DBMS > Derby vs. Lovefield vs. SpaceTime vs. Spark SQL

System Properties Comparison Derby vs. Lovefield vs. SpaceTime vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Embeddable relational database for web apps written in pure JavaScript

SpaceTime is a spatio-temporal DBMS with a focus on performance.

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

Primary database model

Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

db.apache.org/derby

google.github.io/lovefield

www.mireo.com/spacetime

spark.apache.org/sql

Technical documentation

db.apache.org/derby/manuals/index.html

github.com/google/lovefield/blob/master/docs/spec_index.md

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

Developer

Apache Software Foundation

Google

Mireo

Apache Software Foundation

Initial release

1997

2014

2020

2014

Current release

10.17.1.0, November 2023

2.1.12, February 2017

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

JavaScript

C++

Scala

Server operating systems

All OS with a Java VM

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

tested with Chrome, Firefox, IE, Safari

Linux

Linux
OS X
Windows

Data scheme

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

A subset of ANSI SQL is implemented

SQL-like DML and DDL statements

APIs and other access methods

JDBC

RESTful HTTP API

JDBC
ODBC

Supported programming languages

Java

JavaScript

C#
C++
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Fixed-grid hypercubes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Real-time block device replication (DRBD)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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