DBMS > BigObject vs. Drizzle vs. etcd vs. eXtremeDB vs. Sequoiadb

System Properties Comparison BigObject vs. Drizzle vs. etcd vs. eXtremeDB vs. Sequoiadb

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

Analytic DBMS for real-time computations and queries

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

A distributed reliable key-value store

Natively in-memory DBMS with options for persistency, high-availability and clustering

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Key-value store

Relational DBMS
Time Series DBMS

Document store
Relational DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

bigobject.io

etcd.io
github.com/etcd-io/etcd

www.mcobject.com

www.sequoiadb.com

Technical documentation

docs.bigobject.io

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

www.mcobject.com/docs/extremedb.htm

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

Developer

BigObject, Inc.

Drizzle project, originally started by Brian Aker

McObject

Sequoiadb Ltd.

Initial release

2015

2008

2001

2013

Current release

7.2.4, September 2012

3.4, August 2019

8.2, 2021

License

Commercial or Open Source

commercial

free community edition available

Open Source

GNU GPL

Open Source

Apache Version 2.0

commercial

Open Source

Server: AGPL; Client: Apache V2

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 C++

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

FreeBSD
Linux
Windows

experimental

AIX
HP-UX
Linux
macOS
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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.

support of XML interfaces available

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

with the option: eXtremeSQL

SQL-like query language

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC

gRPC
JSON over HTTP

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

C
C++
Java
PHP

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
C
C#
C++
Java
Lua
Python
Scala

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

Lua

yes

JavaScript

Triggers

hooks for callbacks inside the server can be used.

yes, watching key changes

yes

by defining events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning / sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

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

simple password-based access control

More information provided by the system vendor

Specific characteristics

eXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
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Competitive advantages

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more

Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
» more

Licensing and pricing models

For server use cases, there is a simple per-server license irrespective of the number...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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