DBMS > Apache IoTDB vs. HugeGraph vs. Hyprcubd vs. OpenTSDB vs. PostgreSQL

System Properties Comparison Apache IoTDB vs. HugeGraph vs. Hyprcubd vs. OpenTSDB vs. PostgreSQL

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it 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

A fast-speed and highly-scalable Graph DBMS

Serverless Time Series DBMS

Scalable Time Series DBMS based on HBase

Widely used open source RDBMS

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

Primary database model

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

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	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.68
Rank	#146	Overall
	#12	Time Series DBMS

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Website

iotdb.apache.org

github.com/hugegraph
hugegraph.apache.org

hyprcubd.com (offline)

opentsdb.net

www.postgresql.org

Technical documentation

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

hugegraph.apache.org/docs

opentsdb.net/docs/build/html/index.html

www.postgresql.org/docs

Developer

Apache Software Foundation

Baidu

Hyprcubd, Inc.

currently maintained by Yahoo and other contributors

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2018

2011

1989

1989: Postgres, 1996: PostgreSQL

Current release

1.1.0, April 2023

0.9

16.3, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Open Source

LGPL

Open Source

BSD

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)

Linux
macOS
Unix

hosted

Linux
Windows

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

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

numeric data for metrics, strings for tags

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

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language

yes

standard with numerous extensions

APIs and other access methods

JDBC
Native API

Java API
RESTful HTTP API
TinkerPop Gremlin

gRPC (https)

HTTP API
Telnet API

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Groovy
Java
Python

Erlang
Go
Java
Python
R
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

user defined functions

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

based on HBase

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

yes

depending on used storage backend, e.g. Cassandra and HBase

selectable replication factor

based on HBase

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

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Immediate Consistency

based on HBase

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

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

Users, roles and permissions

token access

fine grained access rights according to SQL-standard

More information provided by the system vendor

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