DBMS > Apache Druid vs. Firebird vs. Microsoft SQL Server vs. Milvus vs. Riak KV

System Properties Comparison Apache Druid vs. Firebird vs. Microsoft SQL Server vs. Milvus vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Firebird is an open source RDBMS forked from Borland's InterBase

Microsofts flagship relational DBMS

A DBMS designed for efficient storage of vector data and vector similarity searches

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Vector DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

druid.apache.org

www.firebirdsql.org

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

milvus.io

Technical documentation

druid.apache.org/docs/latest/design

www.firebirdsql.org/en/reference-manuals

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

milvus.io/docs/overview.md

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Apache Software Foundation and contributors

Firebird Foundation

Microsoft

OpenSource, formerly Basho Technologies

Initial release

2012

2000

As fork of Borland's InterBase

1989

2019

2009

Current release

29.0.1, April 2024

5.0.0, January 2024

SQL Server 2022, November 2022

2.3.4, January 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Initial Developer's Public License

commercial

restricted free version is available

Open Source

Apache Version 2.0

Open Source

Apache version 2, commercial enterprise edition

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

Java

C and C++

C++

C++, Go

Erlang

Server operating systems

Linux
OS X
Unix

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Linux
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X

Data scheme

yes

schema-less columns are supported

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

restricted

SQL

Support of SQL

SQL for querying

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

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

RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

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

C++
Go
Java
JavaScript (Node.js)
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

PSQL

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

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

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

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Source-replica replication

yes, but depending on the SQL-Server Edition

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

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

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

yes, using Riak Security

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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