DBMS > AlaSQL vs. Drizzle vs. Faircom EDGE vs. Splice Machine vs. TerminusDB

System Properties Comparison AlaSQL vs. Drizzle vs. Faircom EDGE vs. Splice Machine vs. TerminusDB

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

JavaScript DBMS library

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

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS

Secondary database models

Document store
RDF store

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

alasql.org

www.faircom.com/products/faircom-edge

splicemachine.com

terminusdb.com

Technical documentation

github.com/AlaSQL/alasql

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

splicemachine.com/how-it-works

terminusdb.github.io/terminusdb/#

Developer

Andrey Gershun & Mathias R. Wulff

Drizzle project, originally started by Brian Aker

FairCom Corporation

Splice Machine

DataChemist Ltd.

Initial release

2014

2008

1979

2014

2018

Current release

7.2.4, September 2012

V3, October 2020

3.1, March 2021

11.0.0, January 2023

License

Commercial or Open Source

Open Source

MIT-License

Open Source

GNU GPL

commercial

Restricted, free version available

Open Source

AGPL 3.0, commercial license available

Open Source

GPL 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

JavaScript

C++

ANSI C, C++

Java

Prolog, Rust

Server operating systems

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

FreeBSD
Linux
OS X

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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.

yes

with proprietary extensions

yes

ANSI SQL queries

yes

SQL-like query language (WOQL)

APIs and other access methods

JavaScript API

JDBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

JavaScript

C
C++
Java
PHP

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

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

JavaScript
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Java

yes

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

File partitioning

Customizable business rules for partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication
Source-replica replication

Journaling Streams

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
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

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

across SQL and NoSQL

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

in-memory journaling

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

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

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

Role-based access control

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More resources

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