DBMS > Amazon Redshift vs. Datomic vs. InfinityDB vs. NSDb vs. openGemini

System Properties Comparison Amazon Redshift vs. Datomic vs. InfinityDB vs. NSDb vs. openGemini

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 Java embedded Key-Value Store which extends the Java Map interface

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

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

Primary database model

Relational DBMS

Key-value 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	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.01
Rank	#375	Overall
	#39	Time Series DBMS

Website

aws.amazon.com/redshift

www.datomic.com

boilerbay.com

nsdb.io

www.opengemini.org
github.com/openGemini

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

boilerbay.com/infinitydb/manual

nsdb.io/Architecture

docs.opengemini.org/guide

Developer

Amazon (based on PostgreSQL)

Cognitect

Boiler Bay Inc.

Huawei and openGemini community

Initial release

2012

2002

2017

2022

Current release

1.0.6735, June 2023

4.0

1.1, July 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

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, Clojure

Java

Java, Scala

Server operating systems

hosted

All OS with a Java VM

Linux
macOS

Linux
Windows

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes: int, bigint, decimal, string

Integer, Float, Boolean, String

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

manual creation possible, using inversions based on multi-value capability

all fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

gRPC
HTTP REST
WebSocket

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

Java

Java
Scala

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

Administrators and common users accounts

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