DBMS > Amazon Redshift vs. Apache Druid vs. Apache Impala vs. Apache Kylin vs. GridDB

System Properties Comparison Amazon Redshift vs. Apache Druid vs. Apache Impala vs. Apache Kylin vs. GridDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Analytic DBMS for Hadoop

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Scalable in-memory time series database optimized for IoT and Big Data

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store

Key-value store
Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

impala.apache.org/impala-docs.html

kylin.apache.org/docs

docs.griddb.net

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation, originally contributed from eBay Inc

Toshiba Corporation

Initial release

2012

2013

2015

2013

Current release

29.0.1, April 2024

4.1.0, June 2022

3.1.0, July 2020

5.1, August 2022

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

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++

Server operating systems

hosted

Linux
OS X
Unix

Linux

Data scheme

yes

schema-less columns are supported

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

SQL-like DML and DDL statements

ANSI SQL for queries (using Apache Calcite)

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC

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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

ACID at container level

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

fine grained access rights according to SQL-standard

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

Access rights for users can be defined per database

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.