DBMS > FileMaker vs. GridDB vs. RocksDB vs. Stardog vs. Vertica

System Properties Comparison FileMaker vs. GridDB vs. RocksDB vs. Stardog vs. Vertica

Editorial information provided by DB-Engines

Name

Description

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

Scalable in-memory time series database optimized for IoT and Big Data

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Key-value store
Relational DBMS

Spatial DBMS
Time Series DBMS

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

Score	49.73
Rank	#20	Overall
	#14	Relational DBMS

Score	2.02
Rank	#132	Overall
	#11	Time Series DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	2.05
Rank	#129	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

www.claris.com/filemaker

Technical documentation

www.claris.com/resources/documentation

docs.griddb.net

github.com/facebook/rocksdb/wiki

docs.stardog.com

vertica.com/documentation

Developer

Claris

a subsidiary of Apple

Toshiba Corporation

Facebook, Inc.

Stardog-Union

OpenText

previously Micro Focus and Hewlett Packard

Initial release

1983

2013

2010

2005

Current release

19.4.1, November 2021

5.1, August 2022

8.11.4, April 2024

7.3.0, May 2020

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

BSD

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

C++

Server operating systems

iOS

client part only
Linux
OS X
Windows

Linux

Linux
macOS
Windows

Linux

Data scheme

yes

schema-free

schema-free and OWL/RDFS-schema support

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

via plugins

SQL92, SQL-like TQL (Toshiba Query Language)

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

Filemaker WebDirect
JDBC
ODBC

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

C++ API
Java API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

PHP

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

user defined functions and aggregates, HTTP Server extensions in Java

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

via event handlers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication, warm standby

since Version 14

Source-replica replication

yes

Multi-source replication in HA-Cluster

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

yes

Transaction concepts

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

ACID at container level

yes

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

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

Access rights for users can be defined per database

Access rights for users and roles

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
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Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
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Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
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Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

Cost-based models and subscription-based models are both available. One license is...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.