DBMS > Apache IoTDB vs. EsgynDB vs. ITTIA vs. JaguarDB vs. Spark SQL

System Properties Comparison Apache IoTDB vs. EsgynDB vs. ITTIA vs. JaguarDB vs. Spark SQL

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Edge Time Series DBMS with Real-Time Stream Processing

Performant, highly scalable DBMS for AI and IoT applications

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

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.39
Rank	#273	Overall
	#24	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

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

www.jaguardb.com/support.html

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

Developer

Apache Software Foundation

Esgyn

ITTIA L.L.C.

DataJaguar, Inc.

Apache Software Foundation

Initial release

2018

2015

2007

2015

2014

Current release

1.1.0, April 2023

8.7

3.3 July 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL V3.0

Open Source

Apache 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

C++, Java

C and C++

C++

the server part. Clients available in other languages

Scala

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

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

SQL

Support of SQL

SQL-like query language

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like DML and DDL statements

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC

JDBC
ODBC
RESTful HTTP API
WebSocket

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages supporting JDBC/ODBC/ADO.Net

C
C++
JavaScript (Node.js)
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

yes, utilizing Spark Core

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 between multi datacenters

yes

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

Database file passwords

rights management via user accounts

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