jaysh

Off the top of my head:

- substantially better performance on the same hardware, even moreso for larger range queries (multiple days)

- no new query language to learn

- significantly more expressive as you said

- agents for scraping logs use way less CPU (I used to use grafana-agent which used about 80%, vector uses sub 5%)

- very intuitive to manage TTLs - I can keep some logs for 10 years, and some for 1 week based on the event in the JSON

- more compact storage, I didn't check scientifically but CH storage is better compressed at least 4-5x for us

- no running into maximum stream limits - struggled with these even on Grafana Cloud and didn't realise we silently lost a lot of logs

Honestly: why wouldn't you. Loki always felt like a mistake to me. A brand new query language, really counter-intuitive configuration, large ramp-up time for complex queries, lots of arguing about labels/cardinality etc. It all goes away when you drop it. I think logging should not be exotic or behave in unexpected ways.

Just replied to that question! Let me know if you have other questions.

Not really, ClickHouse is super forgiving so you can do something like:

    CREATE TABLE default.events (
      `timestamp` DateTime
      `event` String -- e.g. 'product.updated' or empty/null
      `message` -- human readable message
      `raw` -- the raw message - this is very useful when pushing logs that aren't JSON - you just let the `event` be null and dump the entire message here
    )
    ENGINE = MergeTree
    PARTITION BY toDate(timestamp)
    ORDER BY (timestamp, event)
    TTL timestamp + toIntervalMonth(6)

ClickHouse is extremely performant even in the cases of e.g.: SELECT count(*) FROM `events` WHERE `raw` LIKE '%hello world%'

Of course, the more columns you splat out (e.g. like correlation_id, user_id, order_id, etc) the better you can index and expect those queries to perform but in general I don't bother outside the obvious core domain ones (exampled above), the performance is so good that unindexed queries are significantly faster than indexed queries in Loki. I have reached the point where I JSON extract on-the-fly for the WHERE clause with very large queries with no meaningful performance issues.

Still via Grafana. I ran it side-by-side with Loki and despite trying to optimise Loki and using ClickHouse out of the box - it really was shocking how much faster ClickHouse was for every single query (e.g. in the last 12 hours give my the frequency of logs with a particular JSON event or even "find this log entry, then join back and find the number of times a different entry appears within the same correlation_id)

ClickHouse replacing Loki finally made our observability stack feel 'right'. It really is a powerhouse for logs and general analytical queries.

It's worth noting that YouTube is optimising for the opposite: they want to prevent artificial view count inflation. Therefore, this behaviour (the view count not increasing when the videos are automatically accessed) would make sense.

If you're interested, an interesting measure for difficulty is how much companies charge to "buy youtube views" (e.g, $16 per 1000 views is ~8x the cost you'd pay for typical CAPTCHA solving).

Spotify are not enabling audio piracy by much because they don't provide an audio stream for songs via their API (beyond a short sample).

Looks slick! I've used Google Calendar to achieve something similar in the past but this looks better.

Minor suggestion would be to remove the unlimited text messages from the plans. It's harder to change later when this takes off, is ripe for abuse, and (to me) makes it feel unsustainable: everyone know unlimited is not actually unlimited. I'd reserve that for for the "call us" enterprise pricing options.

There's one airline in particular that I dislike simply due multiple bad experiences. I vocalise this significantly. I still book their flights if it's the cheapest: I'd argue to myself that (a) flying is a transient experience and (b) I'll use the saving for a 'free' lounge pass, taxi to the airport, etc.

I wouldn't assume that just because people vocalise brand loyalty, means that they stick with it when it's time to open their wallets.

Landed in Sydney a few weeks ago. First visit so I ordered an Uber (as I've used it a lot, trust it, etc.).

After we were picked up, the driver helpfully informed us if we'd ordered the same ride via Ola (he drives both), it'd have been ~50% cheaper (as a new user).

Naturally, I installed it and now I'll switch between Uber and m Ola. This then expands to Bolt, Grab, and so on, and you realise it's virtually the same experience with different branding and the sole differentiating factor is price.

I definitely buy the convenience aspect: it's way more comfortable to pull out your phone and order a ride on a platform you're already familiar with. My trouble is the barrier is so low to install another app (and new joiner incentives are financially motivating) that this leads to virtually no brand loyalty.

Perhaps in a different world, where there are no up significant upfront financial incentives and perhaps drivers were exclusive to a platform (i.e. to pass their rigorous screening processes is a sign of quality), that'd certainly buy some loyalty from me.

It's easy to stick with something when it's working for you:

> If I need to go somewhere, or get home, Uber has never let me down.

The real test of brand loyalty it staying with them after a few bad experiences.

Not OP, but in the same boat, and currently travel regularly. I always almost all of them installed (e.g. Uber/Lyft/Ola/Bolt/Grab etc.) and pick the cheapest. For now, there's the added incentive that you never pay full-fare for your first ride, too, so new competition is great.

Thank you so much for your incredible public service. I wish I could offer more than words. Lots of organisations discuss their values, but when push comes to shove, I don't believe many would even consider, let alone commit resources to, something that has such positive externalities.