DBMS > Amazon Redshift vs. Apache Doris vs. Apache Druid vs. ArcadeDB vs. Google Cloud Spanner

System Properties Comparison Amazon Redshift vs. Apache Doris vs. Apache Druid vs. ArcadeDB vs. Google Cloud Spanner

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An MPP-based analytics DBMS embracing the MySQL protocol

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

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

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.

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

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

Score	0.10
Rank	#358	Overall
	#48	Document stores
	#38	Graph DBMS
	#52	Key-value stores
	#35	Time Series DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Website

aws.amazon.com/redshift

doris.apache.org
github.com/apache/doris

druid.apache.org

arcadedb.com

cloud.google.com/spanner

Technical documentation

docs.aws.amazon.com/redshift

github.com/apache/doris/wiki

druid.apache.org/docs/latest/design

docs.arcadedb.com

cloud.google.com/spanner/docs

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation and contributors

Arcade Data

Google

Initial release

2012

2017

2012

2021

2017

Current release

1.2.2, February 2023

29.0.1, April 2024

September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache license v2

Open Source

Apache Version 2.0

commercial

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

Server operating systems

hosted

Linux

Linux
OS X
Unix

All OS with a Java VM

hosted

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL for querying

SQL-like query language, no joins

yes

Query statements complying to ANSI 2011

APIs and other access methods

JDBC
ODBC

JDBC
MySQL client

JDBC
RESTful HTTP/JSON API

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

gRPC (using protocol buffers) API
JDBC

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

Supported programming languages

All languages supporting JDBC/ODBC

Java

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Java

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

manual/auto, time-based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes, via HDFS, S3 or other storage engines

Source-replica replication

Multi-source replication with 3 replicas for regional instances.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

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

relationship in graphs

yes

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

Transaction concepts

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

ACID

Strict serializable isolation

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

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)

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