DBMS > Apache Druid vs. Cubrid vs. Google BigQuery vs. mSQL vs. Sphinx

System Properties Comparison Apache Druid vs. Cubrid vs. Google BigQuery 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

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Large scale data warehouse service with append-only tables

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

Relational DBMS

Search engine

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	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

druid.apache.org

cubrid.com (korean)
cubrid.org (english)

cloud.google.com/bigquery

hughestech.com.au/products/msql

sphinxsearch.com

Technical documentation

druid.apache.org/docs/latest/design

cubrid.org/manuals

cloud.google.com/bigquery/docs

sphinxsearch.com/docs

Developer

Apache Software Foundation and contributors

CUBRID Corporation, CUBRID Foundation

Google

Hughes Technologies

Sphinx Technologies Inc.

Initial release

2012

2008

2010

1994

2001

Current release

29.0.1, April 2024

11.0, January 2021

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

free licenses can be provided

Open Source

GPL version 2, commercial licence available

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

Java

C, C++, Java

C++

Server operating systems

Linux
OS X
Unix

Linux
Windows

hosted

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.

Secondary indexes

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

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

JDBC
ODBC

Proprietary protocol

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java Stored Procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

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

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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.

User concepts

Access control

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

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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