DBMS > Blueflood vs. GridGain vs. H2 vs. Microsoft Azure Cosmos DB

System Properties Comparison Blueflood vs. GridGain vs. H2 vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

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.

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Time Series DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.06
Rank	#352	Overall
	#36	Time Series DBMS

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	7.57
Rank	#47	Overall
	#30	Relational DBMS

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

blueflood.io

www.gridgain.com

www.h2database.com

azure.microsoft.com/services/cosmos-db

Technical documentation

github.com/rax-maas/blueflood/wiki

www.gridgain.com/docs/index.html

www.h2database.com/html/main.html

learn.microsoft.com/azure/cosmos-db

Developer

Rackspace

GridGain Systems, Inc.

Thomas Mueller

Microsoft

Initial release

2013

2007

2005

2014

Current release

GridGain 8.5.1

2.2.220, July 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial, open source

Open Source

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

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, C++, .Net, Python, REST, SQL

Java

Server operating systems

Linux
OS X

Linux
OS X
Solaris
Windows
z/OS

All OS with a Java VM

hosted

Data scheme

predefined scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

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

yes

SQL-like query language

APIs and other access methods

HTTP REST

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

JDBC
ODBC

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

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Java

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

Server-side scripts

Stored procedures

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

Java Stored Procedures and User-Defined Functions

JavaScript

Triggers

yes (cache interceptors and events)

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes (replicated cache)

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

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

Role-based access control
Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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