DBMS > Amazon DocumentDB vs. Graph Engine vs. Oracle Berkeley DB

System Properties Comparison Amazon DocumentDB vs. Graph Engine vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Widely used in-process key-value store

Primary database model

Document store

Graph DBMS
Key-value store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Website

aws.amazon.com/documentdb

www.graphengine.io

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

aws.amazon.com/documentdb/resources

www.graphengine.io/docs/manual

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2019

2010

1994

Current release

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

commercial license 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

.NET and C

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

hosted

.NET

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

RESTful HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C#
C++
F#
Visual Basic

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

yes

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

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

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

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent 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

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