DBMS > Bangdb vs. CockroachDB vs. Drizzle vs. Oracle Berkeley DB

System Properties Comparison Bangdb vs. CockroachDB vs. Drizzle vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Converged and high performance database for device data, events, time series, document and graph

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Widely used in-process key-value store

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	6.15
Rank	#55	Overall
	#33	Relational DBMS

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

Website

bangdb.com

www.cockroachlabs.com

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

Technical documentation

docs.bangdb.com

www.cockroachlabs.com/docs

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

Developer

Sachin Sinha, BangDB

Cockroach Labs

Drizzle project, originally started by Brian Aker

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2012

2015

2008

1994

Current release

BangDB 2.0, October 2021

23.1.1, May 2023

7.2.4, September 2012

18.1.40, May 2020

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0, commercial license available

Open Source

GNU GPL

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

C, C++

C++

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

Server operating systems

Linux

Linux
macOS
Windows

FreeBSD
Linux
OS X

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes, wire compatible with PostgreSQL

yes

with proprietary extensions

yes

SQL interfaced based on SQLite is available

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC

Supported programming languages

C
C#
C++
Java
Python

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

C
C++
Java
PHP

.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

Triggers

yes, Notifications (with Streaming only)

hooks for callbacks inside the server can be used.

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication using RAFT

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

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, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Role-based access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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