DBMS > GridDB vs. Oracle NoSQL vs. Splunk vs. Typesense

System Properties Comparison GridDB vs. Oracle NoSQL vs. Splunk vs. Typesense

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Editorial information provided by DB-Engines

Name

Description

Scalable in-memory time series database optimized for IoT and Big Data

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Analytics Platform for Big Data

A typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity

Primary database model

Time Series DBMS

Document store
Key-value store
Relational DBMS

Search engine

Secondary database models

Key-value store
Relational DBMS

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

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	89.10
Rank	#14	Overall
	#2	Search engines

Score	0.76
Rank	#219	Overall
	#14	Search engines

Website

griddb.net

www.oracle.com/database/nosql/technologies/nosql

www.splunk.com

typesense.org

Technical documentation

docs.griddb.net

docs.oracle.com/en/database/other-databases/nosql-database/index.html

docs.splunk.com/Documentation/Splunk

typesense.org/docs

Developer

Toshiba Corporation

Oracle

Splunk Inc.

Initial release

2013

2011

2003

2015

Current release

5.1, August 2022

24.1, May 2024

License

Commercial or Open Source

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

commercial

Limited free edition and free developer edition available

Open Source

GPL V3

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

C++

Java

C++

Server operating systems

Linux

Linux
Solaris SPARC/x86

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

schema-free

pre-defined schema optional

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

optional

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

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like DML and DDL statements

Splunk Search Processing Language for search commands

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

RESTful HTTP API

HTTP REST

RESTful HTTP API

Supported programming languages

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

C
C#
Go
Java
JavaScript (Node.js)
Python

C#
Java
JavaScript
PHP
Python
Ruby

.Net

community maintained
Clojure

community maintained
Dart

community maintained
Go

community maintained
Java

community maintained
JavaScript
Perl

community maintained
PHP
Python
Ruby
Rust

community maintained
Swift

community maintained

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Multi-source replication

Multi-source replication using RAFT

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID at container level

configurable

ACID within a storage node (=shard)

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

off heap cache

yes

User concepts

Access control

Access rights for users can be defined per database

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

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