DBMS > Badger vs. Citus vs. FileMaker vs. Microsoft SQL Server vs. Sphinx

System Properties Comparison Badger vs. Citus vs. FileMaker vs. Microsoft SQL Server vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

Microsofts flagship relational DBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Key-value store

Relational DBMS

Search engine

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	47.91
Rank	#20	Overall
	#14	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

github.com/dgraph-io/badger

www.citusdata.com

www.claris.com/filemaker

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

sphinxsearch.com

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.citusdata.com

www.claris.com/resources/documentation

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

sphinxsearch.com/docs

Developer

DGraph Labs

Claris

a subsidiary of Apple

Microsoft

Sphinx Technologies Inc.

Initial release

2017

2010

1983

1989

2001

Current release

8.1, December 2018

19.4.1, November 2021

SQL Server 2022, November 2022

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL, commercial license also available

commercial

restricted free version is available

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

iOS

client part only
Linux
OS X
Windows

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

specific XML type available, but no XML query functionality

yes

A http query request can return the data in XML format

yes

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

yes

standard, with numerous extensions

yes

via plugins

yes

SQL-like query language (SphinxQL)

APIs and other access methods

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

Filemaker WebDirect
JDBC
ODBC

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

Proprietary protocol

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

PHP

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

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

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

other methods possible by using 3rd party extensions

Source-replica replication, warm standby

since Version 14

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

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

simple rights management via user accounts and connection to external directory services

fine grained access rights according to SQL-standard

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