DBMS > Ehcache vs. Google Cloud Datastore vs. IBM Db2 Event Store vs. MySQL vs. SiriDB

System Properties Comparison Ehcache vs. Google Cloud Datastore vs. IBM Db2 Event Store vs. MySQL vs. SiriDB

Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Distributed Event Store optimized for Internet of Things use cases

Widely used open source RDBMS

Open Source Time Series DBMS

Primary database model

Key-value store

Document store

Event Store
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

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	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

www.ehcache.org

cloud.google.com/datastore

www.ibm.com/products/db2-event-store

www.mysql.com

siridb.com

Technical documentation

www.ehcache.org/documentation

cloud.google.com/datastore/docs

www.ibm.com/docs/en/db2-event-store

dev.mysql.com/doc

docs.siridb.com

Developer

Terracotta Inc, owned by Software AG

Google

IBM

Oracle

since 2010, originally MySQL AB, then Sun

Cesbit

Initial release

2009

2008

2017

1995

2017

Current release

3.10.0, March 2022

2.0

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

free developer edition available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

MIT License

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.

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Implementation language

Java

C and C++

Server operating systems

All OS with a Java VM

hosted

Linux

Linux, macOS, Windows for the developer addition

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

Numeric data

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 (GQL)

yes

through the embedded Spark runtime

yes

with proprietary extensions

APIs and other access methods

JCache

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

Supported programming languages

Java

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

using Google App Engine

yes

proprietary syntax

Triggers

yes

Cache Event Listeners

Callbacks using the Google Apps Engine

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Multi-source replication using Paxos

Active-active shard replication

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

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

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

simple rights management via user accounts

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