DBMS > Microsoft Azure Cosmos DB vs. NSDb vs. RethinkDB vs. Rockset vs. Solr

System Properties Comparison Microsoft Azure Cosmos DB vs. NSDb vs. RethinkDB vs. Rockset vs. Solr

Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

A scalable, reliable search and analytics service in the cloud, built on RocksDB

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

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Document store

Search engine

Secondary database models

Spatial DBMS

Relational DBMS
Search engine

Spatial DBMS

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

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

azure.microsoft.com/services/cosmos-db

Technical documentation

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

rethinkdb.com/docs

docs.rockset.com

solr.apache.org/resources.html

Developer

Microsoft

The Linux Foundation

since July 2017

Rockset

Apache Software Foundation

Initial release

2014

2017

2009

2019

2006

Current release

2.4.1, August 2020

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2

commercial

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

Java, Scala

C++

Java

Server operating systems

hosted

Linux
macOS

Linux
OS X
Windows

hosted

All OS with a Java VM

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

Data scheme

schema-free

yes

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

Typing

predefined data types such as float or date

yes

JSON types

yes: int, bigint, decimal, string

yes

string, binary, float, bool, date, geometry

dynamic typing

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.

ingestion from XML files supported

yes

Secondary indexes

yes

All properties auto-indexed by default

all fields are automatically indexed

yes

all fields are automatically indexed

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

Read-only SQL queries, including JOINs

Solr Parallel SQL Interface

APIs and other access methods

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

gRPC
HTTP REST
WebSocket

HTTP REST

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Java
Scala

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

JavaScript

Java plugins

Triggers

JavaScript

Client-side triggers through changefeeds

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

range based

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

Atomic single-document operations

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

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

User concepts

Access control

Access rights can be defined down to the item level

yes

users and table-level permissions

Access rights for users and organizations can be defined via Rockset console

yes

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