DBMS > atoti vs. BoltDB vs. Couchbase vs. Microsoft SQL Server vs. NSDb

System Properties Comparison atoti vs. BoltDB vs. Couchbase vs. Microsoft SQL Server vs. NSDb

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Microsofts flagship relational DBMS

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

Primary database model

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

atoti.io

github.com/boltdb/bolt

www.couchbase.com

www.microsoft.com/en-us/sql-server

nsdb.io

Technical documentation

docs.atoti.io

docs.couchbase.com

learn.microsoft.com/en-US/sql/sql-server

nsdb.io/Architecture

Developer

ActiveViam

Couchbase, Inc.

Microsoft

Initial release

2013

2011

1989

2017

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

free versions available

Open Source

MIT License

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

restricted free version is available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C, C++, Go and Erlang

C++

Java, Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux
Windows

Linux
macOS

Data scheme

schema-free

yes

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.

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

SQL-like query language

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

gRPC
HTTP REST
WebSocket

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Java
Scala

Server-side scripts

Stored procedures

Python

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Automatic Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

including cross data center replication
Source-replica replication

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

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

yes

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

fine grained access rights according to SQL-standard

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