DBMS > Databend vs. Ehcache vs. IBM Db2 Event Store vs. SiriDB

System Properties Comparison Databend vs. Ehcache vs. IBM Db2 Event Store vs. SiriDB

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

A widely adopted Java cache with tiered storage options

Distributed Event Store optimized for Internet of Things use cases

Open Source Time Series DBMS

Primary database model

Relational DBMS

Key-value store

Event Store
Time Series DBMS

Time Series DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

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

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

github.com/datafuselabs/databend
www.databend.com

www.ehcache.org

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

siridb.com

Technical documentation

docs.databend.com

www.ehcache.org/documentation

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

docs.siridb.com

Developer

Databend Labs

Terracotta Inc, owned by Software AG

IBM

Cesbit

Initial release

2021

2009

2017

Current release

1.0.59, April 2023

3.10.0, March 2022

2.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

commercial

free developer edition available

Open Source

MIT License

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

Rust

Java

C and C++

Server operating systems

hosted
Linux
macOS

All OS with a Java VM

Linux

Linux, macOS, Windows for the developer addition

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

JCache

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

Java

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

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

Server-side scripts

Stored procedures

yes

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

by using Terracotta Server

Active-active shard replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

supports JTA and can work as an XA resource

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

simple rights management via user accounts

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