DBMS > HugeGraph vs. IRONdb vs. Spark SQL vs. SQLite vs. TDengine

System Properties Comparison HugeGraph vs. IRONdb vs. Spark SQL vs. SQLite vs. TDengine

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A fast-speed and highly-scalable Graph DBMS

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Spark SQL is a component on top of 'Spark Core' for structured data processing

Widely used embeddable, in-process RDBMS

Time Series DBMS and big data platform

Primary database model

Secondary database models

Relational DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	2.60
Rank	#107	Overall
	#8	Time Series DBMS

Website

github.com/hugegraph
hugegraph.apache.org

www.circonus.com/solutions/time-series-database/

spark.apache.org/sql

www.sqlite.org

github.com/taosdata/TDengine
tdengine.com

Technical documentation

hugegraph.apache.org/docs

docs.circonus.com/irondb/category/getting-started

spark.apache.org/docs/latest/sql-programming-guide.html

www.sqlite.org/docs.html

docs.tdengine.com

Developer

Baidu

Circonus LLC.

Apache Software Foundation

Dwayne Richard Hipp

TDEngine, previously Taos Data

Initial release

2018

2017

2014

2000

2019

Current release

0.9

V0.10.20, January 2018

3.5.0 ( 2.13), September 2023

3.46.0 (23 May 2024), May 2024

3.0, August 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

AGPL V3, also commercial editions available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Scala

Server operating systems

Linux
macOS
Unix

Linux

Linux
OS X
Windows

server-less

Linux
Windows

Data scheme

yes

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

not rigid because of 'dynamic typing' concept.

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

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

Standard SQL with extensions for time-series applications

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
RESTful HTTP API

Supported programming languages

Groovy
Java
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes, in Lua

Triggers

yes

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

yes

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

Automatic, metric affinity per node

yes, utilizing Spark Core

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

configurable replication factor, datacenter aware

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

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

via file-system locks

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

Users, roles and permissions

yes

More information provided by the system vendor

Specific characteristics

TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
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Competitive advantages

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Typical application scenarios

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
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Market metrics

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
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Licensing and pricing models

TDengine OSS is an open source, cloud native time series database. It includes built-in...
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News

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22 May 2024

Solving Long Query Performance Bottlenecks
22 May 2024

What Is Predictive Maintenance?
17 May 2024

Can Typical Time-Series Databases Replace Data Historians?
8 May 2024

TDengine 3.3.0.0 Release Notes
7 May 2024

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