DBMS > CockroachDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure SQL Database vs. Stardog vs. Yanza

System Properties Comparison CockroachDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure SQL Database vs. Stardog vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

Globally distributed, horizontally scalable, multi-model database service

Database as a Service offering with high compatibility to Microsoft SQL Server

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Graph DBMS
RDF store

Time Series DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	6.15
Rank	#55	Overall
	#33	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.cockroachlabs.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/products/azure-sql/database

www.stardog.com

yanza.com

Technical documentation

www.cockroachlabs.com/docs

learn.microsoft.com/azure/cosmos-db

docs.microsoft.com/en-us/azure/azure-sql

docs.stardog.com

Developer

Cockroach Labs

Microsoft

Stardog-Union

Yanza

Initial release

2015

2014

2010

2015

Current release

23.1.1, May 2023

V12

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java

Server operating systems

Linux
macOS
Windows

hosted

Linux
macOS
Windows

Windows

Data scheme

dynamic schema

schema-free

yes

schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

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

Import/export of XML data possible

Secondary indexes

yes

All properties auto-indexed by default

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes, wire compatible with PostgreSQL

SQL-like query language

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC

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

ADO.NET
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

HTTP API

Supported programming languages

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

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

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

any language that supports HTTP calls

Server-side scripts

Stored procedures

JavaScript

Transact SQL

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

JavaScript

yes

via event handlers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using RAFT

yes

Implicit feature of the cloud service

yes, with always 3 replicas available

Multi-source replication in HA-Cluster

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

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

User concepts

Access control

Role-based access control

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

Access rights for users and roles

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