DBMS > Amazon Redshift vs. Drizzle vs. HarperDB vs. Kinetica vs. Percona Server for MongoDB

System Properties Comparison Amazon Redshift vs. Drizzle vs. HarperDB vs. Kinetica vs. Percona Server for MongoDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Fully vectorized database across both GPUs and CPUs

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

Primary database model

Secondary database models

Spatial DBMS
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	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Website

aws.amazon.com/redshift

www.harperdb.io

www.kinetica.com

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

Technical documentation

docs.aws.amazon.com/redshift

docs.harperdb.io/docs

docs.kinetica.com

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

Developer

Amazon (based on PostgreSQL)

Drizzle project, originally started by Brian Aker

HarperDB

Kinetica

Percona

Initial release

2012

2008

2017

2012

2015

Current release

7.2.4, September 2012

3.1, August 2021

7.1, August 2021

3.4.10-2.10, November 2017

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

free community edition available

commercial

Open Source

GPL 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

C++

Node.js

C, C++

C++

Server operating systems

hosted

FreeBSD
Linux
OS X

Linux
OS X

Linux

Data scheme

yes

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

with proprietary extensions

SQL-like data manipulation statements

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C++
Java
JavaScript (Node.js)
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

Server-side scripts

Stored procedures

user defined functions

in Python

Custom Functions

since release 3.1

user defined functions

JavaScript

Triggers

hooks for callbacks inside the server can be used.

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

the nodes on which a table resides can be defined

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

via In-Memory Engine

User concepts

Access control

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users and roles

Access rights for users and roles on table level

Access rights for users and roles

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