DBMS > Blueflood vs. Milvus vs. NCache vs. Snowflake vs. Spark SQL

System Properties Comparison Blueflood vs. Milvus vs. NCache vs. Snowflake vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

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

Open-Source and Enterprise in-memory Key-Value Store

Cloud-based data warehousing service for structured and semi-structured data

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

blueflood.io

milvus.io

www.alachisoft.com/ncache

www.snowflake.com

spark.apache.org/sql

Technical documentation

github.com/rax-maas/blueflood/wiki

milvus.io/docs/overview.md

www.alachisoft.com/resources/docs

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Rackspace

Alachisoft

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2013

2019

2005

2014

Current release

2.3.4, January 2024

5.3.3, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

Enterprise Edition available

commercial

Open Source

Apache 2.0

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++, Go

C#, .NET, .NET Core, Java

Scala

Server operating systems

Linux
OS X

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
Windows

hosted

Linux
OS X
Windows

Data scheme

predefined scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST

RESTful HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

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

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

support for stored procedures with SQL-Server CLR

user defined functions

Triggers

yes

Notifications

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes, with selectable consistency level

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking and pessimistic locking

ACID

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

User concepts

Access control

Role based access control and fine grained access rights

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

Users with fine-grained authorization concept, user roles and pluggable authentication

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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NCache has been the market leader in .NET Distributed Caching since 2005 . NCache...
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Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
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NCache is 100% .NET/ .NET Core based which fully supports ASP.NET Core Sessions ,...
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Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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NCache enables industries like retail, finance, banking IoT, travel, ecommerce, healthcare...
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Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
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Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
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Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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Market Leader in .NET Distributed Caching since 2005.
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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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NCache Open Source is free on an as-is basis without any support. NCache Enterprise...
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