DBMS > Blueflood vs. Drizzle vs. Google BigQuery vs. Graph Engine

System Properties Comparison Blueflood vs. Drizzle vs. Google BigQuery vs. Graph Engine

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Scalable TimeSeries DBMS based on Cassandra

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Primary database model

Time Series DBMS

Relational DBMS

Graph DBMS
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	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.56
Rank	#241	Overall
	#21	Graph DBMS
	#34	Key-value stores

Website

blueflood.io

cloud.google.com/bigquery

www.graphengine.io

Technical documentation

github.com/rax-maas/blueflood/wiki

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

Developer

Rackspace

Drizzle project, originally started by Brian Aker

Google

Microsoft

Initial release

2013

2008

2010

Current release

7.2.4, September 2012

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GNU GPL

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

.NET and C

Server operating systems

Linux
OS X

FreeBSD
Linux
OS X

hosted

.NET

Data scheme

predefined scheme

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

HTTP REST

JDBC

RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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