DBMS > Couchbase vs. Kinetica vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. Solr

System Properties Comparison Couchbase vs. Kinetica vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

Fully vectorized database across both GPUs and CPUs

A Wide Column Store for rapid development using massive semi-structured datasets

Microsofts flagship relational DBMS

A widely used distributed, scalable search engine based on Apache Lucene

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

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.couchbase.com

www.kinetica.com

azure.microsoft.com/en-us/services/storage/tables

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

solr.apache.org

Technical documentation

docs.couchbase.com

docs.kinetica.com

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

solr.apache.org/resources.html

Developer

Couchbase, Inc.

Kinetica

Microsoft

Apache Software Foundation

Initial release

2011

2012

1989

2006

Current release

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

7.1, August 2021

SQL Server 2022, November 2022

9.6.1, May 2024

License

Commercial or Open Source

Open Source

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

commercial

restricted free version is available

Open Source

Apache 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

C, C++, Go and Erlang

C, C++

C++

Java

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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 search fields are automatically indexed

SQL

Support of SQL

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

SQL-like DML and DDL statements

yes

Solr Parallel SQL Interface

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

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

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

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

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

user defined functions

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

Java plugins

Triggers

yes

via the TAP protocol

yes

triggers when inserted values for one or more columns fall within a specified range

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

Implicit feature of the cloud service

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Ephemeral buckets

yes

GPU vRAM or System RAM

yes

User concepts

Access control

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

Access rights for users and roles on table level

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

yes

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