DBMS > Apache IoTDB vs. Blueflood vs. Drizzle vs. VictoriaMetrics

System Properties Comparison Apache IoTDB vs. Blueflood vs. Drizzle vs. VictoriaMetrics

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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

Scalable TimeSeries DBMS based on Cassandra

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

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	1.23
Rank	#172	Overall
	#15	Time Series DBMS

Website

iotdb.apache.org

blueflood.io

victoriametrics.com

Technical documentation

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

github.com/rax-maas/blueflood/wiki

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Apache Software Foundation

Rackspace

Drizzle project, originally started by Brian Aker

VictoriaMetrics

Initial release

2018

2013

2008

2018

Current release

1.1.0, April 2023

7.2.4, September 2012

v1.91, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

GNU GPL

Open Source

Apache Version 2.0

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++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X

FreeBSD
Linux
OS X

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

predefined scheme

yes

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

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC
Native API

HTTP REST

JDBC

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Java
PHP

Server-side scripts

Stored procedures

yes

Triggers

yes

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

based on Cassandra

Sharding

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

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Foreign keys

Referential integrity

yes

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

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More resources

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