DBMS > JanusGraph vs. Microsoft Azure Table Storage vs. PouchDB vs. SQLite vs. Vertica

System Properties Comparison JanusGraph vs. Microsoft Azure Table Storage vs. PouchDB vs. SQLite vs. Vertica

Editorial information provided by DB-Engines

Name

Description

A Graph DBMS optimized for distributed clusters

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

A Wide Column Store for rapid development using massive semi-structured datasets

JavaScript DBMS with an API inspired by CouchDB

Widely used embeddable, in-process RDBMS

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
Time Series DBMS

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

Score	1.91
Rank	#135	Overall
	#12	Graph DBMS

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	2.35
Rank	#116	Overall
	#22	Document stores

Score	116.01
Rank	#10	Overall
	#7	Relational DBMS

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

janusgraph.org

azure.microsoft.com/en-us/services/storage/tables

pouchdb.com

www.sqlite.org

www.vertica.com

Technical documentation

docs.janusgraph.org

pouchdb.com/guides

www.sqlite.org/docs.html

vertica.com/documentation

Developer

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

Apache Software Foundation

Dwayne Richard Hipp

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2017

2012

2000

2005

Current release

0.6.3, February 2023

7.1.1, June 2019

3.45.2 (12 March 2024), March 2024

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Public Domain

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

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

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

JavaScript

C++

Server operating systems

Linux
OS X
Unix
Windows

hosted

server-less, requires a JavaScript environment (browser, Node.js)

server-less

Linux

Data scheme

yes

schema-free

yes

dynamic column types

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

not rigid because of 'dynamic typing' concept.

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

SQL-92 is not fully supported

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

APIs and other access methods

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

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

character-based, interactive, front-end utility

Supported programming languages

Clojure
Java
Python

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

JavaScript

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

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

Server-side scripts

Stored procedures

yes

View functions in JavaScript

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

Implicit feature of the cloud service

Sharding

with a proxy-based framework, named couchdb-lounge

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

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

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual 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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

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

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

User authentification and security via Rexster Graph Server

Access rights based on private key authentication or shared access signatures

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

More information provided by the system vendor

Specific characteristics

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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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