DBMS > Heroic vs. JanusGraph vs. Machbase Neo vs. Microsoft Azure Synapse Analytics

System Properties Comparison Heroic vs. JanusGraph vs. Machbase Neo vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A Graph DBMS optimized for distributed clusters

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

TimeSeries DBMS for AIoT and BigData

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Primary database model

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Website

github.com/spotify/heroic

janusgraph.org

machbase.com

azure.microsoft.com/services/synapse-analytics

Technical documentation

spotify.github.io/heroic

docs.janusgraph.org

machbase.com/dbms

docs.microsoft.com/azure/synapse-analytics

Developer

Spotify

Linux Foundation; originally developed as Titan by Aurelius

Machbase

Microsoft

Initial release

2014

2017

2013

2016

Current release

0.6.3, February 2023

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

free test version available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix
Windows

Linux
macOS
Windows

hosted

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

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

Clojure
Java
Python

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C#
Java
PHP

Server-side scripts

Stored procedures

yes

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

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

In-memory capabilities

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

yes

volatile and lookup table

User concepts

Access control

User authentification and security via Rexster Graph Server

simple password-based access control

yes

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