DBMS > Apache IoTDB vs. GridGain

System Properties Comparison Apache IoTDB vs. GridGain

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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

GridGain is an in-memory computing platform, built on Apache Ignite

Primary database model

Time Series DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

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

Score	1.38
Rank	#151	Overall
	#14	Time Series DBMS

Score	1.31
Rank	#157	Overall
	#1	Columnar
	#26	Key-value stores
	#3	Object oriented DBMS
	#70	Relational DBMS

Website

iotdb.apache.org

www.gridgain.com

Technical documentation

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

www.gridgain.com/docs/index.html

Developer

Apache Software Foundation

GridGain Systems, Inc.

Initial release

2018

2007

Current release

1.1.0, April 2023

GridGain 8.5.1

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial, open source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, C++, .Net, Python, REST, SQL

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Solaris
Windows
z/OS

Data scheme

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

JDBC
Native API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

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

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 (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

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

Role-based access control
Security Hooks for custom implementations

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