DBMS > Apache Phoenix vs. Bangdb vs. Faircom EDGE vs. InfinityDB

System Properties Comparison Apache Phoenix vs. Bangdb vs. Faircom EDGE vs. InfinityDB

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Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Converged and high performance database for device data, events, time series, document and graph

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

A Java embedded Key-Value Store which extends the Java Map interface

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Key-value store
Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

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

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Website

phoenix.apache.org

bangdb.com

www.faircom.com/products/faircom-edge

boilerbay.com

Technical documentation

phoenix.apache.org

docs.bangdb.com

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

boilerbay.com/infinitydb/manual

Developer

Apache Software Foundation

Sachin Sinha, BangDB

FairCom Corporation

Boiler Bay Inc.

Initial release

2014

2012

1979

2002

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

BangDB 2.0, October 2021

V3, October 2020

4.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD 3

commercial

Restricted, free version available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++

ANSI C, C++

Java

Server operating systems

Linux
Unix
Windows

Linux

Android
Linux

ARM, x86
Raspbian
Windows

All OS with a Java VM

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

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

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

yes, ANSI Standard SQL Types

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

secondary, composite, nested, reverse, geospatial

yes

manual creation possible, using inversions based on multi-value capability

SQL

Support of SQL

yes

SQL like support with command line tool

yes

ANSI SQL queries

APIs and other access methods

JDBC

Proprietary protocol
RESTful HTTP API

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

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C
C#
C++
Java
Python

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

Java

Server-side scripts

Stored procedures

user defined functions

yes

.Net, JavaScript, C/C++

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

File partitioning

Customizable business rules for partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor, Knob for CAP (enterprise version only)

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Tunable consistency, set CAP knob accordingly

Immediate Consistency
Tunable Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Foreign keys

Referential integrity

yes

when using SQL

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes

yes, run db with in-memory only mode

yes

User concepts

Access control

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

yes (enterprise version only)

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

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More resources

DB-Engines blog posts

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11 August 2021, Krishna Maheshwari (sponsor)

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