DBMS > GridGain vs. H2 vs. Snowflake vs. Yanza

System Properties Comparison GridGain vs. H2 vs. Snowflake vs. Yanza

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Editorial information provided by DB-Engines

Name

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

Description

GridGain is an in-memory computing platform, built on Apache Ignite

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

Cloud-based data warehousing service for structured and semi-structured data

Time Series DBMS for IoT Applications

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.gridgain.com

www.h2database.com

www.snowflake.com

yanza.com

Technical documentation

www.gridgain.com/docs/index.html

www.h2database.com/html/main.html

docs.snowflake.net/manuals/index.html

Developer

GridGain Systems, Inc.

Thomas Mueller

Snowflake Computing Inc.

Yanza

Initial release

2007

2005

2014

2015

Current release

GridGain 8.5.1

2.2.220, July 2023

License

Commercial or Open Source

commercial

Open Source

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

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

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

Java, C++, .Net

Java

Server operating systems

Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

Windows

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

CLI Client
JDBC
ODBC

HTTP API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Java

JavaScript (Node.js)
Python

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Java Stored Procedures and User-Defined Functions

user defined functions

Triggers

yes (cache interceptors and events)

yes

similar concept for controling cloud resources

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

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

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate 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

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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