DBMS > Apache IoTDB vs. Drizzle vs. RethinkDB vs. SAP IQ vs. SwayDB

System Properties Comparison Apache IoTDB vs. Drizzle vs. RethinkDB vs. SAP IQ 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 IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Columnar RDBMS optimized for Big Data analytics

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

Primary database model

column-oriented

Secondary database models

Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	2.64
Rank	#108	Overall
	#52	Relational DBMS

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

Website

iotdb.apache.org

rethinkdb.com

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

swaydb.simer.au

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

rethinkdb.com/docs

help.sap.com/docs/SAP_IQ

Developer

Apache Software Foundation

Drizzle project, originally started by Brian Aker

The Linux Foundation

since July 2017

SAP, formerly Sybase

Simer Plaha

Initial release

2018

2008

2009

1994

2018

Current release

1.1.0, April 2023

7.2.4, September 2012

2.4.1, August 2020

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GNU GPL

Open Source

Apache Version 2

commercial

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++

Scala

Server operating systems

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
OS X

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

SQL-like query language

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
Native API

JDBC

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Java
PHP

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

C
C#
C++
Java
Perl
PHP
Python
Ruby

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

hooks for callbacks inside the server can be used.

Client-side triggers through changefeeds

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

range based

shared disk or shared nothing architectures with SAP IQ Multiplexer

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication
Source-replica replication

Source-replica replication

SAP/Sybase Replication Server

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-document operations

ACID

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

yes

Pluggable authentication mechanisms

e.g. LDAP, HTTP

yes

users and table-level permissions

fine grained access rights according to SQL-standard

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