DBMS > FileMaker vs. searchxml vs. Snowflake vs. Solr vs. VoltDB

System Properties Comparison FileMaker vs. searchxml vs. Snowflake vs. Solr vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

DBMS for structured and unstructured content wrapped with an application server

Cloud-based data warehousing service for structured and semi-structured data

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Native XML DBMS
Search engine

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.claris.com/filemaker

www.searchxml.net/category/products

www.snowflake.com

solr.apache.org

www.voltdb.com

Technical documentation

www.claris.com/resources/documentation

www.searchxml.net/support/handouts

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

docs.voltdb.com

Developer

Claris

a subsidiary of Apple

informationpartners gmbh

Snowflake Computing Inc.

Apache Software Foundation

VoltDB Inc.

Initial release

1983

2015

2014

2006

2010

Current release

19.4.1, November 2021

1.0

9.6.0, April 2024

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Java, C++

Server operating systems

iOS

client part only
Linux
OS X
Windows

Windows

hosted

All OS with a Java VM

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

Linux
OS X

for development

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

A http query request can return the data in XML format

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

via plugins

yes

Solr Parallel SQL Interface

yes

only a subset of SQL 99

APIs and other access methods

Filemaker WebDirect
JDBC
ODBC

RESTful HTTP API
WebDAV
XQuery
XSLT

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

PHP

C++

most other programming languages supported via APIs

JavaScript (Node.js)
Python

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

on the application server

user defined functions

Java plugins

Java

Triggers

yes

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication, warm standby

since Version 14

yes

sychronisation to multiple collections

yes

Multi-source replication
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

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

multiple readers, single writer

ACID

optimistic locking

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

simple rights management via user accounts and connection to external directory services

Domain, group and role-based access control at the document level and for application services

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

Users and roles with access to stored procedures

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