Greenplum Database vs MySQL

Overview

MySQL

Stacks129.6K

Followers108.6K

Votes3.8K

GitHub Stars11.8K

Forks4.1K

Greenplum Database

Stacks47

Followers111

Votes0

GitHub Stars6.2K

Forks1.7K

Greenplum Database vs MySQL: What are the differences?

<Greenplum Database vs MySQL>

1. **Data Storage**: One key difference between Greenplum Database and MySQL is their approach to data storage. Greenplum Database is designed for large-scale data warehousing, utilizing a shared-nothing architecture with massively parallel processing capabilities. In contrast, MySQL is more commonly used for smaller scale applications and relies on a traditional client-server architecture.
2. **SQL Dialect**: Another significant difference lies in the SQL dialect supported by each database. Greenplum Database is based on PostgreSQL, which provides advanced SQL functionalities. On the other hand, MySQL has its own SQL dialect with some unique features and syntax differences. This can impact the way queries are written and executed in each database.
3. **Scalability**: Greenplum Database is well-known for its scalability and ability to handle petabytes of data efficiently. It allows for horizontal scaling by adding more nodes to the cluster, making it suitable for data-intensive workloads. MySQL, while capable of scaling vertically by adding more resources to a single server, may face limitations in handling extremely large datasets.
4. **Performance**: In terms of performance, Greenplum Database offers superior analytics processing capabilities, making it a popular choice for complex analytics queries. It utilizes MPP (Massively Parallel Processing) architecture to distribute query workloads across multiple computing nodes. MySQL, on the other hand, is more optimized for transactional workloads and may not perform as well for analytical queries.
5. **Data Types**: Greenplum Database supports a wide range of data types, including specialized types for data warehousing and analytics. This allows for more flexibility in data modeling and processing. In comparison, MySQL has a more limited set of data types, catering more towards traditional database applications.
6. **Concurrency Control**: Greenplum Database supports advanced concurrency control mechanisms such as MVCC (Multi-Version Concurrency Control) to handle simultaneous transactions efficiently. This ensures data consistency and isolation in a multi-user environment. MySQL also provides concurrency control features, but they may not be as robust as those offered by Greenplum Database.

In Summary, Greenplum Database and MySQL differ in their approach to data storage, SQL dialect, scalability, performance, data types, and concurrency control mechanisms.

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Advice on MySQL, Greenplum Database

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	Greenplum Database
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 is a massively parallel processing (MPP) database server with an architecture specially designed to manage large-scale analytic data warehouses and business intelligence workloads. It is based on PostgreSQL open-source technology.
-	Core SQL Conformance; MPP Architecture; Innovative Query Optimization; Polymorphic Data Storage; Integrated In-Database Analytics
Statistics
GitHub Stars 11.8K	GitHub Stars 6.2K
GitHub Forks 4.1K	GitHub Forks 1.7K
Stacks 129.6K	Stacks 47
Followers 108.6K	Followers 111
Votes 3.8K	Votes 0
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	No community feedback yet
Integrations
No integrations available	PostgreSQL Kong Slick Heroku Apache Hive Clever Cloud Couchbase Sequelize Sails.js Metabase

What are some alternatives to MySQL, Greenplum Database?

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.

Related Comparisons

Greenplum Database vs MySQL: What are the differences?

<Greenplum Database vs MySQL>

1. **Data Storage**: One key difference between Greenplum Database and MySQL is their approach to data storage. Greenplum Database is designed for large-scale data warehousing, utilizing a shared-nothing architecture with massively parallel processing capabilities. In contrast, MySQL is more commonly used for smaller scale applications and relies on a traditional client-server architecture.
2. **SQL Dialect**: Another significant difference lies in the SQL dialect supported by each database. Greenplum Database is based on PostgreSQL, which provides advanced SQL functionalities. On the other hand, MySQL has its own SQL dialect with some unique features and syntax differences. This can impact the way queries are written and executed in each database.
3. **Scalability**: Greenplum Database is well-known for its scalability and ability to handle petabytes of data efficiently. It allows for horizontal scaling by adding more nodes to the cluster, making it suitable for data-intensive workloads. MySQL, while capable of scaling vertically by adding more resources to a single server, may face limitations in handling extremely large datasets.
4. **Performance**: In terms of performance, Greenplum Database offers superior analytics processing capabilities, making it a popular choice for complex analytics queries. It utilizes MPP (Massively Parallel Processing) architecture to distribute query workloads across multiple computing nodes. MySQL, on the other hand, is more optimized for transactional workloads and may not perform as well for analytical queries.
5. **Data Types**: Greenplum Database supports a wide range of data types, including specialized types for data warehousing and analytics. This allows for more flexibility in data modeling and processing. In comparison, MySQL has a more limited set of data types, catering more towards traditional database applications.
6. **Concurrency Control**: Greenplum Database supports advanced concurrency control mechanisms such as MVCC (Multi-Version Concurrency Control) to handle simultaneous transactions efficiently. This ensures data consistency and isolation in a multi-user environment. MySQL also provides concurrency control features, but they may not be as robust as those offered by Greenplum Database.

In Summary, Greenplum Database and MySQL differ in their approach to data storage, SQL dialect, scalability, performance, data types, and concurrency control mechanisms.

Greenplum Database vs MySQL

Overview