DBMS > GridDB vs. OpenSearch vs. Oracle Berkeley DB vs. ToroDB

System Properties Comparison GridDB vs. OpenSearch vs. Oracle Berkeley DB vs. ToroDB

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Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

Widely used in-process key-value store

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Time Series DBMS

Search engine

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

Secondary database models

Key-value store
Relational DBMS

Vector DBMS

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

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	16.03
Rank	#37	Overall
	#4	Search engines

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

griddb.net

github.com/opensearch-project
opensearch.org

www.oracle.com/database/technologies/related/berkeleydb.html

github.com/torodb/server

Technical documentation

docs.griddb.net

opensearch.org/docs/latest

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Toshiba Corporation

Amazon Web Services

Oracle

originally developed by Sleepycat, which was acquired by Oracle

8Kdata

Initial release

2013

2021

1994

2016

Current release

5.1, August 2022

2.5.0, January 2023

18.1.40, May 2020

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

Apache Version 2.0

Open Source

commercial license available

Open Source

AGPL-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.

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Implementation language

C++

Java

C, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

Linux

All OS with a Java VM

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

All OS with a Java 7 VM

Data scheme

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

string, integer, double, boolean, date, object_id

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

only with the Berkeley DB XML edition

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

Supported programming languages

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

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

yes

Triggers

yes

by using the 'percolation' feature

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Source-replica replication

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID at container level

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

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

OpenSearch is a highly scalable and extensible open-source software suite for search,...
» more

Competitive advantages

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

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
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Typical application scenarios

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

The OpenSearch platform is used across a broad range of search , analytics, and observability...
» 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

The OpenSearch Project offers open source software licensed under Apache License...
» more

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