DBMS > Apache IoTDB vs. Google Cloud Datastore vs. Kyligence Enterprise vs. MySQL

System Properties Comparison Apache IoTDB vs. Google Cloud Datastore vs. Kyligence Enterprise vs. MySQL

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Editorial information provided by DB-Engines

Name

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A distributed analytics engine for big data, built on top of Apache Kylin

Widely used open source RDBMS

Primary database model

Time Series DBMS

Document store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

iotdb.apache.org

cloud.google.com/datastore

kyligence.io/kyligence-enterprise

www.mysql.com

Technical documentation

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

cloud.google.com/datastore/docs

dev.mysql.com/doc

Developer

Apache Software Foundation

Google

Kyligence, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2018

2008

2016

1995

Current release

1.1.0, April 2023

8.3.0, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

ANSI SQL for queries (using Apache Calcite)

yes

with proprietary extensions

APIs and other access methods

JDBC
Native API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

proprietary syntax

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

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 using Paxos

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users with fine-grained authorization concept

no user groups or roles

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