Latency trade-offs and architecture benefits

Turbopuffer search engine supports such products as Cursor, Notion, Linear, Superhuman and Readwise.

This episode on YouTube: https://youtu.be/I8Ztqajighg

Medium: https://dmitry-kan.medium.com/vector-podcast-simon-eskildsen-turbopuffer-69e456da8df3

Dev: https://dev.to/vectorpodcast/vector-podcast-simon-eskildsen-turbopuffer-cfa

If you are on Lucene / OpenSearch stack, you can go managed by signing up here: https://console.aiven.io/signup?utm_source=youtube&utm_medium=&&utm_content=vectorpodcast

Time codes:

00:00 Intro

00:15 Napkin Problem 4: Throughput of Redis

01:35 Episode intro

02:45 Simon's background, including implementation of Turbopuffer

09:23 How Cursor became an early client

11:25 How to test pre-launch

14:38 Why a new vector DB deserves to exist?

20:39 Latency aspect

26:27 Implementation language for Turbopuffer

28:11 Impact of LLM coding tools on programmer craft

30:02 Engineer 2 CEO transition

35:10 Architecture of Turbopuffer

43:25 Disk vs S3 latency, NVMe disks, DRAM

48:27 Multitenancy

50:29 Recall@N benchmarking

59:38 filtered ANN and Big-ANN Benchmarks

1:00:54 What users care about more (than Recall@N benchmarking)

1:01:28 Spicy question about benchmarking in competition

1:06:01 Interesting challenges ahead to tackle

1:10:13 Simon's announcement

Show notes:

- Turbopuffer in Cursor: https://www.youtube.com/watch?v=oFfVt3S51T4&t=5223s

transcript: https://lexfridman.com/cursor-team-transcript

- https://turbopuffer.com/

- Napkin Math: https://sirupsen.com/napkin

- Follow Simon on X: https://x.com/Sirupsen

- Not All Vector Databases Are Made Equal: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696/