DBMS > GridGain vs. KairosDB vs. NSDb vs. ObjectBox

System Properties Comparison GridGain vs. KairosDB vs. NSDb vs. ObjectBox

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

Distributed Time Series DBMS based on Cassandra or H2

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Lightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors

Primary database model

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Time Series DBMS

Object oriented DBMS
Vector DBMS

Secondary database models

Time Series DBMS

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

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.60
Rank	#238	Overall
	#20	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	1.08
Rank	#179	Overall
	#6	Object oriented DBMS
	#9	Vector DBMS

Website

www.gridgain.com

github.com/kairosdb/kairosdb

nsdb.io

github.com/objectbox
objectbox.io

Technical documentation

www.gridgain.com/docs/index.html

kairosdb.github.io

nsdb.io/Architecture

docs.objectbox.io

Developer

GridGain Systems, Inc.

ObjectBox Limited

Initial release

2007

2013

2017

Current release

GridGain 8.5.1

1.2.2, November 2018

4.0 (May 2024)

License

Commercial or Open Source

commercial, open source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Bindings are released under Apache 2.0

Apache License 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++, .Net, Python, REST, SQL

Java

Java, Scala

C and C++

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

Linux
OS X
Windows

Linux
macOS

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes, plus "flex" map-like 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.

yes

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

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

SQL-like query language

APIs and other access methods

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

Graphite protocol
HTTP REST
Telnet API

gRPC
HTTP REST
WebSocket

Proprietary native API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Java
JavaScript

Node.js
PHP
Python

Java
Scala

C
C++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift

Server-side scripts

Stored procedures

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

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor

based on Cassandra

Data sync between devices allowing occasional connected databases to work completely offline

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

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

Using Apache Lucene

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

Role-based access control
Security Hooks for custom implementations

simple password-based access control

yes

More information provided by the system vendor

News

MongoDB Realm & Device Sync alternatives – ObjectBox
17 September 2024

Local AI – what it is and why we need it
11 September 2024

First on-device Vector Database (aka Semantic Index) for iOS
24 July 2024

Retrieval Augmented Generation (RAG) with vector databases: Expanding AI Capabilities
18 June 2024

The on-device Vector Database for Android and Java
29 May 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

GridGain Advances Its Unified Real-Time Data Platform to Help Enterprises Accelerate AI-Driven Data Processing
10 July 2024, insideBIGDATA

GridGain Sponsoring Strategic AI and Kafka Conferences This Month
4 September 2024, Datanami

GridGain in-memory data and generative AI
10 May 2024, Blocks & Files

Data Management News for the Week of July 12; Updates from Cloudera, HerculesAI, Oracle & More
12 July 2024, Solutions Review

GridGain Announces Call for Speakers for Virtual Apache Ignite Summit 2024
8 February 2024, Datanami

provided by Google News

Real-Time Performance and Health Monitoring Using Netdata
2 September 2019, CNX Software

provided by Google News

Share this page