DBMS > atoti vs. Google Cloud Datastore vs. RethinkDB vs. searchxml vs. Sequoiadb

System Properties Comparison atoti vs. Google Cloud Datastore vs. RethinkDB vs. searchxml vs. Sequoiadb

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

DBMS for structured and unstructured content wrapped with an application server

NewSQL database with distributed OLTP and SQL

Primary database model

Object oriented DBMS

Document store

Native XML DBMS
Search engine

Document store
Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

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

Website

atoti.io

cloud.google.com/datastore

rethinkdb.com

www.searchxml.net/category/products

www.sequoiadb.com

Technical documentation

docs.atoti.io

cloud.google.com/datastore/docs

rethinkdb.com/docs

www.searchxml.net/support/handouts

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

Developer

ActiveViam

Google

The Linux Foundation

since July 2017

informationpartners gmbh

Sequoiadb Ltd.

Initial release

2008

2009

2015

2013

Current release

2.4.1, August 2020

1.0

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

Apache Version 2

commercial

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

Java

C++

Server operating systems

hosted

Linux
OS X
Windows

Windows

Linux

Data scheme

schema-free

Typing

predefined data types such as float or date

yes, details here

yes

string, binary, float, bool, date, geometry

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like query language (GQL)

SQL-like query language

APIs and other access methods

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

RESTful HTTP API
WebDAV
XQuery
XSLT

proprietary protocol using JSON

Supported programming languages

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

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

C++

most other programming languages supported via APIs

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

Python

using Google App Engine

yes

on the application server

JavaScript

Triggers

Callbacks using the Google Apps Engine

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

range based

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

yes

sychronisation to multiple collections

Source-replica replication

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 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.

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Atomic single-document operations

multiple readers, single writer

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

MVCC based

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

yes

users and table-level permissions

Domain, group and role-based access control at the document level and for application services

simple password-based access control

More information provided by the system vendor

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Related products and services

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More resources

DB-Engines blog posts

Meet some database management systems you are likely to hear more about in the future
4 August 2014, Paul Andlinger

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