DBMS > Fauna vs. FoundationDB vs. Snowflake vs. Spark SQL vs. Tkrzw

System Properties Comparison Fauna vs. FoundationDB vs. Snowflake vs. Spark SQL vs. Tkrzw

Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

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.

Ordered key-value store. Core features are complimented by layers.

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

Key-value store

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

Score	1.55
Rank	#151	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	1.06
Rank	#185	Overall
	#31	Document stores
	#28	Key-value stores
	#85	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

fauna.com

github.com/apple/foundationdb

www.snowflake.com

spark.apache.org/sql

dbmx.net/tkrzw

Technical documentation

docs.fauna.com

apple.github.io/foundationdb

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Fauna, Inc.

FoundationDB

Snowflake Computing Inc.

Apache Software Foundation

Mikio Hirabayashi

Initial release

2014

2013

2014

2020

Current release

6.2.28, November 2020

3.5.0 ( 2.13), September 2023

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

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++

Scala

C++

Server operating systems

hosted

Linux
OS X
Windows

hosted

Linux
OS X
Windows

Linux
macOS

Data scheme

schema-free

some layers support schemas

yes

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

yes

schema-free

Typing

predefined data types such as float or date

some layers support typing

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

supported in specific SQL layer only

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

JavaScript (Node.js)
Python

Java
Python
R
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in SQL-layer only

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

yes

yes, utilizing Spark Core

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

Linearizable consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

in SQL-layer only

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

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

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