DBMS > HEAVY.AI vs. Hypertable vs. Microsoft Azure Cosmos DB vs. RRDtool

System Properties Comparison HEAVY.AI vs. Hypertable vs. Microsoft Azure Cosmos DB vs. RRDtool

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

An open source BigTable implementation based on distributed file systems such as Hadoop

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

Relational DBMS

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.90
Rank	#132	Overall
	#11	Time Series DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

azure.microsoft.com/services/cosmos-db

oss.oetiker.ch/rrdtool

Technical documentation

docs.heavy.ai

learn.microsoft.com/azure/cosmos-db

oss.oetiker.ch/rrdtool/doc

Developer

HEAVY.AI, Inc.

Hypertable Inc.

Microsoft

Tobias Oetiker

Initial release

2016

2009

2014

1999

Current release

5.10, January 2022

0.9.8.11, March 2016

1.8.0, 2022

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

GNU version 3. Commercial license 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

C++ and CUDA

C++

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

Server operating systems

Linux

Linux
OS X
Windows

an inofficial Windows port is available

hosted

HP-UX
Linux

Data scheme

yes

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.

Exporting into and restoring from XML files possible

Secondary indexes

restricted

only exact value or prefix value scans

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
Thrift
Vega

C++ API
Thrift

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

in-process shared library
Pipes

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
Perl
PHP
Python
Ruby

.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

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor on file system level

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

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

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.