DBMS > Apache Druid vs. Microsoft Azure Cosmos DB vs. OrigoDB vs. Solr vs. Trafodion

System Properties Comparison Apache Druid vs. Microsoft Azure Cosmos DB vs. OrigoDB vs. Solr vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

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

Globally distributed, horizontally scalable, multi-model database service

A fully ACID in-memory object graph database

A widely used distributed, scalable search engine based on Apache Lucene

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS
Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store
Object oriented DBMS

Search engine

Relational DBMS

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

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#19	Object oriented DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

druid.apache.org

azure.microsoft.com/services/cosmos-db

origodb.com

solr.apache.org

trafodion.apache.org

Technical documentation

druid.apache.org/docs/latest/design

learn.microsoft.com/azure/cosmos-db

origodb.com/docs

solr.apache.org/resources.html

trafodion.apache.org/documentation.html

Developer

Apache Software Foundation and contributors

Microsoft

Robert Friberg et al

Apache Software Foundation

Apache Software Foundation, originally developed by HP

Initial release

2012

2014

2009

under the name LiveDB

2006

2014

Current release

29.0.1, April 2024

9.6.1, May 2024

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache Version 2

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

Java

C++, Java

Server operating systems

Linux
OS X
Unix

hosted

Linux
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux

Data scheme

yes

schema-less columns are supported

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

JSON types

User defined using .NET types and collections

yes

supports customizable data types and automatic typing

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.

can be achieved using .NET

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL for querying

SQL-like query language

Solr Parallel SQL Interface

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

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

.NET Client API
HTTP API
LINQ

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

.Net

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

JavaScript

yes

Java plugins

Java Stored Procedures

Triggers

JavaScript

yes

Domain Events

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Implicit feature of the cloud service

horizontal partitioning

client side managed; servers are not synchronized

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Implicit feature of the cloud service

Source-replica replication

yes

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

yes

via user defined functions and HBase

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

depending on model

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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

Access rights can be defined down to the item level

Role based authorization

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.