DBMS > Amazon Neptune vs. Google BigQuery vs. Memcached vs. Oracle Berkeley DB

System Properties Comparison Amazon Neptune vs. Google BigQuery vs. Memcached vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Large scale data warehouse service with append-only tables

In-memory key-value store, originally intended for caching

Widely used in-process key-value store

Primary database model

Graph DBMS
RDF store

Relational DBMS

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	2.20
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	16.84
Rank	#32	Overall
	#4	Key-value stores

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Website

aws.amazon.com/neptune

cloud.google.com/bigquery

www.memcached.org

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

Technical documentation

aws.amazon.com/neptune/developer-resources

cloud.google.com/bigquery/docs

github.com/memcached/memcached/wiki

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

Developer

Amazon

Google

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2017

2010

2003

1994

Current release

1.6.29, June 2024

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

BSD 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

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

Server operating systems

hosted

FreeBSD
Linux
OS X
Unix
Windows

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

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

.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

user defined functions

in JavaScript

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

none

Repcached, a Memcached patch, provides this functionallity

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

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 can be defined via the AWS Identity and Access Management (IAM)

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

using SASL (Simple Authentication and Security Layer) protocol

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