DBMS > Cachelot.io vs. EsgynDB vs. Google Cloud Bigtable vs. PouchDB vs. ToroDB

System Properties Comparison Cachelot.io vs. EsgynDB vs. Google Cloud Bigtable vs. PouchDB vs. ToroDB

Editorial information provided by DB-Engines

Name

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

Description

In-memory caching system

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

JavaScript DBMS with an API inspired by CouchDB

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

Primary database model

Key-value store

Relational DBMS

Key-value store
Wide column store

Document store

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

cachelot.io

www.esgyn.cn

cloud.google.com/bigtable

pouchdb.com

github.com/torodb/server

Technical documentation

cloud.google.com/bigtable/docs

pouchdb.com/guides

Developer

Esgyn

Google

Apache Software Foundation

8Kdata

Initial release

2015

2012

2016

Current release

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Open Source

AGPL-V3

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++, Java

JavaScript

Java

Server operating systems

FreeBSD
Linux
OS X

Linux

hosted

server-less, requires a JavaScript environment (browser, Node.js)

All OS with a Java 7 VM

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

via views

SQL

Support of SQL

yes

APIs and other access methods

Memcached protocol

ADO.NET
JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

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

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users and roles

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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