DBMS > Apache Druid vs. Apache Impala vs. Blueflood vs. DataFS vs. GBase

System Properties Comparison Apache Druid vs. Apache Impala vs. Blueflood vs. DataFS vs. GBase

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Analytic DBMS for Hadoop

Scalable TimeSeries DBMS based on Cassandra

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Primary database model

Relational DBMS
Time Series DBMS

Secondary database models

Document store

Graph DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

impala.apache.org/impala-docs.html

github.com/rax-maas/blueflood/wiki

dev.mobiland.com/Overview.xsp

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Rackspace

Mobiland AG

General Data Technology Co., Ltd.

Initial release

2012

2013

2018

2004

Current release

29.0.1, April 2024

4.1.0, June 2022

1.1.263, October 2022

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2

Open Source

Apache 2.0

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.

Implementation language

Java

C++

Java

C, Java, Python

Server operating systems

Linux
OS X
Unix

Linux

Linux
OS X

Windows

Linux

Data scheme

yes

schema-less columns are supported

yes

predefined scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

SQL-like DML and DDL statements

Standard with numerous extensions

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC

HTTP REST

.NET Client API
Proprietary client DLL
WinRT client

ADO.NET
C API
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC

.Net
C
C#
C++
VB.Net

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

based on Cassandra

Proprietary Sharding system

horizontal partitioning (by range, list and hash) and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

based on Cassandra

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

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.

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Windows-Profile

yes

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More resources

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