DBMS > Dgraph vs. FatDB vs. LevelDB vs. Oracle Berkeley DB vs. Tibero

System Properties Comparison Dgraph vs. FatDB vs. LevelDB vs. Oracle Berkeley DB vs. Tibero

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Distributed and scalable native Graph DBMS

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Widely used in-process key-value store

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Graph DBMS

Document store
Key-value store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

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

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

dgraph.io

github.com/google/leveldb

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

us.tmaxsoft.com/products/tibero

Technical documentation

dgraph.io/docs

github.com/google/leveldb/blob/main/doc/index.md

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

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Dgraph Labs, Inc.

FatCloud

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

TmaxSoft

Initial release

2016

2012

2011

1994

2003

Current release

1.23, February 2021

18.1.40, May 2020

6, April 2015

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

BSD

Open Source

commercial license available

commercial

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

C++

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

C and Assembler

Server operating systems

Linux
OS X
Windows

Windows

Illumos
Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

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

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

SQL

Support of SQL

Via inetgration in SQL Server

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.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

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

via applications

Persistent Stored Procedure (PSM)

Triggers

yes

via applications

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

selectable replication factor

none

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

planned for next version

User concepts

Access control

Planned for future releases

Can implement custom security layer via applications

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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