DBMS > Blueflood vs. Google Cloud Datastore vs. Oracle Berkeley DB vs. PieCloudDB

System Properties Comparison Blueflood vs. Google Cloud Datastore vs. Oracle Berkeley DB vs. PieCloudDB

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Widely used in-process key-value store

A cloud-native analytic database platform with new technologoy for elastic MPP

Primary database model

Time Series DBMS

Document store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.25
Rank	#304	Overall
	#138	Relational DBMS

Website

blueflood.io

cloud.google.com/datastore

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

www.openpie.com

Technical documentation

github.com/rax-maas/blueflood/wiki

cloud.google.com/datastore/docs

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

Developer

Rackspace

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

OpenPie

Initial release

2013

2008

1994

Current release

18.1.40, May 2020

2.1, January 2023

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

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

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

Server operating systems

Linux
OS X

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

hosted

Data scheme

predefined scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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 query language (GQL)

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

HTTP REST

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

Supported programming languages

.Net
Go
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

Java
PL/SQL
Python
R

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication using Paxos

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

User Roles and pluggable authentication with full SQL Standard

More information provided by the system vendor

Specific characteristics

PieCloudDB, OpenPie's flagship product, is a cutting-edge cloud-native data warehouse....
» more

Competitive advantages

Extreme Elastic: PieCloudDB utilizes a cutting-edge eMPP cloud-native architecture...
» more

Typical application scenarios

PieCloudDB is ideal for Data mining applications that require extreme scalability...
» more

Key customers

Sail-Cloud China Shipbuilding Group Haizhou System Soochow Securities etc.,
» more

Licensing and pricing models

PieCloudDB Community Edition: Community License, Free Download, Self-Hosted Deployment；...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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