DBMS > Cachelot.io vs. EJDB vs. GridDB vs. gStore vs. TimescaleDB

System Properties Comparison Cachelot.io vs. EJDB vs. GridDB vs. gStore vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

In-memory caching system

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

A native Graph DBMS to store and maintain very large RDF datasets.

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Key-value store
Relational DBMS

Relational DBMS

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

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	0.14
Rank	#342	Overall
	#34	Graph DBMS
	#16	RDF stores

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

cachelot.io

github.com/Softmotions/ejdb

griddb.net

en.gstore.cn

www.timescale.com

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

docs.griddb.net

en.gstore.cn/#/enDocs

docs.timescale.com

Developer

Softmotions

Toshiba Corporation

Timescale

Initial release

2015

2012

2013

2016

2017

Current release

5.1, August 2022

1.2, November 2023

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Simplified BSD License

Open Source

GPLv2

Open Source

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

Open Source

BSD

Open Source

Apache 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

C++

Server operating systems

FreeBSD
Linux
OS X

server-less

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

numerical, string, blob, geometry, boolean, timestamp

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

full PostgreSQL SQL syntax

APIs and other access methods

Memcached protocol

in-process shared library

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

HTTP API
SPARQL 1.1

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

C++
Java
JavaScript (Node.js)
PHP
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Source-replica replication with hot standby and reads on replicas

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

none

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID at container level

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

Users, roles and permissions, Role-Based Access Control (RBAC) supported

fine grained access rights according to SQL-standard

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

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.

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