As of PG16, HOT updates are tolerated against summarizing indexes, such as BRIN.

https://www.postgresql.org/docs/16/storage-hot.html

Besides, you probably don't want "done" jobs in the same table as pending or retriable jobs - as you scale up, you likely want to archive them as it provides various operational advantages, at no cost.

F DRMs though. Good news is those AAA games are rarely worth anyone's time anyways. Better spin up indies or classic games - a good SNES game is worth a hundred of those garbo AAA license rehashes.

Total rage bait that doesn’t have its place on platforms catering for higher educated and science oriented people.

Nuclear plants evaporates water, water rains down a few kilometers away, gets collected in sewer, recycled/filtered and re-injected in the system.

You know what doesn’t get recycled? Coal & gas burning pollution & CO2. And you know what doesn't get re-injected? Lost lives to pollution and global warming.

> All this stuff is just done automatically in Rust. If you have to write out the code by yourself, that's more work, and a huge chance for bugs. The compiler is doing some very complex tracking to figure out what to drop when and where. You'd have to do that all in your head correctly without its help.

What prevents anyone from dedicating a Zig memory allocator to the job (and all of its subtasks), and simply freeing the entire allocator at the end of the job? No baby-sitting needed.

Or if the mindset is really to be assisted, because “very complex” and too “much work”, may as well use a garbage collected language.

> It's knowing the compiler is on your side and taking care of all this that makes it magical.

Until you got used to it, and trusted it so much, and it suddenly misses something - either after a compiler update, or after some unsupported code introduced, and that shit takes down prod on a Friday. I’m not going to take the chance, thank you, I can call free() and valgrind.

Besides, I am not sure what sort of "security through obscurity" you are talking about? Ian and Sam found it, and frankly - with a public page, page title + first h1 tag clearly stating that this relates to a Cockpit Access system, this has got to show up in a shit ton of security research search engines instantly.

We’ve maintained a financial exchange w/ margining for 8 years with it, and I guarantee you that everyone was more than relieved - customers and employees alike, once we were able to lift and shift the whole thing to Java.

The readability and scalability is abysmal as soon as you move on from a quant desk scenario (which everyone agrees, it is more than amazing at.. panda and dask frames all feel like kindergarten toys compared), the disaster recovery options are basically bound to having distributed storage - which are by the way “too slow” for any real KDB application given the whole KDB concept marries storage and compute in a single thread.. and use-cases of data historical data, such as mentioned in the article, become very quickly awful: one kdb process handles one request at once, so you end up having to deploy & maintain hundreds of RDB keeping the last hour in memory, HDBs with the actual historical data, pausing for hourly write downs of the data, mirroring trees replicating the data using IPC over TCP from the matching engine down to the RDBs/HDBs, recon jobs to verify that the data across all the hosts.. Not to mention that such a TCP-IPC distribution tree with single threaded applications means that any single replica stuck down the line (e.g. big query, or too slow to restart) will typically lead to a complete lockup - all the way to the matching engine - so then you need to start writing logic for circuit breakers to trip both the distribution & the querying (nothing out of the box). And then at some point you need to start implementing custom sharding mechanisms for both distribution & querying (nothing out of the box once again..!) across the hundreds of processes and dozens of servers (which has implications with the circuit breakers) because replicating the whole KDB dataset across dozens of servers (to scale the requests/sec you can factually serve in a reasonable timeframe) get absolutely batshit crazy expensive.

And this is the architecture as designed and recommended by the KX consultants that you end up having to hire to “scale” to service nothing but a few billions dollars in daily leveraged trades.

Everything we have is now in Java - all financial/mathematical logic ported over 1:1 with no changes in data schema (neither in house neither for customers), uses disruptors, convenient chronicle/aeron queues that we can replay anytime (recovery, certifying, troubleshooting, rollback, benchmarks, etc), and infinitely scalable and sharded s3/trino/scylladb for historical.. Performance is orders of magnitude up (despite the thousands of hours micro-optimizing the KDB stack + the millions in KX consultants - and without any Java optimizations really), incidents became essentially non-existent overnight, and the payroll + infra bills got also divided by a very meaningful factor :]

First Google Result for "nofx retire":

> The punk group have all got normal jobs, which they will continue to pursue, but are pulling out all the stops for one final tour for their army of fans.

