DBMS > Apache Druid vs. EJDB vs. Microsoft Azure Cosmos DB vs. Prometheus vs. Quasardb

System Properties Comparison Apache Druid vs. EJDB vs. Microsoft Azure Cosmos DB vs. Prometheus vs. Quasardb

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Globally distributed, horizontally scalable, multi-model database service

Open-source Time Series DBMS and monitoring system

Distributed, high-performance timeseries database

Primary database model

Relational DBMS
Time Series DBMS

Document store

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

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

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

druid.apache.org

github.com/Softmotions/ejdb

azure.microsoft.com/services/cosmos-db

prometheus.io

quasar.ai

Technical documentation

druid.apache.org/docs/latest/design

github.com/Softmotions/ejdb/blob/master/README.md

learn.microsoft.com/azure/cosmos-db

prometheus.io/docs

doc.quasar.ai/master

Developer

Apache Software Foundation and contributors

Softmotions

Microsoft

quasardb

Initial release

2012

2014

2015

2009

Current release

29.0.1, April 2024

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

GPLv2

commercial

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

C++

Server operating systems

Linux
OS X
Unix

server-less

hosted

Linux
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

JSON types

Numeric data only

yes

integer and binary

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.

Import of XML data possible

Secondary indexes

yes

All properties auto-indexed by default

yes

with tags

SQL

Support of SQL

SQL for querying

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

in-process shared library

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

RESTful HTTP/JSON API

HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

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

Server-side scripts

Stored procedures

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

yes

Implicit feature of the cloud service

yes

by Federation

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

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

none

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

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

Access rights can be defined down to the item level

Cryptographically strong user authentication and audit trail

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