![]() ![]() ![]() However, there is a little bit more speculation possible based upon something that is not written in Uber’s article: The article doesn’t mention PostgreSQL Heap-Only-Tuples (HOT). If this is such a big problem to Uber, these updates might be a big part of their overall workload. The PostgreSQL approach causes more disk IOs for updates that change non-indexed columns (“Write Amplification” in the article). ![]() MySQL with InnoDB, on the other hand, needs to update only those indexes that contain updated columns. The first problem Uber’s article describes in great, yet incomplete detail is that PostgreSQL always needs to update all indexes on a table when updating rows in the table. In this post I’ll explain why I think Uber’s article must not be taken as general advice about the choice of databases, why MySQL might still be a good fit for Uber, and why success might cause more problems than just scaling the data store. In case you don’t have an update-heavy use-case, don’t worry about the problems described in Uber’s article. Instead of writing “PostgreSQL has some limitations for update-heavy use-cases” the article just says “Inefficient architecture for writes,” for example. However, the article does a lousy job to transport this message. In my opinion Uber’s article basically says that they found MySQL to be a better fit for their environment as PostgreSQL. This post is an attempt to make sense out of Uber’s article. Knowing that PostgreSQL is not generally lousy, these messages made me wonder what the heck is written in this article. While doing so my mailbox was filling up with questions like “Is PostgreSQL really that lousy?”. I didn’t read the article right away because my inner nerd told me to do some home improvements instead. A Japanese translation of this article is available here.Ī Chinese translation of this article is available here.Ī few days ago Uber published the article “ Why Uber Engineering Switched from Postgres to MySQL”. ![]()
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