DBMS > Badger vs. CrateDB vs. jBASE vs. Oracle Berkeley DB

System Properties Comparison Badger vs. CrateDB vs. jBASE vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Distributed Database based on Lucene

A robust multi-value DBMS comprising development tools and middleware

Widely used in-process key-value store

Primary database model

Key-value store

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Multivalue DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Relational DBMS

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

Score	0.14
Rank	#328	Overall
	#48	Key-value stores

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	1.36
Rank	#157	Overall
	#3	Multivalue DBMS

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

Website

github.com/dgraph-io/badger

cratedb.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

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

Technical documentation

godoc.org/github.com/dgraph-io/badger

cratedb.com/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

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

Developer

DGraph Labs

Crate

Rocket Software (formerly Zumasys)

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2017

2013

1991

1994

Current release

5.8.1, August 2024

5.7

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial

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

Java

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

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

AIX
Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Typing

predefined data types such as float or date

yes

optional

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, but no triggers and constraints, and PostgreSQL compatibility

Embedded SQL for jBASE in BASIC

yes

SQL interfaced based on SQLite is available

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

.Net
Basic
Jabbascript
Java

.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 (Javascript)

yes

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Configurable replication on table/partition-level

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Read-after-write consistency on record level

Foreign keys

Referential integrity

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Concurrency

Support for concurrent manipulation of data

yes

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

rights management via user accounts

Access rights can be defined down to the item level

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