DBMS > AgensGraph vs. Amazon Redshift vs. Datomic vs. LevelDB vs. NSDb

System Properties Comparison AgensGraph vs. Amazon Redshift vs. Datomic vs. LevelDB vs. NSDb

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

Key-value store

Time Series DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

bitnine.net/agensgraph

aws.amazon.com/redshift

www.datomic.com

github.com/google/leveldb

nsdb.io

Technical documentation

bitnine.net/documentation

docs.aws.amazon.com/redshift

docs.datomic.com

github.com/google/leveldb/blob/main/doc/index.md

nsdb.io/Architecture

Developer

Bitnine Global Inc.

Amazon (based on PostgreSQL)

Cognitect

Google

Initial release

2016

2012

2011

2017

Current release

2.1, December 2018

1.0.6735, June 2023

1.23, February 2021

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

limited edition free

Open Source

BSD

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

C++

Java, Scala

Server operating systems

Linux
OS X
Windows

hosted

All OS with a Java VM

Illumos
Linux
OS X
Windows

Linux
macOS

Data scheme

depending on used data model

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, 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

yes

restricted

yes

all fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

APIs and other access methods

Cypher Query Language
JDBC

JDBC
ODBC

RESTful HTTP API

gRPC
HTTP REST
WebSocket

Supported programming languages

C
Java
JavaScript
Python

All languages supporting JDBC/ODBC

Clojure
Java

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Java
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in Python

yes

Transaction Functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

none

But extensive use of caching in the application peers

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

none

But extensive use of caching in the application peers

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

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

yes

with automatic compression on writes

Using Apache Lucene

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

User concepts

Access control

fine grained access rights according to SQL-standard

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