DBMS > Graph Engine vs. Spark SQL vs. STSdb vs. TigerGraph vs. Yanza

System Properties Comparison Graph Engine vs. Spark SQL vs. STSdb vs. TigerGraph vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Time Series DBMS for IoT Applications

Primary database model

Graph DBMS
Key-value store

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

Score	0.62
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.06
Rank	#365	Overall
	#55	Key-value stores

Score	1.83
Rank	#141	Overall
	#13	Graph DBMS

Website

www.graphengine.io

spark.apache.org/sql

github.com/STSSoft/STSdb4

www.tigergraph.com

yanza.com

Technical documentation

www.graphengine.io/docs/manual

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

docs.tigergraph.com

Developer

Microsoft

Apache Software Foundation

STS Soft SC

Yanza

Initial release

2010

2014

2011

2017

2015

Current release

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

.NET and C

Scala

C++

Server operating systems

.NET

Linux
OS X
Windows

Windows

Linux

Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (GSQL)

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

.NET Client API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

HTTP API

Supported programming languages

C#
C++
F#
Visual Basic

Java
Python
R
Scala

C#
Java

C++
Java

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

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More resources

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20 June 2023, SiliconANGLE News

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10 January 2024, VnExpress International

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