DBMS > Amazon DocumentDB vs. Dragonfly vs. IBM Db2 Event Store vs. Microsoft SQL Server vs. SAP SQL Anywhere

System Properties Comparison Amazon DocumentDB vs. Dragonfly vs. IBM Db2 Event Store vs. Microsoft SQL Server vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

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

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Distributed Event Store optimized for Internet of Things use cases

Microsofts flagship relational DBMS

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Document store

Key-value store

Event Store
Time Series DBMS

Relational DBMS

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	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

aws.amazon.com/documentdb

github.com/dragonflydb/dragonfly
www.dragonflydb.io

www.ibm.com/products/db2-event-store

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

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

aws.amazon.com/documentdb/resources

www.dragonflydb.io/docs

www.ibm.com/docs/en/db2-event-store

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

help.sap.com/docs/SAP_SQL_Anywhere

Developer

DragonflyDB team and community contributors

IBM

Microsoft

SAP

formerly Sybase

Initial release

2019

2023

2017

1989

1992

Current release

1.0, March 2023

2.0

SQL Server 2022, November 2022

17, July 2015

License

Commercial or Open Source

commercial

Open Source

BSL 1.1

commercial

free developer edition available

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

C and C++

C++

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

schema-free

scheme-free

yes

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit 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

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

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

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

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

Lua

yes

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

yes, in C/C++, Java, .Net or Perl

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

Active-active shard replication

yes, but depending on the SQL-Server Edition

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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

Transaction concepts

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

Atomic single-document operations

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

Password-based authentication

fine grained access rights according to SQL-standard

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