DBMS > AlaSQL vs. AnzoGraph DB vs. Apache Drill vs. Faircom EDGE vs. H2

System Properties Comparison AlaSQL vs. AnzoGraph DB vs. Apache Drill vs. Faircom EDGE vs. H2

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Primary database model

Document store
Relational DBMS

Graph DBMS
RDF store

Document store
Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Website

alasql.org

cambridgesemantics.com/anzograph

drill.apache.org

www.faircom.com/products/faircom-edge

www.h2database.com

Technical documentation

github.com/AlaSQL/alasql

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

drill.apache.org/docs

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

www.h2database.com/html/main.html

Developer

Andrey Gershun & Mathias R. Wulff

Cambridge Semantics

Apache Software Foundation

FairCom Corporation

Thomas Mueller

Initial release

2014

2018

2012

1979

2005

Current release

2.3, January 2021

1.20.3, January 2023

V3, October 2020

2.2.220, July 2023

License

Commercial or Open Source

Open Source

MIT-License

commercial

free trial version available

Open Source

Apache Version 2

commercial

Restricted, free version available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Cloud-based only

Only available as a cloud service

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)

Linux

Linux
OS X
Windows

Android
Linux

ARM, x86
Raspbian
Windows

All OS with a Java VM

Data scheme

schema-free

Schema-free and OWL/RDFS-schema support

schema-free

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

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

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

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL SELECT statement is SQL:2003 compliant

yes

ANSI SQL queries

yes

APIs and other access methods

JavaScript API

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

JDBC
ODBC
RESTful HTTP API

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

JDBC
ODBC

Supported programming languages

JavaScript

C++
Java
Python

C++

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

Java

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

yes

.Net, JavaScript, C/C++

Java Stored Procedures and User-Defined Functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic sharding

Sharding

File partitioning

Customizable business rules for partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication in MPP-Cluster

yes

Synchronous and asynchronous realtime replication based on transaction logs

With clustering: 2 database servers on different computers operate on identical copies of a database

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency in MPP-Cluster

none

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not needed in graphs

yes

when using SQL

yes

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

Depending on the underlying data source

yes

In-memory capabilities

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

yes

Depending on the underlying data source

yes

User concepts

Access control

Access rights for users and roles

Depending on the underlying data source

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

fine grained access rights according to SQL-standard

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