DBMS > EventStoreDB vs. Machbase Neo vs. Solr vs. Yaacomo

System Properties Comparison EventStoreDB vs. Machbase Neo vs. Solr vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Industrial-strength, open-source database solution built from the ground up for event sourcing.

TimeSeries DBMS for AIoT and BigData

A widely used distributed, scalable search engine based on Apache Lucene

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.eventstore.com

machbase.com

solr.apache.org

yaacomo.com

Technical documentation

developers.eventstore.com

machbase.com/dbms

solr.apache.org/resources.html

Developer

Event Store Limited

Machbase

Apache Software Foundation

Q2WEB GmbH

Initial release

2012

2013

2006

2009

Current release

21.2, February 2021

V8.0, August 2023

9.5.0, February 2024

License

Commercial or Open Source

Open Source

commercial

free test version available

Open Source

Apache Version 2

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

Linux
Windows

Linux
macOS
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Android
Linux
Windows

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

Solr Parallel SQL Interface

yes

APIs and other access methods

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Java API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Java plugins

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

volatile and lookup table

yes

User concepts

Access control

simple password-based access control

yes

fine grained access rights according to SQL-standard

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