DBMS > Google Cloud Bigtable vs. Graph Engine vs. Milvus vs. Qdrant

System Properties Comparison Google Cloud Bigtable vs. Graph Engine vs. Milvus vs. Qdrant

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Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

A high-performance vector database with neural network or semantic-based matching

Primary database model

Key-value store
Wide column store

Graph DBMS
Key-value store

Vector DBMS

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Website

cloud.google.com/bigtable

www.graphengine.io

milvus.io

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

cloud.google.com/bigtable/docs

www.graphengine.io/docs/manual

milvus.io/docs/overview.md

qdrant.tech/documentation

Developer

Google

Microsoft

Qdrant

Initial release

2015

2010

2019

2021

Current release

2.3.4, January 2024

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

Apache Version 2.0

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

.NET and C

C++, Go

Rust

Server operating systems

hosted

.NET

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Docker
Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

Numbers, Strings, Geo, Boolean

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

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

RESTful HTTP API

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

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

C#
C++
F#
Visual Basic

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

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

Collection-level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Role based access control and fine grained access rights

Key-based 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

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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