DBMS > Faircom DB vs. Manticore Search vs. Postgres-XL vs. Solr

System Properties Comparison Faircom DB vs. Manticore Search vs. Postgres-XL vs. Solr

Please select another system to include it in the comparison.

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.

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Key-value store
Relational DBMS

Search engine

Relational DBMS

Search engine

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Spatial DBMS

Spatial DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.faircom.com/products/faircom-db

manticoresearch.com

www.postgres-xl.org

solr.apache.org

Technical documentation

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

manual.manticoresearch.com

www.postgres-xl.org/documentation

solr.apache.org/resources.html

Developer

FairCom Corporation

Manticore Software

Apache Software Foundation

Initial release

1979

2017

2014

since 2012, originally named StormDB

2006

Current release

V12, November 2020

6.0, February 2023

10 R1, October 2018

9.6.0, April 2024

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

GPL version 2

Open Source

Mozilla public license

Open Source

Apache Version 2

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

ANSI C, C++

C++

Java

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

Linux
macOS

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

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

Fixed schema

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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

yes

supports customizable data types and automatic typing

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

yes

XML type, but no XML query functionality

yes

Secondary indexes

yes

full-text index on all search fields

yes

All search fields are automatically indexed

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

SQL-like query language

yes

distributed, parallel query execution

Solr Parallel SQL Interface

APIs and other access methods

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

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
RESTful HTTP/JSON 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++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

Java plugins

Triggers

yes

User configurable commands triggered on index changes

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

horizontal partitioning

Sharding

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).

Synchronous replication based on Galera library

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate Consistency

Eventual 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

ACID

MVCC

optimistic locking

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

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Elasticsearch replaced Solr as the most popular search engine
12 January 2016, Paul Andlinger

Enterprise Search Engines almost double their popularity in the last 12 months
2 July 2014, Paul Andlinger

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

show all

Recent citations in the news

FairCom kicks off new era of database technology USA - English
10 November 2020, PR Newswire

provided by Google News

Integrating Manticore Search with Apache Superset
8 August 2023, hackernoon.com

Clickhouse vs Elasticsearch vs Manticore Search Query Times With a 1.7B NYC Taxi Rides Benchmark
1 June 2022, hackernoon.com

Google's Gemini comes to databases
9 April 2024, Yahoo Canada Shine On

Comparing Meilisearch and Manticore Search Using Key Benchmarks
2 May 2023, hackernoon.com

8 Google Alternatives: How to Search Crypto, the Dark Web, More
1 February 2023, Gizmodo

provided by Google News

SOLR-led walkout demands better conditions for Compass workers
27 February 2024, Daily Northwestern

Best Practices from Rackspace for Modernizing a Legacy HBase/Solr Architecture Using AWS Services | Amazon Web ...
9 October 2023, AWS Blog

SOLR hosts teach-in of labor movements at Northwestern
28 January 2024, Daily Northwestern

Top 5 stock gainers and losers: SOLR.V, GRSL.V, ANON.C
21 November 2023, Equity.Guru

SearchStax Launches Serverless Solr Service to Accelerate Cloud-Native Application Development
5 April 2023, PR Newswire

provided by Google News

Share this page