DBMS > Apache IoTDB vs. HugeGraph vs. TimescaleDB

System Properties Comparison Apache IoTDB vs. HugeGraph vs. TimescaleDB

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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

A fast-speed and highly-scalable Graph DBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Time Series DBMS

Graph DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	0.15
Rank	#339	Overall
	#33	Graph DBMS

Score	4.87
Rank	#74	Overall
	#4	Time Series DBMS

Website

iotdb.apache.org

github.com/hugegraph
hugegraph.apache.org

www.timescale.com

Technical documentation

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

hugegraph.apache.org/docs

docs.timescale.com

Developer

Apache Software Foundation

Baidu

Timescale

Initial release

2018

2017

Current release

1.1.0, April 2023

0.9

2.13.0, November 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
macOS
Unix

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
Native API

Java API
RESTful HTTP API
TinkerPop Gremlin

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

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

yes, across time and space (hash partitioning) attributes

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

Source-replica replication with hot standby and reads on replicas

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

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

fine grained access rights according to SQL-standard

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