DBMS > Amazon Redshift vs. Apache Drill vs. Apache Phoenix vs. GeoSpock vs. HBase

System Properties Comparison Amazon Redshift vs. Apache Drill vs. Apache Phoenix vs. GeoSpock vs. HBase

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

A scale-out RDBMS with evolutionary schema built on Apache HBase

Spatial and temporal data processing engine for extreme data scale

Wide-column store based on Apache Hadoop and on concepts of BigTable

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Website

aws.amazon.com/redshift

drill.apache.org

phoenix.apache.org

geospock.com

hbase.apache.org

Technical documentation

docs.aws.amazon.com/redshift

drill.apache.org/docs

phoenix.apache.org

hbase.apache.org/book.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

GeoSpock

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2012

2014

2008

Current release

1.20.3, January 2023

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

2.0, September 2019

2.3.4, January 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Open Source

Apache version 2

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

Java, Javascript

Java

Server operating systems

hosted

Linux
OS X
Windows

Linux
Unix
Windows

hosted

Linux
Unix
Windows

using Cygwin

Data scheme

yes

schema-free

yes

late-bound, schema-on-read capabilities

yes

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

temporal, categorical

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL SELECT statement is SQL:2003 compliant

yes

ANSI SQL for query only (using Presto)

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC

Java API
RESTful HTTP API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

C++

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency or Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency

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

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Depending on the underlying data source

yes

In-memory capabilities

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

yes

Depending on the underlying data source

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Depending on the underlying data source

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Access rights for users can be defined per table

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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