DBMS > Drizzle vs. Faircom EDGE vs. GridDB vs. Sequoiadb vs. Splice Machine

System Properties Comparison Drizzle vs. Faircom EDGE vs. GridDB vs. Sequoiadb vs. Splice Machine

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.

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

Scalable in-memory time series database optimized for IoT and Big Data

NewSQL database with distributed OLTP and SQL

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Key-value store
Relational DBMS

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

Score	0.02
Rank	#369	Overall
	#57	Key-value stores
	#156	Relational DBMS

Score	1.91
Rank	#122	Overall
	#10	Time Series DBMS

Score	0.39
Rank	#267	Overall
	#42	Document stores
	#125	Relational DBMS

Score	0.54
Rank	#242	Overall
	#113	Relational DBMS

Website

www.faircom.com/products/faircom-edge

griddb.net

www.sequoiadb.com

splicemachine.com

Technical documentation

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

docs.griddb.net

www.sequoiadb.com/en/index.php?m=Files&a=index

splicemachine.com/how-it-works

Developer

Drizzle project, originally started by Brian Aker

FairCom Corporation

Toshiba Corporation

Sequoiadb Ltd.

Splice Machine

Initial release

2008

1979

2013

2014

Current release

7.2.4, September 2012

V3, October 2020

5.1, August 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Restricted, free version available

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Server: AGPL; Client: Apache V2

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

C++

ANSI C, C++

C++

Java

Server operating systems

FreeBSD
Linux
OS X

Android
Linux

ARM, x86
Raspbian
Windows

Linux

Linux
OS X
Solaris
Windows

Data scheme

yes

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

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

numerical, string, blob, geometry, boolean, timestamp

yes

oid, date, timestamp, binary, regex

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

with proprietary extensions

yes

ANSI SQL queries

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language

yes

APIs and other access methods

JDBC

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

proprietary protocol using JSON

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
C++
Java
PHP

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

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net
C++
Java
PHP
Python

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

JavaScript

yes

Java

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate consistency within container, eventual consistency across containers

Eventual 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

ACID

ACID at container level

Document is locked during a transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

yes, multi-version concurrency control (MVCC)

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

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

Access rights for users can be defined per database

simple password-based access control

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

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