DBMS > Couchbase vs. FatDB vs. RavenDB vs. SAP Adaptive Server vs. Spark SQL

System Properties Comparison Couchbase vs. FatDB vs. RavenDB vs. SAP Adaptive Server vs. Spark SQL

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

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.

Open Source Operational and Transactional Enterprise NoSQL Document Database

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store

Document store
Key-value store

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

Graph DBMS
Spatial DBMS
Time Series DBMS

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	18.46
Rank	#36	Overall
	#5	Document stores

Score	3.01
Rank	#101	Overall
	#17	Document stores

Score	37.12
Rank	#25	Overall
	#17	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

www.couchbase.com

ravendb.net

www.sap.com/products/technology-platform/sybase-ase.html

spark.apache.org/sql

Technical documentation

docs.couchbase.com

ravendb.net/docs

help.sap.com/docs/SAP_ASE

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Couchbase, Inc.

FatCloud

Hibernating Rhinos

SAP, Sybase

Apache Software Foundation

Initial release

2011

2012

2010

1987

2014

Current release

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

5.4, July 2022

16.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

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

commercial

Open Source

AGPL version 3, commercial license available

commercial

Open Source

Apache 2.0

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++, Go and Erlang

C and C++

Scala

Server operating systems

Linux
OS X
Windows

Windows

Linux
macOS
Raspberry Pi
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

Linux
OS X
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.

Secondary indexes

yes

SQL

Support of SQL

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

Via inetgration in SQL Server

SQL-like query language (RQL)

yes

SQL-like DML and DDL statements

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

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

C
C++
Cobol
Java
Java
Perl
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

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

yes

via applications

yes

Java and Transact-SQL

Triggers

yes

via the TAP protocol

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

Multi-source replication

Multi-source replication
Source-replica replication

none

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

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

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

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

Authorization levels configured per client per database

fine grained access rights according to SQL-standard

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