DBMS > Apache IoTDB vs. EventStoreDB vs. LokiJS vs. MySQL vs. Sphinx

System Properties Comparison Apache IoTDB vs. EventStoreDB vs. LokiJS vs. MySQL vs. Sphinx

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

Industrial-strength, open-source database solution built from the ground up for event sourcing.

In-memory JavaScript DBMS

Widely used open source RDBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Key/Value like access via memcached API

Search engine

Secondary database models

Document store
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	0.49
Rank	#260	Overall
	#42	Document stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

iotdb.apache.org

www.eventstore.com

github.com/techfort/LokiJS

www.mysql.com

sphinxsearch.com

Technical documentation

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

developers.eventstore.com

techfort.github.io/LokiJS

dev.mysql.com/doc

sphinxsearch.com/docs

Developer

Apache Software Foundation

Event Store Limited

Oracle

since 2010, originally MySQL AB, then Sun

Sphinx Technologies Inc.

Initial release

2018

2012

2014

1995

2001

Current release

1.1.0, April 2023

21.2, February 2021

8.4.0, April 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

JavaScript

C and C++

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

FreeBSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

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

via views

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
Native API

JavaScript API

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

JavaScript

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

View functions in JavaScript

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Partitioning is done manually, search queries against distributed index is supported

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

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

none

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

atomic operations within a single collection possible

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

yes

The original contents of fields are not stored in the Sphinx index.

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

Users with fine-grained authorization concept

no user groups or roles

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