DBMS > Couchbase vs. FatDB vs. Oracle Berkeley DB vs. Rockset vs. Yaacomo

System Properties Comparison Couchbase vs. FatDB vs. Oracle Berkeley DB vs. Rockset vs. Yaacomo

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.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

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

Widely used in-process key-value store

A scalable, reliable search and analytics service in the cloud, built on RocksDB

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store

Document store
Key-value store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

Relational DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS
Search engine

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.82
Rank	#212	Overall
	#36	Document stores

Website

www.couchbase.com

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

rockset.com

yaacomo.com

Technical documentation

docs.couchbase.com

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

docs.rockset.com

Developer

Couchbase, Inc.

FatCloud

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Rockset

Q2WEB GmbH

Initial release

2011

2012

1994

2019

2009

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

Open Source

commercial license available

commercial

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, C++, Go and Erlang

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

C++

Server operating systems

Linux
OS X
Windows

Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

hosted

Android
Linux
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

dynamic typing

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

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

Via inetgration in SQL Server

yes

SQL interfaced based on SQLite is available

Read-only SQL queries, including JOINs

yes

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

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

HTTP REST

JDBC
ODBC

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

via applications

Triggers

yes

via the TAP protocol

yes

via applications

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

none

Automatic sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

Source-replica replication

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency
Immediate Consistency

Eventual 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

In-memory capabilities

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

yes

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Can implement custom security layer via applications

Access rights for users and organizations can be defined via Rockset console

fine grained access rights according to SQL-standard

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