DBMS > Google BigQuery vs. Milvus vs. PouchDB vs. Prometheus vs. SAP SQL Anywhere

System Properties Comparison Google BigQuery vs. Milvus vs. PouchDB vs. Prometheus vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

JavaScript DBMS with an API inspired by CouchDB

Open-source Time Series DBMS and monitoring system

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

cloud.google.com/bigquery

milvus.io

pouchdb.com

prometheus.io

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

cloud.google.com/bigquery/docs

milvus.io/docs/overview.md

pouchdb.com/guides

prometheus.io/docs

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Google

Apache Software Foundation

SAP

formerly Sybase

Initial release

2010

2019

2012

2015

1992

Current release

2.4.4, May 2024

7.1.1, June 2019

17, July 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache 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

C++, Go

JavaScript

Server operating systems

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

server-less, requires a JavaScript environment (browser, Node.js)

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

Numeric data only

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.

Import of XML data possible

yes

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

RESTful HTTP/JSON API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

JavaScript

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

View functions in JavaScript

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

by Federation

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Role based access control and fine grained access rights

fine grained access rights according to SQL-standard

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...
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