DBMS > Amazon DocumentDB vs. Apache Phoenix vs. Blueflood vs. IBM Db2 vs. MySQL

System Properties Comparison Amazon DocumentDB vs. Apache Phoenix vs. Blueflood vs. IBM Db2 vs. MySQL

Editorial information provided by DB-Engines

Name

Description

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

A scale-out RDBMS with evolutionary schema built on Apache HBase

Scalable TimeSeries DBMS based on Cassandra

Common in IBM host environments, 2 different versions for host and Windows/Linux

Widely used open source RDBMS

Primary database model

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store
Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

aws.amazon.com/documentdb

phoenix.apache.org

blueflood.io

www.ibm.com/products/db2

www.mysql.com

Technical documentation

aws.amazon.com/documentdb/resources

phoenix.apache.org

github.com/rax-maas/blueflood/wiki

www.ibm.com/docs/en/db2

dev.mysql.com/doc

Developer

Apache Software Foundation

Rackspace

IBM

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2019

2014

2013

1983

host version

1995

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

12.1, October 2016

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

free version is available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

Server operating systems

hosted

Linux
Unix
Windows

Linux
OS X

AIX
HP-UX
Linux
Solaris
Windows
z/OS

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

predefined scheme

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

with proprietary extensions

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC

HTTP REST

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

only with Windows/Unix/Linux Version

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

yes

with separate tools (MQ, InfoSphere)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic single-document operations

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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