DBMS > Amazon Redshift vs. Apache Druid vs. Google Cloud Spanner vs. Quasardb vs. Sadas Engine

System Properties Comparison Amazon Redshift vs. Apache Druid vs. Google Cloud Spanner vs. Quasardb vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Distributed, high-performance timeseries database

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

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

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Trend Chart

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	2.69
Rank	#101	Overall
	#51	Relational DBMS

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Website

aws.amazon.com/redshift

druid.apache.org

cloud.google.com/spanner

quasar.ai

www.sadasengine.com

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

cloud.google.com/spanner/docs

doc.quasar.ai/master

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

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Google

quasardb

SADAS s.r.l.

Initial release

2012

2017

2009

2006

Current release

30.0.0, June 2024

3.14.1, January 2024

8.0

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

free trial version 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++

Server operating systems

hosted

Linux
OS X
Unix

hosted

BSD
Linux
OS X
Windows

AIX
Linux
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

restricted

yes

with tags

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

yes

Query statements complying to ANSI 2011

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

HTTP API

JDBC
ODBC
Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Go
Java
JavaScript (Node.js)
Python

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

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

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

consistent hashing

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

Multi-source replication with 3 replicas for regional instances.

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

yes

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

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)

Cryptographically strong user authentication and audit trail

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

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