DBMS > Atos Standard Common Repository vs. Blueflood vs. Hypertable vs. Pinecone vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. Blueflood vs. Hypertable vs. Pinecone vs. Yaacomo

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

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

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Scalable TimeSeries DBMS based on Cassandra

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

A managed, cloud-native vector database

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Key-value store

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

Score	0.06
Rank	#352	Overall
	#36	Time Series DBMS

Score	3.02
Rank	#87	Overall
	#3	Vector DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

blueflood.io

www.pinecone.io

yaacomo.com

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.pinecone.io/docs/overview

Developer

Atos Convergence Creators

Rackspace

Hypertable Inc.

Pinecone Systems, Inc

Q2WEB GmbH

Initial release

2016

2013

2009

2019

2009

Current release

1703

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GNU version 3. Commercial license available

commercial

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

C++

Server operating systems

Linux

Linux
OS X

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Android
Linux
Windows

Data scheme

Schema and schema-less with LDAP views

predefined scheme

schema-free

yes

Typing

predefined data types such as float or date

optional

yes

String, Number, Boolean

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

APIs and other access methods

LDAP

HTTP REST

C++ API
Thrift

RESTful HTTP API

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

C++
Java
Perl
PHP
Python
Ruby

Python

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

based on Cassandra

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

selectable replication factor on file system level

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

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

LDAP bind authentication

fine grained access rights according to SQL-standard

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