DBMS > Apache IoTDB vs. Ehcache vs. Heroic vs. SAP IQ

System Properties Comparison Apache IoTDB vs. Ehcache vs. Heroic vs. SAP IQ

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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

A widely adopted Java cache with tiered storage options

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Columnar RDBMS optimized for Big Data analytics

Primary database model

column-oriented

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

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	5.23
Rank	#68	Overall
	#8	Key-value stores

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	3.27
Rank	#96	Overall
	#50	Relational DBMS

Website

iotdb.apache.org

www.ehcache.org

github.com/spotify/heroic

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

Technical documentation

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

www.ehcache.org/documentation

spotify.github.io/heroic

help.sap.com/docs/SAP_IQ

Developer

Apache Software Foundation

Terracotta Inc, owned by Software AG

Spotify

SAP, formerly Sybase

Initial release

2018

2009

2014

1994

Current release

1.1.0, April 2023

3.10.0, March 2022

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

AIX
HP-UX
Linux
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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

JCache

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java

C
C#
C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

by using Terracotta Server

Sharding

shared disk or shared nothing architectures with SAP IQ Multiplexer

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

yes

by using Terracotta Server

yes

SAP/Sybase Replication Server

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

fine grained access rights according to SQL-standard

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