The key difference is that it's not just about schema metadata (tables, indexes, views, columns, etc...). PostgreSQL is fabulous regarding this. Even native types are part of the catalog (pg_catalog).
Things are great in your DB... until they aren't. The post is about making observability a first-class citizen. Plans and query execution statistics, for example, queryable using a uniform interface (SQL) without the need to install DB extensions.
By making the entire architecture of the database visible via system objects - you allow the user to form a mental model of how the database itself works. Instead of it being just a magic box that runs queries - it becomes a fully instrumented data model of itself.
Now, you could say: "The database should just work" and perhaps claim that it is design error when it doesn't. Why do I need instrumentation at this level?
To that I can say: Every database ever made makes query planning mistakes or has places where it misbehaves. That's just the way this field works - because data is fiendishly complicated - particularly at high concurrency of when there is a lot of it. The solution isn't (just) to keep improving and fixing edge cases - it is to make those edge cases easy to detect for all users.
This sounds conceptually similar to performance_schema [1] in MySQL or MariaDB, which is a built-in feature originally introduced in MySQL 5.5 (2010). Or perhaps the easier-to-use sys schema [2], which wraps performance_schema among other things, introduced in MySQL 5.7 (2015).
It's great to have that observability functionality, but I don't really understand the purpose of writing a new DBMS from scratch to add this though. Why not get something merged into Postgres core?
The key difference is that it's not just about schema metadata (tables, indexes, views, columns, etc...). PostgreSQL is fabulous regarding this. Even native types are part of the catalog (pg_catalog).
Things are great in your DB... until they aren't. The post is about making observability a first-class citizen. Plans and query execution statistics, for example, queryable using a uniform interface (SQL) without the need to install DB extensions.
Thank you and yes!
By making the entire architecture of the database visible via system objects - you allow the user to form a mental model of how the database itself works. Instead of it being just a magic box that runs queries - it becomes a fully instrumented data model of itself.
Now, you could say: "The database should just work" and perhaps claim that it is design error when it doesn't. Why do I need instrumentation at this level?
To that I can say: Every database ever made makes query planning mistakes or has places where it misbehaves. That's just the way this field works - because data is fiendishly complicated - particularly at high concurrency of when there is a lot of it. The solution isn't (just) to keep improving and fixing edge cases - it is to make those edge cases easy to detect for all users.
This sounds conceptually similar to performance_schema [1] in MySQL or MariaDB, which is a built-in feature originally introduced in MySQL 5.5 (2010). Or perhaps the easier-to-use sys schema [2], which wraps performance_schema among other things, introduced in MySQL 5.7 (2015).
It's great to have that observability functionality, but I don't really understand the purpose of writing a new DBMS from scratch to add this though. Why not get something merged into Postgres core?
[1] https://dev.mysql.com/doc/refman/8.4/en/performance-schema.h...
[2] https://dev.mysql.com/doc/refman/8.4/en/sys-schema.html
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