DBMS > AnzoGraph DB vs. Firebase Realtime Database vs. KeyDB vs. Yanza

System Properties Comparison AnzoGraph DB vs. Firebase Realtime Database vs. KeyDB vs. Yanza

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Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it 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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Time Series DBMS for IoT Applications

Primary database model

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

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	0.63
Rank	#232	Overall
	#32	Key-value stores

Website

cambridgesemantics.com/anzograph

firebase.google.com/products/realtime-database

github.com/Snapchat/KeyDB
keydb.dev

yanza.com

Technical documentation

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

firebase.google.com/docs/database

docs.keydb.dev

Developer

Cambridge Semantics

Google

acquired by Google 2014

EQ Alpha Technology Ltd.

Yanza

Initial release

2018

2012

2019

2015

Current release

2.3, January 2021

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

BSD-3

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

Linux

hosted

Linux

Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

SQL

Support of SQL

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

APIs and other access methods

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

Android
iOS
JavaScript API
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

HTTP API

Supported programming languages

C++
Java
Python

Java
JavaScript
Objective-C

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions and aggregates

limited functionality with using 'rules'

Lua

Triggers

Callbacks are triggered when data changes

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication
Source-replica replication

none

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

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

yes

Optimistic locking, atomic execution of commands blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

yes, based on authentication and database rules

simple password-based access control and ACL

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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