DBMS > Graph Engine vs. H2 vs. JaguarDB vs. Spark SQL

System Properties Comparison Graph Engine vs. H2 vs. JaguarDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Performant, highly scalable DBMS for AI and IoT applications

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

Primary database model

Graph DBMS
Key-value store

Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

www.graphengine.io/docs/manual

www.h2database.com/html/main.html

www.jaguardb.com/support.html

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

Developer

Microsoft

Thomas Mueller

DataJaguar, Inc.

Apache Software Foundation

Initial release

2010

2005

2015

2014

Current release

2.2.220, July 2023

3.3 July 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

MIT License

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

GPL V3.0

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

.NET and C

Java

C++

the server part. Clients available in other languages

Scala

Server operating systems

.NET

All OS with a Java VM

Linux

Linux
OS X
Windows

Data scheme

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

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

Supported programming languages

C#
C++
F#
Visual Basic

Java

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

With clustering: 2 database servers on different computers operate on identical copies of a database

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

rights management via user accounts

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