase vs. Spark SQL vs. ToroDB

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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.

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

Description

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

Scalable, ACID-compliant native distributed parallel graph platform

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

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

Primary database model

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

Score	0.00
Rank	#383	Overall
	#39	Graph DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

galaxybase.com

spark.apache.org/sql

github.com/torodb/server

Technical documentation

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

Developer

Drizzle project, originally started by Brian Aker

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

Apache Software Foundation

8Kdata

Initial release

2008

2017

2014

2016

Current release

7.2.4, September 2012

Nov 20, November 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Apache 2.0

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C and Java

Scala

Java

Server operating systems

FreeBSD
Linux
OS X

Linux

Linux
OS X
Windows

All OS with a Java 7 VM

Data scheme

yes

Strong typed schema

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

Go
Java
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined procedures and functions

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

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

Role-based access control

Access rights for users and roles

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
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1 July 2013, Matthias Gelbmann

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