DBMS > JanusGraph vs. Microsoft Azure Cosmos DB vs. Trino vs. VelocityDB

System Properties Comparison JanusGraph vs. Microsoft Azure Cosmos DB vs. Trino vs. VelocityDB

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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

Globally distributed, horizontally scalable, multi-model database service

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Graph DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Spatial DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	5.00
Rank	#66	Overall
	#36	Relational DBMS

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Website

janusgraph.org

azure.microsoft.com/services/cosmos-db

trino.io

velocitydb.com

Technical documentation

docs.janusgraph.org

learn.microsoft.com/azure/cosmos-db

trino.io/broadcast
trino.io/docs/current

velocitydb.com/UserGuide

Social network pages

Developer

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

Trino Software Foundation

VelocityDB Inc

Initial release

2017

2014

2012

2020 rebranded from PrestoSQL

2011

Current release

0.6.3, February 2023

7.x

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Starburst Galaxy offers a feature-rich user interface to connect all your data sources, manage your Trino clusters, and query your data.

Implementation language

Java

Server operating systems

Linux
OS X
Unix
Windows

hosted

Linux
macOS

for devlopment

Any that supports .NET

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

depending on connected data-source

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
RESTful HTTP API
Trino CLI

.Net

Supported programming languages

Clojure
Java
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Go
Java
JavaScript (Node.js)
Python
R
Ruby

.Net

Server-side scripts

Stored procedures

yes

JavaScript

yes, depending on connected data-source

Triggers

yes

JavaScript

Callbacks are triggered when data changes

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

depending on connected data-source

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

depending on connected data-source

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

depending on connected data-source

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes

depending on connected data-source

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 can be defined down to the item level

SQL standard access control

Based on Windows Authentication

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Performant analytics query engine for data warehouses, data lakes, and data lakehouses...
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Key customers

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Market metrics

33000+ commits in GitHub 8200+ stargazers in GitHub 1200+ pull requests merged in...
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Licensing and pricing models

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