DBMS > Apache IoTDB vs. JaguarDB vs. Spark SQL vs. SQream DB

System Properties Comparison Apache IoTDB vs. JaguarDB vs. Spark SQL vs. SQream DB

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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

Performant, highly scalable DBMS for AI and IoT applications

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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Time Series DBMS

Key-value store
Vector DBMS

Relational DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#14	Vector DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.74
Rank	#224	Overall
	#103	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

docs.sqream.com

Developer

Apache Software Foundation

DataJaguar, Inc.

Apache Software Foundation

SQream Technologies

Initial release

2018

2015

2014

2017

Current release

1.1.0, April 2023

3.3 July 2023

3.5.0 ( 2.13), September 2023

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL V3.0

Open Source

Apache 2.0

commercial

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++

the server part. Clients available in other languages

Scala

C++, CUDA, Haskell, Java, Scala

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
OS X
Windows

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL 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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
Native API

JDBC
ODBC

.Net
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Java
Python
R
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions in Python

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

horizontal and vertical partitioning

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and 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

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

rights management via user accounts

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