PostgreSQL VS ClickHouse

While both Postgres and ClickHouse serve different purposes, the key distinction lies in how they handle replication and sharding. Postgres is primarily designed for transactional workloads (OLTP), where data consistency and durability are prioritized. On the other hand, ClickHouse is tailored for analytical workloads (OLAP), and optimized for high-speed querying and large-scale data analysis.

For your information, the tuplesort.c module flags when the Sort algorithm will use disk by setting the state to SORTEDONTAPE at this line. Disk interactions is handled by the logtape.c module.

This error happens when using a relational database, like PostgreSQL (one of the most popular, and advanced open-source SQL databases on the market).

This tutorial is going to use Postgres for its database because it is well battle-tested, but you're going to use the Prisma ORM to abstract away the database layer. This gives you the flexibility to use a variety of databases, and simplifies the API that you use to interact with it.

Yes, it's a balancing act. I think it's better to have safer defaults here, not prioritizing extra performance. People who are concerned with performance to the degree where checksums would matter need to consult configuration anyway (in many aspects, not only this one) and can disable this specific thing easily.

For the reference, change is in https://github.com/postgres/postgres/commit/04bec894a04c (so should land in PostgreSQL 18 in a year, unless it will get reverted for some reason).

PostgreSQL, commonly referred to as Postgres, is a powerful, open-source relational database management system (RDBMS) known for its robustness, extensibility, and standards compliance. It was introduced in 1986 by Michael Stonebraker and his team at the University of California, Berkeley, as part of the POSTGRES project, which aimed to address some of the limitations of existing database systems at the time.

PostgreSQL: An open-source object-relational database management system, used early on by Uber.

PostgreSQL

Link: https://www.postgresql.org/

When building web applications, authentication is almost always a core requirement. I needed something simple and secure that would let me get started quickly on new projects without over-complicating the setup. My goal was to have a solution that's easy to implement, handles email verification out of the box, and just works. In this post, I'm sharing how to build a straightforward magic link authentication system using Next.js, Auth.js, Prisma, PostgreSQL, and Resend. It's a powerful yet simple solution that accomplishes exactly what I needed, and I hope it'll be useful for your projects too.

ClickHouse is rapidly gaining traction for its unmatched speed and efficiency in processing big data. Cloudflare, for example, uses ClickHouse to process millions of rows per second and reduce memory usage by over four times, making it a key player in large-scale analytics. With its advanced features and real-time query performance, ClickHouse is becoming a go-to choice for companies handling massive datasets. In this article, we'll explore why ClickHouse is increasingly favored for analytics, its key features, and how to deploy it on Kubernetes. We'll also cover some best practices for scaling ClickHouse to handle growing workloads and maximize performance.

I have this dataset being updated in ClickHouse in real-time: https://play.clickhouse.com/play?user=play#U0VMRUNUIG1heCh0a...

I also provide a way to export it or attach it to clickhouse-local and analyze it locally: https://github.com/ClickHouse/ClickHouse/issues/29693#issuec...

And you can try it right now.

Install ClickHouse:

    curl https://clickhouse.com/ | sh

ClickHouse is a high-performance, column-oriented SQL database management system (DBMS) designed for online analytical processing (OLAP). ClickHouse allows using Arrow Stream as an output format.

We started to look for alternatives and quickly landed at Clickhouse.

In today’s story, we will discuss CVE-2024-22412 which affected ClickHouse a popular open-source column-oriented database management system typically used for online analytical processing (OLAP) in real-time. You can find the full write-up of the vulnerability here.

This is really great!

I can suggest more ideas that will be easy to add:

- a spark line or heat map of upvotes for every thread: https://github.com/ClickHouse/ClickHouse/issues/59020

- a built-in SQL editor for custom queries;

If you need help in supporting or hosting it, write to milovidov at clickhouse.com

We chose ClickHouse to store the dictionary as we ran into deadlock and performance issues with Postgres writes as we scaled the number of workers. ClickHouse's async inserts are fantastic for this task and allowed us to ingest the entire 38M+ document dataset in < 1hr.