DBMS > Atos Standard Common Repository vs. Google Cloud Datastore vs. PouchDB vs. Warp 10

System Properties Comparison Atos Standard Common Repository vs. Google Cloud Datastore vs. PouchDB vs. Warp 10

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

JavaScript DBMS with an API inspired by CouchDB

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store
Key-value store

Document store

Time Series DBMS

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

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	2.18
Rank	#114	Overall
	#21	Document stores

Score	0.06
Rank	#351	Overall
	#35	Time Series DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

cloud.google.com/datastore

pouchdb.com

www.warp10.io

Technical documentation

cloud.google.com/datastore/docs

pouchdb.com/guides

www.warp10.io/content/02_Getting_started

Developer

Atos Convergence Creators

Google

Apache Software Foundation

SenX

Initial release

2016

2008

2012

2015

Current release

1703

7.1.1, June 2019

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

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

Java

JavaScript

Java

Server operating systems

Linux

hosted

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

Linux
OS X
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

Typing

predefined data types such as float or date

optional

yes, details here

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.

yes

Secondary indexes

yes

via views

SQL

Support of SQL

SQL-like query language (GQL)

APIs and other access methods

LDAP

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP REST

only for PouchDB Server
JavaScript API

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages with LDAP bindings

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript

Server-side scripts

Stored procedures

using Google App Engine

View functions in JavaScript

yes

WarpScript

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

Atomic execution of specific operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

LDAP bind authentication

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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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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