DBMS > Amazon Redshift vs. Bangdb vs. GridDB vs. Sequoiadb

System Properties Comparison Amazon Redshift vs. Bangdb vs. GridDB vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Converged and high performance database for device data, events, time series, document and graph

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

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Document store
Relational DBMS

Secondary database models

Spatial DBMS

Key-value store
Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

aws.amazon.com/redshift

bangdb.com

griddb.net

www.sequoiadb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.bangdb.com

docs.griddb.net

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Amazon (based on PostgreSQL)

Sachin Sinha, BangDB

Toshiba Corporation

Sequoiadb Ltd.

Initial release

2012

2013

Current release

BangDB 2.0, October 2021

5.1, August 2022

License

Commercial or Open Source

commercial

Open Source

BSD 3

Open Source

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

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

C++

Server operating systems

hosted

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

yes

numerical, string, blob, geometry, boolean, timestamp

yes

oid, date, timestamp, binary, regex

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

restricted

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL like support with command line tool

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language

APIs and other access methods

JDBC
ODBC

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

proprietary protocol using JSON

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
Python

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

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

JavaScript

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor, Knob for CAP (enterprise version only)

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

Tunable consistency, set CAP knob accordingly

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

ACID at container level

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, run db with in-memory only mode

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes (enterprise version only)

Access rights for users can be defined per database

simple password-based access control

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)
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