DBMS > AlaSQL vs. atoti vs. Derby vs. Splice Machine

System Properties Comparison AlaSQL vs. atoti vs. Derby vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Relational DBMS

Object oriented DBMS

Relational DBMS

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

Score	0.51
Rank	#256	Overall
	#40	Document stores
	#118	Relational DBMS

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

Technical documentation

github.com/AlaSQL/alasql

docs.atoti.io

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

splicemachine.com/how-it-works

Developer

Andrey Gershun & Mathias R. Wulff

ActiveViam

Apache Software Foundation

Splice Machine

Initial release

2014

1997

2014

Current release

10.17.1.0, November 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

MIT-License

commercial

free versions available

Open Source

Apache version 2

Open Source

AGPL 3.0, commercial license available

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

JavaScript

Java

Server operating systems

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

All OS with a Java VM

Linux
OS X
Solaris
Windows

Data scheme

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

Multidimensional Expressions (MDX)

yes

APIs and other access methods

JavaScript API

JDBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

JavaScript

Java

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Python

Java Stored Procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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