DBMS > Apache IoTDB vs. Google Cloud Bigtable vs. Lovefield vs. Machbase Neo vs. RethinkDB

System Properties Comparison Apache IoTDB vs. Google Cloud Bigtable vs. Lovefield vs. Machbase Neo vs. RethinkDB

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Embeddable relational database for web apps written in pure JavaScript

TimeSeries DBMS for AIoT and BigData

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

Primary database model

Time Series DBMS

Key-value store
Wide column store

Relational DBMS

Time Series DBMS

Document store

Secondary database models

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	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Website

iotdb.apache.org

cloud.google.com/bigtable

google.github.io/lovefield

machbase.com

rethinkdb.com

Technical documentation

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

cloud.google.com/bigtable/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

machbase.com/dbms

rethinkdb.com/docs

Developer

Apache Software Foundation

Google

Machbase

The Linux Foundation

since July 2017

Initial release

2018

2015

2014

2013

2009

Current release

1.1.0, April 2023

2.1.12, February 2017

V8.0, August 2023

2.4.1, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

commercial

free test version available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

JavaScript

C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

via JavaScript builder pattern

SQL-like query language

APIs and other access methods

JDBC
Native API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
Go
Java
JavaScript (Node.js)
Python

JavaScript

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Using read-only observers

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

Sharding

range based

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

Internal replication in Colossus, and regional replication between two clusters in different zones

none

selectable replication factor

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 (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

volatile and lookup table

User concepts

Access control

yes

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

simple password-based access control

yes

users and table-level permissions

More information provided by the system vendor

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Related products and services

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