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DBMS > Heroic vs. Nebula Graph vs. Weaviate

System Properties Comparison Heroic vs. Nebula Graph vs. Weaviate

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Editorial information provided by DB-Engines
NameHeroic  Xexclude from comparisonNebula Graph  Xexclude from comparisonWeaviate  Xexclude from comparison
DescriptionTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchA distributed, linear scalable, high perfomant Graph DBMSA cloud-native, realtime vector search engine integrating scalable machine learning models
Primary database modelTime Series DBMSGraph DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.07
Rank#315  Overall
#29  Time Series DBMS
Score1.14
Rank#162  Overall
#15  Graph DBMS
Score0.18
Rank#292  Overall
#15  Search engines
Websitespotify.github.io/­heroic/­#!/­indexgithub.com/­vesoft-inc/­nebula
nebula-graph.io
github.com/­semi-technologies/­weaviate
www.semi.technology/­solutions/­weaviate
Technical documentationspotify.github.io/­heroic/­#!/­docs/­overviewdocs.nebula-graph.iowww.semi.technology/­developers/­weaviate/­current
DeveloperSpotifyVesoft Inc.SeMI Technologies B.V.
Initial release201420192017
Current release1.0.3, January 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2.0 + Common Clause 1.0Open Source infocommercial license available with Weaviate Enterprise
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++Go
Server operating systemsLinux
Data schemeschema-freeStrong typed schemayes, maps to GraphQL interface
Typing infopredefined data types such as float or dateyesyesyes infostring, int, float, geo point, date, cross reference, fuzzy references
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nonono
Secondary indexesyes infovia Elasticsearchyes infoNebula Graph internally uses the Key-Value store RocksDB for persistency. The vertices, edges, and their properties are stored as Key while their values are stored as Value. The primary indexes are per Key and secondary indexes are per Value.yes infoall data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed
SQL infoSupport of SQLnoSQL-like query languageGraphQL is used as query language
APIs and other access methodsHQL (Heroic Query Language, a JSON-based language)
HTTP API
Browser interface
console (shell)
Proprietary native API
Query language nGQL
GraphQL query language
RESTful HTTP/JSON API
Supported programming languagesC++
Go
Java
Python
Server-side scripts infoStored proceduresnono
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesCausal Clustering using Raft protocolyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyes infoRelationships in graphsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes infousing RocksDByes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlRole-based access controlOpenID Connect Discovery
More information provided by the system vendor
HeroicNebula GraphWeaviate
Specific characteristicsNebula Graph is a truly distributed, linearly scalable, lightning-fast graph database,...
» more
Competitive advantagesNebula Graph boasts the world's only graph database solution that is able to host...
» more
Typical application scenariosSocial networking Fraud detection Knowledge graph Data warehouse management Anti...
» more
Key customersCurrently we have some top internet companies in China implementing Nebula Graph...
» more
Market metricsAt our very early stage, Nebula Graph has already received 3.5k stars and thousands...
» more
Licensing and pricing modelsNebula Graph is open source and free to use under Apache 2.0 license and Common Clause...
» more
News

Nebula Graph meetup: New Release & Use Cases
12 October 2021

Nebula Graph SWAG Ready for You!
12 October 2021

Nebula Graph Source Code Explained: Planner
11 October 2021

Nebula Graph Source Code Explained: Scheduler & Executor
8 October 2021

Ecosystem Tools: Nebula Graph Dashboard for Monitoring
22 September 2021

Weaviate in IEEE

Weaviate on DB-engines.com

Introduction & Demo video

FLOSS Weekly Podcast

FOSDEM 2020 video

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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
HeroicNebula GraphWeaviate
DB-Engines blog posts

Weaviate, an ANN Database with CRUD support
2 February 2021,  Etienne Dilocker, SeMI Technologies (sponsor) 

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Recent citations in the news

Third Time Is The Charm For Nebula Graph Database
19 January 2021, The Next Platform

The future of graph database: vesoft unveils Nebula Graph 2.0 GA
24 March 2021, PR Web

Graph Database Startup Vesoft Seeks Funds at $1 Billion Value
5 July 2021, Bloomberg

Nebula Graphâ„¢ Database Secures $10 Million VC Funding in New Round
15 December 2020, PR Web

Nebula Graph Joins Database Race
15 June 2020, Datanami

provided by Google News

Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL
7 April 2021, ZDNet

Getting Started with the Weaviate Vector Search Engine
18 May 2020, hackernoon.com

The History of the Weaviate Vector Search Engine
9 May 2020, hackernoon.com

Weaviate Is A Search Engine For Vector Embeddings
11 May 2020, hackernoon.com

Why the rise of vector databases is a revolution for data analysis
7 July 2021, TechGenix

provided by Google News



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