DBMS > Apache Druid vs. CockroachDB vs. Microsoft Azure Cosmos DB vs. Oracle Coherence

System Properties Comparison Apache Druid vs. CockroachDB vs. Microsoft Azure Cosmos DB vs. Oracle Coherence

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Oracles in-memory data grid solution

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Secondary database models

Spatial DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

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	1.92
Rank	#130	Overall
	#22	Key-value stores

Website

druid.apache.org

www.cockroachlabs.com

azure.microsoft.com/services/cosmos-db

www.oracle.com/java/coherence

Technical documentation

druid.apache.org/docs/latest/design

www.cockroachlabs.com/docs

learn.microsoft.com/azure/cosmos-db

docs.oracle.com/en/middleware/standalone/coherence

Developer

Apache Software Foundation and contributors

Cockroach Labs

Microsoft

Oracle

Initial release

2012

2015

2014

2007

Current release

29.0.1, April 2024

23.1.1, May 2023

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache 2.0, commercial license available

commercial

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

Server operating systems

Linux
OS X
Unix

Linux
macOS
Windows

hosted

All OS with a Java VM

Data scheme

yes

schema-less columns are supported

dynamic schema

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

SQL for querying

yes, wire compatible with PostgreSQL

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC

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

JCache
JPA
RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

Server-side scripts

Stored procedures

JavaScript

Triggers

JavaScript

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication using RAFT

yes

Implicit feature of the cloud service

yes, with selectable consistency level

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
Immediate Consistency

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

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

In-memory capabilities

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

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Role-based access control

Access rights can be defined down to the item level

authentification to access the cache via certificates or http basic authentication

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