quickwit
qdrant
quickwit | qdrant | |
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64 | 140 | |
6,098 | 17,943 | |
4.9% | 3.4% | |
9.8 | 9.9 | |
5 days ago | 3 days ago | |
Rust | Rust | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
quickwit
- Show HN: Search on S3 Using AWS Lambda
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Show HN: Quickwit – OSS Alternative to Elasticsearch, Splunk, Datadog
Hi folks, Quickwit cofounder here.
We started Quickwit 3 years ago with a POC, "Searching the web for under $1000/month" (see HN discussions [0]), with the goal of making a robust OSS alternative to Elasticsearch / Splunk / Datadog.
We have reached a significant milestone with our latest release (0.7) [1], as we have witnessed users of the nightly version of Quickwit deploy clusters with hundreds of nodes, ingest hundreds of terabytes of data daily, and enjoy considerable cost savings.
To give you a concrete example, one company is ingesting hundreds of terabytes of logs daily and migrating from Elasticsearch to Quickwit. They divided their compute costs by 5x and storage costs by 2x while increasing retention from 3 to 30 days. They also increased their durability, accuracy with exactly-once semantics thanks to the native Kafka support, and elasticity.
The 0.7 release also brings better integrations with the Observability ecosystem: improvements of the Elasticsearch-compatible API and better support of OpenTelemetry standards, Grafana, and Jaeger.
Of course, we still have a lot of work to be a fully-fledged observability engine, and we would love to get some feedback or suggestions.
To give you a glance at our 2024 roadmap, we planned to focus on Kibana/OpenDashboard integration, metrics support, and pipe-based query language.
[0] Searching the web for under $1000/month: https://news.ycombinator.com/item?id=27074481
[1] Release blog post: https://quickwit.io/blog/quickwit-0.7
[2] Open Source Repo: https://github.com/quickwit-oss/quickwit
[3] Home Page: https://quickwit.io
- Show HN: Quickwit – OSS Alternative to Datadog, Elasticsearch
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S3 Express Is All You Need
We tested S3 Express for our search engine quickwit[0] a couple of weeks ago.
While this was really satisfying on the performance side, we were a bit disappointed by the price, and I mostly agree with the article on this matter.
I can see some very specific use cases where the pricing should be OK but currently, I would say most of our users should just stay on the classic S3 and add some local SSD caching if they have a lot of requests.
[0] https://github.com/quickwit-oss/quickwit/
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Show HN: Quickwit – Cost-Efficient OSS Search Engine for Observability
Hi HN, I’m one of the builders of Quickwit, a cloud-native OSS search engine for observability. As of 2023, we support logs and traces, metrics will come in 2024.
You know the pitch: while software like Datadog or Splunk are great, they often comes with hefty price tags. Our mission is to offer an affordable alternative. So we’ve built Quickwit, we’ve made it compatible with the observabilty ecosystem (OpenTelemetry, Jaeger, Grafana) and above all, we’ve made it cost-efficient / “easy” to scale (well it’s never easy to scale to petabytes..).
To give you a glance at the engine performance I made a benchmark on the GitHub Archive dataset, 23 TB of events, here are the main observations:
Indexing: costs $2 per ingested TB. With 4CPU, throughput is at 20MBs However, you'll observe > 30MB throughput on simpler datasets, like logs and traces.
Search: a typical query costs $0.0002 per TB (considering both CPU time and GET request costs). Using 8CPU, a simple query on 23TB is achieved in under a second.
Storage: on S3, it costs $8 per ingested TB per month on the GitHub Archive dataset. With logs and traces, you might see costs around $5/ingested TB due to a 2x better compression ratio.
I'm eager to get your thoughts on this!
Benchmark: https://quickwit.io/blog/benchmarking-quickwit-engine-on-an-...
Github repo: https://github.com/quickwit-oss/quickwit/
Website: https://quickwit.io/
- On S3, it costs $8 per ingested TB per month on the GitHub Archive dataset. With logs and traces, you might see costs around $4/ingested TB due to a 2x better compression ratio.
I'm eager to get your thoughts on this!
[0] Benchmark: https://quickwit.io/blog/benchmarking-quickwit-engine-on-an-...
- OSS Sub-second search and analytics engine on cloud storage
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Ask HN: Who is hiring? (September 2023)
Quickwit (https://quickwit.io/) | Paris, France | Onsite and remote (based in Europe) | Full-time
The company is fully remote but we also have a small office in Paris. We prefer candidates based in Europe but can make exceptions for the right profiles.
- Senior Software Engineer 80-110k€ + 0.25-1% equity based on experience.
We’re looking for a senior software engineer to contribute to [Quickwit](https://github.com/quickwit-oss/quickwit), our open-source search and analytics engine. We have an ambitious roadmap for the next 18 months (performance optimization, distributed storage, support for SQL, query optimizer, revamp of our execution engine, etc.), and this is a great opportunity to shape the future of Quickwit while tackling fun and challenging problems in the field of distributed databases.
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Observe your Rust application with Quickwit, Jaeger and Grafana
In our latest blog post, we walk you through the steps of instrumenting your Rust application and monitoring the performance on Grafana using Quickwit + Jaeger.
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Quickwit 0.6.0 - Search and analytics on billions of logs with minimal hardware
Link: https://github.com/quickwit-oss/quickwit
qdrant
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Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
I'm currently looking to implement locally, using QDrant [1] for instance.
I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].
[1]. https://qdrant.tech/
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Show HN: A fast HNSW implementation in Rust
Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...
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pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
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Open-source Rust-based RAG
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb
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Qdrant 1.8.0 - Major Performance Enhancements
For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!
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Perform Image-Driven Reverse Image Search on E-Commerce Sites with ImageBind and Qdrant
Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.
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7 Vector Databases Every Developer Should Know!
Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.
- Ask HN: Who is hiring? (February 2024)
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Step-by-Step Guide to Building LLM Applications with Ruby (Using Langchain and Qdrant)
Qdrant serves as a vector database, optimized for handling high-dimensional data typically found in AI and ML applications. It's designed for efficient storage and retrieval of vectors, making it an ideal solution for managing the data produced and consumed by AI models like Mistral 7B. In our setup, Qdrant handles the storage of vectors generated by the language model, facilitating quick and accurate retrievals.
What are some alternatives?
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
Milvus - A cloud-native vector database, storage for next generation AI applications
loki - Like Prometheus, but for logs.
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
elasticsearch-py - Official Python client for Elasticsearch
faiss - A library for efficient similarity search and clustering of dense vectors.
manticoresearch - Easy to use open source fast database for search | Good alternative to Elasticsearch now | Drop-in replacement for E in the ELK soon
pgvector - Open-source vector similarity search for Postgres
openobserve - 🚀 10x easier, 🚀 140x lower storage cost, 🚀 high performance, 🚀 petabyte scale - Elasticsearch/Splunk/Datadog alternative for 🚀 (logs, metrics, traces, RUM, Error tracking, Session replay).
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
zincsearch - ZincSearch . A lightweight alternative to elasticsearch that requires minimal resources, written in Go.
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.