DBMS > FileMaker vs. Google Cloud Spanner vs. searchxml vs. Snowflake vs. Solr

System Properties Comparison FileMaker vs. Google Cloud Spanner vs. searchxml vs. Snowflake vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

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

Primary database model

Relational DBMS

Native XML DBMS
Search engine

Relational DBMS

Search engine

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	2.89
Rank	#103	Overall
	#52	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

Website

www.claris.com/filemaker

cloud.google.com/spanner

www.searchxml.net/category/products

www.snowflake.com

solr.apache.org

Technical documentation

www.claris.com/resources/documentation

cloud.google.com/spanner/docs

www.searchxml.net/support/handouts

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Claris

a subsidiary of Apple

Google

informationpartners gmbh

Snowflake Computing Inc.

Apache Software Foundation

Initial release

1983

2017

2015

2014

2006

Current release

19.4.1, November 2021

1.0

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

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

C++

Java

Server operating systems

iOS

client part only
Linux
OS X
Windows

hosted

Windows

hosted

All OS with a Java VM

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

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

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

SQL

Support of SQL

yes

via plugins

yes

Query statements complying to ANSI 2011

yes

Solr Parallel SQL Interface

APIs and other access methods

Filemaker WebDirect
JDBC
ODBC

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

RESTful HTTP API
WebDAV
XQuery
XSLT

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

PHP

Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes

on the application server

user defined functions

Java plugins

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

Sharding

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication, warm standby

since Version 14

Multi-source replication with 3 replicas for regional instances.

yes

sychronisation to multiple collections

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

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

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

multiple readers, single writer

ACID

optimistic locking

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

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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

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