DBMS > AnzoGraph DB vs. CouchDB vs. Microsoft Azure Data Explorer vs. Oracle vs. Sphinx

System Properties Comparison AnzoGraph DB vs. CouchDB vs. Microsoft Azure Data Explorer vs. Oracle vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Fully managed big data interactive analytics platform

Widely used RDBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

column oriented

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

cambridgesemantics.com/anzograph

couchdb.apache.org

azure.microsoft.com/services/data-explorer

www.oracle.com/database

sphinxsearch.com

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

docs.couchdb.org/en/stable

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

docs.oracle.com/en/database

sphinxsearch.com/docs

Developer

Cambridge Semantics

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Microsoft

Oracle

Sphinx Technologies Inc.

Initial release

2018

2005

2019

1980

2001

Current release

2.3, January 2021

3.3.3, December 2023

cloud service with continuous releases

23c, September 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache version 2

commercial

restricted free version is available

Open Source

GPL version 2, commercial licence available

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

Erlang

C and C++

C++

Server operating systems

Linux

Android
BSD
Linux
OS X
Solaris
Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

Fixed schema with schema-less datatypes (dynamic)

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

yes

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

yes

Secondary indexes

yes

via views

all fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

Kusto Query Language (KQL), SQL subset

yes

with proprietary extensions

SQL-like query language (SphinxQL)

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

RESTful HTTP/JSON API

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Proprietary protocol

Supported programming languages

C++
Java
Python

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions and aggregates

View functions in JavaScript

Yes, possible languages: KQL, Python, R

PL/SQL

also stored procedures in Java possible

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

improved architecture with release 2.0

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication
Source-replica replication

yes

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

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

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

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

atomic operations within a single document possible

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Access rights for users and roles

Access rights for users can be defined per database

Azure Active Directory Authentication

fine grained access rights according to SQL-standard

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