DBMS > Apache Druid vs. DataFS vs. Google BigQuery vs. InfinityDB vs. Microsoft SQL Server

System Properties Comparison Apache Druid vs. DataFS vs. Google BigQuery vs. InfinityDB vs. Microsoft SQL Server

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Large scale data warehouse service with append-only tables

A Java embedded Key-Value Store which extends the Java Map interface

Microsofts flagship relational DBMS

Primary database model

Relational DBMS
Time Series DBMS

Secondary database models

Graph DBMS

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	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

druid.apache.org

newdatabase.com

cloud.google.com/bigquery

boilerbay.com

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

Technical documentation

druid.apache.org/docs/latest/design

dev.mobiland.com/Overview.xsp

cloud.google.com/bigquery/docs

boilerbay.com/infinitydb/manual

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

Developer

Apache Software Foundation and contributors

Mobiland AG

Google

Boiler Bay Inc.

Microsoft

Initial release

2012

2018

2010

2002

1989

Current release

29.0.1, April 2024

1.1.263, October 2022

4.0

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache license v2

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix

Windows

hosted

All OS with a Java VM

Linux
Windows

Data scheme

yes

schema-less columns are supported

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

SQL for querying

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP/JSON API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

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

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
VB.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Proprietary Sharding system

none

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

yes

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

Windows-Profile

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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