DBMS > Apache IoTDB vs. IBM Db2 warehouse vs. LeanXcale vs. SQream DB

System Properties Comparison Apache IoTDB vs. IBM Db2 warehouse vs. LeanXcale 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

Cloud-based data warehousing service

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

Primary database model

Time Series DBMS

Relational DBMS

Key-value store
Relational 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	1.37
Rank	#160	Overall
	#74	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

iotdb.apache.org

www.ibm.com/products/db2/warehouse

www.leanxcale.com

sqream.com

Technical documentation

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

docs.sqream.com

Developer

Apache Software Foundation

IBM

LeanXcale

SQream Technologies

Initial release

2018

2014

2015

2017

Current release

1.1.0, April 2023

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

through Apache Derby

yes

APIs and other access methods

JDBC
Native API

.NET Client API
JDBC
ODBC
OLE DB

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

.Net
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C
Java
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

PL/SQL, SQL PL

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

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

yes

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

Immediate Consistency

Foreign keys

Referential integrity

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

fine grained access rights according to SQL-standard

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