Overview

MySQL

Stacks129.6K

Followers108.6K

Votes3.8K

GitHub Stars11.8K

Forks4.1K

Vertica

Stacks88

Followers120

Votes16

MySQL vs Vertica: What are the differences?

Introduction

In this Markdown code, we will compare MySQL and Vertica, highlighting the key differences between the two databases.

Scalability: MySQL is known for its horizontal scalability, where data can be distributed across multiple servers. On the other hand, Vertica is designed for high-performance analytics and data warehousing, offering vertical scalability for handling massive data volumes efficiently.
Data Storage: MySQL uses InnoDB as the default storage engine, which focuses on transactional processing and reliability. In contrast, Vertica utilizes a columnar storage layout that offers compressed data storage, enabling fast query performance for analytical workloads.
Query Optimization: MySQL leverages indexes and query plans for query optimization, allowing developers to create custom indexes to improve performance. In Vertica, query optimization is achieved through advanced techniques like projections, segmentation, and column encoding, providing faster execution for complex analytical queries.
Data Loading: MySQL supports real-time data loading, making it suitable for transactional systems. Vertica, on the other hand, is optimized for bulk data loading. It offers features like COPY command and direct loading from various sources, enabling efficient loading of large datasets for analytics.
Analytics Capabilities: MySQL offers basic analytics functions and limited support for complex analytical queries. In contrast, Vertica is built for analytics, providing a wide range of advanced analytical functions, including time series analysis, geospatial analysis, and machine learning, making it suitable for complex analytical workloads.
Concurrency: MySQL handles concurrent transactions using a multi-versioning concurrency control mechanism, ensuring the isolation of transactions. Vertica takes advantage of the shared-nothing architecture and columnar storage, allowing parallel processing of queries and significantly improving concurrency for analytical workloads.

In summary, MySQL excels in transactional processing and horizontal scalability, while Vertica specializes in analytical workloads with its columnar storage, query optimization techniques, and advanced analytical capabilities.

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Advice on MySQL, Vertica

Kyle

Web Application Developer at Redacted DevWorks

Dec 3, 2019

Decidedon

PostGIS

While there's been some very clever techniques that has allowed non-natively supported geo querying to be performed, it is incredibly slow in the long game and error prone at best.

MySQL finally introduced it's own GEO functions and special indexing operations for GIS type data. I prototyped with this, as MySQL is the most familiar database to me. But no matter what I did with it, how much tuning i'd give it, how much I played with it, the results would come back inconsistent.

It was very disappointing.

I figured, at this point, that SQL Server, being an enterprise solution authored by one of the biggest worldwide software developers in the world, Microsoft, might contain some decent GIS in it.

I was very disappointed.

Postgres is a Database solution i'm still getting familiar with, but I noticed it had no built in support for GIS. So I hilariously didn't pay it too much attention. That was until I stumbled upon PostGIS and my world changed forever.

449k views449k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Navraj

CEO at SuPragma

Apr 16, 2020

Needs adviceon

MySQL

PostgreSQL

I asked my last question incorrectly. Rephrasing it here.

I am looking for the most secure open source database for my project I'm starting: https://github.com/SuPragma/SuPragma/wiki

Which database is more secure? MySQL or PostgreSQL? Are there others I should be considering? Is it possible to change the encryption keys dynamically?

Thanks,

Raj

401k views401k

Comments

Detailed Comparison

MySQL	Vertica
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.	It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.
-	Analyze All of Your Data. No longer move data or settle for siloed views;Achieve Scale and Performance;Fear of growing data volumes and users is a thing of the past;Future-Proof Your Analytics
Statistics
GitHub Stars 11.8K	GitHub Stars -
GitHub Forks 4.1K	GitHub Forks -
Stacks 129.6K	Stacks 88
Followers 108.6K	Followers 120
Votes 3.8K	Votes 16
Pros & Cons
Pros 800 Sql 679 Free 562 Easy 528 Widely used 490 Open source Cons 16 Owned by a company with their own agenda 3 Can't roll back schema changes	Pros 3 Shared nothing or shared everything architecture 1 Partition pruning and predicate push down on Parquet 1 Vertica is the only product which offers partition prun 1 Query-Optimized Storage 1 Fully automated Database Designer tool
Integrations
No integrations available	Oracle Golang MongoDB Sass Mode PowerBI Tableau Talend

What are some alternatives to MySQL, Vertica?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.