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Postgresql similar8/17/2023 Let’s analyze Postgres vs ClickHouse with a (very simplified) hedgehog database. What about ClickHouse? Well, it would need to load every Employer value for every entry, go to John Doe’s index, alter it, and write the entire “column” back into data. Postgres would do this seamlessly: access John Doe’s row (including his other attributes), alter the Employer value and write. Let’s start with some obvious uses cases that sharply lean towards Postgres or ClickHouse.Ī Simple Case where Postgres is used over ClickHouse: You operate a dating app and need to change Employer in a John Doe’s row. Read our ClickHouse and Druid comparison for an in-depth look at how they solve the same problem in two contrasting ways. □ Further reading: ClickHouse is just one of many OLAP databases on the market. A principal (akin to Clickhouse) wouldn’t know who Johnny is, but would be able to quickly provide the student body’s national exam pass rate. A teacher (akin to Postgres) would be able to efficiently answer the question “How is Johnny, the 4th grader, doing in Math?”. One previously-used analogy to compare OLTP databases (Postgres) with OLAP databases (ClickHouse) is Teachers vs Principals. Anyone who uses ClickHouse is also using Postgres or another rows-based relational database for the non-specialized bits of their product. If ClickHouse powered Tinder, the only match users would have is with a loading modal. Because ClickHouse is bad at update-heavy data, it’s not a great database to run the day-to-day usage of an app. It’s important to realize that ClickHouse is rarely used alone. Basically, it’s for data that doesn’t need to be changed ClickHouse is downright terrible at mutations. The big difference is that those queries perform differently from the analog queries in Postgres or other row-based relational database.ĬlickHouse was designed for products that require fetched aggregate data, such as analytics, financial real-time products, ad-bidding technology, content delivery networks, or log management. You continue to utilize SQL to interface with ClickHouse. The difference – to be clear – is how the data is stored to the user, no mental-inversion is needed. ClickHouse tables in memory are inverted - data is ingested as a column, meaning you’ve a large number of columns and a sizable set of rows. In contrast, ClickHouse is a columnar database. In each of these, data / objects are stored as rows, like a phone book. The majority of popular solutions - MySQL, Postgres, SQLite - are all row-based. Good chance, the professor referred to them as simply relational databases or even normal databases. If you’ve ever taken a databases 101 course, you’ve likely heard lectures on row-based relational databases. Here, we’re diving deep into how and why ClickHouse saved the day. Suddenly, all those data problems were solved, Thanos-snapped from existence. Eventually, PostHog migrated client data to ClickHouse. Turns out, that wasn’t sustainable (who would’ve thought!). It was obvious Postgres couldn't handle the scale necessary for an analytics platform like PostHog.Īt first, the team tried a ton of hack-y and wacky solutions in attempts to get Postgres to work. Eventually, that all-purpose Postgres database was tasked to store millions of rows of data. In 2020, PostHog used Postgres to store client data. PostHog's database journey was no different. So why even compare them? Because most companies that invest in an online analytical processing (OLAP) database like ClickHouse originally used an online transaction processing (OLTP) stack like MySQL or Postgres. ClickHouse was created for a specific purpose PostgreSQL (aka Postgres) was designed to be flexible and all-purpose. The two database solutions are as similar as grapes and grapefruit. Honestly, it is a bit ridiculous to compare Postgres and ClickHouse.
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