DBMS > FatDB vs. LeanXcale vs. Solr vs. Yaacomo

System Properties Comparison FatDB vs. LeanXcale vs. Solr vs. Yaacomo

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

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

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

Document store
Key-value store

Key-value store
Relational DBMS

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.leanxcale.com

solr.apache.org

yaacomo.com

Technical documentation

solr.apache.org/resources.html

Developer

FatCloud

LeanXcale

Apache Software Foundation

Q2WEB GmbH

Initial release

2012

2015

2006

2009

Current release

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

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

schema-free

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

Via inetgration in SQL Server

yes

through Apache Derby

Solr Parallel SQL Interface

yes

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Java API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

C
Java
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

yes

via applications

Java plugins

Triggers

yes

via applications

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

yes

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

User concepts

Access control

Can implement custom security layer via applications

yes

fine grained access rights according to SQL-standard

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