Tag: PostgreSQL

Why Uber Engineering Switched from Postgres to MySQL

“Why Uber Engineering Switched from Postgres to MySQL” is an interesting study with plenty of technical detail of how MySQL was a better choice than PostgreSQL for the very demanding growth of Uber.  These kinds of issues are probably way out of scope for any “regular Joe” application, but the insight into the differences of MySQL and PostgreSQL architectures is still useful.

Main PostgreSQL limitations covered by the study are:

  • Inefficient architecture for writes
  • Inefficient data replication
  • Issues with table corruption
  • Poor replica MVCC support
  • Difficulty upgrading to newer releases

Database Flow – modern, self-hosted web interface for SQL and GraphQL

Database Flow is a modern, Open Source, self-hosted, web-based tool for working with SQL databases and GraphQL APIs.  It supports a variety of the database engines: IBM DB2, Oracle, H2, PostgreSQL, MySQL, SQLite, Informix, and Microsoft SQL Server.  It features an advanced SQL editor, query plan analyzer, GraphQL client, schema explorer, charting, query history, and more.

The only visible downside so far is that it’s written in Java.

Database Engines Ranking

db-engines-ranking-table

DB-Engines.com provides some insight into some of the most popular database engines (312 of them to be precise).  Nothing too surprising there – Oracle and MySQL leading the charts, but it’s nice to have the numbers and trends.

db-engines-ranking

There are, of course, many different ways how the popularity can be calculated.  Their method is based on the popularity of each engine in a variety of online outlets, from Google Search to social networks.

  • Number of mentions of the system on websites, measured as number of results in search engines queries. At the moment, we use Google, Bing and Yandex for this measurement. In order to count only relevant results, we are searching for <system name> together with the term database, e.g. “Oracle” and “database”.
  • General interest in the system. For this measurement, we use the frequency of searches in Google Trends.
  • Frequency of technical discussions about the system. We use the number of related questions and the number of interested users on the well-known IT-related Q&A sites Stack Overflow and DBA Stack Exchange.
  • Number of job offers, in which the system is mentioned. We use the number of offers on the leading job search engines Indeed and Simply Hired.
  • Number of profiles in professional networks, in which the system is mentioned. We use the internationally most popular professional networks LinkedIn and Upwork.
  • Relevance in social networks. We count the number of Twitter tweets, in which the system is mentioned.

It seems objective and representative enough to me.