DBMS > CockroachDB vs. Drizzle vs. Microsoft Azure Cosmos DB vs. Newts

System Properties Comparison CockroachDB vs. Drizzle vs. Microsoft Azure Cosmos DB vs. Newts

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle 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.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Globally distributed, horizontally scalable, multi-model database service

Time Series DBMS based on Cassandra

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

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	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

www.cockroachlabs.com

azure.microsoft.com/services/cosmos-db

opennms.github.io/newts

Technical documentation

www.cockroachlabs.com/docs

learn.microsoft.com/azure/cosmos-db

github.com/OpenNMS/newts/wiki

Developer

Cockroach Labs

Drizzle project, originally started by Brian Aker

Microsoft

OpenNMS Group

Initial release

2015

2008

2014

Current release

23.1.1, May 2023

7.2.4, September 2012

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

Open Source

GNU GPL

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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

FreeBSD
Linux
OS X

hosted

Linux
OS X
Windows

Data scheme

dynamic schema

yes

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

yes, wire compatible with PostgreSQL

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

JDBC

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

HTTP REST
Java API

Supported programming languages

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

C
C++
Java
PHP

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

Java

Server-side scripts

Stored procedures

JavaScript

Triggers

hooks for callbacks inside the server can be used.

JavaScript

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

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using RAFT

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

selectable replication factor

based on Cassandra

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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.

User concepts

Access control

Role-based access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights can be defined down to the item level

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