DBMS > GridGain vs. JanusGraph vs. Snowflake vs. Weaviate

System Properties Comparison GridGain vs. JanusGraph vs. Snowflake vs. Weaviate

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Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Key-value store
Relational DBMS

Graph DBMS

Relational DBMS

Vector DBMS

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

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	1.91
Rank	#135	Overall
	#12	Graph DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	1.89
Rank	#138	Overall
	#4	Vector DBMS

Website

www.gridgain.com

janusgraph.org

www.snowflake.com

github.com/weaviate/weaviate
weaviate.io

Technical documentation

www.gridgain.com/docs/index.html

docs.janusgraph.org

docs.snowflake.net/manuals/index.html

weaviate.io/developers/weaviate

Developer

GridGain Systems, Inc.

Linux Foundation; originally developed as Titan by Aurelius

Snowflake Computing Inc.

Weaviate B.V.

Initial release

2007

2017

2014

2019

Current release

GridGain 8.5.1

0.6.3, February 2023

1.19, May 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

commercial license available with Weaviate Enterprise

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Java

Server operating systems

Linux
OS X
Solaris
Windows

Linux
OS X
Unix
Windows

hosted

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

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

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

GraphQL is used as query language

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

CLI Client
JDBC
ODBC

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Clojure
Java
Python

JavaScript (Node.js)
Python

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

user defined functions

Triggers

yes (cache interceptors and events)

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

Security Hooks for custom implementations

User authentification and security via Rexster Graph Server

Users with fine-grained authorization concept, user roles and pluggable authentication

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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