Switching from Elastic to Typesense on k8s Cluster
TLDR Nicolo needed to know capabilities and features of Typesense. Jason and William provided explanations and shared experiences using it along with documentation links. Nicolo appreciated the insights.
Mar 10, 2022 (22 months ago)
Right now I'm using elastic but is driving me nuts with costs, and a lot of issues on my k8s cluster.
I only have 1k documents of 3 different kind of collections.
I was wondering if:
• I can search on multiple collections at the same time.
• the collections and the pushed documents are persitent? (on container volume or something)
◦ if not Is there some sort of backend that will be implemented in future like mongo?
• I can scale horizontally or there are some limitaions?
Yup. It's called federated search / multi search in Typesense: https://typesense.org/docs/0.22.2/api/documents.html#federated-multi-search
> the collections and the pushed documents are persitent?
They are, as long as you make sure the underlying disk / volume is persistent.
You can setup a cluster with 3 or 5 nodes and use that as a form of horizontal read scaling, since data is replicated to all those nodes.
Mar 11, 2022 (22 months ago)
One thing - you may be surprised how well a small instance performs.. we now use Typesense Cloud, but when we ran things on-prem, we were absolutely hammering a tiny typesense instance and I don’t think we ever managed to really bog it down enough to need much scaling.
Any docs on how scale it horizontally? I suppose that is peering what you mean with scaling... is that right?
BTW I've seen tha tthe docker image dont have a volume specified on the dockerfile!
Correct. Here are the docs for this: https://typesense.org/docs/guide/high-availability.html
> BTW I've seen tha tthe docker image dont have a volume specified on the dockerfile!
You will have to mount an external volume and get Typesense to use it like this: https://typesense.org/docs/guide/install-typesense.html#from-the-docker-image
Indexed 3015 threads (79% resolved)
Understanding Typesense's Functionality and Usage as a Search Engine
Tim asked about Typesense's function as a DB storage. Jason clarified Typesense functions primarily as a search engine and is not meant to be a primary data store. They also discussed data management strategies using Typesense. Joe provided further insight and recommended resources.
Troubleshooting Typesense Document Import Error
Christopher had trouble importing 2.1M documents into Typesense due to memory errors. Jason clarified the system requirements, explaining the correlation between RAM and dataset size, and ways to tackle the issue. They both also discussed database-like query options.
Optimizing Typesense Implementation for Large Collections
Oskar faced performance issues with his document collection in Typesense due to filter additions. Jason suggested trying a newer Typesense build and potentially partitioning the data into country-wise collections. They also discussed reducing network latency with CDN solutions.
Discussing Multi-tenant Indexing and Typesense Clusters
Semyon enquired about search and indexing performance for multi-tenant indexing. Virginio suggested using Typesense cloud, but Semyon noted the cost difference against bare metal instances. Jason proposed vertical scaling before multi-cluster setup.
Addressing Typesense Server Issues and Optimization Needs
Robert had an issue with a 'stuck' typesense server. Jason and Kishore Nallan gave advice on handling writes, configuration for high search volumes, and running multiple typesense instances. They also recommended monitoring CPU usage and updating the server version for bug fixes.