DBMS > Cachelot.io vs. Faircom DB vs. Linter vs. Solr

System Properties Comparison Cachelot.io vs. Faircom DB vs. Linter vs. Solr

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Editorial information provided by DB-Engines

Name

Description

In-memory caching system

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

RDBMS for high security requirements

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

Primary database model

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

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

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

cachelot.io

www.faircom.com/products/faircom-db

linter.ru

solr.apache.org

Technical documentation

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

solr.apache.org/resources.html

Developer

FairCom Corporation

relex.ru

Apache Software Foundation

Initial release

2015

1979

1990

2006

Current release

V12, November 2020

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Restricted, free version available

commercial

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

C++

ANSI C, C++

C and C++

Java

Server operating systems

FreeBSD
Linux
OS X

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

easily portable to other OSs

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

All OS with a Java VM

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

Data scheme

schema-free

schema free, schema optional, schema required, partial 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

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

Solr Parallel SQL Interface

APIs and other access methods

Memcached protocol

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

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
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++

yes

proprietary syntax with the possibility to convert from PL/SQL

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Source-replica replication

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

none

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

ACID

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

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

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More resources

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