DBMS > Faircom DB vs. Microsoft SQL Server vs. Solr vs. YDB

System Properties Comparison Faircom DB vs. Microsoft SQL Server vs. Solr vs. YDB

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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.

Microsofts flagship relational DBMS

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

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Search engine

Document store
Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	0.24
Rank	#311	Overall
	#44	Key-value stores
	#140	Relational DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Score	0.34
Rank	#285	Overall
	#43	Document stores
	#130	Relational DBMS

Website

www.faircom.com/products/faircom-db

www.microsoft.com/en-us/sql-server

solr.apache.org

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

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

learn.microsoft.com/en-US/sql/sql-server

solr.apache.org/resources.html

ydb.tech/en/docs

Developer

FairCom Corporation

Microsoft

Apache Software Foundation

Yandex

Initial release

1979

1989

2006

2019

Current release

V12, November 2020

SQL Server 2022, November 2022

9.5.0, February 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

restricted free version is available

Open Source

Apache Version 2

Open Source

Apache 2.0; commercial license available

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

Linux
Windows

All OS with a Java VM

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

Linux

Data scheme

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

yes

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

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

supports customizable data types and automatic typing

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

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

Solr Parallel SQL Interface

SQL-like query language (YQL)

APIs and other access methods

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Java API
RESTful HTTP/JSON API

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

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).

yes, but depending on the SQL-Server Edition

yes

Active-passive shard replication

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

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

ACID

optimistic locking

ACID

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

Access rights defined for Yandex Cloud users

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