DBMS > GridDB vs. Hive vs. OpenSearch vs. Sphinx

System Properties Comparison GridDB vs. Hive vs. OpenSearch vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Time Series DBMS

Relational DBMS

Search engine

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	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	16.03
Rank	#37	Overall
	#4	Search engines

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

griddb.net

hive.apache.org

github.com/opensearch-project
opensearch.org

sphinxsearch.com

Technical documentation

docs.griddb.net

cwiki.apache.org/confluence/display/Hive/Home

opensearch.org/docs/latest

sphinxsearch.com/docs

Developer

Toshiba Corporation

Apache Software Foundation

initially developed by Facebook

Amazon Web Services

Sphinx Technologies Inc.

Initial release

2013

2012

2021

2001

Current release

5.1, August 2022

3.1.3, April 2022

2.5.0, January 2023

3.5.1, February 2023

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

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

Linux

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

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

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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.

Secondary indexes

yes

All search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like DML and DDL statements

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC
Thrift

Java API
RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

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

C++
Java
PHP
Python

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

none

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

yes

query execution via MapReduce

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

Foreign keys

Referential integrity

Transaction concepts

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

ACID at container level

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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, groups 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...
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OpenSearch is a highly scalable and extensible open-source software suite for search,...
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Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
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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...
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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)
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The OpenSearch Project offers open source software licensed under Apache License...
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