DBMS > AlaSQL vs. EJDB vs. Faircom EDGE vs. Google BigQuery vs. HBase

System Properties Comparison AlaSQL vs. EJDB vs. Faircom EDGE vs. Google BigQuery vs. HBase

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Large scale data warehouse service with append-only tables

Wide-column store based on Apache Hadoop and on concepts of BigTable

Primary database model

Document store
Relational DBMS

Document store

Key-value store
Relational DBMS

Relational DBMS

Wide column store

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

Score	0.49
Rank	#259	Overall
	#40	Document stores
	#120	Relational DBMS

Score	0.30
Rank	#294	Overall
	#44	Document stores

Score	0.03
Rank	#380	Overall
	#59	Key-value stores
	#157	Relational DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Website

alasql.org

github.com/Softmotions/ejdb

www.faircom.com/products/faircom-edge

cloud.google.com/bigquery

hbase.apache.org

Technical documentation

github.com/AlaSQL/alasql

github.com/Softmotions/ejdb/blob/master/README.md

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

cloud.google.com/bigquery/docs

hbase.apache.org/book.html

Developer

Andrey Gershun & Mathias R. Wulff

Softmotions

FairCom Corporation

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2014

2012

1979

2010

2008

Current release

V3, October 2020

2.3.4, January 2021

License

Commercial or Open Source

Open Source

MIT-License

Open Source

GPLv2

commercial

Restricted, free version available

commercial

Open Source

Apache version 2

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

JavaScript

ANSI C, C++

Java

Server operating systems

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

server-less

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux
Unix
Windows

using Cygwin

Data scheme

schema-free

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

yes

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes, ANSI Standard SQL Types

yes

options to bring your own types, AVRO

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

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

ANSI SQL queries

yes

APIs and other access methods

JavaScript API

in-process shared library

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

RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

Supported programming languages

JavaScript

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

in JavaScript

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication
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
Tunable Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

typically not needed, however similar functionality with collection joins possible

yes

when using SQL

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Since BigQuery is designed for querying data

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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