The United States held an election recently, and there has been some … mild controversy regarding the results. Many raised issues about this before the election itself, but what if we had used instant-runoff voting instead? More importantly, can we implement it with Postgres?
Well, the answer to the last question is a strong affirmative. So long as we don’t break the results down into voting districts, and make wild unsupported assumptions regarding rankings, that is.
Through the wonderful magic of corporate agreements, I’ve been pulled back into (hopefully temporarily) managing a small army of MySQL servers. No! Why can’t this just be a terrible nightmare?! Does anyone deserve such debasement?
Side effects of using MySQL may include…
Hyperbole? Maybe a little. If MySQL was really that terrible, it wouldn’t be in such widespread use. However, as a Postgres DBA for so many years, I’ve come to appreciate what really sets it apart from engines and development approaches like those showcased in MySQL.
Partitioning tables in Postgres can be an extremely risky endeavor. Unfortunately on many larger systems, it’s also essentially a requirement; the maximum size of a Postgres table is 32TB. This isn’t just because converting an existing table to a series of partitions is expensive or time consuming. We must consider how the query planner will react to the partitioned version of a table. There’s also the very real risk we will (or already have) implement flaws in the trigger or constraint logic.
One of the things Postgres has been “missing” for a while is logical replication based on activity replay. Until fairly recently, in order to replicate single tables from one database to another, we had to encumber the table with performance-robbing triggers coupled to a third party daemon to manage transport. Those days might finally be behind us thanks to pglogical.
But is it easy to use? Let’s give it a try on our trusty sensor_log table.
For all of those warehouse queries that never seem to complete before the heat death of the universe, there’s often a faster version. Sometimes this is due to a fundamental misunderstanding of how queries work, or how Postgres specifically functions. The trick is knowing when to back away slowly from an ugly but efficient query, and when to inject a flurry of predicates to fully illustrate the original intent of the query so the planner makes better decisions.
