DBMS > Apache IoTDB vs. DataFS vs. Drizzle vs. Google Cloud Datastore

System Properties Comparison Apache IoTDB vs. DataFS vs. Drizzle vs. Google Cloud Datastore

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle 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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

Primary database model

Secondary database models

Graph DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Website

iotdb.apache.org

newdatabase.com

cloud.google.com/datastore

Technical documentation

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

dev.mobiland.com/Overview.xsp

cloud.google.com/datastore/docs

Developer

Apache Software Foundation

Mobiland AG

Drizzle project, originally started by Brian Aker

Google

Initial release

2018

2008

Current release

1.1.0, April 2023

1.1.263, October 2022

7.2.4, September 2012

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GNU GPL

commercial

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++

Server operating systems

All OS with a Java VM (>= 1.8)

Windows

FreeBSD
Linux
OS X

hosted

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

yes

with proprietary extensions

SQL-like query language (GQL)

APIs and other access methods

JDBC
Native API

.NET Client API
Proprietary client DLL
WinRT client

JDBC

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C#
C++
VB.Net

C
C++
Java
PHP

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

Server-side scripts

Stored procedures

yes

using Google App Engine

Triggers

yes

no, except callback-events from server when changes happened

hooks for callbacks inside the server can be used.

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

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

Proprietary Sharding system

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
Source-replica replication

Multi-source replication using Paxos

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

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.

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

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

Windows-Profile

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

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More resources

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