Under conventional wisdom, pruning can only be achieved when querying by the partition key. However, if your data follows certain patterns, using some clever tricks you can achieve pruning even when filtering by non-partition key columns.
You execute a query that joins two tables with a valid an enforces foreign key and it returns no results. How is it possible? We thought it wasn't possible, but a recent incident revealed an edge case we never thought about. In this article I show how under some circumstances row locks with joins can produce surprising results, and suggest ways to prevent it.
When it comes to database optimization, developers often reach for the same old tools: rewrite the query slightly differently, slap an index on a column, denormalize, analyze, vacuum, cluster, repeat. Conventional techniques are effective, but sometimes being creative can really pay off!
Django signals are extremely useful for decoupling modules and implementing complicated workflows. However, the underlying transport for signals makes them unreliable and subject to unexpected failures.In this article, I present an alternative transport implementation for Django signals using background tasks which makes them reliable and safer to use in mission critical workflows.
Constraints keep the integrity of your system and prevent you from shooting yourself in the foot. Foreign keys are a special type of constraint because, unlike unique, check, and primary keys, they span more than one relation. This makes foreign keys harder to enforce and harder to get right. In this article, I demonstrate common pitfalls, potential optimizations, and implicit behavior related to foreign keys.
"Get or create" is a very common operation for syncing data in the database, but implementing it correctly may be trickier than you may expect. If you ever had to implement it in a real system with real-life load, you may have overlooked potential race conditions, concurrency issues and even bloat!
I'm fairly sure that Excel is the most common way to store data, manipulate data, and yes(!), even pass data around. This is why it's not uncommon to find yourself reading Excel in Python. In this article I compare several ways to read Excel from Python.
Choosing to use a block range index (BRIN) to query a field with high correlation is a no-brainer for the optimizer. However, under some easily reproducible circumstances, a BRIN index can result in significantly slower execution even when the indexed field has very high correlation. In this article I describe how using a BRIN index in presumably "ideal circumstances" can result in degraded performance, and suggest a recent new feature of PostgreSQL as a remedy.
There are many best practices for maintaining backward and forward compatibility in application code, but it's not very commonly mentioned in relation to SQL. SQL is used to produce critical business information for applications and decision-making, so there's no reason it shouldn't benefit from similar practices. In this article, I present a simple way to future-proof SQL.
Concurrency is not very intuitive - you need to train your brain to consider what happens when multiple processes execute a certain code block at the same time. In this article I present common concurrency challenges and how to overcome them with minimal locking.
