DBMS > CouchDB vs. EsgynDB vs. JanusGraph vs. Vertica

System Properties Comparison CouchDB vs. EsgynDB vs. JanusGraph vs. Vertica

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Name

Description

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial DBMS
Time Series DBMS

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

Score	7.46
Rank	#51	Overall
	#7	Document stores

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

Technical documentation

docs.couchdb.org/en/stable

docs.janusgraph.org

vertica.com/documentation

Developer

Apache Software Foundation

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

Esgyn

Linux Foundation; originally developed as Titan by Aurelius

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2005

2015

2017

2005

Current release

3.3.3, December 2023

1.0.0, October 2023

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

C++, Java

Java

C++

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Unix
Windows

Linux

Data scheme

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

via views

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

Clojure
Java
Python

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

View functions in JavaScript

Java Stored Procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication between multi datacenters

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources

DB-Engines blog posts

Couchbase climbs up the DB-Engines Ranking, increasing its popularity by 10% every month
2 June 2014, Matthias Gelbmann

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