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DBMS > Axibase vs. Faircom EDGE vs. Google Cloud Datastore vs. Newts vs. SQL.JS

System Properties Comparison Axibase vs. Faircom EDGE vs. Google Cloud Datastore vs. Newts vs. SQL.JS

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

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

Time Series DBMS based on Cassandra

Port of SQLite to JavaScript

Primary database model

Time Series DBMS

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.63
Rank	#241	Overall
	#112	Relational DBMS

Website

axibase.com/docs/atsd/finance

www.faircom.com/products/faircom-edge

cloud.google.com/datastore

opennms.github.io/newts

Technical documentation

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

cloud.google.com/datastore/docs

github.com/OpenNMS/newts/wiki

sql.js.org/documentation/index.html

Developer

Axibase Corporation

FairCom Corporation

Google

OpenNMS Group

Alon Zakai

enhancements implemented by others

Initial release

2013

1979

2008

2014

2012

Current release

15585

V3, October 2020

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0

Open Source

Cloud-based only

Only available as a cloud service

no

no

yes

no

no

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

ANSI C, C++

Java

JavaScript

Server operating systems

Linux

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux
OS X
Windows

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

Data scheme

yes

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

schema-free

schema-free

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes, ANSI Standard SQL Types

yes, details here

yes

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.

no

yes

no

no

no

Secondary indexes

no

yes

yes

no

SQL

Support of SQL

SQL-like query language

yes

ANSI SQL queries

SQL-like query language (GQL)

no

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

HTTP REST
Java API

JavaScript API

Supported programming languages

Go
Java
PHP
Python
R
Ruby

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

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

Java

JavaScript

Server-side scripts

Stored procedures

yes

yes

.Net, JavaScript, C/C++

using Google App Engine

no

no

Triggers

yes

yes

Callbacks using the Google Apps Engine

no

no

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

no

yes

using Google Cloud Dataflow

no

no

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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

none

Foreign keys

Referential integrity

no

yes

when using SQL

yes

via ReferenceProperties or Ancestor paths

no

yes

Transaction concepts

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

no

ACID

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

no

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes

across SQL and NoSQL

yes

yes

Durability

Support for making data persistent

yes

yes

yes

yes

no

except by serializing a db to a file

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

no

no

yes

User concepts

Access control

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)

no

no

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More resources

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