DBMS > Apache IoTDB vs. Drizzle vs. Kdb vs. Riak KV

System Properties Comparison Apache IoTDB vs. Drizzle vs. Kdb vs. Riak KV

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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

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.

High performance Time Series DBMS

Distributed, fault tolerant key-value store

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS
Vector DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Relational DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	7.73
Rank	#45	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Website

iotdb.apache.org

kx.com

Technical documentation

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

code.kx.com

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Apache Software Foundation

Drizzle project, originally started by Brian Aker

Kx Systems, a division of First Derivatives plc

OpenSource, formerly Basho Technologies

Initial release

2018

2008

2000

kdb was released 2000, kdb+ in 2003

2009

Current release

1.1.0, April 2023

7.2.4, September 2012

3.6, May 2018

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GNU GPL

commercial

free 32-bit version

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
OS X

Linux
OS X
Solaris
Windows

Linux
OS X

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.

yes

Secondary indexes

yes

table attribute 'grouped'

restricted

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like query language (q)

APIs and other access methods

JDBC
Native API

JDBC

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

HTTP API
Native Erlang Interface

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Java
PHP

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions

Erlang

Triggers

yes

hooks for callbacks inside the server can be used.

yes

with views

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal partitioning

Sharding

no "single point of failure"

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

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

similar paradigm used for internal processing

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

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

User concepts

Access control

yes

Pluggable authentication mechanisms

e.g. LDAP, HTTP

rights management via user accounts

yes, using Riak Security

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

Licensing and pricing models

upon request
» more

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

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