DBMS > Apache Doris vs. Microsoft Azure Table Storage vs. Riak TS vs. Transbase vs. Yaacomo

System Properties Comparison Apache Doris vs. Microsoft Azure Table Storage vs. Riak TS vs. Transbase vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

An MPP-based analytics DBMS embracing the MySQL protocol

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

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

A resource-optimized, high-performance, universally applicable RDBMS

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

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

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

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

www.transaction.de/en/products/transbase.html

yaacomo.com

Technical documentation

github.com/apache/doris/wiki

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

www.transaction.de/en/products/transbase/features.html

Developer

Apache Software Foundation, originally contributed from Baidu

Microsoft

Open Source, formerly Basho Technologies

Transaction Software GmbH

Q2WEB GmbH

Initial release

2017

2012

2015

1987

2009

Current release

1.2.2, February 2023

3.0.0, September 2022

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

commercial

free development license

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

C and C++

Server operating systems

Linux

hosted

Linux
OS X

FreeBSD
Linux
macOS
Solaris
Windows

Android
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

yes

SQL

Support of SQL

yes

yes, limited

yes

APIs and other access methods

JDBC
MySQL client

RESTful HTTP API

HTTP API
Native Erlang Interface

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

Java

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

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

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

Erlang

yes

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

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

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

yes

Transaction concepts

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

optimistic locking

yes

ACID

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

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

Apache Doris for Log and Time Series Data Analysis in NetEase: Why Not Elasticsearch and InfluxDB?
5 June 2024, hackernoon.com

Workload Isolation in Apache Doris: Optimizing Resource Management and Performance
25 May 2024, hackernoon.com

How to Digest 15 Billion Logs Per Day and Keep Big Queries Within 1 Second
1 September 2023, KDnuggets

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Streamlining Data Operations: How a Grocery Chain Optimizes Workloads with Apache Doris
16 May 2024, hackernoon.com

provided by Google News

Working with Azure to Use and Manage Data Lakes
7 March 2024, Simplilearn

How to use Azure Table storage in .Net
14 January 2019, InfoWorld

How to Use C# Azure.Data.Tables SDK with Azure Cosmos DB
9 July 2021, hackernoon.com

Inside Azure File Storage
7 October 2015, Microsoft

How to write data to Azure Table Store with an Azure Function
14 April 2017, Experts Exchange

provided by Google News

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