DBMS > Apache Druid vs. Google Cloud Bigtable vs. Microsoft SQL Server vs. Sadas Engine vs. Sphinx

System Properties Comparison Apache Druid vs. Google Cloud Bigtable vs. Microsoft SQL Server vs. Sadas Engine 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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Microsofts flagship relational DBMS

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

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

Primary database model

Relational DBMS
Time Series DBMS

Key-value store
Wide column store

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	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

druid.apache.org

cloud.google.com/bigtable

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

www.sadasengine.com

sphinxsearch.com

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigtable/docs

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

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

sphinxsearch.com/docs

Developer

Apache Software Foundation and contributors

Google

Microsoft

SADAS s.r.l.

Sphinx Technologies Inc.

Initial release

2012

2015

1989

2006

2001

Current release

29.0.1, April 2024

SQL Server 2022, November 2022

8.0

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

restricted free version is available

commercial

free trial version available

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix

hosted

Linux
Windows

AIX
Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-less columns are supported

schema-free

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

full-text index on all search fields

SQL

Support of SQL

SQL for querying

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
RESTful HTTP/JSON API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

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

JDBC
ODBC
Proprietary protocol

Proprietary protocol

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

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

Sharding

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

horizontal partitioning

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

Internal replication in Colossus, and regional replication between two clusters in different zones

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

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

managed by 'Learn by Usage'

User concepts

Access control

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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