DBMS > Amazon Redshift vs. Apache Cassandra vs. CrateDB vs. Manticore Search vs. Milvus

System Properties Comparison Amazon Redshift vs. Apache Cassandra vs. CrateDB vs. Manticore Search vs. Milvus

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Distributed Database based on Lucene

Multi-storage database for search, including full-text search.

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

Primary database model

Relational DBMS

Wide column store

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Search engine

Vector DBMS

Secondary database models

Vector DBMS

starting with release V5

Relational DBMS

Time Series DBMS

using the Manticore Columnar Library

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	98.94
Rank	#11	Overall
	#1	Wide column stores

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	0.23
Rank	#301	Overall
	#21	Search engines

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

cassandra.apache.org/doc/latest

cratedb.com/docs

manual.manticoresearch.com

milvus.io/docs/overview.md

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top level project, originally developped by Facebook

Crate

Manticore Software

Initial release

2012

2008

2013

2017

2019

Current release

5.0-rc1, July 2024

5.8.1, August 2024

6.0, February 2023

2.4.4, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

GPL version 2

Open Source

Apache Version 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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

Java

C++

C++, Go

Server operating systems

hosted

BSD
Linux
OS X
Windows

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

FreeBSD
Linux
macOS
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

Fixed schema

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

Vector, Numeric and String

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.

Can index from XML

Secondary indexes

restricted

only equality queries, not always the best performing solution

yes

full-text index on all search fields

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like SELECT, DML and DDL statements (CQL)

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like query language

APIs and other access methods

JDBC
ODBC

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions (Javascript)

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Representation of geographical distribution of servers is possible

Configurable replication on table/partition-level

Synchronous replication based on Galera library

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Eventual Consistency
Read-after-write consistency on record level

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

unique row identifiers can be used for implementing an optimistic concurrency control strategy

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

rights management via user accounts

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
» more

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

Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
» more

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

Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
» more

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

Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
» more

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

Market metrics

Apache Cassandra is used by 40% of the Fortune 100.
» more

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

Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
» more

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

News

How to Monitor DataStax-Powered RAG Applications with Fiddler
19 September 2024

How Knowledge Graph RAG Boosts LLM Results
18 September 2024

How to Chunk Text in JavaScript for Your RAG Application
18 September 2024

Precision and Efficiency in Domain-Specific Text2SQL Conversion: Introducing Skypoint AI Platform’s SherloQ
11 September 2024

Now in LangChain: Graph Vector Store, a Simple Way to Add Structured Data to RAG Applications
11 September 2024

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.