DBMS > Apache IoTDB vs. Fauna vs. Percona Server for MySQL vs. RocksDB vs. STSdb

System Properties Comparison Apache IoTDB vs. Fauna vs. Percona Server for MySQL vs. RocksDB vs. STSdb

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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.

Enhanced drop-in replacement for MySQL based on XtraDB or TokuDB storage engines with improved performance and additional diagnostic and management features.

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Time Series DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

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

Score	2.10
Rank	#119	Overall
	#57	Relational DBMS

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

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

iotdb.apache.org

fauna.com

www.percona.com/software/mysql-database/percona-server

rocksdb.org

github.com/STSSoft/STSdb4

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.fauna.com

www.percona.com/downloads/Percona-Server-LATEST

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

Fauna, Inc.

Percona

Facebook, Inc.

STS Soft SC

Initial release

2018

2014

2008

2013

2011

Current release

1.1.0, April 2023

8.0.36-28, 2024

9.2.1, May 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2

Open Source

BSD

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Scala

C and C++

C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux

Windows

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

RESTful HTTP API

ADO.NET
JDBC
ODBC

C++ API
Java API

.NET Client API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

Ada
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C++
Go
Java
Perl
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

yes

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

horizontal partitioning

consistent hashing

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication

Multi-source replication
Source-replica replication
XtraDB Cluster

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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

Identity management, authentication, and access control

Users with fine-grained authorization concept

no user groups or roles

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