DBMS > HugeGraph vs. LokiJS vs. Microsoft Azure Cosmos DB vs. Sadas Engine vs. SAP SQL Anywhere

System Properties Comparison HugeGraph vs. LokiJS vs. Microsoft Azure Cosmos DB vs. Sadas Engine vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

In-memory JavaScript DBMS

Globally distributed, horizontally scalable, multi-model database service

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Graph DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.43
Rank	#264	Overall
	#42	Document stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

github.com/hugegraph
hugegraph.apache.org

github.com/techfort/LokiJS

azure.microsoft.com/services/cosmos-db

www.sadasengine.com

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

hugegraph.apache.org/docs

techfort.github.io/LokiJS

learn.microsoft.com/azure/cosmos-db

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Baidu

Microsoft

SADAS s.r.l.

SAP

formerly Sybase

Initial release

2018

2014

2006

1992

Current release

0.9

8.0

17, July 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

free trial version available

commercial

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

Java

JavaScript

C++

Server operating systems

Linux
macOS
Unix

server-less, requires a JavaScript environment (browser, Node.js)

hosted

AIX
Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

also supports composite index and range index

yes

via views

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

JavaScript API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC
Proprietary protocol

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

Groovy
Java
Python

JavaScript

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

View functions in JavaScript

JavaScript

yes, in C/C++, Java, .Net or Perl

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

none

Sharding

Implicit feature of the cloud service

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

yes

Implicit feature of the cloud service

none

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

atomic operations within a single collection possible

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

Users, roles and permissions

Access rights can be defined down to the item level

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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