DBMS > Amazon Redshift vs. Hive vs. Percona Server for MongoDB vs. Prometheus vs. Quasardb

System Properties Comparison Amazon Redshift vs. Hive vs. Percona Server for MongoDB vs. Prometheus vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Open-source Time Series DBMS and monitoring system

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Document store

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	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

aws.amazon.com/redshift

hive.apache.org

www.percona.com/mongodb/software/percona-server-for-mongodb

prometheus.io

quasar.ai

Technical documentation

docs.aws.amazon.com/redshift

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

docs.percona.com/percona-distribution-for-mongodb

prometheus.io/docs

doc.quasar.ai/master

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

initially developed by Facebook

Percona

quasardb

Initial release

2012

2015

2009

Current release

3.1.3, April 2022

3.4.10-2.10, November 2017

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GPL Version 2

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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++

Server operating systems

hosted

All OS with a Java VM

Linux

Linux
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data only

yes

integer and binary

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.

Import of XML data possible

Secondary indexes

restricted

yes

with tags

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift

proprietary protocol using JSON

RESTful HTTP/JSON API

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
PHP
Python

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Source-replica replication

yes

by Federation

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

none

Immediate 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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

via In-Memory Engine

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

Access rights for users and roles

Cryptographically strong user authentication and audit trail

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