MySQL vs Apache Spark: What are the differences?
MySQL: The world's most popular open source 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; Apache Spark: Fast and general engine for large-scale data processing. 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.
MySQL belongs to "Databases" category of the tech stack, while Apache Spark can be primarily classified under "Big Data Tools".
"Sql" is the top reason why over 778 developers like MySQL, while over 45 developers mention "Open-source" as the leading cause for choosing Apache Spark.
MySQL and Apache Spark are both open source tools. It seems that Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub has more adoption than MySQL with 3.98K GitHub stars and 1.56K GitHub forks.
Airbnb, Uber Technologies, and Netflix are some of the popular companies that use MySQL, whereas Apache Spark is used by Uber Technologies, Slack, and Shopify. MySQL has a broader approval, being mentioned in 2994 company stacks & 3053 developers stacks; compared to Apache Spark, which is listed in 266 company stacks and 112 developer stacks.
MySQL has a lot of strengths working for it. It's simple and easy to set up and use. It's JSON engine is also really good these days. Mongo is also simple to setup and use, and it's speed as a document-object storage engine is first class.
Where Postgres has both beat is in it's combining of all of the features that make both MySQL and Mongo great, while adding on enterprise grade level scalability and replication. It's Postgres' stability and robustness, while still fulfilling the roles of it's contemporaries extremely well that edge Postgre for me.
When I was new with web development, I was using PHP for backend and MySQL for database. But after improving my JS skills, I chosen Node.js. Because of too many reasons including npm, express, community, fast coding and etc. MongoDB is so good for using with Node.js. If your JS skills are enough good, I recommend to migrate to Node.js and MongoDB.
Easier scalability of MongoDB prompted this migration from MySQL.
As Runtastic grew, at some point it would have outgrown our MySQL installation. We looked for a couple of alternatives and found MongoDB as a great replacement for our use case. Read how a migration of live data from one database to another worked for us.
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