DBMS > EsgynDB vs. Graphite vs. Microsoft Azure AI Search vs. Riak TS

System Properties Comparison EsgynDB vs. Graphite vs. Microsoft Azure AI Search vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Secondary database models

Vector DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

www.esgyn.cn

github.com/graphite-project/graphite-web

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

Technical documentation

graphite.readthedocs.io

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

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Esgyn

Chris Davis

Microsoft

Open Source, formerly Basho Technologies

Initial release

2015

2006

2015

Current release

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

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

Python

Erlang

Server operating systems

Linux

Linux
Unix

hosted

Linux
OS X

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data only

yes

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

restricted

SQL

Support of SQL

yes

yes, limited

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP API
Sockets

RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

JavaScript (Node.js)
Python

C#
Java
JavaScript
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Java Stored Procedures

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

none

yes

Implicit feature of the cloud service

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

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.

User concepts

Access control

fine grained access rights according to SQL-standard

yes

using Azure authentication

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More resources

DB-Engines blog posts

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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The Billion Data Point Challenge: Building a Query Engine for High Cardinality Time Series Data
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