DBMS > atoti vs. BaseX vs. Faircom EDGE vs. Google Cloud Datastore vs. searchxml

System Properties Comparison atoti vs. BaseX vs. Faircom EDGE vs. Google Cloud Datastore vs. searchxml

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.

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

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

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Object oriented DBMS

Native XML DBMS

Key-value store
Relational DBMS

Document store

Native XML DBMS
Search engine

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

Score	0.45
Rank	#253	Overall
	#13	Object oriented DBMS

Score	1.56
Rank	#141	Overall
	#4	Native XML DBMS

Score	0.02
Rank	#370	Overall
	#55	Key-value stores
	#157	Relational DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	0.00
Rank	#385	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

atoti.io

basex.org

www.faircom.com/products/faircom-edge

cloud.google.com/datastore

www.searchxml.net/category/products

Technical documentation

docs.atoti.io

docs.basex.org

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

cloud.google.com/datastore/docs

www.searchxml.net/support/handouts

Developer

ActiveViam

BaseX GmbH

FairCom Corporation

Google

informationpartners gmbh

Initial release

2007

1979

2008

2015

Current release

11.2, August 2024

V3, October 2020

1.0

License

Commercial or Open Source

commercial

free versions available

Open Source

BSD license

commercial

Restricted, free version available

commercial

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

ANSI C, C++

C++

Server operating systems

Linux
OS X
Windows

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Windows

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Typing

predefined data types such as float or date

XQuery supports types

yes, ANSI Standard SQL Types

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

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

ANSI SQL queries

SQL-like query language (GQL)

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

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

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

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

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

Python

yes

.Net, JavaScript, C/C++

using Google App Engine

yes

on the application server

Triggers

yes

via events

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

File partitioning

Customizable business rules for partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication using Paxos

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

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

Foreign keys

Referential integrity

yes

when using SQL

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

multiple readers, single writer

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

across SQL and NoSQL

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

Users with fine-grained authorization concept on 4 levels

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

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

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

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