DBMS > FatDB vs. HugeGraph vs. Percona Server for MongoDB vs. Spark SQL

System Properties Comparison FatDB vs. HugeGraph vs. Percona Server for MongoDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A fast-speed and highly-scalable Graph DBMS

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Key-value store

Graph DBMS

Document store

Relational DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/hugegraph
hugegraph.apache.org

www.percona.com/mongodb/software/percona-server-for-mongodb

spark.apache.org/sql

Technical documentation

hugegraph.apache.org/docs

docs.percona.com/percona-distribution-for-mongodb

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

FatCloud

Baidu

Percona

Apache Software Foundation

Initial release

2012

2018

2015

2014

Current release

0.9

3.4.10-2.10, November 2017

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL Version 2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Scala

Server operating systems

Windows

Linux
macOS
Unix

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like DML and DDL statements

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

Java API
RESTful HTTP API
TinkerPop Gremlin

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

Groovy
Java
Python

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

asynchronous Gremlin script jobs

JavaScript

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

depending on used storage backend, e.g. Cassandra and HBase

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

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

via In-Memory Engine

User concepts

Access control

Can implement custom security layer via applications

Users, roles and permissions

Access rights for users and roles

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