DBMS > Ehcache vs. Heroic vs. IRONdb vs. Microsoft Azure Synapse Analytics

System Properties Comparison Ehcache vs. Heroic vs. IRONdb vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A widely adopted Java cache with tiered storage options

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Primary database model

Key-value store

Time Series DBMS

Relational DBMS

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Website

www.ehcache.org

github.com/spotify/heroic

www.circonus.com/solutions/time-series-database/

azure.microsoft.com/services/synapse-analytics

Technical documentation

www.ehcache.org/documentation

spotify.github.io/heroic

docs.circonus.com/irondb/category/getting-started

docs.microsoft.com/azure/synapse-analytics

Developer

Terracotta Inc, owned by Software AG

Spotify

Circonus LLC.

Microsoft

Initial release

2009

2014

2017

2016

Current release

3.10.0, March 2022

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

commercial

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

C and C++

C++

Server operating systems

All OS with a Java VM

Linux

hosted

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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 (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

JCache

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

HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

Java

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C#
Java
PHP

Server-side scripts

Stored procedures

yes, in Lua

Transact SQL

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

Automatic, metric affinity per node

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

configurable replication factor, datacenter aware

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency
Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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

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