DBMS > Apache Drill vs. Apache IoTDB vs. Dragonfly

System Properties Comparison Apache Drill vs. Apache IoTDB vs. Dragonfly

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Primary database model

Document store
Relational DBMS

Time Series DBMS

Key-value store

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

Score	2.28
Rank	#119	Overall
	#23	Document stores
	#56	Relational DBMS

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	0.42
Rank	#271	Overall
	#38	Key-value stores

Website

drill.apache.org

iotdb.apache.org

github.com/dragonflydb/dragonfly
www.dragonflydb.io

Technical documentation

drill.apache.org/docs

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

www.dragonflydb.io/docs

Developer

Apache Software Foundation

DragonflyDB team and community contributors

Initial release

2012

2018

2023

Current release

1.20.3, January 2023

1.1.0, April 2023

1.0, March 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

BSL 1.1

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++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM (>= 1.8)

Linux

Data scheme

schema-free

yes

scheme-free

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

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 SELECT statement is SQL:2003 compliant

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
Native API

Proprietary protocol

RESP - REdis Serialization Protocol

Supported programming languages

C++

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

Server-side scripts

Stored procedures

user defined functions

yes

Lua

Triggers

yes

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of command blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

yes

Password-based authentication

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