DBMS > Apache Druid vs. HugeGraph vs. Microsoft SQL Server vs. mSQL vs. Sphinx

System Properties Comparison Apache Druid vs. HugeGraph vs. Microsoft SQL Server vs. mSQL vs. Sphinx

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

A fast-speed and highly-scalable Graph DBMS

Microsofts flagship relational DBMS

mSQL (Mini SQL) is a simple and lightweight RDBMS

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

Primary database model

Relational DBMS
Time Series DBMS

Graph DBMS

Relational DBMS

Search engine

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	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

druid.apache.org

github.com/hugegraph
hugegraph.apache.org

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

hughestech.com.au/products/msql

sphinxsearch.com

Technical documentation

druid.apache.org/docs/latest/design

hugegraph.apache.org/docs

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

sphinxsearch.com/docs

Developer

Apache Software Foundation and contributors

Baidu

Microsoft

Hughes Technologies

Sphinx Technologies Inc.

Initial release

2012

2018

1989

1994

2001

Current release

29.0.1, April 2024

0.9

SQL Server 2022, November 2022

4.4, October 2021

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

free licenses can be provided

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix

Linux
macOS
Unix

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-less columns are supported

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

Secondary indexes

yes

also supports composite index and range index

yes

full-text index on all search fields

SQL

Support of SQL

SQL for querying

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

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

JDBC
ODBC

Proprietary protocol

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Groovy
Java
Python

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

C
C++
Delphi
Java
Perl
PHP
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

yes

depending on used storage backend, e.g. Cassandra and HBase

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

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

depending on used storage backend, e.g. Cassandra and HBase

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

edges in graph

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

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

Users, roles and permissions

fine grained access rights according to SQL-standard

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