DBMS > AnzoGraph DB vs. JaguarDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Solr

System Properties Comparison AnzoGraph DB vs. JaguarDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Solr

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

Performant, highly scalable DBMS for AI and IoT applications

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

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

Primary database model

Graph DBMS
RDF store

Key-value store
Vector DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Search engine

Secondary database models

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	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

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

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

cambridgesemantics.com/anzograph

www.jaguardb.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

solr.apache.org

Technical documentation

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

www.jaguardb.com/support.html

learn.microsoft.com/azure/cosmos-db

solr.apache.org/resources.html

Developer

Cambridge Semantics

DataJaguar, Inc.

Microsoft

Apache Software Foundation

Initial release

2018

2015

2014

2012

2006

Current release

2.3, January 2021

3.3 July 2023

9.6.0, April 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

GPL V3.0

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

the server part. Clients available in other languages

Java

Server operating systems

Linux

hosted

All OS with a Java VM

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

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

JSON types

yes

supports customizable data types and automatic typing

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 properties auto-indexed by default

yes

All search fields are automatically indexed

SQL

Support of SQL

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

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

JDBC
ODBC

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

RESTful HTTP API

Java API
RESTful HTTP/JSON API

Supported programming languages

C++
Java
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

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

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

Server-side scripts

Stored procedures

user defined functions and aggregates

JavaScript

Java plugins

Triggers

JavaScript

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication

yes

Implicit feature of the cloud service

yes

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

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

Access rights for users and roles

rights management via user accounts

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

yes

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