DBMS > IBM Db2 warehouse vs. OpenTSDB vs. Vitess vs. Yaacomo

System Properties Comparison IBM Db2 warehouse vs. OpenTSDB vs. Vitess vs. Yaacomo

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

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

Description

Cloud-based data warehousing service

Scalable Time Series DBMS based on HBase

Scalable, distributed, cloud-native DBMS, extending MySQL

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

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.26
Rank	#162	Overall
	#73	Relational DBMS

Score	1.59
Rank	#140	Overall
	#12	Time Series DBMS

Score	0.86
Rank	#202	Overall
	#95	Relational DBMS

Website

www.ibm.com/products/db2/warehouse

opentsdb.net

vitess.io

yaacomo.com

Technical documentation

opentsdb.net/docs/build/html/index.html

vitess.io/docs

Developer

IBM

currently maintained by Yahoo and other contributors

The Linux Foundation, PlanetScale

Q2WEB GmbH

Initial release

2014

2011

2013

2009

Current release

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

LGPL

Open Source

Apache Version 2.0, commercial licenses available

commercial

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

Server operating systems

hosted

Linux
Windows

Docker
Linux
macOS

Android
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

.NET Client API
JDBC
ODBC
OLE DB

HTTP API
Telnet API

ADO.NET
JDBC
MySQL protocol
ODBC

JDBC
ODBC

Supported programming languages

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Erlang
Go
Java
Python
R
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

PL/SQL, SQL PL

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on HBase

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Eventual Consistency across shards
Immediate Consistency within a shard

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

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

show all

Recent citations in the news

A major upgrade to Db2® Warehouse on IBM Cloud®
26 June 2024, IBM

Announcing the deprecation of prior generation Db2 Warehouse plans on AWS
16 October 2023, IBM

The 10 Best Cloud Data Warehouse Solutions to Consider in 2024
22 October 2023, Solutions Review

Data Mining in Db2 Warehouse: the basics
23 June 2020, Towards Data Science

Top 7 Cloud Data Warehouse Companies
31 May 2023, Datamation

provided by Google News

Comparing Different Time-Series Databases
10 February 2022, hackernoon.com

Brain Monitoring with Kafka, OpenTSDB, and Grafana
5 August 2016, KDnuggets

LogicMonitor Rolls a Time Series Database for Finer-Grain Reporting
1 June 2016, The New Stack

provided by Google News

Deepthi Sigireddi on Distributed Database Architecture in the Cloud Native Era
20 May 2024, InfoQ.com

They scaled YouTube — now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

PlanetScale Unveils Distributed MySQL Database Service Based on Vitess
18 May 2021, Datanami

PlanetScale offers undo button to reverse schema migration without losing data
24 March 2022, The Register

CNCF’s Vitess Scales MySQL with the Help of Kubernetes
9 February 2018, The New Stack

provided by Google News

Share this page