DBMS > Apache Druid vs. Infobright vs. Spark SQL vs. SWC-DB

System Properties Comparison Apache Druid vs. Infobright vs. Spark SQL vs. SWC-DB

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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

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Spark SQL is a component on top of 'Spark Core' for structured data processing

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

druid.apache.org

ignitetech.com/softwarelibrary/infobrightdb

spark.apache.org/sql

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

druid.apache.org/docs/latest/design

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation and contributors

Ignite Technologies Inc.; formerly InfoBright Inc.

Apache Software Foundation

Alex Kashirin

Initial release

2012

2005

2014

2020

Current release

29.0.1, April 2024

3.5.0 ( 2.13), September 2023

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache license v2

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

Apache 2.0

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

C++

Server operating systems

Linux
OS X
Unix

Linux
Windows

Linux
OS X
Windows

Linux

Data scheme

yes

schema-less columns are supported

yes

schema-free

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

Knowledge Grid Technology used instead

SQL

Support of SQL

SQL for querying

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
ODBC

Proprietary protocol
Thrift

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

C++

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

yes, utilizing Spark Core

Sharding

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

Foreign keys

Referential integrity

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

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

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

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