DBMS > FoundationDB vs. GridGain vs. H2 vs. Vertica

System Properties Comparison FoundationDB vs. GridGain vs. H2 vs. Vertica

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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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

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

GridGain is an in-memory computing platform, built on Apache Ignite

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.97
Rank	#191	Overall
	#32	Document stores
	#28	Key-value stores
	#87	Relational DBMS

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	7.57
Rank	#47	Overall
	#30	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

github.com/apple/foundationdb

www.gridgain.com

www.h2database.com

www.vertica.com

Technical documentation

apple.github.io/foundationdb

www.gridgain.com/docs/index.html

www.h2database.com/html/main.html

vertica.com/documentation

Developer

FoundationDB

GridGain Systems, Inc.

Thomas Mueller

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2013

2007

2005

Current release

6.2.28, November 2020

GridGain 8.5.1

2.2.220, July 2023

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial, open source

Open Source

dual-licence (Mozilla public license, Eclipse public license)

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++, .Net, Python, REST, SQL

Java

C++

Server operating systems

Linux
OS X
Windows

Linux
OS X
Solaris
Windows
z/OS

All OS with a Java VM

Linux

Data scheme

schema-free

some layers support schemas

yes

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

some layers support 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

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

SQL

Support of SQL

supported in specific SQL layer only

ANSI-99 for query and DML statements, subset of DDL

yes

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

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

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

character-based, interactive, front-end utility

Supported programming languages

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

C#
C++
Java
PHP
Python
Ruby
Scala

Java

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

Server-side scripts

Stored procedures

in SQL-layer only

yes (compute grid and cache interceptors can be used instead)

Java Stored Procedures and User-Defined Functions

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes (cache interceptors and events)

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

With clustering: 2 database servers on different computers operate on identical copies of a database

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

yes (compute grid and hadoop accelerator)

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Linearizable consistency

Immediate Consistency

Foreign keys

Referential integrity

in SQL-layer only

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Role-based access control
Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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

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