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Comment by jturpin | Hacker News Reader

Elasticsearch is not a pain in the ass to scale, it is one of the easiest databases to scale. Kafka is medium, since they ditched Zookeeper.

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That hasn't been our experience. Our elasticsearch cluster has been a pain in the ass since day one with the main fix always "just double the size of the server" to the point where our ES cluster ended up costing more than our entire AWS bill pre-ES. (but that might be our limited experience) whereas something like postgres has required nearly 0 maintenance apart from adding the occasional index but even that has been just due to tuning, not that the DB fell over.

Both are AWS hosted products (RDS, AWS Elasticsearch).

Easiest database to scale is a pretty low bar. Databases are typically really hard to scale and Elasticsearch is no exception. Aside from the issue of ease, one thing that has been universally true for me is that Elasticsearch is incredibly expensive to scale in terms of compute costs.

Elasticsearch has built in horizontal scaling abilities, unlike Postgres/other SQL databases. It also has integrations with cloud providers for peer discovery, or can use DNS. Once a new data node is detected and reachable, the masters will start sending it shards of data, distributing the load. This all happens without any user intervention. I can't really speak to cost, it is somewhat easy to blow up the memory usage in Elastic for sure, but I can't say its been more expensive than similarly sized Postgres clusters.

Right, GB for GB ES is much easier to scale than Postgres (or any other DB) but probably also more expensive since ES is much more memory and compute hungry. But I can't say I have an apples-to-apples comparison since the use case for ES is usually "dump massive amounts of raw data in and index everything" which you wouldn't typically do with a Postgres instance. But in places where we have run large ES clusters my experience has not really been that it works without any user intervention (at least once you reach a certain scale) and that it involved a lot of operational support. Not that any other solution with comparable features would have been easier necessarily but still not easy in any absolute sense.

I go hilariously out of my way to eliminate elasticsearch at any org I join. Usually because it's only being used for logs and modern tools like loki are immeasurably easier to scale and cheaper to run. But I also find many many developers using it don't know about time series databases or anything at all about which data structures go in which kind of database and just dump everything into a horrifically organized search database. Its at least one order of magnitude worse to scale and operate than a mongo-type nosql database being used incorrectly by a developer who doesn't know any better and two orders of magnitude worse than a sql database being used incorrectly by a developer who doesn't know any better.

Loki's fine if you are very cost sensitive and are comfortable with Prometheus, but it's not really a replacement for a text-search database like Elasticsearch. It also scales about the same, both being horizontally scalable (I'm not sure what Loki's sharding strategy is). Our ELK stack runs on 3 2cpu/8gb ram nodes totaling about $160 a month and can handle 50+ million of records or so (I haven't ran it to its absolute limit). This is a comfortable price to performance ratio for us and I imagine many other companies.

I think people that have issues scaling any modern distributed data stack are because a) Don't have experts or b) Bad practices/stretching the use case. I worked on a project once where the ES cluster performance was degrading because they kept increasing the number of fields. At some point, they had more than 5k for a single document schema even though ES docs mention going over the limit (1k) is not a good idea. I mean if any of these big tech companies can manage clusters of hundreds of nodes for any of these data stacks I'm sure your scaling issues aren't because of the tool.

Easy/hard is depending on the experience of the user. Someone with a lot of experience with Elasticsearch will have a easy time scaling Elasticsearch and hard time scaling Kafka, and vice-versa.

Better to compare how complex they are to scale in terms of actions required.

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