DBMS > Drizzle vs. LeanXcale vs. Machbase Neo vs. SwayDB

System Properties Comparison Drizzle vs. LeanXcale vs. Machbase Neo vs. SwayDB

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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.

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

TimeSeries DBMS for AIoT and BigData

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Key-value store

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

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

www.leanxcale.com

machbase.com

swaydb.simer.au

Technical documentation

machbase.com/dbms

Developer

Drizzle project, originally started by Brian Aker

LeanXcale

Machbase

Simer Plaha

Initial release

2008

2015

2013

2018

Current release

7.2.4, September 2012

V8.0, August 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

free test version available

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

C++

Scala

Server operating systems

FreeBSD
Linux
OS X

Linux
macOS
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

through Apache Derby

SQL-like query language

APIs and other access methods

JDBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Supported programming languages

C
C++
Java
PHP

C
Java
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Java
Kotlin
Scala

Server-side scripts

Stored procedures

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

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

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

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

volatile and lookup table

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

simple password-based access control

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