DBMS > Atos Standard Common Repository vs. Datomic vs. LevelDB vs. NSDb

System Properties Comparison Atos Standard Common Repository vs. Datomic vs. LevelDB vs. NSDb

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

Document store
Key-value store

Relational DBMS

Key-value store

Time Series DBMS

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

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

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.datomic.com

github.com/google/leveldb

nsdb.io

Technical documentation

docs.datomic.com

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

nsdb.io/Architecture

Developer

Atos Convergence Creators

Cognitect

Google

Initial release

2016

2012

2011

2017

Current release

1703

1.0.6735, June 2023

1.23, February 2021

License

Commercial or Open Source

commercial

limited edition free

Open Source

BSD

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Java, Clojure

C++

Java, Scala

Server operating systems

Linux

All OS with a Java VM

Illumos
Linux
OS X
Windows

Linux
macOS

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

Typing

predefined data types such as float or date

optional

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.

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

APIs and other access methods

LDAP

RESTful HTTP API

gRPC
HTTP REST
WebSocket

Supported programming languages

All languages with LDAP bindings

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

Transaction Functions

Triggers

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

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

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

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

LDAP bind authentication

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