DBMS > Heroic vs. Riak KV vs. RocksDB vs. TigerGraph vs. Weaviate

System Properties Comparison Heroic vs. Riak KV vs. RocksDB vs. TigerGraph vs. Weaviate

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Distributed, fault tolerant key-value store

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

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

Primary database model

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Key-value store

Graph DBMS

Vector DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Score	1.52
Rank	#153	Overall
	#6	Vector DBMS

Website

github.com/spotify/heroic

rocksdb.org

www.tigergraph.com

github.com/weaviate/weaviate
weaviate.io

Technical documentation

spotify.github.io/heroic

www.tiot.jp/riak-docs/riak/kv/latest

github.com/facebook/rocksdb/wiki

docs.tigergraph.com

weaviate.io/developers/weaviate

Developer

Spotify

OpenSource, formerly Basho Technologies

Facebook, Inc.

Weaviate B.V.

Initial release

2014

2009

2013

2017

2019

Current release

3.2.0, December 2022

9.2.1, May 2024

1.19, May 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache version 2, commercial enterprise edition

Open Source

BSD

commercial

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

Java

Erlang

C++

Server operating systems

Linux
OS X

Linux

Data scheme

schema-free

yes

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

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

via Elasticsearch

restricted

yes

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

SQL

Support of SQL

SQL-like query language (GSQL)

GraphQL is used as query language

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

HTTP API
Native Erlang Interface

C++ API
Java API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Go
Java
Perl
Python
Ruby

C++
Java

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

Erlang

yes

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

yes

Relationships in graphs

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

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

yes, using Riak Security

Role-based access control

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
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Key customers

All companies that have data.
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Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

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