DBMS > Elasticsearch vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Data Explorer vs. Oracle Coherence vs. Quasardb

System Properties Comparison Elasticsearch vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Data Explorer vs. Oracle Coherence vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Globally distributed, horizontally scalable, multi-model database service

Fully managed big data interactive analytics platform

Oracles in-memory data grid solution

Distributed, high-performance timeseries database

Primary database model

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

column oriented

Key-value store

Time Series DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	135.35
Rank	#7	Overall
	#1	Search engines

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

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

www.elastic.co/elasticsearch

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/services/data-explorer

www.oracle.com/java/coherence

quasar.ai

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

learn.microsoft.com/azure/cosmos-db

docs.microsoft.com/en-us/azure/data-explorer

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

doc.quasar.ai/master

Developer

Elastic

Microsoft

Oracle

quasardb

Initial release

2010

2014

2019

2007

2009

Current release

8.6, January 2023

cloud service with continuous releases

14.1, August 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Elastic License

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

All OS with a Java VM

hosted

All OS with a Java VM

BSD
Linux
OS X
Windows

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

Fixed schema with schema-less datatypes (dynamic)

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

All properties auto-indexed by default

all fields are automatically indexed

yes

with tags

SQL

Support of SQL

SQL-like query language

Kusto Query Language (KQL), SQL subset

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP/JSON API

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

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

JCache
JPA
RESTful HTTP API

HTTP API

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

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

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

.Net
C++
Java

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

Server-side scripts

Stored procedures

yes

JavaScript

Yes, possible languages: KQL, Python, R

Triggers

yes

by using the 'percolation' feature

JavaScript

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, with selectable consistency level

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Spark connector (open source): github.com/Azure/azure-kusto-spark

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

by using LevelDB

In-memory capabilities

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

Memcached and Redis integration

yes

Transient mode

User concepts

Access control

Access rights can be defined down to the item level

Azure Active Directory Authentication

authentification to access the cache via certificates or http basic authentication

Cryptographically strong user authentication and audit trail

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