DBMS > Apache IoTDB vs. Apache Kylin vs. Graph Engine vs. RethinkDB vs. ToroDB

System Properties Comparison Apache IoTDB vs. Apache Kylin vs. Graph Engine vs. RethinkDB vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB 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 distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Time Series DBMS

Relational DBMS

Graph DBMS
Key-value store

Document store

Secondary database models

Spatial DBMS

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

Score	1.41
Rank	#151	Overall
	#14	Time Series DBMS

Score	1.10
Rank	#170	Overall
	#77	Relational DBMS

Score	0.59
Rank	#235	Overall
	#21	Graph DBMS
	#33	Key-value stores

Score	2.52
Rank	#105	Overall
	#20	Document stores

Website

github.com/torodb/server

Technical documentation

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

kylin.apache.org/docs

www.graphengine.io/docs/manual

rethinkdb.com/docs

Developer

Apache Software Foundation

Apache Software Foundation, originally contributed from eBay Inc

Microsoft

The Linux Foundation

since July 2017

8Kdata

Initial release

2018

2015

2010

2009

2016

Current release

1.1.0, April 2023

3.1.0, July 2020

2.4.1, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

Apache Version 2

Open Source

AGPL-V3

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

.NET and C

C++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

.NET

Linux
OS X
Windows

All OS with a Java 7 VM

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes

string, integer, double, boolean, date, object_id

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

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

JDBC
Native API

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
F#
Visual Basic

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

Server-side scripts

Stored procedures

yes

Triggers

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning

Sharding

range based

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

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

users and table-level permissions

Access rights for users and roles

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