DBMS > Apache IoTDB vs. Google Cloud Datastore vs. PostgreSQL vs. TempoIQ

System Properties Comparison Apache IoTDB vs. Google Cloud Datastore vs. PostgreSQL vs. TempoIQ

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Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed 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

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

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Primary database model

Time Series DBMS

Document store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Time Series DBMS

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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	636.25
Rank	#4	Overall
	#4	Relational DBMS

Website

iotdb.apache.org

cloud.google.com/datastore

www.postgresql.org

tempoiq.com (offline)

Technical documentation

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

cloud.google.com/datastore/docs

www.postgresql.org/docs

Developer

Apache Software Foundation

Google

PostgreSQL Global Development Group

www.postgresql.org/developer

TempoIQ

Initial release

2018

2008

1989

1989: Postgres, 1996: PostgreSQL

2012

Current release

1.1.0, April 2023

16.3, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

yes

standard with numerous extensions

APIs and other access methods

JDBC
Native API

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

yes

using Google App Engine

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Callbacks using the Google Apps Engine

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

partitioning by range, list and (since PostgreSQL 11) by hash

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

Source-replica replication

other methods possible by using 3rd party extensions

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

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

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)

fine grained access rights according to SQL-standard

simple authentication-based access control

More information provided by the system vendor

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