How Postgres stores data on disk – this one's a page turner

I was on a mailing list once where messages were blocked because of msexchange being in the headers.

What why ? Would people browse bigcocks.net unless it’s explicitly blocked? What about cox?

Is „tube“ on a blocklist as well?

A classic case of the Scunthorpe problem: https://en.wikipedia.org/wiki/Scunthorpe_problem

In this case the substring is part of the author's name. Such names are not at all uncommon.

I once worked for a company that blocked Cuban sites because of .cu (which is the Portuguese word for the end of your digestive system), but did not block porn sites (or so I was told ;-).

My company also blocks it, but as phishing.

Well if it isn't my arch-nemesis – my legally designated name. Maybe I should've gone for something with just my first name like drewsexpertblog.dev

I remember us once giving a supplier access to our internal bug tracker for a collaborative project. They were unable to get to the “…/openissue” endpoint.

Shut off like a petcock.

Depends very much on how the SSDs are designed internally. I think these days we have to settle for "can never be sure" of the real page size for atomicity. Pages can also become corrupt in other ways.

It is weird that "--data-checksums" isn't the default for new databases, even when it cost a bit in performance. Integrity should be more important than performance.

8k is a very common page size, but 4k isn't unheard of. Oracle's default is 4k.

The issue is that page size caps row size (for on-row storage). Also, if you have a smart clustering index, larger pages can be more efficient use of index addressing. So it's a trade-off.

Historically there was no atomicity at 4k boundaries, just at 512 byte boundaries (sectors). That'd have been too limiting. Lowering the limit now would prove problematic due to the smaller row sizes/ lower number of columns.

I believe HN auto-strips such "filler" words, needs to be added again by a mod

IIRC, if the original submitter edits the title once it has been posted, the edited version sticks, i.e. the filter only works the first time and you can override it if you notice it.

Very good point, I've rephrased this.

But in a power loss the WAL is also read to restore and transactions that were in flight, right?

Thanks for the feedback, I really appreciate it :-) I've added the RSS feed to my home page now, as the other poster noted the URL is https://drew.silcock.dev/rss.xml.

Found it: https://drew.silcock.dev/rss.xml

On mobile, it appears to open regardless of where you tap. This appears to be the culprit:

```const n = document.getElementById('nav-header');

  document.addEventListener(

    'click',

    s => {

      u.hidden ||

      s.target === null ||

      n === null ||

      n.contains(s.target) ||

      r()

    }```

Apache Arrow Columnar Format: https://arrow.apache.org/docs/format/Columnar.html :

The Arrow columnar format includes a language-agnostic in-memory data structure specification, metadata serialization, and a protocol for serialization and generic data transport. This document is intended to provide adequate detail to create a new implementation of the columnar format without the aid of an existing implementation. We utilize Google’s Flatbuffers project for metadata serialization, so it will be necessary to refer to the project’s Flatbuffers protocol definition files while reading this document. The columnar format has some key features:

Data adjacency for sequential access (scans)

O(1) (constant-time) random access

SIMD and vectorization-friendly

Relocatable without “pointer swizzling”, allowing for true zero-copy access in shared memory

Are the major SQL file formats already SIMD optimized and zero-copy across TCP/IP?

Arrow doesn't do full or partial indexes.

Apache Arrow supports Feather and Parquet on-disk file formats. Feather is on-disk Arrow IPC, now with default LZ4 compression or optionally ZSTD.

Some databases support Parquet as the database flat file format (that a DBMS process like PostgreSQL or MySQL provides a logged, permissioned, and cached query interface with query planning to).

IIUC with Parquet it's possible both to use normal tools to offline query data tables as files on disk and also to online query tables with a persistent process with tunable parameters and optionally also centrally enforce schema and referential integrity.

From https://stackoverflow.com/questions/48083405/what-are-the-di... :

Parquet format is designed for long-term storage, where Arrow is more intended for short term or ephemeral storage

Parquet is more expensive to write than Feather as it features more layers of encoding and compression. Feather is unmodified raw columnar Arrow memory. We will probably add simple compression to Feather in the future.

Due to dictionary encoding, RLE encoding, and data page compression, Parquet files will often be much smaller than Feather files

Parquet is a standard storage format for analytics that's supported by many different systems: Spark, Hive, Impala, various AWS services, in future by BigQuery, etc. So if you are doing analytics, Parquet is a good option as a reference storage format for query by multiple systems

Those systems index Parquet. Can they also index Feather IPC, which an application might already have to journal and/or log, and checkpoint?

Edit: What are some of the DLT solutions for indexing given a consensus-controlled message spec designed for synchronization?

- cosmos/iavl: a Merkleized AVL+ tree (a balanced search tree with Merkle hashes and snapshots to prevent tampering and enable synchronization) https://github.com/cosmos/iavl/blob/master/docs/overview.md

- Google/trillion has Merkle hashed edges between rows in order in the table but is centralized

- "EVM Query Language: SQL-Like Language for Ethereum" (2024) https://news.ycombinator.com/item?id=41124567 : [...]

SQLite is for sure not the one size fits all solution

Nor is Postgres. PG is surprisingly versatile. E.g. with some extensions can be used as key-value storage (hashtable), document database, time-series db and so on. And it works quite well. Beyond "good enough" for many use cases. Added benefit, aside from having to run only one db-server, is that you can mix it: part relational, part document, etc.

But the PG versions nearly ever get as good as focused, dedicated solutions get. Which makes sense if you think about it: a team developing a dedicated key-value storage that does that and only that, for years, will always produce a better key-value storage then one bolted onto a generic RDBMS.

A practical example was where we used ltree extension to store ever growing hierarchies. We needed access control over subtrees (so that the X report for John only includes the entities of Johns devision and lower). While it worked in PG, it turned out that "simply replacing" it with OpenLDAP, which had all this built in, made it faster, easier and above all easier to maintain.