DBMS > AnzoGraph DB vs. Google Cloud Datastore vs. PouchDB vs. RRDtool

System Properties Comparison AnzoGraph DB vs. Google Cloud Datastore vs. PouchDB vs. RRDtool

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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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

JavaScript DBMS with an API inspired by CouchDB

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Graph DBMS
RDF store

Document store

Time Series 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	4.47
Rank	#76	Overall
	#12	Document stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

cambridgesemantics.com/anzograph

cloud.google.com/datastore

pouchdb.com

oss.oetiker.ch/rrdtool

Technical documentation

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

cloud.google.com/datastore/docs

pouchdb.com/guides

oss.oetiker.ch/rrdtool/doc

Developer

Cambridge Semantics

Google

Apache Software Foundation

Tobias Oetiker

Initial release

2018

2008

2012

1999

Current release

2.3, January 2021

7.1.1, June 2019

1.8.0, 2022

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

GPL V2 and FLOSS

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

JavaScript

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

Linux

hosted

server-less, requires a JavaScript environment (browser, Node.js)

HP-UX
Linux

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

yes

via views

SQL

Support of SQL

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

SQL-like query language (GQL)

APIs and other access methods

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

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP REST

only for PouchDB Server
JavaScript API

in-process shared library
Pipes

Supported programming languages

C++
Java
Python

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

using Google App Engine

View functions in JavaScript

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication using Paxos

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

none

Foreign keys

Referential integrity

not needed in graphs

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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Recent citations in the news

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15 April 2019, InfoWorld

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