DBMS > Blueflood vs. Datastax Enterprise vs. Oracle Berkeley DB vs. Oracle Rdb

System Properties Comparison Blueflood vs. Datastax Enterprise vs. Oracle Berkeley DB vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

Widely used in-process key-value store

Primary database model

Time Series DBMS

Wide column store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	0.06
Rank	#352	Overall
	#36	Time Series DBMS

Score	4.82
Rank	#65	Overall
	#4	Wide column stores

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	1.04
Rank	#184	Overall
	#84	Relational DBMS

Website

blueflood.io

www.datastax.com/products/datastax-enterprise

www.oracle.com/database/technologies/related/berkeleydb.html

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.datastax.com

docs.oracle.com/cd/E17076_05/html/index.html

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Rackspace

DataStax

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2013

2011

1994

1984

Current release

6.8, April 2020

18.1.40, May 2020

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

commercial license available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

Java

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux
OS X

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

HP Open VMS

Data scheme

predefined scheme

schema-free

Flexible Schema (defined schema, partial schema, 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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements (CQL); Spark SQL

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

HTTP REST

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

Supported programming languages

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

configurable replication factor, datacenter aware, advanced replication for edge computing

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

yes, on a single node

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

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more

Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
» more

Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
» more

Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more

Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more

Licensing and pricing models

Annual subscription
» more

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