DBMS > HugeGraph vs. MonetDB vs. Postgres-XL vs. ScyllaDB

System Properties Comparison HugeGraph vs. MonetDB vs. Postgres-XL vs. ScyllaDB

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Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

A relational database management system that stores data in columns

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Cassandra and DynamoDB compatible wide column store

Primary database model

Graph DBMS

Relational DBMS

Wide column store

Secondary database models

Document store
Spatial DBMS

Key-value store

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Website

github.com/hugegraph
hugegraph.apache.org

www.monetdb.org

www.postgres-xl.org

www.scylladb.com

Technical documentation

hugegraph.apache.org/docs

www.monetdb.org/Documentation

www.postgres-xl.org/documentation

docs.scylladb.com

Developer

Baidu

MonetDB BV

ScyllaDB

Initial release

2018

2004

2014

since 2012, originally named StormDB

2015

Current release

0.9

Dec2023 (11.49), December 2023

10 R1, October 2018

ScyllaDB Open Source 5.4.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Mozilla Public License 2.0

Open Source

Mozilla public license

Open Source

Open Source (AGPL), commercial license available

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.

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Implementation language

Java

C++

Server operating systems

Linux
macOS
Unix

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

Linux

Data scheme

yes

schema-free

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

also supports composite index and range index

yes

cluster global secondary indices

SQL

Support of SQL

yes

SQL 2003 with some extensions

yes

distributed, parallel query execution

SQL-like DML and DDL statements (CQL)

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

Supported programming languages

Groovy
Java
Python

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes, in SQL, C, R

user defined functions

yes, Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding via remote tables

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

Source-replica replication available in experimental status

selectable replication factor

Representation of geographical distribution of servers is possible

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

MVCC

Atomicity and isolation are supported for single operations

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

in-memory tables

User concepts

Access control

Users, roles and permissions

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
» more

Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
» more

Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
» more

Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
» more

Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
» more

Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
» more

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

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