Seems in direct contradiction with this anarchist click bait title.

Besides, gluetun+chihaya+qbit containers do the job without breaking a sweat, and without ever having to remember that you run a VPN - as it’d only be tunneling the containers of your choice. gluetun is the best image ever made!

How is no one thinking of a mere pub-sub topic? Set the TTL on the topic to whatever your max JWT TTL is, make your applications subscribe to the beginning of the topic upon startup, problem solved.

You need to load up the certificates from configuration to verify the signatures anyways, it doesn’t cost any more to load up a Kafka consumer writing to a tiny map.

https://immich.app/docs/features/libraries#external-librarie...

And it’s only “pre-optimized” if you are cool with PHP memory limit crashes, PHP operation timeouts, PHP request size limits, and the works.

Another joy associated with using Nextcloud sync is that uploads don’t even seem to support multi-part resumable uploads. So not only is it crazy slow, if there’s any error during the auto-upload of a 2G video clip, or the app is temporally backgrounded by iOS, it’ll go into an exponential back off (which you can force start), and eventually just start the upload for that/those file(s) over from scratch - good ways to waste days burning in your screen while in a trip and trying to ensure your medias are backed up in case you lose your phone on a trip. Try uploading raw images & 4k clips shot on iPhone to Nextcloud using the Nextcloud app + the AIO image from abroad.

I’m telling you, I’ve tried to use them for quite some time, and I’m far from DevOps-illiterate - I’ve been using k8s since it’s infancy, we wrote the original Operators at CoreOS way back.

A big part of the problem, it seems, is that, when you have a large library, and you jump/scroll to a specific year or so, it won’t cancel the previous page(s) worth of thumbnails loading. So as soon as you’re scrolling to search for something, it quickly accumulates hundreds of useless requests that quickly overload the PHP workers, and make everything crawl to a standstill.

I personally had to give up. When trying to grab photos from abroad for my shortly upcoming proposal, I’ve literally deleted Nextcloud/Memories, plopped Immich in docker compose, let it index/transcode/generate thumbnails from scratch against my “external library” (so Immich doesn’t duplicate the medias), and that ended up savings me days of buffering, and was able to find the nice pictures for the occasion!

(R740xd with 48 cores and 96TB SSD-backed ZFS pool)

- Immich supports external libraries

- Use docker compose and never worry about versions breaking

Decently sized EKS nodes can easily hold nearly 800 pods each (as documented), that'd make it 75 nodes. Each EKS cluster supports up to 13,500 nodes. Spread in a couple of regions to improve your customer experience, you're looking at 20 EKS nodes per cluster. This is a nothingburger.

Besides, it's far from being rocket science to co-locate tenant schemas on medium-sized pg instances, monitor tenant growth, and re-balance schemas as necessary. Tenants' contracts does not evolve overnight, and certainly does not grow orders of magnitude on week over week basis - a company using Figma either has 10 seats, 100 seats, 1000, or 10,000 seats. It's easy to plan ahead for. And I would MUCH rather having to think of re-balancing a heavy hitter customer's schema to another instance every now and then (can be 100% automated too), compared to facing a business-wide SPOF, and having to hire L07+ DBAs to maintain a proprietary query parser / planner / router.

Hell, OVH does tenant-based deployments of Ceph clusters, with collocated/coscheduled SSD/HDD hardware and does hot-spot resolution. And running Ceph is significantly more demanding and admin+monitoring heavy.

There's no virtually no excuse not spinning up a pg pod (or two) for each tenant - heck even a namespace with the whole stack.

Embed your 4-phases migrations directly in your releases / deployments, slap a py script to manage progressive rollouts, and you're done.

Discovery is automated, blast / loss radius is reduced to the smallest denominator, you can now monitor / pin / adjust the stack for each customer individually as necessary, sort the release ordering / schedule based on client criticality / sensitivity, you can now easily geolocate the deployment to the tenant's location, charge by resource usage, and much more.

And you can still query & roll-up all of your databases at once for analytics with Trino/DBT with nothing more but a yaml inventory.

No magic, no proprietary garbage.

> 14:50 - 16:28

Or

> 14:50 to 16:28

It makes sense when you think of it.

I’ve been using this for years, and it works wonders for datetimes too.