DBMS > Amazon Redshift vs. Datomic vs. Hazelcast vs. NSDb vs. Percona Server for MySQL

System Properties Comparison Amazon Redshift vs. Datomic vs. Hazelcast vs. NSDb vs. Percona Server for MySQL

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A widely adopted in-memory data grid

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Enhanced drop-in replacement for MySQL based on XtraDB or TokuDB storage engines with improved performance and additional diagnostic and management features.

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.03
Rank	#122	Overall
	#57	Relational DBMS

Website

aws.amazon.com/redshift

www.datomic.com

hazelcast.com

nsdb.io

www.percona.com/software/mysql-database/percona-server

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

hazelcast.org/imdg/docs

nsdb.io/Architecture

www.percona.com/downloads/Percona-Server-LATEST

Developer

Amazon (based on PostgreSQL)

Cognitect

Hazelcast

Percona

Initial release

2012

2008

2017

2008

Current release

1.0.6735, June 2023

5.3.6, November 2023

8.0.36-28, 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2.0

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

Java, Clojure

Java

Java, Scala

C and C++

Server operating systems

hosted

All OS with a Java VM

Linux
macOS

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

yes

Secondary indexes

restricted

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

gRPC
HTTP REST
WebSocket

ADO.NET
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

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

Java
Scala

Ada
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

yes

Event Listeners, Executor Services

yes

Triggers

By using transaction functions

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes

Replicated Map

Multi-source replication
Source-replica replication
XtraDB Cluster

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 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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

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