DBMS > Faircom DB vs. Heroic vs. Manticore Search vs. Microsoft Azure Table Storage vs. Oracle Rdb

System Properties Comparison Faircom DB vs. Heroic vs. Manticore Search vs. Microsoft Azure Table Storage vs. Oracle Rdb

Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Key-value store
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	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Website

www.faircom.com/products/faircom-db

github.com/spotify/heroic

manticoresearch.com

azure.microsoft.com/en-us/services/storage/tables

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

spotify.github.io/heroic

manual.manticoresearch.com

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

FairCom Corporation

Spotify

Manticore Software

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

1979

2014

2017

2012

1984

Current release

V12, November 2020

6.0, February 2023

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache 2.0

Open Source

GPL version 2

commercial

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

ANSI C, C++

Java

C++

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

FreeBSD
Linux
macOS
Windows

hosted

HP Open VMS

Data scheme

schema free, schema optional, schema required, partial schema,

schema-free

Fixed schema

schema-free

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

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

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.

Can index from XML

Secondary indexes

yes

via Elasticsearch

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

SQL-like query language

yes

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

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

RESTful HTTP API

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

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

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

Synchronous replication based on Galera library

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

yes

isolated transactions for atomic changes and binary logging for safe writes

optimistic locking

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

yes

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

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 access rights according to SQL-standard with additional protections for files

Access rights based on private key authentication or shared access signatures

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