DBMS > Fauna vs. HEAVY.AI vs. Snowflake vs. Tkrzw

System Properties Comparison Fauna vs. HEAVY.AI vs. Snowflake vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

fauna.com

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

www.snowflake.com

dbmx.net/tkrzw

Technical documentation

docs.fauna.com

docs.heavy.ai

docs.snowflake.net/manuals/index.html

Developer

Fauna, Inc.

HEAVY.AI, Inc.

Snowflake Computing Inc.

Mikio Hirabayashi

Initial release

2014

2016

2014

2020

Current release

5.10, January 2022

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

C++ and CUDA

C++

Server operating systems

hosted

Linux

hosted

Linux
macOS

Data scheme

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

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

yes

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

CLI Client
JDBC
ODBC

Supported programming languages

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript (Node.js)
Python

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

consistent hashing

Sharding

Round robin

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using specific database classes

User concepts

Access control

Identity management, authentication, and access control

fine grained access rights according to SQL-standard

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

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