DBMS > Heroic vs. Milvus vs. SAP HANA vs. ScyllaDB

System Properties Comparison Heroic vs. Milvus vs. SAP HANA vs. ScyllaDB

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

In-memory, column based data store. Available as appliance or cloud service

Cassandra and DynamoDB compatible wide column store

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Key-value store

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Website

github.com/spotify/heroic

milvus.io

www.sap.com/products/hana.html

www.scylladb.com

Technical documentation

spotify.github.io/heroic

milvus.io/docs/overview.md

help.sap.com/hana

docs.scylladb.com

Developer

Spotify

SAP

ScyllaDB

Initial release

2014

2019

2010

2015

Current release

2.3.4, January 2024

2.0 SPS07 (April 4, 2023), April 2023

ScyllaDB Open Source 5.4.1, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

Open Source

Open Source (AGPL), commercial license available

Cloud-based only

Only available as a cloud service

also available as a cloud based service

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

Scylla Cloud: Create real-time applications that run at global scale with Scylla Cloud, the industry’s most powerful NoSQL DBaaS

Implementation language

Java

C++, Go

C++

Server operating systems

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Appliance or cloud-service

Linux

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

cluster global secondary indices

SQL

Support of SQL

yes

SQL-like DML and DDL statements (CQL)

APIs and other access methods

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

RESTful HTTP API

JDBC
ODBC

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

Supported programming languages

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

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Server-side scripts

Stored procedures

SQLScript, R

yes, Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Representation of geographical distribution of servers is possible

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

in-memory tables

User concepts

Access control

Role based access control and fine grained access rights

yes

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
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ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
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Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
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