DBMS > GBase vs. HEAVY.AI vs. Lovefield vs. Pinecone

System Properties Comparison GBase vs. HEAVY.AI vs. Lovefield vs. Pinecone

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

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 managed, cloud-native vector database

Primary database model

Relational DBMS

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	1.11
Rank	#173	Overall
	#78	Relational DBMS

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	3.02
Rank	#87	Overall
	#3	Vector DBMS

Website

www.gbase.cn

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

google.github.io/lovefield

www.pinecone.io

Technical documentation

docs.heavy.ai

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

docs.pinecone.io/docs/overview

Developer

General Data Technology Co., Ltd.

HEAVY.AI, Inc.

Google

Pinecone Systems, Inc

Initial release

2004

2016

2014

2019

Current release

GBase 8a, GBase 8s, GBase 8c

5.10, January 2022

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

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.

Implementation language

C, Java, Python

C++ and CUDA

JavaScript

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

hosted

Data scheme

yes

Typing

predefined data types such as float or date

yes

String, Number, 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.

yes

Secondary indexes

yes

SQL

Support of SQL

Standard with numerous extensions

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

Python

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

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

using MemoryDB

User concepts

Access control

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

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

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

5 Q’s for Mike Flaxman, Vice President of Heavy.AI
15 August 2024, Center for Data Innovation

HEAVY.AI Accelerates Big Data Analytics with Vultr's High-Performance GPU Cloud Infrastructure
11 September 2024, insideBIGDATA

Dr. Mike Flaxman, VP or Product Management at HEAVY.AI – Interview Series
19 September 2024, Unite.AI

HEAVY.AI Accelerates Big Data Analytics with Vultr’s High-Performance GPU Cloud Infrastructure
11 September 2024, insideBIGDATA

HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities
19 April 2023, businesswire.com

provided by Google News

Using the Pinecone vector database in .NET
12 September 2024, InfoWorld

Pinecone serverless goes multicloud as vector database market heats up
27 August 2024, VentureBeat

Pinecone launches its serverless vector database out of preview
21 May 2024, TechCrunch

Pinecone vector database available on AWS, Azure, and Google clouds
27 August 2024, Blocks & Files

Pinecone launches serverless vector database on Azure, GCP
27 August 2024, TechTarget

provided by Google News

Share this page