DBMS > 4D vs. CouchDB vs. Hive vs. LeanXcale

System Properties Comparison 4D vs. CouchDB vs. Hive vs. LeanXcale

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Relational DBMS

Document store

Relational DBMS

Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	2.89
Rank	#107	Overall
	#56	Relational DBMS

Score	16.97
Rank	#41	Overall
	#7	Document stores

Score	81.61
Rank	#17	Overall
	#11	Relational DBMS

Score	0.18
Rank	#292	Overall
	#42	Key-value stores
	#136	Relational DBMS

Website

Technical documentation

livedoc.4d.com

docs.couchdb.org/en/stable

cwiki.apache.org/confluence/display/Hive/Home

Developer

4D, Inc

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Apache Software Foundation

initially developed by Facebook

LeanXcale

Initial release

1984

2005

2012

2015

Current release

v18 R5, January 2021

3.2.2, April 2022

3.1.2, August 2019

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache Version 2

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

Erlang

Java

Server operating systems

OS X
Windows

Android
BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Data scheme

yes

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

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like DML and DDL statements

yes

through Apache Derby

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

4D proprietary IDE
PHP

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C++
Java
PHP
Python

C
Java
Scala

Server-side scripts

Stored procedures

yes

View functions in JavaScript

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

improved architecture with release 2.0

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

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

atomic operations within a single document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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

Users and groups

Access rights for users can be defined per database

Access rights for users, groups and roles

More information provided by the system vendor

Specific characteristics

Full ACID and full SQL operational database with analytical queries over operational...
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Competitive advantages

Linear scalability upto 100s of nodes (tested with 100 nodes and TPC-C).
» more

Typical application scenarios

Hybrid Transactional-Analytical Processing (HTAP) requiring an operational database...
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Key customers

banking/finance/fintech, telco, insurance, traveltech, M2M/IoT, retail
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Market metrics

15 beta testers
» more

Licensing and pricing models

annual fee covering license and support based on number of physical cores
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