DBMS > Apache IoTDB vs. Google Cloud Datastore vs. HarperDB vs. Machbase Neo vs. MySQL

System Properties Comparison Apache IoTDB vs. Google Cloud Datastore vs. HarperDB vs. Machbase Neo vs. MySQL

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

TimeSeries DBMS for AIoT and BigData

Widely used open source RDBMS

Primary database model

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	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

iotdb.apache.org

cloud.google.com/datastore

www.harperdb.io

machbase.com

www.mysql.com

Technical documentation

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

cloud.google.com/datastore/docs

docs.harperdb.io/docs

machbase.com/dbms

dev.mysql.com/doc

Developer

Apache Software Foundation

Google

HarperDB

Machbase

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2018

2008

2017

2013

1995

Current release

1.1.0, April 2023

3.1, August 2021

V8.0, August 2023

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

commercial

free test version available

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

Node.js

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux
OS X

Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

dynamic schema

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

JSON data types

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)

SQL-like data manipulation statements

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC
Native API

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Custom Functions

since release 3.1

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

A table resides as a whole on one (or more) nodes in a cluster

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

yes

the nodes on which a table resides can be defined

selectable replication factor

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

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

Atomic execution of specific operations

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

yes, using LMDB

yes

In-memory capabilities

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

yes

volatile and lookup table

yes

User concepts

Access control

yes

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

Access rights for users and roles

simple password-based access control

Users with fine-grained authorization concept

no user groups or roles

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