DBMS > Apache IoTDB vs. GBase vs. IBM Db2 warehouse vs. Rockset

System Properties Comparison Apache IoTDB vs. GBase vs. IBM Db2 warehouse vs. Rockset

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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

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Cloud-based data warehousing service

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Time Series DBMS

Relational DBMS

Document store

Secondary database models

Relational DBMS
Search engine

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Website

iotdb.apache.org

www.gbase.cn

www.ibm.com/products/db2/warehouse

rockset.com

Technical documentation

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

docs.rockset.com

Developer

Apache Software Foundation

General Data Technology Co., Ltd.

IBM

Rockset

Initial release

2018

2004

2014

2019

Current release

1.1.0, April 2023

GBase 8a, GBase 8s, GBase 8c

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, Java, Python

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

dynamic typing

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.

yes

Import/export of XML data possible

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

Standard with numerous extensions

yes

Read-only SQL queries, including JOINs

APIs and other access methods

JDBC
Native API

ADO.NET
C API
JDBC
ODBC

.NET Client API
JDBC
ODBC
OLE DB

HTTP REST

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

PL/SQL, SQL PL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Automatic sharding

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

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

Eventual 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

Access rights for users and organizations can be defined via Rockset console

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