DBMS > Dragonfly vs. Fauna vs. IBM Db2 Event Store vs. Snowflake vs. Tkrzw

System Properties Comparison Dragonfly vs. Fauna vs. IBM Db2 Event Store vs. Snowflake vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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.

Distributed Event Store optimized for Internet of Things use cases

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

Key-value store

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Event Store
Time Series DBMS

Relational DBMS

Key-value store

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

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

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

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

fauna.com

www.ibm.com/products/db2-event-store

www.snowflake.com

dbmx.net/tkrzw

Technical documentation

www.dragonflydb.io/docs

docs.fauna.com

www.ibm.com/docs/en/db2-event-store

docs.snowflake.net/manuals/index.html

Developer

DragonflyDB team and community contributors

Fauna, Inc.

IBM

Snowflake Computing Inc.

Mikio Hirabayashi

Initial release

2023

2014

2017

2014

2020

Current release

1.0, March 2023

2.0

0.9.3, August 2020

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

free developer 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

C++

Scala

C and C++

C++

Server operating systems

Linux

hosted

Linux

Linux, macOS, Windows for the developer addition

hosted

Linux
macOS

Data scheme

scheme-free

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

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

through the embedded Spark runtime

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

JavaScript (Node.js)
Python

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

Lua

user defined functions

yes

user defined functions

Triggers

publish/subscribe channels provide some trigger functionality

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

consistent hashing

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

Active-active shard replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

Password-based authentication

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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