DBMS > Apache Solr vs. FoundationDB vs. SvectorDB

System Properties Comparison Apache Solr vs. FoundationDB vs. SvectorDB

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Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

Description

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

Ordered key-value store. Core features are complimented by layers.

Serverless cloud-native vector database

Primary database model

Search engine

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	32.79
Rank	#23	Overall
	#3	Search engines

Score	0.89
Rank	#197	Overall
	#32	Document stores
	#29	Key-value stores
	#92	Relational DBMS

Score	0.00
Rank	#385	Overall
	#21	Vector DBMS

Website

solr.apache.org

github.com/apple/foundationdb

svectordb.com

Technical documentation

solr.apache.org/resources.html

apple.github.io/foundationdb

www.svectordb.com/docs

Developer

Apache Software Foundation

FoundationDB

SvectorDB

Initial release

2006

2013

2023

Current release

9.6.1, May 2024

6.2.28, November 2020

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

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.

Implementation language

Java

C++

Server operating systems

All OS with a Java VM

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

Linux
OS X
Windows

server-less

Data scheme

yes

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

schema-free

some layers support schemas

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

some layers support typing

string, double, vector

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

SQL

Support of SQL

Solr Parallel SQL Interface

supported in specific SQL layer only

APIs and other access methods

Java API
RESTful HTTP/JSON API

OpenAPI 3.0, RESTful HTTP API, Python SDK, JavaScript / TypeScript SDK

Supported programming languages

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

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

Server-side scripts

Stored procedures

Java plugins

in SQL-layer only

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Linearizable consistency

Immediate Consistency

Foreign keys

Referential integrity

in SQL-layer only

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

User concepts

Access control

yes

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