DBMS > GreptimeDB vs. HEAVY.AI vs. SQLite vs. Weaviate

System Properties Comparison GreptimeDB vs. HEAVY.AI vs. SQLite vs. Weaviate

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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

Widely used embeddable, in-process RDBMS

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Time Series DBMS

Relational DBMS

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	0.09
Rank	#343	Overall
	#31	Time Series DBMS

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	1.48
Rank	#149	Overall
	#8	Vector DBMS

Website

greptime.com

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

www.sqlite.org

github.com/weaviate/weaviate
weaviate.io

Technical documentation

docs.greptime.com

docs.heavy.ai

www.sqlite.org/docs.html

weaviate.io/developers/weaviate

Developer

Greptime Inc.

HEAVY.AI, Inc.

Dwayne Richard Hipp

Weaviate B.V.

Initial release

2022

2016

2000

2019

Current release

5.10, January 2022

3.46.1 (13 August 2024), August 2024

1.19, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Public Domain

Open Source

commercial license available with Weaviate Enterprise

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.

Implementation language

Rust

C++ and CUDA

Server operating systems

Android
Docker
FreeBSD
Linux
macOS
Windows

Linux

server-less

Data scheme

schema-free, schema definition possible

yes

dynamic column types

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

yes

SQL-92 is not fully supported

GraphQL is used as query language

APIs and other access methods

gRPC
HTTP API
JDBC

JDBC
ODBC
Thrift
Vega

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

C++
Erlang
Go
Java
JavaScript

All languages supporting JDBC/ODBC/Thrift
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

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

yes

Durability

Support for making data persistent

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

Simple rights management via user accounts

fine grained access rights according to SQL-standard

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

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

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
» more

Competitive advantages

- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
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Flexible deployment - Free, open source or fully-managed cloud vector database service...
» more

Typical application scenarios

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

As a database supporting the development of generative AI and semantic search applications...
» more

Key customers

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

All companies that have data.
» more

Market metrics

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

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more

Licensing and pricing models

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

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
» more

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