DBMS > Apache IoTDB vs. EJDB vs. Graphite vs. HugeGraph

System Properties Comparison Apache IoTDB vs. EJDB vs. Graphite vs. HugeGraph

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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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

A fast-speed and highly-scalable Graph DBMS

Primary database model

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

iotdb.apache.org

github.com/Softmotions/ejdb

github.com/graphite-project/graphite-web

github.com/hugegraph
hugegraph.apache.org

Technical documentation

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

github.com/Softmotions/ejdb/blob/master/README.md

graphite.readthedocs.io

hugegraph.apache.org/docs

Developer

Apache Software Foundation

Softmotions

Chris Davis

Baidu

Initial release

2018

2012

2006

2018

Current release

1.1.0, April 2023

0.9

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPLv2

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

Python

Java

Server operating systems

All OS with a Java VM (>= 1.8)

server-less

Linux
Unix

Linux
macOS
Unix

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

Numeric data only

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

SQL

Support of SQL

SQL-like query language

APIs and other access methods

JDBC
Native API

in-process shared library

HTTP API
Sockets

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

JavaScript (Node.js)
Python

Groovy
Java
Python

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

yes

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

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

none

yes

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

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

none

Eventual Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

edges in graph

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

locking

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

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More resources

DB-Engines blog posts

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4 July 2016, Matthias Gelbmann

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1 June 2015, Paul Andlinger

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