DBMS > Apache Druid vs. EsgynDB vs. HugeGraph vs. Microsoft Azure Cosmos DB

System Properties Comparison Apache Druid vs. EsgynDB vs. HugeGraph vs. Microsoft Azure Cosmos DB

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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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A fast-speed and highly-scalable Graph DBMS

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Graph DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

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

Website

druid.apache.org

www.esgyn.cn

github.com/hugegraph
hugegraph.apache.org

azure.microsoft.com/services/cosmos-db

Technical documentation

druid.apache.org/docs/latest/design

hugegraph.apache.org/docs

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation and contributors

Esgyn

Baidu

Microsoft

Initial release

2012

2015

2018

2014

Current release

29.0.1, April 2024

0.9

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache Version 2.0

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

C++, Java

Java

Server operating systems

Linux
OS X
Unix

Linux

Linux
macOS
Unix

hosted

Data scheme

yes

schema-less columns are supported

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

also supports composite index and range index

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL for querying

yes

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

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

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

Groovy
Java
Python

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

Server-side scripts

Stored procedures

Java Stored Procedures

asynchronous Gremlin script jobs

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Multi-source replication between multi datacenters

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

edges in graph

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.

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

Users, roles and permissions

Access rights can be defined down to the item level

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