DBMS > AnzoGraph DB vs. Drizzle vs. Elasticsearch vs. Microsoft Azure Cosmos DB

System Properties Comparison AnzoGraph DB vs. Drizzle vs. Elasticsearch vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

Primary database model

Graph DBMS
RDF store

Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

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

Website

cambridgesemantics.com/anzograph

www.elastic.co/elasticsearch

azure.microsoft.com/services/cosmos-db

Technical documentation

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

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

learn.microsoft.com/azure/cosmos-db

Developer

Cambridge Semantics

Drizzle project, originally started by Brian Aker

Elastic

Microsoft

Initial release

2018

2008

2010

2014

Current release

2.3, January 2021

7.2.4, September 2012

8.6, January 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

GNU GPL

Open Source

Elastic License

commercial

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

C++

Java

Server operating systems

Linux

FreeBSD
Linux
OS X

All OS with a Java VM

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

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

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

All search fields are automatically indexed

yes

All properties auto-indexed by default

SQL

Support of SQL

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

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

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

JDBC

Java API
RESTful HTTP/JSON API

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

Supported programming languages

C++
Java
Python

C
C++
Java
PHP

.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

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

JavaScript

Triggers

hooks for callbacks inside the server can be used.

yes

by using the 'percolation' feature

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

Implicit feature of the cloud service

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

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

ES-Hadoop Connector

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

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

Foreign keys

Referential integrity

not needed in graphs

yes

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

Memcached and Redis integration

User concepts

Access control

Access rights for users and roles

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights can be defined down to the item level

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