![]() ![]() ![]() The reason is complex and described in more detail here, but this is the short version: Before Postgres does anything with the data you provide, it resolves the schema’s default values. Since we only insert two schemas, we would expect the second schema to have an id of 2, but it doesn’t! There’s a gap between the id of the first schema ( 1) and the id of the second schema ( 3) of exactly the number of upserts we executed. Query = from ( u in Usage, update:, set: ] ) Repo. With upserts, you only need one write call to the database (the read is optional though, more about this later). In both scenarios, you always need a read and a write call. If no schema exists, you insert a new one. If a schema exists, you bump its counter and execute an update. Without upserts, you would need two database calls: First, a read to fetch any existing schema for the user and today’s date combination. You just want to increase the number of API calls the user made. In this case, you don’t care whether it’s the first or the hundredth API call. Let’s say you want to track how often a user calls your API on a given day. Upserts are useful in situations where you don’t care whether a schema exists or not, you just want to update it. An upsert updates the schema if it exists and creates it if it doesn’t. A conventional update throws an error if it doesn’t exist. They allow you to update a schema without checking if it exists in your database first. Upserts are a combination of insert and update. In particular, this post provides a complete guide to their funky fusion: the upsert operation. Just as assassins have two butts, we will also talk about two outstanding things today: insert and update operations on the database. Before we dive into today’s blog post, please consider the following question: ![]()
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