DBMS > Apache IoTDB vs. Google Cloud Datastore vs. Google Cloud Firestore vs. HEAVY.AI vs. Heroic

System Properties Comparison Apache IoTDB vs. Google Cloud Datastore vs. Google Cloud Firestore vs. HEAVY.AI vs. Heroic

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

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Secondary database models

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	7.85
Rank	#51	Overall
	#8	Document stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Website

iotdb.apache.org

cloud.google.com/datastore

firebase.google.com/products/firestore

github.com/heavyai/heavydb
www.heavy.ai

github.com/spotify/heroic

Technical documentation

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

cloud.google.com/datastore/docs

firebase.google.com/docs/firestore

docs.heavy.ai

spotify.github.io/heroic

Developer

Apache Software Foundation

Google

HEAVY.AI, Inc.

Spotify

Initial release

2018

2008

2017

2016

2014

Current release

1.1.0, April 2023

5.10, January 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

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

C++ and CUDA

Java

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux

Data scheme

yes

schema-free

yes

schema-free

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.

Secondary indexes

yes

via Elasticsearch

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

yes

APIs and other access methods

JDBC
Native API

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

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

yes

using Google App Engine

yes, Firebase Rules & Cloud Functions

Triggers

yes

Callbacks using the Google Apps Engine

yes, with Cloud Functions

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Round robin

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

Multi-source replication using Paxos

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

using Google Cloud Dataflow

Using 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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

yes

Concurrency

Support for concurrent manipulation of data

yes

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)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

fine grained access rights according to SQL-standard

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More resources

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