DBMS > Ehcache vs. Google Cloud Bigtable vs. Heroic vs. MySQL vs. SAP IQ

System Properties Comparison Ehcache vs. Google Cloud Bigtable vs. Heroic vs. MySQL vs. SAP IQ

Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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

Widely used open source RDBMS

Columnar RDBMS optimized for Big Data analytics

Primary database model

Key-value store

Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

column-oriented

Secondary database models

Document store
Spatial DBMS

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

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Website

www.ehcache.org

cloud.google.com/bigtable

github.com/spotify/heroic

www.mysql.com

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

Technical documentation

www.ehcache.org/documentation

cloud.google.com/bigtable/docs

spotify.github.io/heroic

dev.mysql.com/doc

help.sap.com/docs/SAP_IQ

Developer

Terracotta Inc, owned by Software AG

Google

Spotify

Oracle

since 2010, originally MySQL AB, then Sun

SAP, formerly Sybase

Initial release

2009

2015

2014

1995

1994

Current release

3.10.0, March 2022

8.4.0, April 2024

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

Java

C and C++

Server operating systems

All OS with a Java VM

hosted

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

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 Elasticsearch

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

JCache

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

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

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

Java

C#
C++
Go
Java
JavaScript (Node.js)
Python

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

C
C#
C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

proprietary syntax

yes

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

shared disk or shared nothing architectures with SAP IQ Multiplexer

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

Multi-source replication
Source-replica replication

SAP/Sybase Replication Server

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

Atomic single-row operations

ACID

not for MyISAM storage engine

ACID

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

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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