DBMS > AnzoGraph DB vs. Badger vs. Microsoft Azure AI Search vs. NSDb

System Properties Comparison AnzoGraph DB vs. Badger vs. Microsoft Azure AI Search vs. NSDb

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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

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Search-as-a-service for web and mobile app development

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Secondary database models

Vector DBMS

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	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

cambridgesemantics.com/anzograph

github.com/dgraph-io/badger

azure.microsoft.com/en-us/services/search

nsdb.io

Technical documentation

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

godoc.org/github.com/dgraph-io/badger

learn.microsoft.com/en-us/azure/search

nsdb.io/Architecture

Developer

Cambridge Semantics

DGraph Labs

Microsoft

Initial release

2018

2017

2015

2017

Current release

2.3, January 2021

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache 2.0

commercial

Open Source

Apache Version 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, Scala

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
macOS

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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 fields are automatically indexed

SQL

Support of SQL

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

SQL-like query language

APIs and other access methods

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

RESTful HTTP API

gRPC
HTTP REST
WebSocket

Supported programming languages

C++
Java
Python

C#
Java
JavaScript
Python

Java
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

none

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

yes

using Azure authentication

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More resources

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