There are so many switches and dials to tune, and unless you really learn it in depth, you won't know which ones you need. It's difficult to even determine what hardware requirements you have. And it's a hard sell to tell your business guys "I think elasticsearch will work better if we give it more... CPU? Memory? Disk speed? I'm not really sure." and can't provide any concrete metrics to back that up.
Another place where footguns abound is upgrading from one version to another, especially if you've got plugins installed. There are tricks that you have to learn the hard way.
At this point, I think long and hard before reaching for a solution like elasticsearch. If I've got a DBA whose entire job it is to master the tech and wield it expertly, that's one thing. But if I'm part of an early stage startup, I just can't justify the lost time and potential for catastrophe.
But that's true for any data store. This isn't any different. Nor is an RDBMS. They all need HA/replication. And that is rarely trivial.
Honestly, I think this is why managed/hosted solutions (AWS RDS for example) are so popular - they remove a large part of the complexity for you.
Personally I don't understand why there are so few search libraries/systems to choose from, given that "search" is one of the fundamental pillars of CS.
First time it happened for me and I was pretty angry at it
Here is a little comparison to enlighten your questions: https://docs.meilisearch.com/resources/comparison_to_alterna....
[1] https://vespa.ai
I run a few 10TiB ES clusters (which, is not much to be fair) but infrequently find that I have to reindex or reshard the cluster because I can’t just add another node. There’s something to be said for understanding the index rotation too, and access patterns.
It’s easy to make an ES cluster, it’s difficult to maintain one, it’s nearly impossible to debug one.
- if you consider that “it’s slow” is what you have to debug.
- Hey, a node died! - Run Terraform to stand up a whole new cluster and restore it from a snapshot. - Update the app to point at the new cluster. - Run Terraform to delete the old cluster.
I'm pretty happy with this arrangement.
This is exactly it. This is a problem you encounter with every database engine, but in most of them you can quickly find the bottleneck and fix it. With elasticsearch... it's a frustrating and expensive game of trial and error.
For information, what does "10TiB" refer to in this context?
Is it the size of what ES takes in RAM, or the size of ES' index, or is it the total size of the corpus that ES must index? Or corpus size + index ?
(Contrived example for the sake of illustrating the point)
In all there were about 50k documents, and we mostly cared about the title field. Elasticsearch would randomly bloat up to occupy a huge amount of RAM. Restarting it would make it work for a few days. It would also occasionally crash.
We got rid of it and went with some levenshtein distance based database query
I'd love to use it again sometime but the experience was not good, and Googling for information brought up all kinds of very complex use-cases shared by others
It will take you something like 10 minutes to start and populate MeiliSearch, you will be able to test it just by going to the server HTTP url in no time!
I implemented the student facing course catalog web interface for a single org. One of the funnests (most fun) parts was the heuristics in the query parser. Like patterns for recognizing course numbers and boosting those exact match results. Really helps you appreciate all the fit & finish that goes into proper search engines.
This was the olden days, when we just used Lucene directly.
The problem is that ES is deceptively simple to operate. As millions of people who have found things like their medical records shared with the world can attest.
Clustered ElasticSearch has been rock-solid for me and I've used it in anger many times. The level of maintenance needed is close to zero, both initially and long-term. Compare that with the abysmal experience of setting up a sharded MongoDB cluster for example...
Please enlighten me how ElasticSearch is "a lot of work to operate" (heard that one multiple times), and what you're comparing it to.