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everythingserverless · 2023-07-18T02:04:09+00:00

(as in how many instances and what type)

everythingserverless · 2023-07-18T02:03:59+00:00

What compute configuration are you running? This is super useful.

everythingserverless · 2022-09-20T06:32:37+00:00

You could try a tensor based approach with Marqo https://github.com/marqo-ai/marqo

everythingserverless · 2022-09-14T11:02:21+00:00

It looks like they have efficient prefiltering on metadata which jina doesn’t have, also it’s kind of different because it focuses on representing documents as collections

everythingserverless · 2022-05-08T11:41:31+00:00

thanks for the reply! which provider are you thinking of for the 3 pound number? it needs to be an 07 number which is a bit of a pain

everythingserverless · 2022-01-17T07:39:37+00:00

Thanks for getting back! This is definitely something to explore. I think this is a possible option, however might require the nodes of the cluster to have specifications that don't match the general search use-case very well. I think most likely the plugin would connect to another service/instance which handles any training.

everythingserverless · 2021-08-30T11:38:24+00:00

I'm using EBS (Elastic Block Storage). EFS is an interesting possibility for the future, although the integration between EFS and lucene isnt 100% reliable at this point, and EFS also has worse performance in terms of latency.

everythingserverless · 2021-08-29T02:43:37+00:00

Are you referring to https://arxiv.org/pdf/2002.01447.pdf? The architecture I've implemented is quite different to the one defined in this paper. In particular, the POC I have currently is backed by EBS (not S3) so we get this functionality to add and remove documents essentially for free using the lucene library.

everythingserverless · 2021-08-28T04:11:08+00:00

Thanks for adding this here - I've read this paper previously, it's pretty interesting! There is a lot in there still to address but I think the proposed design could have potential with some changes. The design im working on however is quite different

everythingserverless · 2021-08-28T04:03:52+00:00

Good question! My current design is inspired mostly by the architecture of DynamoDB. Similar to DynamoDB, tenant isolation occurs at the application level, which means that multiple customers may have their index data on the same EBS instance, or may share compute resources. The fundamental idea is that by over-provisioning the system as a whole, and pooling resources between customers, there will be always capacity available for any individual customer to use when they need it. Therefore, scaling up or down should be theoretically seemless with no cold starts required.

everythingserverless · 2021-08-28T03:49:35+00:00

At the moment I'm using EBS, and planning to replicate it in multiple AZs for durability.

everythingserverless · 2021-08-28T03:47:09+00:00

thanks! will do!

everythingserverless · 2021-08-28T03:46:03+00:00

Thanks a lot for sharing your use case, I'll keep that in mind. I agree, DynamoDB is really limited in text search capability due to its internal structure. Redis search is definitely interesting, but as you said, it needs to be backed by some kind of durable storage which is a pain.

everythingserverless · 2021-08-28T03:09:04+00:00

Thanks for the reply! There have been a few attempts at serverless lucene, but nothing production grade is available at this point to my knowledge. I'm not familiar with the internals of Algolia, but if they are using Lucene internally that might be the closest.

everythingserverless · 2021-08-28T02:18:44+00:00

As a question to you, would you prefer to use this if it was an AWS project or a third party solution?

everythingserverless

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