DBMS > Vertica vs. Vitess vs. WakandaDB

System Properties Comparison Vertica vs. Vitess vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Scalable, distributed, cloud-native DBMS, extending MySQL

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Column oriented

Relational DBMS

Object oriented DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	9.53
Rank	#42	Overall
	#26	Relational DBMS

Score	0.92
Rank	#197	Overall
	#92	Relational DBMS

Score	0.03
Rank	#366	Overall
	#17	Object oriented DBMS

Website

www.vertica.com

vitess.io

wakanda.github.io

Technical documentation

vertica.com/documentation

vitess.io/docs

wakanda.github.io/doc

Developer

OpenText

previously Micro Focus and Hewlett Packard

The Linux Foundation, PlanetScale

Wakanda SAS

Initial release

2005

2013

2012

Current release

12.0.3, January 2023

15.0.2, December 2022

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

Apache Version 2.0, commercial licenses available

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C++, JavaScript

Server operating systems

Linux

Docker
Linux
macOS

Linux
OS X
Windows

Data scheme

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

ADO.NET
JDBC
MySQL protocol
ODBC

RESTful HTTP API

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

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

JavaScript

Server-side scripts

Stored procedures

yes, PostgreSQL PL/pgSQL, with minor differences

yes

proprietary syntax

yes

Triggers

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning, hierarchical partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

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; supports Kerberos, LDAP, Ident and hash

Users with fine-grained authorization concept

no user groups or roles

yes

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More resources

Recent citations in the news

New browser-based query editor for OpenText Core Analytics Database accelerates and simplifies querying your data
25 November 2024, blogs.opentext.com

Vertica on Kubernetes
20 June 2024, blogs.opentext.com

Querying a Vertica data source in Amazon Athena using the Athena Federated Query SDK
11 February 2021, AWS Blog

Solving Big Data Challenges with Data Science at Uber
20 March 2019, Uber

VAST links arms with Vertica for fast analytics
19 April 2022, Blocks and Files

provided by Google News

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

PlanetScale Announces $30M Series B Funding to Accelerate Adoption of Instantly Provisioned and Infinitely Scalable Database
23 June 2021, Business Wire

Update CNCF’s Vitess Scales MySQL with the Help of Kubernetes
29 March 2022, InApps Technology

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

Scaling MySQL with Sam Lambert from PlanetScale
29 August 2023, substack.com

provided by Google News

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