DBMS > Amazon DocumentDB vs. Apache Doris vs. Geode vs. Google Cloud Spanner vs. Microsoft SQL Server

System Properties Comparison Amazon DocumentDB vs. Apache Doris vs. Geode vs. Google Cloud Spanner vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

An MPP-based analytics DBMS embracing the MySQL protocol

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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.

Microsofts flagship relational DBMS

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	1.86
Rank	#134	Overall
	#24	Key-value stores

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

aws.amazon.com/documentdb

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

geode.apache.org

cloud.google.com/spanner

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

Technical documentation

aws.amazon.com/documentdb/resources

github.com/apache/doris/wiki

geode.apache.org/docs

cloud.google.com/spanner/docs

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

Developer

Apache Software Foundation, originally contributed from Baidu

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

Microsoft

Initial release

2019

2017

2002

2017

1989

Current release

1.2.2, February 2023

1.1, February 2017

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

hosted

Linux

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux
Windows

Data scheme

schema-free

yes

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

SQL

Support of SQL

yes

SQL-like query language (OQL)

yes

Query statements complying to ANSI 2011

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
MySQL client

Java Client API
Memcached protocol
RESTful HTTP API

gRPC (using protocol buffers) API
JDBC

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

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

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Java

.Net
All JVM based languages
C++
Groovy
Java
Scala

Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions

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

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

none

Multi-source replication

Multi-source replication with 3 replicas for regional instances.

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

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

Atomic single-document operations

yes, on a single node

ACID

Strict serializable isolation

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

Access rights for users and roles

fine grained access rights according to SQL-standard

Access rights per client and object definable

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

fine grained access rights according to SQL-standard

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