DBMS > Apache Druid vs. Apache Impala vs. Blueflood vs. GBase vs. Riak KV

System Properties Comparison Apache Druid vs. Apache Impala vs. Blueflood vs. GBase vs. Riak KV

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

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

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS
Time Series DBMS

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store

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	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

Technical documentation

druid.apache.org/docs/latest/design

impala.apache.org/impala-docs.html

github.com/rax-maas/blueflood/wiki

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Rackspace

General Data Technology Co., Ltd.

OpenSource, formerly Basho Technologies

Initial release

2012

2013

2004

2009

Current release

29.0.1, April 2024

4.1.0, June 2022

GBase 8a, GBase 8s, GBase 8c

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

Linux
OS X
Unix

Linux

Linux
OS X

Linux

Linux
OS X

Data scheme

yes

schema-less columns are supported

yes

predefined scheme

yes

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

Secondary indexes

yes

restricted

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

ADO.NET
C API
JDBC
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

based on Cassandra

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

Sharding

no "single point of failure"

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

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

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

yes

yes, using Riak Security

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More resources

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