DBMS > atoti vs. Drizzle vs. EXASOL vs. ReductStore vs. SwayDB

System Properties Comparison atoti vs. Drizzle vs. EXASOL vs. ReductStore vs. SwayDB

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

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

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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

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

Primary database model

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

atoti.io

www.exasol.com

github.com/reductstore
www.reduct.store

swaydb.simer.au

Technical documentation

docs.atoti.io

www.exasol.com/resources

www.reduct.store/docs

Developer

ActiveViam

Drizzle project, originally started by Brian Aker

Exasol

ReductStore LLC

Simer Plaha

Initial release

2008

2000

2023

2018

Current release

7.2.4, September 2012

1.9, March 2024

License

Commercial or Open Source

commercial

free versions available

Open Source

GNU GPL

commercial

Open Source

Business Source License 1.1

Open Source

GNU Affero GPL V3.0

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

Java

C++

C++, Rust

Scala

Server operating systems

FreeBSD
Linux
OS X

Docker
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

Multidimensional Expressions (MDX)

yes

with proprietary extensions

yes

APIs and other access methods

JDBC

.Net
JDBC
ODBC
WebSocket

HTTP API

Supported programming languages

C
C++
Java
PHP

Java
Lua
Python
R

C++
JavaScript (Node.js)
Python
Rust

Java
Kotlin
Scala

Server-side scripts

Stored procedures

Python

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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, multi-version concurrency control (MVCC)

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

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

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