I'm noticing a growing trend of using #S3 instead of traditional databases or block storage in products, which significantly reduces costs.
We've transitioned to using #Quickwit, an analytics and search engine for #OpenTelemetry tracing and logging. It can also be used as a #Jaeger storage backend.
You can easily integrate Jaeger with #Grafana, using Quickwit for S3-based storage. All data is stored in S3, resulting in a time-to-search of just 45 seconds, which is impressive. The time-to-read is nearly 70 ms.
Quickwit is written in Rust and packaged as a single binary, easy to install and deploy. Combined with Grafana, Jaeger and S3, it creates a powerful setup for observability.
Quickwit stores logs and traces in S3, while Grafana provides a flexible dashboard for visualizing and analyzing the data. This combination offers fast search performance and scalable storage without the complexity of traditional ELK stacks—no Java, yay!
#Turbopuffer also caught my attention—a serverless vector database that leverages S3 for storage. It offers a cost advantage of 10x-100x compared to alternative vector database solutions. The biggest user of this technology is https://t.co/MhcU7pXuvz, which is my favorite AI tool for daily use.
https://t.co/oTLZ8CkvBI now supports OpenTelemetry logs & traces! From now on, you can have the full insights into your server, including all the necessary details. If you're looking for a blazingly fast OTel compatible tool, check @Quickwit_Inc :)
#iggyrs#otel#opentelemetry
The new Quickwit Grafana plugin 0.4 is out!
Quality of Life features ahead:
- Autocomplete hints
- Enhanced log context XP
- Adhoc Filters and more!
https://t.co/vFgXNXmo3z
💃🏼New blog post about Quickwit's serverless search performance on AWS Lambda & S3 🏇
On the menu:
- Latency on search/analytics queries
- Impact of Lambda size (1GB to 8GB)
- Impact of caching layers
- Beautiful charts to illustrate our findings 😍
=> https://t.co/g8JUpIhx3H
Just finished a short tutorial to get started with @Quickwit_Inc and @redpandadata.
=> https://t.co/BXMXF0aGEi
The main goal is to demonstrate how easily the indexing is distributed over 3 indexers, each taking a set of partitions of a given topic.
I've heard Bloomberg in NYC might have event space they'd let someone use to host public tech talks.
Do I know anyone who could help me figure out if that's so?
Also, do I know any devs in NYC interested in giving tech talks on any Systems Programming topics?
DMs open. 😀
Playing with Quickwit (https://t.co/FNKgwkJNMS) and my favorite dataset: @githubarchive.
𝟭𝟲𝗧𝗕 𝗼𝗳 𝗝𝗦𝗢𝗡. 𝟱.𝟮𝗯𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗱𝗼𝗰𝘀
𝗧𝗵𝗲 𝗶𝗻𝗱𝗲𝘅 𝗶𝘀 𝗼𝗻 𝗦𝟯
With a single server 8vCPUs, 16GB of RAM
`actor.login:fulmicoton` returns 21,473 docs in 5.83s
😍
⭐ Quickwit 0.3 is released ⭐.
A lot of new features for our log Search Engine!
- Aggregations
- Schemaless
- UI
- and more!
https://t.co/T6YuvPtKI9
#rust#quickwit🦀🔍
📽We will be streaming our next event on 25 May, 12pm ET/18pm CEST!
@guilload, our Co-Founder, will present how to use Quickwit to ingest data from #Kafka & #Kinesis and how our indexing engine works.
👉Register:https://t.co/WAfLlt6dKN
#search#devops#dev#selfhosted#rustlang
We just opensourced chitchat: Quickwit's gossip cluster membership. Here is a blog post from @evanxg852000!
https://t.co/c2CVd17vHn
https://t.co/ZMN2eryAie
Rustaceans in NYC, please join us for @guilload's talk "Intro to Tantivy: a full-text search engine library written in Rust"
https://t.co/qLDSppuOsz
@Quickwit_Inc#rust#event
« French people are... very polite. » Pattern extracted from 1 billion web pages with #Quickwit search engine #Search#S3 https://t.co/MIa21USoiM via @Quickwit_inc