DBMS > Graph Engine vs. Graphite vs. Oracle Berkeley DB

System Properties Comparison Graph Engine vs. Graphite vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

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

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

Widely used in-process key-value store

Primary database model

Graph DBMS
Key-value store

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

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

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

Website

www.graphengine.io

github.com/graphite-project/graphite-web

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

Technical documentation

www.graphengine.io/docs/manual

graphite.readthedocs.io

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

Developer

Microsoft

Chris Davis

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2010

2006

1994

Current release

18.1.40, May 2020

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

Open Source

commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

.NET and C

Python

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

Server operating systems

.NET

Linux
Unix

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data only

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

RESTful HTTP API

HTTP API
Sockets

Supported programming languages

C#
C++
F#
Visual Basic

JavaScript (Node.js)
Python

.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

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

Durability

Support for making data persistent

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

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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Recent citations in the news

Trinity
2 June 2023, Microsoft

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13 February 2017, InfoWorld

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27 July 2016, GeekWire

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21 June 2023, Datanami

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29 October 2019, TechTarget

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The Billion Data Point Challenge: Building a Query Engine for High Cardinality Time Series Data
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3 September 2009, The Guardian

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29 April 2018, Open Source For You

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