DBMS > EsgynDB vs. FoundationDB vs. Google Cloud Datastore vs. Ignite

System Properties Comparison EsgynDB vs. FoundationDB vs. Google Cloud Datastore vs. Ignite

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Primary database model

Relational DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Document store

Key-value store
Relational DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.03
Rank	#190	Overall
	#31	Document stores
	#28	Key-value stores
	#89	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Website

www.esgyn.cn

github.com/apple/foundationdb

cloud.google.com/datastore

ignite.apache.org

Technical documentation

apple.github.io/foundationdb

cloud.google.com/datastore/docs

apacheignite.readme.io/docs

Developer

Esgyn

FoundationDB

Google

Apache Software Foundation

Initial release

2015

2013

2008

2015

Current release

6.2.28, November 2020

Apache Ignite 2.6

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

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

C++

C++, Java, .Net

Server operating systems

Linux

Linux
OS X
Windows

hosted

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

some layers support schemas

schema-free

yes

Typing

predefined data types such as float or date

yes

some layers support typing

yes, details here

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

SQL

Support of SQL

yes

supported in specific SQL layer only

SQL-like query language (GQL)

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

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

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

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

in SQL-layer only

using Google App Engine

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

Triggers

Callbacks using the Google Apps Engine

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

Multi-source replication using Paxos

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Linearizable consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

in SQL-layer only

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

fine grained access rights according to SQL-standard

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

Security Hooks for custom implementations

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