DBMS > Badger vs. Microsoft Azure SQL Database vs. Sphinx vs. SQLite

System Properties Comparison Badger vs. Microsoft Azure SQL Database vs. Sphinx vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

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

Database as a Service offering with high compatibility to Microsoft SQL Server

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

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

github.com/dgraph-io/badger

azure.microsoft.com/en-us/products/azure-sql/database

sphinxsearch.com

www.sqlite.org

Technical documentation

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

docs.microsoft.com/en-us/azure/azure-sql

sphinxsearch.com/docs

www.sqlite.org/docs.html

Developer

DGraph Labs

Microsoft

Sphinx Technologies Inc.

Dwayne Richard Hipp

Initial release

2017

2010

2001

2000

Current release

V12

3.5.1, February 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

GPL version 2, commercial licence available

Open Source

Public Domain

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

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

server-less

Data scheme

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

yes

SQL-92 is not fully supported

APIs and other access methods

ADO.NET
JDBC
ODBC

Proprietary protocol

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, with always 3 replicas available

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

via file-system locks

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx 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

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