DBMS > Heroic vs. HugeGraph vs. Microsoft Azure Synapse Analytics vs. Milvus vs. Oracle Coherence

System Properties Comparison Heroic vs. HugeGraph vs. Microsoft Azure Synapse Analytics vs. Milvus vs. Oracle Coherence

Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

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

A DBMS designed for efficient storage of vector data and vector similarity searches

Oracles in-memory data grid solution

Primary database model

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Website

github.com/spotify/heroic

github.com/hugegraph
hugegraph.apache.org

azure.microsoft.com/services/synapse-analytics

milvus.io

www.oracle.com/java/coherence

Technical documentation

spotify.github.io/heroic

hugegraph.apache.org/docs

docs.microsoft.com/azure/synapse-analytics

milvus.io/docs/overview.md

docs.oracle.com/en/middleware/standalone/coherence

Developer

Spotify

Baidu

Microsoft

Oracle

Initial release

2014

2018

2016

2019

2007

Current release

0.9

2.3.4, January 2024

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 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

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

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

Java

C++

C++, Go

Java

Server operating systems

Linux
macOS
Unix

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

All OS with a Java VM

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

APIs and other access methods

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

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

RESTful HTTP API

JCache
JPA
RESTful HTTP API

Supported programming languages

Groovy
Java
Python

C#
Java
PHP

C++
Go
Java
JavaScript (Node.js)
Python

.Net
C++
Java

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

Transact SQL

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

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

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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, roles and permissions

yes

Role based access control and fine grained access rights

authentification to access the cache via certificates or http basic authentication

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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