DBMS > Apache IoTDB vs. Dragonfly vs. HBase vs. JSqlDb vs. Riak TS

System Properties Comparison Apache IoTDB vs. Dragonfly vs. HBase vs. JSqlDb vs. Riak TS

Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it 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

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

JavaScript Query Language Database, stores JavaScript objects and primitives

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Time Series DBMS

Key-value store

Wide column store

Document store
Object oriented 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	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Website

iotdb.apache.org

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

hbase.apache.org

jsqldb.org (offline)

Technical documentation

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

www.dragonflydb.io/docs

hbase.apache.org/book.html

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

Developer

Apache Software Foundation

DragonflyDB team and community contributors

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Konrad von Backstrom

Open Source, formerly Basho Technologies

Initial release

2018

2023

2008

2018

2015

Current release

1.1.0, April 2023

1.0, March 2023

2.3.4, January 2021

0.8, December 2018

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSL 1.1

Open Source

Apache version 2

Open Source

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++

Java

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
Unix
Windows

using Cygwin

Linux
macOS
Windows

Linux
OS X

Data scheme

yes

scheme-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

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

options to bring your own types, AVRO

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

restricted

SQL

Support of SQL

SQL-like query language

yes, limited

APIs and other access methods

JDBC
Native API

Proprietary protocol

RESP - REdis Serialization Protocol

Java API
RESTful HTTP API
Thrift

HTTP API
Native Erlang Interface

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

JavaScript

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

Lua

yes

Coprocessors in Java

functions in JavaScript

Erlang

Triggers

yes

publish/subscribe channels provide some trigger functionality

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

none

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

Source-replica replication

Multi-source replication
Source-replica replication

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

Atomic execution of command blocks and scripts

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

using RocksDB

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

Password-based authentication

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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