Apache Spark vs MySQL

Overview

MySQL

Stacks129.6K

Followers108.6K

Votes3.8K

GitHub Stars11.8K

Forks4.1K

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Spark vs MySQL: What are the differences?

Introduction

Apache Spark and MySQL are both powerful tools used in data processing and analysis. However, they have some key differences that set them apart.

Data Processing Speed: Apache Spark is known for its fast data processing capabilities, as it performs in-memory computation. It stores data in memory, allowing for quick access and manipulation. On the other hand, MySQL follows a traditional disk-based approach, which can be slower compared to Spark's in-memory processing.
Scalability: Apache Spark is designed to handle large-scale data processing tasks and can scale horizontally across multiple machines. It uses the concept of RDDs (Resilient Distributed Datasets), which allows for fault-tolerant distributed processing. In contrast, MySQL is more suited for smaller-scale applications and is limited by the capacity of a single machine.
Supported Data Types: MySQL has a rich set of data types that cover a wide range of applications, including numerical, string, and date types. Apache Spark, on the other hand, provides a more limited set of data types natively but can work with different data formats like CSV, JSON, and Parquet.
Processing Paradigm: Spark follows a distributed data processing paradigm, where data is processed in parallel across a cluster of machines. It provides a high-level API for processing structured and unstructured data. MySQL, on the other hand, follows a traditional relational database model and is optimized for structured data processing using SQL.
Stream Processing: Apache Spark has built-in support for stream processing through its streaming module, allowing for real-time data processing and analytics. MySQL does not have native support for stream processing and requires additional tools or plugins to achieve similar functionality.
Usage: Apache Spark is commonly used for big data processing, machine learning, and interactive data analysis. It provides a more flexible and versatile framework for processing different types of data. MySQL, on the other hand, is widely used as a relational database management system for transactional applications, such as web applications and e-commerce platforms.

In summary, Apache Spark and MySQL differ in their data processing speed, scalability, supported data types, processing paradigm, stream processing capabilities, and usage scenarios.

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Advice on MySQL, Apache Spark

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	Apache Spark
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.	Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
-	Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars 11.8K	GitHub Stars 42.2K
GitHub Forks 4.1K	GitHub Forks 28.9K
Stacks 129.6K	Stacks 3.1K
Followers 108.6K	Followers 3.5K
Votes 3.8K	Votes 140
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 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed

What are some alternatives to MySQL, Apache Spark?

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.

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Apache Spark vs MySQL: What are the differences?

Introduction

Apache Spark and MySQL are both powerful tools used in data processing and analysis. However, they have some key differences that set them apart.

Data Processing Speed: Apache Spark is known for its fast data processing capabilities, as it performs in-memory computation. It stores data in memory, allowing for quick access and manipulation. On the other hand, MySQL follows a traditional disk-based approach, which can be slower compared to Spark's in-memory processing.
Scalability: Apache Spark is designed to handle large-scale data processing tasks and can scale horizontally across multiple machines. It uses the concept of RDDs (Resilient Distributed Datasets), which allows for fault-tolerant distributed processing. In contrast, MySQL is more suited for smaller-scale applications and is limited by the capacity of a single machine.
Supported Data Types: MySQL has a rich set of data types that cover a wide range of applications, including numerical, string, and date types. Apache Spark, on the other hand, provides a more limited set of data types natively but can work with different data formats like CSV, JSON, and Parquet.
Processing Paradigm: Spark follows a distributed data processing paradigm, where data is processed in parallel across a cluster of machines. It provides a high-level API for processing structured and unstructured data. MySQL, on the other hand, follows a traditional relational database model and is optimized for structured data processing using SQL.
Stream Processing: Apache Spark has built-in support for stream processing through its streaming module, allowing for real-time data processing and analytics. MySQL does not have native support for stream processing and requires additional tools or plugins to achieve similar functionality.
Usage: Apache Spark is commonly used for big data processing, machine learning, and interactive data analysis. It provides a more flexible and versatile framework for processing different types of data. MySQL, on the other hand, is widely used as a relational database management system for transactional applications, such as web applications and e-commerce platforms.

In summary, Apache Spark and MySQL differ in their data processing speed, scalability, supported data types, processing paradigm, stream processing capabilities, and usage scenarios.