DBMS > Blueflood vs. GigaSpaces vs. Microsoft Azure Cosmos DB vs. RRDtool

System Properties Comparison Blueflood vs. GigaSpaces vs. Microsoft Azure Cosmos DB vs. RRDtool

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Globally distributed, horizontally scalable, multi-model database service

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Time Series DBMS

Document store
Object oriented DBMS

Values are user defined objects

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Graph DBMS
Search engine

Spatial DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

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

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

blueflood.io

www.gigaspaces.com

azure.microsoft.com/services/cosmos-db

oss.oetiker.ch/rrdtool

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.gigaspaces.com/latest/landing.html

learn.microsoft.com/azure/cosmos-db

oss.oetiker.ch/rrdtool/doc

Developer

Rackspace

Gigaspaces Technologies

Microsoft

Tobias Oetiker

Initial release

2013

2000

2014

1999

Current release

15.5, September 2020

1.8.0, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

GPL V2 and FLOSS

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

Java, C++, .Net

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

Linux
OS X

Linux
macOS
Solaris
Windows

hosted

HP-UX
Linux

Data scheme

predefined scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

Numeric data only

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.

XML can be used for describing objects metadata

Exporting into and restoring from XML files possible

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language

APIs and other access methods

HTTP REST

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

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

in-process shared library
Pipes

Supported programming languages

.Net
C++
Java
Python
Scala

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

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

yes, event driven architecture

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

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

Consistency level configurable: ALL, QUORUM, ANY

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

none

Foreign keys

Referential integrity

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

by using the rrdcached daemon

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

Access rights can be defined down to the item level

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