DBMS > Dragonfly vs. FileMaker vs. InfluxDB vs. Microsoft SQL Server vs. OushuDB

System Properties Comparison Dragonfly vs. FileMaker vs. InfluxDB vs. Microsoft SQL Server vs. OushuDB

Editorial information provided by DB-Engines

Name

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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

DBMS for storing time series, events and metrics

Microsofts flagship relational DBMS

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Primary database model

Secondary database models

Spatial DBMS

with GEO package

Document store
Graph DBMS
Spatial DBMS

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

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	47.91
Rank	#20	Overall
	#14	Relational DBMS

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

www.claris.com/filemaker

www.influxdata.com/products/influxdb-overview

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

www.oushu.com/product/oushuDB

Technical documentation

www.dragonflydb.io/docs

www.claris.com/resources/documentation

docs.influxdata.com/influxdb

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

www.oushu.com/documentation

Developer

DragonflyDB team and community contributors

Claris

a subsidiary of Apple

Microsoft

Oushu

Initial release

2023

1983

2013

1989

Current release

1.0, March 2023

19.4.1, November 2021

2.7.6, April 2024

SQL Server 2022, November 2022

4.0.1, August 2020

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

MIT-License; commercial enterprise version available

commercial

restricted free version is available

commercial

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

Linux

iOS

client part only
Linux
OS X
Windows

Linux
OS X

through Homebrew

Linux
Windows

Linux

Data scheme

scheme-free

yes

schema-free

yes

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

yes

Numeric data and Strings

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

A http query request can return the data in XML format

yes

Secondary indexes

yes

SQL

Support of SQL

yes

via plugins

SQL-like query language

yes

Full-featured ANSI SQL support

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

Filemaker WebDirect
JDBC
ODBC

HTTP API
JSON over UDP

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

JDBC
ODBC

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

PHP

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

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

C
C++

Server-side scripts

Stored procedures

Lua

yes

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

yes

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

in enterprise version only

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

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Source-replica replication, warm standby

since Version 14

selectable replication factor

in enterprise version only

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Depending on used storage engine

yes

User concepts

Access control

Password-based authentication

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

simple rights management via user accounts

fine grained access rights according to SQL-standard

Kerberos, SSL and role based access

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