DBMS > Apache Phoenix vs. Faircom EDGE vs. Manticore Search vs. Spark SQL vs. VoltDB

System Properties Comparison Apache Phoenix vs. Faircom EDGE vs. Manticore Search vs. Spark SQL vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

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

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

Multi-storage database for search, including full-text search.

Spark SQL is a component on top of 'Spark Core' for structured data processing

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Search engine

Relational DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

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

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

phoenix.apache.org

www.faircom.com/products/faircom-edge

manticoresearch.com

spark.apache.org/sql

www.voltdb.com

Technical documentation

phoenix.apache.org

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

manual.manticoresearch.com

spark.apache.org/docs/latest/sql-programming-guide.html

docs.voltdb.com

Developer

Apache Software Foundation

FairCom Corporation

Manticore Software

Apache Software Foundation

VoltDB Inc.

Initial release

2014

1979

2017

2014

2010

Current release

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

V3, October 2020

6.0, February 2023

3.5.0 ( 2.13), September 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

GPL version 2

Open Source

Apache 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java

ANSI C, C++

C++

Scala

Java, C++

Server operating systems

Linux
Unix
Windows

Android
Linux

ARM, x86
Raspbian
Windows

FreeBSD
Linux
macOS
Windows

Linux
OS X
Windows

Linux
OS X

for development

Data scheme

yes

late-bound, schema-on-read capabilities

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

Fixed schema

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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

Can index from XML

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

ANSI SQL queries

SQL-like query language

SQL-like DML and DDL statements

yes

only a subset of SQL 99

APIs and other access methods

JDBC

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

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

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

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

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Java
Python
R
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

.Net, JavaScript, C/C++

user defined functions

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

Synchronous replication based on Galera library

none

Multi-source replication
Source-replica replication

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

Immediate Consistency
Tunable Consistency

Foreign keys

Referential integrity

yes

when using SQL

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

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

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

Users and roles with access to stored procedures

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