DBMS > Amazon Redshift vs. Apache Phoenix vs. Hazelcast vs. Hive vs. LeanXcale

System Properties Comparison Amazon Redshift vs. Apache Phoenix vs. Hazelcast vs. Hive vs. LeanXcale

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

A widely adopted in-memory data grid

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Key-value store
Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

phoenix.apache.org

hazelcast.org/imdg/docs

cwiki.apache.org/confluence/display/Hive/Home

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Hazelcast

Apache Software Foundation

initially developed by Facebook

LeanXcale

Initial release

2012

2014

2008

2012

2015

Current release

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

5.3.6, November 2023

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2

commercial

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

Server operating systems

hosted

Linux
Unix
Windows

All OS with a Java VM

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

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

the object must implement a serialization strategy

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language

SQL-like DML and DDL statements

yes

through Apache Derby

APIs and other access methods

JDBC
ODBC

JDBC

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
Thrift

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C++
Java
PHP
Python

C
Java
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

Event Listeners, Executor Services

yes

user defined functions and integration of map-reduce

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

Replicated Map

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Role-based access control

Access rights for users, groups and roles

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