DBMS > Badger vs. InfluxDB vs. Infobright vs. Lovefield vs. Milvus

System Properties Comparison Badger vs. InfluxDB vs. Infobright vs. Lovefield vs. Milvus

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

DBMS for storing time series, events and metrics

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Embeddable relational database for web apps written in pure JavaScript

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

Primary database model

Secondary database models

Spatial DBMS

with GEO package

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	26.56
Rank	#28	Overall
	#1	Time Series DBMS

Score	1.00
Rank	#193	Overall
	#90	Relational DBMS

Score	0.32
Rank	#290	Overall
	#132	Relational DBMS

Score	1.81
Rank	#144	Overall
	#5	Vector DBMS

Website

github.com/dgraph-io/badger

www.influxdata.com/products/influxdb-overview

ignitetech.com/softwarelibrary/infobrightdb

google.github.io/lovefield

milvus.io

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.influxdata.com/influxdb

github.com/google/lovefield/blob/master/docs/spec_index.md

milvus.io/docs/overview.md

Developer

DGraph Labs

Ignite Technologies Inc.; formerly InfoBright Inc.

Google

Initial release

2017

2013

2005

2014

2019

Current release

2.7.5, January 2024

2.1.12, February 2017

2.3.4, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

MIT-License; commercial enterprise version available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud 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

Implementation language

JavaScript

C++, Go

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X

through Homebrew

Linux
Windows

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

tested with Chrome, Firefox, IE, Safari

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

Numeric data and Strings

yes

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.

Secondary indexes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

HTTP API
JSON over UDP

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript

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

Server-side scripts

Stored procedures

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

in enterprise version only

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

in enterprise version only

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

Depending on used storage engine

yes

using MemoryDB

yes

User concepts

Access control

simple rights management via user accounts

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

InfluxData is the creator of InfluxDB , the open source time series database. It...
» more

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

Competitive advantages

Time to Value InfluxDB is available in all the popular languages and frameworks,...
» more

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

Typical application scenarios

IoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more

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

Key customers

InfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more

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

Market metrics

Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more

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

Licensing and pricing models

Open source core with closed source clustering available either on-premise or on...
» more

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

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8 April 2024

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29 March 2024

Time Series, InfluxDB, and Vector Databases
26 March 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.

Related products and services

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More resources

DB-Engines blog posts

Why Build a Time Series Data Platform?
20 July 2017, Paul Dix (guest author)

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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Vector databases
2 June 2023, Matthias Gelbmann

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