DBMS > GreptimeDB vs. HEAVY.AI vs. Lovefield vs. Milvus vs. SQLite

System Properties Comparison GreptimeDB vs. HEAVY.AI vs. Lovefield vs. Milvus vs. SQLite

Editorial information provided by DB-Engines

Name

Description

An open source Time Series DBMS built for increased scalability, high performance and efficiency

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Embeddable relational database for web apps written in pure JavaScript

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

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.06
Rank	#352	Overall
	#33	Time Series DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

greptime.com

github.com/heavyai/heavydb
www.heavy.ai

google.github.io/lovefield

milvus.io

www.sqlite.org

Technical documentation

docs.greptime.com

docs.heavy.ai

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

milvus.io/docs/overview.md

www.sqlite.org/docs.html

Developer

Greptime Inc.

HEAVY.AI, Inc.

Google

Dwayne Richard Hipp

Initial release

2022

2016

2014

2019

2000

Current release

5.10, January 2022

2.1.12, February 2017

2.3.4, January 2024

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

Public Domain

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

Rust

C++ and CUDA

JavaScript

C++, Go

Server operating systems

Android
Docker
FreeBSD
Linux
macOS
Windows

Linux

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

server-less

Data scheme

schema-free, schema definition possible

yes

dynamic column types

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

SQL-92 is not fully supported

APIs and other access methods

gRPC
HTTP API
JDBC

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C++
Erlang
Go
Java
JavaScript

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Python

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

via file-system locks

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

using MemoryDB

yes

User concepts

Access control

Simple rights management via user accounts

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

GreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more

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

Competitive advantages

- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
» more

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

For IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more

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

Key customers

Greptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more

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

Market metrics

GreptimeDB has garnered global recognition by topping GitHub trends following its...
» more

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

Licensing and pricing models

GreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
» more

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.